Brad Carson was the Army's General Counsel, served two terms in Congress and was Acting Under Secretary of Defense for Personnel and Readiness. He now heads Americans for Responsible Innovation, the AI-policy advocacy group he co-founded. Keith Duggar spends roughly eighty minutes pushing back. SPONSOR: --- Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open. Apply now: https://cyber.fund --- Carson's whole case rests on one line: the genie is not out of the bottle. We have pulled dangerous tech back before. Asilomar halted recombinant DNA in 1975, and the West still controls the chips AI runs on. Calling it unstoppable, he says, is the most dangerous idea in the room. Then Keith drags him somewhere darker. A Palantir heat map scores you 0.73 on whether you are a combatant, and a strike follows. The model is wrong some accepted share of the time, and when it is, nobody answers for it. You cannot court-martial a model, and not even the interpretability researchers can say why it picked you. — Note: after recording, we learned that Americans for Responsible Innovation is backed by EA-aligned philanthropy (not sponsored) --- TIMESTAMPS: 00:00:00 From the Pentagon to AI governance 00:04:52 Regulatory capture vs Silicon Valley networks 00:07:56 Transparency and the Claude tier changes 00:09:40 Tort liability when AI tools cause harm 00:13:40 AI is a product, not a person 00:16:01 Children, suicide, and the suicide business 00:19:59 Opaque neural nets and the law of war 00:25:54 Probabilistic targeting and the death of accountability 00:28:47 The arms race fallacy: Asilomar and restraint 00:34:02 Talking to China: track 2 talks and chip leverage 00:39:45 Air power never wins: capital for labour 00:43:29 Anthropic vs the Department of War 00:51:29 Concentration, open source, and brain drain 01:00:18 DeepSeek, Chinese culture, and AI as diplomacy 01:12:25 Upskilling Congress and why public trust matters --- REFERENCES: organization: [00:02:45] ICRC position on autonomous weapons https://www.icrc.org/en/law-and-policy/autonomous-weapons [00:05:22] Americans for Responsible Innovation (ARI) https://ari.us [00:07:20] Andreessen Horowitz (a16z) https://a16z.com/ [00:43:29] Anthropic https://www.anthropic.com/ [01:00:18] DeepSeek https://www.deepseek.com [01:03:05] Moonshot AI (Kimi) https://www.moonshot.cn [01:16:05] Office of Technology Assessment https://en.wikipedia.org/wiki/Office_of_Technology_Assessment other: [00:03:35] Beneficial AGI 2019 Conference (Future of Life Institute, Puerto Rico) https://futureoflife.org/event/beneficial-agi-2019/ [00:18:30] Section 230 of the Communications Decency Act https://en.wikipedia.org/wiki/Section_230 [00:19:59] Lethal Autonomous Weapons (LAWS) https://en.wikipedia.org/wiki/Lethal_autonomous_weapon [00:31:35] Strategic Arms Limitation Talks (SALT) https://en.wikipedia.org/wiki/Strategic_Arms_Limitation_Talks [00:32:28] Asilomar Conference on Recombinant DNA (1975) https://en.wikipedia.org/wiki/Asilomar_Conference_on_Recombinant_DNA [00:39:45] The New Iron Triangle (ARI policy byte) https://ari.us/policy-bytes/the-new-iron-triangle/ [00:48:05] Defense Production Act https://en.wikipedia.org/wiki/Defense_Production_Act person: [00:03:35] Anthony Aguirre https://en.wikipedia.org/wiki/Anthony_Aguirre [00:06:48] Dean Ball — Hyperdimensional https://www.hyperdimensional.co/ [00:23:13] Neel Nanda — mechanistic interpretability https://www.neelnanda.io/ [00:36:02] Jack Clark (Anthropic) on Conversations with Tyler https://conversationswithtyler.com/episodes/jack-clark/ [00:36:45] Dean Acheson https://en.wikipedia.org/wiki/Dean_Acheson [00:37:05] Paul Nitze https://en.wikipedia.org/wiki/Paul_Nitze [00:39:15] Robert Trager — Centre for the Governance of AI https://www.governance.ai/team/robert-trager [00:41:55] Giulio Douhet https://en.wikipedia.org/wiki/Giulio_Douhet [01:15:05] Don Beyer (US Congress) https://en.wikipedia.org/wiki/Don_Beyer tool: [00:22:19] Phalanx CIWS https://en.wikipedia.org/wiki/Phalanx_CIWS [00:24:50] Palantir Foundry https://www.palantir.com/ [01:07:17] Qwen (Alibaba) https://qwenlm.github.io --- ReScript: https://app.rescript.info/public/share/9405ff35c0215b7cdae6402d41284171 https://app.rescript.info/api/public/sessions/0a6c081b8e5fe413/pdf

Michael I. Jordan, described by Science magazine as the most influential computer scientist alive, has never thought of himself as an AI researcher. In this conversation he explains why that distinction matters. SPONSOR: --- Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open. Apply now: https://cyber.fund --- Jordan trained as a statistician and cognitive scientist, and his career has been spent building machine learning systems that work in the real world: supply chains, commerce, healthcare, and large economic systems. When the field rebranded itself as AI and then AGI, he did not follow. Instead he argues that the framing is wrong. AI is better understood as a collective economic system than as a race to build a disembodied superintelligence. We talk about why AGI is mostly a PR term, what machine learning achieved before the LLM hype cycle, and why the assistant-on-your-shoulder vision may be less compelling than it sounds. Jordan explains why explanations need to be actionable, not merely mechanistic; why AlphaFold's missing error bars matter; how prediction-powered inference changes the picture; and why drug discovery is an incentive-design problem rather than a pure pattern-matching problem. ERRATA: Science magazine ranked him the most influential computer scientist, not Nature --- TIMESTAMPS: 00:00:00 Cold open: A demoralizing message to young builders 00:02:04 CyberFund sponsor read 00:02:50 From symbolic AI to machine learning systems 00:05:42 Why AGI is mostly a PR term 00:08:48 A collectivist, economic perspective on AI 00:11:33 Why LLMs need system design, not hype 00:14:50 Predictability beats faux understanding 00:17:55 AlphaFold, bias, and prediction-powered inference 00:21:48 Stop anthropomorphizing intelligence 00:27:44 Drug discovery as an incentive problem 00:32:29 The three-layer data market 