From 7130aa57021528348e218230dd6609559355ed38 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Mon, 30 Mar 2026 22:14:08 +0100 Subject: [PATCH 01/46] First draft: the oldest pattern matcher --- src/pages/blog/the-oldest-pattern-matcher.md | 85 ++++ the-pattern-matching-medicine-notes.md | 440 +++++++++++++++++++ 2 files changed, 525 insertions(+) create mode 100644 src/pages/blog/the-oldest-pattern-matcher.md create mode 100644 the-pattern-matching-medicine-notes.md diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md new file mode 100644 index 000000000..19362b8dd --- /dev/null +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -0,0 +1,85 @@ +--- +author: 'hga' +title: 'The oldest pattern matcher' +description: 'Two people with no medical training, a chatbot and a protein-folding model did what years of specialist consultations could not.' +category: 'ai' +layout: '../../layouts/BlogPost.astro' +publishedDate: '2026-04-06' +heroImage: 'capability-hyperinflation.jpg' +tags: + - 'ai' + - 'engineering' + - 'medicine' +--- + +

In 1985, Katalin Karikó left Hungary with her husband and two-year-old daughter. The government limited how much money citizens could take out of the country: $100. She sold the family car on the black market and sewed $1,246 into her daughter's teddy bear.

+ +At Penn, she spent a decade trying to make synthetic mRNA work as a therapeutic. Nobody was interested. "The people who reviewed the grants said mRNA will not be a good therapeutic, so don't bother," her future collaborator [Drew Weissman](https://en.wikipedia.org/wiki/Drew_Weissman) recalled. The university demoted her in 1995. Three years later she met Weissman at a photocopier. + +Their early experiments produced a problem that looked fatal: injected synthetic mRNA triggered violent inflammation because the immune system recognised the molecules as foreign and attacked. Karikó [modified a single nucleoside](https://pubmed.ncbi.nlm.nih.gov/16111635/) so the mRNA could slip past the body's pattern-matching machinery, evading one layer of recognition to arm another. The modified mRNA taught the immune system to identify the spike protein of a virus it had never encountered. *Nature* and *Science* rejected the paper. It was published in *Immunity* in 2005. Fifteen years later, that technique became the [Pfizer-BioNTech and Moderna COVID-19 vaccines](https://en.wikipedia.org/wiki/COVID-19_vaccine). Karikó and Weissman shared the [2023 Nobel Prize](https://www.nobelprize.org/prizes/medicine/2023/kariko/facts/). + +Karikó spent her career negotiating with pattern matchers. The people who dismiss AI as "just pattern matching" have never reckoned with what it can do. + +## Recognition + +In March 2026, a Sydney tech entrepreneur named Paul Conyngham [designed a personalised cancer vaccine for his dog](https://fortune.com/2026/03/15/australian-tech-entrepreneur-ai-cancer-vaccine-dog-rosie-unsw-mrna/). His eight-year-old rescue Staffy, Rosie, was dying of [mast cell cancer](https://en.wikipedia.org/wiki/Mast_cell_tumor). Surgery and chemotherapy had failed. Vets gave her months. + +Conyngham used ChatGPT to research immunotherapy approaches, paid $3,000 to sequence Rosie's tumour DNA at UNSW's Ramaciotti Centre for Genomics, then fed the mutations into [AlphaFold](https://en.wikipedia.org/wiki/AlphaFold) to predict which proteins would be visible to the immune system. He used Grok to help design an mRNA vaccine targeting those neoantigens, and Professor Pall Thordarson at [UNSW's RNA Institute synthesised it](https://news.unsw.edu.au/en/meet-the-man-who-designed-a-cancer-vaccine-for-his-dog). By mid-March the largest tumour had shrunk by roughly 75%. Thordarson called it the first personalised cancer vaccine designed for a dog. + + + +Days later, a user on Reddit [described feeding 25 years of medical records to Claude](https://www.reddit.com/r/ClaudeAI/comments/1s41fny/25_years_multiple_specialists_zero_answers_one/). His uncle, a 62-year-old man in India on dialysis three times a week, had suffered positional headaches his entire adult life. Neurologists, nephrologists: none found a cause. Claude spotted a pattern connecting the positional headaches, loud snoring and dialysis, referencing research showing that [40-57% of dialysis patients have undiagnosed sleep apnea](https://pubmed.ncbi.nlm.nih.gov/11207682/). A sleep study confirmed it: breathing stopping 119 times per night, oxygen dropping to 78%. A CPAP machine resolved headaches that had persisted for a quarter of a century. + +Each specialist had seen their own slice: the nephrologist saw kidneys, the neurologist saw headaches. Claude saw the constellation. In both cases the AI matched signals across domains that human specialists kept in separate departments. [Charles Janeway](https://en.wikipedia.org/wiki/Charles_Janeway), the immunologist who [named the immune system's own mechanism "pattern recognition receptors"](https://pmc.ncbi.nlm.nih.gov/articles/PMC3117407/) in 1989, would have recognised the principle. The innate immune system matches molecular patterns on pathogens without understanding what they mean. It just matches. Matching is enough to activate the deeper response. + +These are anecdotes, not clinical trials. An oncologist [urged caution in The Conversation](https://theconversation.com/a-man-used-ai-to-help-make-a-cancer-vaccine-for-his-dog-an-oncologist-urges-caution-278735): a single case, no control group, mast cell tumours that behave unpredictably. The Reddit post is unverified. The question is not whether these two cases prove that AI can practise medicine. It is what cognitive mechanism made them possible at all. + +## Horror autotoxicus + +In 1901, [Paul Ehrlich](https://en.wikipedia.org/wiki/Paul_Ehrlich) coined the term *horror autotoxicus* to express his conviction that the immune system would never attack the body's own tissues. Self-destruction would be "[dysteleological to the highest degree](https://pioneerworks.org/broadcast/kavin-senapathy-paul-ehrlichs-horror-autotoxicus)". His authority was so formidable that evidence of autoimmunity was dismissed for fifty years, until a young researcher named [Noel Rose](https://pmc.ncbi.nlm.nih.gov/articles/PMC7983605/) proved that rabbits' immune systems could be made to attack their own thyroids. His mentor, trained in the lineage of Ehrlich's own students, made him repeat the experiment again and again. When the results became undeniable: "My boy, you've done it. You've demolished the dogma". + +The immune system's pattern matcher can misfire. When it does, it attacks the self. Hallucination is the autoimmune disorder of artificial intelligence: the same mechanism that recognises threats matching against the wrong thing. + + + +[Yann LeCun](https://en.wikipedia.org/wiki/Yann_LeCun), Meta's chief AI scientist, calls LLMs "glorified autocomplete" and maintains they cannot reason or plan. His [position paper](https://openreview.net/pdf?id=BZ5a1r-kVsf) proposes Joint Embedding Predictive Architectures as the path toward genuine world models. [Karl Friston](https://en.wikipedia.org/wiki/Karl_Friston), the neuroscientist behind the [Free Energy Principle](https://en.wikipedia.org/wiki/Free_energy_principle), is [blunter](https://deniseholt.us/deep-learning-is-rubbish-friston-lecun-face-off-at-davos-2024/): "Deep learning is rubbish". LLMs are "just a mapping between content and content". His most striking line: large language models, in their failure to encode uncertainty, are "extremely prone to psychiatric disorders". + +These are serious people. Pattern matching can find what is latent in existing knowledge, but it cannot construct new theories about unobserved phenomena. Nobody pattern-matched their way to general relativity. Yet both critics prescribe more AI, not less. LeCun wants world models. Friston wants active inference systems that encode uncertainty and curiosity. They are calling for deeper pattern matching, not a different kind of intelligence. + +## The dirty little secret + +Janeway called the innate immune system's role "the immunologist's dirty little secret" because without its pattern recognition the adaptive immune system never activates. Shallow matching is the gatekeeper for deeper intelligence. The relationship holds beyond immunology. + +[Douglas Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) has spent four decades arguing that analogy is not a special variety of reasoning but the core of all cognition. In [*Surfaces and Essences*](https://en.wikipedia.org/wiki/Surfaces_and_Essences), he and Emmanuel Sander present pairs of proverbs that assert contradictory things: "don't judge a book by its cover" alongside "where there's smoke, there's fire". The critics' argument against LLMs is the first proverb: surface patterns cannot grasp deep truth. The medical cases are the second: surface patterns reliably point to underlying conditions. Both proverbs coexist in every language because they are not rules. They are analogical frames, and intelligence is selecting which frame fits. + + + +"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. Pattern matching finds that a situation resembles a known one. Analogy asks why the resemblance holds, what structural property the two situations share. [Einstein's equivalence principle](https://en.wikipedia.org/wiki/Equivalence_principle) started as a pattern match: a person in freefall feels weightless, and that feels like the absence of gravity. The analogy was not the proof. It was the generative spark that told him where to point the mathematics. His analogical thinking preceded the formalisation. + +Friston's own framework contains the same tension. His Free Energy Principle holds that all perception is prediction error minimisation: the brain generating patterns, comparing them against sensory data, adjusting. The man who formalised cognition as pattern matching also says LLMs are insufficient. Both positions are true. What varies is the depth of the model, the encoding of uncertainty, whether the system can act and learn from consequences. A difference of depth, not kind. + +## Immune memory + +In 430 BC, [Thucydides](https://en.wikipedia.org/wiki/Thucydides) contracted the [Plague of Athens](https://en.wikipedia.org/wiki/Plague_of_Athens) and survived. He observed that "the same man was never attacked twice" and that survivors nursed the sick because they "had now no fear for themselves". He was describing immune memory: the body recognising a pattern it had encountered before. He had no framework to explain the mechanism. The observation stood for 2,300 years before anyone did. + +Software engineering has its own immune memory. When we praise code as "idiomatic" we are praising pattern matching: this solution selected the right analogy from a repertoire of known forms. [Design patterns](https://en.wikipedia.org/wiki/Design_Patterns) are named analogies. The Gang of Four catalogue is a compendium of "this situation is like that situation". Every abstraction in computing is a metaphor: files, folders, windows, streams, pipes, threads. + +When an experienced developer looks at an unfamiliar problem and reaches for a known pattern, they are doing what Hofstadter describes: stripping away irrelevancies to obtain a conceptual nugget, then stepping to a related one in another domain. When an LLM does the same thing, we call it shallow. But AlphaFold sits at the interesting middle of the gradient. It is a model built from patterns in protein sequence data, now capable of predicting the three-dimensional structure of proteins it has never encountered. That is not retrieval. It is pattern matching deep enough to be generative: a narrow world model, emerging from the mechanism LeCun dismisses. + +## Checkpoint + + + +[James Allison](https://en.wikipedia.org/wiki/James_P._Allison)'s mother died of lymphoma when he was ten. Two of his uncles died of cancer. His brother died of prostate cancer. At UC Berkeley in 1995, a postdoc in his lab injected mice with antibodies blocking CTLA-4, a molecular brake on T cells. Allison's reaction: "[What the hell? I've never seen that](https://cancerhistoryproject.com/article/jim-allison-believed-in-the-power-of-t-cellswhen-hardly-anyone-else-did/)". He demanded a double-blind repeat and measured the tumours himself. Cages labelled A, B, C, D. For weeks, all tumours grew. Then some cages stopped. Tumours necrosing, melting away. + +He spent three and a half years trying to persuade a pharmaceutical company to develop the humanised antibody. [Sharon Belvin](https://www.cancerresearch.org/stories/patients/sharon-belvin) was 22, diagnosed with stage 4 melanoma two weeks before her wedding. She enrolled in the first trial of ipilimumab, one of three responders out of seventeen. Her tumours disappeared. She is alive decades later. + +Allison didn't teach the immune system anything new. He removed a checkpoint, a constraint on pattern matching the body was already doing. The T cells already recognised the cancer. They were being held back. + +The debate about whether LLMs "really think" is a checkpoint of its own. It constrains recognition of what these systems already achieve while the field waits for an architecture that may turn out to be pattern matching at greater depth. Karikó spent a career learning the immune system's vocabulary, finding its blind spots, teaching it new patterns. She didn't dismiss its recognition as shallow. She worked with it. + +The teddy bear made it out of Hungary. The vaccine made it into the body. Both had to pass a checkpoint to do their work. + +--- + +Pattern recognition is how we approach AI-assisted engineering at JUXT: finding what the system already knows and removing what holds it back. If that sounds like work worth doing together, [let's talk](https://www.juxt.pro/contact/). diff --git a/the-pattern-matching-medicine-notes.md b/the-pattern-matching-medicine-notes.md new file mode 100644 index 000000000..4abf4417c --- /dev/null +++ b/the-pattern-matching-medicine-notes.md @@ -0,0 +1,440 @@ +# Research notes: pattern matching in medicine + +## Thesis + +LLMs are dismissed as "just pattern matching" or "pseudo-thinking". Two recent cases of lay people using AI in medicine show that pattern matching, the ability to find signals and match against known patterns across vast bodies of knowledge, is itself a form of creative intelligence with real-world consequences. The piece should be a hymn to the value of similarity, metaphor and analogy even without causal reasoning. It should also gesture toward world models as the next frontier. + +## Central metaphor: the immune system as pattern matcher + +The immune system is the body's own pattern-matching system. It recognises surface features (antigens) that signal threats, without "understanding" cause and effect. The piece is about pattern matching at different levels, and the immune system vocabulary threads through every section. + +**Vocabulary for section headings and threading:** +- Recognition, response, memory +- Self vs. non-self +- Pattern recognition receptors (Janeway's term) +- Adjuvant (something that helps the immune response work) +- Horror autotoxicus (Ehrlich's term — when pattern matching goes wrong) +- Checkpoint (the brakes on the immune response — Allison's discovery) +- Immune memory (patterns the body has seen before) +- Tolerance (the immune system learning what to ignore) + +--- + +## Immunology stories for opening / threading + +### Metchnikoff, Messina, Christmas 1882 + +Élie Metchnikoff, Russian zoologist, living in Messina with his wife and her family. The whole family went to the circus to see trained monkeys. He stayed behind, alone with his microscope, watching motile cells in a transparent starfish larva. He picked rose thorns from the shrubbery where "we had just a few days before assembled a Christmas tree for the children on a mandarin bush" and pushed them under the skin of the larva. + +> "I was so excited I couldn't fall asleep all night in trepidation of the result of my experiment, and the next morning, at a very early hour, I observed with immense joy that the experiment was a perfect success!" + +Cells had gathered around the thorns, surrounding them. He recognised this as the same process that happens when white blood cells gather at a site of inflammation. He hypothesised white blood cells attack and kill bacteria the same way. Carl Claus suggested naming it "phagocytosis" (devouring cells). He shared the 1908 Nobel Prize. + +**For the piece:** The starfish larva has no brain. Its cells recognise a foreign body and surround it. Pattern recognition without any central intelligence. Metchnikoff saw in this the foundation of immunity itself. + +**Personal colour:** Survived two suicide attempts. Once deliberately infected himself with relapsing fever. Drank soured milk daily, believing it extended lifespan. + +### Onesimus and Cotton Mather, Boston, 1721 + +In 1706, Cotton Mather's congregation purchased an enslaved West African man. Around 1716, Mather asked Onesimus if he had ever had smallpox. Onesimus answered "yes and no" and showed Mather a scar on his arm. He explained that in Africa he had undergone an operation that gave him immunity. + +When smallpox hit Boston in 1721, Mather urged physician Zabdiel Boylston to try the method. Boylston inoculated 280 people: 6 died (2.2%). Among the 5,889 uninoculated Bostonians who caught smallpox, 844 died (14.3%). Mather was publicly ridiculed for relying on the testimony of an enslaved person. A firebomb was thrown through his window. + +**For the piece:** An enslaved man's bodily knowledge, passed through African medical tradition, outperformed European medicine. Pure empirical pattern recognition without any causal theory of infection. + +### Charles Janeway: "The immunologist's dirty little secret" (1989) + +At the 1989 Cold Spring Harbor Symposium, Janeway exposed "the immunologist's dirty little secret": foreign antigen alone wasn't enough to trigger an adaptive immune response. Every experiment needed adjuvant, a mysterious mixture. Why? + +He proposed that the immune system evolved to distinguish "noninfectious self from infectious non-self." He hypothesised that "pattern recognition receptors" (PRRs) on innate immune cells detect conserved molecular patterns on microbes (pathogen-associated molecular patterns, or PAMPs). These are the gatekeepers: they decide whether the adaptive immune system switches on. + +Confirmed when Janeway and Ruslan Medzhitov identified human Toll-like receptor 4 (TLR4). + +**For the piece:** Janeway literally named the mechanism "pattern recognition receptors." The innate immune system recognises molecular patterns without understanding what they mean. + +### Paul Ehrlich and "horror autotoxicus" (1901) + +Ehrlich coined this term to express his conviction that the immune system would never attack the body's own tissues. Self-destruction would be "dysteleological to the highest degree." His authority was so overwhelming that when Donath and Landsteiner demonstrated genuine autoimmunity in 1904, Ehrlich dismissed their findings. The dogma held for half a century. + +In 1956, Noel Rose (in Ernest Witebsky's lab, Buffalo) proved the body attacks itself: rabbits injected with their own thyroglobulin developed autoimmune thyroiditis. Witebsky, whose mentor was Ehrlich's student, was sceptical. He made Rose repeat the experiment again and again. Rose later said Witebsky "almost killed the whole story." + +When the results were undeniable, Witebsky told him: "My boy, you've done it. You've demolished the dogma." + +**For the piece:** When pattern matching goes wrong, it matches against the body's own tissues. The immune system's pattern matcher has no concept of "self"; it just matches. This is the honest parallel to hallucination: the same mechanism that recognises threats can misfire. + +### James Allison and checkpoint inhibitors + +Allison's mother died of lymphoma when he was 10. Two uncles died of cancer. His brother died of prostate cancer. + +At UC Berkeley in 1995, his postdoc reported that mice injected with anti-CTLA-4 antibodies showed tumour regression. Allison's reaction: "What the hell? I've never seen that." He demanded a double-blind repeat and measured tumours himself. + +Cages labelled A, B, C, D. For weeks, all tumours grew. Then some cages stopped. Tumours necrosing, melting away. + +He spent 3.5 years trying to find a pharma company to make the humanised antibody. "Pharma thought immunology was nice but they were going to work on autoimmune disease." Eventually Medarex (small biotech, Princeton) took the rights in 1999. + +Sharon Belvin, 22, stage 4 melanoma in brain, lungs, liver. Diagnosed two weeks before her wedding. Months to live. Enrolled in ipilimumab trial. One of three responders out of 17. Tumours disappeared. Allison met her in 2006. He cried. + +Ipilimumab (Yervoy) approved 2011. 22% of advanced melanoma patients alive at 10 years. Allison: 2018 Nobel Prize. Plays harmonica in a band of immunologists called the Checkpoints. + +**For the piece:** Checkpoint inhibitors are about removing brakes on the immune system's pattern matching. The immune system already knew how to fight the cancer; it was being held back. The parallel to LeCun/Friston as "checkpoints" on AI is almost too neat. + +### Katalin Karikó and mRNA + +Left Hungary in 1985. $100 limit on money leaving the country. Sold the family car on the black market, sewed the proceeds ($1,246) into her daughter's teddy bear. + +At Penn, she consistently failed to win grants. "People were not interested in mRNA." Demoted in 1995. Met Drew Weissman at the photocopier in 1998. "I had always wanted to try mRNA, and here was somebody at the Xerox machine telling me that's what she does." + +Early experiments: synthetic mRNA triggered violent inflammation. The immune system treated it as a foreign invader. They found the fix: altering one of mRNA's four nucleosides made modified mRNA invisible to immune surveillance. Paper rejected by Nature and Science. "Not novel." "Not of interest." Published in Immunity, 2005. + +In 2020, their technology became the Pfizer-BioNTech and Moderna COVID vaccines. 2023 Nobel Prize. + +**For the piece:** The immune system's pattern matcher was too good: it recognised synthetic mRNA as foreign and attacked it. Karikó had to evade one layer of pattern matching to enable another. She had to make the delivery mechanism invisible to the immune system's pattern recognition so that the payload (the spike protein pattern) could teach the immune system a new pattern to recognise. + +### Thucydides and immune memory (430 BC) + +Contracted the Plague of Athens and survived. Made the first recorded observation of acquired immunity: + +> "The same man was never attacked twice — never at least fatally." + +> "Yet it was with those who had recovered from the disease that the sick and the dying found most compassion. These knew what it was from experience, and had now no fear for themselves." + +He recognised the specificity of this immunity: survivors were resistant to the plague but not to other diseases. + +**For the piece:** Thucydides observed the immune system's memory without any framework to explain it. The body remembered a pattern it had seen before. 2,300 years before anyone understood why. + +### Emily Whitehead and CAR-T therapy (2012) + +Five years old, acute lymphoblastic leukaemia. After 16 months of chemo, cancer relapsed. Ineligible for bone marrow transplant. + +Phase 1 trial at Children's Hospital of Philadelphia (Carl June, Bruce Levine, David Porter at Penn Medicine). T cells extracted, reprogrammed with chimeric antigen receptors, multiplied over six weeks, reinfused. + +After the third infusion: 105-degree fever, face swelled beyond recognition, blood pressure 53/29, fluid flooded lungs, ventilator, induced coma. One-in-a-thousand chance of surviving the night. + +The team noticed IL-6 was vastly elevated. IL-6 happened to have an FDA-approved blocker: tocilizumab, a rheumatoid arthritis drug never used in cancer. They gave it as a desperate measure. She improved within hours. + +Emily woke on her seventh birthday. Hospital staff sang happy birthday. Cancer-free for over 10 years as of 2022. + +**For the piece:** CAR-T therapy is literally engineering the immune system's pattern-matching capability. Scientists design a receptor that recognises a specific surface pattern on cancer cells, then hand it to T cells. The intelligence is in the pattern, not in the T cell's understanding of cancer. + +--- + +### Sources for immunology stories + +- Metchnikoff: pmc.ncbi.nlm.nih.gov/articles/PMC6738810/ +- Lady Mary Montagu: theconversation.com/lady-mary-wortley-montagu-the-forgotten-immunisation-pioneer-164256 +- Onesimus: history.com/articles/smallpox-vaccine-onesimus-slave-cotton-mather +- Milkmaid myth debunking: npr.org/sections/goatsandsoda/2018/02/01/582370199/ +- Janeway "dirty little secret": pmc.ncbi.nlm.nih.gov/articles/PMC3117407/ +- Ehrlich / Rose / horror autotoxicus: pioneerworks.org/broadcast/kavin-senapathy-paul-ehrlichs-horror-autotoxicus +- James Allison: cancerhistoryproject.com; laskerfoundation.org; nbcnews.com; time.com/5778987/ +- Sharon Belvin: cancerresearch.org/stories/patients/sharon-belvin +- Emily Whitehead: cancerresearch.org/stories/patients/emily-whitehead; chop.edu +- Katalin Karikó: scientificamerican.com; bu.edu/articles/2021/ +- Benjamin Jesty: history.org.uk/publications/resource/4826/ +- Chinese variolation: scmp.com/magazines/post-magazine/short-reads/article/3078436/ +- Jenner: pmc.ncbi.nlm.nih.gov/articles/PMC1200696/ + +## Tentpole example 1: Paul Conyngham and Rosie (the mRNA vaccine) + +**Who:** Paul Conyngham, a tech entrepreneur and electrical/computing engineer based in Sydney, Australia. Co-founder of Core Intelligence Technologies, former director of the Data Science and AI Association of Australia. + +**The patient:** Rosie, an 8-year-old rescue Staffordshire bull terrier cross. + +**The cancer:** Mast cell cancer (common and aggressive skin cancer in dogs). Initially misdiagnosed as a rash for nearly a year before biopsy confirmed cancer in 2024. After surgery and chemotherapy, tumours kept returning. Vets said she had months to live. + +**What he did, step by step:** + +1. Used ChatGPT, Gemini and Grok for initial research and literature review. ChatGPT suggested immunotherapy and pointed him toward UNSW's Ramaciotti Centre for Genomics. +2. Paid $3,000 to Professor Martin Smith at the UNSW Ramaciotti Centre for Genomics to sequence DNA from both Rosie and her tumour, identifying mutations that differentiated cancer cells from healthy tissue. +3. Used AlphaFold (Google DeepMind) for protein structure prediction, to determine which mutated proteins (neoantigens) would be visible to the immune system and suitable as vaccine targets. +4. Used Grok to help "design an mRNA vaccine that boosts the production of tumour-associated antigens". +5. Approached Professor Pall Thordarson, head of UNSW's RNA Institute, who agreed to review the data and synthesise the physical mRNA vaccine. +6. Used chatbots to navigate ethics approval paperwork (100-page document, took three months, which he described as harder than the vaccine creation itself). + +**Timeline:** +- Pre-2024: Multiple vet visits, misdiagnosis as rash +- 2024: Biopsy confirms mast cell cancer. Surgery and chemo attempted, tumours recurred. Given months to live. +- 2024 mid-year onward: AI-assisted research, genome sequencing, AlphaFold analysis, UNSW collaboration +- December 2025: First vaccine injection (Brisbane, under ethical approval from Professor Rachel Allavena, University of Queensland) +- February 2026: Booster injection +- Mid-March 2026: Tennis ball-sized tumour had shrunk ~75%. Most tumours shrank significantly. Rosie regained energy, "back chasing rabbits" + +**Outcome:** Partial remission. Most tumours shrank dramatically. One tumour didn't respond; team was sequencing it for a second vaccine. Professor Thordarson described it as the first personalised cancer vaccine designed for a dog. + +**Important caveats (Justin Stebbing, oncologist, The Conversation):** +- Single case, not a controlled study +- Mast cell tumours can behave unpredictably +- Impossible to determine how much improvement is due to the vaccine +- AI did not "cure cancer" alone; qualified scientists checked work and manufactured vaccine +- Conyngham did not make a vaccine in his garage + +**Key sources:** +- Fortune (15 Mar 2026): fortune.com/2026/03/15/australian-tech-entrepreneur-ai-cancer-vaccine-dog-rosie-unsw-mrna/ +- The Conversation: theconversation.com/a-man-used-ai-to-help-make-a-cancer-vaccine-for-his-dog-an-oncologist-urges-caution-278735 +- Reason (19 Mar 2026): reason.com/2026/03/19/man-successfully-designs-mrna-vaccine-to-treat-his-dogs-cancer/ +- The Scientist: the-scientist.com/chatgpt-and-alphafold-help-design-personalized-vaccine-for-dog-with-cancer-74227 +- Phys.org: phys.org/news/2026-03-dog-chatgpt-australia-ai-vaccine.html +- UNSW News: news.unsw.edu.au/en/meet-the-man-who-designed-a-cancer-vaccine-for-his-dog +- Newsweek: newsweek.com/owner-no-medical-background-invents-cure-dogs-terminal-cancer-11684882 +- Bioplatforms Australia: bioplatforms.com/ai-and-genomics-combine-to-create-personalised-cancer-vaccine-for-dog/ + +--- + +## Tentpole example 2: Claude diagnoses 25 years of sleep apnea (India) + +**Who:** Reddit user u/the_kuka + +**The patient:** The poster's uncle, a 62-year-old man in India. (NB: Henry remembered this as father or father-in-law; it was actually the uncle.) + +**Medical history:** +- Kidney failure requiring dialysis three times per week +- Diabetes +- Hypertension +- Stroke six years prior +- Severe headaches/migraines specifically when lying down, lasting 25+ years +- Loud snoring for 25 years +- Daily afternoon sleeping for 25 years + +**Previous medical consultations:** Multiple specialists including neurologists and nephrologists. Brain MRI scans, blood work. No definitive cause found for the positional headaches. + +**What the AI did:** The poster compiled medical reports, test results and symptom details and shared them with Claude over several days. Claude spotted a pattern linking the positional nature of the headaches with potential sleep-related issues. It referenced research indicating that 40-57% of dialysis patients may have undiagnosed sleep apnea. It flagged relevant findings in the uploaded MRI reports that other doctors had overlooked, and prompted questions about snoring and daytime sleepiness. + +**The diagnosis:** Undiagnosed obstructive sleep apnea. A subsequent sleep study confirmed severe sleep apnea: +- Breathing stopping 119 times per night +- Oxygen levels dropping to 78% (dangerously low) +- 47 oxygen desaturations per hour +- 28 minutes per night below safe oxygen levels + +**Treatment:** CPAP machine. + +**Outcome:** Headaches/migraines subsided after CPAP treatment. + +**Key insight from the poster:** Claude didn't replace medical professionals but helped connect insights across multiple disciplines (nephrology, neurology, pulmonology, ENT) that had not been synthesised in earlier consultations. + +**Timeline:** Reddit post went viral 27-29 March 2026. News articles appeared 28-29 March 2026. + +**Reddit post:** r/ClaudeAI: "25 years. Multiple specialists. Zero answers. One Claude conversation cracked it." + +**Key sources:** +- Storyboard18 (29 Mar 2026): storyboard18.com/digital/claude-ai-flags-undiagnosed-sleep-apnea-in-indian-patient-after-25-years-claims-reddit-user-ws-l-93573.htm +- NewsBytes: newsbytesapp.com/news/science/reddit-user-says-claude-suggested-sleep-apnea-caused-uncles-migraines/tldr +- The CSR Journal (28 Mar 2026): thecsrjournal.in/reddit-user-says-ai-conversation-diagnosed-uncles-25-year-condition/ +- NPR (Jan 2026, broader piece): npr.org/2026/01/30/nx-s1-5693219/chatgpt-chatbot-ai-health-medical-advice + +**Caveats:** All sources note these are unverified user-generated claims. + +--- + +## Thematic threads to develop + +## Proposed arc + +### 1. Pattern matching is undervalued + +The medical cases show that matching against known patterns across domains is already powerful enough to save lives. The critics who dismiss this as shallow are wrong. + +- The sleep apnea case: Claude matched a constellation of symptoms (positional headaches, snoring, dialysis) against a statistical pattern (40-57% of dialysis patients have undiagnosed sleep apnea) that no individual specialist had synthesised. Each specialist saw their slice; the pattern matcher saw the whole. +- The Conyngham case: AI matched tumour mutation profiles against known immunological literature to identify viable vaccine targets. Pattern matching across genomics, immunology and pharmacology simultaneously. +- Hofstadter's contradictory proverbs: the critics' argument is essentially "don't judge a book by its cover". The medical cases are "where there's smoke, there's fire". Both are valid frames. The intelligence lies in selecting the right one. + +### 2. Pattern matching has a ceiling (the honest version of the critique) + +It can find what's already latent in existing knowledge. It can't construct new theories about unobserved phenomena. This is the honest "stochastic parrot" critique: not that pattern matching is worthless, but that it's insufficient for science at the frontier. Nobody pattern-matched their way to general relativity. + +### 3. Analogy bridges the gap (Hofstadter + Einstein) + +Einstein's equivalence principle started as a pattern match: a person falling from a roof feels weightless, and that *feels like* the absence of gravity. The analogy wasn't the proof. It was the generative spark that told him where to look. The mathematical formalisation came after. Analogy is the scout; causal reasoning is the surveyor. The scout finds the territory worth mapping. + +Hofstadter's Chapter 8 argument: "the history of mathematics and physics consists of a series of snowballing analogies." Einstein's analogical thinking preceded his mathematical formalisations. Analogy isn't shallow retrieval; it's *structural* similarity that suggests shared underlying principles. + +### 4. The bridge is already forming (world models) + +The Conyngham case sits between pure pattern matching and hypothesis construction. AlphaFold predicting which mutated proteins would be visible to the immune system isn't retrieval. It's a learned model of protein folding (built from patterns, but now capable of prediction about novel structures) generating a hypothesis about a specific dog's specific cancer. That's a narrow world model. + +World models that understand cause and effect can ask "what would happen if..." and follow the chain, the way Einstein followed his thought experiments. They're not an alternative to pattern matching. They're what pattern matching becomes when the patterns are rich enough to be generative. + +### 5. The software connection (bring it home to the reader) + +- "Idiomatic" literally means "looks like something we've seen before". When we praise idiomatic code, we're praising pattern matching. We're saying: this code selected the right analogy from a repertoire of known forms. +- Design patterns are named analogies. The Gang of Four book is a catalogue of "this situation is like that situation". Observer is an analogy to newspaper subscriptions. Factory is an analogy to a factory. +- Every abstraction in software is a metaphor: files, folders, windows, streams, pipes, threads, garbage collection. The entire conceptual vocabulary of computing is built on "this new thing is like that familiar thing." +- When an experienced developer looks at a problem and reaches for a known pattern, they're doing exactly what Hofstadter describes. When an LLM does the same thing, we call it "just" pattern matching. +- Idiomatic code is pattern matching. But the best architecture is analogical thinking: selecting the right structural metaphor for a system that doesn't exist yet. + +### Relationship to the blog's broader arc +- "Capability hyperinflation" argued that AI capability gains devalue planning horizons +- "Three paradoxes" explored Braess's Paradox (removing capacity can improve networks) +- "Software's second heroic age" (published today): individuals operating at institutional scale +- This piece extends: pattern matching as a capability previously only available at institutional scale (research teams, cross-disciplinary collaborations) now available to individuals. And the line from pattern matching through analogy to world models traces the trajectory of AI itself. + +--- + +## Hofstadter: Surfaces and Essences + +*Surfaces and Essences: Analogy as the Fuel and Fire of Thinking* (2013, Basic Books, 533pp). Co-authored with Emmanuel Sander. + +### Core thesis + +Analogy-making and categorisation are not two separate cognitive operations but "two sides of the same coin". Every act of recognising what something *is* is itself an act of analogy: matching a new perception against prior experience. + +> "The central thesis of our book, a simple yet nonstandard idea, is that the spotting of analogies pervades every moment of our thought, thus constituting thought's core." + +> "Every concept we have is essentially nothing but a tightly packaged bundle of analogies, and all we do when we think is to move fluidly from concept to concept — in other words, to leap from one analogy-bundle to another." + +> "It's the very blue that fills the whole sky of cognition — analogy is everything, or very nearly so, in my view." (from "Analogy as the Core of Cognition", 2001 essay) + +> "Without the ceaseless pulsating heartbeat of our 'categorization engine', we would understand nothing around us, could not reason in any form whatever, could not communicate with anyone else, and would have no basis on which to take any action." (p. 15) + +### Against the dismissal of analogy as shallow + +> "One should not think of analogy-making as a special variety of reasoning (as in the dull and uninspiring phrase 'analogical reasoning and problem-solving', a long-standing cliché in the cognitive-science world), for that is to do analogy a terrible disservice." + +> "Even if you think you're the one pulling the strings, you're merely a marionette unaware of its strings. You think you're consciously crafting an analogy to convey a particular standpoint, but in truth it's the other way around: your standpoint rests on a myriad of hidden analogies that prescribe a particular view of things." (p. 512) + +> "Unconscious analogical processes thus dominate how we interact with our environment; they underlie how we understand the world and the situations we find ourselves in." (p. 519) + +Alternative framing from a 1995 Wired interview: "by stripping away the irrelevancies, you obtain a conceptual nugget; then you step to the next one, which is closely related, and the next nugget you've stepped to takes you to some other domain after you put back all the irrelevancies." + +### The contradictory proverbs (Chapter 2, "The Evocation of Phrases", pp. 101-102) + +Hofstadter and Sander present pairs of proverbs that assert contradictory things: + +- "Don't judge a book by its cover" vs. "Where there's smoke, there's fire" +- "Look before you leap" vs. "He who hesitates is lost" +- "To thine own self be true" vs. "When in Rome, do as the Romans do" + +The argument: proverbs function as *categories*, not repositories of universal truth. A proverb is a compact analogical lens for framing a situation. The fact that contradictory proverbs coexist in a language proves they are not literal truths but analogical frameworks. We reach for whichever frame best matches the essence of the situation we're encountering. + +> "The use of a proverb as a label is a way of making sense, albeit perhaps a biased type of sense, of what one is seeing." + +> "These two opposite stances, embodied in short and familiar phrases, can be used to pin pithy labels on, and thus concisely categorize, novel situations that are very complex, thereby implicitly conveying entire attitudes about them." + +**The connection to the piece's argument:** Intelligence is not the application of fixed rules but the ability to select, from a vast repertoire of prior patterns, the one that best illuminates the current situation. This is directly analogous to how language models work. + +### Experts vs. novices (p. 342-346) + +"Experts perceive subtle features invisible to novices and associate hidden traits with surface-level cues." What distinguishes an expert is richer category repertoires: they see features that elude novices, and associate hidden traits with subtle surface cues. Expert observations are "doubly opaque" to novices. + +### Einstein and snowballing analogies (Chapter 8, "Analogies that Shook the World") + +~50 pages on Einstein's thought processes. "The history of mathematics and physics consists of a series of snowballing analogies." Einstein described as "the greatest metaphorical thinker". His thought experiments (riding alongside a beam of light) are paradigmatic acts of analogy. His analogical thinking preceded his mathematical formalisations. + +### How the contradictory proverbs could fit the piece + +The proverbs list could work as a structural device or opening. The argument against LLMs is essentially "don't judge a book by its cover" (surface-level pattern matching can't grasp deep truth), while the medical cases demonstrate "where there's smoke, there's fire" (surface patterns reliably point to underlying conditions). The two proverbs are contradictory, yet both are deployed by intelligent humans choosing the frame that fits. LLMs do the same thing. The question is not whether pattern matching is "real" thinking, but whether the right pattern has been matched to the situation at hand. + +Alternatively, the contradictory proverbs could illustrate a broader point about how even human wisdom is pattern matching all the way down. We just don't notice because we've dignified our patterns with names like "experience" and "intuition". + +--- + +## The foils: LeCun and Friston + +### Yann LeCun + +Chief AI Scientist at Meta. Turing Award winner. The most prominent critic of LLMs from within the AI research community. + +**Core position:** LLMs "cannot reason" and "cannot plan". They are "glorified autocomplete". Autoregressive token prediction is fundamentally insufficient for real understanding. + +> "A system trained on all the text in the world cannot understand the world the way a baby can after a few months of life." (recurring framing, X/Twitter, 2023-2024) + +He's called the idea that scaling LLMs will produce AGI "a mirage" and described autoregressive LLMs as having "exponentially diverging errors" because each token prediction compounds uncertainty. + +**Important distinction:** LeCun is NOT in the "stochastic parrots" camp (Bender/Gebru). Their critique comes from a safety/ethics direction. LeCun's prescription is to build *better* neural systems (world models), not to retreat from the approach. He wants more AI, differently architected. + +**His alternative: JEPA (Joint Embedding Predictive Architecture).** Published in "A Path Towards Autonomous Machine Intelligence" (June 2022). Rather than predicting raw inputs (tokens, pixels), JEPA predicts abstract representations. Two encoders map different views of reality into a shared embedding space. The system learns to predict one representation from the other. This, he argues, is how you get world models that support planning and causal reasoning. + +**What he thinks LLMs lack:** +1. Persistent memory and world state +2. Grounding (connection between words and physical reality) +3. Planning (can't search through possible action sequences) +4. Causal reasoning (captures correlations, not causes) +5. Learning efficiency (humans learn from far less data) + +**The tensions in his position (useful for the piece):** + +1. His proposed world models are themselves learned from data using neural networks. JEPA learns by processing large amounts of sensory data and extracting statistical regularities. The distinction between "mere pattern matching" in LLMs and "learning world models" in JEPA is arguably one of degree rather than kind. + +2. Moving goalposts: as LLMs get better at tasks LeCun said they couldn't do (reasoning, code generation, planning-like behaviour in chain-of-thought), the critique has had to adjust. He now distinguishes System 1 (intuitive) from System 2 (deliberate) thinking, arguing LLMs can only do System 1. But chain-of-thought blurs this. + +3. The Meta conflict: he publicly dismisses LLMs while his employer bets billions on Llama. + +4. The deepest tension (Jacob Steinhardt's point): a system trained to predict tokens well enough must develop internal representations that capture real-world structure, which may *be* a world model in practice. The training objective and the resulting capabilities are not the same thing. + +**Key sources:** +- "A Path Towards Autonomous Machine Intelligence" (2022 position paper, OpenReview) +- Lex Fridman interview, January 2024 +- Davos 2024 panel appearances +- X/Twitter feed (extremely active, most provocative one-liners appear here) + +--- + +### Karl Friston + +Professor of neuroscience at UCL. Creator of the Free Energy Principle. Approaching world models from physics and neuroscience rather than engineering. + +**On LLMs, bluntly:** + +> "Deep Learning is rubbish, largely because it doesn't have the calculus of the machinery or the physics to have a calculus of beliefs, a calculus of inference, of planning, of situational awareness." (Davos 2024) + +> "It's just a mapping between content and content." + +> "You can never be a large language model that prompts, that asks questions. You don't know what you don't know before asking the right questions." + +> "To be agentic, the large language model would have to start prompting you... information seeking. It's curiosity." + +> "If that's right, what you are saying is that large language models, in their failure to encode uncertainty, are extremely prone to psychiatric disorders." (drawing from clinical neuroscience: if most psychiatric disorders stem from failure to encode uncertainty, and LLMs structurally can't encode uncertainty...) + +On LLMs and science: + +> "No scientist goes out and trawls data. They carefully design an experiment that generates exactly the right kind of data to resolve their uncertainty about their hypothesis." (Singularity University podcast) + +**His framework: the Free Energy Principle and active inference.** + +All perception is prediction error minimisation. The brain generates predictions (patterns), compares them against sensory data, adjusts. A "world model" (generative model) is an internal model that generates predictions about sensory input and the consequences of actions. + +> "We are prediction machines. Our brains are based in the game of trying to make sense of all the sensory data that our senses gather in terms of what could have caused that." + +> "We don't extract information from sensory data. What we do is a much more constructive act of perception. It's a sort of inside-out generation of predictions." + +> "Your brain cells can compare what's coming in and what you thought should be coming in, and then the difference is the newsworthy information." + +> "Our brains probably entail the deepest, most expressive generative models in our knowable world. Here, 'deep' refers to hierarchical depth." + +**Intelligence defined:** + +> "Intelligence is sense making that can be characterized as gathering evidence for my world models." (Davos 2024) + +> "Intelligence requires not just statistical modeling... but recognizing structural composition." (Psychology Today, Feb 2025) + +What a genuine world model must do (in Friston's framework): +1. Predict the consequences of actions (not just the next token) +2. Encode uncertainty (know what it doesn't know) +3. Support information-seeking behaviour (curiosity) +4. Be grounded in sensorimotor interaction with an environment + +**Friston vs. LeCun (Davos 2024 panel):** + +Both want world models. Both agree current AI is fundamentally lacking. They disagree on how to build them. + +> "I think it's a very simple difference. I am committed to first principles and was trained as a physicist and think as a physicist. He is a really skillful engineer." (Friston on LeCun) + +LeCun thinks deep learning can be extended to support world models (via JEPA). Friston thinks deep learning's mathematical foundations are structurally incapable of encoding the uncertainty required. Friston's critique: LeCun's approach "sets the temperature to zero, removing uncertainty from the objective function." + +**THE KEY TENSION FOR THE PIECE (the gradient argument):** + +Friston's framework treats all cognition as prediction error minimisation on a continuum. Simple organisms do it with shallow models. Brains do it with deep hierarchical generative models. LLMs do it with next-token prediction over learned statistical regularities. The mechanism is universal. What varies is the depth, the encoding of uncertainty, the causal structure, and whether the system can act on the world. + +The man who says "all perception is prediction" also says LLMs are "just a mapping between content and content." Both statements are true simultaneously. The difference is not in kind but in what the prediction is built from. + +This is exactly the gradient argument: pattern matching → analogy → world models is a continuum of depth, not a set of discrete categories. + +**Key sources:** +- Davos 2024 panel: deniseholt.us/deep-learning-is-rubbish-friston-lecun-face-off-at-davos-2024/ +- "What Yann LeCun is Missing": deniseholt.substack.com/p/what-yann-lecun-is-missing-karl-friston +- Psychology Today, Feb 2025: psychologytoday.com/us/blog/experimentations/202502/designing-a-curious-machine-intelligence-that-actually-thinks +- Singularity University podcast: su.org/resources/how-free-energy-shapes-the-future-of-ai +- National Science Review, May 2024: pmc.ncbi.nlm.nih.gov/articles/PMC11060478/ +- "Generating meaning" paper, Trends in Cognitive Sciences, Feb 2024 +- IAI TV: iai.tv/articles/reality-is-a-creation-of-consciousness-auid-3365 +- VERSES / Active inference: diginomica.com/why-karl-friston-betting-cultivating-curiosity-sustainable-agi +- StarTalk podcast with Neil deGrasse Tyson, Oct 2024 (most accessible entry point) From 62bf0ed76358c7e493f9251ea2246f4bd6f0d408 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Fri, 3 Apr 2026 07:59:47 +0100 Subject: [PATCH 02/46] Strengthen argumentative structure and engage with LeCun/Friston critiques --- src/pages/blog/the-oldest-pattern-matcher.md | 34 ++++++++++++-------- 1 file changed, 20 insertions(+), 14 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 19362b8dd..692082cc8 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -1,7 +1,7 @@ --- author: 'hga' -title: 'The oldest pattern matcher' -description: 'Two people with no medical training, a chatbot and a protein-folding model did what years of specialist consultations could not.' +title: 'Just pattern matching' +description: 'The 2023 Nobel Prize' category: 'ai' layout: '../../layouts/BlogPost.astro' publishedDate: '2026-04-06' @@ -12,13 +12,13 @@ tags: - 'medicine' --- -

In 1985, Katalin Karikó left Hungary with her husband and two-year-old daughter. The government limited how much money citizens could take out of the country: $100. She sold the family car on the black market and sewed $1,246 into her daughter's teddy bear.

+

In 1985, Katalin Karikó left Hungary with her husband and two-year-old daughter. The government limited how much money citizens could take out of the country: $100. She sold the family car on the black market and sewed $1,246 into her daughter's teddy bear to evade detection at the checkpoints.

At Penn, she spent a decade trying to make synthetic mRNA work as a therapeutic. Nobody was interested. "The people who reviewed the grants said mRNA will not be a good therapeutic, so don't bother," her future collaborator [Drew Weissman](https://en.wikipedia.org/wiki/Drew_Weissman) recalled. The university demoted her in 1995. Three years later she met Weissman at a photocopier. -Their early experiments produced a problem that looked fatal: injected synthetic mRNA triggered violent inflammation because the immune system recognised the molecules as foreign and attacked. Karikó [modified a single nucleoside](https://pubmed.ncbi.nlm.nih.gov/16111635/) so the mRNA could slip past the body's pattern-matching machinery, evading one layer of recognition to arm another. The modified mRNA taught the immune system to identify the spike protein of a virus it had never encountered. *Nature* and *Science* rejected the paper. It was published in *Immunity* in 2005. Fifteen years later, that technique became the [Pfizer-BioNTech and Moderna COVID-19 vaccines](https://en.wikipedia.org/wiki/COVID-19_vaccine). Karikó and Weissman shared the [2023 Nobel Prize](https://www.nobelprize.org/prizes/medicine/2023/kariko/facts/). +Their early experiments produced a problem that looked fatal: injected synthetic mRNA triggered violent inflammation because the immune system recognised the molecules as foreign and attacked. Karikó [modified a single nucleoside](https://pubmed.ncbi.nlm.nih.gov/16111635/) so the mRNA could slip past the body's pattern-matching machinery, evading one layer of recognition to arm another. The modified mRNA taught the immune system to identify the spike protein of a virus it had never encountered. *Nature* and *Science* rejected the paper. Fifteen years later, that technique became the [Pfizer-BioNTech and Moderna COVID-19 vaccines](https://en.wikipedia.org/wiki/COVID-19_vaccine). Karikó and Weissman shared the [2023 Nobel Prize](https://www.nobelprize.org/prizes/medicine/2023/kariko/facts/). -Karikó spent her career negotiating with pattern matchers. The people who dismiss AI as "just pattern matching" have never reckoned with what it can do. +Karikó spent her career negotiating with pattern matchers. The people who dismiss AI as "just pattern matching" have never reckoned with what they can do. ## Recognition @@ -28,27 +28,31 @@ Conyngham used ChatGPT to research immunotherapy approaches, paid $3,000 to sequ -Days later, a user on Reddit [described feeding 25 years of medical records to Claude](https://www.reddit.com/r/ClaudeAI/comments/1s41fny/25_years_multiple_specialists_zero_answers_one/). His uncle, a 62-year-old man in India on dialysis three times a week, had suffered positional headaches his entire adult life. Neurologists, nephrologists: none found a cause. Claude spotted a pattern connecting the positional headaches, loud snoring and dialysis, referencing research showing that [40-57% of dialysis patients have undiagnosed sleep apnea](https://pubmed.ncbi.nlm.nih.gov/11207682/). A sleep study confirmed it: breathing stopping 119 times per night, oxygen dropping to 78%. A CPAP machine resolved headaches that had persisted for a quarter of a century. +And then, days later, a user on Reddit [described feeding 25 years of medical records to Claude](https://www.reddit.com/r/ClaudeAI/comments/1s41fny/25_years_multiple_specialists_zero_answers_one/). His uncle, a 62-year-old man in India on dialysis three times a week, had suffered positional headaches his entire adult life. Neurologists, nephrologists: none found a cause. Claude spotted a pattern connecting the positional headaches, loud snoring and dialysis, referencing research showing that [40-57% of dialysis patients have undiagnosed sleep apnea](https://pubmed.ncbi.nlm.nih.gov/11207682/). A sleep study confirmed it: breathing stopping 119 times per night, oxygen dropping to 78%. A CPAP machine resolved headaches that had persisted for a quarter of a century. -Each specialist had seen their own slice: the nephrologist saw kidneys, the neurologist saw headaches. Claude saw the constellation. In both cases the AI matched signals across domains that human specialists kept in separate departments. [Charles Janeway](https://en.wikipedia.org/wiki/Charles_Janeway), the immunologist who [named the immune system's own mechanism "pattern recognition receptors"](https://pmc.ncbi.nlm.nih.gov/articles/PMC3117407/) in 1989, would have recognised the principle. The innate immune system matches molecular patterns on pathogens without understanding what they mean. It just matches. Matching is enough to activate the deeper response. +Each specialist had seen their own slice: the nephrologist saw kidneys, the neurologist saw headaches. Claude saw the constellation. In both cases the AI matched signals across domains that human specialists kept in separate departments. [Charles Janeway](https://en.wikipedia.org/wiki/Charles_Janeway), the immunologist who [named the immune system's own mechanism "pattern recognition receptors"](https://pmc.ncbi.nlm.nih.gov/articles/PMC3117407/) in 1989, would have recognised the principle. -These are anecdotes, not clinical trials. An oncologist [urged caution in The Conversation](https://theconversation.com/a-man-used-ai-to-help-make-a-cancer-vaccine-for-his-dog-an-oncologist-urges-caution-278735): a single case, no control group, mast cell tumours that behave unpredictably. The Reddit post is unverified. The question is not whether these two cases prove that AI can practise medicine. It is what cognitive mechanism made them possible at all. +The innate immune system is the body's first responder: a set of receptors that recognise broad molecular patterns shared by whole classes of pathogen, without needing to have encountered any specific one before. It cannot learn or remember. It doesn't understand what the patterns mean. But matching is enough to activate the deeper response, the adaptive immune system that learns, remembers and targets with precision. Shallow recognition is the gatekeeper for deeper intelligence. ## Horror autotoxicus In 1901, [Paul Ehrlich](https://en.wikipedia.org/wiki/Paul_Ehrlich) coined the term *horror autotoxicus* to express his conviction that the immune system would never attack the body's own tissues. Self-destruction would be "[dysteleological to the highest degree](https://pioneerworks.org/broadcast/kavin-senapathy-paul-ehrlichs-horror-autotoxicus)". His authority was so formidable that evidence of autoimmunity was dismissed for fifty years, until a young researcher named [Noel Rose](https://pmc.ncbi.nlm.nih.gov/articles/PMC7983605/) proved that rabbits' immune systems could be made to attack their own thyroids. His mentor, trained in the lineage of Ehrlich's own students, made him repeat the experiment again and again. When the results became undeniable: "My boy, you've done it. You've demolished the dogma". -The immune system's pattern matcher can misfire. When it does, it attacks the self. Hallucination is the autoimmune disorder of artificial intelligence: the same mechanism that recognises threats matching against the wrong thing. +The immune system's pattern matcher can misfire. When it does, it attacks the self. Hallucination is the autoimmune disorder of artificial intelligence: the same mechanism that recognises threats matching against the wrong thing. Ehrlich's dogma held back immunology for half a century by insisting the mechanism couldn't do what it demonstrably did. The dismissal of LLMs as "just pattern matching" has the same shape: an authority-backed conviction that constrains recognition of what the system already achieves. [Yann LeCun](https://en.wikipedia.org/wiki/Yann_LeCun), Meta's chief AI scientist, calls LLMs "glorified autocomplete" and maintains they cannot reason or plan. His [position paper](https://openreview.net/pdf?id=BZ5a1r-kVsf) proposes Joint Embedding Predictive Architectures as the path toward genuine world models. [Karl Friston](https://en.wikipedia.org/wiki/Karl_Friston), the neuroscientist behind the [Free Energy Principle](https://en.wikipedia.org/wiki/Free_energy_principle), is [blunter](https://deniseholt.us/deep-learning-is-rubbish-friston-lecun-face-off-at-davos-2024/): "Deep learning is rubbish". LLMs are "just a mapping between content and content". His most striking line: large language models, in their failure to encode uncertainty, are "extremely prone to psychiatric disorders". -These are serious people. Pattern matching can find what is latent in existing knowledge, but it cannot construct new theories about unobserved phenomena. Nobody pattern-matched their way to general relativity. Yet both critics prescribe more AI, not less. LeCun wants world models. Friston wants active inference systems that encode uncertainty and curiosity. They are calling for deeper pattern matching, not a different kind of intelligence. +These are serious people, and the failures they point to are real. LLMs have what you might call a lethal trifecta: they can be confident, wrong and persuasive at the same time, with no internal signal distinguishing knowledge from confabulation. They are vulnerable to [prompt injection](https://simonwillison.net/2025/Jan/9/prompt-injection-attack/), where an attacker's instructions embedded in data override the system's own goals, because LLMs have no persistent self-model to violate. They cannot update their beliefs through interaction with the world. These are not minor engineering problems. They are architectural gaps. + +LeCun's proposed fix is [Joint Embedding Predictive Architecture](https://openreview.net/pdf?id=BZ5a1r-kVsf): rather than predicting the next token in a sequence, the system learns to predict abstract representations in a latent space, building an internal world model it can reason over without generating text. Friston's is [active inference](https://mitpress.mit.edu/9780262045353/active-inference/): agents that encode uncertainty as a first-class quantity, that act on their environment and learn from consequences, that are driven by something like curiosity to seek out information that would reduce their uncertainty. Both architectures would address the lethal trifecta directly. A system that encodes what it doesn't know cannot be confidently wrong. A system with a persistent self-model is harder to hijack. + +They may be right that current LLMs are insufficient for the hardest problems. Nobody pattern-matched their way to general relativity. But look at what both critics actually propose. LeCun's JEPA still learns by comparing representations and minimising prediction error, the same core operation as an LLM, applied to abstract structures rather than token sequences. Friston is more explicit: his Free Energy Principle holds that all perception, all cognition, is prediction error minimisation. The brain generates patterns, compares them against sensory data, adjusts. His own framework formalises intelligence as pattern matching. What he objects to in LLMs is not the matching but the shallowness: no uncertainty, no action, no updating through consequences. Both critics prescribe more AI, not less. Their proposed architectures add depth to the pattern matching. They do not replace it with something else. ## The dirty little secret -Janeway called the innate immune system's role "the immunologist's dirty little secret" because without its pattern recognition the adaptive immune system never activates. Shallow matching is the gatekeeper for deeper intelligence. The relationship holds beyond immunology. +Janeway called the innate immune system's role "the immunologist's dirty little secret" because immunologists had spent decades studying the sophisticated adaptive response while ignoring the crude pattern matching that gates it. Without innate recognition, the adaptive system never activates. The relationship holds beyond immunology. [Douglas Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) has spent four decades arguing that analogy is not a special variety of reasoning but the core of all cognition. In [*Surfaces and Essences*](https://en.wikipedia.org/wiki/Surfaces_and_Essences), he and Emmanuel Sander present pairs of proverbs that assert contradictory things: "don't judge a book by its cover" alongside "where there's smoke, there's fire". The critics' argument against LLMs is the first proverb: surface patterns cannot grasp deep truth. The medical cases are the second: surface patterns reliably point to underlying conditions. Both proverbs coexist in every language because they are not rules. They are analogical frames, and intelligence is selecting which frame fits. @@ -56,11 +60,11 @@ Janeway called the innate immune system's role "the immunologist's dirty little "Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. Pattern matching finds that a situation resembles a known one. Analogy asks why the resemblance holds, what structural property the two situations share. [Einstein's equivalence principle](https://en.wikipedia.org/wiki/Equivalence_principle) started as a pattern match: a person in freefall feels weightless, and that feels like the absence of gravity. The analogy was not the proof. It was the generative spark that told him where to point the mathematics. His analogical thinking preceded the formalisation. -Friston's own framework contains the same tension. His Free Energy Principle holds that all perception is prediction error minimisation: the brain generating patterns, comparing them against sensory data, adjusting. The man who formalised cognition as pattern matching also says LLMs are insufficient. Both positions are true. What varies is the depth of the model, the encoding of uncertainty, whether the system can act and learn from consequences. A difference of depth, not kind. +This is where Friston's critique and Hofstadter's framework converge. Friston says LLMs lack the depth to encode uncertainty and learn from action. Hofstadter says intelligence is selecting the right analogy from competing frames. Both describe a gradient. At one end, surface matching: token prediction, molecular pattern recognition, the innate immune response. At the other, something that looks like understanding: world models, structural analogy, adaptive immunity. The question is whether the gradient has a discontinuity, a point where pattern matching stops and "real" intelligence begins. Neither Friston's mathematics nor Hofstadter's cognitive science suggests one. ## Immune memory -In 430 BC, [Thucydides](https://en.wikipedia.org/wiki/Thucydides) contracted the [Plague of Athens](https://en.wikipedia.org/wiki/Plague_of_Athens) and survived. He observed that "the same man was never attacked twice" and that survivors nursed the sick because they "had now no fear for themselves". He was describing immune memory: the body recognising a pattern it had encountered before. He had no framework to explain the mechanism. The observation stood for 2,300 years before anyone did. +In 430 BC, [Thucydides](https://en.wikipedia.org/wiki/Thucydides) contracted the [Plague of Athens](https://en.wikipedia.org/wiki/Plague_of_Athens) and survived. He observed that "the same man was never attacked twice" and that survivors nursed the sick because they "had now no fear for themselves". He was describing immune memory: the body recognising a pattern it had encountered before. He had no framework to explain the mechanism, and the observation stood for 2,300 years before anyone did. The pattern matching worked long before the theory caught up. We may be in a similar lag with LLMs: the systems produce results we can observe but not yet fully explain. Software engineering has its own immune memory. When we praise code as "idiomatic" we are praising pattern matching: this solution selected the right analogy from a repertoire of known forms. [Design patterns](https://en.wikipedia.org/wiki/Design_Patterns) are named analogies. The Gang of Four catalogue is a compendium of "this situation is like that situation". Every abstraction in computing is a metaphor: files, folders, windows, streams, pipes, threads. @@ -68,9 +72,11 @@ When an experienced developer looks at an unfamiliar problem and reaches for a k ## Checkpoint +If the immune system can misfire (autoimmunity) and can be dismissed by authority (Ehrlich's dogma), can it also be right about something and still be held back? That is what [James Allison](https://en.wikipedia.org/wiki/James_P._Allison) discovered. + -[James Allison](https://en.wikipedia.org/wiki/James_P._Allison)'s mother died of lymphoma when he was ten. Two of his uncles died of cancer. His brother died of prostate cancer. At UC Berkeley in 1995, a postdoc in his lab injected mice with antibodies blocking CTLA-4, a molecular brake on T cells. Allison's reaction: "[What the hell? I've never seen that](https://cancerhistoryproject.com/article/jim-allison-believed-in-the-power-of-t-cellswhen-hardly-anyone-else-did/)". He demanded a double-blind repeat and measured the tumours himself. Cages labelled A, B, C, D. For weeks, all tumours grew. Then some cages stopped. Tumours necrosing, melting away. +Allison's mother died of lymphoma when he was ten. Two of his uncles died of cancer. His brother died of prostate cancer. At UC Berkeley in 1995, a postdoc in his lab injected mice with antibodies blocking CTLA-4, a molecular brake on T cells. Allison's reaction: "[What the hell? I've never seen that](https://cancerhistoryproject.com/article/jim-allison-believed-in-the-power-of-t-cellswhen-hardly-anyone-else-did/)". He demanded a double-blind repeat and measured the tumours himself. Cages labelled A, B, C, D. For weeks, all tumours grew. Then some cages stopped. Tumours necrosing, melting away. He spent three and a half years trying to persuade a pharmaceutical company to develop the humanised antibody. [Sharon Belvin](https://www.cancerresearch.org/stories/patients/sharon-belvin) was 22, diagnosed with stage 4 melanoma two weeks before her wedding. She enrolled in the first trial of ipilimumab, one of three responders out of seventeen. Her tumours disappeared. She is alive decades later. From 7e92425ffe61332a2d0586dcb130ffc5aed8a080 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 4 Apr 2026 18:27:26 +0100 Subject: [PATCH 03/46] Second draft: structural edits, idiom headings, engage with LeCun/Friston --- src/pages/blog/the-oldest-pattern-matcher.md | 54 +++++++++----------- 1 file changed, 24 insertions(+), 30 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 692082cc8..b37efdc43 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -14,47 +14,47 @@ tags:

In 1985, Katalin Karikó left Hungary with her husband and two-year-old daughter. The government limited how much money citizens could take out of the country: $100. She sold the family car on the black market and sewed $1,246 into her daughter's teddy bear to evade detection at the checkpoints.