00:38:07 Social knowledge, markets, and culture 00:45:39 Creator economics beyond Spotify 00:48:30 How science-fiction AI narratives mislead young builders 00:51:45 AI should improve humans, not replace them 00:56:42 Safety is a property of the whole system 00:58:12 Silicon Valley gurus and the cream off the top 01:00:47 Game theory, mechanism design, and contracts 01:04:39 Conformal prediction, e-values, and anytime inference 01:08:11 A new liberal arts triangle for the AI era 01:11:30 The Bayesian duck and markets as uncertainty reduction --- REFERENCES: person: [00:02:50] Michael I. Jordan (homepage) https://people.eecs.berkeley.edu/~jordan/ paper: [00:06:01] A Collectivist, Economic Perspective on AI https://arxiv.org/abs/2507.06268 [00:18:09] AlphaFold https://www.nature.com/articles/s41586-021-03819-2 [00:20:36] Prediction-Powered Inference https://arxiv.org/abs/2301.09633 [00:24:38] On the Measure of Intelligence https://arxiv.org/abs/1911.01547 [00:33:47] On Three-Layer Data Markets https://arxiv.org/abs/2402.09697 [01:04:39] Conformal Prediction with Conditional Guarantees https://arxiv.org/abs/2107.07511 [01:04:51] A Tutorial on Conformal Prediction https://www.jmlr.org/papers/v9/shafer08a.html [01:06:00] E-Values Expand the Scope of Conformal Prediction https://arxiv.org/abs/2503.13050 [01:08:23] Computational Thinking https://www.cs.cmu.edu/~CompThink/papers/Wing06.pdf other: [00:11:33] The Bitter Lesson http://www.incompleteideas.net/IncIdeas/BitterLesson.html [00:20:50] How to use AI for discovery without leading science astray https://news.berkeley.edu/2023/11/09/how-to-use-ai-for-discovery-without-leading-science-astray/ [00:28:20] How Should the FDA Test? https://rdi.berkeley.edu/events/sbc-assets/pdfs/Summit%20session%20speaker%20slides%20submission%20form-s1-5%20%28File%20responses%29/Slides%20in%20PDF%20%28Please%20name%20the%20submitted%20file%20as%20_firstname_-_lastname_-slides.pdf%29.%20%28File%20responses%29/27-Michael%20Jordan-Session%20V.pdf#page=15 [00:28:40] Michael I. Jordan Session V Slides https://rdi.berkeley.edu/events/sbc-assets/pdfs/Summit%20session%20speaker%20slides%20submission%20form-s1-5%20%28File%20responses%29/Slides%20in%20PDF%20%28Please%20name%20the%20submitted%20file%20as%20_firstname_-_lastname_-slides.pdf%29.%20%28File%20responses%29/27-Michael%20Jordan-Session%20V.pdf#page=1 [01:08:13] Three Foundational Disciplines https://rdi.berkeley.edu/events/sbc-assets/pdfs/Summit%20session%20speaker%20slides%20submission%20form-s1-5%20%28File%20responses%29/Slides%20in%20PDF%20%28Please%20name%20the%20submitted%20file%20as%20_firstname_-_lastname_-slides.pdf%29.%20%28File%20responses%29/27-Michael%20Jordan-Session%20V.pdf#page=35 organization: [00:45:58] UnitedMasters https://www.unitedmasters.com/ book: [00:48:30] Human Compatible: Artificial Intelligence and the Problem of Control https://aima.cs.berkeley.edu/~russell/hc.html [01:00:56] Theory of Games and Economic Behavior https://press.princeton.edu/books/paperback/9780691130613/theory-of-games-and-economic-behavior

Beth Barnes and David Rein on the one graph that ate the AI timelines discourse, and why the two people who built it are the most careful about how you read it. **SPONSOR** Prolific - Quality data. From real people. For faster breakthroughs. https://www.prolific.com/?utm_source=mlst Interview: https://youtu.be/cnxZZTl1tkk --- Beth Barnes and David Rein from METR on the one graph that ate the AI timelines discourse, and why the people who built it are the most careful about how it gets read. Beth founded METR after leaving OpenAI alignment. David is first author on GPQA and co-author on HCAST and the METR Time Horizons paper. Together they built the measurement Daniel Kokotajlo called the single most important piece of evidence on AI timelines: the log-linear line of "how long a task a frontier model can complete at 50% reliability" vs release date. The conversation opens on reward hacking. Current models can articulate in chat why a behaviour is undesired and then execute it anyway as agents. From there: construct validity, Melanie Mitchell's four-problem taxonomy, and the ARC-AGI 1-to-2 collapse as a worked example of adversarially-selected benchmarks regressing once labs target them. Beth's counter: METR deliberately does not adversarially select. David's: models do not have to do the right thing for the right reasons. Methodology, then specification — David's compiler analogy, Beth on four-month tasks as expensive to evaluate rather than unspecifiable. Then the SWE-bench reality check, the METR finding that half of passing PRs would not be merged, and Beth's horses-versus-bank-tellers analogy for the labour market. The close: monitorability, the coin-spinning boat, two-year recursive self-improvement, and Beth's line that "overhyped now" and "big deal later" are not correlated claims. --- TIMESTAMPS: 00:00:00 Intro 00:02:06 Sponsor break: Prolific human-feedback infrastructure 00:02:33 Welcome and the scalable oversight motivation 00:06:02 Construct validity, benchmark pathologies and the Chollet worry 00:15:45 Time Horizons: human time, HCAST tasks and the 50% logistic 00:24:50 Is human difficulty really one variable? 00:33:05 Agent harness evolution and the inference-compute dividend 00:40:00 Scaffolding bells, token budgets and the credit-assignment problem 00:44:15 Look at the damn graph: regularisation bug and reliability nuance 00:50:00 Why 50%? Reliability, reward hacking and pizza-party transcripts 00:55:20 Extrapolation risk and straight lines on graphs 00:59:25 Software engineering as a specification acquisition problem 01:07:40 Compilers also made ugly code: vibe-coding quality and Claude on METR Slack 01:15:15 Strongest defensible claim, Carlini's compiler swarm and AI 2027 01:23:45 SWE-bench merge rates, the bank-teller analogy and horses 01:31:45 Scheming, alignment faking and the mentalistic vocabulary problem 01:40:45 Reward hacking, monitorability and chain-of-thought faithfulness 01:45:25 Recursive self-improvement, knowledge vs intelligence and closing See top comment for references!