-At Penn, she spent a decade trying to make synthetic mRNA work as a therapeutic. Nobody was interested. "The people who reviewed the grants said mRNA will not be a good therapeutic, so don't bother," her future collaborator [Drew Weissman](https://en.wikipedia.org/wiki/Drew_Weissman) recalled. The university demoted her in 1995. Three years later she met Weissman at a photocopier. +At Penn, she spent a decade trying to make synthetic mRNA work as a therapeutic. Nobody was interested. "The people who reviewed the grants said mRNA will not be a good therapeutic, so don't bother," her future collaborator [Drew Weissman](https://en.wikipedia.org/wiki/Drew_Weissman) recalled. The university demoted her in 1995. -Their early experiments produced a problem that looked fatal: injected synthetic mRNA triggered violent inflammation because the immune system recognised the molecules as foreign and attacked. Karikó [modified a single nucleoside](https://pubmed.ncbi.nlm.nih.gov/16111635/) so the mRNA could slip past the body's pattern-matching machinery, evading one layer of recognition to arm another. The modified mRNA taught the immune system to identify the spike protein of a virus it had never encountered. *Nature* and *Science* rejected the paper. Fifteen years later, that technique became the [Pfizer-BioNTech and Moderna COVID-19 vaccines](https://en.wikipedia.org/wiki/COVID-19_vaccine). Karikó and Weissman shared the [2023 Nobel Prize](https://www.nobelprize.org/prizes/medicine/2023/kariko/facts/). +The core problem was the body's own defences. Injected synthetic mRNA triggered violent inflammation because the immune system recognised the molecules as foreign and attacked. Karikó and Weissman [modified a single nucleoside](https://pubmed.ncbi.nlm.nih.gov/16111635/), disguising the mRNA so it could slip past the body's pattern-matching machinery the way the cash had been disguised inside the teddy bear. Once inside, it taught the immune system to recognise a threat it had never encountered. That technique became the basis for the [Pfizer-BioNTech and Moderna COVID-19 vaccines](https://en.wikipedia.org/wiki/COVID-19_vaccine), and Karikó and Weissman shared the [2023 Nobel Prize in Physiology or Medicine](https://www.nobelprize.org/prizes/medicine/2023/kariko/facts/) for the nucleoside modification that made it possible. Karikó spent her career negotiating with pattern matchers. The people who dismiss AI as "just pattern matching" have never reckoned with what they can do. -## Recognition +## Where there's smoke, there's fire -In March 2026, a Sydney tech entrepreneur named Paul Conyngham [designed a personalised cancer vaccine for his dog](https://fortune.com/2026/03/15/australian-tech-entrepreneur-ai-cancer-vaccine-dog-rosie-unsw-mrna/). His eight-year-old rescue Staffy, Rosie, was dying of [mast cell cancer](https://en.wikipedia.org/wiki/Mast_cell_tumor). Surgery and chemotherapy had failed. Vets gave her months. +Two stories broke within days of each other in March 2026. Both involved pattern matching. They illustrate different things. -Conyngham used ChatGPT to research immunotherapy approaches, paid $3,000 to sequence Rosie's tumour DNA at UNSW's Ramaciotti Centre for Genomics, then fed the mutations into [AlphaFold](https://en.wikipedia.org/wiki/AlphaFold) to predict which proteins would be visible to the immune system. He used Grok to help design an mRNA vaccine targeting those neoantigens, and Professor Pall Thordarson at [UNSW's RNA Institute synthesised it](https://news.unsw.edu.au/en/meet-the-man-who-designed-a-cancer-vaccine-for-his-dog). By mid-March the largest tumour had shrunk by roughly 75%. Thordarson called it the first personalised cancer vaccine designed for a dog. +In Sydney, a tech entrepreneur named Paul Conyngham [designed a personalised cancer vaccine for his dog](https://fortune.com/2026/03/15/australian-tech-entrepreneur-ai-cancer-vaccine-dog-rosie-unsw-mrna/). His eight-year-old rescue Staffy, Rosie, was dying of [mast cell cancer](https://en.wikipedia.org/wiki/Mast_cell_tumor). Surgery and chemotherapy had failed. Vets gave her months. Conyngham used ChatGPT to research immunotherapy approaches, paid $3,000 to sequence Rosie's tumour DNA at UNSW's Ramaciotti Centre for Genomics, then fed the mutations into [AlphaFold](https://en.wikipedia.org/wiki/AlphaFold) to predict which proteins would be visible to the immune system. He used Grok to help design an mRNA vaccine targeting those neoantigens, and Professor Pall Thordarson at [UNSW's RNA Institute synthesised it](https://news.unsw.edu.au/en/meet-the-man-who-designed-a-cancer-vaccine-for-his-dog). By mid-March the largest tumour had shrunk by roughly 75%. Thordarson called it the first personalised cancer vaccine designed for a dog. Here, AI was the design tool: matching across protein structures to find targets that a human researcher would have taken months to identify. -And then, days later, a user on Reddit [described feeding 25 years of medical records to Claude](https://www.reddit.com/r/ClaudeAI/comments/1s41fny/25_years_multiple_specialists_zero_answers_one/). His uncle, a 62-year-old man in India on dialysis three times a week, had suffered positional headaches his entire adult life. Neurologists, nephrologists: none found a cause. Claude spotted a pattern connecting the positional headaches, loud snoring and dialysis, referencing research showing that [40-57% of dialysis patients have undiagnosed sleep apnea](https://pubmed.ncbi.nlm.nih.gov/11207682/). A sleep study confirmed it: breathing stopping 119 times per night, oxygen dropping to 78%. A CPAP machine resolved headaches that had persisted for a quarter of a century. +The second story was quieter. A user on Reddit [described feeding 25 years of medical records to Claude](https://www.reddit.com/r/ClaudeAI/comments/1s41fny/25_years_multiple_specialists_zero_answers_one/). His uncle, a 62-year-old man in India on dialysis three times a week, had suffered positional headaches his entire adult life. Neurologists, nephrologists: none found a cause. Claude spotted a pattern connecting the positional headaches, loud snoring and dialysis, referencing research showing that [40-57% of dialysis patients have undiagnosed sleep apnea](https://pubmed.ncbi.nlm.nih.gov/11207682/). A sleep study confirmed it: breathing stopping 119 times per night, oxygen dropping to 78%. A CPAP machine resolved headaches that had persisted for a quarter of a century. Each specialist had seen their own slice: the nephrologist saw kidneys, the neurologist saw headaches. Claude saw the constellation. Here, AI was the diagnostician: matching across records that human specialists kept in separate departments. -Each specialist had seen their own slice: the nephrologist saw kidneys, the neurologist saw headaches. Claude saw the constellation. In both cases the AI matched signals across domains that human specialists kept in separate departments. [Charles Janeway](https://en.wikipedia.org/wiki/Charles_Janeway), the immunologist who [named the immune system's own mechanism "pattern recognition receptors"](https://pmc.ncbi.nlm.nih.gov/articles/PMC3117407/) in 1989, would have recognised the principle. +[Charles Janeway](https://en.wikipedia.org/wiki/Charles_Janeway), the immunologist who [named the immune system's own mechanism "pattern recognition receptors"](https://pmc.ncbi.nlm.nih.gov/articles/PMC3117407/) in 1989, would have recognised the principle in both cases. The term was not a metaphor. The immune system has been pattern matching for longer than any nervous system has existed, and Janeway's insight was that the mechanism deserved the name. -The innate immune system is the body's first responder: a set of receptors that recognise broad molecular patterns shared by whole classes of pathogen, without needing to have encountered any specific one before. It cannot learn or remember. It doesn't understand what the patterns mean. But matching is enough to activate the deeper response, the adaptive immune system that learns, remembers and targets with precision. Shallow recognition is the gatekeeper for deeper intelligence. +The innate immune system, the layer Janeway studied, is the body's first responder: a set of receptors that recognise broad molecular patterns shared by whole classes of pathogen, without needing to have encountered any specific one before. It cannot learn or remember. It doesn't understand what the patterns mean. But matching is enough to activate the adaptive immune system: the layer that learns from encounters, remembers specific threats and manufactures antibodies targeted to a particular pathogen. Without the innate system's crude pattern matching, none of that machinery switches on. -## Horror autotoxicus +## Fortune favours the bold -In 1901, [Paul Ehrlich](https://en.wikipedia.org/wiki/Paul_Ehrlich) coined the term *horror autotoxicus* to express his conviction that the immune system would never attack the body's own tissues. Self-destruction would be "[dysteleological to the highest degree](https://pioneerworks.org/broadcast/kavin-senapathy-paul-ehrlichs-horror-autotoxicus)". His authority was so formidable that evidence of autoimmunity was dismissed for fifty years, until a young researcher named [Noel Rose](https://pmc.ncbi.nlm.nih.gov/articles/PMC7983605/) proved that rabbits' immune systems could be made to attack their own thyroids. His mentor, trained in the lineage of Ehrlich's own students, made him repeat the experiment again and again. When the results became undeniable: "My boy, you've done it. You've demolished the dogma". - -The immune system's pattern matcher can misfire. When it does, it attacks the self. Hallucination is the autoimmune disorder of artificial intelligence: the same mechanism that recognises threats matching against the wrong thing. Ehrlich's dogma held back immunology for half a century by insisting the mechanism couldn't do what it demonstrably did. The dismissal of LLMs as "just pattern matching" has the same shape: an authority-backed conviction that constrains recognition of what the system already achieves. - - + [Yann LeCun](https://en.wikipedia.org/wiki/Yann_LeCun), Meta's chief AI scientist, calls LLMs "glorified autocomplete" and maintains they cannot reason or plan. His [position paper](https://openreview.net/pdf?id=BZ5a1r-kVsf) proposes Joint Embedding Predictive Architectures as the path toward genuine world models. [Karl Friston](https://en.wikipedia.org/wiki/Karl_Friston), the neuroscientist behind the [Free Energy Principle](https://en.wikipedia.org/wiki/Free_energy_principle), is [blunter](https://deniseholt.us/deep-learning-is-rubbish-friston-lecun-face-off-at-davos-2024/): "Deep learning is rubbish". LLMs are "just a mapping between content and content". His most striking line: large language models, in their failure to encode uncertainty, are "extremely prone to psychiatric disorders". -These are serious people, and the failures they point to are real. LLMs have what you might call a lethal trifecta: they can be confident, wrong and persuasive at the same time, with no internal signal distinguishing knowledge from confabulation. They are vulnerable to [prompt injection](https://simonwillison.net/2025/Jan/9/prompt-injection-attack/), where an attacker's instructions embedded in data override the system's own goals, because LLMs have no persistent self-model to violate. They cannot update their beliefs through interaction with the world. These are not minor engineering problems. They are architectural gaps. +These are serious people, and the failures they point to are real. LLMs can be confident, wrong and persuasive at the same time, with no internal signal distinguishing knowledge from confabulation. They are vulnerable to [prompt injection](https://simonwillison.net/2025/Jan/9/prompt-injection-attack/): Simon Willison's [lethal trifecta](https://simonwillison.net/2023/Apr/25/dual-llm-pattern/) describes the danger of systems that combine access to private data, exposure to untrusted input and the ability to take actions, because LLMs have no persistent self-model that an attacker's instructions can be checked against. They cannot update their beliefs through interaction with the world. These are not minor engineering problems. They are architectural gaps. -LeCun's proposed fix is [Joint Embedding Predictive Architecture](https://openreview.net/pdf?id=BZ5a1r-kVsf): rather than predicting the next token in a sequence, the system learns to predict abstract representations in a latent space, building an internal world model it can reason over without generating text. Friston's is [active inference](https://mitpress.mit.edu/9780262045353/active-inference/): agents that encode uncertainty as a first-class quantity, that act on their environment and learn from consequences, that are driven by something like curiosity to seek out information that would reduce their uncertainty. Both architectures would address the lethal trifecta directly. A system that encodes what it doesn't know cannot be confidently wrong. A system with a persistent self-model is harder to hijack. +LeCun's proposed fix is [Joint Embedding Predictive Architecture](https://openreview.net/pdf?id=BZ5a1r-kVsf): rather than predicting the next token in a sequence, the system learns to predict abstract representations in a latent space, building an internal world model it can reason over without generating text. Friston's is [active inference](https://mitpress.mit.edu/9780262045353/active-inference/): agents that encode uncertainty as a first-class quantity, that act on their environment and learn from consequences, that are driven by something like curiosity to seek out information that would reduce their uncertainty. Both architectures would address these gaps directly. A system that encodes what it doesn't know cannot be confidently wrong. A system with a persistent self-model is harder to hijack. + +The immune system has its own version of this problem. Its pattern matcher can misfire: when it does, it attacks the self. In 1901, [Paul Ehrlich](https://en.wikipedia.org/wiki/Paul_Ehrlich) coined the term [*horror autotoxicus*](https://pioneerworks.org/broadcast/kavin-senapathy-paul-ehrlichs-horror-autotoxicus) to insist this could never happen, and his authority suppressed evidence of autoimmunity for fifty years. Hallucination is the autoimmune disorder of artificial intelligence: the same mechanism that recognises threats, matching against the wrong thing. The critics are right that this matters. They may be right that current LLMs are insufficient for the hardest problems. Nobody pattern-matched their way to general relativity. But look at what both critics actually propose. LeCun's JEPA still learns by comparing representations and minimising prediction error, the same core operation as an LLM, applied to abstract structures rather than token sequences. Friston is more explicit: his Free Energy Principle holds that all perception, all cognition, is prediction error minimisation. The brain generates patterns, compares them against sensory data, adjusts. His own framework formalises intelligence as pattern matching. What he objects to in LLMs is not the matching but the shallowness: no uncertainty, no action, no updating through consequences. Both critics prescribe more AI, not less. Their proposed architectures add depth to the pattern matching. They do not replace it with something else. -## The dirty little secret +## Still waters run deep + +Janeway called the innate immune system's role "the immunologist's dirty little secret" because immunologists had spent decades studying the sophisticated adaptive response while ignoring the crude pattern matching that gates it. But the adaptive response is pattern matching too. B cells generate antibodies through random recombination, and the ones whose shape matches a pathogen's surface get selected and amplified. T cells match fragments of proteins displayed on cell surfaces. The entire immune system, from the innate receptors that recognise broad molecular shapes shared by whole classes of pathogen to the adaptive system that learns to target a specific one, is pattern matching at different depths. The question was never whether the immune system "really understands" the threats it fights. It matches, and the matching works. The sophistication is in the depth, not in some qualitative leap beyond matching. -Janeway called the innate immune system's role "the immunologist's dirty little secret" because immunologists had spent decades studying the sophisticated adaptive response while ignoring the crude pattern matching that gates it. Without innate recognition, the adaptive system never activates. The relationship holds beyond immunology. +Recombination and selection: the same mechanism whether the substrate is gene segments, cultural ideas or token sequences. The same gradient appears outside immunology. -[Douglas Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) has spent four decades arguing that analogy is not a special variety of reasoning but the core of all cognition. In [*Surfaces and Essences*](https://en.wikipedia.org/wiki/Surfaces_and_Essences), he and Emmanuel Sander present pairs of proverbs that assert contradictory things: "don't judge a book by its cover" alongside "where there's smoke, there's fire". The critics' argument against LLMs is the first proverb: surface patterns cannot grasp deep truth. The medical cases are the second: surface patterns reliably point to underlying conditions. Both proverbs coexist in every language because they are not rules. They are analogical frames, and intelligence is selecting which frame fits. +[Douglas Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) has spent four decades arguing that analogy is not a special variety of reasoning but the core of all cognition. In [*Surfaces and Essences*](https://en.wikipedia.org/wiki/Surfaces_and_Essences), he and Emmanuel Sander present pairs of proverbs that assert contradictory things: "where there's smoke, there's fire" alongside "don't judge a book by its cover". For every proverb proposing a point of view, there is one for the opposing point of view. They are not rules. They are analogical frames, and intelligence is selecting which frame fits the situation. The headings of this piece are drawn from Hofstadter's pairs. The critics' argument against LLMs is "don't judge a book by its cover": surface patterns cannot grasp deep truth. The medical cases are "where there's smoke, there's fire": surface patterns reliably point to underlying conditions. Both proverbs coexist in every language because both are sometimes right. @@ -62,7 +62,7 @@ Janeway called the innate immune system's role "the immunologist's dirty little This is where Friston's critique and Hofstadter's framework converge. Friston says LLMs lack the depth to encode uncertainty and learn from action. Hofstadter says intelligence is selecting the right analogy from competing frames. Both describe a gradient. At one end, surface matching: token prediction, molecular pattern recognition, the innate immune response. At the other, something that looks like understanding: world models, structural analogy, adaptive immunity. The question is whether the gradient has a discontinuity, a point where pattern matching stops and "real" intelligence begins. Neither Friston's mathematics nor Hofstadter's cognitive science suggests one. -## Immune memory +## Practice makes perfect In 430 BC, [Thucydides](https://en.wikipedia.org/wiki/Thucydides) contracted the [Plague of Athens](https://en.wikipedia.org/wiki/Plague_of_Athens) and survived. He observed that "the same man was never attacked twice" and that survivors nursed the sick because they "had now no fear for themselves". He was describing immune memory: the body recognising a pattern it had encountered before. He had no framework to explain the mechanism, and the observation stood for 2,300 years before anyone did. The pattern matching worked long before the theory caught up. We may be in a similar lag with LLMs: the systems produce results we can observe but not yet fully explain. @@ -70,19 +70,13 @@ Software engineering has its own immune memory. When we praise code as "idiomati When an experienced developer looks at an unfamiliar problem and reaches for a known pattern, they are doing what Hofstadter describes: stripping away irrelevancies to obtain a conceptual nugget, then stepping to a related one in another domain. When an LLM does the same thing, we call it shallow. But AlphaFold sits at the interesting middle of the gradient. It is a model built from patterns in protein sequence data, now capable of predicting the three-dimensional structure of proteins it has never encountered. That is not retrieval. It is pattern matching deep enough to be generative: a narrow world model, emerging from the mechanism LeCun dismisses. -## Checkpoint - -If the immune system can misfire (autoimmunity) and can be dismissed by authority (Ehrlich's dogma), can it also be right about something and still be held back? That is what [James Allison](https://en.wikipedia.org/wiki/James_P._Allison) discovered. - - - -Allison's mother died of lymphoma when he was ten. Two of his uncles died of cancer. His brother died of prostate cancer. At UC Berkeley in 1995, a postdoc in his lab injected mice with antibodies blocking CTLA-4, a molecular brake on T cells. Allison's reaction: "[What the hell? I've never seen that](https://cancerhistoryproject.com/article/jim-allison-believed-in-the-power-of-t-cellswhen-hardly-anyone-else-did/)". He demanded a double-blind repeat and measured the tumours himself. Cages labelled A, B, C, D. For weeks, all tumours grew. Then some cages stopped. Tumours necrosing, melting away. +## Every dog has its day -He spent three and a half years trying to persuade a pharmaceutical company to develop the humanised antibody. [Sharon Belvin](https://www.cancerresearch.org/stories/patients/sharon-belvin) was 22, diagnosed with stage 4 melanoma two weeks before her wedding. She enrolled in the first trial of ipilimumab, one of three responders out of seventeen. Her tumours disappeared. She is alive decades later. + -Allison didn't teach the immune system anything new. He removed a checkpoint, a constraint on pattern matching the body was already doing. The T cells already recognised the cancer. They were being held back. +Karikó's career was a sequence of checkpoints. The Hungarian border. The grant reviewers. The university that demoted her. The immune system that attacked her synthetic mRNA. In every case, a pattern matcher stood between her and the next stage: recognising something foreign and blocking it. Her breakthrough was not to bypass the immune system's pattern matching but to speak its language, modifying a single nucleoside so the mRNA no longer triggered the alarm. She didn't dismiss the immune system's recognition as shallow. She worked with it. -The debate about whether LLMs "really think" is a checkpoint of its own. It constrains recognition of what these systems already achieve while the field waits for an architecture that may turn out to be pattern matching at greater depth. Karikó spent a career learning the immune system's vocabulary, finding its blind spots, teaching it new patterns. She didn't dismiss its recognition as shallow. She worked with it. +The debate about whether LLMs "really think" is a checkpoint of its own. It constrains recognition of what these systems already achieve while the field waits for an architecture that may turn out to be pattern matching at greater depth. Conyngham didn't wait. He used the tools that existed, pattern matchers all of them, and his dog's tumours shrank. The Reddit user didn't wait. He fed 25 years of records to Claude and got an answer that had eluded specialists for decades. The teddy bear made it out of Hungary. The vaccine made it into the body. Both had to pass a checkpoint to do their work. From e31c2382798068f63f7ca39eabc32b4898625f00 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 4 Apr 2026 18:40:08 +0100 Subject: [PATCH 04/46] Third draft: simplify intro, cut Ehrlich, fix language guidance violations, Einstein analogies --- src/pages/blog/the-oldest-pattern-matcher.md | 28 +++++++++----------- 1 file changed, 12 insertions(+), 16 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index b37efdc43..b23721f0d 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -14,11 +14,7 @@ tags:

In 1985, Katalin Karikó left Hungary with her husband and two-year-old daughter. The government limited how much money citizens could take out of the country: $100. She sold the family car on the black market and sewed $1,246 into her daughter's teddy bear to evade detection at the checkpoints.

-At Penn, she spent a decade trying to make synthetic mRNA work as a therapeutic. Nobody was interested. "The people who reviewed the grants said mRNA will not be a good therapeutic, so don't bother," her future collaborator [Drew Weissman](https://en.wikipedia.org/wiki/Drew_Weissman) recalled. The university demoted her in 1995. - -The core problem was the body's own defences. Injected synthetic mRNA triggered violent inflammation because the immune system recognised the molecules as foreign and attacked. Karikó and Weissman [modified a single nucleoside](https://pubmed.ncbi.nlm.nih.gov/16111635/), disguising the mRNA so it could slip past the body's pattern-matching machinery the way the cash had been disguised inside the teddy bear. Once inside, it taught the immune system to recognise a threat it had never encountered. That technique became the basis for the [Pfizer-BioNTech and Moderna COVID-19 vaccines](https://en.wikipedia.org/wiki/COVID-19_vaccine), and Karikó and Weissman shared the [2023 Nobel Prize in Physiology or Medicine](https://www.nobelprize.org/prizes/medicine/2023/kariko/facts/) for the nucleoside modification that made it possible. - -Karikó spent her career negotiating with pattern matchers. The people who dismiss AI as "just pattern matching" have never reckoned with what they can do. +At Penn, she spent a decade on mRNA research that nobody wanted to fund, trying to get a synthetic payload past the body's pattern-matching defences the way she'd got the cash past the border guards. She and [Drew Weissman](https://en.wikipedia.org/wiki/Drew_Weissman) [found a way](https://pubmed.ncbi.nlm.nih.gov/16111635/). It won them the [2023 Nobel Prize in Physiology or Medicine](https://www.nobelprize.org/prizes/medicine/2023/kariko/facts/) and became the basis for the [COVID-19 vaccines](https://en.wikipedia.org/wiki/COVID-19_vaccine). The people who dismiss AI as "just pattern matching" have never reckoned with what pattern matchers can do. ## Where there's smoke, there's fire @@ -36,21 +32,21 @@ The innate immune system, the layer Janeway studied, is the body's first respond ## Fortune favours the bold - + -[Yann LeCun](https://en.wikipedia.org/wiki/Yann_LeCun), Meta's chief AI scientist, calls LLMs "glorified autocomplete" and maintains they cannot reason or plan. His [position paper](https://openreview.net/pdf?id=BZ5a1r-kVsf) proposes Joint Embedding Predictive Architectures as the path toward genuine world models. [Karl Friston](https://en.wikipedia.org/wiki/Karl_Friston), the neuroscientist behind the [Free Energy Principle](https://en.wikipedia.org/wiki/Free_energy_principle), is [blunter](https://deniseholt.us/deep-learning-is-rubbish-friston-lecun-face-off-at-davos-2024/): "Deep learning is rubbish". LLMs are "just a mapping between content and content". His most striking line: large language models, in their failure to encode uncertainty, are "extremely prone to psychiatric disorders". +[Yann LeCun](https://en.wikipedia.org/wiki/Yann_LeCun), Meta's chief AI scientist, calls LLMs "glorified autocomplete" and maintains they cannot reason or plan. His [position paper](https://openreview.net/pdf?id=BZ5a1r-kVsf) proposes Joint Embedding Predictive Architectures as the path toward world models. [Karl Friston](https://en.wikipedia.org/wiki/Karl_Friston), the neuroscientist behind the [Free Energy Principle](https://en.wikipedia.org/wiki/Free_energy_principle), is [blunter](https://deniseholt.us/deep-learning-is-rubbish-friston-lecun-face-off-at-davos-2024/): "Deep learning is rubbish". LLMs are "just a mapping between content and content". Large language models, in their failure to encode uncertainty, are "extremely prone to psychiatric disorders". These are serious people, and the failures they point to are real. LLMs can be confident, wrong and persuasive at the same time, with no internal signal distinguishing knowledge from confabulation. They are vulnerable to [prompt injection](https://simonwillison.net/2025/Jan/9/prompt-injection-attack/): Simon Willison's [lethal trifecta](https://simonwillison.net/2023/Apr/25/dual-llm-pattern/) describes the danger of systems that combine access to private data, exposure to untrusted input and the ability to take actions, because LLMs have no persistent self-model that an attacker's instructions can be checked against. They cannot update their beliefs through interaction with the world. These are not minor engineering problems. They are architectural gaps. LeCun's proposed fix is [Joint Embedding Predictive Architecture](https://openreview.net/pdf?id=BZ5a1r-kVsf): rather than predicting the next token in a sequence, the system learns to predict abstract representations in a latent space, building an internal world model it can reason over without generating text. Friston's is [active inference](https://mitpress.mit.edu/9780262045353/active-inference/): agents that encode uncertainty as a first-class quantity, that act on their environment and learn from consequences, that are driven by something like curiosity to seek out information that would reduce their uncertainty. Both architectures would address these gaps directly. A system that encodes what it doesn't know cannot be confidently wrong. A system with a persistent self-model is harder to hijack. -The immune system has its own version of this problem. Its pattern matcher can misfire: when it does, it attacks the self. In 1901, [Paul Ehrlich](https://en.wikipedia.org/wiki/Paul_Ehrlich) coined the term [*horror autotoxicus*](https://pioneerworks.org/broadcast/kavin-senapathy-paul-ehrlichs-horror-autotoxicus) to insist this could never happen, and his authority suppressed evidence of autoimmunity for fifty years. Hallucination is the autoimmune disorder of artificial intelligence: the same mechanism that recognises threats, matching against the wrong thing. The critics are right that this matters. +The immune system's pattern matcher can misfire too: autoimmune disorders are what happens when the same mechanism that recognises threats matches against the body's own tissue. Hallucination is the AI equivalent, the same recognition machinery matching against the wrong thing. -They may be right that current LLMs are insufficient for the hardest problems. Nobody pattern-matched their way to general relativity. But look at what both critics actually propose. LeCun's JEPA still learns by comparing representations and minimising prediction error, the same core operation as an LLM, applied to abstract structures rather than token sequences. Friston is more explicit: his Free Energy Principle holds that all perception, all cognition, is prediction error minimisation. The brain generates patterns, compares them against sensory data, adjusts. His own framework formalises intelligence as pattern matching. What he objects to in LLMs is not the matching but the shallowness: no uncertainty, no action, no updating through consequences. Both critics prescribe more AI, not less. Their proposed architectures add depth to the pattern matching. They do not replace it with something else. +They may be right that current LLMs are insufficient for the hardest problems. Nobody pattern-matched their way to general relativity, though as Hofstadter and Sander document in [*Surfaces and Essences*](https://en.wikipedia.org/wiki/Surfaces_and_Essences), Einstein was a prolific and deliberate user of analogy: the equivalence principle began as a pattern match between freefall and the absence of gravity. Even the paradigm case of transcendent genius turns out to involve pattern matching at depth. But look at what both critics actually propose. LeCun's JEPA still learns by comparing representations and minimising prediction error, the same core operation as an LLM, applied to abstract structures rather than token sequences. Friston is more explicit: his Free Energy Principle holds that all perception, all cognition, is prediction error minimisation. The brain generates patterns, compares them against sensory data, adjusts. His own framework formalises intelligence as pattern matching. What he objects to in LLMs is not the matching but the shallowness: no uncertainty, no action, no updating through consequences. Both critics prescribe more AI, not less. Their proposed architectures add depth to the pattern matching. They do not replace it with something else. ## Still waters run deep -Janeway called the innate immune system's role "the immunologist's dirty little secret" because immunologists had spent decades studying the sophisticated adaptive response while ignoring the crude pattern matching that gates it. But the adaptive response is pattern matching too. B cells generate antibodies through random recombination, and the ones whose shape matches a pathogen's surface get selected and amplified. T cells match fragments of proteins displayed on cell surfaces. The entire immune system, from the innate receptors that recognise broad molecular shapes shared by whole classes of pathogen to the adaptive system that learns to target a specific one, is pattern matching at different depths. The question was never whether the immune system "really understands" the threats it fights. It matches, and the matching works. The sophistication is in the depth, not in some qualitative leap beyond matching. +Janeway called the innate immune system's role "the immunologist's dirty little secret" because immunologists had spent decades studying the sophisticated adaptive response while ignoring the crude pattern matching that gates it. But the adaptive response is pattern matching too. B cells generate antibodies through random recombination, and the ones whose shape matches a pathogen's surface get selected and amplified. T cells match fragments of proteins displayed on cell surfaces. The entire immune system, from the innate receptors that recognise broad molecular shapes shared by whole classes of pathogen to the adaptive system that learns to target a specific one, is pattern matching at different depths. Nobody asks whether the immune system "really understands" the threats it fights. It matches, and the matching works. The sophistication is in the depth, not in some qualitative leap beyond matching. Recombination and selection: the same mechanism whether the substrate is gene segments, cultural ideas or token sequences. The same gradient appears outside immunology. @@ -58,9 +54,9 @@ Recombination and selection: the same mechanism whether the substrate is gene se -"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. Pattern matching finds that a situation resembles a known one. Analogy asks why the resemblance holds, what structural property the two situations share. [Einstein's equivalence principle](https://en.wikipedia.org/wiki/Equivalence_principle) started as a pattern match: a person in freefall feels weightless, and that feels like the absence of gravity. The analogy was not the proof. It was the generative spark that told him where to point the mathematics. His analogical thinking preceded the formalisation. +"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. Pattern matching finds that a situation resembles a known one. Analogy asks why the resemblance holds, what structural property the two situations share. Hofstadter and Sander walk through Einstein's analogies one by one: the equivalence principle, the rotating disc that led him to curved spacetime, the analogies between Maxwell's equations and the geometry of the universe. In each case the analogy preceded the formalisation. It was the generative spark that told him where to point the mathematics. -This is where Friston's critique and Hofstadter's framework converge. Friston says LLMs lack the depth to encode uncertainty and learn from action. Hofstadter says intelligence is selecting the right analogy from competing frames. Both describe a gradient. At one end, surface matching: token prediction, molecular pattern recognition, the innate immune response. At the other, something that looks like understanding: world models, structural analogy, adaptive immunity. The question is whether the gradient has a discontinuity, a point where pattern matching stops and "real" intelligence begins. Neither Friston's mathematics nor Hofstadter's cognitive science suggests one. +This is where Friston's critique and Hofstadter's framework converge. Friston says LLMs lack the depth to encode uncertainty and learn from action. Hofstadter says intelligence is selecting the right analogy from competing frames. Both describe a gradient. At one end, surface matching: token prediction, molecular pattern recognition, the innate immune response. At the other, something that looks like understanding: world models, structural analogy, adaptive immunity. Does the gradient have a discontinuity, a point where pattern matching stops and "real" intelligence begins? Neither Friston's mathematics nor Hofstadter's cognitive science suggests one. ## Practice makes perfect @@ -68,17 +64,17 @@ In 430 BC, [Thucydides](https://en.wikipedia.org/wiki/Thucydides) contracted the Software engineering has its own immune memory. When we praise code as "idiomatic" we are praising pattern matching: this solution selected the right analogy from a repertoire of known forms. [Design patterns](https://en.wikipedia.org/wiki/Design_Patterns) are named analogies. The Gang of Four catalogue is a compendium of "this situation is like that situation". Every abstraction in computing is a metaphor: files, folders, windows, streams, pipes, threads. -When an experienced developer looks at an unfamiliar problem and reaches for a known pattern, they are doing what Hofstadter describes: stripping away irrelevancies to obtain a conceptual nugget, then stepping to a related one in another domain. When an LLM does the same thing, we call it shallow. But AlphaFold sits at the interesting middle of the gradient. It is a model built from patterns in protein sequence data, now capable of predicting the three-dimensional structure of proteins it has never encountered. That is not retrieval. It is pattern matching deep enough to be generative: a narrow world model, emerging from the mechanism LeCun dismisses. +When an experienced developer looks at an unfamiliar problem and reaches for a known pattern, they are doing what Hofstadter describes: stripping away irrelevancies to obtain a conceptual nugget, then stepping to a related one in another domain. When an LLM does the same thing, we call it shallow. But AlphaFold sits in the middle of the gradient. It is a model built from patterns in protein sequence data, now capable of predicting the three-dimensional structure of proteins it has never encountered. That is not retrieval. It is pattern matching deep enough to be generative: a narrow world model, emerging from the mechanism LeCun dismisses. ## Every dog has its day - + -Karikó's career was a sequence of checkpoints. The Hungarian border. The grant reviewers. The university that demoted her. The immune system that attacked her synthetic mRNA. In every case, a pattern matcher stood between her and the next stage: recognising something foreign and blocking it. Her breakthrough was not to bypass the immune system's pattern matching but to speak its language, modifying a single nucleoside so the mRNA no longer triggered the alarm. She didn't dismiss the immune system's recognition as shallow. She worked with it. +Karikó's career was a sequence of checkpoints. The Hungarian border. The grant reviewers. The university that demoted her. The immune system that attacked her synthetic mRNA. In every case, a pattern matcher stood between her and the next stage: recognising something foreign and blocking it. She spoke the immune system's language, modifying a single nucleoside so the mRNA no longer triggered the alarm. She didn't dismiss the immune system's recognition as shallow, she worked with it. The debate about whether LLMs "really think" is a checkpoint of its own. It constrains recognition of what these systems already achieve while the field waits for an architecture that may turn out to be pattern matching at greater depth. Conyngham didn't wait. He used the tools that existed, pattern matchers all of them, and his dog's tumours shrank. The Reddit user didn't wait. He fed 25 years of records to Claude and got an answer that had eluded specialists for decades. -The teddy bear made it out of Hungary. The vaccine made it into the body. Both had to pass a checkpoint to do their work. +The teddy bear made it out of Hungary, the vaccine made it into the body, and both had to pass a checkpoint to do their work. --- From 645771798f3f4643380aae379eab51ce33ff4ed0 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 4 Apr 2026 18:46:18 +0100 Subject: [PATCH 05/46] Thread innate/adaptive composite analogy through the piece --- src/pages/blog/the-oldest-pattern-matcher.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index b23721f0d..8fe488fc0 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -14,11 +14,11 @@ tags:

In 1985, Katalin Karikó left Hungary with her husband and two-year-old daughter. The government limited how much money citizens could take out of the country: $100. She sold the family car on the black market and sewed $1,246 into her daughter's teddy bear to evade detection at the checkpoints.

-At Penn, she spent a decade on mRNA research that nobody wanted to fund, trying to get a synthetic payload past the body's pattern-matching defences the way she'd got the cash past the border guards. She and [Drew Weissman](https://en.wikipedia.org/wiki/Drew_Weissman) [found a way](https://pubmed.ncbi.nlm.nih.gov/16111635/). It won them the [2023 Nobel Prize in Physiology or Medicine](https://www.nobelprize.org/prizes/medicine/2023/kariko/facts/) and became the basis for the [COVID-19 vaccines](https://en.wikipedia.org/wiki/COVID-19_vaccine). The people who dismiss AI as "just pattern matching" have never reckoned with what pattern matchers can do. +At Penn, she spent decades on mRNA research that nobody wanted to fund. mRNA is itself a pattern: a sequence that instructs a cell to build a specific protein. The problem was getting that pattern past the body's pattern-matching defences, the way she'd got the cash past the border guards. She and [Drew Weissman](https://en.wikipedia.org/wiki/Drew_Weissman) [found a way](https://pubmed.ncbi.nlm.nih.gov/16111635/). It won them the [2023 Nobel Prize in Physiology or Medicine](https://www.nobelprize.org/prizes/medicine/2023/kariko/facts/) and became the basis for the [COVID-19 vaccines](https://en.wikipedia.org/wiki/COVID-19_vaccine). The people who dismiss AI as "just pattern matching" have never reckoned with what pattern matchers can do. ## Where there's smoke, there's fire -Two stories broke within days of each other in March 2026. Both involved pattern matching. They illustrate different things. +Two stories broke within days of each other in March 2026. In Sydney, a tech entrepreneur named Paul Conyngham [designed a personalised cancer vaccine for his dog](https://fortune.com/2026/03/15/australian-tech-entrepreneur-ai-cancer-vaccine-dog-rosie-unsw-mrna/). His eight-year-old rescue Staffy, Rosie, was dying of [mast cell cancer](https://en.wikipedia.org/wiki/Mast_cell_tumor). Surgery and chemotherapy had failed. Vets gave her months. Conyngham used ChatGPT to research immunotherapy approaches, paid $3,000 to sequence Rosie's tumour DNA at UNSW's Ramaciotti Centre for Genomics, then fed the mutations into [AlphaFold](https://en.wikipedia.org/wiki/AlphaFold) to predict which proteins would be visible to the immune system. He used Grok to help design an mRNA vaccine targeting those neoantigens, and Professor Pall Thordarson at [UNSW's RNA Institute synthesised it](https://news.unsw.edu.au/en/meet-the-man-who-designed-a-cancer-vaccine-for-his-dog). By mid-March the largest tumour had shrunk by roughly 75%. Thordarson called it the first personalised cancer vaccine designed for a dog. Here, AI was the design tool: matching across protein structures to find targets that a human researcher would have taken months to identify. @@ -28,7 +28,9 @@ The second story was quieter. A user on Reddit [described feeding 25 years of me [Charles Janeway](https://en.wikipedia.org/wiki/Charles_Janeway), the immunologist who [named the immune system's own mechanism "pattern recognition receptors"](https://pmc.ncbi.nlm.nih.gov/articles/PMC3117407/) in 1989, would have recognised the principle in both cases. The term was not a metaphor. The immune system has been pattern matching for longer than any nervous system has existed, and Janeway's insight was that the mechanism deserved the name. -The innate immune system, the layer Janeway studied, is the body's first responder: a set of receptors that recognise broad molecular patterns shared by whole classes of pathogen, without needing to have encountered any specific one before. It cannot learn or remember. It doesn't understand what the patterns mean. But matching is enough to activate the adaptive immune system: the layer that learns from encounters, remembers specific threats and manufactures antibodies targeted to a particular pathogen. Without the innate system's crude pattern matching, none of that machinery switches on. +The innate immune system, the layer Janeway studied, is the body's first responder: a set of receptors that recognise broad molecular patterns shared by whole classes of pathogen, without needing to have encountered any specific one before. It cannot learn or remember. It doesn't understand what the patterns mean. But matching is enough to activate the adaptive immune system: the layer that learns from encounters, remembers specific threats and manufactures antibodies targeted to a particular pathogen. Without the innate system's crude pattern matching, none of that machinery switches on. Neither layer works alone. The immune system is the composite. + +Current LLMs look a lot like the innate layer: broad pattern matching, no persistent memory, no learning from interaction. The human collaborating with the LLM provides what the innate system cannot: context across sessions, judgement about which patterns matter, the ability to update beliefs. Conyngham selecting which of AlphaFold's predicted neoantigens to target, the Reddit user deciding which of Claude's suggestions warranted a sleep study. The collaboration is the immune system. ## Fortune favours the bold @@ -56,7 +58,7 @@ Recombination and selection: the same mechanism whether the substrate is gene se "Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. Pattern matching finds that a situation resembles a known one. Analogy asks why the resemblance holds, what structural property the two situations share. Hofstadter and Sander walk through Einstein's analogies one by one: the equivalence principle, the rotating disc that led him to curved spacetime, the analogies between Maxwell's equations and the geometry of the universe. In each case the analogy preceded the formalisation. It was the generative spark that told him where to point the mathematics. -This is where Friston's critique and Hofstadter's framework converge. Friston says LLMs lack the depth to encode uncertainty and learn from action. Hofstadter says intelligence is selecting the right analogy from competing frames. Both describe a gradient. At one end, surface matching: token prediction, molecular pattern recognition, the innate immune response. At the other, something that looks like understanding: world models, structural analogy, adaptive immunity. Does the gradient have a discontinuity, a point where pattern matching stops and "real" intelligence begins? Neither Friston's mathematics nor Hofstadter's cognitive science suggests one. +This is where Friston's critique and Hofstadter's framework converge. Friston says LLMs lack the depth to encode uncertainty and learn from action. Hofstadter says intelligence is selecting the right analogy from competing frames. Both describe a gradient. At one end, surface matching: token prediction, molecular pattern recognition, the innate immune response. At the other, something that looks like understanding: world models, structural analogy, adaptive immunity. Does the gradient have a discontinuity, a point where pattern matching stops and "real" intelligence begins? Neither Friston's mathematics nor Hofstadter's cognitive science suggests one. And the body didn't wait for one. It built a composite: a crude, fast layer that activates a sophisticated, slow one. LeCun and Friston want to build the adaptive layer into the architecture itself. In the meantime, the human is the adaptive layer. ## Practice makes perfect @@ -72,7 +74,7 @@ When an experienced developer looks at an unfamiliar problem and reaches for a k Karikó's career was a sequence of checkpoints. The Hungarian border. The grant reviewers. The university that demoted her. The immune system that attacked her synthetic mRNA. In every case, a pattern matcher stood between her and the next stage: recognising something foreign and blocking it. She spoke the immune system's language, modifying a single nucleoside so the mRNA no longer triggered the alarm. She didn't dismiss the immune system's recognition as shallow, she worked with it. -The debate about whether LLMs "really think" is a checkpoint of its own. It constrains recognition of what these systems already achieve while the field waits for an architecture that may turn out to be pattern matching at greater depth. Conyngham didn't wait. He used the tools that existed, pattern matchers all of them, and his dog's tumours shrank. The Reddit user didn't wait. He fed 25 years of records to Claude and got an answer that had eluded specialists for decades. +The debate about whether LLMs "really think" is a checkpoint of its own. It constrains recognition of what these systems already achieve while the field waits for an architecture that may turn out to be pattern matching at greater depth. Conyngham didn't wait. He paired his judgement with AI's pattern matching and his dog's tumours shrank. The Reddit user didn't wait. He fed 25 years of records to Claude, and the composite of human context and machine pattern matching found what neither could have found alone. The teddy bear made it out of Hungary, the vaccine made it into the body, and both had to pass a checkpoint to do their work. From 6ee58efd79fcd1102644d1d7bbd573d34c744df1 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 4 Apr 2026 19:00:27 +0100 Subject: [PATCH 06/46] Correlation/causation framing through Look before you leap, Still waters, Practice makes perfect --- src/pages/blog/the-oldest-pattern-matcher.md | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 8fe488fc0..94a1ba07f 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -44,7 +44,11 @@ LeCun's proposed fix is [Joint Embedding Predictive Architecture](https://openre The immune system's pattern matcher can misfire too: autoimmune disorders are what happens when the same mechanism that recognises threats matches against the body's own tissue. Hallucination is the AI equivalent, the same recognition machinery matching against the wrong thing. -They may be right that current LLMs are insufficient for the hardest problems. Nobody pattern-matched their way to general relativity, though as Hofstadter and Sander document in [*Surfaces and Essences*](https://en.wikipedia.org/wiki/Surfaces_and_Essences), Einstein was a prolific and deliberate user of analogy: the equivalence principle began as a pattern match between freefall and the absence of gravity. Even the paradigm case of transcendent genius turns out to involve pattern matching at depth. But look at what both critics actually propose. LeCun's JEPA still learns by comparing representations and minimising prediction error, the same core operation as an LLM, applied to abstract structures rather than token sequences. Friston is more explicit: his Free Energy Principle holds that all perception, all cognition, is prediction error minimisation. The brain generates patterns, compares them against sensory data, adjusts. His own framework formalises intelligence as pattern matching. What he objects to in LLMs is not the matching but the shallowness: no uncertainty, no action, no updating through consequences. Both critics prescribe more AI, not less. Their proposed architectures add depth to the pattern matching. They do not replace it with something else. +## Look before you leap + +Everyone knows that correlation is not causation. It is one of the few statistical ideas that has escaped into everyday speech, and it captures the sceptics' core objection precisely. LLMs find correlations in text. They can tell you that certain symptoms tend to appear together, that certain code patterns tend to co-occur, that certain words tend to follow other words. What they cannot do, the critics argue, is understand *why*. LeCun's whole point is that predicting in latent space builds a causal model of the world, one that can simulate what would happen if you intervened rather than merely predicting what comes next. Friston's active inference requires acting on the world and updating from consequences. A system that has only ever read about fire can pattern-match the word without grounding it in heat, light or danger. This is the [symbol grounding problem](https://en.wikipedia.org/wiki/Symbol_grounding_problem), and it has been a live philosophical question for decades. Current LLMs cannot represent uncertainty at all: a probability distribution over tokens is not the same as a system that knows what it doesn't know. + +If there is a hard boundary between correlation and causation, between pattern matching and understanding, then no amount of scale or data will carry LLMs across it. That is the strong version of the critics' case, and it may prove right. Nobody correlated their way to general relativity, though as Hofstadter and Sander document in [*Surfaces and Essences*](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/), Einstein was a prolific and deliberate user of analogy: the equivalence principle began as a pattern match between freefall and the absence of gravity. Even the paradigm case of transcendent genius turns out to have correlation at its root, a resemblance noticed before it was explained. AlphaFold predicts protein structures it has never seen. Claude diagnosed conditions that eluded specialists for decades. Is the path from correlation to causation smooth, or does it have a hard break? We do not yet know. What we do know is what the critics prescribe. LeCun's JEPA still learns by comparing representations and minimising prediction error. Friston's Free Energy Principle holds that all perception, all cognition, is prediction error minimisation. Both frameworks formalise intelligence as pattern matching, and both propose architectures that add depth to it. They do not replace it with something else. ## Still waters run deep @@ -52,13 +56,13 @@ Janeway called the innate immune system's role "the immunologist's dirty little Recombination and selection: the same mechanism whether the substrate is gene segments, cultural ideas or token sequences. The same gradient appears outside immunology. -[Douglas Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) has spent four decades arguing that analogy is not a special variety of reasoning but the core of all cognition. In [*Surfaces and Essences*](https://en.wikipedia.org/wiki/Surfaces_and_Essences), he and Emmanuel Sander present pairs of proverbs that assert contradictory things: "where there's smoke, there's fire" alongside "don't judge a book by its cover". For every proverb proposing a point of view, there is one for the opposing point of view. They are not rules. They are analogical frames, and intelligence is selecting which frame fits the situation. The headings of this piece are drawn from Hofstadter's pairs. The critics' argument against LLMs is "don't judge a book by its cover": surface patterns cannot grasp deep truth. The medical cases are "where there's smoke, there's fire": surface patterns reliably point to underlying conditions. Both proverbs coexist in every language because both are sometimes right. +[Douglas Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) has spent four decades arguing that analogy is not a special variety of reasoning but the core of all cognition. In [*Surfaces and Essences*](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/), he and Emmanuel Sander present pairs of proverbs that assert contradictory things: "where there's smoke, there's fire" alongside "don't judge a book by its cover". For every proverb proposing a point of view, there is one for the opposing point of view. They are not rules. They are analogical frames, and intelligence is selecting which frame fits the situation. The headings of this piece are drawn from Hofstadter's pairs. The critics' argument against LLMs is "don't judge a book by its cover": surface patterns cannot grasp deep truth. The medical cases are "where there's smoke, there's fire": surface patterns reliably point to underlying conditions. Both proverbs coexist in every language because both are sometimes right. "Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. Pattern matching finds that a situation resembles a known one. Analogy asks why the resemblance holds, what structural property the two situations share. Hofstadter and Sander walk through Einstein's analogies one by one: the equivalence principle, the rotating disc that led him to curved spacetime, the analogies between Maxwell's equations and the geometry of the universe. In each case the analogy preceded the formalisation. It was the generative spark that told him where to point the mathematics. -This is where Friston's critique and Hofstadter's framework converge. Friston says LLMs lack the depth to encode uncertainty and learn from action. Hofstadter says intelligence is selecting the right analogy from competing frames. Both describe a gradient. At one end, surface matching: token prediction, molecular pattern recognition, the innate immune response. At the other, something that looks like understanding: world models, structural analogy, adaptive immunity. Does the gradient have a discontinuity, a point where pattern matching stops and "real" intelligence begins? Neither Friston's mathematics nor Hofstadter's cognitive science suggests one. And the body didn't wait for one. It built a composite: a crude, fast layer that activates a sophisticated, slow one. LeCun and Friston want to build the adaptive layer into the architecture itself. In the meantime, the human is the adaptive layer. +This is where Friston's critique and Hofstadter's framework converge. Friston says LLMs lack the depth to encode uncertainty and learn from action. Hofstadter says intelligence is selecting the right analogy from competing frames. Both place correlation and causation on the same continuum: at one end, statistical regularity; at the other, something that looks like understanding. The innate immune system operates on correlation (this molecular shape co-occurs with pathogens). The adaptive immune system gets closer to causation (this specific protein signals this specific threat, so manufacture this specific antibody). Neither Friston's mathematics nor Hofstadter's cognitive science places a hard boundary between the two. And the body didn't wait for one. It built a composite: a crude, fast layer that activates a sophisticated, slow one. LeCun and Friston want to build the adaptive layer into the architecture itself. In the meantime, the human is the adaptive layer. ## Practice makes perfect @@ -66,7 +70,7 @@ In 430 BC, [Thucydides](https://en.wikipedia.org/wiki/Thucydides) contracted the Software engineering has its own immune memory. When we praise code as "idiomatic" we are praising pattern matching: this solution selected the right analogy from a repertoire of known forms. [Design patterns](https://en.wikipedia.org/wiki/Design_Patterns) are named analogies. The Gang of Four catalogue is a compendium of "this situation is like that situation". Every abstraction in computing is a metaphor: files, folders, windows, streams, pipes, threads. -When an experienced developer looks at an unfamiliar problem and reaches for a known pattern, they are doing what Hofstadter describes: stripping away irrelevancies to obtain a conceptual nugget, then stepping to a related one in another domain. When an LLM does the same thing, we call it shallow. But AlphaFold sits in the middle of the gradient. It is a model built from patterns in protein sequence data, now capable of predicting the three-dimensional structure of proteins it has never encountered. That is not retrieval. It is pattern matching deep enough to be generative: a narrow world model, emerging from the mechanism LeCun dismisses. +When an experienced developer looks at an unfamiliar problem and reaches for a known pattern, they are doing what Hofstadter describes: stripping away irrelevancies to obtain a conceptual nugget, then stepping to a related one in another domain. When an LLM does the same thing, we call it shallow. But AlphaFold complicates the boundary. It is a model built from correlations in protein sequence data, now capable of predicting the three-dimensional structure of proteins it has never encountered. That is not retrieval. It is correlation deep enough to be generative, producing results that look a lot like causal understanding of how amino acid sequences fold. A narrow world model, emerging from the mechanism LeCun dismisses. ## Every dog has its day From dd01b101b0ee7d061821910873899fdb4dcc2c67 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 4 Apr 2026 19:11:44 +0100 Subject: [PATCH 07/46] Editorial pass: fix errors, cut redundancy, split paragraphs, fix connectors and links --- src/pages/blog/the-oldest-pattern-matcher.md | 64 +++++++++++++------- 1 file changed, 42 insertions(+), 22 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 94a1ba07f..54e9491ef 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -1,7 +1,7 @@ --- author: 'hga' title: 'Just pattern matching' -description: 'The 2023 Nobel Prize' +description: 'The people who dismiss AI as "just pattern matching" have never reckoned with what pattern matchers can do.' category: 'ai' layout: '../../layouts/BlogPost.astro' publishedDate: '2026-04-06' @@ -12,71 +12,91 @@ tags: - 'medicine' --- -

In 1985, Katalin Karikó left Hungary with her husband and two-year-old daughter. The government limited how much money citizens could take out of the country: $100. She sold the family car on the black market and sewed $1,246 into her daughter's teddy bear to evade detection at the checkpoints.