Robert Lange, founding researcher at Sakana AI, joins Tim to discuss *Shinka Evolve* — a framework that combines LLMs with evolutionary algorithms to do open-ended program search. The core claim: systems like AlphaEvolve can optimize solutions to fixed problems, but real scientific progress requires co-evolving the problems themselves. GTC is coming, the premier AI conference, great opportunity to learn about AI. NVIDIA and partners will showcase breakthroughs in physical AI, AI factories, agentic AI, and inference, exploring the next wave of AI innovation for developers and researchers. Register for virtual GTC for free, using my link and win NVIDIA DGX Spark (https://nvda.ws/4qQ0LMg) In this episode: • Why AlphaEvolve gets stuck — it needs a human to hand it the right problem. Shinka tries to invent new problems automatically, drawing on ideas from POET, PowerPlay, and MAP-Elites quality-diversity search. • The *architecture* of Shinka: an archive of programs organized as islands, LLMs used as mutation operators, and a UCB bandit that adaptively selects between frontier models (GPT-5, Sonnet 4.5, Gemini) mid-run. The credit-assignment problem across models turns out to be genuinely hard. • Concrete results — state-of-the-art circle packing with dramatically fewer evaluations, second place in an AtCoder competitive programming challenge, evolved load-balancing loss functions for mixture-of-experts models, and agent scaffolds for AIME math benchmarks. • Are these systems actually thinking outside the box, or are they parasitic on their starting conditions? When LLMs run autonomously, "nothing interesting happens." Robert pushes back with the stepping-stone argument — evolution doesn't need to extrapolate, just recombine usefully. • The AI Scientist question: can automated research pipelines produce real science, or just workshop-level slop that passes surface-level review? Robert is honest that the current version is more co-pilot than autonomous researcher. • Where this lands in 5-20 years — Robert's prediction that scientific research will be fundamentally transformed, and Tim's thought experiment about alien mathematical artifacts that no human could have conceived. Robert Lange: https://roberttlange.com/ --- TIMESTAMPS: 00:00:00 Introduction: Robert Lange, Sakana AI and Shinka Evolve 00:04:15 AlphaEvolve's Blind Spot: Co-Evolving Problems with Solutions 00:09:05 Unknown Unknowns, POET, and Auto-Curricula for AI Science 00:14:20 MAP-Elites and Quality-Diversity: Shinka's Evolutionary Architecture 00:28:00 UCB Bandits, Mutations and the Vibe Research Vision 00:40:00 Scaling Shinka: Meta-Evolution, Democratisation and the Three-Axis Model 00:47:10 Applications, ARC-AGI and the Future of Work 00:57:00 The AI Scientist and the Human Co-Pilot: Who Steers the Search? 01:06:00 AI Scientist v2, Slop Critique and the Future of Scientific Publishing --- REFERENCES: paper: [00:03:30] ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution https://arxiv.org/abs/2509.19349 [00:04:15] AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery https://arxiv.org/abs/2506.13131 [00:06:30] Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents https://arxiv.org/abs/2505.22954 [00:09:05] Paired Open-Ended Trailblazer (POET) https://arxiv.org/abs/1901.01753 [00:10:00] PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem https://arxiv.org/abs/1112.5309 [00:10:40] Automated Capability Discovery via Foundation Model Self-Exploration https://arxiv.org/abs/2502.07577 [00:15:30] Illuminating Search Spaces by Mapping Elites (MAP-Elites) https://arxiv.org/abs/1504.04909 [00:47:10] Automated Design of Agentic Systems (ADAS) https://arxiv.org/abs/2408.08435 [00:49:50] Discovering Preference Optimization Algorithms with and for Large Language Models (DiscoPOP) https://arxiv.org/abs/2406.08414 [00:57:00] The AI Scientist v2: Automating the Full Research Pipeline https://arxiv.org/abs/2504.08066 book: [00:06:48] Why Greatness Cannot Be Planned https://link.springer.com/book/10.1007/978-3-319-15524-1 benchmark: [00:47:10] ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering https://arxiv.org/abs/2506.09050 [00:50:50] On the Measure of Intelligence (ARC-AGI) https://arxiv.org/abs/1911.01547 --- LINKS: Download PDF transcript: https://app.rescript.info/api/sessions/b8a9dcf60623657c/pdf/download Full Transcript: https://app.rescript.info/public/share/SDOD_3oXOcli3zTqcAtR8eibT5U3gam84oo4KRtI-Vk

Dive into the realities of AI-assisted coding, the origins of modern fine-tuning, and the cognitive science behind machine learning with fast.ai founder Jeremy Howard. In this episode, we unpack why AI might be turning software engineering into a slot machine and how to maintain true technical intuition in the age of large language models. GTC is coming, the premier AI conference, great opportunity to learn about AI. NVIDIA and partners will showcase breakthroughs in physical AI, AI factories, agentic AI, and inference, exploring the next wave of AI innovation for developers and researchers. Register for virtual GTC for free, using my link and win NVIDIA DGX Spark (https://nvda.ws/4qQ0LMg) Jeremy Howard is a renowned data scientist, researcher, entrepreneur, and educator. As the co-founder of fast.ai, former President of Kaggle, and the creator of ULMFiT, Jeremy has spent decades democratizing deep learning. His pioneering work laid the foundation for modern transfer learning and the pre-training and fine-tuning paradigm that powers today's language models. Key Topics and Main Insights Discussed: - The Origins of ULMFiT and Fine-Tuning - The Vibe Coding Illusion and Software Engineering - Cognitive Science, Friction, and Learning - The Future of Developers RESCRIPT: https://app.rescript.info/public/share/BhX5zP3b0m63srLOQDKBTFTooSzEMh_ARwmDG_h_izk https://app.rescript.info/api/public/sessions/62d06c0336c567d6/pdf Jeremy Howard: https://x.com/jeremyphoward https://www.answer.ai/ --- TIMESTAMPS (fixed): 00:00:00 Introduction & GTC Sponsor 00:04:30 ULMFiT & The Birth of Fine-Tuning 00:12:00 Intuition & The Mechanics of Learning 00:18:30 Abstraction Hierarchies & AI Creativity 00:23:00 Claude Code & The Interpolation Illusion 00:27:30 Coding vs. Software Engineering 00:30:00 Cosplaying Intelligence: Dennett vs. Searle 00:36:30 Automation, Radiology & Desirable Difficulty 00:42:30 Organizational Knowledge & The Slope 00:48:00 Vibe Coding as a Slot Machine 00:54:00 The Erosion of Control in Software 01:01:00 Interactive Programming & REPL Environments 01:05:00 The Notebook Debate & Exploratory Science 01:17:30 AI Existential Risk & Power Centralization 01:24:20 Current Risks, Privacy & Enfeeblement --- REFERENCES: Blog Post: [00:03:00] fast.ai Blog: Self-Supervised Learning https://www.fast.ai/posts/2020-01-13-self_supervised.html [00:13:30] DeepMind Blog: Gemini Deep Think https://deepmind.google/blog/accelerating-mathematical-and-scientific-discovery-with-gemini-deep-think/ [00:19:30] Modular Blog: Claude C Compiler analysis https://www.modular.com/blog/the-claude-c-compiler-what-it-reveals-about-the-future-of-software [00:19:45] Anthropic Engineering Blog: Building C Compiler https://www.anthropic.com/engineering/building-c-compiler [00:48:00] Cursor Blog: Scaling Agents https://cursor.com/blog/scaling-agents [01:05:15] fast.ai Blog: NB Dev Merged Driver https://www.fast.ai/posts/2022-08-25-jupyter-git.html [01:17:30] Jeremy Howard: Response to AI Risk Letter https://www.normaltech.ai/p/is-avoiding-extinction-from-ai-really Book: [00:08:30] M. Chirimuuta: The Brain Abstracted https://mitpress.mit.edu/9780262548045/the-brain-abstracted/ [00:30:00] Daniel Dennett: Consciousness Explained https://www.amazon.com/Consciousness-Explained-Daniel-C-Dennett/dp/0316180661 [00:42:30] Cesar Hidalgo: Infinite Alphabet / Laws of Knowledge https://www.amazon.com/Infinite-Alphabet-Laws-Knowledge/dp/0241655676 Archive Article: [00:13:45] MLST Archive: Why Creativity Cannot Be Interpolated https://archive.mlst.ai/read/why-creativity-cannot-be-interpolated Research Study: [00:24:30] METR Study: AI OS Development https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/ Paper: [00:24:45] Fred Brooks: No Silver Bullet https://www.cs.unc.edu/techreports/86-020.pdf [00:30:15] John Searle: Minds, Brains, and Programs https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/minds-brains-and-programs/DC644B47A4299C637C89772FACC2706A Research Paper: [00:13:50] Mathilde Caron et al.: Emerging Properties in Self-Supervised Vision Transformers (DINO) https://arxiv.org/abs/2104.14294 [00:25:00] Oxford VGG: Sculptor Identification Paper https://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12a/arandjelovic12a.pdf [00:36:30] Anthropic Paper: AI Skill Formation https://arxiv.org/pdf/2601.20245 Historical Reference: [00:36:45] Ebbinghaus: Memory / Spaced Repetition https://www.loc.gov/item/e11000616/ Technical Note: [00:42:45] John Ousterhout: Slope vs Intercept https://gist.github.com/gtallen1187/e83ed02eac6cc8d7e185 Video: [00:59:00] Bret Victor: Inventing on Principle https://vimeo.com/906418692 [01:05:00] Joel Grus: I Don't Like Notebooks https://www.youtube.com/watch?v=7jiPeIFXb6U

Blaise Agüera y Arcas presenting at ALife 2025 — the most technically detailed public walkthrough of the ideas in his *What is Life?* and *What is Intelligence?* books that we've come across. He covers the BFF experiments (self-replicating programs emerging spontaneously from random noise), the mathematical framework connecting Lotka-Volterra population dynamics with Smoluchowski coagulation, eigenvalue analysis of cooperation matrices, and his central claim that symbiogenesis — not mutation — is the primary engine of evolutionary novelty. The experimental results are genuinely striking: complex self-replicating code arising from random byte strings with zero mutation, a sharp phase transition that looks like gelation, and a proof that blocking deep symbiogenetic ancestry trees prevents the transition entirely. A few things worth flagging for critical viewers: — The substrate is more carefully engineered than the framing sometimes suggests. The choice of language, tape length, interaction protocol, and step limits all shape what emerges. Their own SUBLEQ counterexample (where self-replicators *don't* arise despite being theoretically possible) highlights that these design choices matter substantially — and a general theory of which substrates support this transition is still missing. — The leap from "self-replicating programs on fixed-length tapes" to "life was computational and intelligent from the start" involves significant philosophical extrapolation beyond what the experiments directly demonstrate. — The Bedau et