+

In 1985, Katalin Karikó left Hungary with her husband and two-year-old daughter. The government limited how much money citizens could take out of the country: $100. She sold the family car on the black market and sewed $1,246 into her daughter's teddy bear to evade detection at the checkpoints.

At Penn, she spent decades on mRNA research that nobody wanted to fund. mRNA is itself a pattern: a sequence that instructs a cell to build a specific protein. The problem was getting that pattern past the body's pattern-matching defences, the way she'd got the cash past the border guards. She and [Drew Weissman](https://en.wikipedia.org/wiki/Drew_Weissman) [found a way](https://pubmed.ncbi.nlm.nih.gov/16111635/). It won them the [2023 Nobel Prize in Physiology or Medicine](https://www.nobelprize.org/prizes/medicine/2023/kariko/facts/) and became the basis for the [COVID-19 vaccines](https://en.wikipedia.org/wiki/COVID-19_vaccine). The people who dismiss AI as "just pattern matching" have never reckoned with what pattern matchers can do. ## Where there's smoke, there's fire -Two stories broke within days of each other in March 2026. +Two stories were circulating in early 2026. -In Sydney, a tech entrepreneur named Paul Conyngham [designed a personalised cancer vaccine for his dog](https://fortune.com/2026/03/15/australian-tech-entrepreneur-ai-cancer-vaccine-dog-rosie-unsw-mrna/). His eight-year-old rescue Staffy, Rosie, was dying of [mast cell cancer](https://en.wikipedia.org/wiki/Mast_cell_tumor). Surgery and chemotherapy had failed. Vets gave her months. Conyngham used ChatGPT to research immunotherapy approaches, paid $3,000 to sequence Rosie's tumour DNA at UNSW's Ramaciotti Centre for Genomics, then fed the mutations into [AlphaFold](https://en.wikipedia.org/wiki/AlphaFold) to predict which proteins would be visible to the immune system. He used Grok to help design an mRNA vaccine targeting those neoantigens, and Professor Pall Thordarson at [UNSW's RNA Institute synthesised it](https://news.unsw.edu.au/en/meet-the-man-who-designed-a-cancer-vaccine-for-his-dog). By mid-March the largest tumour had shrunk by roughly 75%. Thordarson called it the first personalised cancer vaccine designed for a dog. Here, AI was the design tool: matching across protein structures to find targets that a human researcher would have taken months to identify. +In Sydney, a tech entrepreneur named Paul Conyngham [designed a personalised cancer vaccine for his dog](https://fortune.com/2026/03/15/australian-tech-entrepreneur-ai-cancer-vaccine-dog-rosie-unsw-mrna/). His eight-year-old rescue Staffy, Rosie, was dying of [mast cell cancer](https://en.wikipedia.org/wiki/Mast_cell_tumor). Surgery and chemotherapy had failed. Vets gave her months. Conyngham used ChatGPT to research immunotherapy approaches, paid $3,000 to sequence Rosie's tumour DNA at UNSW's Ramaciotti Centre for Genomics, then fed the mutations into [AlphaFold](https://en.wikipedia.org/wiki/AlphaFold) to predict which proteins would be visible to the immune system. He used Grok to help design an mRNA vaccine targeting those neoantigens, and Professor Pall Thordarson at [UNSW's RNA Institute synthesised it](https://news.unsw.edu.au/en/meet-the-man-who-designed-a-cancer-vaccine-for-his-dog). By mid-March the largest tumour had shrunk by roughly 75%. Thordarson called it the first personalised cancer vaccine designed for a dog. + +Here, AI was the design tool: matching across protein structures to find targets that a human researcher would have taken months to identify. -The second story was quieter. A user on Reddit [described feeding 25 years of medical records to Claude](https://www.reddit.com/r/ClaudeAI/comments/1s41fny/25_years_multiple_specialists_zero_answers_one/). His uncle, a 62-year-old man in India on dialysis three times a week, had suffered positional headaches his entire adult life. Neurologists, nephrologists: none found a cause. Claude spotted a pattern connecting the positional headaches, loud snoring and dialysis, referencing research showing that [40-57% of dialysis patients have undiagnosed sleep apnea](https://pubmed.ncbi.nlm.nih.gov/11207682/). A sleep study confirmed it: breathing stopping 119 times per night, oxygen dropping to 78%. A CPAP machine resolved headaches that had persisted for a quarter of a century. Each specialist had seen their own slice: the nephrologist saw kidneys, the neurologist saw headaches. Claude saw the constellation. Here, AI was the diagnostician: matching across records that human specialists kept in separate departments. +The second story was quieter. A user on Reddit [described feeding 25 years of medical records to Claude](https://www.reddit.com/r/ClaudeAI/comments/1s41fny/25_years_multiple_specialists_zero_answers_one/). His uncle, a 62-year-old man in India on dialysis three times a week, had suffered positional headaches his entire adult life. Neurologists, nephrologists: none found a cause. Claude spotted a pattern connecting the positional headaches, loud snoring and dialysis, referencing research showing that [40-57% of dialysis patients have undiagnosed sleep apnea](https://pubmed.ncbi.nlm.nih.gov/11207682/). A sleep study confirmed it: breathing stopping 119 times per night, oxygen dropping to 78%. A CPAP machine resolved headaches that had persisted for a quarter of a century. + +Each specialist had seen their own slice: the nephrologist saw kidneys, the neurologist saw headaches. Claude saw the constellation. Here, AI was the diagnostician: matching across records that human specialists kept in separate departments. + +[Charles Janeway](https://en.wikipedia.org/wiki/Charles_Janeway), the immunologist who [named the immune system's own mechanism "pattern recognition receptors"](https://pmc.ncbi.nlm.nih.gov/articles/PMC3117407/) in 1989, would have recognised the principle in both cases. The immune system has been pattern matching for longer than any nervous system has existed. Janeway's insight was that the mechanism deserved the name. -[Charles Janeway](https://en.wikipedia.org/wiki/Charles_Janeway), the immunologist who [named the immune system's own mechanism "pattern recognition receptors"](https://pmc.ncbi.nlm.nih.gov/articles/PMC3117407/) in 1989, would have recognised the principle in both cases. The term was not a metaphor. The immune system has been pattern matching for longer than any nervous system has existed, and Janeway's insight was that the mechanism deserved the name. +The innate immune system, the layer Janeway studied, is the body's first responder: a set of receptors that recognise broad molecular patterns shared by whole classes of pathogen, without needing to have encountered any specific one before. It cannot learn or remember. It doesn't understand what the patterns mean. -The innate immune system, the layer Janeway studied, is the body's first responder: a set of receptors that recognise broad molecular patterns shared by whole classes of pathogen, without needing to have encountered any specific one before. It cannot learn or remember. It doesn't understand what the patterns mean. But matching is enough to activate the adaptive immune system: the layer that learns from encounters, remembers specific threats and manufactures antibodies targeted to a particular pathogen. Without the innate system's crude pattern matching, none of that machinery switches on. Neither layer works alone. The immune system is the composite. +But matching is enough to activate the adaptive immune system: the layer that learns from encounters, remembers specific threats and manufactures antibodies targeted to a particular pathogen. Without the innate system's crude pattern matching, none of that machinery switches on. -Current LLMs look a lot like the innate layer: broad pattern matching, no persistent memory, no learning from interaction. The human collaborating with the LLM provides what the innate system cannot: context across sessions, judgement about which patterns matter, the ability to update beliefs. Conyngham selecting which of AlphaFold's predicted neoantigens to target, the Reddit user deciding which of Claude's suggestions warranted a sleep study. The collaboration is the immune system. +Current LLMs look a lot like the innate layer: broad pattern matching, no persistent memory, no learning from interaction. The human collaborating with the LLM provides what the innate system cannot: context across sessions, judgement about which patterns matter, the ability to update beliefs: Conyngham selecting which of AlphaFold's predicted neoantigens to target, the Reddit user deciding which of Claude's suggestions warranted a sleep study. ## Fortune favours the bold - + -[Yann LeCun](https://en.wikipedia.org/wiki/Yann_LeCun), Meta's chief AI scientist, calls LLMs "glorified autocomplete" and maintains they cannot reason or plan. His [position paper](https://openreview.net/pdf?id=BZ5a1r-kVsf) proposes Joint Embedding Predictive Architectures as the path toward world models. [Karl Friston](https://en.wikipedia.org/wiki/Karl_Friston), the neuroscientist behind the [Free Energy Principle](https://en.wikipedia.org/wiki/Free_energy_principle), is [blunter](https://deniseholt.us/deep-learning-is-rubbish-friston-lecun-face-off-at-davos-2024/): "Deep learning is rubbish". LLMs are "just a mapping between content and content". Large language models, in their failure to encode uncertainty, are "extremely prone to psychiatric disorders". +[Yann LeCun](https://en.wikipedia.org/wiki/Yann_LeCun), Meta's chief AI scientist, calls LLMs "glorified autocomplete" and maintains they cannot reason or plan. His [position paper](https://openreview.net/pdf?id=BZ5a1r-kVsf) proposes Joint Embedding Predictive Architectures as the path toward world models. [Karl Friston](https://en.wikipedia.org/wiki/Karl_Friston), the neuroscientist behind the [Free Energy Principle](https://en.wikipedia.org/wiki/Free_energy_principle), is blunter: ["Deep learning is rubbish"](https://deniseholt.us/deep-learning-is-rubbish-friston-lecun-face-off-at-davos-2024/). LLMs are "just a mapping between content and content". Large language models, in their failure to encode uncertainty, are "extremely prone to psychiatric disorders". -These are serious people, and the failures they point to are real. LLMs can be confident, wrong and persuasive at the same time, with no internal signal distinguishing knowledge from confabulation. They are vulnerable to [prompt injection](https://simonwillison.net/2025/Jan/9/prompt-injection-attack/): Simon Willison's [lethal trifecta](https://simonwillison.net/2023/Apr/25/dual-llm-pattern/) describes the danger of systems that combine access to private data, exposure to untrusted input and the ability to take actions, because LLMs have no persistent self-model that an attacker's instructions can be checked against. They cannot update their beliefs through interaction with the world. These are not minor engineering problems. They are architectural gaps. +These are serious people, and the failures they point to are real. LLMs can be confident, wrong and persuasive at the same time, with no internal signal distinguishing knowledge from confabulation. They are vulnerable to [prompt injection](https://simonwillison.net/2025/Jan/9/prompt-injection-attack/): Simon Willison's [lethal trifecta](https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/) describes the danger of systems that combine access to private data, exposure to untrusted input and the ability to take actions, because LLMs have no persistent self-model that an attacker's instructions can be checked against. They cannot update their beliefs through interaction with the world. -LeCun's proposed fix is [Joint Embedding Predictive Architecture](https://openreview.net/pdf?id=BZ5a1r-kVsf): rather than predicting the next token in a sequence, the system learns to predict abstract representations in a latent space, building an internal world model it can reason over without generating text. Friston's is [active inference](https://mitpress.mit.edu/9780262045353/active-inference/): agents that encode uncertainty as a first-class quantity, that act on their environment and learn from consequences, that are driven by something like curiosity to seek out information that would reduce their uncertainty. Both architectures would address these gaps directly. A system that encodes what it doesn't know cannot be confidently wrong. A system with a persistent self-model is harder to hijack. +These are architectural gaps. -The immune system's pattern matcher can misfire too: autoimmune disorders are what happens when the same mechanism that recognises threats matches against the body's own tissue. Hallucination is the AI equivalent, the same recognition machinery matching against the wrong thing. +LeCun's proposed fix is Joint Embedding Predictive Architecture: rather than predicting the next token in a sequence, the system learns to predict abstract representations in a latent space, building an internal world model it can reason over without generating text. Friston's is [active inference](https://mitpress.mit.edu/9780262045353/active-inference/): agents that encode uncertainty as a first-class quantity, that act on their environment and learn from consequences, that are driven by something like curiosity to seek out information that would reduce their uncertainty. A system that encodes what it doesn't know cannot be confidently wrong. A system with a persistent self-model is harder to hijack. + +The immune system's pattern matcher can misfire too: autoimmune disorders are what happens when the same mechanism that recognises threats matches against the body's own tissue. Hallucination is the AI equivalent: the same recognition machinery matching against the wrong thing. ## Look before you leap -Everyone knows that correlation is not causation. It is one of the few statistical ideas that has escaped into everyday speech, and it captures the sceptics' core objection precisely. LLMs find correlations in text. They can tell you that certain symptoms tend to appear together, that certain code patterns tend to co-occur, that certain words tend to follow other words. What they cannot do, the critics argue, is understand *why*. LeCun's whole point is that predicting in latent space builds a causal model of the world, one that can simulate what would happen if you intervened rather than merely predicting what comes next. Friston's active inference requires acting on the world and updating from consequences. A system that has only ever read about fire can pattern-match the word without grounding it in heat, light or danger. This is the [symbol grounding problem](https://en.wikipedia.org/wiki/Symbol_grounding_problem), and it has been a live philosophical question for decades. Current LLMs cannot represent uncertainty at all: a probability distribution over tokens is not the same as a system that knows what it doesn't know. +Everyone knows that correlation is not causation. It is one of the few statistical ideas that has escaped into everyday speech, and it captures the sceptics' core objection precisely. LLMs find correlations in text. They can tell you that certain symptoms tend to appear together, that certain code patterns tend to co-occur, that certain words tend to follow other words. What they cannot do, the critics argue, is understand *why*. LeCun's whole point is that predicting in latent space builds a causal model of the world, one that can simulate what would happen if you intervened rather than merely predicting what comes next. Friston's active inference requires acting on the world and updating from consequences. A system that has only ever read about fire can pattern-match the word without grounding it in heat, light or danger. This is the [symbol grounding problem](https://en.wikipedia.org/wiki/Symbol_grounding_problem): a live philosophical question for decades. Current LLMs cannot represent uncertainty at all. A probability distribution over tokens is not the same as a system that knows what it doesn't know. + +If there is a hard boundary between correlation and causation, between pattern matching and understanding, then no amount of scale or data will carry LLMs across it. That is the strong version of the critics' case, and it may prove right. -If there is a hard boundary between correlation and causation, between pattern matching and understanding, then no amount of scale or data will carry LLMs across it. That is the strong version of the critics' case, and it may prove right. Nobody correlated their way to general relativity, though as Hofstadter and Sander document in [*Surfaces and Essences*](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/), Einstein was a prolific and deliberate user of analogy: the equivalence principle began as a pattern match between freefall and the absence of gravity. Even the paradigm case of transcendent genius turns out to have correlation at its root, a resemblance noticed before it was explained. AlphaFold predicts protein structures it has never seen. Claude diagnosed conditions that eluded specialists for decades. Is the path from correlation to causation smooth, or does it have a hard break? We do not yet know. What we do know is what the critics prescribe. LeCun's JEPA still learns by comparing representations and minimising prediction error. Friston's Free Energy Principle holds that all perception, all cognition, is prediction error minimisation. Both frameworks formalise intelligence as pattern matching, and both propose architectures that add depth to it. They do not replace it with something else. +Nobody correlated their way to general relativity. Yet as [Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) and Sander document in [*Surfaces and Essences*](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/), Einstein was a prolific and deliberate user of analogy: the equivalence principle began as a pattern match between freefall and the absence of gravity. Even the paradigm case of transcendent genius turns out to have correlation at its root, a resemblance noticed before it was explained. + +AlphaFold predicts protein structures it has never seen. Claude diagnosed conditions that eluded specialists for decades. Is the path from correlation to causation smooth, or does it have a hard break? We do not yet know. + +What we do know is what the critics prescribe. LeCun's JEPA still learns by comparing representations and minimising prediction error. Friston's Free Energy Principle holds that all perception, all cognition, is prediction error minimisation. Both frameworks formalise intelligence as pattern matching, and both propose architectures that add depth to it. ## Still waters run deep -Janeway called the innate immune system's role "the immunologist's dirty little secret" because immunologists had spent decades studying the sophisticated adaptive response while ignoring the crude pattern matching that gates it. But the adaptive response is pattern matching too. B cells generate antibodies through random recombination, and the ones whose shape matches a pathogen's surface get selected and amplified. T cells match fragments of proteins displayed on cell surfaces. The entire immune system, from the innate receptors that recognise broad molecular shapes shared by whole classes of pathogen to the adaptive system that learns to target a specific one, is pattern matching at different depths. Nobody asks whether the immune system "really understands" the threats it fights. It matches, and the matching works. The sophistication is in the depth, not in some qualitative leap beyond matching. +Janeway called the innate immune system's role "the immunologist's dirty little secret" because immunologists had spent decades studying the sophisticated adaptive response while ignoring the crude pattern matching that gates it. But the adaptive response is pattern matching too. B cells generate antibodies through random recombination, and the ones whose shape matches a pathogen's surface get selected and amplified. T cells match fragments of proteins displayed on cell surfaces. The entire system is pattern matching at different depths. + +Nobody asks whether the immune system "really understands" the threats it fights. It matches, and the matching works. -Recombination and selection: the same mechanism whether the substrate is gene segments, cultural ideas or token sequences. The same gradient appears outside immunology. +Recombination and selection: the same mechanism whether the substrate is gene segments, cultural ideas or token sequences. -[Douglas Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) has spent four decades arguing that analogy is not a special variety of reasoning but the core of all cognition. In [*Surfaces and Essences*](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/), he and Emmanuel Sander present pairs of proverbs that assert contradictory things: "where there's smoke, there's fire" alongside "don't judge a book by its cover". For every proverb proposing a point of view, there is one for the opposing point of view. They are not rules. They are analogical frames, and intelligence is selecting which frame fits the situation. The headings of this piece are drawn from Hofstadter's pairs. The critics' argument against LLMs is "don't judge a book by its cover": surface patterns cannot grasp deep truth. The medical cases are "where there's smoke, there's fire": surface patterns reliably point to underlying conditions. Both proverbs coexist in every language because both are sometimes right. +Hofstadter has spent four decades arguing that analogy is not a special variety of reasoning but the core of all cognition. In *Surfaces and Essences*, he and Emmanuel Sander present pairs of proverbs that assert contradictory things: "where there's smoke, there's fire" alongside "don't judge a book by its cover". For every proverb proposing a point of view, there is one for the opposing point of view. They are not rules. They are analogical frames, and intelligence is selecting which frame fits the situation. The headings of this piece are drawn from Hofstadter's pairs. The critics' argument against LLMs is "don't judge a book by its cover": surface patterns cannot grasp deep truth. The medical cases are "where there's smoke, there's fire": surface patterns reliably point to underlying conditions. Both proverbs coexist in every language because both are sometimes right. "Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. Pattern matching finds that a situation resembles a known one. Analogy asks why the resemblance holds, what structural property the two situations share. Hofstadter and Sander walk through Einstein's analogies one by one: the equivalence principle, the rotating disc that led him to curved spacetime, the analogies between Maxwell's equations and the geometry of the universe. In each case the analogy preceded the formalisation. It was the generative spark that told him where to point the mathematics. -This is where Friston's critique and Hofstadter's framework converge. Friston says LLMs lack the depth to encode uncertainty and learn from action. Hofstadter says intelligence is selecting the right analogy from competing frames. Both place correlation and causation on the same continuum: at one end, statistical regularity; at the other, something that looks like understanding. The innate immune system operates on correlation (this molecular shape co-occurs with pathogens). The adaptive immune system gets closer to causation (this specific protein signals this specific threat, so manufacture this specific antibody). Neither Friston's mathematics nor Hofstadter's cognitive science places a hard boundary between the two. And the body didn't wait for one. It built a composite: a crude, fast layer that activates a sophisticated, slow one. LeCun and Friston want to build the adaptive layer into the architecture itself. In the meantime, the human is the adaptive layer. +This is where Friston's critique and Hofstadter's framework converge. Friston says LLMs lack the depth to encode uncertainty and learn from action. Hofstadter says intelligence is selecting the right analogy from competing frames. Both place correlation and causation on the same continuum: at one end, statistical regularity; at the other, something that looks like understanding. The innate immune system operates on correlation (this molecular shape co-occurs with pathogens). The adaptive immune system gets closer to causation (this specific protein signals this specific threat, so manufacture this specific antibody). Neither Friston's mathematics nor Hofstadter's cognitive science places a hard boundary between the two. + +The body didn't wait for one. It built a composite: a crude, fast layer that activates a sophisticated, slow one. LeCun and Friston want to build the adaptive layer into the architecture itself. In the meantime, the human is the adaptive layer. ## Practice makes perfect -In 430 BC, [Thucydides](https://en.wikipedia.org/wiki/Thucydides) contracted the [Plague of Athens](https://en.wikipedia.org/wiki/Plague_of_Athens) and survived. He observed that "the same man was never attacked twice" and that survivors nursed the sick because they "had now no fear for themselves". He was describing immune memory: the body recognising a pattern it had encountered before. He had no framework to explain the mechanism, and the observation stood for 2,300 years before anyone did. The pattern matching worked long before the theory caught up. We may be in a similar lag with LLMs: the systems produce results we can observe but not yet fully explain. +In 430 BC, [Thucydides](https://en.wikipedia.org/wiki/Thucydides) contracted the [Plague of Athens](https://en.wikipedia.org/wiki/Plague_of_Athens) and survived. He observed that "the same man was never attacked twice, never at least fatally" and that survivors nursed the sick because they "had now no fear for themselves". He was describing immune memory: the body recognising a pattern it had encountered before. He had no framework to explain the mechanism, and the observation stood for 2,300 years before anyone did. The pattern matching worked long before the theory caught up, and we may be in a similar position with LLMs. Software engineering has its own immune memory. When we praise code as "idiomatic" we are praising pattern matching: this solution selected the right analogy from a repertoire of known forms. [Design patterns](https://en.wikipedia.org/wiki/Design_Patterns) are named analogies. The Gang of Four catalogue is a compendium of "this situation is like that situation". Every abstraction in computing is a metaphor: files, folders, windows, streams, pipes, threads. -When an experienced developer looks at an unfamiliar problem and reaches for a known pattern, they are doing what Hofstadter describes: stripping away irrelevancies to obtain a conceptual nugget, then stepping to a related one in another domain. When an LLM does the same thing, we call it shallow. But AlphaFold complicates the boundary. It is a model built from correlations in protein sequence data, now capable of predicting the three-dimensional structure of proteins it has never encountered. That is not retrieval. It is correlation deep enough to be generative, producing results that look a lot like causal understanding of how amino acid sequences fold. A narrow world model, emerging from the mechanism LeCun dismisses. +When an experienced developer looks at an unfamiliar problem and reaches for a known pattern, they are doing what Hofstadter describes: stripping away irrelevancies to obtain a conceptual nugget, then stepping to a related one in another domain. When an LLM does the same thing, we call it shallow. But AlphaFold complicates the boundary. It is a model built from correlations in protein sequence data, now capable of predicting the three-dimensional structure of proteins it has never encountered. That is not retrieval. It is correlation deep enough to be generative, producing results that look a lot like causal understanding of how amino acid sequences fold. + +A narrow world model, emerging from the mechanism LeCun dismisses. ## Every dog has its day -Karikó's career was a sequence of checkpoints. The Hungarian border. The grant reviewers. The university that demoted her. The immune system that attacked her synthetic mRNA. In every case, a pattern matcher stood between her and the next stage: recognising something foreign and blocking it. She spoke the immune system's language, modifying a single nucleoside so the mRNA no longer triggered the alarm. She didn't dismiss the immune system's recognition as shallow, she worked with it. +Karikó's career was a sequence of checkpoints. The Hungarian border. The grant reviewers. The university that demoted her. The immune system that attacked her synthetic mRNA. In every case, a pattern matcher stood between her and the next stage: recognising something foreign and blocking it. She spoke the immune system's language, modifying a single nucleoside so the mRNA no longer triggered the alarm. She didn't dismiss the immune system's recognition as shallow. She worked with it. The debate about whether LLMs "really think" is a checkpoint of its own. It constrains recognition of what these systems already achieve while the field waits for an architecture that may turn out to be pattern matching at greater depth. Conyngham didn't wait. He paired his judgement with AI's pattern matching and his dog's tumours shrank. The Reddit user didn't wait. He fed 25 years of records to Claude, and the composite of human context and machine pattern matching found what neither could have found alone. From 52283481dad25f222077058de46440f87348ba5a Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 25 Apr 2026 18:44:04 +0100 Subject: [PATCH 08/46] First draft: Feynman/pattern-matching piece with Karpathy framing --- feynman.md | 54 ++++++++++++ src/pages/blog/the-oldest-pattern-matcher.md | 93 +++++++------------- 2 files changed, 87 insertions(+), 60 deletions(-) create mode 100644 feynman.md diff --git a/feynman.md b/feynman.md new file mode 100644 index 000000000..3e841a6e8 --- /dev/null +++ b/feynman.md @@ -0,0 +1,54 @@ + +# Lede + +Pocono Conference, spring 1948. Feynman at the blackboard, twirling chalk, drawing straight lines and wavy lines and vertices. Schwinger had lectured for hours the day before: polished, formal, complete. Now Feynman is sketching what look like cartoons. Teller interrupts: this violates the exclusion principle. Dirac keeps asking "Is it unitary?" Bohr strides to the stage and lectures Feynman on the uncertainty principle, mistaking the diagrams for literal particle trajectories. The presentation fails. + +Eighteen months later, those cartoons are doing in hours what formal methods had taken months to achieve. Schwinger later compared the diagrams to the silicon chip: "bringing computation to the masses." Within a decade they have reshaped theoretical physics. + +Feynman's Nobel lecture, 1965: "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks." The formal reconstruction is the residue of the work, presented as though it were the work itself. + +{Seed for the closing: something about what distinguishes a frozen pattern from a living one — planted lightly, not labelled.} + +# The elevation of reason + +Institutions privilege formal reasoning because it scales: it can be taught in classrooms, examined, audited, transferred between people. Tacit expertise dies when the expert leaves. So institutions create incentives to formalise, even when formalisation degrades the underlying capability (Scott's metis). Professions claim status by claiming formal bodies of knowledge (Abbott). The hierarchy runs through how status is allocated: formal reasoning is associated with intellectual seriousness, intuition with mere craft. + +Pearl's ladder of causation — association, intervention, counterfactual — is a useful taxonomy. It is also an exhibit of this hierarchy, ranking formal causal models above pattern matching. The ranking may have the directionality wrong. + +# Reasoning backwards + +In the 1940s, the psychologist Adriaan de Groot showed chess masters a board position for five seconds, then asked them to reconstruct it. Masters reproduced nearly everything. Amateurs got fragments. But when the pieces were placed randomly, the advantage vanished. The skill was perception of meaningful patterns, not calculation or memory. Masters don't think further ahead than amateurs. They see more. + +The same pattern appears across formally respected fields. Einstein described his thinking as visual and muscular, translating to mathematics only for communication. Working mathematicians surveyed in the 1940s nearly universally reported that results arrived through sudden recognition after periods of unconscious incubation, with formal proofs following after. Feynman, in the same Nobel lecture, described publishing results he hadn't yet proven because "a very great deal more truth can become known than can be proven." + +Gary Klein studied firefighters, military commanders and ICU nurses making high-stakes decisions under pressure. They almost never compared options. They recognised the situation and acted. When forced to enumerate alternatives — the prescribed rational method — they performed worse. + +Formal reasoning is what novices do while the pattern library is being built. Once built, the pattern library is the superior tool. Asking an expert to show their working is asking them to operate at a lower level of skill. + +# Pattern matching forwards + +Hofstadter spent four decades arguing that analogy is the core of all cognition. "Every concept we have is essentially nothing but a tightly packaged bundle of analogies," he writes in Surfaces and Essences. His chapter on Einstein walks through the analogies one by one: the equivalence principle began as an analogical identification between freefall and the absence of gravity. The light quantum hypothesis mapped the statistical behaviour of a gas onto radiation in a cavity. In each case the analogy preceded the formalisation. It was the generative spark that told Einstein where to point the mathematics. + +Hofstadter and Sander present pairs of proverbs that assert contradictory things: "where there's smoke, there's fire" alongside "don't judge a book by its cover." We inherit a whole library of patterns, but they point in every direction. Intelligence is selecting which pattern fits the situation. + +What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers, "selection pressure" from engineering. Mechanism is analogy that has ratcheted into robustness through repeated testing. + +# Metapatterns and reinforcement + +The contradictory proverbs show that a pattern library alone can't tell you which pattern to apply. What separates useful pattern matching from noise is continuous refinement against reality. + +Expert intuition is built through sustained engagement with hard problems at the edge of current ability — deliberate discomfort, not accumulated hours. Through active reconstruction rather than passive reception: Feynman refused to accept results he hadn't rederived, treating each rederivation as a way of building perceptual capacity. Through breadth: his lockpicking, his Mayan codices, his drawing were fuel for the analogical engine. And through repeated cycles of being wrong, integrating the consequences, and refining the library. + +A pattern library frozen in a textbook is different from one being continuously tested against reality. The expert's advantage is having patterns that are still being refined. + +LLMs inherit enormous pattern libraries from the serialised outputs of minds that already did the causal work. Text encodes causal structure because it was written by causal reasoners. LLMs succeed where naive "pattern matcher" readings can't explain because they're pattern matching over this substrate. They struggle where continuously-refined, intervention-based expertise matters — the parts of cognition that require being wrong about the world, repeatedly, and integrating the consequences. + +The conventional framing says LLMs lack world models, so the fix is adding explicit causal structure to the architecture. But what matters about world models may be that they update, not that they represent cause and effect. A frozen causal model is just another static pattern. Reasoning models jump capability without adding anything explicitly causal. Chain-of-thought and search over hypotheses add dynamism to what was static, something like the expert's intervention-and-feedback loop compressed into a single inference. The research frontier is about making pattern libraries adaptive: reasoning, tool use, agentic loops, memory. + +For practitioners: the LLM's pattern library is vast but frozen, the practitioner's is smaller but alive. The productive collaboration is the system's breadth meeting the human's adaptive refinement — a partnership with a pattern matcher whose library complements your own. + +# Conclusion + +The audience at Pocono saw cartoons where there was physics. They mistook the representation for a lack of rigour because they'd internalised a hierarchy that equates formalism with seriousness. Feynman's diagrams were the physics. The formalism came after. + +We're still internalising that hierarchy, and it shapes how we evaluate AI. The formal capability these systems have commoditised was supposed to be the valuable thing. The valuable thing was always the patient, effortful construction of a pattern library refined by sustained contact with reality. The question worth asking about these systems is how fast their pattern matching is becoming adaptive — and what that means for the people whose expertise was always built the same way. diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 54e9491ef..8038fa551 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -9,99 +9,72 @@ heroImage: 'capability-hyperinflation.jpg' tags: - 'ai' - 'engineering' - - 'medicine' --- -

In 1985, Katalin Karikó left Hungary with her husband and two-year-old daughter. The government limited how much money citizens could take out of the country: $100. She sold the family car on the black market and sewed $1,246 into her daughter's teddy bear to evade detection at the checkpoints.

+

Schwinger had spent hours at the blackboard the day before, deriving quantum electrodynamics through pages of formal mathematics. The twenty-eight physicists assembled at the Pocono Conference in the spring of 1948, Dirac and Bohr and Oppenheimer among them, were impressed. Then Feynman got up and drew what looked like cartoons.