al. (2000) open problems paper he references at the start actually sets a higher bar for Challenge 3.2 than BFF currently meets: it asks that "the internal organization of these 'organisms' and the boundaries separating them from their environment arise and be sustained through the activities of lower-level primitives" — whereas BFF's tape boundaries are fixed by design, not emergent. --- TIMESTAMPS: 00:00:00 Introduction: From Noise to Programs & ALife History 00:03:15 Defining Life: Function as the "Spirit" 00:05:45 Von Neumann's Insight: Life is Embodied Computation 00:09:15 Physics of Computation: Irreversibility & Fallacies 00:15:00 The BFF Experiment: Spontaneous Generation of Code 00:23:45 The Mystery: Complexity Growth Without Mutation 00:27:00 Symbiogenesis: The Engine of Novelty 00:33:15 Mathematical Proof: Blocking Symbiosis Stops Life 00:40:15 Evolutionary Implications: It's Symbiogenesis All The Way Down 00:44:30 Intelligence as Modeling Others 00:46:49 Q&A: Levels of Abstraction & Definitions --- REFERENCES: Paper: [00:01:16] Open Problems in Artificial Life https://direct.mit.edu/artl/article/6/4/363/2354/Open-Problems-in-Artificial-Life [00:09:30] When does a physical system compute? https://arxiv.org/abs/1309.7979 [00:15:00] Computational Life https://arxiv.org/abs/2406.19108 [00:27:30] On the Origin of Mitosing Cells https://pubmed.ncbi.nlm.nih.gov/11541392/ [00:42:00] The Major Evolutionary Transitions https://www.nature.com/articles/374227a0 [00:44:00] The ARC gene https://www.nih.gov/news-events/news-releases/memory-gene-goes-viral Person: [00:05:45] Alan Turing https://plato.stanford.edu/entries/turing/ [00:07:30] John von Neumann https://en.wikipedia.org/wiki/John_von_Neumann [00:11:15] Hector Zenil https://hectorzenil.net/ [00:12:00] Robert Sapolsky https://profiles.stanford.edu/robert-sapolsky [00:29:30] Marian Smoluchowski https://en.wikipedia.org/wiki/Marian_Smoluchowski Book: [00:06:15] What is Life? https://mitpress.mit.edu/9780262554091/what-is-life/ [00:19:45] What is Life? How Chemistry Becomes Biology https://amazon.com/dp/0199641013 Technical Concept: [00:15:45] Brainfuck https://esolangs.org/wiki/Brainfuck --- LINKS: RESCRIPT: https://app.rescript.info/public/share/ff7gb6HpezOR3DF-gr9-rCoMFzzEgUjLQK6voV5XVWY

What makes something truly *intelligent?* Is a rock an agent? Could a perfect simulation of your brain actually *be* you? In this fascinating conversation, Dr. Jeff Beck takes us on a journey through the philosophical and technical foundations of agency, intelligence, and the future of AI. Jeff doesn't hold back on the big questions. He argues that from a purely mathematical perspective, there's no structural difference between an agent and a rock – both execute policies that map inputs to outputs. The real distinction lies in *sophistication* – how complex are the internal computations? Does the system engage in planning and counterfactual reasoning, or is it just a lookup table that happens to give the right answers? *Key topics explored in this conversation:* *The Black Box Problem of Agency* – How can we tell if something is truly planning versus just executing a pre-computed response? Jeff explains why this question is nearly impossible to answer from the outside, and why the best we can do is ask which model gives us the simplest explanation. *Energy-Based Models Explained* – A masterclass on how EBMs differ from standard neural networks. The key insight: traditional networks only optimize weights, while energy-based models optimize *both* weights and internal states – a subtle but profound distinction that connects to Bayesian inference. *Why Your Brain Might Have Evolved from Your Nose* – One of the most surprising moments in the conversation. Jeff proposes that the complex, non-smooth nature of olfactory space may have driven the evolution of our associative cortex and planning abilities. *The JEPA Revolution* – A deep dive into Yann LeCun's Joint Embedding Prediction Architecture and why learning in latent space (rather than predicting every pixel) might be the key to more robust AI representations. *AI Safety Without Skynet Fears* – Jeff takes a refreshingly grounded stance on AI risk. He's less worried about rogue superintelligences and more concerned about humans becoming "reward function selectors" – couch potatoes who just approve or reject AI outputs. His proposed solution? Use inverse reinforcement learning to derive AI goals from observed human behavior, then make *small* perturbations rather than naive commands like "end world hunger." Whether you're interested in the philosophy of mind, the technical details of modern machine learning, or just want to understand what makes intelligence *tick,* this conversation delivers insights you won't find anywhere else. --- TIMESTAMPS: 00:00:00 Geometric Deep Learning & Physical Symmetries 00:00:56 Defining Agency: From Rocks to Planning 00:05:25 The Black Box Problem & Counterfactuals 00:08:45 Simulated Agency vs. Physical Reality 00:12:55 Energy-Based Models & Test-Time Training 00:17:30 Bayesian Inference & Free Energy 00:20:07 JEPA, Latent Space, & Non-Contrastive Learning 00:27:07 Evolution of Intelligence & Modular Brains 00:34:00 Scientific Discovery & Automated Experimentation 00:38:04 AI Safety, Enfeeblement & The Future of Work --- REFERENCES: Concept: [00:00:58] Free Energy Principle (FEP) https://en.wikipedia.org/wiki/Free_energy_principle [00:06:00] Monte Carlo Tree Search https://en.wikipedia.org/wiki/Monte_Carlo_tree_search Book: [00:09:00] The Intentional Stance https://mitpress.mit.edu/9780262540537/the-intentional-stance/ Paper: [00:13:00] A Tutorial on Energy-Based Learning (LeCun 2006) http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf [00:15:00] Auto-Encoding Variational Bayes (VAE) https://arxiv.org/abs/1312.6114 [00:20:15] JEPA (Joint Embedding Prediction Architecture) https://openreview.net/forum?id=BZ5a1r-kVsf [00:22:30] The Wake-Sleep Algorithm https://www.cs.toronto.edu/~hinton/absps/ws.pdf [00:22:45] Barlow Twins: Self-Supervised Learning https://arxiv.org/abs/2103.03230 [00:30:40] GFlowNets (Generative Flow Networks) https://arxiv.org/abs/2111.09266 [00:45:00] Maximum Entropy Inverse Reinforcement Learning https://www.aaai.org/Papers/AAAI/2008/AAAI08-227.pdf Challenge: [00:27:15] ARC Prize (Abstraction and Reasoning Corpus) https://arcprize.org/ --- RESCRIPT: https://app.rescript.info/public/share/DJlSbJ_Qx080q315tWaqMWn3PixCQsOcM4Kf1IW9_Eo PDF: https://app.rescript.info/api/public/sessions/0efec296b9b6e905/pdf

Professor Mazviita Chirimuuta joins us for a fascinating deep dive into the philosophy of neuroscience and what it really means to understand the mind. *What can neuroscience actually tell us about how the mind works?* In this thought-provoking conversation, we explore the hidden assumptions behind computational theories of the brain, the limits of scientific abstraction, and why the question of machine consciousness might be more complicated than AI researchers assume. Mazviita, author of *The Brain Abstracted,* brings a unique perspective shaped by her background in both neuroscience research and philosophy. She challenges us to think critically about the metaphors we use to understand cognition — from the reflex theory of the late 19th century to today's dominant view of the brain as a computer. *Key topics explored:* *The problem of oversimplification* — Why scientific models necessarily leave things out, and how this can sometimes lead entire fields astray. The cautionary tale of reflex theory shows how elegant explanations can blind us to biological complexity. *Is the brain really a computer?* — Mazviita unpacks the philosophical assumptions behind computational neuroscience and asks: if we can model anything computationally, what makes brains special? The answer might challenge everything you thought you knew about AI. *Haptic realism* — A fresh way of thinking about scientific knowledge that emphasizes interaction over passive observation. Knowledge isn't about reading the "source code of the universe" — it's something we actively construct through engagement with the world. *Why embodiment matters for understanding* — Can a disembodied language model truly understand? Mazviita makes a compelling case that human cognition is deeply entangled with our sensory-motor engagement and biological existence in ways that can't simply be abstracted away. *Technology and human finitude* — Drawing on Heidegger, we discuss how the dream of transcending our physical limitations through technology might reflect a fundamental misunderstanding of what it means to be a knower. This conversation is essential viewing for anyone interested in AI, consciousness, philosophy of mind, or the future of cognitive science. Whether you're skeptical of strong AI claims or a true believer in machine consciousness, Mazviita's careful philosophical analysis will give you new tools for thinking through these profound questions. --- TIMESTAMPS: 00:00:00 The Problem of Generalizing Neuroscience 00:02:51 Abstraction vs. Idealization: The "Kaleidoscope" 00:05:39 Platonism in AI: Discovering or Inventing Patterns? 00:09:42 When Simplification Fails: The Reflex Theory 00:12:23 Behaviorism and the "Black Box" Trap 00:14:20 Haptic Realism: Knowledge Through Interaction 00:20:23 Is Nature Protean? The Myth of Converging Truth 00:23:23 The Computational Theory of Mind: A Useful Fiction? 00:27:25 Biological Constraints: Why Brains Aren't Just Neural Nets 00:31:01 Agency, Distal Causes, and Dennett's Stances 00:37:13 Searle's Challenge: Causal Powers and Understanding 00:41:58 Heidegger's Warning & The Experiment on Children --- REFERENCES: Book: [00:01:28] The Brain Abstracted https://mitpress.mit.edu/9780262548045/the-brain-abstracted/ [00:11:05] The Integrated Action of the Nervous System https://www.amazon.sg/integrative-action-nervous-system/dp/9354179029 [00:18:15] The Quest for Certainty (Dewey) https://www.amazon.com/Quest-Certainty-Relation-Knowledge-Lectures/dp/0399501916 [00:19:45] Realism for Realistic People (Chang) https://www.cambridge.org/core/books/realism-for-realistic-people/ACC93A7F03B15AA4D6F3A466E3FC5AB7 [00:38:15] The Rediscovery of the Mind (Searle) https://mitpress.mit.edu/9780262691543/the-rediscovery-of-the-mind/ [00:47:18] So You've Been Publicly Shamed (Ronson) https://www.amazon.com/So-Youve-Been-Publicly-Shamed/dp/1594634017 [00:50:30] Reality+ (Chalmers) https://consc.net/reality/ Person: [00:05:00] Francois Chollet https://arcprize.org/ Paper: [00:08:03] Real Patterns (Dennett) https://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/class-info/FP2012/FP2012_readings/Dennett_RealPatterns.pdf [00:25:30] A Logical Calculus of Ideas... (1943) https://link.springer.com/article/10.1007/BF02478259 [00:29:17] The Lottery Ticket Hypothesis https://arxiv.org/abs/1803.03635 Philosophy: [00:17:30] Transcendental Idealism (Kant) https://plato.stanford.edu/entries/kant-transcendental-idealism/ --- RESCRIPT: https://app.rescript.info/public/share/A6cZ1TY35p8ORMmYCWNBI0no9ChU3-Kx7dPXGJURvZ0 PDF Transcript: https://app.rescript.info/api/public/sessions/0fb7767e066cf712/pdf