-At Penn, she spent decades on mRNA research that nobody wanted to fund. mRNA is itself a pattern: a sequence that instructs a cell to build a specific protein. The problem was getting that pattern past the body's pattern-matching defences, the way she'd got the cash past the border guards. She and [Drew Weissman](https://en.wikipedia.org/wiki/Drew_Weissman) [found a way](https://pubmed.ncbi.nlm.nih.gov/16111635/). It won them the [2023 Nobel Prize in Physiology or Medicine](https://www.nobelprize.org/prizes/medicine/2023/kariko/facts/) and became the basis for the [COVID-19 vaccines](https://en.wikipedia.org/wiki/COVID-19_vaccine). The people who dismiss AI as "just pattern matching" have never reckoned with what pattern matchers can do. +[Teller](https://en.wikipedia.org/wiki/Edward_Teller) interrupted: the approach violated the exclusion principle. Dirac kept asking "Is it unitary?" Bohr strode to the stage and lectured Feynman on the uncertainty principle, having mistaken the diagrams for literal pictures of particle paths. The presentation was a disaster. -## Where there's smoke, there's fire +Within eighteen months, those cartoons were [doing in hours](https://en.wikipedia.org/wiki/Feynman_diagram) what formal methods took months to achieve. Schwinger later compared them to the silicon chip: "bringing computation to the masses." -Two stories were circulating in early 2026. +Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." And: "A very great deal more truth can become known than can be proven." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. -In Sydney, a tech entrepreneur named Paul Conyngham [designed a personalised cancer vaccine for his dog](https://fortune.com/2026/03/15/australian-tech-entrepreneur-ai-cancer-vaccine-dog-rosie-unsw-mrna/). His eight-year-old rescue Staffy, Rosie, was dying of [mast cell cancer](https://en.wikipedia.org/wiki/Mast_cell_tumor). Surgery and chemotherapy had failed. Vets gave her months. Conyngham used ChatGPT to research immunotherapy approaches, paid $3,000 to sequence Rosie's tumour DNA at UNSW's Ramaciotti Centre for Genomics, then fed the mutations into [AlphaFold](https://en.wikipedia.org/wiki/AlphaFold) to predict which proteins would be visible to the immune system. He used Grok to help design an mRNA vaccine targeting those neoantigens, and Professor Pall Thordarson at [UNSW's RNA Institute synthesised it](https://news.unsw.edu.au/en/meet-the-man-who-designed-a-cancer-vaccine-for-his-dog). By mid-March the largest tumour had shrunk by roughly 75%. Thordarson called it the first personalised cancer vaccine designed for a dog. +Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs. The tweet reached sixteen million people. The system uses an AI agent to ingest sources, maintain cross-references and synthesise connections, while the human curates what goes in, asks the questions and decides what it means. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy described the principle behind it: strip the model down to the "algorithms for thought" and offload the stored knowledge to an external system. Separate the reasoning from the memory. The reasoning is the valuable part. -Here, AI was the design tool: matching across protein structures to find targets that a human researcher would have taken months to identify. +That separation, between a frozen library and the adaptive process that operates over it, is a old one. It was playing out at the blackboard at Pocono. - +## The elevation of reason -The second story was quieter. A user on Reddit [described feeding 25 years of medical records to Claude](https://www.reddit.com/r/ClaudeAI/comments/1s41fny/25_years_multiple_specialists_zero_answers_one/). His uncle, a 62-year-old man in India on dialysis three times a week, had suffered positional headaches his entire adult life. Neurologists, nephrologists: none found a cause. Claude spotted a pattern connecting the positional headaches, loud snoring and dialysis, referencing research showing that [40-57% of dialysis patients have undiagnosed sleep apnea](https://pubmed.ncbi.nlm.nih.gov/11207682/). A sleep study confirmed it: breathing stopping 119 times per night, oxygen dropping to 78%. A CPAP machine resolved headaches that had persisted for a quarter of a century. +The audience at Pocono were pattern matching too. They had a pattern for what serious physics looks like: dense formalism, systematic derivation, the kind of thing Schwinger had spent hours demonstrating the day before. Feynman's diagrams didn't match. So the room concluded, quickly and confidently, that what they were seeing couldn't be physics. -Each specialist had seen their own slice: the nephrologist saw kidneys, the neurologist saw headaches. Claude saw the constellation. Here, AI was the diagnostician: matching across records that human specialists kept in separate departments. +This is a pattern we have all absorbed. Institutions reinforce it because formal knowledge is what they can transmit: it can be taught in classrooms, examined, audited and passed between people who have never met. [James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*. Professions reinforce it because a formal body of knowledge is what justifies their authority and gatekeeping. The hierarchy is old and stable: formal reasoning is associated with intellectual seriousness, intuition with craft. The boundary between knowledge work and skilled trades follows the same line. -[Charles Janeway](https://en.wikipedia.org/wiki/Charles_Janeway), the immunologist who [named the immune system's own mechanism "pattern recognition receptors"](https://pmc.ncbi.nlm.nih.gov/articles/PMC3117407/) in 1989, would have recognised the principle in both cases. The immune system has been pattern matching for longer than any nervous system has existed. Janeway's insight was that the mechanism deserved the name. +[Judea Pearl's](https://en.wikipedia.org/wiki/Judea_Pearl) [ladder of causation](https://en.wikipedia.org/wiki/Causal_model) is a recent and rigorous version of this hierarchy: association at the bottom, intervention in the middle, counterfactual reasoning at the top. A useful taxonomy, and a ranking that assumes the formal end is where understanding lives. -The innate immune system, the layer Janeway studied, is the body's first responder: a set of receptors that recognise broad molecular patterns shared by whole classes of pathogen, without needing to have encountered any specific one before. It cannot learn or remember. It doesn't understand what the patterns mean. +## Reasoning backwards -But matching is enough to activate the adaptive immune system: the layer that learns from encounters, remembers specific threats and manufactures antibodies targeted to a particular pathogen. Without the innate system's crude pattern matching, none of that machinery switches on. +In the 1940s, the Dutch psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Groot) showed chess masters a board position for five seconds, then asked them to reconstruct it. Masters reproduced nearly everything. Amateurs managed fragments. The obvious explanation was superior memory or deeper calculation. Then de Groot scrambled the pieces into positions that couldn't arise in a real game, and the masters' advantage vanished. They weren't remembering better or thinking harder. They were recognising patterns that only exist in meaningful play. -Current LLMs look a lot like the innate layer: broad pattern matching, no persistent memory, no learning from interaction. The human collaborating with the LLM provides what the innate system cannot: context across sessions, judgement about which patterns matter, the ability to update beliefs: Conyngham selecting which of AlphaFold's predicted neoantigens to target, the Reddit user deciding which of Claude's suggestions warranted a sleep study. +Judges do something similar. The [legal realist](https://en.wikipedia.org/wiki/Legal_realism) tradition, running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), holds that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. The law looks like formal reasoning from the outside. From the inside, it is pattern matching with a formal audit trail. -## Fortune favours the bold +Working mathematicians [surveyed in the 1940s](https://en.wikipedia.org/wiki/Jacques_Hadamard) reported the same shape: results arrived through sudden recognition, often after periods of unconscious incubation, with formal proofs constructed afterwards. The proof was a way of showing others what the mathematician already knew. Feynman, in his Nobel lecture, described publishing results he couldn't yet prove. He covered the tracks, and so did every mathematician in the survey. - +Formal reasoning, in this light, is what novices do while the pattern library is sparse. Once the library is rich enough, the expert perceives rather than derives. Asking them to show their working is asking them to operate at a lower level of skill. -[Yann LeCun](https://en.wikipedia.org/wiki/Yann_LeCun), Meta's chief AI scientist, calls LLMs "glorified autocomplete" and maintains they cannot reason or plan. His [position paper](https://openreview.net/pdf?id=BZ5a1r-kVsf) proposes Joint Embedding Predictive Architectures as the path toward world models. [Karl Friston](https://en.wikipedia.org/wiki/Karl_Friston), the neuroscientist behind the [Free Energy Principle](https://en.wikipedia.org/wiki/Free_energy_principle), is blunter: ["Deep learning is rubbish"](https://deniseholt.us/deep-learning-is-rubbish-friston-lecun-face-off-at-davos-2024/). LLMs are "just a mapping between content and content". Large language models, in their failure to encode uncertainty, are "extremely prone to psychiatric disorders". +## Pattern matching forwards -These are serious people, and the failures they point to are real. LLMs can be confident, wrong and persuasive at the same time, with no internal signal distinguishing knowledge from confabulation. They are vulnerable to [prompt injection](https://simonwillison.net/2025/Jan/9/prompt-injection-attack/): Simon Willison's [lethal trifecta](https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/) describes the danger of systems that combine access to private data, exposure to untrusted input and the ability to take actions, because LLMs have no persistent self-model that an attacker's instructions can be checked against. They cannot update their beliefs through interaction with the world. +A sceptic could grant all of this and still hold the line. Fine, experts recognise situations they've seen before. But what about genuinely new ideas? Surely the creative act, the moment where something is understood for the first time, requires more than matching against a library of known patterns? -These are architectural gaps. +Einstein is the test case, because no one's creative process has been more carefully studied. [Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) and [Sander](https://en.wikipedia.org/wiki/Emmanuel_Sander) devote a chapter of [*Surfaces and Essences*](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/) to it. In 1905, Einstein noticed that [Wien's law](https://en.wikipedia.org/wiki/Wien%27s_radiation_law) for the entropy of radiation at low density had the same mathematical form as the entropy of an ideal gas. The equations looked alike. He concluded the physics must be alike too, and proposed that light comes in discrete packets, [quanta](https://en.wikipedia.org/wiki/Photon), by analogy with gas molecules. That paper [won him the Nobel Prize](https://www.nobelprize.org/prizes/physics/1921/einstein/facts/). A decade later, the [equivalence principle](https://en.wikipedia.org/wiki/Equivalence_principle) began with a man falling from a roof: Einstein realised that a person in freefall would feel weightless, and identified that feeling with the absence of gravity. The analogy between two physical sensations became the foundation of [general relativity](https://en.wikipedia.org/wiki/General_relativity). He then spent eight years working out the mathematics. -LeCun's proposed fix is Joint Embedding Predictive Architecture: rather than predicting the next token in a sequence, the system learns to predict abstract representations in a latent space, building an internal world model it can reason over without generating text. Friston's is [active inference](https://mitpress.mit.edu/9780262045353/active-inference/): agents that encode uncertainty as a first-class quantity, that act on their environment and learn from consequences, that are driven by something like curiosity to seek out information that would reduce their uncertainty. A system that encodes what it doesn't know cannot be confidently wrong. A system with a persistent self-model is harder to hijack. +In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. [Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his own breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) arriving unbidden as he [stepped onto a bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy), after weeks of failed formal effort. The bus didn't help him reason. It gave the unconscious pattern matching room to surface. -The immune system's pattern matcher can misfire too: autoimmune disorders are what happens when the same mechanism that recognises threats matches against the body's own tissue. Hallucination is the AI equivalent: the same recognition machinery matching against the wrong thing. +"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers, "selection pressure" from engineering. These began as metaphors. They became mechanisms by being good enough analogies to predict and intervene with, tested and refined until they earned the status of formal knowledge. The scientific vocabulary we treat as precise and literal is, at root, a library of analogies that survived. -## Look before you leap +## Metapatterns and reinforcement -Everyone knows that correlation is not causation. It is one of the few statistical ideas that has escaped into everyday speech, and it captures the sceptics' core objection precisely. LLMs find correlations in text. They can tell you that certain symptoms tend to appear together, that certain code patterns tend to co-occur, that certain words tend to follow other words. What they cannot do, the critics argue, is understand *why*. LeCun's whole point is that predicting in latent space builds a causal model of the world, one that can simulate what would happen if you intervened rather than merely predicting what comes next. Friston's active inference requires acting on the world and updating from consequences. A system that has only ever read about fire can pattern-match the word without grounding it in heat, light or danger. This is the [symbol grounding problem](https://en.wikipedia.org/wiki/Symbol_grounding_problem): a live philosophical question for decades. Current LLMs cannot represent uncertainty at all. A probability distribution over tokens is not the same as a system that knows what it doesn't know. +But Hofstadter and Sander also present pairs of proverbs that assert contradictory things: "where there's smoke, there's fire" alongside "don't judge a book by its cover." Every language has these. For each proverb proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. The patterns point in every direction. -If there is a hard boundary between correlation and causation, between pattern matching and understanding, then no amount of scale or data will carry LLMs across it. That is the strong version of the critics' case, and it may prove right. +This is the gap between having patterns and having expertise. Feynman didn't just accumulate a library of physical intuitions. He built each one himself, refusing to accept results he hadn't rederived from scratch. Each rederivation was an act of reconstruction that connected the result to everything else he knew, forging links that a passively received result would lack. His eclectic interests, the [lockpicking](https://en.wikipedia.org/wiki/Surely_You%27re_Joking,_Mr._Feynman!), the Mayan codices, the drawing, weren't distractions from physics. They were breadth, and breadth is what fuels analogy. The wider the library, the more unexpected the connections it can make. -Nobody correlated their way to general relativity. Yet as [Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) and Sander document in [*Surfaces and Essences*](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/), Einstein was a prolific and deliberate user of analogy: the equivalence principle began as a pattern match between freefall and the absence of gravity. Even the paradigm case of transcendent genius turns out to have correlation at its root, a resemblance noticed before it was explained. +What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened. Patterns that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences. The textbook's library is frozen. -AlphaFold predicts protein structures it has never seen. Claude diagnosed conditions that eluded specialists for decades. Is the path from correlation to causation smooth, or does it have a hard break? We do not yet know. +This distinction matters now because LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. Text encodes causal structure because it was written by causal reasoners. When an LLM recognises a pattern, it is drawing on a substrate where the causal work has already been performed and linguistically encoded. This is why these systems succeed in ways that a naive "pattern matcher" reading can't explain: they are pattern matching over the distilled outputs of experts, not over raw data. Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. -What we do know is what the critics prescribe. LeCun's JEPA still learns by comparing representations and minimising prediction error. Friston's Free Energy Principle holds that all perception, all cognition, is prediction error minimisation. Both frameworks formalise intelligence as pattern matching, and both propose architectures that add depth to it. +The conventional framing says LLMs lack [world models](https://en.wikipedia.org/wiki/World_model), so the fix is adding explicit causal structure to the architecture. But what matters about a world model may be that it updates, not that it represents cause and effect. A frozen causal model is just another static pattern. Reasoning models jump capability without adding anything explicitly causal. Chain-of-thought and search over hypotheses add something like the expert's intervention-and-feedback loop, compressed into a single inference. The research frontier is about making pattern libraries adaptive: [reasoning](https://en.wikipedia.org/wiki/Chain-of-thought_prompting), tool use, agentic loops, persistent memory. -## Still waters run deep +Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this separation. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the frozen library cannot: judgement about what to read, what questions to ask, what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." The LLM handles everything that can be pattern-matched from a frozen library. The human handles everything that requires an adaptive one. -Janeway called the innate immune system's role "the immunologist's dirty little secret" because immunologists had spent decades studying the sophisticated adaptive response while ignoring the crude pattern matching that gates it. But the adaptive response is pattern matching too. B cells generate antibodies through random recombination, and the ones whose shape matches a pathogen's surface get selected and amplified. T cells match fragments of proteins displayed on cell surfaces. The entire system is pattern matching at different depths. +The productive collaboration is the system's breadth meeting the human's adaptive refinement, a partnership with a pattern matcher whose library complements your own. -Nobody asks whether the immune system "really understands" the threats it fights. It matches, and the matching works. +## Conclusion -Recombination and selection: the same mechanism whether the substrate is gene segments, cultural ideas or token sequences. +The audience at Pocono saw cartoons where there was physics. They had internalised a hierarchy that equates formalism with seriousness, and it blinded them to the most powerful computational tool their field would produce. Schwinger's formal derivation was correct, complete and slow. Feynman's diagrams were correct, complete and fast. The diagrams won because they matched how physicists actually think: visually, analogically, in patterns. -Hofstadter has spent four decades arguing that analogy is not a special variety of reasoning but the core of all cognition. In *Surfaces and Essences*, he and Emmanuel Sander present pairs of proverbs that assert contradictory things: "where there's smoke, there's fire" alongside "don't judge a book by its cover". For every proverb proposing a point of view, there is one for the opposing point of view. They are not rules. They are analogical frames, and intelligence is selecting which frame fits the situation. The headings of this piece are drawn from Hofstadter's pairs. The critics' argument against LLMs is "don't judge a book by its cover": surface patterns cannot grasp deep truth. The medical cases are "where there's smoke, there's fire": surface patterns reliably point to underlying conditions. Both proverbs coexist in every language because both are sometimes right. +We have been applying the same hierarchy to AI, and it is leading us to the same mistake. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission. The systems we have built are doing a version of what Einstein did with Wien's law and what Feynman did at the blackboard: matching patterns across a vast library of human knowledge, finding resemblances that carry real structure. - - -"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. Pattern matching finds that a situation resembles a known one. Analogy asks why the resemblance holds, what structural property the two situations share. Hofstadter and Sander walk through Einstein's analogies one by one: the equivalence principle, the rotating disc that led him to curved spacetime, the analogies between Maxwell's equations and the geometry of the universe. In each case the analogy preceded the formalisation. It was the generative spark that told him where to point the mathematics. - -This is where Friston's critique and Hofstadter's framework converge. Friston says LLMs lack the depth to encode uncertainty and learn from action. Hofstadter says intelligence is selecting the right analogy from competing frames. Both place correlation and causation on the same continuum: at one end, statistical regularity; at the other, something that looks like understanding. The innate immune system operates on correlation (this molecular shape co-occurs with pathogens). The adaptive immune system gets closer to causation (this specific protein signals this specific threat, so manufacture this specific antibody). Neither Friston's mathematics nor Hofstadter's cognitive science places a hard boundary between the two. - -The body didn't wait for one. It built a composite: a crude, fast layer that activates a sophisticated, slow one. LeCun and Friston want to build the adaptive layer into the architecture itself. In the meantime, the human is the adaptive layer. - -## Practice makes perfect - -In 430 BC, [Thucydides](https://en.wikipedia.org/wiki/Thucydides) contracted the [Plague of Athens](https://en.wikipedia.org/wiki/Plague_of_Athens) and survived. He observed that "the same man was never attacked twice, never at least fatally" and that survivors nursed the sick because they "had now no fear for themselves". He was describing immune memory: the body recognising a pattern it had encountered before. He had no framework to explain the mechanism, and the observation stood for 2,300 years before anyone did. The pattern matching worked long before the theory caught up, and we may be in a similar position with LLMs. - -Software engineering has its own immune memory. When we praise code as "idiomatic" we are praising pattern matching: this solution selected the right analogy from a repertoire of known forms. [Design patterns](https://en.wikipedia.org/wiki/Design_Patterns) are named analogies. The Gang of Four catalogue is a compendium of "this situation is like that situation". Every abstraction in computing is a metaphor: files, folders, windows, streams, pipes, threads. - -When an experienced developer looks at an unfamiliar problem and reaches for a known pattern, they are doing what Hofstadter describes: stripping away irrelevancies to obtain a conceptual nugget, then stepping to a related one in another domain. When an LLM does the same thing, we call it shallow. But AlphaFold complicates the boundary. It is a model built from correlations in protein sequence data, now capable of predicting the three-dimensional structure of proteins it has never encountered. That is not retrieval. It is correlation deep enough to be generative, producing results that look a lot like causal understanding of how amino acid sequences fold. - -A narrow world model, emerging from the mechanism LeCun dismisses. - -## Every dog has its day - - - -Karikó's career was a sequence of checkpoints. The Hungarian border. The grant reviewers. The university that demoted her. The immune system that attacked her synthetic mRNA. In every case, a pattern matcher stood between her and the next stage: recognising something foreign and blocking it. She spoke the immune system's language, modifying a single nucleoside so the mRNA no longer triggered the alarm. She didn't dismiss the immune system's recognition as shallow. She worked with it. - -The debate about whether LLMs "really think" is a checkpoint of its own. It constrains recognition of what these systems already achieve while the field waits for an architecture that may turn out to be pattern matching at greater depth. Conyngham didn't wait. He paired his judgement with AI's pattern matching and his dog's tumours shrank. The Reddit user didn't wait. He fed 25 years of records to Claude, and the composite of human context and machine pattern matching found what neither could have found alone. - -The teddy bear made it out of Hungary, the vaccine made it into the body, and both had to pass a checkpoint to do their work. +Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. It is the capacity we are now building into these systems, through reasoning, tool use and memory. How fast it develops is the question that matters. --- -Pattern recognition is how we approach AI-assisted engineering at JUXT: finding what the system already knows and removing what holds it back. If that sounds like work worth doing together, [let's talk](https://www.juxt.pro/contact/). +Pattern matching, refined by contact with reality, is how we approach AI-assisted engineering at JUXT. If that framing resonates, [let's talk](https://www.juxt.pro/contact/). From 25f93a766d30fe09cff65c275c0f2c53f902d977 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 25 Apr 2026 19:15:23 +0100 Subject: [PATCH 09/46] Editorial passes 1-9: register, economy, evidence, rhythm, connectors, accuracy --- src/pages/blog/the-oldest-pattern-matcher.md | 56 ++++++++++++-------- 1 file changed, 34 insertions(+), 22 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 8038fa551..909a16933 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -1,7 +1,7 @@ --- author: 'hga' title: 'Just pattern matching' -description: 'The people who dismiss AI as "just pattern matching" have never reckoned with what pattern matchers can do.' +description: "Schwinger's derivation was correct, complete and slow. Feynman's cartoons were correct, complete and fast. We're making the same mistake with AI." category: 'ai' layout: '../../layouts/BlogPost.astro' publishedDate: '2026-04-06' @@ -15,41 +15,49 @@ tags: [Teller](https://en.wikipedia.org/wiki/Edward_Teller) interrupted: the approach violated the exclusion principle. Dirac kept asking "Is it unitary?" Bohr strode to the stage and lectured Feynman on the uncertainty principle, having mistaken the diagrams for literal pictures of particle paths. The presentation was a disaster. -Within eighteen months, those cartoons were [doing in hours](https://en.wikipedia.org/wiki/Feynman_diagram) what formal methods took months to achieve. Schwinger later compared them to the silicon chip: "bringing computation to the masses." +Within eighteen months, [Freeman Dyson](https://en.wikipedia.org/wiki/Freeman_Dyson) had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. -Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." And: "A very great deal more truth can become known than can be proven." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. +Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. -Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs. The tweet reached sixteen million people. The system uses an AI agent to ingest sources, maintain cross-references and synthesise connections, while the human curates what goes in, asks the questions and decides what it means. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy described the principle behind it: strip the model down to the "algorithms for thought" and offload the stored knowledge to an external system. Separate the reasoning from the memory. The reasoning is the valuable part. +Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs. The tweet reached sixteen million people. The system uses an AI agent to ingest sources, maintain cross-references and synthesise connections, while the human curates what goes in, asks the questions and decides what it means. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy described the principle behind it: strip the model down to the "algorithms for thought" and offload the stored knowledge to an external system. -That separation, between a frozen library and the adaptive process that operates over it, is a old one. It was playing out at the blackboard at Pocono. +Separate the reasoning from the memory. + +That separation, between a frozen library and the adaptive process that operates over it, is an old one. ## The elevation of reason -The audience at Pocono were pattern matching too. They had a pattern for what serious physics looks like: dense formalism, systematic derivation, the kind of thing Schwinger had spent hours demonstrating the day before. Feynman's diagrams didn't match. So the room concluded, quickly and confidently, that what they were seeing couldn't be physics. +The audience at Pocono were pattern matching too. They had a pattern for what serious physics looks like: dense formalism, systematic derivation, the kind of thing Schwinger had spent hours demonstrating the day before. So the room concluded, quickly and confidently, that what they were seeing couldn't be physics. + +This is a pattern we have all absorbed. Institutions reinforce it because the formal version is the part they can freeze and transmit: taught in classrooms, examined, audited, passed between people who have never met. [James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*: the living part, the part that can't survive the freezing. -This is a pattern we have all absorbed. Institutions reinforce it because formal knowledge is what they can transmit: it can be taught in classrooms, examined, audited and passed between people who have never met. [James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*. Professions reinforce it because a formal body of knowledge is what justifies their authority and gatekeeping. The hierarchy is old and stable: formal reasoning is associated with intellectual seriousness, intuition with craft. The boundary between knowledge work and skilled trades follows the same line. +Professions reinforce the same hierarchy because a formal body of knowledge is what justifies their authority and gatekeeping. Formal reasoning is associated with intellectual seriousness, intuition with craft. The boundary between knowledge work and skilled trades follows the same line. -[Judea Pearl's](https://en.wikipedia.org/wiki/Judea_Pearl) [ladder of causation](https://en.wikipedia.org/wiki/Causal_model) is a recent and rigorous version of this hierarchy: association at the bottom, intervention in the middle, counterfactual reasoning at the top. A useful taxonomy, and a ranking that assumes the formal end is where understanding lives. +[Judea Pearl's](https://en.wikipedia.org/wiki/Judea_Pearl) [ladder of causation](https://en.wikipedia.org/wiki/Causal_model) is a recent and rigorous version of this hierarchy: association at the bottom, intervention in the middle, counterfactual reasoning at the top. A useful taxonomy, but a ranking that assumes the formal end is where understanding lives. ## Reasoning backwards -In the 1940s, the Dutch psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Groot) showed chess masters a board position for five seconds, then asked them to reconstruct it. Masters reproduced nearly everything. Amateurs managed fragments. The obvious explanation was superior memory or deeper calculation. Then de Groot scrambled the pieces into positions that couldn't arise in a real game, and the masters' advantage vanished. They weren't remembering better or thinking harder. They were recognising patterns that only exist in meaningful play. +In the 1940s, the Dutch psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Groot) showed chess masters a board position for five seconds, then asked them to reconstruct it. Masters reproduced about 90% of the pieces. Amateurs managed roughly half. The obvious explanation was superior memory or deeper calculation. + +Then de Groot scrambled the pieces into positions that couldn't arise in a real game, and the masters' advantage vanished. They weren't remembering better or thinking harder: they were recognising patterns that only exist in meaningful play. Judges do something similar. The [legal realist](https://en.wikipedia.org/wiki/Legal_realism) tradition, running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), holds that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. The law looks like formal reasoning from the outside. From the inside, it is pattern matching with a formal audit trail. Working mathematicians [surveyed in the 1940s](https://en.wikipedia.org/wiki/Jacques_Hadamard) reported the same shape: results arrived through sudden recognition, often after periods of unconscious incubation, with formal proofs constructed afterwards. The proof was a way of showing others what the mathematician already knew. Feynman, in his Nobel lecture, described publishing results he couldn't yet prove. He covered the tracks, and so did every mathematician in the survey. -Formal reasoning, in this light, is what novices do while the pattern library is sparse. Once the library is rich enough, the expert perceives rather than derives. Asking them to show their working is asking them to operate at a lower level of skill. +[Hubert Dreyfus](https://en.wikipedia.org/wiki/Hubert_Dreyfus) spent decades arguing this point: formal reasoning is what novices do while the pattern library is sparse. Once the library is rich enough, the expert perceives rather than derives. Asking them to show their working is asking them to operate at a lower level of skill. ## Pattern matching forwards -A sceptic could grant all of this and still hold the line. Fine, experts recognise situations they've seen before. But what about genuinely new ideas? Surely the creative act, the moment where something is understood for the first time, requires more than matching against a library of known patterns? +Even granting that experts recognise situations they've seen before, what about new ideas? Surely the creative act requires more than matching against a library of known patterns? -Einstein is the test case, because no one's creative process has been more carefully studied. [Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) and [Sander](https://en.wikipedia.org/wiki/Emmanuel_Sander) devote a chapter of [*Surfaces and Essences*](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/) to it. In 1905, Einstein noticed that [Wien's law](https://en.wikipedia.org/wiki/Wien%27s_radiation_law) for the entropy of radiation at low density had the same mathematical form as the entropy of an ideal gas. The equations looked alike. He concluded the physics must be alike too, and proposed that light comes in discrete packets, [quanta](https://en.wikipedia.org/wiki/Photon), by analogy with gas molecules. That paper [won him the Nobel Prize](https://www.nobelprize.org/prizes/physics/1921/einstein/facts/). A decade later, the [equivalence principle](https://en.wikipedia.org/wiki/Equivalence_principle) began with a man falling from a roof: Einstein realised that a person in freefall would feel weightless, and identified that feeling with the absence of gravity. The analogy between two physical sensations became the foundation of [general relativity](https://en.wikipedia.org/wiki/General_relativity). He then spent eight years working out the mathematics. +Einstein is the test case, because no one's creative process has been more carefully studied. [Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) and Sander devote a chapter of [*Surfaces and Essences*](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/) to it. In 1905, Einstein noticed that [Wien's law](https://en.wikipedia.org/wiki/Wien%27s_radiation_law) for the entropy of radiation at low density had the same mathematical form as the entropy of an ideal gas. The equations looked alike. He concluded the physics must be alike too, and proposed that light comes in discrete packets, [quanta](https://en.wikipedia.org/wiki/Photon), by analogy with gas molecules. That paper [won him the Nobel Prize](https://www.nobelprize.org/prizes/physics/1921/einstein/facts/). -In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. [Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his own breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) arriving unbidden as he [stepped onto a bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy), after weeks of failed formal effort. The bus didn't help him reason. It gave the unconscious pattern matching room to surface. +Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equivalence_principle) began with a man falling from a roof: Einstein realised that a person in freefall would feel weightless, and identified that feeling with the absence of gravity. The analogy between two physical sensations became the foundation of [general relativity](https://en.wikipedia.org/wiki/General_relativity). He then spent eight years working out the mathematics. -"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers, "selection pressure" from engineering. These began as metaphors. They became mechanisms by being good enough analogies to predict and intervene with, tested and refined until they earned the status of formal knowledge. The scientific vocabulary we treat as precise and literal is, at root, a library of analogies that survived. +In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. [Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his own breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) arriving unbidden as he [stepped onto a bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy), after weeks of failed formal effort. It gave the unconscious pattern matching room to surface. + +"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers, "selection pressure" from engineering. These began as metaphors. They became mechanisms by being good enough analogies to predict and intervene with, tested and refined until they earned the status of formal knowledge. ## Metapatterns and reinforcement @@ -59,22 +67,26 @@ This is the gap between having patterns and having expertise. Feynman didn't jus What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened. Patterns that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences. The textbook's library is frozen. -This distinction matters now because LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. Text encodes causal structure because it was written by causal reasoners. When an LLM recognises a pattern, it is drawing on a substrate where the causal work has already been performed and linguistically encoded. This is why these systems succeed in ways that a naive "pattern matcher" reading can't explain: they are pattern matching over the distilled outputs of experts, not over raw data. Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. +LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. Experts covered the tracks, as Feynman described: they did the intuitive, analogical work and then wrote up the formal reconstruction. But the tracks are still there in the text, encoded in the causal language, the mechanism descriptions, the "because" and "led to" and "if... then" that saturate human writing. LLMs are doing a kind of forensics on those covered tracks, recovering the pattern-matching residue that the formal presentation tried to hide. + +Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. -The conventional framing says LLMs lack [world models](https://en.wikipedia.org/wiki/World_model), so the fix is adding explicit causal structure to the architecture. But what matters about a world model may be that it updates, not that it represents cause and effect. A frozen causal model is just another static pattern. Reasoning models jump capability without adding anything explicitly causal. Chain-of-thought and search over hypotheses add something like the expert's intervention-and-feedback loop, compressed into a single inference. The research frontier is about making pattern libraries adaptive: [reasoning](https://en.wikipedia.org/wiki/Chain-of-thought_prompting), tool use, agentic loops, persistent memory. +The conventional framing says LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure to the architecture. But a frozen causal model is just another static library: what matters about a world model is that it updates. -Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this separation. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the frozen library cannot: judgement about what to read, what questions to ask, what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." The LLM handles everything that can be pattern-matched from a frozen library. The human handles everything that requires an adaptive one. +[Reasoning models](https://en.wikipedia.org/wiki/Chain_of_thought) improve without adding anything explicitly causal. Chain-of-thought and search over hypotheses let the pattern library refine itself during a single inference, a compressed version of the expert's cycle of perceiving, testing and revising. The research frontier is about making pattern libraries adaptive: reasoning, tool use, agentic loops, persistent memory. -The productive collaboration is the system's breadth meeting the human's adaptive refinement, a partnership with a pattern matcher whose library complements your own. +Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this separation. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the frozen library cannot: judgement about what to read, what questions to ask, what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." The LLM handles everything that can be pattern-matched from a frozen library, the human everything that requires an adaptive one. ## Conclusion -The audience at Pocono saw cartoons where there was physics. They had internalised a hierarchy that equates formalism with seriousness, and it blinded them to the most powerful computational tool their field would produce. Schwinger's formal derivation was correct, complete and slow. Feynman's diagrams were correct, complete and fast. The diagrams won because they matched how physicists actually think: visually, analogically, in patterns. +The audience at Pocono saw cartoons where there was physics. Bohr mistook the diagrams for literal pictures and lectured Feynman on principles he understood perfectly well. The room had internalised a hierarchy that equates formalism with seriousness, which blinded them to the most powerful computational tool their field would produce. + +Schwinger's formal derivation was correct, complete and slow. Feynman's diagrams were correct, complete and fast. Schwinger conceded as much when he compared them to the silicon chip, bringing computation to the masses. -We have been applying the same hierarchy to AI, and it is leading us to the same mistake. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission. The systems we have built are doing a version of what Einstein did with Wien's law and what Feynman did at the blackboard: matching patterns across a vast library of human knowledge, finding resemblances that carry real structure. +We are applying the same hierarchy to AI. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence from chess, law, mathematics and physics runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, the *metis* frozen for institutional use. The systems we have built are doing forensics on the tracks that experts covered: extracting the intuitive structure that the formal write-up concealed, finding resemblances that carry real weight. -Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. It is the capacity we are now building into these systems, through reasoning, tool use and memory. How fast it develops is the question that matters. +Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. Karpathy's architecture separates the frozen library from the adaptive process. The research frontier is building the adaptive process into the systems themselves. Within eighteen months of Pocono, the cartoons had won. How quickly their pattern matching becomes adaptive will determine what happens to the people whose expertise was always built the same way. --- -Pattern matching, refined by contact with reality, is how we approach AI-assisted engineering at JUXT. If that framing resonates, [let's talk](https://www.juxt.pro/contact/). +Pattern matching, refined by contact with reality, is how we approach AI-assisted engineering at JUXT. If you're approaching AI this way, [let's talk](https://www.juxt.pro/contact/). From a1119b69e4df78612f4e9b1ba629d2a160c662ba Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 25 Apr 2026 19:42:44 +0100 Subject: [PATCH 10/46] Add pull quotes, complete editorial passes --- src/pages/blog/the-oldest-pattern-matcher.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 909a16933..c2bb760af 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -41,6 +41,8 @@ In the 1940s, the Dutch psychologist [Adriaan de Groot](https://en.wikipedia.org Then de Groot scrambled the pieces into positions that couldn't arise in a real game, and the masters' advantage vanished. They weren't remembering better or thinking harder: they were recognising patterns that only exist in meaningful play. + + Judges do something similar. The [legal realist](https://en.wikipedia.org/wiki/Legal_realism) tradition, running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), holds that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. The law looks like formal reasoning from the outside. From the inside, it is pattern matching with a formal audit trail. Working mathematicians [surveyed in the 1940s](https://en.wikipedia.org/wiki/Jacques_Hadamard) reported the same shape: results arrived through sudden recognition, often after periods of unconscious incubation, with formal proofs constructed afterwards. The proof was a way of showing others what the mathematician already knew. Feynman, in his Nobel lecture, described publishing results he couldn't yet prove. He covered the tracks, and so did every mathematician in the survey. @@ -55,6 +57,8 @@ Einstein is the test case, because no one's creative process has been more caref Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equivalence_principle) began with a man falling from a roof: Einstein realised that a person in freefall would feel weightless, and identified that feeling with the absence of gravity. The analogy between two physical sensations became the foundation of [general relativity](https://en.wikipedia.org/wiki/General_relativity). He then spent eight years working out the mathematics. + + In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. [Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his own breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) arriving unbidden as he [stepped onto a bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy), after weeks of failed formal effort. It gave the unconscious pattern matching room to surface. "Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers, "selection pressure" from engineering. These began as metaphors. They became mechanisms by being good enough analogies to predict and intervene with, tested and refined until they earned the status of formal knowledge. @@ -67,6 +71,8 @@ This is the gap between having patterns and having expertise. Feynman didn't jus What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened. Patterns that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences. The textbook's library is frozen. + + LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. Experts covered the tracks, as Feynman described: they did the intuitive, analogical work and then wrote up the formal reconstruction. But the tracks are still there in the text, encoded in the causal language, the mechanism descriptions, the "because" and "led to" and "if... then" that saturate human writing. LLMs are doing a kind of forensics on those covered tracks, recovering the pattern-matching residue that the formal presentation tried to hide. Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. From da8898c1a7a2229a9b207e262a8e881d3cb855f5 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 25 Apr 2026 20:31:26 +0100 Subject: [PATCH 11/46] Feedback round: diagrams explained, Lagrange, Kahneman-Klein, causal pattern matching, cut Pearl --- src/pages/blog/the-oldest-pattern-matcher.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index c2bb760af..0ae88e896 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -15,7 +15,7 @@ tags: [Teller](https://en.wikipedia.org/wiki/Edward_Teller) interrupted: the approach violated the exclusion principle. Dirac kept asking "Is it unitary?" Bohr strode to the stage and lectured Feynman on the uncertainty principle, having mistaken the diagrams for literal pictures of particle paths. The presentation was a disaster. -Within eighteen months, [Freeman Dyson](https://en.wikipedia.org/wiki/Freeman_Dyson) had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. +The diagrams were a notation: straight lines for particles, wavy lines for forces, vertices for interactions. Each element mapped to a term in the mathematics, but physicists could reason through the spatial arrangement of the picture instead of grinding through pages of algebra. Within eighteen months, [Freeman Dyson](https://en.wikipedia.org/wiki/Freeman_Dyson) had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. @@ -31,9 +31,7 @@ The audience at Pocono were pattern matching too. They had a pattern for what se This is a pattern we have all absorbed. Institutions reinforce it because the formal version is the part they can freeze and transmit: taught in classrooms, examined, audited, passed between people who have never met. [James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*: the living part, the part that can't survive the freezing. -Professions reinforce the same hierarchy because a formal body of knowledge is what justifies their authority and gatekeeping. Formal reasoning is associated with intellectual seriousness, intuition with craft. The boundary between knowledge work and skilled trades follows the same line. - -[Judea Pearl's](https://en.wikipedia.org/wiki/Judea_Pearl) [ladder of causation](https://en.wikipedia.org/wiki/Causal_model) is a recent and rigorous version of this hierarchy: association at the bottom, intervention in the middle, counterfactual reasoning at the top. A useful taxonomy, but a ranking that assumes the formal end is where understanding lives. +Professions reinforce the same hierarchy because a formal body of knowledge is what justifies their authority and gatekeeping. Formal reasoning is associated with intellectual seriousness, intuition with craft. The boundary between knowledge work and skilled trades follows the same line. [Lagrange](https://en.wikipedia.org/wiki/Joseph-Louis_Lagrange) boasted in 1788 that his [*Mécanique analytique*](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) contained "no diagrams": algebraic method had superseded geometric intuition. Two centuries later, the room at Pocono was still operating on the same assumption. ## Reasoning backwards @@ -49,19 +47,21 @@ Working mathematicians [surveyed in the 1940s](https://en.wikipedia.org/wiki/Jac [Hubert Dreyfus](https://en.wikipedia.org/wiki/Hubert_Dreyfus) spent decades arguing this point: formal reasoning is what novices do while the pattern library is sparse. Once the library is rich enough, the expert perceives rather than derives. Asking them to show their working is asking them to operate at a lower level of skill. +[Kahneman](https://en.wikipedia.org/wiki/Daniel_Kahneman) [reached the same conclusion](https://pubmed.ncbi.nlm.nih.gov/19739881/) after a six-year collaboration with Klein: in regular environments with adequate feedback, the expert's [fast, intuitive judgement](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow) outperforms deliberate analysis. + ## Pattern matching forwards Even granting that experts recognise situations they've seen before, what about new ideas? Surely the creative act requires more than matching against a library of known patterns? Einstein is the test case, because no one's creative process has been more carefully studied. [Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) and Sander devote a chapter of [*Surfaces and Essences*](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/) to it. In 1905, Einstein noticed that [Wien's law](https://en.wikipedia.org/wiki/Wien%27s_radiation_law) for the entropy of radiation at low density had the same mathematical form as the entropy of an ideal gas. The equations looked alike. He concluded the physics must be alike too, and proposed that light comes in discrete packets, [quanta](https://en.wikipedia.org/wiki/Photon), by analogy with gas molecules. That paper [won him the Nobel Prize](https://www.nobelprize.org/prizes/physics/1921/einstein/facts/). -Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equivalence_principle) began with a man falling from a roof: Einstein realised that a person in freefall would feel weightless, and identified that feeling with the absence of gravity. The analogy between two physical sensations became the foundation of [general relativity](https://en.wikipedia.org/wiki/General_relativity). He then spent eight years working out the mathematics. +Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equivalence_principle) began with a man falling from a roof: Einstein realised that a person in freefall would feel weightless, and identified that feeling with the absence of gravity. He was pattern matching a causal relationship: acceleration produces felt weight, and so does gravity, so perhaps they are the same phenomenon. The analogy between two physical causes became the foundation of [general relativity](https://en.wikipedia.org/wiki/General_relativity). He then spent eight years working out the mathematics. In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. [Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his own breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) arriving unbidden as he [stepped onto a bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy), after weeks of failed formal effort. It gave the unconscious pattern matching room to surface. -"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers, "selection pressure" from engineering. These began as metaphors. They became mechanisms by being good enough analogies to predict and intervene with, tested and refined until they earned the status of formal knowledge. +"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers, "selection pressure" from engineering. [Cognitive scientists](https://en.wikipedia.org/wiki/Force_dynamics) have shown that our causal vocabulary itself runs on physical schemas learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. These began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. ## Metapatterns and reinforcement From 08d65a4593bcb5b0543bf04764d634f980089600 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 25 Apr 2026 22:17:45 +0100 Subject: [PATCH 12/46] Restructure: maths-first in reasoning, stochastic parrots opening, CoT expansion, Karpathy rewrite --- src/pages/blog/the-oldest-pattern-matcher.md | 22 +++++++------------- 1 file changed, 8 insertions(+), 14 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 0ae88e896..d8256da1a 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -19,11 +19,7 @@ The diagrams were a notation: straight lines for particles, wavy lines for force Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. -Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs. The tweet reached sixteen million people. The system uses an AI agent to ingest sources, maintain cross-references and synthesise connections, while the human curates what goes in, asks the questions and decides what it means. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy described the principle behind it: strip the model down to the "algorithms for thought" and offload the stored knowledge to an external system. - -Separate the reasoning from the memory. - -That separation, between a frozen library and the adaptive process that operates over it, is an old one. +Feynman's diagrams did something specific: they separated the reasoning from the formalism. The physicist could think in spatial patterns while the algebra took care of itself. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that makes the same move. An AI agent handles the stored knowledge while the human does the pattern matching: choosing what to read, asking the questions, deciding what it means. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy described the principle: strip the model down to the "algorithms for thought" and offload the storage. Sixteen million people engaged with the idea. The separation between a frozen library and the living process that operates over it turns out to be a question people are ready to think about. ## The elevation of reason @@ -35,23 +31,21 @@ Professions reinforce the same hierarchy because a formal body of knowledge is w ## Reasoning backwards -In the 1940s, the Dutch psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Groot) showed chess masters a board position for five seconds, then asked them to reconstruct it. Masters reproduced about 90% of the pieces. Amateurs managed roughly half. The obvious explanation was superior memory or deeper calculation. +Working mathematicians [surveyed in the 1940s](https://en.wikipedia.org/wiki/Jacques_Hadamard) consistently reported the same experience: results arrived through sudden recognition, often after periods of unconscious incubation, with formal proofs constructed afterwards. The proof was a way of showing others what the mathematician already knew. [Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) arriving unbidden as he [stepped onto a bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy), after weeks of failed formal effort. It gave the unconscious pattern matching room to surface. Feynman, in his Nobel lecture, described publishing results he couldn't yet prove. He covered the tracks, and so did every mathematician in the survey. -Then de Groot scrambled the pieces into positions that couldn't arise in a real game, and the masters' advantage vanished. They weren't remembering better or thinking harder: they were recognising patterns that only exist in meaningful play. +Experienced mathematicians describe something similar when reviewing the work of others: a sense of whether a proof is correct before they have checked the steps, a feeling for the shape of a valid argument that precedes the line-by-line verification. Judges do something similar. The [legal realist](https://en.wikipedia.org/wiki/Legal_realism) tradition, running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), holds that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. The law looks like formal reasoning from the outside. From the inside, it is pattern matching with a formal audit trail. -Working mathematicians [surveyed in the 1940s](https://en.wikipedia.org/wiki/Jacques_Hadamard) reported the same shape: results arrived through sudden recognition, often after periods of unconscious incubation, with formal proofs constructed afterwards. The proof was a way of showing others what the mathematician already knew. Feynman, in his Nobel lecture, described publishing results he couldn't yet prove. He covered the tracks, and so did every mathematician in the survey. - -[Hubert Dreyfus](https://en.wikipedia.org/wiki/Hubert_Dreyfus) spent decades arguing this point: formal reasoning is what novices do while the pattern library is sparse. Once the library is rich enough, the expert perceives rather than derives. Asking them to show their working is asking them to operate at a lower level of skill. +The psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Groot) found the same thing in chess. Masters shown a board position for five seconds reproduced about 90% of the pieces. Amateurs managed roughly half. When de Groot scrambled the pieces into positions that couldn't arise in a real game, the masters' advantage vanished. They weren't remembering better or thinking harder: they were recognising patterns that only exist in meaningful play. -[Kahneman](https://en.wikipedia.org/wiki/Daniel_Kahneman) [reached the same conclusion](https://pubmed.ncbi.nlm.nih.gov/19739881/) after a six-year collaboration with Klein: in regular environments with adequate feedback, the expert's [fast, intuitive judgement](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow) outperforms deliberate analysis. +[Hubert Dreyfus](https://en.wikipedia.org/wiki/Hubert_Dreyfus) spent decades arguing this point: formal reasoning is what novices do while the pattern library is sparse. Once the library is rich enough, the expert perceives rather than derives. Asking them to show their working is asking them to operate at a lower level of skill. [Kahneman](https://en.wikipedia.org/wiki/Daniel_Kahneman) [reached the same conclusion](https://pubmed.ncbi.nlm.nih.gov/19739881/) after a six-year collaboration with Klein: in regular environments with adequate feedback, the expert's [fast, intuitive judgement](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow) outperforms deliberate analysis. ## Pattern matching forwards -Even granting that experts recognise situations they've seen before, what about new ideas? Surely the creative act requires more than matching against a library of known patterns? +The most common dismissal of LLMs is that they are "[stochastic parrots](https://en.wikipedia.org/wiki/Stochastic_parrot)": statistical machines that regurgitate patterns from training data without understanding. The assumption underneath is that pattern matching can recognise what already exists but cannot produce anything new. Creativity, on this view, is where pattern matching hits its ceiling. Einstein is the test case, because no one's creative process has been more carefully studied. [Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) and Sander devote a chapter of [*Surfaces and Essences*](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/) to it. In 1905, Einstein noticed that [Wien's law](https://en.wikipedia.org/wiki/Wien%27s_radiation_law) for the entropy of radiation at low density had the same mathematical form as the entropy of an ideal gas. The equations looked alike. He concluded the physics must be alike too, and proposed that light comes in discrete packets, [quanta](https://en.wikipedia.org/wiki/Photon), by analogy with gas molecules. That paper [won him the Nobel Prize](https://www.nobelprize.org/prizes/physics/1921/einstein/facts/). @@ -59,7 +53,7 @@ Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equiv -In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. [Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his own breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) arriving unbidden as he [stepped onto a bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy), after weeks of failed formal effort. It gave the unconscious pattern matching room to surface. +In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. Einstein wasn't transcending pattern matching. He was pattern matching at a level of abstraction most people never reach: matching causal structures rather than surface features, matching how things behave rather than how they look. "Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers, "selection pressure" from engineering. [Cognitive scientists](https://en.wikipedia.org/wiki/Force_dynamics) have shown that our causal vocabulary itself runs on physical schemas learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. These began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. @@ -79,7 +73,7 @@ Where they struggle is where continuously refined expertise matters, where being The conventional framing says LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure to the architecture. But a frozen causal model is just another static library: what matters about a world model is that it updates. -[Reasoning models](https://en.wikipedia.org/wiki/Chain_of_thought) improve without adding anything explicitly causal. Chain-of-thought and search over hypotheses let the pattern library refine itself during a single inference, a compressed version of the expert's cycle of perceiving, testing and revising. The research frontier is about making pattern libraries adaptive: reasoning, tool use, agentic loops, persistent memory. +[Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. This explains why chain-of-thought is so effective despite the absence of explicit causal reasoning or world models. It introduces dynamism into what would otherwise be a single static pass over a frozen library. The research frontier is about deepening this: reasoning, tool use, agentic loops, persistent memory. Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this separation. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the frozen library cannot: judgement about what to read, what questions to ask, what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." The LLM handles everything that can be pattern-matched from a frozen library, the human everything that requires an adaptive one. From abcb4a5681761876571de7928ca45ecf8f3ccbe0 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 25 Apr 2026 22:19:46 +0100 Subject: [PATCH 13/46] Expand Hofstadter proverb pairs from Surfaces and Essences --- src/pages/blog/the-oldest-pattern-matcher.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index d8256da1a..dcf876efa 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -59,7 +59,7 @@ In each case the analogy preceded the formalism, often by years. The creative ac ## Metapatterns and reinforcement -But Hofstadter and Sander also present pairs of proverbs that assert contradictory things: "where there's smoke, there's fire" alongside "don't judge a book by its cover." Every language has these. For each proverb proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. The patterns point in every direction. +But Hofstadter and Sander also present pairs of proverbs that assert contradictory things. "Nothing ventured, nothing gained," but then again, "better safe than sorry." "Absence makes the heart grow fonder," but then again, "out of sight, out of mind." "Many hands make light work," but then again, "too many cooks spoil the broth." "He who hesitates is lost," but then again, "fools rush in where angels fear to tread." "Where there's smoke, there's fire," but then again, "don't judge a book by its cover." Every language has these. For each proverb proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. The patterns point in every direction. This is the gap between having patterns and having expertise. Feynman didn't just accumulate a library of physical intuitions. He built each one himself, refusing to accept results he hadn't rederived from scratch. Each rederivation was an act of reconstruction that connected the result to everything else he knew, forging links that a passively received result would lack. His eclectic interests, the [lockpicking](https://en.wikipedia.org/wiki/Surely_You%27re_Joking,_Mr._Feynman!), the Mayan codices, the drawing, weren't distractions from physics. They were breadth, and breadth is what fuels analogy. The wider the library, the more unexpected the connections it can make. From 30c6cd59f09cf53426fb69fda2684db913b1f7f7 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 25 Apr 2026 22:28:08 +0100 Subject: [PATCH 14/46] Add meta-pattern example: patterns at multiple levels of abstraction --- src/pages/blog/the-oldest-pattern-matcher.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index dcf876efa..964388d4d 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -61,6 +61,8 @@ In each case the analogy preceded the formalism, often by years. The creative ac But Hofstadter and Sander also present pairs of proverbs that assert contradictory things. "Nothing ventured, nothing gained," but then again, "better safe than sorry." "Absence makes the heart grow fonder," but then again, "out of sight, out of mind." "Many hands make light work," but then again, "too many cooks spoil the broth." "He who hesitates is lost," but then again, "fools rush in where angels fear to tread." "Where there's smoke, there's fire," but then again, "don't judge a book by its cover." Every language has these. For each proverb proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. The patterns point in every direction. +Knowing when to apply "nothing ventured, nothing gained" rather than "better safe than sorry" is itself a pattern, just at a higher level of abstraction. A senior engineer deciding whether to ship fast or plan carefully is selecting between these two idioms based on the shape of the situation: how reversible is the decision, how much is at stake, how much is unknown. That selection is a meta-pattern, learned the same way the idioms themselves were learned: by over-planning something that needed speed, by rushing something that needed care, and by refining the judgement through repeated contact with reality. Patterns all the way up. + This is the gap between having patterns and having expertise. Feynman didn't just accumulate a library of physical intuitions. He built each one himself, refusing to accept results he hadn't rederived from scratch. Each rederivation was an act of reconstruction that connected the result to everything else he knew, forging links that a passively received result would lack. His eclectic interests, the [lockpicking](https://en.wikipedia.org/wiki/Surely_You%27re_Joking,_Mr._Feynman!), the Mayan codices, the drawing, weren't distractions from physics. They were breadth, and breadth is what fuels analogy. The wider the library, the more unexpected the connections it can make. What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened. Patterns that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences. The textbook's library is frozen. From 53dd4484306907004b02bf4bf80787579668396b Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 25 Apr 2026 22:29:48 +0100 Subject: [PATCH 15/46] Name concrete meta-patterns: reversibility, decomposability --- src/pages/blog/the-oldest-pattern-matcher.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 964388d4d..4005419b4 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -61,7 +61,7 @@ In each case the analogy preceded the formalism, often by years. The creative ac But Hofstadter and Sander also present pairs of proverbs that assert contradictory things. "Nothing ventured, nothing gained," but then again, "better safe than sorry." "Absence makes the heart grow fonder," but then again, "out of sight, out of mind." "Many hands make light work," but then again, "too many cooks spoil the broth." "He who hesitates is lost," but then again, "fools rush in where angels fear to tread." "Where there's smoke, there's fire," but then again, "don't judge a book by its cover." Every language has these. For each proverb proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. The patterns point in every direction. -Knowing when to apply "nothing ventured, nothing gained" rather than "better safe than sorry" is itself a pattern, just at a higher level of abstraction. A senior engineer deciding whether to ship fast or plan carefully is selecting between these two idioms based on the shape of the situation: how reversible is the decision, how much is at stake, how much is unknown. That selection is a meta-pattern, learned the same way the idioms themselves were learned: by over-planning something that needed speed, by rushing something that needed care, and by refining the judgement through repeated contact with reality. Patterns all the way up. +Knowing when to apply "nothing ventured, nothing gained" rather than "better safe than sorry" is itself a pattern, just at a higher level of abstraction. A senior engineer choosing a database for a prototype applies the first: move fast, learn from the choice, change it later if needed. The same engineer choosing a database for a system that will run in production for a decade applies the second. The meta-pattern operating here is something like "how long will I live with this decision?" — a pattern about reversibility that tells you which lower-level pattern to trust. "Many hands make light work" applies when migrating a well-documented API with clear contracts. "Too many cooks spoil the broth" applies when designing a system's core abstraction. The meta-pattern: is the work decomposable, or does it need a coherent vision? These meta-patterns are learned the same way the proverbs were: by getting it wrong both ways and refining the judgement through contact with reality. Patterns all the way up. This is the gap between having patterns and having expertise. Feynman didn't just accumulate a library of physical intuitions. He built each one himself, refusing to accept results he hadn't rederived from scratch. Each rederivation was an act of reconstruction that connected the result to everything else he knew, forging links that a passively received result would lack. His eclectic interests, the [lockpicking](https://en.wikipedia.org/wiki/Surely_You%27re_Joking,_Mr._Feynman!), the Mayan codices, the drawing, weren't distractions from physics. They were breadth, and breadth is what fuels analogy. The wider the library, the more unexpected the connections it can make. From ff1e20e0f29e431523ff7a435470ece23bc4d141 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 25 Apr 2026 22:32:04 +0100 Subject: [PATCH 16/46] Pithy meta-patterns: prototype vs production, deep thinking --- src/pages/blog/the-oldest-pattern-matcher.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 4005419b4..62a53f3f9 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -61,7 +61,7 @@ In each case the analogy preceded the formalism, often by years. The creative ac But Hofstadter and Sander also present pairs of proverbs that assert contradictory things. "Nothing ventured, nothing gained," but then again, "better safe than sorry." "Absence makes the heart grow fonder," but then again, "out of sight, out of mind." "Many hands make light work," but then again, "too many cooks spoil the broth." "He who hesitates is lost," but then again, "fools rush in where angels fear to tread." "Where there's smoke, there's fire," but then again, "don't judge a book by its cover." Every language has these. For each proverb proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. The patterns point in every direction. -Knowing when to apply "nothing ventured, nothing gained" rather than "better safe than sorry" is itself a pattern, just at a higher level of abstraction. A senior engineer choosing a database for a prototype applies the first: move fast, learn from the choice, change it later if needed. The same engineer choosing a database for a system that will run in production for a decade applies the second. The meta-pattern operating here is something like "how long will I live with this decision?" — a pattern about reversibility that tells you which lower-level pattern to trust. "Many hands make light work" applies when migrating a well-documented API with clear contracts. "Too many cooks spoil the broth" applies when designing a system's core abstraction. The meta-pattern: is the work decomposable, or does it need a coherent vision? These meta-patterns are learned the same way the proverbs were: by getting it wrong both ways and refining the judgement through contact with reality. Patterns all the way up. +Knowing when to apply "nothing ventured, nothing gained" rather than "better safe than sorry" is itself a pattern, just at a higher level of abstraction. "This is more like a prototype than a production system" is a meta-pattern: it tells you which lower-level pattern to trust. "This is a piece of work that requires deep thinking" is another, and it selects "too many cooks" over "many hands." These meta-patterns are learned the same way the proverbs were: by getting it wrong both ways and refining the judgement through contact with reality. Patterns all the way up. This is the gap between having patterns and having expertise. Feynman didn't just accumulate a library of physical intuitions. He built each one himself, refusing to accept results he hadn't rederived from scratch. Each rederivation was an act of reconstruction that connected the result to everything else he knew, forging links that a passively received result would lack. His eclectic interests, the [lockpicking](https://en.wikipedia.org/wiki/Surely_You%27re_Joking,_Mr._Feynman!), the Mayan codices, the drawing, weren't distractions from physics. They were breadth, and breadth is what fuels analogy. The wider the library, the more unexpected the connections it can make. From 39b2d5dd98149ca5488d30cea2b780ef581f0a87 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 25 Apr 2026 22:35:49 +0100 Subject: [PATCH 17/46] Core reframe: making complex formalisms accessible through pattern and analogy --- src/pages/blog/the-oldest-pattern-matcher.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 62a53f3f9..eb40b9a53 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -19,7 +19,7 @@ The diagrams were a notation: straight lines for particles, wavy lines for force Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. -Feynman's diagrams did something specific: they separated the reasoning from the formalism. The physicist could think in spatial patterns while the algebra took care of itself. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that makes the same move. An AI agent handles the stored knowledge while the human does the pattern matching: choosing what to read, asking the questions, deciding what it means. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy described the principle: strip the model down to the "algorithms for thought" and offload the storage. Sixteen million people engaged with the idea. The separation between a frozen library and the living process that operates over it turns out to be a question people are ready to think about. +Feynman's diagrams did something specific: they made a complex formalism accessible through visual metaphor. Lines for particles, vertices for interactions, spatial arrangement for logical structure. The physics didn't get simpler. It got more accessible to the kind of thinking humans are good at: seeing patterns, reasoning by analogy, recognising shapes. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. The LLM handles the formal overhead, ingesting sources and maintaining structure, while the human thinks in connections and questions. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." Sixteen million people engaged with the idea. Making complex systems accessible through pattern matching turns out to be a question people are ready to think about. ## The elevation of reason From 1122cf00c6be16efa226d116be2368109e66352f Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sat, 25 Apr 2026 22:37:43 +0100 Subject: [PATCH 18/46] Karpathy as pattern-matching collaboration at two levels of abstraction --- src/pages/blog/the-oldest-pattern-matcher.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index eb40b9a53..39223c23b 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -19,7 +19,7 @@ The diagrams were a notation: straight lines for particles, wavy lines for force Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. -Feynman's diagrams did something specific: they made a complex formalism accessible through visual metaphor. Lines for particles, vertices for interactions, spatial arrangement for logical structure. The physics didn't get simpler. It got more accessible to the kind of thinking humans are good at: seeing patterns, reasoning by analogy, recognising shapes. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. The LLM handles the formal overhead, ingesting sources and maintaining structure, while the human thinks in connections and questions. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." Sixteen million people engaged with the idea. Making complex systems accessible through pattern matching turns out to be a question people are ready to think about. +Feynman's diagrams did something specific: they made a complex formalism accessible through visual metaphor. Lines for particles, vertices for interactions, spatial arrangement for logical structure. The physics didn't get simpler. It got more accessible to the kind of thinking humans are good at: seeing patterns, reasoning by analogy, recognising shapes. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that extends the same principle. The LLM finds connections across sources, recognises relationships, synthesises. The human decides which patterns matter, which questions to pursue, what it all means. Both are pattern matching, at different levels: the LLM across a vast frozen library, the human with the adaptive judgement that comes from living in the problem. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." Sixteen million people engaged with the idea. A pattern-matching collaboration, operating at two levels of abstraction, turns out to be a question people are ready to think about. ## The elevation of reason From 9e3569438681904a08cef9263e85e34316b37be4 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 07:40:22 +0100 Subject: [PATCH 19/46] Simplify Karpathy lede, establish vocabulary, update CTA --- src/pages/blog/the-oldest-pattern-matcher.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 39223c23b..d9640d03a 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -19,7 +19,9 @@ The diagrams were a notation: straight lines for particles, wavy lines for force Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. -Feynman's diagrams did something specific: they made a complex formalism accessible through visual metaphor. Lines for particles, vertices for interactions, spatial arrangement for logical structure. The physics didn't get simpler. It got more accessible to the kind of thinking humans are good at: seeing patterns, reasoning by analogy, recognising shapes. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that extends the same principle. The LLM finds connections across sources, recognises relationships, synthesises. The human decides which patterns matter, which questions to pursue, what it all means. Both are pattern matching, at different levels: the LLM across a vast frozen library, the human with the adaptive judgement that comes from living in the problem. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." Sixteen million people engaged with the idea. A pattern-matching collaboration, operating at two levels of abstraction, turns out to be a question people are ready to think about. +Feynman's diagrams did something specific: they made a complex formalism accessible through visual metaphor. Lines for particles, vertices for interactions, spatial arrangement for logical structure. The physics didn't get simpler. It got more accessible to the kind of thinking humans are good at: seeing patterns, reasoning by analogy, recognising shapes. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections, synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." Sixteen million people engaged with the idea. + +Pattern matching, analogy, recognition, intuition, metaphor: these words all describe facets of the same cognitive faculty. This piece will use them interchangeably, because the phenomenon they point to is one thing viewed from different angles. When a physicist reads a diagram, a mathematician feels a proof is right, or an LLM finds structure in text, the underlying operation is the same: recognising that this situation resembles that one, and acting on the resemblance. ## The elevation of reason @@ -91,4 +93,4 @@ Their limitation is the one Hofstadter's proverbs predict. A frozen library, how --- -Pattern matching, refined by contact with reality, is how we approach AI-assisted engineering at JUXT. If you're approaching AI this way, [let's talk](https://www.juxt.pro/contact/). +JUXT understand the strengths and limitations of LLMs. If you'd like us to speak with you or your team, [get in touch](https://www.juxt.pro/contact/). From 2154fb42c63fdb25b0b030a19fd7393efafafd95 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 07:43:43 +0100 Subject: [PATCH 20/46] Fix vocabulary paragraph, escalate Karpathy two-levels in conclusion --- src/pages/blog/the-oldest-pattern-matcher.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index d9640d03a..8fe921976 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -21,7 +21,7 @@ Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman Feynman's diagrams did something specific: they made a complex formalism accessible through visual metaphor. Lines for particles, vertices for interactions, spatial arrangement for logical structure. The physics didn't get simpler. It got more accessible to the kind of thinking humans are good at: seeing patterns, reasoning by analogy, recognising shapes. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections, synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." Sixteen million people engaged with the idea. -Pattern matching, analogy, recognition, intuition, metaphor: these words all describe facets of the same cognitive faculty. This piece will use them interchangeably, because the phenomenon they point to is one thing viewed from different angles. When a physicist reads a diagram, a mathematician feels a proof is right, or an LLM finds structure in text, the underlying operation is the same: recognising that this situation resembles that one, and acting on the resemblance. +Pattern matching, analogy, recognition, intuition, metaphor: these are different names for the same cognitive operation. When a physicist reads a diagram, a mathematician feels a proof is right, or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. ## The elevation of reason @@ -89,7 +89,7 @@ Schwinger's formal derivation was correct, complete and slow. Feynman's diagrams We are applying the same hierarchy to AI. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence from chess, law, mathematics and physics runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, the *metis* frozen for institutional use. The systems we have built are doing forensics on the tracks that experts covered: extracting the intuitive structure that the formal write-up concealed, finding resemblances that carry real weight. -Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. Karpathy's architecture separates the frozen library from the adaptive process. The research frontier is building the adaptive process into the systems themselves. Within eighteen months of Pocono, the cartoons had won. How quickly their pattern matching becomes adaptive will determine what happens to the people whose expertise was always built the same way. +Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. In Karpathy's architecture, both sides are pattern matching: the LLM across its vast frozen library, the human with the adaptive judgement that comes from living in the problem. The collaboration works because pattern matching operates at multiple levels of abstraction, the same structure the proverbs and meta-patterns revealed. The research frontier is building the adaptive layer into the systems themselves. Within eighteen months of Pocono, the cartoons had won. How quickly these systems develop their own adaptive layer will determine what happens to the people whose expertise was always built the same way. --- From e6349b74adf6a3526d2f320e236820fdea87995d Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 07:46:46 +0100 Subject: [PATCH 21/46] =?UTF-8?q?Weave=20maths=20material:=20Atiyah,=20Gow?= =?UTF-8?q?ers,=20Erd=C5=91s=20Book=20proof,=20arithmetic=20paradox?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/pages/blog/the-oldest-pattern-matcher.md | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 8fe921976..cc8a7fd49 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -35,7 +35,7 @@ Professions reinforce the same hierarchy because a formal body of knowledge is w Working mathematicians [surveyed in the 1940s](https://en.wikipedia.org/wiki/Jacques_Hadamard) consistently reported the same experience: results arrived through sudden recognition, often after periods of unconscious incubation, with formal proofs constructed afterwards. The proof was a way of showing others what the mathematician already knew. [Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) arriving unbidden as he [stepped onto a bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy), after weeks of failed formal effort. It gave the unconscious pattern matching room to surface. Feynman, in his Nobel lecture, described publishing results he couldn't yet prove. He covered the tracks, and so did every mathematician in the survey. -Experienced mathematicians describe something similar when reviewing the work of others: a sense of whether a proof is correct before they have checked the steps, a feeling for the shape of a valid argument that precedes the line-by-line verification. +[Michael Atiyah](https://en.wikipedia.org/wiki/Michael_Atiyah) described the same faculty when reviewing the work of others: "Once you've seen it, the truth or veracity, it just stares you in the face. The truth is looking back at you." The feeling for the shape of a valid argument precedes the line-by-line verification. It is, as Atiyah put it, "[pre all that](https://www.scientificamerican.com/article/beauty-equals-truth/)" — pre words, pre formulas, pre statements. @@ -71,7 +71,7 @@ What separates a useful pattern library from a contradictory heap of proverbs is -LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. Experts covered the tracks, as Feynman described: they did the intuitive, analogical work and then wrote up the formal reconstruction. But the tracks are still there in the text, encoded in the causal language, the mechanism descriptions, the "because" and "led to" and "if... then" that saturate human writing. LLMs are doing a kind of forensics on those covered tracks, recovering the pattern-matching residue that the formal presentation tried to hide. +LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. Experts covered the tracks, as Feynman described: they did the intuitive, analogical work and then wrote up the formal reconstruction. [Timothy Gowers](https://en.wikipedia.org/wiki/Timothy_Gowers) [makes the same observation](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) about mathematics specifically: LLMs are "trained on the product of mathematics rather than the process." They learn to [imitate pulling rabbits out of hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) "without learning how to catch the rabbit or put it in the hat in the first place." But the tracks are still there in the text, encoded in the causal language, the mechanism descriptions, the "because" and "led to" and "if... then" that saturate human writing. LLMs are doing a kind of forensics on those covered tracks, recovering the pattern-matching residue that the formal presentation tried to hide. Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. @@ -81,13 +81,19 @@ The conventional framing says LLMs lack [world models](https://en.wikipedia.org/ Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this separation. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the frozen library cannot: judgement about what to read, what questions to ask, what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." The LLM handles everything that can be pattern-matched from a frozen library, the human everything that requires an adaptive one. +The results of this collaboration are already visible. Since late 2025, mathematicians working with LLMs have been [solving open problems](https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/) from [Paul Erdős's](https://en.wikipedia.org/wiki/Paul_Erd%C5%91s) celebrated problem lists at a pace that would have been unthinkable a year earlier. [Mehtaab Sawhney and Mark Sellke](https://x.com/MarkSellke/status/1979226538059931886) used thousands of GPT-5 queries to solve ten Erdős problems listed as open. Roughly 80% of the model's generated arguments [were wrong](https://arxiv.org/html/2510.23513v1). The collaboration still worked, because the mathematician's adaptive judgement could sift the useful patterns from the noise. + +The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/status/2044307082275618993). Every mathematician since Erdős's 1935 paper had approached it by transforming the problem into continuous analysis. GPT-5.4 stayed in the arithmetic realm and deployed the [von Mangoldt function](https://en.wikipedia.org/wiki/Von_Mangoldt_function) in a way nobody had tried. [Jared Duker Lichtman](https://en.wikipedia.org/wiki/Jared_Duker_Lichtman), who had spent seven years on primitive set problems, read the proof and called it a [Book proof](https://en.wikipedia.org/wiki/Proofs_from_THE_BOOK): Erdős's term for a proof so compact and elegant it could have been written by God. [Tao](https://terrytao.wordpress.com/2025/12/08/the-story-of-erdos-problem-126/) suggested it "may reveal previously unexplored connections between integer anatomy and flow network theory." + +A system that cannot reliably add two numbers produced a proof that an expert in the field called divine. That is not what brute-force computation looks like. It is what pattern matching looks like when it operates at sufficient depth, in the hands of someone who knows which patterns matter. + ## Conclusion The audience at Pocono saw cartoons where there was physics. Bohr mistook the diagrams for literal pictures and lectured Feynman on principles he understood perfectly well. The room had internalised a hierarchy that equates formalism with seriousness, which blinded them to the most powerful computational tool their field would produce. Schwinger's formal derivation was correct, complete and slow. Feynman's diagrams were correct, complete and fast. Schwinger conceded as much when he compared them to the silicon chip, bringing computation to the masses. -We are applying the same hierarchy to AI. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence from chess, law, mathematics and physics runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, the *metis* frozen for institutional use. The systems we have built are doing forensics on the tracks that experts covered: extracting the intuitive structure that the formal write-up concealed, finding resemblances that carry real weight. +We are applying the same hierarchy to AI. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence from mathematics, physics and law runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, the *metis* frozen for institutional use. These systems cannot reliably add two numbers, yet in expert hands they are producing proofs that mathematicians call divine. That paradox dissolves the moment you stop treating pattern matching as a lesser faculty. They are doing forensics on the tracks that experts covered: extracting the intuitive structure that the formal write-up concealed, finding resemblances that carry real weight. Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. In Karpathy's architecture, both sides are pattern matching: the LLM across its vast frozen library, the human with the adaptive judgement that comes from living in the problem. The collaboration works because pattern matching operates at multiple levels of abstraction, the same structure the proverbs and meta-patterns revealed. The research frontier is building the adaptive layer into the systems themselves. Within eighteen months of Pocono, the cartoons had won. How quickly these systems develop their own adaptive layer will determine what happens to the people whose expertise was always built the same way. From 5916c86087bb58390d8ce4cc5ec714e4592ba295 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 08:00:31 +0100 Subject: [PATCH 22/46] Split metapatterns section, re-seed Schwinger, fix tricolons and mirrors, replace pull quote --- src/pages/blog/the-oldest-pattern-matcher.md | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index cc8a7fd49..8cf3c0efe 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -15,11 +15,11 @@ tags: [Teller](https://en.wikipedia.org/wiki/Edward_Teller) interrupted: the approach violated the exclusion principle. Dirac kept asking "Is it unitary?" Bohr strode to the stage and lectured Feynman on the uncertainty principle, having mistaken the diagrams for literal pictures of particle paths. The presentation was a disaster. -The diagrams were a notation: straight lines for particles, wavy lines for forces, vertices for interactions. Each element mapped to a term in the mathematics, but physicists could reason through the spatial arrangement of the picture instead of grinding through pages of algebra. Within eighteen months, [Freeman Dyson](https://en.wikipedia.org/wiki/Freeman_Dyson) had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. +The diagrams were a notation: straight lines for particles, wavy lines for forces, vertices for interactions. Each element mapped to a term in the mathematics, but physicists could reason through the spatial arrangement of the picture instead of grinding through pages of algebra. Within eighteen months, [Freeman Dyson](https://en.wikipedia.org/wiki/Freeman_Dyson) had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. Schwinger later compared them to the silicon chip, "[bringing computation to the masses](https://en.wikipedia.org/wiki/Feynman_diagram#History)." Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. -Feynman's diagrams did something specific: they made a complex formalism accessible through visual metaphor. Lines for particles, vertices for interactions, spatial arrangement for logical structure. The physics didn't get simpler. It got more accessible to the kind of thinking humans are good at: seeing patterns, reasoning by analogy, recognising shapes. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections, synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." Sixteen million people engaged with the idea. +Feynman's diagrams did something specific: they made a complex formalism accessible through visual metaphor. Lines for particles, vertices for interactions. The physics didn't get simpler. It got more accessible to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections, synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." Sixteen million people engaged with the idea. Pattern matching, analogy, recognition, intuition, metaphor: these are different names for the same cognitive operation. When a physicist reads a diagram, a mathematician feels a proof is right, or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. @@ -35,7 +35,7 @@ Professions reinforce the same hierarchy because a formal body of knowledge is w Working mathematicians [surveyed in the 1940s](https://en.wikipedia.org/wiki/Jacques_Hadamard) consistently reported the same experience: results arrived through sudden recognition, often after periods of unconscious incubation, with formal proofs constructed afterwards. The proof was a way of showing others what the mathematician already knew. [Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) arriving unbidden as he [stepped onto a bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy), after weeks of failed formal effort. It gave the unconscious pattern matching room to surface. Feynman, in his Nobel lecture, described publishing results he couldn't yet prove. He covered the tracks, and so did every mathematician in the survey. -[Michael Atiyah](https://en.wikipedia.org/wiki/Michael_Atiyah) described the same faculty when reviewing the work of others: "Once you've seen it, the truth or veracity, it just stares you in the face. The truth is looking back at you." The feeling for the shape of a valid argument precedes the line-by-line verification. It is, as Atiyah put it, "[pre all that](https://www.scientificamerican.com/article/beauty-equals-truth/)" — pre words, pre formulas, pre statements. +[Michael Atiyah](https://en.wikipedia.org/wiki/Michael_Atiyah) described the same faculty when reviewing the work of others: "Once you've seen it, the truth or veracity, it just stares you in the face. The truth is looking back at you." The feeling for the shape of a valid argument precedes the line-by-line verification. It is, as Atiyah put it, "[pre all that](https://www.scientificamerican.com/article/beauty-equals-truth/)" — pre words, pre formulas. @@ -53,11 +53,11 @@ Einstein is the test case, because no one's creative process has been more caref Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equivalence_principle) began with a man falling from a roof: Einstein realised that a person in freefall would feel weightless, and identified that feeling with the absence of gravity. He was pattern matching a causal relationship: acceleration produces felt weight, and so does gravity, so perhaps they are the same phenomenon. The analogy between two physical causes became the foundation of [general relativity](https://en.wikipedia.org/wiki/General_relativity). He then spent eight years working out the mathematics. - + In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. Einstein wasn't transcending pattern matching. He was pattern matching at a level of abstraction most people never reach: matching causal structures rather than surface features, matching how things behave rather than how they look. -"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers, "selection pressure" from engineering. [Cognitive scientists](https://en.wikipedia.org/wiki/Force_dynamics) have shown that our causal vocabulary itself runs on physical schemas learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. These began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. +"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. [Cognitive scientists](https://en.wikipedia.org/wiki/Force_dynamics) have shown that our causal vocabulary itself runs on physical schemas learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. These began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. ## Metapatterns and reinforcement @@ -67,7 +67,9 @@ Knowing when to apply "nothing ventured, nothing gained" rather than "better saf This is the gap between having patterns and having expertise. Feynman didn't just accumulate a library of physical intuitions. He built each one himself, refusing to accept results he hadn't rederived from scratch. Each rederivation was an act of reconstruction that connected the result to everything else he knew, forging links that a passively received result would lack. His eclectic interests, the [lockpicking](https://en.wikipedia.org/wiki/Surely_You%27re_Joking,_Mr._Feynman!), the Mayan codices, the drawing, weren't distractions from physics. They were breadth, and breadth is what fuels analogy. The wider the library, the more unexpected the connections it can make. -What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened. Patterns that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences. The textbook's library is frozen. +What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened. Patterns that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences, while the textbook's library is frozen. + +## Forensics on covered tracks @@ -81,7 +83,7 @@ The conventional framing says LLMs lack [world models](https://en.wikipedia.org/ Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this separation. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the frozen library cannot: judgement about what to read, what questions to ask, what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." The LLM handles everything that can be pattern-matched from a frozen library, the human everything that requires an adaptive one. -The results of this collaboration are already visible. Since late 2025, mathematicians working with LLMs have been [solving open problems](https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/) from [Paul Erdős's](https://en.wikipedia.org/wiki/Paul_Erd%C5%91s) celebrated problem lists at a pace that would have been unthinkable a year earlier. [Mehtaab Sawhney and Mark Sellke](https://x.com/MarkSellke/status/1979226538059931886) used thousands of GPT-5 queries to solve ten Erdős problems listed as open. Roughly 80% of the model's generated arguments [were wrong](https://arxiv.org/html/2510.23513v1). The collaboration still worked, because the mathematician's adaptive judgement could sift the useful patterns from the noise. +Since late 2025, mathematicians working with LLMs have been [solving open problems](https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/) from [Paul Erdős's](https://en.wikipedia.org/wiki/Paul_Erd%C5%91s) celebrated problem lists at a pace that would have been unthinkable a year earlier. [Mehtaab Sawhney and Mark Sellke](https://x.com/MarkSellke/status/1979226538059931886) used thousands of GPT-5 queries to solve ten Erdős problems listed as open. Roughly 80% of the model's generated arguments [were wrong](https://arxiv.org/html/2510.23513v1). The collaboration still worked, because the mathematician's adaptive judgement could sift the useful patterns from the noise. The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/status/2044307082275618993). Every mathematician since Erdős's 1935 paper had approached it by transforming the problem into continuous analysis. GPT-5.4 stayed in the arithmetic realm and deployed the [von Mangoldt function](https://en.wikipedia.org/wiki/Von_Mangoldt_function) in a way nobody had tried. [Jared Duker Lichtman](https://en.wikipedia.org/wiki/Jared_Duker_Lichtman), who had spent seven years on primitive set problems, read the proof and called it a [Book proof](https://en.wikipedia.org/wiki/Proofs_from_THE_BOOK): Erdős's term for a proof so compact and elegant it could have been written by God. [Tao](https://terrytao.wordpress.com/2025/12/08/the-story-of-erdos-problem-126/) suggested it "may reveal previously unexplored connections between integer anatomy and flow network theory." From 67fa47518c3b93c7887a763262497660e62e7990 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 08:05:04 +0100 Subject: [PATCH 23/46] Remaining review fixes: Bohr reframe, cut orphans, transitions, tighten Feynman breadth --- src/pages/blog/the-oldest-pattern-matcher.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 8cf3c0efe..d030e583b 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -19,9 +19,9 @@ The diagrams were a notation: straight lines for particles, wavy lines for force Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. -Feynman's diagrams did something specific: they made a complex formalism accessible through visual metaphor. Lines for particles, vertices for interactions. The physics didn't get simpler. It got more accessible to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections, synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." Sixteen million people engaged with the idea. +Feynman's diagrams did something specific: they made a complex formalism accessible through visual metaphor. Lines for particles, vertices for interactions. The physics didn't get simpler. It got more accessible to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections, synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." -Pattern matching, analogy, recognition, intuition, metaphor: these are different names for the same cognitive operation. When a physicist reads a diagram, a mathematician feels a proof is right, or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. +Pattern matching, analogy, recognition, intuition, metaphor: these are different names for the same cognitive operation. When a mathematician feels a proof is right or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. ## The elevation of reason @@ -35,15 +35,15 @@ Professions reinforce the same hierarchy because a formal body of knowledge is w Working mathematicians [surveyed in the 1940s](https://en.wikipedia.org/wiki/Jacques_Hadamard) consistently reported the same experience: results arrived through sudden recognition, often after periods of unconscious incubation, with formal proofs constructed afterwards. The proof was a way of showing others what the mathematician already knew. [Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) arriving unbidden as he [stepped onto a bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy), after weeks of failed formal effort. It gave the unconscious pattern matching room to surface. Feynman, in his Nobel lecture, described publishing results he couldn't yet prove. He covered the tracks, and so did every mathematician in the survey. -[Michael Atiyah](https://en.wikipedia.org/wiki/Michael_Atiyah) described the same faculty when reviewing the work of others: "Once you've seen it, the truth or veracity, it just stares you in the face. The truth is looking back at you." The feeling for the shape of a valid argument precedes the line-by-line verification. It is, as Atiyah put it, "[pre all that](https://www.scientificamerican.com/article/beauty-equals-truth/)" — pre words, pre formulas. +[Michael Atiyah](https://en.wikipedia.org/wiki/Michael_Atiyah) described the same faculty when reviewing the work of others: "Once you've seen it, the truth or veracity, it just stares you in the face. The truth is looking back at you." The feeling for the shape of a valid argument precedes the line-by-line verification. It is, as Atiyah put it, "[pre all that](https://www.scientificamerican.com/article/beauty-equals-truth/)" — pre words, pre formulas. The formal reconstruction comes after, as justification for a conclusion already reached. -Judges do something similar. The [legal realist](https://en.wikipedia.org/wiki/Legal_realism) tradition, running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), holds that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. The law looks like formal reasoning from the outside. From the inside, it is pattern matching with a formal audit trail. +That phrase — "a conclusion already reached" — describes law as well as mathematics. Judges do something similar. The [legal realist](https://en.wikipedia.org/wiki/Legal_realism) tradition, running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), holds that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. The law looks like formal reasoning from the outside. From the inside, it is pattern matching with a formal audit trail. The psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Groot) found the same thing in chess. Masters shown a board position for five seconds reproduced about 90% of the pieces. Amateurs managed roughly half. When de Groot scrambled the pieces into positions that couldn't arise in a real game, the masters' advantage vanished. They weren't remembering better or thinking harder: they were recognising patterns that only exist in meaningful play. -[Hubert Dreyfus](https://en.wikipedia.org/wiki/Hubert_Dreyfus) spent decades arguing this point: formal reasoning is what novices do while the pattern library is sparse. Once the library is rich enough, the expert perceives rather than derives. Asking them to show their working is asking them to operate at a lower level of skill. [Kahneman](https://en.wikipedia.org/wiki/Daniel_Kahneman) [reached the same conclusion](https://pubmed.ncbi.nlm.nih.gov/19739881/) after a six-year collaboration with Klein: in regular environments with adequate feedback, the expert's [fast, intuitive judgement](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow) outperforms deliberate analysis. +[Hubert Dreyfus](https://en.wikipedia.org/wiki/Hubert_Dreyfus) spent decades arguing this point: formal reasoning is what novices do while the pattern library is sparse. Once the library is rich enough, the expert perceives rather than derives. Asking them to show their working is asking them to operate at a lower level of skill. [Kahneman](https://en.wikipedia.org/wiki/Daniel_Kahneman) [reached the same conclusion](https://pubmed.ncbi.nlm.nih.gov/19739881/) after a six-year collaboration with Klein: in regular environments with adequate feedback, the expert's [fast, intuitive judgement](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow) outperforms deliberate analysis. But all of this concerns recognition: matching a new situation to something already in the library. What about situations nobody has seen before? ## Pattern matching forwards @@ -65,7 +65,7 @@ But Hofstadter and Sander also present pairs of proverbs that assert contradicto Knowing when to apply "nothing ventured, nothing gained" rather than "better safe than sorry" is itself a pattern, just at a higher level of abstraction. "This is more like a prototype than a production system" is a meta-pattern: it tells you which lower-level pattern to trust. "This is a piece of work that requires deep thinking" is another, and it selects "too many cooks" over "many hands." These meta-patterns are learned the same way the proverbs were: by getting it wrong both ways and refining the judgement through contact with reality. Patterns all the way up. -This is the gap between having patterns and having expertise. Feynman didn't just accumulate a library of physical intuitions. He built each one himself, refusing to accept results he hadn't rederived from scratch. Each rederivation was an act of reconstruction that connected the result to everything else he knew, forging links that a passively received result would lack. His eclectic interests, the [lockpicking](https://en.wikipedia.org/wiki/Surely_You%27re_Joking,_Mr._Feynman!), the Mayan codices, the drawing, weren't distractions from physics. They were breadth, and breadth is what fuels analogy. The wider the library, the more unexpected the connections it can make. +This is the gap between having patterns and having expertise. Feynman didn't just accumulate a library of physical intuitions. He [built each one himself](https://en.wikipedia.org/wiki/Surely_You%27re_Joking,_Mr._Feynman!), refusing to accept results he hadn't rederived from scratch, forging links that a passively received result would lack. Breadth is what fuels analogy: the wider the library, the more unexpected the connections it can make. What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened. Patterns that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences, while the textbook's library is frozen. @@ -79,19 +79,19 @@ Where they struggle is where continuously refined expertise matters, where being The conventional framing says LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure to the architecture. But a frozen causal model is just another static library: what matters about a world model is that it updates. -[Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. This explains why chain-of-thought is so effective despite the absence of explicit causal reasoning or world models. It introduces dynamism into what would otherwise be a single static pass over a frozen library. The research frontier is about deepening this: reasoning, tool use, agentic loops, persistent memory. +[Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. This explains why chain-of-thought is so effective despite the absence of explicit causal reasoning or world models. It introduces dynamism into what would otherwise be a single static pass over a frozen library. Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this separation. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the frozen library cannot: judgement about what to read, what questions to ask, what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." The LLM handles everything that can be pattern-matched from a frozen library, the human everything that requires an adaptive one. Since late 2025, mathematicians working with LLMs have been [solving open problems](https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/) from [Paul Erdős's](https://en.wikipedia.org/wiki/Paul_Erd%C5%91s) celebrated problem lists at a pace that would have been unthinkable a year earlier. [Mehtaab Sawhney and Mark Sellke](https://x.com/MarkSellke/status/1979226538059931886) used thousands of GPT-5 queries to solve ten Erdős problems listed as open. Roughly 80% of the model's generated arguments [were wrong](https://arxiv.org/html/2510.23513v1). The collaboration still worked, because the mathematician's adaptive judgement could sift the useful patterns from the noise. -The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/status/2044307082275618993). Every mathematician since Erdős's 1935 paper had approached it by transforming the problem into continuous analysis. GPT-5.4 stayed in the arithmetic realm and deployed the [von Mangoldt function](https://en.wikipedia.org/wiki/Von_Mangoldt_function) in a way nobody had tried. [Jared Duker Lichtman](https://en.wikipedia.org/wiki/Jared_Duker_Lichtman), who had spent seven years on primitive set problems, read the proof and called it a [Book proof](https://en.wikipedia.org/wiki/Proofs_from_THE_BOOK): Erdős's term for a proof so compact and elegant it could have been written by God. [Tao](https://terrytao.wordpress.com/2025/12/08/the-story-of-erdos-problem-126/) suggested it "may reveal previously unexplored connections between integer anatomy and flow network theory." +The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/status/2044307082275618993). Every mathematician since Erdős's 1935 paper had approached it by transforming the problem into continuous analysis. GPT-5.4 stayed in the arithmetic realm and deployed the [von Mangoldt function](https://en.wikipedia.org/wiki/Von_Mangoldt_function) in a way nobody had tried. [Jared Duker Lichtman](https://en.wikipedia.org/wiki/Jared_Duker_Lichtman), who had spent seven years on primitive set problems, read the proof and [called it a Book proof](https://en.wikipedia.org/wiki/Proofs_from_THE_BOOK): [Erdős's](https://terrytao.wordpress.com/2025/12/08/the-story-of-erdos-problem-126/) term for a proof so compact and elegant it could have been written by God. A system that cannot reliably add two numbers produced a proof that an expert in the field called divine. That is not what brute-force computation looks like. It is what pattern matching looks like when it operates at sufficient depth, in the hands of someone who knows which patterns matter. ## Conclusion -The audience at Pocono saw cartoons where there was physics. Bohr mistook the diagrams for literal pictures and lectured Feynman on principles he understood perfectly well. The room had internalised a hierarchy that equates formalism with seriousness, which blinded them to the most powerful computational tool their field would produce. +The audience at Pocono saw cartoons where there was physics. Bohr's mistake was not that he failed to understand quantum mechanics. It was that he had a pattern for what serious physics looks like, and the diagrams didn't match it. The room had internalised a hierarchy that equates formalism with seriousness, which blinded them to the most powerful computational tool their field would produce. Schwinger's formal derivation was correct, complete and slow. Feynman's diagrams were correct, complete and fast. Schwinger conceded as much when he compared them to the silicon chip, bringing computation to the masses. From c5600bc68c14604f3fe52f8b944ff5a485b5d990 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 08:10:00 +0100 Subject: [PATCH 24/46] Final check fixes: fold mirrors, soften vocab claim, split conclusion, fix tautology --- src/pages/blog/the-oldest-pattern-matcher.md | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index d030e583b..bb3b9e4f0 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -21,7 +21,7 @@ Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman Feynman's diagrams did something specific: they made a complex formalism accessible through visual metaphor. Lines for particles, vertices for interactions. The physics didn't get simpler. It got more accessible to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections, synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." -Pattern matching, analogy, recognition, intuition, metaphor: these are different names for the same cognitive operation. When a mathematician feels a proof is right or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. +Pattern matching, analogy, recognition, intuition and metaphor are different names for variations on a single faculty. When a mathematician feels a proof is right or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. ## The elevation of reason @@ -49,13 +49,13 @@ The psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Gro The most common dismissal of LLMs is that they are "[stochastic parrots](https://en.wikipedia.org/wiki/Stochastic_parrot)": statistical machines that regurgitate patterns from training data without understanding. The assumption underneath is that pattern matching can recognise what already exists but cannot produce anything new. Creativity, on this view, is where pattern matching hits its ceiling. -Einstein is the test case, because no one's creative process has been more carefully studied. [Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) and Sander devote a chapter of [*Surfaces and Essences*](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/) to it. In 1905, Einstein noticed that [Wien's law](https://en.wikipedia.org/wiki/Wien%27s_radiation_law) for the entropy of radiation at low density had the same mathematical form as the entropy of an ideal gas. The equations looked alike. He concluded the physics must be alike too, and proposed that light comes in discrete packets, [quanta](https://en.wikipedia.org/wiki/Photon), by analogy with gas molecules. That paper [won him the Nobel Prize](https://www.nobelprize.org/prizes/physics/1921/einstein/facts/). +Einstein is the test case, because no one's creative process has been more carefully studied. [Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) and Sander [walk through his analogies one by one](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/). In 1905, Einstein noticed that [Wien's law](https://en.wikipedia.org/wiki/Wien%27s_radiation_law) for the entropy of radiation at low density had the same mathematical form as the entropy of an ideal gas. The equations looked alike. He concluded the physics must be alike too, and proposed that light comes in discrete packets, [quanta](https://en.wikipedia.org/wiki/Photon), by analogy with gas molecules. That paper [won him the Nobel Prize](https://www.nobelprize.org/prizes/physics/1921/einstein/facts/). Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equivalence_principle) began with a man falling from a roof: Einstein realised that a person in freefall would feel weightless, and identified that feeling with the absence of gravity. He was pattern matching a causal relationship: acceleration produces felt weight, and so does gravity, so perhaps they are the same phenomenon. The analogy between two physical causes became the foundation of [general relativity](https://en.wikipedia.org/wiki/General_relativity). He then spent eight years working out the mathematics. - + -In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. Einstein wasn't transcending pattern matching. He was pattern matching at a level of abstraction most people never reach: matching causal structures rather than surface features, matching how things behave rather than how they look. +In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. Einstein was pattern matching at a level of abstraction most people never reach: matching causal structures rather than surface features, matching how things behave rather than how they look. "Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. [Cognitive scientists](https://en.wikipedia.org/wiki/Force_dynamics) have shown that our causal vocabulary itself runs on physical schemas learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. These began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. @@ -77,7 +77,7 @@ LLMs have inherited enormous pattern libraries from the serialised outputs of hu Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. -The conventional framing says LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure to the architecture. But a frozen causal model is just another static library: what matters about a world model is that it updates. +The conventional framing says LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure to the architecture. But a frozen causal model is still a frozen library: what matters about a world model is that it updates. [Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. This explains why chain-of-thought is so effective despite the absence of explicit causal reasoning or world models. It introduces dynamism into what would otherwise be a single static pass over a frozen library. @@ -87,17 +87,19 @@ Since late 2025, mathematicians working with LLMs have been [solving open proble The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/status/2044307082275618993). Every mathematician since Erdős's 1935 paper had approached it by transforming the problem into continuous analysis. GPT-5.4 stayed in the arithmetic realm and deployed the [von Mangoldt function](https://en.wikipedia.org/wiki/Von_Mangoldt_function) in a way nobody had tried. [Jared Duker Lichtman](https://en.wikipedia.org/wiki/Jared_Duker_Lichtman), who had spent seven years on primitive set problems, read the proof and [called it a Book proof](https://en.wikipedia.org/wiki/Proofs_from_THE_BOOK): [Erdős's](https://terrytao.wordpress.com/2025/12/08/the-story-of-erdos-problem-126/) term for a proof so compact and elegant it could have been written by God. -A system that cannot reliably add two numbers produced a proof that an expert in the field called divine. That is not what brute-force computation looks like. It is what pattern matching looks like when it operates at sufficient depth, in the hands of someone who knows which patterns matter. +A system that cannot reliably add two numbers produced a proof that an expert in the field called divine. That is what pattern matching looks like when it operates at sufficient depth, in the hands of someone who knows which patterns matter. ## Conclusion -The audience at Pocono saw cartoons where there was physics. Bohr's mistake was not that he failed to understand quantum mechanics. It was that he had a pattern for what serious physics looks like, and the diagrams didn't match it. The room had internalised a hierarchy that equates formalism with seriousness, which blinded them to the most powerful computational tool their field would produce. +The audience at Pocono saw cartoons where there was physics. Bohr understood quantum mechanics perfectly well, but he had a pattern for what serious physics looks like, and the diagrams didn't match it. The room had internalised a hierarchy that equates formalism with seriousness, which blinded them to the most powerful computational tool their field would produce. Schwinger's formal derivation was correct, complete and slow. Feynman's diagrams were correct, complete and fast. Schwinger conceded as much when he compared them to the silicon chip, bringing computation to the masses. We are applying the same hierarchy to AI. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence from mathematics, physics and law runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, the *metis* frozen for institutional use. These systems cannot reliably add two numbers, yet in expert hands they are producing proofs that mathematicians call divine. That paradox dissolves the moment you stop treating pattern matching as a lesser faculty. They are doing forensics on the tracks that experts covered: extracting the intuitive structure that the formal write-up concealed, finding resemblances that carry real weight. -Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. In Karpathy's architecture, both sides are pattern matching: the LLM across its vast frozen library, the human with the adaptive judgement that comes from living in the problem. The collaboration works because pattern matching operates at multiple levels of abstraction, the same structure the proverbs and meta-patterns revealed. The research frontier is building the adaptive layer into the systems themselves. Within eighteen months of Pocono, the cartoons had won. How quickly these systems develop their own adaptive layer will determine what happens to the people whose expertise was always built the same way. +Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. + +In Karpathy's architecture, both sides are pattern matching: the LLM across its vast frozen library, the human with the adaptive judgement that comes from living in the problem. The collaboration works because pattern matching operates at multiple levels of abstraction, the same structure the proverbs and meta-patterns revealed. The research frontier is building the adaptive layer into the systems themselves. Within eighteen months of Pocono, the cartoons had won. How quickly these systems develop their own adaptive layer will determine what happens to the people whose expertise was always built the same way. --- From f277e38315309d99e54388dc277aa0ce782601c1 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 09:00:10 +0100 Subject: [PATCH 25/46] Reframe: stepping stones not forensics, compression not concealment --- src/pages/blog/the-oldest-pattern-matcher.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index bb3b9e4f0..a3722419a 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -69,11 +69,11 @@ This is the gap between having patterns and having expertise. Feynman didn't jus What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened. Patterns that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences, while the textbook's library is frozen. -## Forensics on covered tracks +## Following the stepping stones - + -LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. Experts covered the tracks, as Feynman described: they did the intuitive, analogical work and then wrote up the formal reconstruction. [Timothy Gowers](https://en.wikipedia.org/wiki/Timothy_Gowers) [makes the same observation](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) about mathematics specifically: LLMs are "trained on the product of mathematics rather than the process." They learn to [imitate pulling rabbits out of hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) "without learning how to catch the rabbit or put it in the hat in the first place." But the tracks are still there in the text, encoded in the causal language, the mechanism descriptions, the "because" and "led to" and "if... then" that saturate human writing. LLMs are doing a kind of forensics on those covered tracks, recovering the pattern-matching residue that the formal presentation tried to hide. +LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. Experts covered the tracks, as Feynman described: they did the intuitive work and then laid down formal stepping stones for others to follow — proofs, derivations, case law, mechanism descriptions. [Timothy Gowers](https://en.wikipedia.org/wiki/Timothy_Gowers) [makes the same observation](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) about mathematics specifically: LLMs are "trained on the product of mathematics rather than the process." They learn to [imitate pulling rabbits out of hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) "without learning how to catch the rabbit or put it in the hat in the first place." But those stepping stones are rich with structure, encoded in the causal language, the "because" and "led to" and "if... then" that saturate human writing. LLMs follow the same stepping stones that practitioners follow, and abstract them into compact patterns. The patterns are useful because the stepping stones were laid down by causal reasoners. Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. @@ -95,7 +95,7 @@ The audience at Pocono saw cartoons where there was physics. Bohr understood qua Schwinger's formal derivation was correct, complete and slow. Feynman's diagrams were correct, complete and fast. Schwinger conceded as much when he compared them to the silicon chip, bringing computation to the masses. -We are applying the same hierarchy to AI. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence from mathematics, physics and law runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, the *metis* frozen for institutional use. These systems cannot reliably add two numbers, yet in expert hands they are producing proofs that mathematicians call divine. That paradox dissolves the moment you stop treating pattern matching as a lesser faculty. They are doing forensics on the tracks that experts covered: extracting the intuitive structure that the formal write-up concealed, finding resemblances that carry real weight. +We are applying the same hierarchy to AI. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence from mathematics, physics and law runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, the *metis* frozen for institutional use. These systems cannot reliably add two numbers, yet in expert hands they are producing proofs that mathematicians call divine. That paradox dissolves the moment you stop treating pattern matching as a lesser faculty. They are following the stepping stones that experts laid down, finding compact patterns in the formal reconstructions, and those patterns carry real weight because the reconstructions were written by causal reasoners. Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. From 12c28a67c0e5707f1bb6872e493f3e710be6e3ff Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 09:02:59 +0100 Subject: [PATCH 26/46] Language fixes: fold mirrors, remove em-dashes, cut AI tells --- src/pages/blog/the-oldest-pattern-matcher.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index a3722419a..21858afc9 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -15,11 +15,11 @@ tags: [Teller](https://en.wikipedia.org/wiki/Edward_Teller) interrupted: the approach violated the exclusion principle. Dirac kept asking "Is it unitary?" Bohr strode to the stage and lectured Feynman on the uncertainty principle, having mistaken the diagrams for literal pictures of particle paths. The presentation was a disaster. -The diagrams were a notation: straight lines for particles, wavy lines for forces, vertices for interactions. Each element mapped to a term in the mathematics, but physicists could reason through the spatial arrangement of the picture instead of grinding through pages of algebra. Within eighteen months, [Freeman Dyson](https://en.wikipedia.org/wiki/Freeman_Dyson) had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. Schwinger later compared them to the silicon chip, "[bringing computation to the masses](https://en.wikipedia.org/wiki/Feynman_diagram#History)." +The diagrams were a notation: straight lines for particles, wavy lines for forces and vertices for interactions. Each element mapped to a term in the mathematics, but physicists could reason through the spatial arrangement of the picture instead of grinding through pages of algebra. Within eighteen months, [Freeman Dyson](https://en.wikipedia.org/wiki/Freeman_Dyson) had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. Schwinger later compared them to the silicon chip, "[bringing computation to the masses](https://en.wikipedia.org/wiki/Feynman_diagram#History)." Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. -Feynman's diagrams did something specific: they made a complex formalism accessible through visual metaphor. Lines for particles, vertices for interactions. The physics didn't get simpler. It got more accessible to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections, synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." +Feynman's diagrams did something specific: they made a complex formalism accessible through visual metaphor. Lines for particles, vertices for interactions. The physics didn't get simpler, but it became accessible to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections, synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." Pattern matching, analogy, recognition, intuition and metaphor are different names for variations on a single faculty. When a mathematician feels a proof is right or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. @@ -35,11 +35,11 @@ Professions reinforce the same hierarchy because a formal body of knowledge is w Working mathematicians [surveyed in the 1940s](https://en.wikipedia.org/wiki/Jacques_Hadamard) consistently reported the same experience: results arrived through sudden recognition, often after periods of unconscious incubation, with formal proofs constructed afterwards. The proof was a way of showing others what the mathematician already knew. [Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) arriving unbidden as he [stepped onto a bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy), after weeks of failed formal effort. It gave the unconscious pattern matching room to surface. Feynman, in his Nobel lecture, described publishing results he couldn't yet prove. He covered the tracks, and so did every mathematician in the survey. -[Michael Atiyah](https://en.wikipedia.org/wiki/Michael_Atiyah) described the same faculty when reviewing the work of others: "Once you've seen it, the truth or veracity, it just stares you in the face. The truth is looking back at you." The feeling for the shape of a valid argument precedes the line-by-line verification. It is, as Atiyah put it, "[pre all that](https://www.scientificamerican.com/article/beauty-equals-truth/)" — pre words, pre formulas. The formal reconstruction comes after, as justification for a conclusion already reached. +[Michael Atiyah](https://en.wikipedia.org/wiki/Michael_Atiyah) described the same faculty when reviewing the work of others: "Once you've seen it, the truth or veracity, it just stares you in the face. The truth is looking back at you." The feeling for the shape of a valid argument precedes the line-by-line verification. It is, as Atiyah put it, "[pre all that](https://www.scientificamerican.com/article/beauty-equals-truth/)": pre words, pre formulas. The formal reconstruction comes after, as justification for a conclusion already reached. -That phrase — "a conclusion already reached" — describes law as well as mathematics. Judges do something similar. The [legal realist](https://en.wikipedia.org/wiki/Legal_realism) tradition, running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), holds that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. The law looks like formal reasoning from the outside. From the inside, it is pattern matching with a formal audit trail. +"A conclusion already reached" describes law as well as mathematics. Judges do something similar. The [legal realist](https://en.wikipedia.org/wiki/Legal_realism) tradition, running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), holds that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. The law looks like formal reasoning from the outside. From the inside, it is pattern matching with a formal audit trail. The psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Groot) found the same thing in chess. Masters shown a board position for five seconds reproduced about 90% of the pieces. Amateurs managed roughly half. When de Groot scrambled the pieces into positions that couldn't arise in a real game, the masters' advantage vanished. They weren't remembering better or thinking harder: they were recognising patterns that only exist in meaningful play. @@ -73,13 +73,13 @@ What separates a useful pattern library from a contradictory heap of proverbs is -LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. Experts covered the tracks, as Feynman described: they did the intuitive work and then laid down formal stepping stones for others to follow — proofs, derivations, case law, mechanism descriptions. [Timothy Gowers](https://en.wikipedia.org/wiki/Timothy_Gowers) [makes the same observation](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) about mathematics specifically: LLMs are "trained on the product of mathematics rather than the process." They learn to [imitate pulling rabbits out of hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) "without learning how to catch the rabbit or put it in the hat in the first place." But those stepping stones are rich with structure, encoded in the causal language, the "because" and "led to" and "if... then" that saturate human writing. LLMs follow the same stepping stones that practitioners follow, and abstract them into compact patterns. The patterns are useful because the stepping stones were laid down by causal reasoners. +LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. Experts covered the tracks, as Feynman described: they did the intuitive work and then laid down formal stepping stones for others to follow. Proofs, derivations, case law, mechanism descriptions. [Timothy Gowers](https://en.wikipedia.org/wiki/Timothy_Gowers) [makes the same observation](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) about mathematics specifically: LLMs are "trained on the product of mathematics rather than the process." They learn to [imitate pulling rabbits out of hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) "without learning how to catch the rabbit or put it in the hat in the first place." But those stepping stones are rich with structure, encoded in the causal language, the "because" and "led to" and "if... then" that saturate human writing. LLMs follow the same stepping stones that practitioners follow, and abstract them into compact patterns. The patterns are useful because the stepping stones were laid down by causal reasoners. Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. The conventional framing says LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure to the architecture. But a frozen causal model is still a frozen library: what matters about a world model is that it updates. -[Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. This explains why chain-of-thought is so effective despite the absence of explicit causal reasoning or world models. It introduces dynamism into what would otherwise be a single static pass over a frozen library. +[Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. Chain-of-thought is effective despite the absence of explicit causal reasoning or world models because it introduces dynamism into what would otherwise be a single static pass over a frozen library. Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this separation. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the frozen library cannot: judgement about what to read, what questions to ask, what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." The LLM handles everything that can be pattern-matched from a frozen library, the human everything that requires an adaptive one. @@ -95,7 +95,7 @@ The audience at Pocono saw cartoons where there was physics. Bohr understood qua Schwinger's formal derivation was correct, complete and slow. Feynman's diagrams were correct, complete and fast. Schwinger conceded as much when he compared them to the silicon chip, bringing computation to the masses. -We are applying the same hierarchy to AI. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence from mathematics, physics and law runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, the *metis* frozen for institutional use. These systems cannot reliably add two numbers, yet in expert hands they are producing proofs that mathematicians call divine. That paradox dissolves the moment you stop treating pattern matching as a lesser faculty. They are following the stepping stones that experts laid down, finding compact patterns in the formal reconstructions, and those patterns carry real weight because the reconstructions were written by causal reasoners. +We are applying the same hierarchy to AI. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence from mathematics, physics and law runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, the *metis* frozen for institutional use. These systems cannot reliably add two numbers, yet in expert hands they are producing proofs that mathematicians call divine. Once you stop treating pattern matching as a lesser faculty, the paradox disappears. They are following the stepping stones that experts laid down, finding compact patterns in the formal reconstructions, and those patterns carry real weight because the reconstructions were written by causal reasoners. Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. From 2702ef8f4ba6fe8ca019b47ca5b0a4786df6726e Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 11:23:40 +0100 Subject: [PATCH 27/46] Integrate research: geometry history, JEPA/Friston, engineering sniff test, causal development, language fixes --- src/pages/blog/the-oldest-pattern-matcher.md | 30 ++++++++++++++------ 1 file changed, 21 insertions(+), 9 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 21858afc9..42838f5a3 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -29,7 +29,7 @@ The audience at Pocono were pattern matching too. They had a pattern for what se This is a pattern we have all absorbed. Institutions reinforce it because the formal version is the part they can freeze and transmit: taught in classrooms, examined, audited, passed between people who have never met. [James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*: the living part, the part that can't survive the freezing. -Professions reinforce the same hierarchy because a formal body of knowledge is what justifies their authority and gatekeeping. Formal reasoning is associated with intellectual seriousness, intuition with craft. The boundary between knowledge work and skilled trades follows the same line. [Lagrange](https://en.wikipedia.org/wiki/Joseph-Louis_Lagrange) boasted in 1788 that his [*Mécanique analytique*](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) contained "no diagrams": algebraic method had superseded geometric intuition. Two centuries later, the room at Pocono was still operating on the same assumption. +Professions reinforce the same hierarchy because a formal body of knowledge is what justifies their authority and gatekeeping. Formal reasoning is associated with intellectual seriousness, intuition with craft. The boundary between knowledge work and skilled trades follows the same line. [Lagrange](https://en.wikipedia.org/wiki/Joseph-Louis_Lagrange) boasted in 1788 that his [*Mécanique analytique*](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) contained "no diagrams": algebraic method had superseded geometric intuition. He had good reasons. Geometry couldn't express higher dimensions or abstract algebraic structures, and the [arithmetisation of analysis](https://en.wikipedia.org/wiki/Arithmetization_of_analysis) in the nineteenth century deliberately set out to abolish geometric intuition from proofs, replacing it with definitions that could be verified without pictures. Two centuries later, the room at Pocono was still operating on the same assumption. ## Reasoning backwards @@ -43,10 +43,12 @@ Working mathematicians [surveyed in the 1940s](https://en.wikipedia.org/wiki/Jac The psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Groot) found the same thing in chess. Masters shown a board position for five seconds reproduced about 90% of the pieces. Amateurs managed roughly half. When de Groot scrambled the pieces into positions that couldn't arise in a real game, the masters' advantage vanished. They weren't remembering better or thinking harder: they were recognising patterns that only exist in meaningful play. -[Hubert Dreyfus](https://en.wikipedia.org/wiki/Hubert_Dreyfus) spent decades arguing this point: formal reasoning is what novices do while the pattern library is sparse. Once the library is rich enough, the expert perceives rather than derives. Asking them to show their working is asking them to operate at a lower level of skill. [Kahneman](https://en.wikipedia.org/wiki/Daniel_Kahneman) [reached the same conclusion](https://pubmed.ncbi.nlm.nih.gov/19739881/) after a six-year collaboration with Klein: in regular environments with adequate feedback, the expert's [fast, intuitive judgement](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow) outperforms deliberate analysis. But all of this concerns recognition: matching a new situation to something already in the library. What about situations nobody has seen before? +[Kahneman](https://en.wikipedia.org/wiki/Daniel_Kahneman) [reached the same conclusion](https://pubmed.ncbi.nlm.nih.gov/19739881/) after a six-year collaboration with Klein: in regular environments with adequate feedback, the expert's [fast, intuitive judgement](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow) outperforms deliberate analysis. [Hubert Dreyfus](https://en.wikipedia.org/wiki/Hubert_Dreyfus) spent decades arguing the deeper point: formal reasoning is what novices do while the pattern library is sparse. Once the library is rich enough, the expert perceives rather than derives. Asking them to show their working is asking them to operate at a lower level of skill. ## Pattern matching forwards +All of this concerns recognition: matching a new situation to something already in the library. What about situations nobody has seen before? + The most common dismissal of LLMs is that they are "[stochastic parrots](https://en.wikipedia.org/wiki/Stochastic_parrot)": statistical machines that regurgitate patterns from training data without understanding. The assumption underneath is that pattern matching can recognise what already exists but cannot produce anything new. Creativity, on this view, is where pattern matching hits its ceiling. Einstein is the test case, because no one's creative process has been more carefully studied. [Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) and Sander [walk through his analogies one by one](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/). In 1905, Einstein noticed that [Wien's law](https://en.wikipedia.org/wiki/Wien%27s_radiation_law) for the entropy of radiation at low density had the same mathematical form as the entropy of an ideal gas. The equations looked alike. He concluded the physics must be alike too, and proposed that light comes in discrete packets, [quanta](https://en.wikipedia.org/wiki/Photon), by analogy with gas molecules. That paper [won him the Nobel Prize](https://www.nobelprize.org/prizes/physics/1921/einstein/facts/). @@ -57,14 +59,24 @@ Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equiv In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. Einstein was pattern matching at a level of abstraction most people never reach: matching causal structures rather than surface features, matching how things behave rather than how they look. -"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. [Cognitive scientists](https://en.wikipedia.org/wiki/Force_dynamics) have shown that our causal vocabulary itself runs on physical schemas learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. These began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. +"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. His claim is that analogy *is* thinking: the core cognitive act from which everything else, including formal reasoning, is built. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. [Cognitive scientists](https://en.wikipedia.org/wiki/Force_dynamics) have shown that our causal vocabulary runs on physical schemas learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. Even [developmental evidence](https://pubmed.ncbi.nlm.nih.gov/14756583/) suggests that children build their causal models from statistical regularities and active experimentation: the formal model is the residue of the pattern matching, not a separate faculty. These began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. It is metaphor all the way down. ## Metapatterns and reinforcement -But Hofstadter and Sander also present pairs of proverbs that assert contradictory things. "Nothing ventured, nothing gained," but then again, "better safe than sorry." "Absence makes the heart grow fonder," but then again, "out of sight, out of mind." "Many hands make light work," but then again, "too many cooks spoil the broth." "He who hesitates is lost," but then again, "fools rush in where angels fear to tread." "Where there's smoke, there's fire," but then again, "don't judge a book by its cover." Every language has these. For each proverb proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. The patterns point in every direction. +But Hofstadter and Sander also present pairs of proverbs that assert contradictory things: + +- "Nothing ventured, nothing gained," but then again, "better safe than sorry." +- "Absence makes the heart grow fonder," but then again, "out of sight, out of mind." +- "Many hands make light work," but then again, "too many cooks spoil the broth." +- "He who hesitates is lost," but then again, "fools rush in where angels fear to tread." +- "Where there's smoke, there's fire," but then again, "don't judge a book by its cover." + +Every language has these. For each proverb proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. The patterns point in every direction. Knowing when to apply "nothing ventured, nothing gained" rather than "better safe than sorry" is itself a pattern, just at a higher level of abstraction. "This is more like a prototype than a production system" is a meta-pattern: it tells you which lower-level pattern to trust. "This is a piece of work that requires deep thinking" is another, and it selects "too many cooks" over "many hands." These meta-patterns are learned the same way the proverbs were: by getting it wrong both ways and refining the judgement through contact with reality. Patterns all the way up. +Software engineers operate the same way. A senior engineer looks at a proposed architecture and [smells](https://martinfowler.com/bliki/CodeSmell.html) whether it will hold: they recognise a "[big ball of mud](https://en.wikipedia.org/wiki/Big_ball_of_mud)" or a distributed monolith on sight, the way a chess master perceives a board. The boxes and arrows on a whiteboard are not precise specifications. They are tokens in a shared pattern-matching conversation, [scaffolding for recognition](https://www.microsoft.com/en-us/research/publication/lets-go-to-the-whiteboard-how-and-why-software-developers-use-drawings/) rather than the recognition itself. + This is the gap between having patterns and having expertise. Feynman didn't just accumulate a library of physical intuitions. He [built each one himself](https://en.wikipedia.org/wiki/Surely_You%27re_Joking,_Mr._Feynman!), refusing to accept results he hadn't rederived from scratch, forging links that a passively received result would lack. Breadth is what fuels analogy: the wider the library, the more unexpected the connections it can make. What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened. Patterns that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences, while the textbook's library is frozen. @@ -73,11 +85,11 @@ What separates a useful pattern library from a contradictory heap of proverbs is -LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. Experts covered the tracks, as Feynman described: they did the intuitive work and then laid down formal stepping stones for others to follow. Proofs, derivations, case law, mechanism descriptions. [Timothy Gowers](https://en.wikipedia.org/wiki/Timothy_Gowers) [makes the same observation](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) about mathematics specifically: LLMs are "trained on the product of mathematics rather than the process." They learn to [imitate pulling rabbits out of hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) "without learning how to catch the rabbit or put it in the hat in the first place." But those stepping stones are rich with structure, encoded in the causal language, the "because" and "led to" and "if... then" that saturate human writing. LLMs follow the same stepping stones that practitioners follow, and abstract them into compact patterns. The patterns are useful because the stepping stones were laid down by causal reasoners. +LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. When experts publish, they do two things at once: they cover the tracks (the dead ends, the wrong ideas, the blind alleys Feynman described) and they lay down stepping stones (proofs, derivations, case law, mechanism descriptions) that mark the clearest route to the useful patterns. The dead ends vanish, and what remains is a refined path. LLMs benefit not from uncovering what was hidden but from following what was explicitly laid down as the most direct route. [Timothy Gowers](https://en.wikipedia.org/wiki/Timothy_Gowers) [makes the same observation](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) about mathematics specifically: LLMs are "trained on the product of mathematics rather than the process." They learn to [imitate pulling rabbits out of hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) "without learning how to catch the rabbit or put it in the hat in the first place." But those stepping stones are rich with structure, encoded in the causal language, the "because" and "led to" and "if... then" that saturate human writing. LLMs follow the same stepping stones that practitioners follow, and abstract them into compact patterns. The patterns are useful because the stepping stones were laid down by causal reasoners. Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. -The conventional framing says LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure to the architecture. But a frozen causal model is still a frozen library: what matters about a world model is that it updates. +The conventional framing says LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure to the architecture. [LeCun's](https://en.wikipedia.org/wiki/Yann_LeCun) [JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) learns to predict abstract representations rather than raw data, capturing temporal structure and causal regularity. But once trained, those representations are fixed. It is a better frozen library: richer, more structural, but still frozen. [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) takes a different approach: agents that sense, act and update their internal model continuously, refining through every interaction. The world model is never frozen because the loop never stops. What matters about a world model, on this reading, is that it updates. [Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. Chain-of-thought is effective despite the absence of explicit causal reasoning or world models because it introduces dynamism into what would otherwise be a single static pass over a frozen library. @@ -95,11 +107,11 @@ The audience at Pocono saw cartoons where there was physics. Bohr understood qua Schwinger's formal derivation was correct, complete and slow. Feynman's diagrams were correct, complete and fast. Schwinger conceded as much when he compared them to the silicon chip, bringing computation to the masses. -We are applying the same hierarchy to AI. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence from mathematics, physics and law runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, the *metis* frozen for institutional use. These systems cannot reliably add two numbers, yet in expert hands they are producing proofs that mathematicians call divine. Once you stop treating pattern matching as a lesser faculty, the paradox disappears. They are following the stepping stones that experts laid down, finding compact patterns in the formal reconstructions, and those patterns carry real weight because the reconstructions were written by causal reasoners. +Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. Karpathy's architecture is the current best attempt to make the most of these systems: pair the frozen library with a human whose adaptive judgement comes from living in the problem. The research frontier is building the adaptive layer into the systems themselves. -Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. +World models and explicitly causal architectures have not been required to get here. These systems cannot reliably add two numbers, yet in expert hands they are producing proofs that mathematicians call divine. They followed the stepping stones that experts laid down, found compact patterns in the formal reconstructions, and those patterns carried real weight because the reconstructions were written by causal reasoners. No one expected pattern matching alone to get this far. -In Karpathy's architecture, both sides are pattern matching: the LLM across its vast frozen library, the human with the adaptive judgement that comes from living in the problem. The collaboration works because pattern matching operates at multiple levels of abstraction, the same structure the proverbs and meta-patterns revealed. The research frontier is building the adaptive layer into the systems themselves. Within eighteen months of Pocono, the cartoons had won. How quickly these systems develop their own adaptive layer will determine what happens to the people whose expertise was always built the same way. +We are applying the same hierarchy to AI that the room at Pocono applied to Feynman. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence from mathematics, physics and law runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, the *metis* frozen for institutional use. Within eighteen months of Pocono, the cartoons had won. The evidence from every discipline surveyed here, and from the unreasonable effectiveness of these systems, says we have the hierarchy backwards. --- From f1717a817c71d1f9c907d581547e07558f105c33 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 16:33:58 +0100 Subject: [PATCH 28/46] Author redraft: The elevation of reason section --- src/pages/blog/the-oldest-pattern-matcher.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 42838f5a3..ac58cf33e 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -27,9 +27,15 @@ Pattern matching, analogy, recognition, intuition and metaphor are different nam The audience at Pocono were pattern matching too. They had a pattern for what serious physics looks like: dense formalism, systematic derivation, the kind of thing Schwinger had spent hours demonstrating the day before. So the room concluded, quickly and confidently, that what they were seeing couldn't be physics. -This is a pattern we have all absorbed. Institutions reinforce it because the formal version is the part they can freeze and transmit: taught in classrooms, examined, audited, passed between people who have never met. [James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*: the living part, the part that can't survive the freezing. +This is a pattern we have all absorbed. There are reasons. A formal body of knowledge is the only kind that survives the journey from one mind to another it has never met. Apprenticeship can transmit intuition, but apprenticeship doesn't scale. Once a discipline has more practitioners than any master can mentor, the formal version is what gets carried: taught in classrooms, examined, audited, written into textbooks. The intuitive part doesn't lose by competition. It loses by selection. It can't be filed. -Professions reinforce the same hierarchy because a formal body of knowledge is what justifies their authority and gatekeeping. Formal reasoning is associated with intellectual seriousness, intuition with craft. The boundary between knowledge work and skilled trades follows the same line. [Lagrange](https://en.wikipedia.org/wiki/Joseph-Louis_Lagrange) boasted in 1788 that his [*Mécanique analytique*](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) contained "no diagrams": algebraic method had superseded geometric intuition. He had good reasons. Geometry couldn't express higher dimensions or abstract algebraic structures, and the [arithmetisation of analysis](https://en.wikipedia.org/wiki/Arithmetization_of_analysis) in the nineteenth century deliberately set out to abolish geometric intuition from proofs, replacing it with definitions that could be verified without pictures. Two centuries later, the room at Pocono was still operating on the same assumption. +[James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*: the living part, the part that can't survive the freezing. His broader argument is worth recovering. Legibility, making knowledge formal enough to govern, is what allows coordination at scale, and there is tragedy in it but no villain. The price of operating beyond face-to-face scale is that whatever can't be made legible falls out of the institutional record. Across generations the formal record becomes synonymous with the discipline, and the *metis* becomes invisible, then forgotten, then suspect. + +The same selection runs through professional life. Certification requires something testable, so the curriculum becomes the part of the practice that can be written down. Appellate review requires opinions that read as derivation from precedent, so the felt sense of the case is officially absent from the judgement. Cumulative progress across generations requires stepping stones a stranger can follow, so what passes between cohorts is the trick, not the catching. None of this requires anyone to choose formalism over intuition. The choice is made by what can travel. + +It is also made, sometimes, by genuine epistemic advance. [Lagrange](https://en.wikipedia.org/wiki/Joseph-Louis_Lagrange) boasted in 1788 that his [*Mécanique analytique*](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) contained "no diagrams": algebraic method had superseded geometric intuition. He had good reasons. Geometry couldn't express higher dimensions or abstract algebraic structures, and the [arithmetisation of analysis](https://en.wikipedia.org/wiki/Arithmetization_of_analysis) in the nineteenth century deliberately set out to abolish geometric intuition from proofs, replacing it with definitions that could be verified without pictures. The motivation was real. Formal methods reach where intuition can't. + +But once a field has been through that move, it tends to assume the move is general. Formalism becomes the marker of seriousness in domains where the trade-off is quite different, where the intuition is doing most of the work and the formalism is reconstruction after the fact. By the time the room at Pocono saw Feynman's diagrams, two centuries of this selection had done its work. The audience hadn't decided that intuition wasn't physics. They had inherited a discipline in which the intuition had long since fallen out of the record, and they were responding to its absence the way any expert responds to an unfamiliar pattern: with suspicion. ## Reasoning backwards From 46df030725c95a60109a528b62efba60d6d6e2d6 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 16:36:32 +0100 Subject: [PATCH 29/46] World models: the real distinction is updating vs frozen, not causal vs semantic --- src/pages/blog/the-oldest-pattern-matcher.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index ac58cf33e..1a39ccf2e 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -95,7 +95,7 @@ LLMs have inherited enormous pattern libraries from the serialised outputs of hu Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. -The conventional framing says LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure to the architecture. [LeCun's](https://en.wikipedia.org/wiki/Yann_LeCun) [JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) learns to predict abstract representations rather than raw data, capturing temporal structure and causal regularity. But once trained, those representations are fixed. It is a better frozen library: richer, more structural, but still frozen. [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) takes a different approach: agents that sense, act and update their internal model continuously, refining through every interaction. The world model is never frozen because the loop never stops. What matters about a world model, on this reading, is that it updates. +The conventional framing draws the line between causal and semantic: LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure. [LeCun's](https://en.wikipedia.org/wiki/Yann_LeCun) [JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) and [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) both aim at this. But if causation is itself a repertoire of learned patterns, as Hofstadter and the developmental evidence suggest, then the causal/semantic distinction is less fundamental than it appears. The distinction that matters is whether the model updates. An LLM's weights are frozen after training, and so are JEPA's learned representations. A world model that captures causal structure but cannot revise it is a more structured frozen library, not a qualitatively different kind of intelligence. What produces results is adaptive process layered on top of the frozen model: chain-of-thought, tool use, human collaboration. [Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. Chain-of-thought is effective despite the absence of explicit causal reasoning or world models because it introduces dynamism into what would otherwise be a single static pass over a frozen library. From ca64c62e6755ebc26cc1e94f1858dc4dc931d5b9 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 18:03:21 +0100 Subject: [PATCH 30/46] Editorial pass: 12 revisions and 10 fact-checks MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Fact-checks: Dyson six months not eighteen, Hadamard dating, Erdős #1196 attribution (Liam Price not Sawhney/Sellke), 80% figure decoupled, conjecture dated 1968 not 1935. Editorial: expand Poincaré bus moment, compress recognition parade, update stochastic parrots critique (Kambhampati, Mitchell, Marcus), recast world-models paragraph, expand chain-of-thought (RLHF, RLVR, deployment-time learning), add survivorship bias sieve, hedge Hofstadter, soften legal realism, reconcile tracks/stepping-stones, remove Dreyfus embodiment dependency, caveat arithmetic claim. --- src/pages/blog/the-oldest-pattern-matcher.md | 34 ++++++++++++-------- 1 file changed, 20 insertions(+), 14 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 1a39ccf2e..87883afe5 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -15,7 +15,7 @@ tags: [Teller](https://en.wikipedia.org/wiki/Edward_Teller) interrupted: the approach violated the exclusion principle. Dirac kept asking "Is it unitary?" Bohr strode to the stage and lectured Feynman on the uncertainty principle, having mistaken the diagrams for literal pictures of particle paths. The presentation was a disaster. -The diagrams were a notation: straight lines for particles, wavy lines for forces and vertices for interactions. Each element mapped to a term in the mathematics, but physicists could reason through the spatial arrangement of the picture instead of grinding through pages of algebra. Within eighteen months, [Freeman Dyson](https://en.wikipedia.org/wiki/Freeman_Dyson) had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. Schwinger later compared them to the silicon chip, "[bringing computation to the masses](https://en.wikipedia.org/wiki/Feynman_diagram#History)." +The diagrams were a notation: straight lines for particles, wavy lines for forces and vertices for interactions. Each element mapped to a term in the mathematics, but physicists could reason through the spatial arrangement of the picture instead of grinding through pages of algebra. Within six months, [Freeman Dyson](https://en.wikipedia.org/wiki/Freeman_Dyson) had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. Schwinger later compared them to the silicon chip, "[bringing computation to the masses](https://en.wikipedia.org/wiki/Feynman_diagram#History)." Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. @@ -39,23 +39,25 @@ But once a field has been through that move, it tends to assume the move is gene ## Reasoning backwards -Working mathematicians [surveyed in the 1940s](https://en.wikipedia.org/wiki/Jacques_Hadamard) consistently reported the same experience: results arrived through sudden recognition, often after periods of unconscious incubation, with formal proofs constructed afterwards. The proof was a way of showing others what the mathematician already knew. [Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) arriving unbidden as he [stepped onto a bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy), after weeks of failed formal effort. It gave the unconscious pattern matching room to surface. Feynman, in his Nobel lecture, described publishing results he couldn't yet prove. He covered the tracks, and so did every mathematician in the survey. +When [Hadamard](https://en.wikipedia.org/wiki/Jacques_Hadamard) [surveyed working mathematicians](https://en.wikipedia.org/wiki/The_Psychology_of_Invention_in_the_Mathematical_Field), they consistently reported the same experience: results arrived through sudden recognition, with formal proofs constructed afterwards. The proof was a way of showing others what the mathematician already knew. + +[Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) in exact detail. He had spent fifteen days at his desk trying every combination he could think of, getting nowhere. Then he travelled to Coutances for a geological excursion, and as he [put his foot on the step of the bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy) the solution arrived complete, with the certainty that it was correct. He didn't work it out on the bus. He sat down for a conversation and picked it up later. The weeks of failed formal effort hadn't been wasted; they had loaded the pattern library. The bus ride gave the unconscious matching room to surface. [Michael Atiyah](https://en.wikipedia.org/wiki/Michael_Atiyah) described the same faculty when reviewing the work of others: "Once you've seen it, the truth or veracity, it just stares you in the face. The truth is looking back at you." The feeling for the shape of a valid argument precedes the line-by-line verification. It is, as Atiyah put it, "[pre all that](https://www.scientificamerican.com/article/beauty-equals-truth/)": pre words, pre formulas. The formal reconstruction comes after, as justification for a conclusion already reached. -"A conclusion already reached" describes law as well as mathematics. Judges do something similar. The [legal realist](https://en.wikipedia.org/wiki/Legal_realism) tradition, running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), holds that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. The law looks like formal reasoning from the outside. From the inside, it is pattern matching with a formal audit trail. +"A conclusion already reached" describes law as well as mathematics. There is a strong tradition arguing that judges do the same thing. The [legal realists](https://en.wikipedia.org/wiki/Legal_realism), running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), hold that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. On this account, the law looks like formal reasoning from the outside. From the inside, it is pattern matching with a formal audit trail. The psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Groot) found the same thing in chess. Masters shown a board position for five seconds reproduced about 90% of the pieces. Amateurs managed roughly half. When de Groot scrambled the pieces into positions that couldn't arise in a real game, the masters' advantage vanished. They weren't remembering better or thinking harder: they were recognising patterns that only exist in meaningful play. -[Kahneman](https://en.wikipedia.org/wiki/Daniel_Kahneman) [reached the same conclusion](https://pubmed.ncbi.nlm.nih.gov/19739881/) after a six-year collaboration with Klein: in regular environments with adequate feedback, the expert's [fast, intuitive judgement](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow) outperforms deliberate analysis. [Hubert Dreyfus](https://en.wikipedia.org/wiki/Hubert_Dreyfus) spent decades arguing the deeper point: formal reasoning is what novices do while the pattern library is sparse. Once the library is rich enough, the expert perceives rather than derives. Asking them to show their working is asking them to operate at a lower level of skill. +[Kahneman and Klein](https://pubmed.ncbi.nlm.nih.gov/19739881/) confirmed the pattern empirically: in regular environments with adequate feedback, [fast intuitive judgement](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow) outperforms deliberate analysis. Formal reasoning is what novices do while the pattern library is still sparse. Once the library is rich enough, the expert perceives rather than derives. Asking them to show their working is asking them to operate at a lower level of skill. ## Pattern matching forwards All of this concerns recognition: matching a new situation to something already in the library. What about situations nobody has seen before? -The most common dismissal of LLMs is that they are "[stochastic parrots](https://en.wikipedia.org/wiki/Stochastic_parrot)": statistical machines that regurgitate patterns from training data without understanding. The assumption underneath is that pattern matching can recognise what already exists but cannot produce anything new. Creativity, on this view, is where pattern matching hits its ceiling. +The most common dismissal of LLMs is that they are "[stochastic parrots](https://en.wikipedia.org/wiki/Stochastic_parrot)": statistical machines that regurgitate patterns from training data without understanding. The critique has sharpened since Bender coined the term in 2021. The stronger versions, from [Kambhampati](https://arxiv.org/abs/2402.01817) on planning, [Mitchell](https://aiguide.substack.com/p/can-large-language-models-reason) on abstraction, and [Marcus](https://en.wikipedia.org/wiki/Gary_Marcus) on world models, identify a more specific boundary: LLMs can recognise and recombine patterns but cannot plan, verify or update themselves. The assumption underneath, old and new, is that pattern matching can recognise what already exists but cannot produce anything genuinely new. Einstein is the test case, because no one's creative process has been more carefully studied. [Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) and Sander [walk through his analogies one by one](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/). In 1905, Einstein noticed that [Wien's law](https://en.wikipedia.org/wiki/Wien%27s_radiation_law) for the entropy of radiation at low density had the same mathematical form as the entropy of an ideal gas. The equations looked alike. He concluded the physics must be alike too, and proposed that light comes in discrete packets, [quanta](https://en.wikipedia.org/wiki/Photon), by analogy with gas molecules. That paper [won him the Nobel Prize](https://www.nobelprize.org/prizes/physics/1921/einstein/facts/). @@ -65,7 +67,9 @@ Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equiv In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. Einstein was pattern matching at a level of abstraction most people never reach: matching causal structures rather than surface features, matching how things behave rather than how they look. -"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. His claim is that analogy *is* thinking: the core cognitive act from which everything else, including formal reasoning, is built. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. [Cognitive scientists](https://en.wikipedia.org/wiki/Force_dynamics) have shown that our causal vocabulary runs on physical schemas learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. Even [developmental evidence](https://pubmed.ncbi.nlm.nih.gov/14756583/) suggests that children build their causal models from statistical regularities and active experimentation: the formal model is the residue of the pattern matching, not a separate faculty. These began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. It is metaphor all the way down. +But Einstein is a survivorship bias case study. For every analogy that opens a new field, thousands lead nowhere. The productive analogy needs a sieve: someone who can tell the difference between a deep structural resemblance and a superficial coincidence of form. Einstein was his own sieve. When mathematicians later in this story used LLMs to attack open problems, roughly 80% of the model's generated arguments were wrong. The collaborations still worked, because the mathematician provided the sieve. + +"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. His claim, which has its critics, is that analogy is the core cognitive act from which everything else, including formal reasoning, is built. The strong version may overreach. The weaker version is hard to deny: more of cognition is pattern matching than the formalist tradition admits. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. [Cognitive scientists](https://en.wikipedia.org/wiki/Force_dynamics) have shown that our causal vocabulary runs on physical schemas learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. Even [developmental evidence](https://pubmed.ncbi.nlm.nih.gov/14756583/) suggests that children build their causal models from statistical regularities and active experimentation: the formal model is the residue of the pattern matching, not a separate faculty. These began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. It is metaphor all the way down. ## Metapatterns and reinforcement @@ -91,21 +95,23 @@ What separates a useful pattern library from a contradictory heap of proverbs is -LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. When experts publish, they do two things at once: they cover the tracks (the dead ends, the wrong ideas, the blind alleys Feynman described) and they lay down stepping stones (proofs, derivations, case law, mechanism descriptions) that mark the clearest route to the useful patterns. The dead ends vanish, and what remains is a refined path. LLMs benefit not from uncovering what was hidden but from following what was explicitly laid down as the most direct route. [Timothy Gowers](https://en.wikipedia.org/wiki/Timothy_Gowers) [makes the same observation](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) about mathematics specifically: LLMs are "trained on the product of mathematics rather than the process." They learn to [imitate pulling rabbits out of hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) "without learning how to catch the rabbit or put it in the hat in the first place." But those stepping stones are rich with structure, encoded in the causal language, the "because" and "led to" and "if... then" that saturate human writing. LLMs follow the same stepping stones that practitioners follow, and abstract them into compact patterns. The patterns are useful because the stepping stones were laid down by causal reasoners. +LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. When experts publish, they do two things at once: they cover the tracks (the dead ends, the wrong ideas, the blind alleys Feynman described) and they lay down stepping stones (proofs, derivations, case law, mechanism descriptions) that mark the clearest route to the useful patterns. Covering tracks and laying stepping stones are the same act. The dead ends vanish, and what remains is a refined path. LLMs benefit not from uncovering what was hidden but from following what was explicitly laid down as the most direct route. [Timothy Gowers](https://en.wikipedia.org/wiki/Timothy_Gowers) [makes the same observation](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) about mathematics specifically: LLMs are "trained on the product of mathematics rather than the process." They learn to [imitate pulling rabbits out of hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) "without learning how to catch the rabbit or put it in the hat in the first place." But those stepping stones are rich with structure, encoded in the causal language, the "because" and "led to" and "if... then" that saturate human writing. LLMs follow the same stepping stones that practitioners follow, and abstract them into compact patterns. The patterns are useful because the stepping stones were laid down by causal reasoners. Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. -The conventional framing draws the line between causal and semantic: LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure. [LeCun's](https://en.wikipedia.org/wiki/Yann_LeCun) [JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) and [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) both aim at this. But if causation is itself a repertoire of learned patterns, as Hofstadter and the developmental evidence suggest, then the causal/semantic distinction is less fundamental than it appears. The distinction that matters is whether the model updates. An LLM's weights are frozen after training, and so are JEPA's learned representations. A world model that captures causal structure but cannot revise it is a more structured frozen library, not a qualitatively different kind of intelligence. What produces results is adaptive process layered on top of the frozen model: chain-of-thought, tool use, human collaboration. +The conventional framing draws the line between causal and semantic: LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure. [LeCun's](https://en.wikipedia.org/wiki/Yann_LeCun) [JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) learns predictive representations of the world in latent space. [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) goes further, building in the expectation that the agent will act on the world and update its model from the consequences. These are complementary bets on different parts of the problem, not rival theories. But if causation is itself a repertoire of learned patterns, as Hofstadter and the developmental evidence suggest, then the causal/semantic distinction is less fundamental than it appears. The distinction that matters is whether the model updates. An LLM's weights are frozen after training. JEPA's learned representations capture richer structure but are equally frozen at inference time. Adding causal architecture to a frozen model produces a more structured frozen library, not a qualitatively different kind of intelligence. What produces results is feedback: adaptive process layered on top of the frozen model through chain-of-thought, tool use and human collaboration. + +[Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. -[Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. Chain-of-thought is effective despite the absence of explicit causal reasoning or world models because it introduces dynamism into what would otherwise be a single static pass over a frozen library. +The frozen/adaptive line is blurrier than it first appears. Deployed weights are frozen, but the training process that produced them was not: [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) and [reinforcement learning from verifiable rewards](https://arxiv.org/abs/2402.03300) both shape reasoning through feedback loops. At inference time, chain-of-thought and in-context learning introduce further adaptation within a single conversation. What is genuinely missing is deployment-time learning: the ability to revise the model's own weights from the consequences of its actions in the world. That is the gap between a frozen library with clever retrieval and a library that is truly alive. Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this separation. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the frozen library cannot: judgement about what to read, what questions to ask, what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." The LLM handles everything that can be pattern-matched from a frozen library, the human everything that requires an adaptive one. -Since late 2025, mathematicians working with LLMs have been [solving open problems](https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/) from [Paul Erdős's](https://en.wikipedia.org/wiki/Paul_Erd%C5%91s) celebrated problem lists at a pace that would have been unthinkable a year earlier. [Mehtaab Sawhney and Mark Sellke](https://x.com/MarkSellke/status/1979226538059931886) used thousands of GPT-5 queries to solve ten Erdős problems listed as open. Roughly 80% of the model's generated arguments [were wrong](https://arxiv.org/html/2510.23513v1). The collaboration still worked, because the mathematician's adaptive judgement could sift the useful patterns from the noise. +Since late 2025, mathematicians working with LLMs have been [solving open problems](https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/) from [Paul Erdős's](https://en.wikipedia.org/wiki/Paul_Erd%C5%91s) celebrated problem lists at a pace that would have been unthinkable a year earlier. [Sawhney and Sellke](https://x.com/MarkSellke/status/1979226538059931886) used GPT-5 to find solutions to ten Erdős problems still listed as open. In [another collaboration](https://arxiv.org/html/2510.23513v1), roughly 80% of the model's generated arguments were wrong. The work still produced results, because the mathematician's adaptive judgement could sift the useful patterns from the noise. -The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/status/2044307082275618993). Every mathematician since Erdős's 1935 paper had approached it by transforming the problem into continuous analysis. GPT-5.4 stayed in the arithmetic realm and deployed the [von Mangoldt function](https://en.wikipedia.org/wiki/Von_Mangoldt_function) in a way nobody had tried. [Jared Duker Lichtman](https://en.wikipedia.org/wiki/Jared_Duker_Lichtman), who had spent seven years on primitive set problems, read the proof and [called it a Book proof](https://en.wikipedia.org/wiki/Proofs_from_THE_BOOK): [Erdős's](https://terrytao.wordpress.com/2025/12/08/the-story-of-erdos-problem-126/) term for a proof so compact and elegant it could have been written by God. +The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/status/2044307082275618993), a conjecture from 1968. Previous approaches had relied on continuous analysis. Working with GPT-5.4, [Liam Price](https://x.com/jdlichtman/status/2044307082275618993) stayed in the arithmetic realm and deployed the [von Mangoldt function](https://en.wikipedia.org/wiki/Von_Mangoldt_function) in a way nobody had tried. [Jared Duker Lichtman](https://en.wikipedia.org/wiki/Jared_Duker_Lichtman), who had spent seven years on primitive set problems, read the proof and [called it a Book proof](https://en.wikipedia.org/wiki/Proofs_from_THE_BOOK): [Erdős's](https://terrytao.wordpress.com/2025/12/08/the-story-of-erdos-problem-126/) term for a proof so compact and elegant it could have been written by God. -A system that cannot reliably add two numbers produced a proof that an expert in the field called divine. That is what pattern matching looks like when it operates at sufficient depth, in the hands of someone who knows which patterns matter. +A system whose underlying network has no arithmetic produced a proof that an expert in the field called divine. That is what pattern matching looks like when it operates at sufficient depth, in the hands of someone who knows which patterns matter. ## Conclusion @@ -115,9 +121,9 @@ Schwinger's formal derivation was correct, complete and slow. Feynman's diagrams Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. Karpathy's architecture is the current best attempt to make the most of these systems: pair the frozen library with a human whose adaptive judgement comes from living in the problem. The research frontier is building the adaptive layer into the systems themselves. -World models and explicitly causal architectures have not been required to get here. These systems cannot reliably add two numbers, yet in expert hands they are producing proofs that mathematicians call divine. They followed the stepping stones that experts laid down, found compact patterns in the formal reconstructions, and those patterns carried real weight because the reconstructions were written by causal reasoners. No one expected pattern matching alone to get this far. +World models and explicitly causal architectures have not been required to get here. These systems have no built-in arithmetic, yet in expert hands they are producing proofs that mathematicians call divine. They followed the stepping stones that experts laid down, found compact patterns in the formal reconstructions, and those patterns carried real weight because the reconstructions were written by causal reasoners. No one expected pattern matching alone to get this far. -We are applying the same hierarchy to AI that the room at Pocono applied to Feynman. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence from mathematics, physics and law runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, the *metis* frozen for institutional use. Within eighteen months of Pocono, the cartoons had won. The evidence from every discipline surveyed here, and from the unreasonable effectiveness of these systems, says we have the hierarchy backwards. +We are applying the same hierarchy to AI that the room at Pocono applied to Feynman. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence from mathematics, physics and law runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, the *metis* frozen for institutional use. Within months of Pocono, the cartoons had won. The evidence from every discipline surveyed here, and from the unreasonable effectiveness of these systems, says we have the hierarchy backwards. --- From 4670eed6ac99f136f7087fc9184f16ee98ea2adc Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 20:04:09 +0100 Subject: [PATCH 31/46] Heisenbug passage, agent redraft cherry-picks, tighten conclusion Add software idiom escalation ending on Heisenbug callback to Pocono. Cherry-pick improvements from agent redraft: tighter lede, better headings (Living libraries, What the room missed), paragraph splits for readability, stronger CTA. Preserve all fact-checks and editorial revisions from prior commit. --- src/pages/blog/the-oldest-pattern-matcher.md | 34 +++++++++++++------- 1 file changed, 22 insertions(+), 12 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 87883afe5..dbf92a700 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -19,7 +19,7 @@ The diagrams were a notation: straight lines for particles, wavy lines for force Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. -Feynman's diagrams did something specific: they made a complex formalism accessible through visual metaphor. Lines for particles, vertices for interactions. The physics didn't get simpler, but it became accessible to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections, synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." +Feynman's diagrams made a complex formalism accessible through visual metaphor. The physics didn't get simpler, but it became accessible to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections and synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." Pattern matching, analogy, recognition, intuition and metaphor are different names for variations on a single faculty. When a mathematician feels a proof is right or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. @@ -55,7 +55,7 @@ The psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Gro ## Pattern matching forwards -All of this concerns recognition: matching a new situation to something already in the library. What about situations nobody has seen before? +Recognition matches a new situation to something already in the library. The deeper question is whether pattern matching can also produce something nobody has seen before. The most common dismissal of LLMs is that they are "[stochastic parrots](https://en.wikipedia.org/wiki/Stochastic_parrot)": statistical machines that regurgitate patterns from training data without understanding. The critique has sharpened since Bender coined the term in 2021. The stronger versions, from [Kambhampati](https://arxiv.org/abs/2402.01817) on planning, [Mitchell](https://aiguide.substack.com/p/can-large-language-models-reason) on abstraction, and [Marcus](https://en.wikipedia.org/wiki/Gary_Marcus) on world models, identify a more specific boundary: LLMs can recognise and recombine patterns but cannot plan, verify or update themselves. The assumption underneath, old and new, is that pattern matching can recognise what already exists but cannot produce anything genuinely new. @@ -69,9 +69,11 @@ In each case the analogy preceded the formalism, often by years. The creative ac But Einstein is a survivorship bias case study. For every analogy that opens a new field, thousands lead nowhere. The productive analogy needs a sieve: someone who can tell the difference between a deep structural resemblance and a superficial coincidence of form. Einstein was his own sieve. When mathematicians later in this story used LLMs to attack open problems, roughly 80% of the model's generated arguments were wrong. The collaborations still worked, because the mathematician provided the sieve. -"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. His claim, which has its critics, is that analogy is the core cognitive act from which everything else, including formal reasoning, is built. The strong version may overreach. The weaker version is hard to deny: more of cognition is pattern matching than the formalist tradition admits. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. [Cognitive scientists](https://en.wikipedia.org/wiki/Force_dynamics) have shown that our causal vocabulary runs on physical schemas learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. Even [developmental evidence](https://pubmed.ncbi.nlm.nih.gov/14756583/) suggests that children build their causal models from statistical regularities and active experimentation: the formal model is the residue of the pattern matching, not a separate faculty. These began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. It is metaphor all the way down. +"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. His claim, which has its critics, is that analogy is the core cognitive act from which everything else, including formal reasoning, is built. The strong version may overreach. The weaker version is hard to deny: more of cognition is pattern matching than the formalist tradition admits. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. [Cognitive scientists](https://en.wikipedia.org/wiki/Force_dynamics) have shown that our causal vocabulary runs on physical schemas learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. -## Metapatterns and reinforcement +Even [developmental evidence](https://pubmed.ncbi.nlm.nih.gov/14756583/) suggests that children build their causal models from statistical regularities and active experimentation, so the formal model is the residue of the pattern matching, not a separate faculty. These began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. It is metaphor all the way down. + +## Living libraries But Hofstadter and Sander also present pairs of proverbs that assert contradictory things: @@ -85,17 +87,23 @@ Every language has these. For each proverb proposing a point of view, another pr Knowing when to apply "nothing ventured, nothing gained" rather than "better safe than sorry" is itself a pattern, just at a higher level of abstraction. "This is more like a prototype than a production system" is a meta-pattern: it tells you which lower-level pattern to trust. "This is a piece of work that requires deep thinking" is another, and it selects "too many cooks" over "many hands." These meta-patterns are learned the same way the proverbs were: by getting it wrong both ways and refining the judgement through contact with reality. Patterns all the way up. -Software engineers operate the same way. A senior engineer looks at a proposed architecture and [smells](https://martinfowler.com/bliki/CodeSmell.html) whether it will hold: they recognise a "[big ball of mud](https://en.wikipedia.org/wiki/Big_ball_of_mud)" or a distributed monolith on sight, the way a chess master perceives a board. The boxes and arrows on a whiteboard are not precise specifications. They are tokens in a shared pattern-matching conversation, [scaffolding for recognition](https://www.microsoft.com/en-us/research/publication/lets-go-to-the-whiteboard-how-and-why-software-developers-use-drawings/) rather than the recognition itself. +Software engineers operate the same way. A senior engineer looks at a proposed architecture and [smells](https://martinfowler.com/bliki/CodeSmell.html) whether it will hold, the way a chess master perceives a board. The boxes and arrows on a whiteboard are not precise specifications. They are tokens in a shared pattern-matching conversation, [scaffolding for recognition](https://www.microsoft.com/en-us/research/publication/lets-go-to-the-whiteboard-how-and-why-software-developers-use-drawings/) rather than the recognition itself. + +The vocabulary gives it away. Code whose tangled dependencies resist comprehension is a [ball of mud](https://en.wikipedia.org/wiki/Big_ball_of_mud). Code whose composition they admire is [clean](https://en.wikipedia.org/wiki/Robert_C._Martin). They hold seams together with glue code, fire [tracer bullets](https://en.wikipedia.org/wiki/Tracer_bullet_(software_development)) through a system to find the fault, and measure the blast radius before deploying a fix. When the bug vanishes the moment they try to observe it, they call it a [Heisenbug](https://en.wikipedia.org/wiki/Heisenbug) — after the same uncertainty principle that Bohr was lecturing Feynman about at Pocono. This is the gap between having patterns and having expertise. Feynman didn't just accumulate a library of physical intuitions. He [built each one himself](https://en.wikipedia.org/wiki/Surely_You%27re_Joking,_Mr._Feynman!), refusing to accept results he hadn't rederived from scratch, forging links that a passively received result would lack. Breadth is what fuels analogy: the wider the library, the more unexpected the connections it can make. -What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened. Patterns that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences, while the textbook's library is frozen. + + +What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened; those that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences, while the textbook's library is frozen. ## Following the stepping stones -LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. When experts publish, they do two things at once: they cover the tracks (the dead ends, the wrong ideas, the blind alleys Feynman described) and they lay down stepping stones (proofs, derivations, case law, mechanism descriptions) that mark the clearest route to the useful patterns. Covering tracks and laying stepping stones are the same act. The dead ends vanish, and what remains is a refined path. LLMs benefit not from uncovering what was hidden but from following what was explicitly laid down as the most direct route. [Timothy Gowers](https://en.wikipedia.org/wiki/Timothy_Gowers) [makes the same observation](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) about mathematics specifically: LLMs are "trained on the product of mathematics rather than the process." They learn to [imitate pulling rabbits out of hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) "without learning how to catch the rabbit or put it in the hat in the first place." But those stepping stones are rich with structure, encoded in the causal language, the "because" and "led to" and "if... then" that saturate human writing. LLMs follow the same stepping stones that practitioners follow, and abstract them into compact patterns. The patterns are useful because the stepping stones were laid down by causal reasoners. +LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. When experts publish, they do two things at once: they cover the tracks (the dead ends, the wrong ideas, the blind alleys Feynman described) and they lay down stepping stones (proofs, derivations, case law, mechanism descriptions) that mark the clearest route to the useful patterns. Covering tracks and laying stepping stones are the same act. The dead ends vanish, and what remains is a refined path. LLMs benefit not from uncovering what was hidden but from following what was explicitly laid down as the most direct route. [Timothy Gowers](https://en.wikipedia.org/wiki/Timothy_Gowers) [makes the same observation](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) about mathematics specifically: LLMs are "trained on the product of mathematics rather than the process." They learn to [imitate pulling rabbits out of hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) "without learning how to catch the rabbit or put it in the hat in the first place." + +The stepping stones are rich with structure, encoded in the causal language, the "because" and "led to" and "if... then" that saturate human writing. LLMs follow the same stones that practitioners follow, and abstract them into compact patterns. The patterns are useful because the stones were laid down by causal reasoners. Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. @@ -113,18 +121,20 @@ The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/sta A system whose underlying network has no arithmetic produced a proof that an expert in the field called divine. That is what pattern matching looks like when it operates at sufficient depth, in the hands of someone who knows which patterns matter. -## Conclusion +## What the room missed -The audience at Pocono saw cartoons where there was physics. Bohr understood quantum mechanics perfectly well, but he had a pattern for what serious physics looks like, and the diagrams didn't match it. The room had internalised a hierarchy that equates formalism with seriousness, which blinded them to the most powerful computational tool their field would produce. +The audience at Pocono saw cartoons where there was physics. Bohr understood quantum mechanics perfectly well, but he had a pattern for what serious physics looks like, and the diagrams didn't match it. The room had internalised a hierarchy that equates formalism with seriousness, blinding them to the most powerful computational tool their field would produce. Schwinger's formal derivation was correct, complete and slow. Feynman's diagrams were correct, complete and fast. Schwinger conceded as much when he compared them to the silicon chip, bringing computation to the masses. Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. Karpathy's architecture is the current best attempt to make the most of these systems: pair the frozen library with a human whose adaptive judgement comes from living in the problem. The research frontier is building the adaptive layer into the systems themselves. -World models and explicitly causal architectures have not been required to get here. These systems have no built-in arithmetic, yet in expert hands they are producing proofs that mathematicians call divine. They followed the stepping stones that experts laid down, found compact patterns in the formal reconstructions, and those patterns carried real weight because the reconstructions were written by causal reasoners. No one expected pattern matching alone to get this far. +World models and explicitly causal architectures have not been required to get here. These systems have no built-in arithmetic, yet in expert hands they are producing proofs that mathematicians call divine. They followed the stepping stones that experts laid down, found compact patterns in the formal reconstructions, and those patterns carried weight because the reconstructions were written by causal reasoners. No one expected pattern matching alone to get this far. + +We are applying the same hierarchy to AI that the room at Pocono applied to Feynman. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is what counts. The evidence from mathematics, physics and law runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, while the *metis* stays with the practitioner. -We are applying the same hierarchy to AI that the room at Pocono applied to Feynman. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is the real thing. The evidence from mathematics, physics and law runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, the *metis* frozen for institutional use. Within months of Pocono, the cartoons had won. The evidence from every discipline surveyed here, and from the unreasonable effectiveness of these systems, says we have the hierarchy backwards. +Within months of Pocono, the cartoons had won. The unreasonable effectiveness of these systems suggests we have the hierarchy backwards again. --- -JUXT understand the strengths and limitations of LLMs. If you'd like us to speak with you or your team, [get in touch](https://www.juxt.pro/contact/). +Pairing the frozen library with adaptive judgement is how we approach AI-assisted engineering at JUXT. If you'd like to think through which patterns matter in your domain, [get in touch](https://www.juxt.pro/contact/). 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and slow. Feynman's c category: 'ai' layout: '../../layouts/BlogPost.astro' publishedDate: '2026-04-06' -heroImage: 'capability-hyperinflation.jpg' +heroImage: 'just-pattern-matching.jpg' tags: - 'ai' - 'engineering' From df2afb2673ce4d5b1b765ad333279a072ee40fbe Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 20:30:07 +0100 Subject: [PATCH 33/46] Update post description --- src/pages/blog/the-oldest-pattern-matcher.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 1cb03ee36..6677d6c31 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -1,7 +1,7 @@ --- author: 'hga' title: 'Just pattern matching' -description: "Schwinger's derivation was correct, complete and slow. Feynman's cartoons were correct, complete and fast. We're making the same mistake with AI." +description: "How a machine which can't natively perform arithmetic produced a proof experts call divine" category: 'ai' layout: '../../layouts/BlogPost.astro' publishedDate: '2026-04-06' From d01209bdbb44f172e32b1f190e80439d7daa6930 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 20:35:56 +0100 Subject: [PATCH 34/46] Author tightening pass: fold sentences, trim hedges, inline proverbs, relocate links --- src/pages/blog/the-oldest-pattern-matcher.md | 44 +++++++++----------- 1 file changed, 19 insertions(+), 25 deletions(-) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/the-oldest-pattern-matcher.md index 6677d6c31..a1e1e3556 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/the-oldest-pattern-matcher.md @@ -1,7 +1,7 @@ --- author: 'hga' title: 'Just pattern matching' -description: "How a machine which can't natively perform arithmetic produced a proof experts call divine" +description: "Schwinger's derivation was correct, complete and slow; Feynman's cartoons were correct, complete and fast. We're making the same mistake with AI." category: 'ai' layout: '../../layouts/BlogPost.astro' publishedDate: '2026-04-06' @@ -19,7 +19,7 @@ The diagrams were a notation: straight lines for particles, wavy lines for force Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. -Feynman's diagrams made a complex formalism accessible through visual metaphor. The physics didn't get simpler, but it became accessible to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. Earlier this month, [Andrej Karpathy](https://en.wikipedia.org/wiki/Andrej_Karpathy) [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections and synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." +Feynman's diagrams made a complex formalism accessible through visual metaphor. The physics didn't get simpler, but it became accessible to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. Earlier this month, Andrej Karpathy [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections and synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." Pattern matching, analogy, recognition, intuition and metaphor are different names for variations on a single faculty. When a mathematician feels a proof is right or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. @@ -27,13 +27,13 @@ Pattern matching, analogy, recognition, intuition and metaphor are different nam The audience at Pocono were pattern matching too. They had a pattern for what serious physics looks like: dense formalism, systematic derivation, the kind of thing Schwinger had spent hours demonstrating the day before. So the room concluded, quickly and confidently, that what they were seeing couldn't be physics. -This is a pattern we have all absorbed. There are reasons. A formal body of knowledge is the only kind that survives the journey from one mind to another it has never met. Apprenticeship can transmit intuition, but apprenticeship doesn't scale. Once a discipline has more practitioners than any master can mentor, the formal version is what gets carried: taught in classrooms, examined, audited, written into textbooks. The intuitive part doesn't lose by competition. It loses by selection. It can't be filed. +This is a pattern we have all absorbed. There are reasons. A formal body of knowledge is the only kind that survives the journey from one mind to another it has never met. Apprenticeship can transmit intuition, but apprenticeship doesn't scale. Once a discipline has more practitioners than any master can mentor, the formal version is what gets carried: taught in classrooms, examined, audited, written into textbooks. The intuitive part doesn't lose by competition but by selection. It can't be filed. -[James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*: the living part, the part that can't survive the freezing. His broader argument is worth recovering. Legibility, making knowledge formal enough to govern, is what allows coordination at scale, and there is tragedy in it but no villain. The price of operating beyond face-to-face scale is that whatever can't be made legible falls out of the institutional record. Across generations the formal record becomes synonymous with the discipline, and the *metis* becomes invisible, then forgotten, then suspect. +[James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*: the living part, the part that can't survive the freezing. Legibility, making knowledge formal enough to govern, is what allows coordination at scale, and there is tragedy in it but no villain. The price of operating beyond face-to-face scale is that whatever can't be made legible falls out of the institutional record. Across generations the formal record becomes synonymous with the discipline, and the *metis* becomes invisible, then forgotten, then suspect. The same selection runs through professional life. Certification requires something testable, so the curriculum becomes the part of the practice that can be written down. Appellate review requires opinions that read as derivation from precedent, so the felt sense of the case is officially absent from the judgement. Cumulative progress across generations requires stepping stones a stranger can follow, so what passes between cohorts is the trick, not the catching. None of this requires anyone to choose formalism over intuition. The choice is made by what can travel. -It is also made, sometimes, by genuine epistemic advance. [Lagrange](https://en.wikipedia.org/wiki/Joseph-Louis_Lagrange) boasted in 1788 that his [*Mécanique analytique*](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) contained "no diagrams": algebraic method had superseded geometric intuition. He had good reasons. Geometry couldn't express higher dimensions or abstract algebraic structures, and the [arithmetisation of analysis](https://en.wikipedia.org/wiki/Arithmetization_of_analysis) in the nineteenth century deliberately set out to abolish geometric intuition from proofs, replacing it with definitions that could be verified without pictures. The motivation was real. Formal methods reach where intuition can't. +It is also made, sometimes, by epistemic advance. [Lagrange](https://en.wikipedia.org/wiki/Joseph-Louis_Lagrange) boasted in 1788 that his [*Mécanique analytique*](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) contained "no diagrams": algebraic method had superseded geometric intuition. He had good reasons. Geometry couldn't express higher dimensions or abstract algebraic structures, and the [arithmetisation of analysis](https://en.wikipedia.org/wiki/Arithmetization_of_analysis) in the nineteenth century deliberately set out to abolish geometric intuition from proofs, replacing it with definitions that could be verified without pictures. Formal methods reach where intuition can't. But once a field has been through that move, it tends to assume the move is general. Formalism becomes the marker of seriousness in domains where the trade-off is quite different, where the intuition is doing most of the work and the formalism is reconstruction after the fact. By the time the room at Pocono saw Feynman's diagrams, two centuries of this selection had done its work. The audience hadn't decided that intuition wasn't physics. They had inherited a discipline in which the intuition had long since fallen out of the record, and they were responding to its absence the way any expert responds to an unfamiliar pattern: with suspicion. @@ -45,9 +45,9 @@ When [Hadamard](https://en.wikipedia.org/wiki/Jacques_Hadamard) [surveyed workin [Michael Atiyah](https://en.wikipedia.org/wiki/Michael_Atiyah) described the same faculty when reviewing the work of others: "Once you've seen it, the truth or veracity, it just stares you in the face. The truth is looking back at you." The feeling for the shape of a valid argument precedes the line-by-line verification. It is, as Atiyah put it, "[pre all that](https://www.scientificamerican.com/article/beauty-equals-truth/)": pre words, pre formulas. The formal reconstruction comes after, as justification for a conclusion already reached. - + -"A conclusion already reached" describes law as well as mathematics. There is a strong tradition arguing that judges do the same thing. The [legal realists](https://en.wikipedia.org/wiki/Legal_realism), running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), hold that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. On this account, the law looks like formal reasoning from the outside. From the inside, it is pattern matching with a formal audit trail. +"A conclusion already reached" describes law as well as mathematics. There is a strong tradition arguing that judges do the same thing. The [legal realists](https://en.wikipedia.org/wiki/Legal_realism), running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), hold that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. On this account, the law looks like formal reasoning from the outside but is pattern matching with a formal audit trail from the inside. The psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Groot) found the same thing in chess. Masters shown a board position for five seconds reproduced about 90% of the pieces. Amateurs managed roughly half. When de Groot scrambled the pieces into positions that couldn't arise in a real game, the masters' advantage vanished. They weren't remembering better or thinking harder: they were recognising patterns that only exist in meaningful play. @@ -55,11 +55,11 @@ The psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Gro ## Pattern matching forwards -Recognition matches a new situation to something already in the library. The deeper question is whether pattern matching can also produce something nobody has seen before. +Recognition matches a new situation to something already in the library. Can pattern matching also produce something nobody has seen before? -The most common dismissal of LLMs is that they are "[stochastic parrots](https://en.wikipedia.org/wiki/Stochastic_parrot)": statistical machines that regurgitate patterns from training data without understanding. The critique has sharpened since Bender coined the term in 2021. The stronger versions, from [Kambhampati](https://arxiv.org/abs/2402.01817) on planning, [Mitchell](https://aiguide.substack.com/p/can-large-language-models-reason) on abstraction, and [Marcus](https://en.wikipedia.org/wiki/Gary_Marcus) on world models, identify a more specific boundary: LLMs can recognise and recombine patterns but cannot plan, verify or update themselves. The assumption underneath, old and new, is that pattern matching can recognise what already exists but cannot produce anything genuinely new. +The most common dismissal of LLMs is that they are "[stochastic parrots](https://en.wikipedia.org/wiki/Stochastic_parrot)": statistical machines that regurgitate patterns from training data without understanding. The critique has tightened since Bender coined the term in 2021. The stronger versions, from [Kambhampati](https://arxiv.org/abs/2402.01817) on planning, [Mitchell](https://aiguide.substack.com/p/can-large-language-models-reason) on abstraction, and [Marcus](https://en.wikipedia.org/wiki/Gary_Marcus) on world models, identify a more specific boundary: LLMs can recognise and recombine patterns but cannot plan, verify or update themselves. The assumption underneath, old and new, is that pattern matching can recognise what already exists but cannot produce anything new. -Einstein is the test case, because no one's creative process has been more carefully studied. [Hofstadter](https://en.wikipedia.org/wiki/Douglas_Hofstadter) and Sander [walk through his analogies one by one](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/). In 1905, Einstein noticed that [Wien's law](https://en.wikipedia.org/wiki/Wien%27s_radiation_law) for the entropy of radiation at low density had the same mathematical form as the entropy of an ideal gas. The equations looked alike. He concluded the physics must be alike too, and proposed that light comes in discrete packets, [quanta](https://en.wikipedia.org/wiki/Photon), by analogy with gas molecules. That paper [won him the Nobel Prize](https://www.nobelprize.org/prizes/physics/1921/einstein/facts/). +Einstein is the test case, because no one's creative process has been more carefully studied. Hofstadter and Sander [walk through his analogies one by one](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/). In 1905, Einstein noticed that [Wien's law](https://en.wikipedia.org/wiki/Wien%27s_radiation_law) for the entropy of radiation at low density had the same mathematical form as the entropy of an ideal gas. The equations looked alike. He concluded the physics must be alike too, and proposed that light comes in discrete packets, [quanta](https://en.wikipedia.org/wiki/Photon), by analogy with gas molecules. That paper [won him the Nobel Prize](https://www.nobelprize.org/prizes/physics/1921/einstein/facts/). Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equivalence_principle) began with a man falling from a roof: Einstein realised that a person in freefall would feel weightless, and identified that feeling with the absence of gravity. He was pattern matching a causal relationship: acceleration produces felt weight, and so does gravity, so perhaps they are the same phenomenon. The analogy between two physical causes became the foundation of [general relativity](https://en.wikipedia.org/wiki/General_relativity). He then spent eight years working out the mathematics. @@ -67,21 +67,15 @@ Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equiv In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. Einstein was pattern matching at a level of abstraction most people never reach: matching causal structures rather than surface features, matching how things behave rather than how they look. -But Einstein is a survivorship bias case study. For every analogy that opens a new field, thousands lead nowhere. The productive analogy needs a sieve: someone who can tell the difference between a deep structural resemblance and a superficial coincidence of form. Einstein was his own sieve. When mathematicians later in this story used LLMs to attack open problems, roughly 80% of the model's generated arguments were wrong. The collaborations still worked, because the mathematician provided the sieve. +But Einstein is a survivorship bias case study. For every analogy that opens a new field, thousands lead nowhere. The productive analogy needs a sieve: someone who can tell the difference between a deep structural resemblance and a superficial coincidence of form. Einstein was his own sieve. -"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. His claim, which has its critics, is that analogy is the core cognitive act from which everything else, including formal reasoning, is built. The strong version may overreach. The weaker version is hard to deny: more of cognition is pattern matching than the formalist tradition admits. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. [Cognitive scientists](https://en.wikipedia.org/wiki/Force_dynamics) have shown that our causal vocabulary runs on physical schemas learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. +"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. His claim, which has its critics, is that analogy is the core cognitive act from which everything else, including formal reasoning, is built. The strong version may overreach, but the weaker version is hard to deny: more of cognition is pattern matching than the formalist tradition admits. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. Cognitive scientists have shown that our causal vocabulary [runs on physical schemas](https://en.wikipedia.org/wiki/Force_dynamics) learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. Even [developmental evidence](https://pubmed.ncbi.nlm.nih.gov/14756583/) suggests that children build their causal models from statistical regularities and active experimentation, so the formal model is the residue of the pattern matching, not a separate faculty. These began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. It is metaphor all the way down. ## Living libraries -But Hofstadter and Sander also present pairs of proverbs that assert contradictory things: - -- "Nothing ventured, nothing gained," but then again, "better safe than sorry." -- "Absence makes the heart grow fonder," but then again, "out of sight, out of mind." -- "Many hands make light work," but then again, "too many cooks spoil the broth." -- "He who hesitates is lost," but then again, "fools rush in where angels fear to tread." -- "Where there's smoke, there's fire," but then again, "don't judge a book by its cover." +But Hofstadter and Sander also present pairs of proverbs that assert contradictory things. "Nothing ventured, nothing gained," but then again, "better safe than sorry." "Absence makes the heart grow fonder," but then again, "out of sight, out of mind." "Many hands make light work," but then again, "too many cooks spoil the broth." "He who hesitates is lost," but then again, "fools rush in where angels fear to tread." "Where there's smoke, there's fire," but then again, "don't judge a book by its cover." Every language has these. For each proverb proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. The patterns point in every direction. @@ -89,7 +83,7 @@ Knowing when to apply "nothing ventured, nothing gained" rather than "better saf Software engineers operate the same way. A senior engineer looks at a proposed architecture and [smells](https://martinfowler.com/bliki/CodeSmell.html) whether it will hold, the way a chess master perceives a board. The boxes and arrows on a whiteboard are not precise specifications. They are tokens in a shared pattern-matching conversation, [scaffolding for recognition](https://www.microsoft.com/en-us/research/publication/lets-go-to-the-whiteboard-how-and-why-software-developers-use-drawings/) rather than the recognition itself. -The vocabulary gives it away. Code whose tangled dependencies resist comprehension is a [ball of mud](https://en.wikipedia.org/wiki/Big_ball_of_mud). Code whose composition they admire is [clean](https://en.wikipedia.org/wiki/Robert_C._Martin). They hold seams together with glue code, fire [tracer bullets](https://en.wikipedia.org/wiki/Tracer_bullet_(software_development)) through a system to find the fault, and measure the blast radius before deploying a fix. When the bug vanishes the moment they try to observe it, they call it a [Heisenbug](https://en.wikipedia.org/wiki/Heisenbug) — after the same uncertainty principle that Bohr was lecturing Feynman about at Pocono. +The vocabulary gives it away. Code whose tangled dependencies resist comprehension is a [ball of mud](https://en.wikipedia.org/wiki/Big_ball_of_mud); code whose composition they admire is [clean](https://en.wikipedia.org/wiki/Robert_C._Martin). They hold seams together with glue code, fire [tracer bullets](https://en.wikipedia.org/wiki/Tracer_bullet_(software_development)) through a system to find the fault, and measure the blast radius before deploying a fix. When the bug vanishes the moment they try to observe it, they call it a [Heisenbug](https://en.wikipedia.org/wiki/Heisenbug), after the same uncertainty principle that Bohr was lecturing Feynman about at Pocono. This is the gap between having patterns and having expertise. Feynman didn't just accumulate a library of physical intuitions. He [built each one himself](https://en.wikipedia.org/wiki/Surely_You%27re_Joking,_Mr._Feynman!), refusing to accept results he hadn't rederived from scratch, forging links that a passively received result would lack. Breadth is what fuels analogy: the wider the library, the more unexpected the connections it can make. @@ -107,17 +101,17 @@ The stepping stones are rich with structure, encoded in the causal language, the Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. -The conventional framing draws the line between causal and semantic: LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure. [LeCun's](https://en.wikipedia.org/wiki/Yann_LeCun) [JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) learns predictive representations of the world in latent space. [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) goes further, building in the expectation that the agent will act on the world and update its model from the consequences. These are complementary bets on different parts of the problem, not rival theories. But if causation is itself a repertoire of learned patterns, as Hofstadter and the developmental evidence suggest, then the causal/semantic distinction is less fundamental than it appears. The distinction that matters is whether the model updates. An LLM's weights are frozen after training. JEPA's learned representations capture richer structure but are equally frozen at inference time. Adding causal architecture to a frozen model produces a more structured frozen library, not a qualitatively different kind of intelligence. What produces results is feedback: adaptive process layered on top of the frozen model through chain-of-thought, tool use and human collaboration. +The conventional framing draws the line between causal and semantic: LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure. [LeCun's](https://en.wikipedia.org/wiki/Yann_LeCun) [JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) learns predictive representations of the world in latent space. [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) builds in the expectation that the agent will act on the world and update its model from the consequences. These are complementary attempts at different parts of the problem, not rival theories. But if causation is itself a repertoire of learned patterns, as Hofstadter and the developmental evidence suggest, then the causal/semantic distinction is less fundamental than it appears. The distinction that matters is whether the model updates. An LLM's weights are frozen after training. JEPA's learned representations capture richer structure but are equally frozen at inference time. Adding causal architecture to a frozen model produces a more structured frozen library, not a qualitatively different kind of intelligence. What produces results is feedback: adaptive process layered on top of the frozen model through chain-of-thought, tool use and human collaboration. [Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. -The frozen/adaptive line is blurrier than it first appears. Deployed weights are frozen, but the training process that produced them was not: [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) and [reinforcement learning from verifiable rewards](https://arxiv.org/abs/2402.03300) both shape reasoning through feedback loops. At inference time, chain-of-thought and in-context learning introduce further adaptation within a single conversation. What is genuinely missing is deployment-time learning: the ability to revise the model's own weights from the consequences of its actions in the world. That is the gap between a frozen library with clever retrieval and a library that is truly alive. +The frozen/adaptive line is blurrier than it first appears. Deployed weights are frozen, but the training process that produced them was not: [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) and [reinforcement learning from verifiable rewards](https://arxiv.org/abs/2402.03300) both shape reasoning through feedback loops. At inference time, chain-of-thought and in-context learning introduce further adaptation within a single conversation. What is missing is deployment-time learning: the ability to revise the model's own weights from the consequences of its actions in the world. That is the gap between a frozen library with clever retrieval and a library that is alive. -Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this separation. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the frozen library cannot: judgement about what to read, what questions to ask, what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." The LLM handles everything that can be pattern-matched from a frozen library, the human everything that requires an adaptive one. +Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this separation. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the frozen library cannot: judgement about what to read, what questions to ask and what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." The LLM handles everything that can be pattern-matched from a frozen library, the human everything that requires an adaptive one. -Since late 2025, mathematicians working with LLMs have been [solving open problems](https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/) from [Paul Erdős's](https://en.wikipedia.org/wiki/Paul_Erd%C5%91s) celebrated problem lists at a pace that would have been unthinkable a year earlier. [Sawhney and Sellke](https://x.com/MarkSellke/status/1979226538059931886) used GPT-5 to find solutions to ten Erdős problems still listed as open. In [another collaboration](https://arxiv.org/html/2510.23513v1), roughly 80% of the model's generated arguments were wrong. The work still produced results, because the mathematician's adaptive judgement could sift the useful patterns from the noise. +Since late 2025, mathematicians working with LLMs have been [solving open problems](https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/) from [Paul Erdős's](https://en.wikipedia.org/wiki/Paul_Erd%C5%91s) celebrated problem lists at a pace that would have been unthinkable a year earlier. Sawhney and Sellke [used GPT-5 to find solutions to ten Erdős problems still listed as open](https://x.com/MarkSellke/status/1979226538059931886). In another collaboration, [roughly 80% of the model's generated arguments were wrong](https://arxiv.org/html/2510.23513v1). The work still produced results, because the mathematician's adaptive judgement could sift the useful patterns from the noise. -The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/status/2044307082275618993), a conjecture from 1968. Previous approaches had relied on continuous analysis. Working with GPT-5.4, [Liam Price](https://x.com/jdlichtman/status/2044307082275618993) stayed in the arithmetic realm and deployed the [von Mangoldt function](https://en.wikipedia.org/wiki/Von_Mangoldt_function) in a way nobody had tried. [Jared Duker Lichtman](https://en.wikipedia.org/wiki/Jared_Duker_Lichtman), who had spent seven years on primitive set problems, read the proof and [called it a Book proof](https://en.wikipedia.org/wiki/Proofs_from_THE_BOOK): [Erdős's](https://terrytao.wordpress.com/2025/12/08/the-story-of-erdos-problem-126/) term for a proof so compact and elegant it could have been written by God. +The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/status/2044307082275618993), a conjecture from 1968. Previous approaches had relied on continuous analysis. Working with GPT-5.4, Liam Price stayed in the arithmetic realm and deployed the [von Mangoldt function](https://en.wikipedia.org/wiki/Von_Mangoldt_function) in a way nobody had tried. [Jared Duker Lichtman](https://en.wikipedia.org/wiki/Jared_Duker_Lichtman), who had spent seven years on primitive set problems, read the proof and [called it a Book proof](https://en.wikipedia.org/wiki/Proofs_from_THE_BOOK): Erdős's term for a proof so compact and elegant it could have been written by God. A system whose underlying network has no arithmetic produced a proof that an expert in the field called divine. That is what pattern matching looks like when it operates at sufficient depth, in the hands of someone who knows which patterns matter. From 9e8ecede5e58573a53c6c79a112a841bb613de71 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 20:45:24 +0100 Subject: [PATCH 35/46] Rename post to just-pattern-matching --- .../{the-oldest-pattern-matcher.md => just-pattern-matching.