What if everything we think we know about the brain is just a really good metaphor that we forgot was a metaphor? This episode takes you on a journey through the history of scientific simplification, from a young Karl Friston watching wood lice in his garden to the bold claims that your mind is literally software running on biological hardware. We bring together some of the most brilliant minds we've interviewed — Professor Mazviita Chirimuuta, Francois Chollet, Joscha Bach, Professor Luciano Floridi, Professor Noam Chomsky, Nobel laureate John Jumper, and more — to wrestle with a deceptively simple question: *When scientists simplify reality to study it, what gets captured and what gets lost?* *Key ideas explored:* *The Spherical Cow Problem* — Science requires simplification. We're limited creatures trying to understand systems far more complex than our working memory can hold. But when does a useful model become a dangerous illusion? *The Kaleidoscope Hypothesis* — Francois Chollet's beautiful idea that beneath all the apparent chaos of reality lies simple, repeating patterns — like bits of colored glass in a kaleidoscope creating infinite complexity. Is this profound truth or Platonic wishful thinking? *Is Software Really Spirit?* — Joscha Bach makes the provocative claim that software is literally spirit, not metaphorically. We push back hard on this, asking whether the "sameness" we see across different computers running the same program exists in nature or only in our descriptions. *The Cultural Illusion of AGI* — Why does artificial general intelligence seem so inevitable to people in Silicon Valley? Professor Chirimuuta suggests we might be caught in a "cultural historical illusion" — our mechanistic assumptions about minds making AI seem like destiny when it might just be a bet. *Prediction vs. Understanding* — Nobel Prize winner John Jumper: AI can predict and control, but understanding requires a human in the loop. Throughout history, we've described the brain as hydraulic pumps, telegraph networks, telephone switchboards, and now computers. Each metaphor felt obviously true at the time. This episode asks: what will we think was naive about our current assumptions in fifty years? Featuring insights from *The Brain Abstracted* by Mazviita Chirimuuta — possibly the most influential book on how we think about thinking in 2025. --- TIMESTAMPS: 00:00:00 The Wood Louse & The Spherical Cow 00:02:04 The Necessity of Abstraction 00:04:42 Simplicius vs. Ignorantio: The Boxing Match 00:06:39 The Kaleidoscope Hypothesis 00:08:40 Is the Mind Software? 00:13:15 Critique of Causal Patterns 00:14:40 Temperature is Not a Thing 00:18:24 The Ship of Theseus & Ontology 00:23:45 Metaphors Hardening into Reality 00:25:41 The Illusion of AGI Inevitability 00:27:45 Prediction vs. Understanding 00:32:00 Climbing the Mountain vs. The Helicopter 00:34:53 Haptic Realism & The Limits of Knowledge --- REFERENCES: Person: [00:00:00] Karl Friston (UCL) https://profiles.ucl.ac.uk/1236-karl-friston [00:06:30] Francois Chollet https://fchollet.com/ [00:14:41] Cesar Hidalgo, MLST interview. https://www.youtube.com/watch?v=vzpFOJRteeI [00:30:30] Terence Tao's Blog https://terrytao.wordpress.com/ Book: [00:02:25] The Brain Abstracted https://mitpress.mit.edu/9780262548045/the-brain-abstracted/ [00:06:00] On Learned Ignorance https://www.amazon.com/Nicholas-Cusa-learned-ignorance-translation/dp/0938060236 [00:24:15] Science and the Modern World https://amazon.com/dp/0684836394 Interview.: [00:02:43] The Brain Abstracted Patreon interview https://www.patreon.com/posts/brain-abstracted-124479979 Interview: [00:04:18] David Krakauer's presentation on intelligence. https://www.youtube.com/watch?v=dY46YsGWMIc [00:06:45] Machine Learning Street Talk interview with Francois Chollet. https://www.youtube.com/watch?v=JTU8Ha4Jyfc [00:09:32] Joscha Bach Patreon interview. https://www.patreon.com/posts/joscha-bach-deep-141884561 [00:18:24] Luciano Floridi, MLST interview. https://www.youtube.com/watch?v=YLNGvvgq3eg [00:25:02] Jeff Beck, MLST interview. https://www.youtube.com/watch?v=9suqiofCiwM [00:28:00] John Jumper https://www.patreon.com/posts/john-jumper-on-144557652 [00:30:48] Noam Chomsky, MLST interview. https://www.youtube.com/watch?v=axuGfh4UR9Q [00:31:59] Anna Ciaunica https://www.patreon.com/posts/dr-anna-ciaunica-144509970 [00:33:12] Mike Israetel debate on functionalism. https://www.youtube.com/watch?v=4yYcN_mFi18 Company/Org: [00:09:25] Neuralink https://neuralink.com/ Paper: [00:23:45] A Logical Calculus of Ideas Immanent in Nervous Activity https://link.springer.com/article/10.1007/BF02478259 [00:28:00] Highly Accurate Protein Structure Prediction with AlphaFold https://www.nature.com/articles/s41586-021-03819-2 Thank you to Dr. Maxwell Ramstead for early script work on this show (Ph.D student of Friston) and the woodlice story came from him!