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename src/pages/blog/{the-oldest-pattern-matcher.md => just-pattern-matching.md} (99%) diff --git a/src/pages/blog/the-oldest-pattern-matcher.md b/src/pages/blog/just-pattern-matching.md similarity index 99% rename from src/pages/blog/the-oldest-pattern-matcher.md rename to src/pages/blog/just-pattern-matching.md index a1e1e3556..bc71bd652 100644 --- a/src/pages/blog/the-oldest-pattern-matcher.md +++ b/src/pages/blog/just-pattern-matching.md @@ -1,7 +1,7 @@ --- author: 'hga' title: 'Just pattern matching' -description: "Schwinger's derivation was correct, complete and slow; Feynman's cartoons were correct, complete and fast. We're making the same mistake with AI." +description: "How a machine which can't natively perform arithmetic produced a proof judged by experts to be divine" category: 'ai' layout: '../../layouts/BlogPost.astro' publishedDate: '2026-04-06' From 4b6e37320efce222682ad7ee45131baaec3f4bdd Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 21:00:05 +0100 Subject: [PATCH 36/46] =?UTF-8?q?Author=20pass:=20tighten=20Nobel=20lectur?= =?UTF-8?q?e,=20Poincar=C3=A9,=20proverbs,=20Einstein;=20legible=20not=20a?= =?UTF-8?q?ccessible?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/pages/blog/just-pattern-matching.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/src/pages/blog/just-pattern-matching.md b/src/pages/blog/just-pattern-matching.md index bc71bd652..1101cfe77 100644 --- a/src/pages/blog/just-pattern-matching.md +++ b/src/pages/blog/just-pattern-matching.md @@ -17,9 +17,9 @@ tags: The diagrams were a notation: straight lines for particles, wavy lines for forces and vertices for interactions. Each element mapped to a term in the mathematics, but physicists could reason through the spatial arrangement of the picture instead of grinding through pages of algebra. Within six months, [Freeman Dyson](https://en.wikipedia.org/wiki/Freeman_Dyson) had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. Schwinger later compared them to the silicon chip, "[bringing computation to the masses](https://en.wikipedia.org/wiki/Feynman_diagram#History)." -Feynman's [Nobel lecture](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/) seventeen years later was unusually candid. "We have a habit in writing articles published in scientific journals to make the work as finished as possible, to cover all the tracks, to not worry about the blind alleys or to describe how you had the wrong idea first." The audience at Pocono saw the absence of formalism and concluded the physics was absent too. +Seventeen years later, accepting the [Nobel Prize](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/), Feynman observed that scientific writing is designed to cover the tracks: the dead ends, the wrong ideas, the blind alleys nobody describes. The audience at Pocono had seen the absence of formalism and concluded the physics was absent too. -Feynman's diagrams made a complex formalism accessible through visual metaphor. The physics didn't get simpler, but it became accessible to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. Earlier this month, Andrej Karpathy [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections and synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." +Feynman's diagrams made a complex formalism legible through visual metaphor. The physics didn't get simpler, but it became tractable to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. Earlier this month, Andrej Karpathy [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections and synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." Pattern matching, analogy, recognition, intuition and metaphor are different names for variations on a single faculty. When a mathematician feels a proof is right or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. @@ -31,9 +31,9 @@ This is a pattern we have all absorbed. There are reasons. A formal body of know [James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*: the living part, the part that can't survive the freezing. Legibility, making knowledge formal enough to govern, is what allows coordination at scale, and there is tragedy in it but no villain. The price of operating beyond face-to-face scale is that whatever can't be made legible falls out of the institutional record. Across generations the formal record becomes synonymous with the discipline, and the *metis* becomes invisible, then forgotten, then suspect. -The same selection runs through professional life. Certification requires something testable, so the curriculum becomes the part of the practice that can be written down. Appellate review requires opinions that read as derivation from precedent, so the felt sense of the case is officially absent from the judgement. Cumulative progress across generations requires stepping stones a stranger can follow, so what passes between cohorts is the trick, not the catching. None of this requires anyone to choose formalism over intuition. The choice is made by what can travel. +The same selection runs through professional life. Certification requires something testable, so the curriculum becomes the part of the practice that can be written down. Appellate review requires opinions that read as derivation from precedent, so the felt sense of the case is officially absent from the judgement. Cumulative progress across generations requires stepping stones a stranger can follow, so what passes between cohorts is the technique, not the path that found it. None of this requires anyone to choose formalism over intuition. The choice is made by what can travel. -It is also made, sometimes, by epistemic advance. [Lagrange](https://en.wikipedia.org/wiki/Joseph-Louis_Lagrange) boasted in 1788 that his [*Mécanique analytique*](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) contained "no diagrams": algebraic method had superseded geometric intuition. He had good reasons. Geometry couldn't express higher dimensions or abstract algebraic structures, and the [arithmetisation of analysis](https://en.wikipedia.org/wiki/Arithmetization_of_analysis) in the nineteenth century deliberately set out to abolish geometric intuition from proofs, replacing it with definitions that could be verified without pictures. Formal methods reach where intuition can't. +It is also made, sometimes, by epistemic advance. [Lagrange](https://en.wikipedia.org/wiki/Joseph-Louis_Lagrange) boasted in 1788 that his [*Mécanique analytique*](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) contained "no diagrams": algebraic method had superseded geometric intuition. Geometry couldn't express higher dimensions or abstract algebraic structures, and the [arithmetisation of analysis](https://en.wikipedia.org/wiki/Arithmetization_of_analysis) in the nineteenth century deliberately set out to abolish geometric intuition from proofs, replacing it with definitions that could be verified without pictures. Formal methods reach where intuition can't. But once a field has been through that move, it tends to assume the move is general. Formalism becomes the marker of seriousness in domains where the trade-off is quite different, where the intuition is doing most of the work and the formalism is reconstruction after the fact. By the time the room at Pocono saw Feynman's diagrams, two centuries of this selection had done its work. The audience hadn't decided that intuition wasn't physics. They had inherited a discipline in which the intuition had long since fallen out of the record, and they were responding to its absence the way any expert responds to an unfamiliar pattern: with suspicion. @@ -41,13 +41,13 @@ But once a field has been through that move, it tends to assume the move is gene When [Hadamard](https://en.wikipedia.org/wiki/Jacques_Hadamard) [surveyed working mathematicians](https://en.wikipedia.org/wiki/The_Psychology_of_Invention_in_the_Mathematical_Field), they consistently reported the same experience: results arrived through sudden recognition, with formal proofs constructed afterwards. The proof was a way of showing others what the mathematician already knew. -[Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) in exact detail. He had spent fifteen days at his desk trying every combination he could think of, getting nowhere. Then he travelled to Coutances for a geological excursion, and as he [put his foot on the step of the bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy) the solution arrived complete, with the certainty that it was correct. He didn't work it out on the bus. He sat down for a conversation and picked it up later. The weeks of failed formal effort hadn't been wasted; they had loaded the pattern library. The bus ride gave the unconscious matching room to surface. +[Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) in exact detail. He had spent fifteen days at his desk trying every combination he could think of, getting nowhere. Then he travelled to Coutances for a geological excursion, and as he [put his foot on the step of the bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy) the solution arrived complete, with the certainty that it was correct. He didn't work it out then. He turned to the conversation he had been about to have, and verified the proof later when he got home. The weeks of failed formal effort hadn't been wasted; they had loaded the pattern library. The bus ride gave the unconscious matching room to surface. [Michael Atiyah](https://en.wikipedia.org/wiki/Michael_Atiyah) described the same faculty when reviewing the work of others: "Once you've seen it, the truth or veracity, it just stares you in the face. The truth is looking back at you." The feeling for the shape of a valid argument precedes the line-by-line verification. It is, as Atiyah put it, "[pre all that](https://www.scientificamerican.com/article/beauty-equals-truth/)": pre words, pre formulas. The formal reconstruction comes after, as justification for a conclusion already reached. -"A conclusion already reached" describes law as well as mathematics. There is a strong tradition arguing that judges do the same thing. The [legal realists](https://en.wikipedia.org/wiki/Legal_realism), running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), hold that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. On this account, the law looks like formal reasoning from the outside but is pattern matching with a formal audit trail from the inside. +"A conclusion already reached" describes law as well as mathematics. The [legal realists](https://en.wikipedia.org/wiki/Legal_realism), running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), hold that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. On this account, the law looks like formal reasoning from the outside but is pattern matching with a formal audit trail from the inside. The psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Groot) found the same thing in chess. Masters shown a board position for five seconds reproduced about 90% of the pieces. Amateurs managed roughly half. When de Groot scrambled the pieces into positions that couldn't arise in a real game, the masters' advantage vanished. They weren't remembering better or thinking harder: they were recognising patterns that only exist in meaningful play. @@ -65,17 +65,17 @@ Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equiv -In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. Einstein was pattern matching at a level of abstraction most people never reach: matching causal structures rather than surface features, matching how things behave rather than how they look. +In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. Einstein was pattern matching at a level of abstraction most people never reach: causal structures rather than surface features. But Einstein is a survivorship bias case study. For every analogy that opens a new field, thousands lead nowhere. The productive analogy needs a sieve: someone who can tell the difference between a deep structural resemblance and a superficial coincidence of form. Einstein was his own sieve. "Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. His claim, which has its critics, is that analogy is the core cognitive act from which everything else, including formal reasoning, is built. The strong version may overreach, but the weaker version is hard to deny: more of cognition is pattern matching than the formalist tradition admits. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. Cognitive scientists have shown that our causal vocabulary [runs on physical schemas](https://en.wikipedia.org/wiki/Force_dynamics) learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. -Even [developmental evidence](https://pubmed.ncbi.nlm.nih.gov/14756583/) suggests that children build their causal models from statistical regularities and active experimentation, so the formal model is the residue of the pattern matching, not a separate faculty. These began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. It is metaphor all the way down. +Even [developmental evidence](https://pubmed.ncbi.nlm.nih.gov/14756583/) suggests that children build their causal models from statistical regularities and active experimentation, so the formal model is the residue of the pattern matching, not a separate faculty. Force and current began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. It is metaphor all the way down. ## Living libraries -But Hofstadter and Sander also present pairs of proverbs that assert contradictory things. "Nothing ventured, nothing gained," but then again, "better safe than sorry." "Absence makes the heart grow fonder," but then again, "out of sight, out of mind." "Many hands make light work," but then again, "too many cooks spoil the broth." "He who hesitates is lost," but then again, "fools rush in where angels fear to tread." "Where there's smoke, there's fire," but then again, "don't judge a book by its cover." +But Hofstadter and Sander also present pairs of proverbs that assert contradictory things. "Nothing ventured, nothing gained," but then again, "better safe than sorry." "Many hands make light work," but then again, "too many cooks spoil the broth." "He who hesitates is lost," but then again, "fools rush in where angels fear to tread." Every language has these. For each proverb proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. The patterns point in every direction. @@ -101,7 +101,7 @@ The stepping stones are rich with structure, encoded in the causal language, the Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. -The conventional framing draws the line between causal and semantic: LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure. [LeCun's](https://en.wikipedia.org/wiki/Yann_LeCun) [JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) learns predictive representations of the world in latent space. [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) builds in the expectation that the agent will act on the world and update its model from the consequences. These are complementary attempts at different parts of the problem, not rival theories. But if causation is itself a repertoire of learned patterns, as Hofstadter and the developmental evidence suggest, then the causal/semantic distinction is less fundamental than it appears. The distinction that matters is whether the model updates. An LLM's weights are frozen after training. JEPA's learned representations capture richer structure but are equally frozen at inference time. Adding causal architecture to a frozen model produces a more structured frozen library, not a qualitatively different kind of intelligence. What produces results is feedback: adaptive process layered on top of the frozen model through chain-of-thought, tool use and human collaboration. +The conventional framing draws the line between causal and semantic: LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure. [LeCun's](https://en.wikipedia.org/wiki/Yann_LeCun) [JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) learns predictive representations of the world in latent space. [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) builds in the expectation that the agent will act on the world and update its model from the consequences. These are complementary attempts at different parts of the problem, not rival theories. But if causation is itself a repertoire of learned patterns, as Hofstadter and the developmental evidence suggest, then the causal/semantic distinction is less fundamental than it appears. What matters is whether the model can update itself. An LLM's weights are frozen after training. JEPA's learned representations capture richer structure but are equally frozen at inference time. Adding causal architecture to a frozen model produces a more structured frozen library, not a qualitatively different kind of intelligence. What produces results is feedback: adaptive process layered on top of the frozen model through chain-of-thought, tool use and human collaboration. [Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. @@ -131,4 +131,4 @@ Within months of Pocono, the cartoons had won. The unreasonable effectiveness of --- -Pairing the frozen library with adaptive judgement is how we approach AI-assisted engineering at JUXT. If you'd like to think through which patterns matter in your domain, [get in touch](https://www.juxt.pro/contact/). +Pairing the frozen library with adaptive judgement is how we approach AI-assisted engineering at JUXT. 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diff --git a/src/pages/blog/just-pattern-matching.md b/src/pages/blog/just-pattern-matching.md index 1101cfe77..089f03591 100644 --- a/src/pages/blog/just-pattern-matching.md +++ b/src/pages/blog/just-pattern-matching.md @@ -15,7 +15,7 @@ tags: [Teller](https://en.wikipedia.org/wiki/Edward_Teller) interrupted: the approach violated the exclusion principle. Dirac kept asking "Is it unitary?" Bohr strode to the stage and lectured Feynman on the uncertainty principle, having mistaken the diagrams for literal pictures of particle paths. The presentation was a disaster. -The diagrams were a notation: straight lines for particles, wavy lines for forces and vertices for interactions. Each element mapped to a term in the mathematics, but physicists could reason through the spatial arrangement of the picture instead of grinding through pages of algebra. Within six months, [Freeman Dyson](https://en.wikipedia.org/wiki/Freeman_Dyson) had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. Schwinger later compared them to the silicon chip, "[bringing computation to the masses](https://en.wikipedia.org/wiki/Feynman_diagram#History)." +The diagrams were a notation: straight lines for particles, wavy lines for forces and vertices for interactions. Each element mapped to a term in the mathematics, but physicists could reason through the spatial arrangement of the picture instead of grinding through pages of algebra. Within six months, Freeman Dyson had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. Schwinger later compared them to the silicon chip, "[bringing computation to the masses](https://en.wikipedia.org/wiki/Feynman_diagram#History)." Seventeen years later, accepting the [Nobel Prize](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/), Feynman observed that scientific writing is designed to cover the tracks: the dead ends, the wrong ideas, the blind alleys nobody describes. The audience at Pocono had seen the absence of formalism and concluded the physics was absent too. @@ -33,7 +33,7 @@ This is a pattern we have all absorbed. There are reasons. A formal body of know The same selection runs through professional life. Certification requires something testable, so the curriculum becomes the part of the practice that can be written down. Appellate review requires opinions that read as derivation from precedent, so the felt sense of the case is officially absent from the judgement. Cumulative progress across generations requires stepping stones a stranger can follow, so what passes between cohorts is the technique, not the path that found it. None of this requires anyone to choose formalism over intuition. The choice is made by what can travel. -It is also made, sometimes, by epistemic advance. [Lagrange](https://en.wikipedia.org/wiki/Joseph-Louis_Lagrange) boasted in 1788 that his [*Mécanique analytique*](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) contained "no diagrams": algebraic method had superseded geometric intuition. Geometry couldn't express higher dimensions or abstract algebraic structures, and the [arithmetisation of analysis](https://en.wikipedia.org/wiki/Arithmetization_of_analysis) in the nineteenth century deliberately set out to abolish geometric intuition from proofs, replacing it with definitions that could be verified without pictures. Formal methods reach where intuition can't. +It is also made, sometimes, by epistemic advance. Lagrange boasted in 1788 that his [*Mécanique analytique*](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) contained "no diagrams": algebraic method had superseded geometric intuition. Geometry couldn't express higher dimensions or abstract algebraic structures, and the [arithmetisation of analysis](https://en.wikipedia.org/wiki/Arithmetization_of_analysis) in the nineteenth century deliberately set out to abolish geometric intuition from proofs, replacing it with definitions that could be verified without pictures. Formal methods reach where intuition can't. But once a field has been through that move, it tends to assume the move is general. Formalism becomes the marker of seriousness in domains where the trade-off is quite different, where the intuition is doing most of the work and the formalism is reconstruction after the fact. By the time the room at Pocono saw Feynman's diagrams, two centuries of this selection had done its work. The audience hadn't decided that intuition wasn't physics. They had inherited a discipline in which the intuition had long since fallen out of the record, and they were responding to its absence the way any expert responds to an unfamiliar pattern: with suspicion. @@ -41,7 +41,7 @@ But once a field has been through that move, it tends to assume the move is gene When [Hadamard](https://en.wikipedia.org/wiki/Jacques_Hadamard) [surveyed working mathematicians](https://en.wikipedia.org/wiki/The_Psychology_of_Invention_in_the_Mathematical_Field), they consistently reported the same experience: results arrived through sudden recognition, with formal proofs constructed afterwards. The proof was a way of showing others what the mathematician already knew. -[Poincaré](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9) described his breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) in exact detail. He had spent fifteen days at his desk trying every combination he could think of, getting nowhere. Then he travelled to Coutances for a geological excursion, and as he [put his foot on the step of the bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy) the solution arrived complete, with the certainty that it was correct. He didn't work it out then. He turned to the conversation he had been about to have, and verified the proof later when he got home. The weeks of failed formal effort hadn't been wasted; they had loaded the pattern library. The bus ride gave the unconscious matching room to surface. +Poincaré described his breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) in exact detail. He had spent fifteen days at his desk trying every combination he could think of, getting nowhere. Then he travelled to Coutances for a geological excursion, and as he [put his foot on the step of the bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy) the solution arrived complete, with the certainty that it was correct. He didn't work it out then. He turned to the conversation he had been about to have, and verified the proof later when he got home. The weeks of failed formal effort hadn't been wasted; they had loaded the pattern library. The bus ride gave the unconscious matching room to surface. [Michael Atiyah](https://en.wikipedia.org/wiki/Michael_Atiyah) described the same faculty when reviewing the work of others: "Once you've seen it, the truth or veracity, it just stares you in the face. The truth is looking back at you." The feeling for the shape of a valid argument precedes the line-by-line verification. It is, as Atiyah put it, "[pre all that](https://www.scientificamerican.com/article/beauty-equals-truth/)": pre words, pre formulas. The formal reconstruction comes after, as justification for a conclusion already reached. @@ -77,7 +77,7 @@ Even [developmental evidence](https://pubmed.ncbi.nlm.nih.gov/14756583/) suggest But Hofstadter and Sander also present pairs of proverbs that assert contradictory things. "Nothing ventured, nothing gained," but then again, "better safe than sorry." "Many hands make light work," but then again, "too many cooks spoil the broth." "He who hesitates is lost," but then again, "fools rush in where angels fear to tread." -Every language has these. For each proverb proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. The patterns point in every direction. +Every language has these. For each proverb proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. Knowing when to apply "nothing ventured, nothing gained" rather than "better safe than sorry" is itself a pattern, just at a higher level of abstraction. "This is more like a prototype than a production system" is a meta-pattern: it tells you which lower-level pattern to trust. "This is a piece of work that requires deep thinking" is another, and it selects "too many cooks" over "many hands." These meta-patterns are learned the same way the proverbs were: by getting it wrong both ways and refining the judgement through contact with reality. Patterns all the way up. @@ -93,15 +93,17 @@ What separates a useful pattern library from a contradictory heap of proverbs is ## Following the stepping stones - - LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. When experts publish, they do two things at once: they cover the tracks (the dead ends, the wrong ideas, the blind alleys Feynman described) and they lay down stepping stones (proofs, derivations, case law, mechanism descriptions) that mark the clearest route to the useful patterns. Covering tracks and laying stepping stones are the same act. The dead ends vanish, and what remains is a refined path. LLMs benefit not from uncovering what was hidden but from following what was explicitly laid down as the most direct route. [Timothy Gowers](https://en.wikipedia.org/wiki/Timothy_Gowers) [makes the same observation](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) about mathematics specifically: LLMs are "trained on the product of mathematics rather than the process." They learn to [imitate pulling rabbits out of hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) "without learning how to catch the rabbit or put it in the hat in the first place." -The stepping stones are rich with structure, encoded in the causal language, the "because" and "led to" and "if... then" that saturate human writing. LLMs follow the same stones that practitioners follow, and abstract them into compact patterns. The patterns are useful because the stones were laid down by causal reasoners. + + +The stepping stones are rich with structure, encoded in the causal language, the "because" and "led to" and "if... then" that saturate human writing. LLMs follow the same stepping stones that practitioners follow, and abstract them into compact patterns. The patterns are useful because the stones were laid down by causal reasoners. Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. -The conventional framing draws the line between causal and semantic: LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure. [LeCun's](https://en.wikipedia.org/wiki/Yann_LeCun) [JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) learns predictive representations of the world in latent space. [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) builds in the expectation that the agent will act on the world and update its model from the consequences. These are complementary attempts at different parts of the problem, not rival theories. But if causation is itself a repertoire of learned patterns, as Hofstadter and the developmental evidence suggest, then the causal/semantic distinction is less fundamental than it appears. What matters is whether the model can update itself. An LLM's weights are frozen after training. JEPA's learned representations capture richer structure but are equally frozen at inference time. Adding causal architecture to a frozen model produces a more structured frozen library, not a qualitatively different kind of intelligence. What produces results is feedback: adaptive process layered on top of the frozen model through chain-of-thought, tool use and human collaboration. +The conventional framing draws the line between causal and semantic: LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure. [LeCun's](https://en.wikipedia.org/wiki/Yann_LeCun) [JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) learns predictive representations of the world in latent space. [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) builds in the expectation that the agent will act on the world and update its model from the consequences. These are complementary attempts at different parts of the problem, not rival theories. + +But if causation is itself a repertoire of learned patterns, as Hofstadter and the developmental evidence suggest, then the causal/semantic distinction is less fundamental than it appears. What matters is whether the model can update itself. An LLM's weights are frozen after training. JEPA's learned representations capture richer structure but are equally frozen at inference time. Adding causal architecture to a frozen model produces a more structured frozen library, not a qualitatively different kind of intelligence. What produces results is feedback: adaptive process layered on top of the frozen model through chain-of-thought, tool use and human collaboration. [Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. @@ -131,4 +133,4 @@ Within months of Pocono, the cartoons had won. The unreasonable effectiveness of --- -Pairing the frozen library with adaptive judgement is how we approach AI-assisted engineering at JUXT. If you'd like to think through which patterns matter in your domain, [get in touch](https://www.juxt.pro/contact/). \ No newline at end of file +Pairing the frozen library with adaptive judgement is how we approach AI-assisted engineering at JUXT. If you'd like to think through which patterns matter in your domain, [get in touch](https://www.juxt.pro/contact/). From 4bc315c5ba1e52a675ca76b4a5f3a167163cf0eb Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 21:30:19 +0100 Subject: [PATCH 39/46] Update the description --- src/pages/blog/just-pattern-matching.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/blog/just-pattern-matching.md b/src/pages/blog/just-pattern-matching.md index 089f03591..eb74e4d5e 100644 --- a/src/pages/blog/just-pattern-matching.md +++ b/src/pages/blog/just-pattern-matching.md @@ -1,7 +1,7 @@ --- author: 'hga' title: 'Just pattern matching' -description: "How a machine which can't natively perform arithmetic produced a proof judged by experts to be divine" +description: "How systems with no built-in arithmetic are producing proofs that mathematicians call divine." category: 'ai' layout: '../../layouts/BlogPost.astro' publishedDate: '2026-04-06' From 731b0a21c7572095dcdac13e2aeefdf1160fa9a2 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Sun, 26 Apr 2026 21:32:49 +0100 Subject: [PATCH 40/46] Reframe survivorship bias, vary frozen/static/fixed vocabulary --- src/pages/blog/just-pattern-matching.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/pages/blog/just-pattern-matching.md b/src/pages/blog/just-pattern-matching.md index eb74e4d5e..bf4cfd931 100644 --- a/src/pages/blog/just-pattern-matching.md +++ b/src/pages/blog/just-pattern-matching.md @@ -67,7 +67,7 @@ Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equiv In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. Einstein was pattern matching at a level of abstraction most people never reach: causal structures rather than surface features. -But Einstein is a survivorship bias case study. For every analogy that opens a new field, thousands lead nowhere. The productive analogy needs a sieve: someone who can tell the difference between a deep structural resemblance and a superficial coincidence of form. Einstein was his own sieve. +But analogy is indiscriminate. For every resemblance that opens a new field, thousands lead nowhere. The productive analogy needs a sieve: someone who can tell a deep structural correspondence from a superficial coincidence of form. Einstein was his own sieve. "Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. His claim, which has its critics, is that analogy is the core cognitive act from which everything else, including formal reasoning, is built. The strong version may overreach, but the weaker version is hard to deny: more of cognition is pattern matching than the formalist tradition admits. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. Cognitive scientists have shown that our causal vocabulary [runs on physical schemas](https://en.wikipedia.org/wiki/Force_dynamics) learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. @@ -103,13 +103,13 @@ Where they struggle is where continuously refined expertise matters, where being The conventional framing draws the line between causal and semantic: LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure. [LeCun's](https://en.wikipedia.org/wiki/Yann_LeCun) [JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) learns predictive representations of the world in latent space. [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) builds in the expectation that the agent will act on the world and update its model from the consequences. These are complementary attempts at different parts of the problem, not rival theories. -But if causation is itself a repertoire of learned patterns, as Hofstadter and the developmental evidence suggest, then the causal/semantic distinction is less fundamental than it appears. What matters is whether the model can update itself. An LLM's weights are frozen after training. JEPA's learned representations capture richer structure but are equally frozen at inference time. Adding causal architecture to a frozen model produces a more structured frozen library, not a qualitatively different kind of intelligence. What produces results is feedback: adaptive process layered on top of the frozen model through chain-of-thought, tool use and human collaboration. +But if causation is itself a repertoire of learned patterns, as Hofstadter and the developmental evidence suggest, then the causal/semantic distinction is less fundamental than it appears. What matters is whether the model can update itself. An LLM's weights are fixed after training. JEPA's learned representations capture richer structure but are equally static at inference time. Adding causal architecture to a static model produces a better-organised library, not a qualitatively different kind of intelligence. What produces results is feedback: adaptive process layered on top of the frozen model through chain-of-thought, tool use and human collaboration. [Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. -The frozen/adaptive line is blurrier than it first appears. Deployed weights are frozen, but the training process that produced them was not: [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) and [reinforcement learning from verifiable rewards](https://arxiv.org/abs/2402.03300) both shape reasoning through feedback loops. At inference time, chain-of-thought and in-context learning introduce further adaptation within a single conversation. What is missing is deployment-time learning: the ability to revise the model's own weights from the consequences of its actions in the world. That is the gap between a frozen library with clever retrieval and a library that is alive. +The fixed/adaptive line is blurrier than it first appears. Deployed weights are fixed, but the training process that produced them was not: [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) and [reinforcement learning from verifiable rewards](https://arxiv.org/abs/2402.03300) both shape reasoning through feedback loops. At inference time, chain-of-thought and in-context learning introduce further adaptation within a single conversation. What is missing is deployment-time learning: the ability to revise the model's own weights from the consequences of its actions in the world. That is the gap between a static library with clever retrieval and a library that is alive. -Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this separation. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the frozen library cannot: judgement about what to read, what questions to ask and what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." The LLM handles everything that can be pattern-matched from a frozen library, the human everything that requires an adaptive one. +Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this separation. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the static library cannot: judgement about what to read, what questions to ask and what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." The LLM handles everything that can be pattern-matched from a fixed library, the human everything that requires an adaptive one. Since late 2025, mathematicians working with LLMs have been [solving open problems](https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/) from [Paul Erdős's](https://en.wikipedia.org/wiki/Paul_Erd%C5%91s) celebrated problem lists at a pace that would have been unthinkable a year earlier. Sawhney and Sellke [used GPT-5 to find solutions to ten Erdős problems still listed as open](https://x.com/MarkSellke/status/1979226538059931886). In another collaboration, [roughly 80% of the model's generated arguments were wrong](https://arxiv.org/html/2510.23513v1). The work still produced results, because the mathematician's adaptive judgement could sift the useful patterns from the noise. From 7ad0a4a9deda7067526d6f8fd76c4ffac8eb5ee6 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Mon, 27 Apr 2026 09:54:05 +0100 Subject: [PATCH 41/46] Restructure article around argument escalation: critique arc, Einstein centerpiece, expert sieve --- src/pages/blog/just-pattern-matching.md | 110 +++++++++++------------- 1 file changed, 48 insertions(+), 62 deletions(-) diff --git a/src/pages/blog/just-pattern-matching.md b/src/pages/blog/just-pattern-matching.md index bf4cfd931..30102c5bc 100644 --- a/src/pages/blog/just-pattern-matching.md +++ b/src/pages/blog/just-pattern-matching.md @@ -17,120 +17,106 @@ tags: The diagrams were a notation: straight lines for particles, wavy lines for forces and vertices for interactions. Each element mapped to a term in the mathematics, but physicists could reason through the spatial arrangement of the picture instead of grinding through pages of algebra. Within six months, Freeman Dyson had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. Schwinger later compared them to the silicon chip, "[bringing computation to the masses](https://en.wikipedia.org/wiki/Feynman_diagram#History)." -Seventeen years later, accepting the [Nobel Prize](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/), Feynman observed that scientific writing is designed to cover the tracks: the dead ends, the wrong ideas, the blind alleys nobody describes. The audience at Pocono had seen the absence of formalism and concluded the physics was absent too. +Feynman's diagrams made a complex formalism legible through visual metaphor. The physics didn't get simpler, but it became tractable to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. -Feynman's diagrams made a complex formalism legible through visual metaphor. The physics didn't get simpler, but it became tractable to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. Earlier this month, Andrej Karpathy [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections and synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." +Pattern matching, analogy, recognition, intuition and metaphor are different names for variations on a single faculty. When a mathematician feels a proof is right, or a physicist sees two equations with the same shape and concludes the underlying reality must be alike, the act is the same: recognising that this situation resembles that one, and acting on the resemblance. At sufficient depth, this faculty is not the absence of reasoning. It is the substrate of it. -Pattern matching, analogy, recognition, intuition and metaphor are different names for variations on a single faculty. When a mathematician feels a proof is right or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. +The boundary that critics keep drawing between pattern matching and "real" reasoning has been relocating for five years. Each version of the critique has been falsified by capability and quietly replaced with a narrower one, but the underlying assumption has not been argued for. It has been assumed: that pattern matching can recombine what exists but never generate anything new. -## The elevation of reason - -The audience at Pocono were pattern matching too. They had a pattern for what serious physics looks like: dense formalism, systematic derivation, the kind of thing Schwinger had spent hours demonstrating the day before. So the room concluded, quickly and confidently, that what they were seeing couldn't be physics. - -This is a pattern we have all absorbed. There are reasons. A formal body of knowledge is the only kind that survives the journey from one mind to another it has never met. Apprenticeship can transmit intuition, but apprenticeship doesn't scale. Once a discipline has more practitioners than any master can mentor, the formal version is what gets carried: taught in classrooms, examined, audited, written into textbooks. The intuitive part doesn't lose by competition but by selection. It can't be filed. +## A moving target -[James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*: the living part, the part that can't survive the freezing. Legibility, making knowledge formal enough to govern, is what allows coordination at scale, and there is tragedy in it but no villain. The price of operating beyond face-to-face scale is that whatever can't be made legible falls out of the institutional record. Across generations the formal record becomes synonymous with the discipline, and the *metis* becomes invisible, then forgotten, then suspect. +The critique has tightened since Bender and Gebru [coined "stochastic parrots"](https://dl.acm.org/doi/10.1145/3442188.3445922) in 2021, but it has also moved. The original claim was stark: LLMs manipulate surface statistical form with no access to meaning. They produce fluent text the way a parrot produces fluent speech, without semantics. Since then, interpretability work has complicated the picture. Researchers have found [emergent board-state representations](https://www.neelnanda.io/mechanistic-interpretability/othello) in networks trained to play Othello and [millions of separable concepts](https://www.anthropic.com/research/mapping-mind-language-model) recoverable from a production model's activations. The internals of these systems have more structure than "stochastic parrot" allows for. The statistical critique hasn't been refuted so much as overtaken by the models' own anatomy. -The same selection runs through professional life. Certification requires something testable, so the curriculum becomes the part of the practice that can be written down. Appellate review requires opinions that read as derivation from precedent, so the felt sense of the case is officially absent from the judgement. Cumulative progress across generations requires stepping stones a stranger can follow, so what passes between cohorts is the technique, not the path that found it. None of this requires anyone to choose formalism over intuition. The choice is made by what can travel. +The next wave was more precise. Kambhampati [argued LLMs cannot plan](https://arxiv.org/abs/2403.04121), Mitchell [questioned their capacity for abstraction](https://aiguide.substack.com/p/can-large-language-models-reason), Marcus insisted they lack world models. Each identified a specific missing capability, and each was right about the systems they were testing. Reasoning models, chain-of-thought training and agentic architectures that search, verify and backtrack have since partly addressed the gap. The boundary didn't hold; it relocated. -It is also made, sometimes, by epistemic advance. Lagrange boasted in 1788 that his [*Mécanique analytique*](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) contained "no diagrams": algebraic method had superseded geometric intuition. Geometry couldn't express higher dimensions or abstract algebraic structures, and the [arithmetisation of analysis](https://en.wikipedia.org/wiki/Arithmetization_of_analysis) in the nineteenth century deliberately set out to abolish geometric intuition from proofs, replacing it with definitions that could be verified without pictures. Formal methods reach where intuition can't. + -But once a field has been through that move, it tends to assume the move is general. Formalism becomes the marker of seriousness in domains where the trade-off is quite different, where the intuition is doing most of the work and the formalism is reconstruction after the fact. By the time the room at Pocono saw Feynman's diagrams, two centuries of this selection had done its work. The audience hadn't decided that intuition wasn't physics. They had inherited a discipline in which the intuition had long since fallen out of the record, and they were responding to its absence the way any expert responds to an unfamiliar pattern: with suspicion. +Then came the phenomenological turn. Apple's [GSM-Symbolic](https://arxiv.org/pdf/2410.05229) paper and its successor, "[The Illusion of Thinking](https://machinelearning.apple.com/research/illusion-of-thinking)", argued that chain-of-thought is rationalisation, not reasoning: the models confabulate plausible-looking derivations without performing the underlying operations. This is a serious claim, but it has a problem. -## Reasoning backwards +Applied consistently, the same standard excludes much of what humans call reasoning too. Hadamard's mathematicians [reported results arriving through sudden recognition](https://en.wikipedia.org/wiki/The_Psychology_of_Invention_in_the_Mathematical_Field), with formal proofs constructed afterwards. The legal realists, from Holmes through Jerome Frank, hold that judges reach verdicts by intuitive recognition and then [reason backwards through case law](https://en.wikipedia.org/wiki/Legal_realism) to construct the justification. Kahneman spent a career showing that [fast intuitive judgement outperforms deliberation](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow) in regular environments with adequate feedback. If post-hoc rationalisation disqualifies an LLM from reasoning, it disqualifies a good portion of human cognition along the way. -When [Hadamard](https://en.wikipedia.org/wiki/Jacques_Hadamard) [surveyed working mathematicians](https://en.wikipedia.org/wiki/The_Psychology_of_Invention_in_the_Mathematical_Field), they consistently reported the same experience: results arrived through sudden recognition, with formal proofs constructed afterwards. The proof was a way of showing others what the mathematician already knew. +The most recent critique has abandoned capability claims altogether. The worry is now about [cognitive offloading](https://drphilippahardman.substack.com/p/the-cognitive-offloading-paradox), parasitic labour, "[slop](https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity)": what happens when people stop thinking for themselves because a machine does it passably. These are legitimate concerns about deployment, but they are not arguments about what the systems can do. They are arguments about what we should let them do, and that is a separate conversation. -Poincaré described his breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) in exact detail. He had spent fifteen days at his desk trying every combination he could think of, getting nowhere. Then he travelled to Coutances for a geological excursion, and as he [put his foot on the step of the bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy) the solution arrived complete, with the certainty that it was correct. He didn't work it out then. He turned to the conversation he had been about to have, and verified the proof later when he got home. The weeks of failed formal effort hadn't been wasted; they had loaded the pattern library. The bus ride gave the unconscious matching room to surface. +Four phases, and the conviction stays constant while the justification adapts. That pattern is itself revealing. When someone holds a conclusion steady and rebuilds the reasoning beneath it each time the ground shifts, the conclusion is being supplied by intuition, and the argument is reconstruction after the fact. The critics are doing the very thing they accuse the models of: pattern matching a hierarchy they already believe in, then constructing the justification. What keeps the hierarchy in place is older than any of these papers. -[Michael Atiyah](https://en.wikipedia.org/wiki/Michael_Atiyah) described the same faculty when reviewing the work of others: "Once you've seen it, the truth or veracity, it just stares you in the face. The truth is looking back at you." The feeling for the shape of a valid argument precedes the line-by-line verification. It is, as Atiyah put it, "[pre all that](https://www.scientificamerican.com/article/beauty-equals-truth/)": pre words, pre formulas. The formal reconstruction comes after, as justification for a conclusion already reached. +## The elevation of reason - +The audience at Pocono were pattern matching too. They had a pattern for what serious physics looks like: dense formalism, systematic derivation, the kind of thing Schwinger had spent hours demonstrating the day before. The room concluded, quickly and confidently, that what they were seeing couldn't be physics. -"A conclusion already reached" describes law as well as mathematics. The [legal realists](https://en.wikipedia.org/wiki/Legal_realism), running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), hold that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. On this account, the law looks like formal reasoning from the outside but is pattern matching with a formal audit trail from the inside. +A formal body of knowledge is the only kind that survives the journey from one mind to another it has never met. Apprenticeship can transmit intuition, but apprenticeship doesn't scale. Once a discipline has more practitioners than any master can mentor, the formal version is what gets carried: taught in classrooms, written into textbooks, examined and audited. [James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*: the living part, the part that can't survive the freezing. The intuitive part doesn't lose by competition but by selection. It can't be filed, and what can't be filed can't travel. -The psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Groot) found the same thing in chess. Masters shown a board position for five seconds reproduced about 90% of the pieces. Amateurs managed roughly half. When de Groot scrambled the pieces into positions that couldn't arise in a real game, the masters' advantage vanished. They weren't remembering better or thinking harder: they were recognising patterns that only exist in meaningful play. +Across generations the formal record becomes synonymous with the discipline, and the *metis* becomes invisible, then forgotten, then suspect. Seventeen years after the Pocono disaster, accepting the [Nobel Prize](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/), Feynman observed that scientific writing is designed to cover the tracks: the dead ends, the wrong ideas, the blind alleys nobody describes. The audience at Pocono had seen the absence of formalism and concluded the physics was absent too. -[Kahneman and Klein](https://pubmed.ncbi.nlm.nih.gov/19739881/) confirmed the pattern empirically: in regular environments with adequate feedback, [fast intuitive judgement](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow) outperforms deliberate analysis. Formal reasoning is what novices do while the pattern library is still sparse. Once the library is rich enough, the expert perceives rather than derives. Asking them to show their working is asking them to operate at a lower level of skill. +Sometimes the hierarchy is earned. Lagrange's algebra [really did reach](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) where geometry couldn't, and the [arithmetisation of analysis](https://en.wikipedia.org/wiki/Arithmetization_of_analysis) deliberately set out to abolish geometric intuition from proofs, replacing it with definitions that could be verified without pictures. -## Pattern matching forwards +But formalism becomes the marker of seriousness in domains where the trade-off is quite different, where the intuition is doing most of the work and the formalism is reconstruction after the fact. By the time the room at Pocono saw Feynman's diagrams, intuition had long since fallen out of the record, and they were responding to its absence the way any expert responds to an unfamiliar pattern: with suspicion. -Recognition matches a new situation to something already in the library. Can pattern matching also produce something nobody has seen before? +The hierarchy was older than anyone in the room, and nobody had chosen it. It had simply accumulated, through selection, across the centuries. What it missed was standing right in front of them. -The most common dismissal of LLMs is that they are "[stochastic parrots](https://en.wikipedia.org/wiki/Stochastic_parrot)": statistical machines that regurgitate patterns from training data without understanding. The critique has tightened since Bender coined the term in 2021. The stronger versions, from [Kambhampati](https://arxiv.org/abs/2402.01817) on planning, [Mitchell](https://aiguide.substack.com/p/can-large-language-models-reason) on abstraction, and [Marcus](https://en.wikipedia.org/wiki/Gary_Marcus) on world models, identify a more specific boundary: LLMs can recognise and recombine patterns but cannot plan, verify or update themselves. The assumption underneath, old and new, is that pattern matching can recognise what already exists but cannot produce anything new. +## The shape of an equation Einstein is the test case, because no one's creative process has been more carefully studied. Hofstadter and Sander [walk through his analogies one by one](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/). In 1905, Einstein noticed that [Wien's law](https://en.wikipedia.org/wiki/Wien%27s_radiation_law) for the entropy of radiation at low density had the same mathematical form as the entropy of an ideal gas. The equations looked alike. He concluded the physics must be alike too, and proposed that light comes in discrete packets, [quanta](https://en.wikipedia.org/wiki/Photon), by analogy with gas molecules. That paper [won him the Nobel Prize](https://www.nobelprize.org/prizes/physics/1921/einstein/facts/). Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equivalence_principle) began with a man falling from a roof: Einstein realised that a person in freefall would feel weightless, and identified that feeling with the absence of gravity. He was pattern matching a causal relationship: acceleration produces felt weight, and so does gravity, so perhaps they are the same phenomenon. The analogy between two physical causes became the foundation of [general relativity](https://en.wikipedia.org/wiki/General_relativity). He then spent eight years working out the mathematics. - - -In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify, extend and communicate. Einstein was pattern matching at a level of abstraction most people never reach: causal structures rather than surface features. - -But analogy is indiscriminate. For every resemblance that opens a new field, thousands lead nowhere. The productive analogy needs a sieve: someone who can tell a deep structural correspondence from a superficial coincidence of form. Einstein was his own sieve. +In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify and extend. Einstein was pattern matching at a level of abstraction most people never reach: causal structures rather than surface features. -"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. His claim, which has its critics, is that analogy is the core cognitive act from which everything else, including formal reasoning, is built. The strong version may overreach, but the weaker version is hard to deny: more of cognition is pattern matching than the formalist tradition admits. What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. Cognitive scientists have shown that our causal vocabulary [runs on physical schemas](https://en.wikipedia.org/wiki/Force_dynamics) learned from the body: pushing, blocking, enabling, containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. + -Even [developmental evidence](https://pubmed.ncbi.nlm.nih.gov/14756583/) suggests that children build their causal models from statistical regularities and active experimentation, so the formal model is the residue of the pattern matching, not a separate faculty. Force and current began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. It is metaphor all the way down. +Recognise formal similarity across domains, carry it from the familiar to the unfamiliar: that is what Einstein did, and it is what LLMs do. They identify correspondences across vast libraries of encoded human reasoning and transport them from one context to another. The mechanism differs, but the cognitive shape is the same. -## Living libraries +If this is pattern matching, then pattern matching produced quanta. If it is not, the term has lost all meaning. The boundary the critics draw between "recognising what exists" and "producing the new" is in the wrong place. Einstein's analogies did not retrieve existing physics. They generated new physics by perceiving that the shape of one domain could be transported to another, and the formal verification that followed was reconstruction, not discovery. -But Hofstadter and Sander also present pairs of proverbs that assert contradictory things. "Nothing ventured, nothing gained," but then again, "better safe than sorry." "Many hands make light work," but then again, "too many cooks spoil the broth." "He who hesitates is lost," but then again, "fools rush in where angels fear to tread." +"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," [Hofstadter writes](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/). His claim, which has its critics, is that analogy is the core cognitive act from which everything else, including formal reasoning, is built. The strong version may overreach, but the weaker version is hard to deny: more of cognition is pattern matching than the formalist tradition admits. -Every language has these. For each proverb proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. +What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. Cognitive scientists have shown that our causal vocabulary [runs on physical schemas](https://en.wikipedia.org/wiki/Force_dynamics) learned from the body: pushing, blocking, enabling and containing. Even [developmental evidence](https://pubmed.ncbi.nlm.nih.gov/14756583/) suggests that children build their causal models from statistical regularities and active experimentation, so the formal model is the residue of the pattern matching, not a separate faculty. Force and current began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. It is metaphor all the way down. -Knowing when to apply "nothing ventured, nothing gained" rather than "better safe than sorry" is itself a pattern, just at a higher level of abstraction. "This is more like a prototype than a production system" is a meta-pattern: it tells you which lower-level pattern to trust. "This is a piece of work that requires deep thinking" is another, and it selects "too many cooks" over "many hands." These meta-patterns are learned the same way the proverbs were: by getting it wrong both ways and refining the judgement through contact with reality. Patterns all the way up. +But analogy is indiscriminate. For every resemblance that opens a new field, thousands lead nowhere. The productive analogy needs a sieve: someone who can tell a deep structural correspondence from a superficial coincidence of form. -Software engineers operate the same way. A senior engineer looks at a proposed architecture and [smells](https://martinfowler.com/bliki/CodeSmell.html) whether it will hold, the way a chess master perceives a board. The boxes and arrows on a whiteboard are not precise specifications. They are tokens in a shared pattern-matching conversation, [scaffolding for recognition](https://www.microsoft.com/en-us/research/publication/lets-go-to-the-whiteboard-how-and-why-software-developers-use-drawings/) rather than the recognition itself. +## The living sieve -The vocabulary gives it away. Code whose tangled dependencies resist comprehension is a [ball of mud](https://en.wikipedia.org/wiki/Big_ball_of_mud); code whose composition they admire is [clean](https://en.wikipedia.org/wiki/Robert_C._Martin). They hold seams together with glue code, fire [tracer bullets](https://en.wikipedia.org/wiki/Tracer_bullet_(software_development)) through a system to find the fault, and measure the blast radius before deploying a fix. When the bug vanishes the moment they try to observe it, they call it a [Heisenbug](https://en.wikipedia.org/wiki/Heisenbug), after the same uncertainty principle that Bohr was lecturing Feynman about at Pocono. +Einstein was his own sieve. Most people are not Einstein, and most analogies are not productive. A pattern library that contains "nothing ventured, nothing gained" alongside "better safe than sorry" cannot tell you which to apply. Every language has [pairs of proverbs](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/) that assert contradictory things. Knowing when to trust one over the other is itself a pattern, just at a higher level of abstraction, a meta-pattern trained by getting it wrong both ways and refining the judgement through contact with reality. -This is the gap between having patterns and having expertise. Feynman didn't just accumulate a library of physical intuitions. He [built each one himself](https://en.wikipedia.org/wiki/Surely_You%27re_Joking,_Mr._Feynman!), refusing to accept results he hadn't rederived from scratch, forging links that a passively received result would lack. Breadth is what fuels analogy: the wider the library, the more unexpected the connections it can make. - - + What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened; those that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences, while the textbook's library is frozen. -## Following the stepping stones - -LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. When experts publish, they do two things at once: they cover the tracks (the dead ends, the wrong ideas, the blind alleys Feynman described) and they lay down stepping stones (proofs, derivations, case law, mechanism descriptions) that mark the clearest route to the useful patterns. Covering tracks and laying stepping stones are the same act. The dead ends vanish, and what remains is a refined path. LLMs benefit not from uncovering what was hidden but from following what was explicitly laid down as the most direct route. [Timothy Gowers](https://en.wikipedia.org/wiki/Timothy_Gowers) [makes the same observation](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) about mathematics specifically: LLMs are "trained on the product of mathematics rather than the process." They learn to [imitate pulling rabbits out of hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) "without learning how to catch the rabbit or put it in the hat in the first place." - - +This is why [Erdős's](https://en.wikipedia.org/wiki/Paul_Erd%C5%91s) [probabilistic method](https://en.wikipedia.org/wiki/Probabilistic_method) was so productive: he imported the machinery of probability theory into combinatorics, using expected-value arguments to prove that certain structures must exist without constructing a single one. The move was not derived within combinatorics. It was transported from another domain, the same structural act Einstein performed. The [Langlands programme](https://en.wikipedia.org/wiki/Langlands_program) and the [Atiyah-Singer index theorem](https://en.wikipedia.org/wiki/Atiyah%E2%80%93Singer_index_theorem) followed the same shape: progress came from recognising that structurally distant objects were the same thing seen from different angles, and the mathematicians who saw the correspondence were the ones with libraries broad enough to hold both sides. -The stepping stones are rich with structure, encoded in the causal language, the "because" and "led to" and "if... then" that saturate human writing. LLMs follow the same stepping stones that practitioners follow, and abstract them into compact patterns. The patterns are useful because the stones were laid down by causal reasoners. +Since late 2025, mathematicians working with LLMs have been [solving open problems](https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/) from Erdős's celebrated problem lists at a pace that would have been unthinkable a year earlier. Sawhney and Sellke [used GPT-5 to find solutions to ten Erdős problems still listed as open](https://x.com/MarkSellke/status/1979226538059931886). In another collaboration, [roughly 80% of the model's generated arguments were wrong](https://arxiv.org/html/2510.23513v1). The work still produced results, because the mathematician's adaptive judgement could sift the useful patterns from the noise. -Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. +The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/status/2044307082275618993), a conjecture from 1968. Previous approaches had relied on continuous analysis. Working with GPT-5.4, Liam Price stayed in the arithmetic realm and deployed the [von Mangoldt function](https://en.wikipedia.org/wiki/Von_Mangoldt_function) in a way nobody had tried. [Jared Duker Lichtman](https://en.wikipedia.org/wiki/Jared_Duker_Lichtman), who had spent seven years on primitive set problems, read the proof and [called it a Book proof](https://en.wikipedia.org/wiki/Proofs_from_THE_BOOK): Erdős's term for a proof so compact and elegant it could have been written by God. -The conventional framing draws the line between causal and semantic: LLMs lack [world models](https://en.wikipedia.org/wiki/World_model_(artificial_intelligence)), so the fix is adding explicit causal structure. [LeCun's](https://en.wikipedia.org/wiki/Yann_LeCun) [JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) learns predictive representations of the world in latent space. [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) builds in the expectation that the agent will act on the world and update its model from the consequences. These are complementary attempts at different parts of the problem, not rival theories. +The objection that LLMs are not reasoning mistakes the division of labour. Analogical generation paired with expert filtering is how a great deal of creative work has always been done. The model proposes; the practitioner sieves. Einstein proposed analogies and sieved them himself. Sawhney discarded 80% and kept the rest. The division is different, the faculty is the same. A system whose underlying network has no arithmetic produced a proof that an expert in the field called divine. That is what pattern matching looks like when it operates at sufficient depth, in the hands of someone who knows which patterns matter. -But if causation is itself a repertoire of learned patterns, as Hofstadter and the developmental evidence suggest, then the causal/semantic distinction is less fundamental than it appears. What matters is whether the model can update itself. An LLM's weights are fixed after training. JEPA's learned representations capture richer structure but are equally static at inference time. Adding causal architecture to a static model produces a better-organised library, not a qualitatively different kind of intelligence. What produces results is feedback: adaptive process layered on top of the frozen model through chain-of-thought, tool use and human collaboration. +What the living sieve cannot do is lend its life to the frozen library. The expert's judgement comes from sustained contact with consequences, from being wrong and updating. The static pattern library, however vast, lacks that contact. -[Chain-of-thought prompting](https://en.wikipedia.org/wiki/Chain_of_thought) is a clue to where the adaptive process might come from. When a reasoning model works through a problem step by step, each intermediate result becomes something the next step can evaluate, revise or abandon. The model generates a tentative pattern match, examines it against other patterns in the library, and refines before committing. The feedback is internal rather than environmental, but the dynamic is structurally similar to what experts do: the practitioner argues with themselves. +## What the stones carry -The fixed/adaptive line is blurrier than it first appears. Deployed weights are fixed, but the training process that produced them was not: [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) and [reinforcement learning from verifiable rewards](https://arxiv.org/abs/2402.03300) both shape reasoning through feedback loops. At inference time, chain-of-thought and in-context learning introduce further adaptation within a single conversation. What is missing is deployment-time learning: the ability to revise the model's own weights from the consequences of its actions in the world. That is the gap between a static library with clever retrieval and a library that is alive. +The world-models research programme is a serious positive account of what intelligence requires. [LeCun's JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) learns predictive representations of the world in latent space. [Friston's active inference](https://en.wikipedia.org/wiki/Free_energy_principle) builds in the expectation that the agent will act on the world and update its model from the consequences. These are complementary approaches to different parts of the problem, and both address something LLMs lack. Whether explicit world models are necessary for these capabilities, or merely one route to them, is an empirical question. The evidence so far: we have got remarkably far without them. That is not proof they are unnecessary. It is reason to treat the necessity claim as a hypothesis rather than a premise. -Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this separation. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the static library cannot: judgement about what to read, what questions to ask and what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." The LLM handles everything that can be pattern-matched from a fixed library, the human everything that requires an adaptive one. + -Since late 2025, mathematicians working with LLMs have been [solving open problems](https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/) from [Paul Erdős's](https://en.wikipedia.org/wiki/Paul_Erd%C5%91s) celebrated problem lists at a pace that would have been unthinkable a year earlier. Sawhney and Sellke [used GPT-5 to find solutions to ten Erdős problems still listed as open](https://x.com/MarkSellke/status/1979226538059931886). In another collaboration, [roughly 80% of the model's generated arguments were wrong](https://arxiv.org/html/2510.23513v1). The work still produced results, because the mathematician's adaptive judgement could sift the useful patterns from the noise. +If causation is itself a repertoire of learned patterns, as Hofstadter and the developmental evidence suggest, then causal structure may be something that sufficient pattern matching recovers rather than a categorically different thing requiring categorically different machinery. The stepping stones that experts laid down are saturated with causal language, the "because" and "led to" and "if... then" of human explanation. LLMs follow those stones and abstract them into compact patterns. The patterns carry weight because the stones were written by causal reasoners. -The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/status/2044307082275618993), a conjecture from 1968. Previous approaches had relied on continuous analysis. Working with GPT-5.4, Liam Price stayed in the arithmetic realm and deployed the [von Mangoldt function](https://en.wikipedia.org/wiki/Von_Mangoldt_function) in a way nobody had tried. [Jared Duker Lichtman](https://en.wikipedia.org/wiki/Jared_Duker_Lichtman), who had spent seven years on primitive set problems, read the proof and [called it a Book proof](https://en.wikipedia.org/wiki/Proofs_from_THE_BOOK): Erdős's term for a proof so compact and elegant it could have been written by God. +[Gowers](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) makes the same observation about mathematics: LLMs are "trained on the product of mathematics rather than the process", [imitating the pulling of rabbits from hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) without learning how the rabbit got there. What the product carries, though, is richer than Gowers's framing implies. The formal reconstructions are dense with the causal structure of the minds that wrote them. -A system whose underlying network has no arithmetic produced a proof that an expert in the field called divine. That is what pattern matching looks like when it operates at sufficient depth, in the hands of someone who knows which patterns matter. +Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this division of labour. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the static library cannot: judgement about what to read, what questions to ask and what the connections mean. -## What the room missed +Deployed weights are fixed, but the line between fixed and adaptive is blurrier than it first appears. [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) and [reinforcement learning from verifiable rewards](https://arxiv.org/abs/2402.03300) shape reasoning through feedback loops during training; [chain-of-thought](https://en.wikipedia.org/wiki/Chain_of_thought) and in-context learning introduce further adaptation within a single conversation. What is missing is deployment-time learning: the ability to revise the model's own weights from the consequences of its actions. That is the gap between a static library with clever retrieval and one that is alive. -The audience at Pocono saw cartoons where there was physics. Bohr understood quantum mechanics perfectly well, but he had a pattern for what serious physics looks like, and the diagrams didn't match it. The room had internalised a hierarchy that equates formalism with seriousness, blinding them to the most powerful computational tool their field would produce. +The world-models programme may close that gap. What it has not been required to do is get us here, to a place where a frozen library in expert hands is producing proofs that mathematicians call divine. -Schwinger's formal derivation was correct, complete and slow. Feynman's diagrams were correct, complete and fast. Schwinger conceded as much when he compared them to the silicon chip, bringing computation to the masses. +## The cartoons win -Their limitation is the one Hofstadter's proverbs predict. A frozen library, however vast, can't tell you which pattern to apply when the patterns contradict. That capacity comes from sustained contact with reality, from being wrong and updating. Karpathy's architecture is the current best attempt to make the most of these systems: pair the frozen library with a human whose adaptive judgement comes from living in the problem. The research frontier is building the adaptive layer into the systems themselves. +The audience at Pocono saw cartoons where there was physics. Within months, the cartoons had won. -World models and explicitly causal architectures have not been required to get here. These systems have no built-in arithmetic, yet in expert hands they are producing proofs that mathematicians call divine. They followed the stepping stones that experts laid down, found compact patterns in the formal reconstructions, and those patterns carried weight because the reconstructions were written by causal reasoners. No one expected pattern matching alone to get this far. +LLMs fail in ways that look causal-structure-shaped. They struggle with counterfactuals and with transfer to regimes their training data did not cover. These failures are real. We argue they are quantitative, the same brittleness humans show outside their own training distribution, not evidence of a missing faculty. The sociocultural critique, that these systems produce slop and invite cognitive offloading, describes a real mode of use. It says something about deployment, not about capability, and we decline to treat it as a verdict on the technology. -We are applying the same hierarchy to AI that the room at Pocono applied to Feynman. The dismissal of LLMs as "just pattern matching" assumes that pattern matching is the lower faculty and formal causal reasoning is what counts. The evidence from mathematics, physics and law runs the other way. Pattern matching is what expertise consists of. Formal reasoning is the reconstruction we produce afterwards, for audit and transmission, while the *metis* stays with the practitioner. +The retreat of the critique is real and documentable. First LLMs could not reason at all; then they could not plan; then they could not verify; now the boundary has contracted to causal structure. Einstein is not a curiosity in this account. His creative process is the best-documented act of scientific reasoning we have, and it shows the boundary between pattern matching and causal reasoning is misplaced. Pattern matching plus an expert sieve is how the most celebrated reasoning in the history of physics was done. -Within months of Pocono, the cartoons had won. The unreasonable effectiveness of these systems suggests we have the hierarchy backwards again. +Feynman's cartoons worked because they made the physics tractable to the kind of thinking humans are best at: seeing patterns and reasoning through spatial arrangement rather than grinding through algebra. LLMs do the same thing for knowledge. What neither the room at Pocono nor the current critique has reckoned with is that pattern matching, at sufficient depth, is what reasoning was all along. --- -Pairing the frozen library with adaptive judgement is how we approach AI-assisted engineering at JUXT. If you'd like to think through which patterns matter in your domain, [get in touch](https://www.juxt.pro/contact/). +Pairing the frozen library with adaptive human judgement is how we approach AI-assisted engineering at JUXT. If you'd like to think through which patterns carry weight in your domain, [get in touch](https://www.juxt.pro/contact/). From c21472f4bfbef86488490b0bf8b4d7c25402f3f3 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Tue, 28 Apr 2026 09:46:56 +0100 Subject: [PATCH 42/46] Reframe around sociotechnical observation; rewrite stepping stones and conclusion --- src/pages/blog/just-pattern-matching.md | 124 ++++++++++++++---------- 1 file changed, 73 insertions(+), 51 deletions(-) diff --git a/src/pages/blog/just-pattern-matching.md b/src/pages/blog/just-pattern-matching.md index 30102c5bc..f6d9909b7 100644 --- a/src/pages/blog/just-pattern-matching.md +++ b/src/pages/blog/just-pattern-matching.md @@ -13,110 +13,132 @@ tags:

    Schwinger had spent hours at the blackboard the day before, deriving quantum electrodynamics through pages of formal mathematics. The twenty-eight physicists assembled at the Pocono Conference in the spring of 1948, Dirac and Bohr and Oppenheimer among them, were impressed. Then Feynman got up and drew what looked like cartoons.

    -[Teller](https://en.wikipedia.org/wiki/Edward_Teller) interrupted: the approach violated the exclusion principle. Dirac kept asking "Is it unitary?" Bohr strode to the stage and lectured Feynman on the uncertainty principle, having mistaken the diagrams for literal pictures of particle paths. The presentation was a disaster. +[Teller](https://en.wikipedia.org/wiki/Edward_Teller) interrupted, objecting that the approach violated the exclusion principle. Dirac kept asking "Is it unitary?" Bohr strode to the stage and lectured Feynman on the uncertainty principle, having mistaken the diagrams for literal pictures of particle paths. The presentation was an absolute disaster. -The diagrams were a notation: straight lines for particles, wavy lines for forces and vertices for interactions. Each element mapped to a term in the mathematics, but physicists could reason through the spatial arrangement of the picture instead of grinding through pages of algebra. Within six months, Freeman Dyson had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. Schwinger later compared them to the silicon chip, "[bringing computation to the masses](https://en.wikipedia.org/wiki/Feynman_diagram#History)." +Had the attendees been more receptive, they'd have heard that diagrams were a kind of notation: straight lines for particles, wavy lines for forces and vertices for interactions. Each element mapped to a term in the mathematics, and the spatial arrangement of the picture enabled scientists to reason through relationships instead of grinding through pages of algebra. Within six months, Freeman Dyson had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. Feynman's diagrams made a complex formalism legible through visual metaphor. The physics didn't get simpler, but it became tractable to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. -Feynman's diagrams made a complex formalism legible through visual metaphor. The physics didn't get simpler, but it became tractable to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. +Earlier this month, Andrej Karpathy [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections and synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." -Pattern matching, analogy, recognition, intuition and metaphor are different names for variations on a single faculty. When a mathematician feels a proof is right, or a physicist sees two equations with the same shape and concludes the underlying reality must be alike, the act is the same: recognising that this situation resembles that one, and acting on the resemblance. At sufficient depth, this faculty is not the absence of reasoning. It is the substrate of it. +Pattern matching, analogy, recognition, intuition and metaphor are different names for variations on a single faculty. When a mathematician feels a proof is right or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. -The boundary that critics keep drawing between pattern matching and "real" reasoning has been relocating for five years. Each version of the critique has been falsified by capability and quietly replaced with a narrower one, but the underlying assumption has not been argued for. It has been assumed: that pattern matching can recombine what exists but never generate anything new. +## The elevation of reason -## A moving target +The audience at Pocono were pattern matching too. They had a pattern for what 'serious physics' looks like: dense formalism, systematic derivation, the kind of thing Schwinger had spent hours demonstrating the day before. So the room concluded, quickly and confidently, that what they were seeing couldn't be physics. -The critique has tightened since Bender and Gebru [coined "stochastic parrots"](https://dl.acm.org/doi/10.1145/3442188.3445922) in 2021, but it has also moved. The original claim was stark: LLMs manipulate surface statistical form with no access to meaning. They produce fluent text the way a parrot produces fluent speech, without semantics. Since then, interpretability work has complicated the picture. Researchers have found [emergent board-state representations](https://www.neelnanda.io/mechanistic-interpretability/othello) in networks trained to play Othello and [millions of separable concepts](https://www.anthropic.com/research/mapping-mind-language-model) recoverable from a production model's activations. The internals of these systems have more structure than "stochastic parrot" allows for. The statistical critique hasn't been refuted so much as overtaken by the models' own anatomy. +A formal body of knowledge is the only kind that survives the journey from one mind to another it has never met. Apprenticeship can transmit intuition, but apprenticeship doesn't scale. Once a discipline has more practitioners than any master can mentor, the formal version is what gets carried: taught in classrooms, examined, audited, written into textbooks. The intuitive part doesn't lose by competition but by selection. It can't be filed. -The next wave was more precise. Kambhampati [argued LLMs cannot plan](https://arxiv.org/abs/2403.04121), Mitchell [questioned their capacity for abstraction](https://aiguide.substack.com/p/can-large-language-models-reason), Marcus insisted they lack world models. Each identified a specific missing capability, and each was right about the systems they were testing. Reasoning models, chain-of-thought training and agentic architectures that search, verify and backtrack have since partly addressed the gap. The boundary didn't hold; it relocated. +[James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*: the living part, the part that can't survive the freezing. Legibility, making knowledge formal enough to govern, is what allows coordination at scale, and there is tragedy in it but no villain. The price of operating beyond face-to-face scale is that whatever can't be made legible falls out of the institutional record. Across generations the formal record becomes synonymous with the discipline, and the *metis* becomes invisible, then forgotten, then suspect. - +Seventeen years after the Pocono disaster, accepting the [Nobel Prize](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/), Feynman observed that scientific writing is designed to cover the tracks: the dead ends, the wrong ideas, the blind alleys nobody describes. The audience at Pocono had seen the absence of formalism and concluded the physics was absent too. -Then came the phenomenological turn. Apple's [GSM-Symbolic](https://arxiv.org/pdf/2410.05229) paper and its successor, "[The Illusion of Thinking](https://machinelearning.apple.com/research/illusion-of-thinking)", argued that chain-of-thought is rationalisation, not reasoning: the models confabulate plausible-looking derivations without performing the underlying operations. This is a serious claim, but it has a problem. +The same selection runs through professional life. Certification requires something testable, so the curriculum becomes the part of the practice that can be written down. Appellate review requires opinions that read as derivation from precedent, so the felt sense of the case is officially absent from the judgement. Cumulative progress across generations requires stepping stones a stranger can follow, so what passes between cohorts is the technique, not the path that found it. None of this requires anyone to choose formalism over intuition. The choice is made by what can travel. -Applied consistently, the same standard excludes much of what humans call reasoning too. Hadamard's mathematicians [reported results arriving through sudden recognition](https://en.wikipedia.org/wiki/The_Psychology_of_Invention_in_the_Mathematical_Field), with formal proofs constructed afterwards. The legal realists, from Holmes through Jerome Frank, hold that judges reach verdicts by intuitive recognition and then [reason backwards through case law](https://en.wikipedia.org/wiki/Legal_realism) to construct the justification. Kahneman spent a career showing that [fast intuitive judgement outperforms deliberation](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow) in regular environments with adequate feedback. If post-hoc rationalisation disqualifies an LLM from reasoning, it disqualifies a good portion of human cognition along the way. +It is also made, sometimes, by epistemic advance. Lagrange boasted in 1788 that his [*Mécanique analytique*](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) contained "no diagrams": algebraic method had superseded geometric intuition. Geometry couldn't express higher dimensions or abstract algebraic structures, and the [arithmetisation of analysis](https://en.wikipedia.org/wiki/Arithmetization_of_analysis) in the nineteenth century deliberately set out to abolish geometric intuition from proofs, replacing it with definitions that could be verified without pictures. Formal methods reach where intuition can't. -The most recent critique has abandoned capability claims altogether. The worry is now about [cognitive offloading](https://drphilippahardman.substack.com/p/the-cognitive-offloading-paradox), parasitic labour, "[slop](https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity)": what happens when people stop thinking for themselves because a machine does it passably. These are legitimate concerns about deployment, but they are not arguments about what the systems can do. They are arguments about what we should let them do, and that is a separate conversation. +Formalism becomes the marker of seriousness in domains where the trade-off is quite different, where the intuition is doing most of the work and the formalism is reconstruction after the fact. By the time the room at Pocono saw Feynman's diagrams, the discipline's formal record contained no trace of how the intuitions were reached, and they were responding to its absence the way any expert responds to an unfamiliar pattern: with suspicion. -Four phases, and the conviction stays constant while the justification adapts. That pattern is itself revealing. When someone holds a conclusion steady and rebuilds the reasoning beneath it each time the ground shifts, the conclusion is being supplied by intuition, and the argument is reconstruction after the fact. The critics are doing the very thing they accuse the models of: pattern matching a hierarchy they already believe in, then constructing the justification. What keeps the hierarchy in place is older than any of these papers. +## Reasoning backwards -## The elevation of reason +When [Hadamard](https://en.wikipedia.org/wiki/Jacques_Hadamard) [surveyed working mathematicians](https://en.wikipedia.org/wiki/The_Psychology_of_Invention_in_the_Mathematical_Field), they consistently reported an experience of arriving at results through sudden recognition. Formal proofs were constructed afterwards: the proof was a way of showing others what the mathematician already knew to be true. + +Poincaré described his breakthrough on [Fuchsian functions](https://en.wikipedia.org/wiki/Fuchsian_group) in exact detail. He had spent fifteen days at his desk trying every combination he could think of, getting nowhere. Then he travelled to Coutances for a geological excursion, and as he [put his foot on the step of the bus](https://en.wikipedia.org/wiki/Henri_Poincar%C3%A9#Philosophy) the solution arrived complete. He didn't work it out then, but he was certain it was correct. He turned to the conversation he had been about to have, and verified the proof later when he got home. The weeks of failed formal effort hadn't been wasted; they had created the circumstances. The bus ride gave the unconscious pattern matching room to surface. + +[Michael Atiyah](https://en.wikipedia.org/wiki/Michael_Atiyah) described the same faculty when reviewing the work of others: "Once you've seen it, the truth or veracity, it just stares you in the face. The truth is looking back at you." The feeling for the shape of a valid argument precedes the line-by-line verification. It is, as Atiyah put it, "[pre all that](https://www.scientificamerican.com/article/beauty-equals-truth/)": pre words, pre formulas. The formal reconstruction comes after, as justification for a conclusion already reached. -The audience at Pocono were pattern matching too. They had a pattern for what serious physics looks like: dense formalism, systematic derivation, the kind of thing Schwinger had spent hours demonstrating the day before. The room concluded, quickly and confidently, that what they were seeing couldn't be physics. + -A formal body of knowledge is the only kind that survives the journey from one mind to another it has never met. Apprenticeship can transmit intuition, but apprenticeship doesn't scale. Once a discipline has more practitioners than any master can mentor, the formal version is what gets carried: taught in classrooms, written into textbooks, examined and audited. [James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*: the living part, the part that can't survive the freezing. The intuitive part doesn't lose by competition but by selection. It can't be filed, and what can't be filed can't travel. +"A conclusion already reached" pops up in many other domains besides mathematics. Take law for example: the [legal realists](https://en.wikipedia.org/wiki/Legal_realism), running from [Oliver Wendell Holmes](https://en.wikipedia.org/wiki/Oliver_Wendell_Holmes_Jr.) through [Jerome Frank](https://en.wikipedia.org/wiki/Jerome_Frank), hold that judges typically reach a verdict through intuitive recognition of the situation and then reason backwards through case law to construct the justification. The formal opinion reads as though the conclusion followed from the precedents. In practice, the precedents were selected to support a conclusion already reached. -Across generations the formal record becomes synonymous with the discipline, and the *metis* becomes invisible, then forgotten, then suspect. Seventeen years after the Pocono disaster, accepting the [Nobel Prize](https://www.nobelprize.org/prizes/physics/1965/feynman/lecture/), Feynman observed that scientific writing is designed to cover the tracks: the dead ends, the wrong ideas, the blind alleys nobody describes. The audience at Pocono had seen the absence of formalism and concluded the physics was absent too. +The psychologist [Adriaan de Groot](https://en.wikipedia.org/wiki/Adriaan_de_Groot) found the same thing in chess. Masters shown a board position for five seconds reproduced about 90% of the pieces. Amateurs managed roughly half. When de Groot scrambled the pieces into positions that couldn't arise in a real game, the masters' advantage vanished. They weren't remembering better or thinking harder: they were recognising patterns that only exist in meaningful play. -Sometimes the hierarchy is earned. Lagrange's algebra [really did reach](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) where geometry couldn't, and the [arithmetisation of analysis](https://en.wikipedia.org/wiki/Arithmetization_of_analysis) deliberately set out to abolish geometric intuition from proofs, replacing it with definitions that could be verified without pictures. +[Kahneman and Klein](https://pubmed.ncbi.nlm.nih.gov/19739881/) confirmed the pattern empirically: in regular environments with adequate feedback, [fast intuitive judgement](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow) outperforms deliberate analysis. Formal reasoning is what novices do while the pattern library is still sparse. Once the library is rich enough, the expert perceives rather than derives. -But formalism becomes the marker of seriousness in domains where the trade-off is quite different, where the intuition is doing most of the work and the formalism is reconstruction after the fact. By the time the room at Pocono saw Feynman's diagrams, intuition had long since fallen out of the record, and they were responding to its absence the way any expert responds to an unfamiliar pattern: with suspicion. +Asking them to show their working is asking them to operate at a lower level of skill. -The hierarchy was older than anyone in the room, and nobody had chosen it. It had simply accumulated, through selection, across the centuries. What it missed was standing right in front of them. +## Pattern matching forwards -## The shape of an equation +The most common dismissal of LLMs is that they are "[stochastic parrots](https://en.wikipedia.org/wiki/Stochastic_parrot)": statistical machines that regurgitate patterns from training data without understanding. The critique has shifted since Bender coined the term in 2021. The stronger versions, from [Kambhampati](https://arxiv.org/abs/2402.01817) on planning, [Mitchell](https://aiguide.substack.com/p/can-large-language-models-reason) on abstraction, and [Marcus](https://en.wikipedia.org/wiki/Gary_Marcus) on world models, identify a more specific boundary: LLMs can recognise and recombine patterns but cannot plan, verify or update themselves. -Einstein is the test case, because no one's creative process has been more carefully studied. Hofstadter and Sander [walk through his analogies one by one](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/). In 1905, Einstein noticed that [Wien's law](https://en.wikipedia.org/wiki/Wien%27s_radiation_law) for the entropy of radiation at low density had the same mathematical form as the entropy of an ideal gas. The equations looked alike. He concluded the physics must be alike too, and proposed that light comes in discrete packets, [quanta](https://en.wikipedia.org/wiki/Photon), by analogy with gas molecules. That paper [won him the Nobel Prize](https://www.nobelprize.org/prizes/physics/1921/einstein/facts/). +The core accusation is that pattern matching can recognise what already exists but cannot produce anything new. + +In their book Surfaces and Essences, Hofstadter and Sander [walk through Einstein's analogies one by one](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/). + +In 1905, Einstein noticed that [Wien's law](https://en.wikipedia.org/wiki/Wien%27s_radiation_law) for the entropy of radiation at low density had the same mathematical form as the entropy of an ideal gas. The equations looked alike. He concluded the physics must be alike too, and proposed that light comes in discrete packets, [quanta](https://en.wikipedia.org/wiki/Photon), by analogy with gas molecules. That paper [won him the Nobel Prize](https://www.nobelprize.org/prizes/physics/1921/einstein/facts/). Two years later, the [equivalence principle](https://en.wikipedia.org/wiki/Equivalence_principle) began with a man falling from a roof: Einstein realised that a person in freefall would feel weightless, and identified that feeling with the absence of gravity. He was pattern matching a causal relationship: acceleration produces felt weight, and so does gravity, so perhaps they are the same phenomenon. The analogy between two physical causes became the foundation of [general relativity](https://en.wikipedia.org/wiki/General_relativity). He then spent eight years working out the mathematics. + + In each case the analogy preceded the formalism, often by years. The creative act was perceiving a resemblance between two situations that nobody had previously connected. The formal derivation was what happened afterwards, to verify and extend. Einstein was pattern matching at a level of abstraction most people never reach: causal structures rather than surface features. - +"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," Hofstadter writes. His strong claim, which is highly contested, is that analogy is the core cognitive act from which everything else (including formal reasoning) is built. -Recognise formal similarity across domains, carry it from the familiar to the unfamiliar: that is what Einstein did, and it is what LLMs do. They identify correspondences across vast libraries of encoded human reasoning and transport them from one context to another. The mechanism differs, but the cognitive shape is the same. +What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. Cognitive scientists have shown that our causal vocabulary [runs on physical schemas](https://en.wikipedia.org/wiki/Force_dynamics) learned from the body: pushing, blocking, enabling and containing. Causation is a repertoire of patterns we match to situations, not a single logical operation. It is metaphor all the way down. A discipline that can only recognise formal cognitive work will keep mistaking the other kind for absence. -If this is pattern matching, then pattern matching produced quanta. If it is not, the term has lost all meaning. The boundary the critics draw between "recognising what exists" and "producing the new" is in the wrong place. Einstein's analogies did not retrieve existing physics. They generated new physics by perceiving that the shape of one domain could be transported to another, and the formal verification that followed was reconstruction, not discovery. +## Living libraries -"Every concept we have is essentially nothing but a tightly packaged bundle of analogies," [Hofstadter writes](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/). His claim, which has its critics, is that analogy is the core cognitive act from which everything else, including formal reasoning, is built. The strong version may overreach, but the weaker version is hard to deny: more of cognition is pattern matching than the formalist tradition admits. +But analogy is indiscriminate. For every resemblance that opens a new field, thousands lead nowhere. The productive analogy needs a sieve: someone who can tell a deep structural correspondence from a superficial coincidence of form. Einstein was his own sieve. -What we call a causal mechanism is itself a stabilised analogy: "force" borrowed from muscular pushing, "current" from rivers. Cognitive scientists have shown that our causal vocabulary [runs on physical schemas](https://en.wikipedia.org/wiki/Force_dynamics) learned from the body: pushing, blocking, enabling and containing. Even [developmental evidence](https://pubmed.ncbi.nlm.nih.gov/14756583/) suggests that children build their causal models from statistical regularities and active experimentation, so the formal model is the residue of the pattern matching, not a separate faculty. Force and current began as metaphors. They became mechanisms by being good enough to predict and intervene with, tested and refined until they earned the status of formal knowledge. It is metaphor all the way down. +Engineers carry their own contradictory proverbs. YAGNI says don't build what you don't need yet, but every architecture review asks whether the design will scale. "Worse is better" says ship the simple thing, but the post-mortem after the outage says do it right the first time. "Make it correct, then make it fast" is good counsel until the market has moved on while you were perfecting the abstraction. -But analogy is indiscriminate. For every resemblance that opens a new field, thousands lead nowhere. The productive analogy needs a sieve: someone who can tell a deep structural correspondence from a superficial coincidence of form. +For each maxim proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. -## The living sieve +Knowing when to apply YAGNI rather than building for extensibility is itself a pattern, just at a higher level of abstraction. "This has the feel of a prototype, not a production system" is a meta-pattern: it tells you which lower-level pattern to trust. "This is the kind of system where the first outage costs more than the extra engineering" is another, and it selects do-it-right over worse-is-better. These meta-patterns are learned the same way the maxims were: by getting it wrong both ways and refining the judgement through contact with reality. Patterns all the way up. -Einstein was his own sieve. Most people are not Einstein, and most analogies are not productive. A pattern library that contains "nothing ventured, nothing gained" alongside "better safe than sorry" cannot tell you which to apply. Every language has [pairs of proverbs](https://www.basicbooks.com/titles/douglas-hofstadter/surfaces-and-essences/9780465018475/) that assert contradictory things. Knowing when to trust one over the other is itself a pattern, just at a higher level of abstraction, a meta-pattern trained by getting it wrong both ways and refining the judgement through contact with reality. +Software engineers operate the same way. A senior engineer looks at a proposed architecture and [smells](https://martinfowler.com/bliki/CodeSmell.html) whether it will hold, the way a chess master perceives a board. The boxes and arrows on a whiteboard are not precise specifications. They are tokens in a shared pattern-matching conversation, [scaffolding for recognition](https://www.microsoft.com/en-us/research/publication/lets-go-to-the-whiteboard-how-and-why-software-developers-use-drawings/) rather than the recognition itself. - +The vocabulary gives it away. Code whose tangled dependencies resist comprehension is a [ball of mud](https://en.wikipedia.org/wiki/Big_ball_of_mud); code whose composition they admire is [clean](https://en.wikipedia.org/wiki/Robert_C._Martin). They hold seams together with glue code, fire [tracer bullets](https://en.wikipedia.org/wiki/Tracer_bullet_(software_development)) through a system to find the fault, and measure the blast radius before deploying a fix. When the bug vanishes the moment they try to observe it, they call it a [Heisenbug](https://en.wikipedia.org/wiki/Heisenbug), after the same uncertainty principle that Bohr was lecturing Feynman about at Pocono. -What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened; those that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences, while the textbook's library is frozen. +This is the gap between having patterns and having expertise. Feynman didn't just accumulate a library of physical intuitions. He [built each one himself](https://en.wikipedia.org/wiki/Surely_You%27re_Joking,_Mr._Feynman!), refusing to accept results he hadn't rederived from scratch, forging links that a passively received result would lack. Breadth is what fuels analogy: the wider the library, the more unexpected the connections it can make. -This is why [Erdős's](https://en.wikipedia.org/wiki/Paul_Erd%C5%91s) [probabilistic method](https://en.wikipedia.org/wiki/Probabilistic_method) was so productive: he imported the machinery of probability theory into combinatorics, using expected-value arguments to prove that certain structures must exist without constructing a single one. The move was not derived within combinatorics. It was transported from another domain, the same structural act Einstein performed. The [Langlands programme](https://en.wikipedia.org/wiki/Langlands_program) and the [Atiyah-Singer index theorem](https://en.wikipedia.org/wiki/Atiyah%E2%80%93Singer_index_theorem) followed the same shape: progress came from recognising that structurally distant objects were the same thing seen from different angles, and the mathematicians who saw the correspondence were the ones with libraries broad enough to hold both sides. + -Since late 2025, mathematicians working with LLMs have been [solving open problems](https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/) from Erdős's celebrated problem lists at a pace that would have been unthinkable a year earlier. Sawhney and Sellke [used GPT-5 to find solutions to ten Erdős problems still listed as open](https://x.com/MarkSellke/status/1979226538059931886). In another collaboration, [roughly 80% of the model's generated arguments were wrong](https://arxiv.org/html/2510.23513v1). The work still produced results, because the mathematician's adaptive judgement could sift the useful patterns from the noise. +What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened; those that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences, while the textbook's library is frozen. This is the living part that can't survive the freezing, the contact with consequences that no filing system preserves. -The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/status/2044307082275618993), a conjecture from 1968. Previous approaches had relied on continuous analysis. Working with GPT-5.4, Liam Price stayed in the arithmetic realm and deployed the [von Mangoldt function](https://en.wikipedia.org/wiki/Von_Mangoldt_function) in a way nobody had tried. [Jared Duker Lichtman](https://en.wikipedia.org/wiki/Jared_Duker_Lichtman), who had spent seven years on primitive set problems, read the proof and [called it a Book proof](https://en.wikipedia.org/wiki/Proofs_from_THE_BOOK): Erdős's term for a proof so compact and elegant it could have been written by God. +## Following the stepping stones -The objection that LLMs are not reasoning mistakes the division of labour. Analogical generation paired with expert filtering is how a great deal of creative work has always been done. The model proposes; the practitioner sieves. Einstein proposed analogies and sieved them himself. Sawhney discarded 80% and kept the rest. The division is different, the faculty is the same. A system whose underlying network has no arithmetic produced a proof that an expert in the field called divine. That is what pattern matching looks like when it operates at sufficient depth, in the hands of someone who knows which patterns matter. +LLMs have inherited enormous pattern libraries from the serialised outputs of human minds. When experts publish, they do two things at once: they cover the tracks (the dead ends, the wrong ideas, the blind alleys Feynman described) and they lay down stepping stones (proofs, derivations, case law and mechanism descriptions) that mark the clearest route to the useful patterns. Covering tracks and laying stepping stones are the same act. The dead ends vanish, and what remains is a refined path. -What the living sieve cannot do is lend its life to the frozen library. The expert's judgement comes from sustained contact with consequences, from being wrong and updating. The static pattern library, however vast, lacks that contact. +LLMs benefit from following what was explicitly laid down as the most direct route. [Timothy Gowers](https://en.wikipedia.org/wiki/Timothy_Gowers) [makes the same observation](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) about mathematics specifically: LLMs are "trained on the product of mathematics rather than the process." They learn to [imitate pulling rabbits out of hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) "without learning how to catch the rabbit or put it in the hat in the first place." -## What the stones carry +The stepping stones are rich with structure, encoded in the causal language, the "because" and "led to" and "if... then" that saturate human writing. LLMs follow the same stepping stones that practitioners follow, and abstract them into compact patterns. The patterns are useful because the stones were laid down by causal reasoners. -The world-models research programme is a serious positive account of what intelligence requires. [LeCun's JEPA](https://openreview.net/pdf?id=BZ5a1r-kVsf) learns predictive representations of the world in latent space. [Friston's active inference](https://en.wikipedia.org/wiki/Free_energy_principle) builds in the expectation that the agent will act on the world and update its model from the consequences. These are complementary approaches to different parts of the problem, and both address something LLMs lack. Whether explicit world models are necessary for these capabilities, or merely one route to them, is an empirical question. The evidence so far: we have got remarkably far without them. That is not proof they are unnecessary. It is reason to treat the necessity claim as a hypothesis rather than a premise. +Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. Several of its architects have staked their reputations on exactly this limitation. LeCun left Meta in November 2025 to [found AMI Labs](https://www.technologyreview.com/2026/01/22/1131661/yann-lecuns-new-venture-ami-labs/) around JEPA, raising over $1B on the explicit thesis that LLMs are a dead end. Sutskever, who built much of the current paradigm, has [framed the field](https://www.dwarkesh.com/p/andrej-karpathy) as moving "from the age of scaling to the age of research" because models "generalise dramatically worse than people". [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) approaches the same problem from a different angle: an agent that acts on the world and updates its beliefs from the consequences. - +These are serious predictions backed by serious capital. World models may well earn their place at the edges where the corpus is thin, in robotics and physical reasoning that demands the kind of contact with consequences a training set cannot supply. -If causation is itself a repertoire of learned patterns, as Hofstadter and the developmental evidence suggest, then causal structure may be something that sufficient pattern matching recovers rather than a categorically different thing requiring categorically different machinery. The stepping stones that experts laid down are saturated with causal language, the "because" and "led to" and "if... then" of human explanation. LLMs follow those stones and abstract them into compact patterns. The patterns carry weight because the stones were written by causal reasoners. +And yet the paradigm keeps exceeding the limits predicted for it. -[Gowers](https://gowers.wordpress.com/2025/09/22/creating-a-database-of-motivated-proofs/) makes the same observation about mathematics: LLMs are "trained on the product of mathematics rather than the process", [imitating the pulling of rabbits from hats](https://www.youtube.com/watch?v=5D3x_Ygv3No) without learning how the rabbit got there. What the product carries, though, is richer than Gowers's framing implies. The formal reconstructions are dense with the causal structure of the minds that wrote them. +When researchers trained a transformer to play Othello from raw move sequences, with no representation of the board, probing studies found that the network had [spontaneously developed an internal model of the board state](https://openreview.net/forum?id=DeG07_TcZvT). The result has been [replicated in chess](https://arxiv.org/abs/2403.15498) and across [seven different LLM architectures](https://openreview.net/forum?id=1OkVexYLct), with [linear spatial representations](https://arxiv.org/pdf/2506.02996) of geographic structure emerging from training on text alone. These are not world models in LeCun's sense, but they are not surface-level pattern matching either. Something in between is surfacing from the training signal. -Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working example of this division of labour. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the static library cannot: judgement about what to read, what questions to ask and what the connections mean. + + +The [Generative EmCom paper](https://arxiv.org/pdf/2501.00226) from January 2025 makes the cleanest version of the argument: an LLM "does not learn a world model from scratch; instead, it learns a statistical approximation of a collective world model that is already implicitly encoded in human language through a society-wide process of embodied, interactive sense-making." A corpus produced by embodied, interactive sense-makers encodes far more causal structure than its critics assume. Gowers sees the same thing from inside mathematics; Karpathy sees it from inside deep learning. Sutskever's own earlier [compression-is-intelligence](https://arxiv.org/pdf/2404.09937) framing is the same thought from a different angle: compress well enough and you recover structure that was never labelled as such. + +Each predicted limit has also been answered by extending the harness rather than replacing the model. Chain-of-thought was the first move. [Agentic Context Engineering](https://arxiv.org/abs/2510.04618) treats context as an evolving playbook that accumulates strategies through generation, reflection and curation, outperforming weight-update approaches on agent benchmarks without touching the model weights. Karpathy's [autoresearch experiment](https://fortune.com/2026/03/17/andrej-karpathy-loop-autonomous-ai-agents-future/) had an LLM agent run 700 training experiments over two days and discover 20 optimisations a human researcher had missed. You can read these two ways: as the model providers conceding that pattern matching alone falls short, or as evidence that pattern matching plus the right scaffolding keeps recovering more of what world-model architectures claim to provide. Both readings may be correct at once. + +Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working instance of the division of labour. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the frozen library cannot, the *metis*: judgement about what to ask and what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." + +The LLM handles everything that can be pattern-matched from a fixed corpus. The human supplies the part that requires sustained contact with consequences. + +Since late 2025, mathematicians working with LLMs have been [solving open problems](https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/) from [Paul Erdős's](https://en.wikipedia.org/wiki/Paul_Erd%C5%91s) celebrated problem lists at a pace that would have been unthinkable a year earlier. Sawhney and Sellke [used GPT-5 to find solutions to ten Erdős problems still listed as open](https://x.com/MarkSellke/status/1979226538059931886). In another collaboration, [roughly 80% of the model's generated arguments were wrong](https://arxiv.org/html/2510.23513v1). The work still produced results, because the mathematician's *metis*, the adaptive judgement that no frozen library can replace, sifted the productive arguments from the noise. + +The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/status/2044307082275618993), a conjecture from 1968. Previous approaches had relied on continuous analysis. Working with GPT-5.4, Liam Price stayed in the arithmetic realm and deployed the [von Mangoldt function](https://en.wikipedia.org/wiki/Von_Mangoldt_function) in a way nobody had tried. [Jared Duker Lichtman](https://en.wikipedia.org/wiki/Jared_Duker_Lichtman), who had spent seven years on primitive set problems, read the proof and [called it a Book proof](https://en.wikipedia.org/wiki/Proofs_from_THE_BOOK): Erdős's term for a proof so compact and elegant it could have been written by God. -Deployed weights are fixed, but the line between fixed and adaptive is blurrier than it first appears. [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) and [reinforcement learning from verifiable rewards](https://arxiv.org/abs/2402.03300) shape reasoning through feedback loops during training; [chain-of-thought](https://en.wikipedia.org/wiki/Chain_of_thought) and in-context learning introduce further adaptation within a single conversation. What is missing is deployment-time learning: the ability to revise the model's own weights from the consequences of its actions. That is the gap between a static library with clever retrieval and one that is alive. +A system whose underlying network has no arithmetic produced a proof that an expert in the field called divine. No one expected pattern matching to reach this far. What is the most urgent use case that the current paradigm cannot address, the one where world models will prove indispensable rather than merely elegant? And is there evidence yet that world-model architectures deliver on the predictions being made for them, or are the early results from AMI Labs and JEPA still running on the same funding confidence that raised $1B before a single benchmark was published? -The world-models programme may close that gap. What it has not been required to do is get us here, to a place where a frozen library in expert hands is producing proofs that mathematicians call divine. +## What the room missed -## The cartoons win +The audience at Pocono saw cartoons where there was physics. Their confidence was the predictable output of two centuries of disciplinary selection. -The audience at Pocono saw cartoons where there was physics. Within months, the cartoons had won. +Institutions select for what can travel. What travels is what gets filed, and what gets filed becomes synonymous with what counts. Within a generation, the *metis* falls out of the institutional record and stops being recognised as work at all. The room at Pocono had inherited a discipline that could not, structurally, see what Feynman was showing them. The tracks had been covered so thoroughly that the audience had forgotten there were tracks. -LLMs fail in ways that look causal-structure-shaped. They struggle with counterfactuals and with transfer to regimes their training data did not cover. These failures are real. We argue they are quantitative, the same brittleness humans show outside their own training distribution, not evidence of a missing faculty. The sociocultural critique, that these systems produce slop and invite cognitive offloading, describes a real mode of use. It says something about deployment, not about capability, and we decline to treat it as a verdict on the technology. +The "just pattern matching" dismissal is produced by the same hierarchy. It is what two centuries of selecting for formalism will reach for when it encounters cognitive work that doesn't fit the format. The world-model camp may turn out to be right at the edges, where deployment-time learning and environmental feedback are what matter. But the wholesale dismissal, pattern matching as the lower faculty and formal reasoning as what counts, is the bias the room at Pocono operated under, applied to a new generation of cartoons. -The retreat of the critique is real and documentable. First LLMs could not reason at all; then they could not plan; then they could not verify; now the boundary has contracted to causal structure. Einstein is not a curiosity in this account. His creative process is the best-documented act of scientific reasoning we have, and it shows the boundary between pattern matching and causal reasoning is misplaced. Pattern matching plus an expert sieve is how the most celebrated reasoning in the history of physics was done. +The cost of operating at scale is that the *metis* gets lost. The cost of a hierarchy that selects against intuition is that we keep failing to recognise it when we encounter it again, in the experts whose tracks we covered and in the systems that followed their stepping stones. -Feynman's cartoons worked because they made the physics tractable to the kind of thinking humans are best at: seeing patterns and reasoning through spatial arrangement rather than grinding through algebra. LLMs do the same thing for knowledge. What neither the room at Pocono nor the current critique has reckoned with is that pattern matching, at sufficient depth, is what reasoning was all along. +Feynman's cartoons won within months. The hierarchy that dismissed them is still selecting. --- -Pairing the frozen library with adaptive human judgement is how we approach AI-assisted engineering at JUXT. If you'd like to think through which patterns carry weight in your domain, [get in touch](https://www.juxt.pro/contact/). +Pairing the frozen library with adaptive judgement is how we approach AI-assisted engineering at JUXT. If you'd like to think through which patterns matter in your domain, [get in touch](https://www.juxt.pro/contact/). From 1b5f2ce058091a22e95c84274f699d7f12d5039c Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Tue, 28 Apr 2026 13:38:54 +0100 Subject: [PATCH 43/46] Plant world-model camp in intro, deepen closing's two-part sociotechnical move --- src/pages/blog/just-pattern-matching.md | 32 +++++++++++++++---------- 1 file changed, 19 insertions(+), 13 deletions(-) diff --git a/src/pages/blog/just-pattern-matching.md b/src/pages/blog/just-pattern-matching.md index f6d9909b7..c4bbfd08f 100644 --- a/src/pages/blog/just-pattern-matching.md +++ b/src/pages/blog/just-pattern-matching.md @@ -19,13 +19,15 @@ Had the attendees been more receptive, they'd have heard that diagrams were a ki Earlier this month, Andrej Karpathy [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections and synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." +Not everyone in the field agrees. Yann LeCun left Meta in November 2025 to [found AMI Labs](https://www.technologyreview.com/2026/01/22/1131661/yann-lecuns-new-venture-ami-labs/) around a different architecture, raising over $1B on the thesis that large language models are a dead end. Sutskever, who built much of the current paradigm at OpenAI, now frames the field as moving "from the age of scaling to the age of research." [Fei-Fei Li's](https://en.wikipedia.org/wiki/Fei-Fei_Li) World Labs is building spatial intelligence from the ground up. In early 2026, [more than $2B has flowed into world-model startups](https://emergent.sh/news/world-models-ai), placed by the architects of the previous generation of systems. The disagreement about whether pattern matching over text can ever be enough is live, well-funded and unresolved. + Pattern matching, analogy, recognition, intuition and metaphor are different names for variations on a single faculty. When a mathematician feels a proof is right or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. ## The elevation of reason The audience at Pocono were pattern matching too. They had a pattern for what 'serious physics' looks like: dense formalism, systematic derivation, the kind of thing Schwinger had spent hours demonstrating the day before. So the room concluded, quickly and confidently, that what they were seeing couldn't be physics. -A formal body of knowledge is the only kind that survives the journey from one mind to another it has never met. Apprenticeship can transmit intuition, but apprenticeship doesn't scale. Once a discipline has more practitioners than any master can mentor, the formal version is what gets carried: taught in classrooms, examined, audited, written into textbooks. The intuitive part doesn't lose by competition but by selection. It can't be filed. +A formal body of knowledge is the only kind that survives the journey from one mind to another it has never met. Apprenticeship can transmit intuition, but apprenticeship doesn't scale. Once a discipline has more practitioners than any master can mentor, the formal version is what gets carried: taught in classrooms, examined, audited and written into textbooks. The intuitive part doesn't lose by competition but by selection. It can't be filed. [James C. Scott](https://en.wikipedia.org/wiki/James_C._Scott) called the practical knowledge that resists this codification *metis*: the living part, the part that can't survive the freezing. Legibility, making knowledge formal enough to govern, is what allows coordination at scale, and there is tragedy in it but no villain. The price of operating beyond face-to-face scale is that whatever can't be made legible falls out of the institutional record. Across generations the formal record becomes synonymous with the discipline, and the *metis* becomes invisible, then forgotten, then suspect. @@ -35,7 +37,7 @@ The same selection runs through professional life. Certification requires someth It is also made, sometimes, by epistemic advance. Lagrange boasted in 1788 that his [*Mécanique analytique*](https://en.wikipedia.org/wiki/M%C3%A9canique_analytique) contained "no diagrams": algebraic method had superseded geometric intuition. Geometry couldn't express higher dimensions or abstract algebraic structures, and the [arithmetisation of analysis](https://en.wikipedia.org/wiki/Arithmetization_of_analysis) in the nineteenth century deliberately set out to abolish geometric intuition from proofs, replacing it with definitions that could be verified without pictures. Formal methods reach where intuition can't. -Formalism becomes the marker of seriousness in domains where the trade-off is quite different, where the intuition is doing most of the work and the formalism is reconstruction after the fact. By the time the room at Pocono saw Feynman's diagrams, the discipline's formal record contained no trace of how the intuitions were reached, and they were responding to its absence the way any expert responds to an unfamiliar pattern: with suspicion. +Formalism becomes the marker of seriousness in domains where the trade-off is quite different, where the intuition is doing most of the work and the formalism is reconstruction after the fact. By the time the room at Pocono saw Feynman's diagrams, the discipline's formal record contained no trace of how the intuitions were reached, and they were responding to its absence the way any expert responds to an unfamiliar pattern: with suspicion. Institutions trained to recognise serious work in one form will keep reaching for it even as the work changes shape. ## Reasoning backwards @@ -57,7 +59,7 @@ Asking them to show their working is asking them to operate at a lower level of ## Pattern matching forwards -The most common dismissal of LLMs is that they are "[stochastic parrots](https://en.wikipedia.org/wiki/Stochastic_parrot)": statistical machines that regurgitate patterns from training data without understanding. The critique has shifted since Bender coined the term in 2021. The stronger versions, from [Kambhampati](https://arxiv.org/abs/2402.01817) on planning, [Mitchell](https://aiguide.substack.com/p/can-large-language-models-reason) on abstraction, and [Marcus](https://en.wikipedia.org/wiki/Gary_Marcus) on world models, identify a more specific boundary: LLMs can recognise and recombine patterns but cannot plan, verify or update themselves. +The most common dismissal of LLMs is that they are "[stochastic parrots](https://en.wikipedia.org/wiki/Stochastic_parrot)": statistical machines that regurgitate patterns from training data without understanding. The critique has shifted since Bender coined the term in 2021. The stronger versions, from [Kambhampati](https://arxiv.org/abs/2402.01817) on planning, [Mitchell](https://aiguide.substack.com/p/can-large-language-models-reason) on abstraction and [Marcus](https://en.wikipedia.org/wiki/Gary_Marcus) on world models, identify a more specific boundary: LLMs can recognise and recombine patterns but cannot plan, verify or update themselves. The core accusation is that pattern matching can recognise what already exists but cannot produce anything new. @@ -83,7 +85,7 @@ Engineers carry their own contradictory proverbs. YAGNI says don't build what yo For each maxim proposing a point of view, another proposes the opposite. A pattern library that contains both can't tell you which to apply. -Knowing when to apply YAGNI rather than building for extensibility is itself a pattern, just at a higher level of abstraction. "This has the feel of a prototype, not a production system" is a meta-pattern: it tells you which lower-level pattern to trust. "This is the kind of system where the first outage costs more than the extra engineering" is another, and it selects do-it-right over worse-is-better. These meta-patterns are learned the same way the maxims were: by getting it wrong both ways and refining the judgement through contact with reality. Patterns all the way up. +Knowing when to apply YAGNI rather than building for extensibility is itself a pattern, just at a higher level of abstraction. "This has the feel of a prototype, not a production system" is a meta-pattern: it tells you which lower-level pattern to trust. "This is the kind of system where the first outage costs more than the extra engineering" is another, and it selects do-it-right over worse-is-better. These meta-patterns are learned the same way the maxims were: by getting it wrong both ways and refining the judgement through contact with reality, the *metis* that no maxim can carry on its own. Patterns all the way up. Software engineers operate the same way. A senior engineer looks at a proposed architecture and [smells](https://martinfowler.com/bliki/CodeSmell.html) whether it will hold, the way a chess master perceives a board. The boxes and arrows on a whiteboard are not precise specifications. They are tokens in a shared pattern-matching conversation, [scaffolding for recognition](https://www.microsoft.com/en-us/research/publication/lets-go-to-the-whiteboard-how-and-why-software-developers-use-drawings/) rather than the recognition itself. @@ -93,7 +95,7 @@ This is the gap between having patterns and having expertise. Feynman didn't jus -What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened; those that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences, while the textbook's library is frozen. This is the living part that can't survive the freezing, the contact with consequences that no filing system preserves. +What separates a useful pattern library from a contradictory heap of proverbs is continuous refinement against reality. Patterns that survive contact with the world get strengthened, while those that fail get pruned or reshaped. The expert's library is alive, constantly being trued up against consequences, while the textbook's library is frozen. This is the living part that can't survive the freezing, the contact with consequences that no filing system preserves. ## Following the stepping stones @@ -103,9 +105,7 @@ LLMs benefit from following what was explicitly laid down as the most direct rou The stepping stones are rich with structure, encoded in the causal language, the "because" and "led to" and "if... then" that saturate human writing. LLMs follow the same stepping stones that practitioners follow, and abstract them into compact patterns. The patterns are useful because the stones were laid down by causal reasoners. -Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. Several of its architects have staked their reputations on exactly this limitation. LeCun left Meta in November 2025 to [found AMI Labs](https://www.technologyreview.com/2026/01/22/1131661/yann-lecuns-new-venture-ami-labs/) around JEPA, raising over $1B on the explicit thesis that LLMs are a dead end. Sutskever, who built much of the current paradigm, has [framed the field](https://www.dwarkesh.com/p/andrej-karpathy) as moving "from the age of scaling to the age of research" because models "generalise dramatically worse than people". [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) approaches the same problem from a different angle: an agent that acts on the world and updates its beliefs from the consequences. - -These are serious predictions backed by serious capital. World models may well earn their place at the edges where the corpus is thin, in robotics and physical reasoning that demands the kind of contact with consequences a training set cannot supply. +Where they struggle is where continuously refined expertise matters, where being wrong about the world and integrating the consequences is how the pattern library stays alive. The architectural alternatives are specific about what's missing. LeCun's JEPA learns predictive representations in latent space rather than predicting the next token, modelling the world directly rather than modelling text about the world. [Friston's](https://en.wikipedia.org/wiki/Karl_Friston) [active inference](https://en.wikipedia.org/wiki/Free_energy_principle) approaches the problem from a different angle: an agent that acts on the world and updates its beliefs from the consequences. Both address a real gap. World models may earn their place where the corpus is thin, in robotics and physical reasoning that demands the kind of feedback a training set cannot supply. And yet the paradigm keeps exceeding the limits predicted for it. @@ -113,13 +113,13 @@ When researchers trained a transformer to play Othello from raw move sequences, -The [Generative EmCom paper](https://arxiv.org/pdf/2501.00226) from January 2025 makes the cleanest version of the argument: an LLM "does not learn a world model from scratch; instead, it learns a statistical approximation of a collective world model that is already implicitly encoded in human language through a society-wide process of embodied, interactive sense-making." A corpus produced by embodied, interactive sense-makers encodes far more causal structure than its critics assume. Gowers sees the same thing from inside mathematics; Karpathy sees it from inside deep learning. Sutskever's own earlier [compression-is-intelligence](https://arxiv.org/pdf/2404.09937) framing is the same thought from a different angle: compress well enough and you recover structure that was never labelled as such. +The [Generative EmCom paper](https://arxiv.org/pdf/2501.00226) from January 2025 makes the cleanest version of the argument: an LLM "does not learn a world model from scratch; instead, it learns a statistical approximation of a collective world model that is already implicitly encoded in human language through a society-wide process of embodied, interactive sense-making." A corpus produced by embodied, interactive sense-makers encodes far more causal structure than its critics assume. Gowers sees the same thing from inside mathematics, Karpathy from inside deep learning. Sutskever's own earlier [compression-is-intelligence](https://arxiv.org/pdf/2404.09937) framing arrives at the same place: compress well enough and you recover structure that was never labelled as such. Each predicted limit has also been answered by extending the harness rather than replacing the model. Chain-of-thought was the first move. [Agentic Context Engineering](https://arxiv.org/abs/2510.04618) treats context as an evolving playbook that accumulates strategies through generation, reflection and curation, outperforming weight-update approaches on agent benchmarks without touching the model weights. Karpathy's [autoresearch experiment](https://fortune.com/2026/03/17/andrej-karpathy-loop-autonomous-ai-agents-future/) had an LLM agent run 700 training experiments over two days and discover 20 optimisations a human researcher had missed. You can read these two ways: as the model providers conceding that pattern matching alone falls short, or as evidence that pattern matching plus the right scaffolding keeps recovering more of what world-model architectures claim to provide. Both readings may be correct at once. Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) is a working instance of the division of labour. The AI agent handles the bookkeeping: ingesting sources, maintaining cross-references, synthesising connections across hundreds of articles. The human provides what the frozen library cannot, the *metis*: judgement about what to ask and what the connections mean. "The tedious part of maintaining a knowledge base is not the reading or the thinking," Karpathy writes. "It's the bookkeeping." -The LLM handles everything that can be pattern-matched from a fixed corpus. The human supplies the part that requires sustained contact with consequences. +The LLM handles everything that can be pattern-matched from a fixed corpus, and the human supplies the part that requires sustained contact with consequences. Since late 2025, mathematicians working with LLMs have been [solving open problems](https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/) from [Paul Erdős's](https://en.wikipedia.org/wiki/Paul_Erd%C5%91s) celebrated problem lists at a pace that would have been unthinkable a year earlier. Sawhney and Sellke [used GPT-5 to find solutions to ten Erdős problems still listed as open](https://x.com/MarkSellke/status/1979226538059931886). In another collaboration, [roughly 80% of the model's generated arguments were wrong](https://arxiv.org/html/2510.23513v1). The work still produced results, because the mathematician's *metis*, the adaptive judgement that no frozen library can replace, sifted the productive arguments from the noise. @@ -133,11 +133,17 @@ The audience at Pocono saw cartoons where there was physics. Their confidence wa Institutions select for what can travel. What travels is what gets filed, and what gets filed becomes synonymous with what counts. Within a generation, the *metis* falls out of the institutional record and stops being recognised as work at all. The room at Pocono had inherited a discipline that could not, structurally, see what Feynman was showing them. The tracks had been covered so thoroughly that the audience had forgotten there were tracks. -The "just pattern matching" dismissal is produced by the same hierarchy. It is what two centuries of selecting for formalism will reach for when it encounters cognitive work that doesn't fit the format. The world-model camp may turn out to be right at the edges, where deployment-time learning and environmental feedback are what matter. But the wholesale dismissal, pattern matching as the lower faculty and formal reasoning as what counts, is the bias the room at Pocono operated under, applied to a new generation of cartoons. +The "just pattern matching" dismissal is produced by the same hierarchy. It is what two centuries of selecting for formalism will reach for when it encounters cognitive work that doesn't fit the format. + + + +And the architectural alternative the discipline is reaching for, with billions of dollars behind it, comes from the same place. The dismissal and the proposed correction are not independent assessments of the evidence but the same bias expressed in both directions: suspicion toward what doesn't fit the format, and confidence in whatever does. The room at Pocono didn't just fail to see Feynman's physics. It would have funded Schwinger. + +The world-model camp may turn out to be right at the edges, where the corpus is thin and environmental feedback is what matters. Nobody knows. But the confidence with which the alternative is held, and the speed with which capital has followed, are partly explained by a bias the discipline has carried for two centuries. Formalism looks like serious cognitive work, and pattern matching doesn't. That assessment feels like technical judgement, but it is also inheritance. -The cost of operating at scale is that the *metis* gets lost. The cost of a hierarchy that selects against intuition is that we keep failing to recognise it when we encounter it again, in the experts whose tracks we covered and in the systems that followed their stepping stones. +The cost of operating at scale is that the *metis* gets lost. The cost of a hierarchy that selects against intuition is that we keep failing to recognise it when we encounter it again, in the experts whose tracks we covered and in the systems that follow their stepping stones, while reaching for whatever alternative the discipline was trained to call serious. -Feynman's cartoons won within months. The hierarchy that dismissed them is still selecting. +The room at Pocono is still in session, and the *metis* is still what it can't see. --- From f7acdbc9442d9c64235c720ea2d9efa24e1fd039 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Tue, 28 Apr 2026 13:47:01 +0100 Subject: [PATCH 44/46] Fix possessive inside link text --- src/pages/blog/just-pattern-matching.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/blog/just-pattern-matching.md b/src/pages/blog/just-pattern-matching.md index c4bbfd08f..48b497dca 100644 --- a/src/pages/blog/just-pattern-matching.md +++ b/src/pages/blog/just-pattern-matching.md @@ -19,7 +19,7 @@ Had the attendees been more receptive, they'd have heard that diagrams were a ki Earlier this month, Andrej Karpathy [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections and synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." -Not everyone in the field agrees. Yann LeCun left Meta in November 2025 to [found AMI Labs](https://www.technologyreview.com/2026/01/22/1131661/yann-lecuns-new-venture-ami-labs/) around a different architecture, raising over $1B on the thesis that large language models are a dead end. Sutskever, who built much of the current paradigm at OpenAI, now frames the field as moving "from the age of scaling to the age of research." [Fei-Fei Li's](https://en.wikipedia.org/wiki/Fei-Fei_Li) World Labs is building spatial intelligence from the ground up. In early 2026, [more than $2B has flowed into world-model startups](https://emergent.sh/news/world-models-ai), placed by the architects of the previous generation of systems. The disagreement about whether pattern matching over text can ever be enough is live, well-funded and unresolved. +Not everyone in the field agrees. Yann LeCun left Meta in November 2025 to [found AMI Labs](https://www.technologyreview.com/2026/01/22/1131661/yann-lecuns-new-venture-ami-labs/) around a different architecture, raising over $1B on the thesis that large language models are a dead end. Sutskever, who built much of the current paradigm at OpenAI, now frames the field as moving "from the age of scaling to the age of research." [Fei-Fei Li](https://en.wikipedia.org/wiki/Fei-Fei_Li)'s World Labs is building spatial intelligence from the ground up. In early 2026, [more than $2B has flowed into world-model startups](https://emergent.sh/news/world-models-ai), placed by the architects of the previous generation of systems. The disagreement about whether pattern matching over text can ever be enough is live, well-funded and unresolved. Pattern matching, analogy, recognition, intuition and metaphor are different names for variations on a single faculty. When a mathematician feels a proof is right or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. From adc499144f4b5ad6d2cfe114e0fd7a405bba5e29 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Tue, 28 Apr 2026 13:55:40 +0100 Subject: [PATCH 45/46] Escape dollar signs to prevent LaTeX math mode rendering --- src/pages/blog/just-pattern-matching.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/pages/blog/just-pattern-matching.md b/src/pages/blog/just-pattern-matching.md index 48b497dca..8df10aab5 100644 --- a/src/pages/blog/just-pattern-matching.md +++ b/src/pages/blog/just-pattern-matching.md @@ -19,7 +19,7 @@ Had the attendees been more receptive, they'd have heard that diagrams were a ki Earlier this month, Andrej Karpathy [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections and synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought." -Not everyone in the field agrees. Yann LeCun left Meta in November 2025 to [found AMI Labs](https://www.technologyreview.com/2026/01/22/1131661/yann-lecuns-new-venture-ami-labs/) around a different architecture, raising over $1B on the thesis that large language models are a dead end. Sutskever, who built much of the current paradigm at OpenAI, now frames the field as moving "from the age of scaling to the age of research." [Fei-Fei Li](https://en.wikipedia.org/wiki/Fei-Fei_Li)'s World Labs is building spatial intelligence from the ground up. In early 2026, [more than $2B has flowed into world-model startups](https://emergent.sh/news/world-models-ai), placed by the architects of the previous generation of systems. The disagreement about whether pattern matching over text can ever be enough is live, well-funded and unresolved. +Not everyone in the field agrees. Yann LeCun left Meta in November 2025 to [found AMI Labs](https://www.technologyreview.com/2026/01/22/1131661/yann-lecuns-new-venture-ami-labs/) around a different architecture, raising over \$1B on the thesis that large language models are a dead end. Sutskever, who built much of the current paradigm at OpenAI, now frames the field as moving "from the age of scaling to the age of research." [Fei-Fei Li](https://en.wikipedia.org/wiki/Fei-Fei_Li)'s World Labs is building spatial intelligence from the ground up. In early 2026, [more than \$2B has flowed into world-model startups](https://emergent.sh/news/world-models-ai), placed by the architects of the previous generation of systems. The disagreement about whether pattern matching over text can ever be enough is live, well-funded and unresolved. Pattern matching, analogy, recognition, intuition and metaphor are different names for variations on a single faculty. When a mathematician feels a proof is right or an LLM finds structure in text, the underlying act is the same: recognising that this situation resembles that one, and acting on the resemblance. @@ -125,7 +125,7 @@ Since late 2025, mathematicians working with LLMs have been [solving open proble The most striking result was [Erdős Problem #1196](https://x.com/jdlichtman/status/2044307082275618993), a conjecture from 1968. Previous approaches had relied on continuous analysis. Working with GPT-5.4, Liam Price stayed in the arithmetic realm and deployed the [von Mangoldt function](https://en.wikipedia.org/wiki/Von_Mangoldt_function) in a way nobody had tried. [Jared Duker Lichtman](https://en.wikipedia.org/wiki/Jared_Duker_Lichtman), who had spent seven years on primitive set problems, read the proof and [called it a Book proof](https://en.wikipedia.org/wiki/Proofs_from_THE_BOOK): Erdős's term for a proof so compact and elegant it could have been written by God. -A system whose underlying network has no arithmetic produced a proof that an expert in the field called divine. No one expected pattern matching to reach this far. What is the most urgent use case that the current paradigm cannot address, the one where world models will prove indispensable rather than merely elegant? And is there evidence yet that world-model architectures deliver on the predictions being made for them, or are the early results from AMI Labs and JEPA still running on the same funding confidence that raised $1B before a single benchmark was published? +A system whose underlying network has no arithmetic produced a proof that an expert in the field called divine. No one expected pattern matching to reach this far. What is the most urgent use case that the current paradigm cannot address, the one where world models will prove indispensable rather than merely elegant? And is there evidence yet that world-model architectures deliver on the predictions being made for them, or are the early results from AMI Labs and JEPA still running on the same funding confidence that raised \$1B before a single benchmark was published? ## What the room missed From 9e24e0b39c5eb7322e395ee3dfe5674b090dad77 Mon Sep 17 00:00:00 2001 From: Henry Garner Date: Tue, 28 Apr 2026 14:12:05 +0100 Subject: [PATCH 46/46] Reframe diagrams as reasoning tool, not notation --- src/pages/blog/just-pattern-matching.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pages/blog/just-pattern-matching.md b/src/pages/blog/just-pattern-matching.md index 8df10aab5..3ccb81e32 100644 --- a/src/pages/blog/just-pattern-matching.md +++ b/src/pages/blog/just-pattern-matching.md @@ -15,7 +15,7 @@ tags: [Teller](https://en.wikipedia.org/wiki/Edward_Teller) interrupted, objecting that the approach violated the exclusion principle. Dirac kept asking "Is it unitary?" Bohr strode to the stage and lectured Feynman on the uncertainty principle, having mistaken the diagrams for literal pictures of particle paths. The presentation was an absolute disaster. -Had the attendees been more receptive, they'd have heard that diagrams were a kind of notation: straight lines for particles, wavy lines for forces and vertices for interactions. Each element mapped to a term in the mathematics, and the spatial arrangement of the picture enabled scientists to reason through relationships instead of grinding through pages of algebra. Within six months, Freeman Dyson had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. Feynman's diagrams made a complex formalism legible through visual metaphor. The physics didn't get simpler, but it became tractable to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. +Had the attendees been more receptive, they'd have heard that the diagrams were a tool for reasoning through quantum interactions: straight lines for particles, wavy lines for forces and vertices for interactions. Each element mapped to a term in the mathematics, and the spatial arrangement of the picture let scientists see relationships instead of grinding through pages of algebra. Within six months, Freeman Dyson had [proved the approaches equivalent](https://en.wikipedia.org/wiki/Feynman_diagram), and the cartoons were doing in hours what formal methods took months to achieve. Feynman's diagrams made a complex formalism legible through visual metaphor. The physics didn't get simpler, but it became tractable to the kind of thinking humans are good at: seeing patterns and reasoning by analogy. Earlier this month, Andrej Karpathy [shared an architecture](https://x.com/karpathy/status/2039805659525644595) for building personal knowledge bases with LLMs that does the same thing for knowledge. An LLM ingests sources, finds connections and synthesises, while the human reasons through the results. In a [conversation with Dwarkesh Patel](https://www.dwarkesh.com/p/andrej-karpathy), Karpathy called it stripping the model down to the "algorithms for thought."

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