Dr. Jeff Beck, mathematician turned computational neuroscientist, joins us for a fascinating deep dive into why the future of AI might look less like ChatGPT and more like your own brain. **SPONSOR MESSAGES START** — Prolific - Quality data. From real people. For faster breakthroughs. https://www.prolific.com/?utm_source=mlst — **END** *What if the key to building truly intelligent machines isn't bigger models, but smarter ones?* In this conversation, Jeff makes a compelling case that we've been building AI backwards. While the tech industry races to scale up transformers and language models, Jeff argues we're missing something fundamental: the brain doesn't work like a giant prediction engine. It works like a scientist, constantly testing hypotheses about a world made of *objects* that interact through *forces* — not pixels and tokens. *The Bayesian Brain* — Jeff explains how your brain is essentially running the scientific method on autopilot. When you combine what you see with what you hear, you're doing optimal Bayesian inference without even knowing it. This isn't just philosophy — it's backed by decades of behavioral experiments showing humans are surprisingly efficient at handling uncertainty. *AutoGrad Changed Everything* — Forget transformers for a moment. Jeff argues the real hero of the AI boom was automatic differentiation, which turned AI from a math problem into an engineering problem. But in the process, we lost sight of what actually makes intelligence work. *The Cat in the Warehouse Problem* — Here's where it gets practical. Imagine a warehouse robot that's never seen a cat. Current AI would either crash or make something up. Jeff's approach? Build models that *know what they don't know*, can phone a friend to download new object models on the fly, and keep learning continuously. It's like giving robots the ability to say "wait, what IS that?" instead of confidently being wrong. *Why Language is a Terrible Model for Thought* — In a provocative twist, Jeff argues that grounding AI in language (like we do with LLMs) is fundamentally misguided. Self-report is the least reliable data in psychology — people routinely explain their own behavior incorrectly. We should be grounding AI in physics, not words. *The Future is Lots of Little Models* — Instead of one massive neural network, Jeff envisions AI systems built like video game engines: thousands of small, modular object models that can be combined, swapped, and updated independently. It's more efficient, more flexible, and much closer to how we actually think. Whether you're an AI researcher, a robotics enthusiast, or just curious about how minds — biological or artificial — actually work, this conversation offers a refreshingly different perspective on where intelligence comes from and where it's going. Rescript: https://app.rescript.info/public/share/D-b494t8DIV-KRGYONJghvg-aelMmxSDjKthjGdYqsE --- TIMESTAMPS: 00:00:00 Introduction & The Bayesian Brain 00:01:25 Bayesian Inference & Information Processing 00:05:17 The Brain Metaphor: From Levers to Computers 00:10:13 Micro vs. Macro Causation & Instrumentalism 00:16:59 The Active Inference Community & AutoGrad 00:22:54 Object-Centered Models & The Grounding Problem 00:35:50 Scaling Bayesian Inference & Architecture Design 00:48:05 The Cat in the Warehouse: Solving Generalization 00:58:17 Alignment via Belief Exchange 01:05:24 Deception, Emergence & Cellular Automata --- REFERENCES: Paper: [00:00:24] Zoubin Ghahramani (Google DeepMind) https://pmc.ncbi.nlm.nih.gov/articles/PMC3538441/pdf/rsta201 [00:19:20] Mamba: Linear-Time Sequence Modeling https://arxiv.org/abs/2312.00752 [00:27:36] xLSTM: Extended Long Short-Term Memory https://arxiv.org/abs/2405.04517 [00:41:12] 3D Gaussian Splatting https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/ [01:07:09] Lenia: Biology of Artificial Life https://arxiv.org/abs/1812.05433 [01:08:20] Growing Neural Cellular Automata https://distill.pub/2020/growing-ca/ [01:14:05] DreamCoder https://arxiv.org/abs/2006.08381 [01:14:58] The Genomic Bottleneck https://www.nature.com/articles/s41467-019-11786-6 Person: [00:16:42] Karl Friston (UCL) https://www.youtube.com/watch?v=PNYWi996Beg

Tim sits down with Max Bennett to explore how our brains evolved over 600 million years—and what that means for understanding both human intelligence and AI. Max isn't a neuroscientist by training. He's a tech entrepreneur who got curious, started reading, and ended up weaving together three fields that rarely talk to each other: comparative psychology (what different animals can actually do), evolutionary neuroscience (how brains changed over time), and AI (what actually works in practice). *Your Brain Is a Guessing Machine* You don't actually "see" the world. Your brain builds a simulation of what it *thinks* is out there and just uses your eyes to check if it's right. That's why optical illusions work—your brain is filling in a triangle that isn't there, or can't decide if it's looking at a duck or a rabbit. *Rats Have Regrets* In a fascinating experiment called "Restaurant Row," rats make choices about waiting for food. When they skip a short wait for something they like and end up stuck with a long wait for something they don't—you can literally watch their brain imagine eating the food they passed up. They regret their choice and make different decisions next time. *Chimps Are Machiavellian* The most gripping story is about two chimps, Rock and Belle. Belle learns where food is hidden. Rock figures out he can just follow her and steal it. So Belle starts hiding the food when she finds it. Then Rock starts *pretending* not to watch her, then sprinting to grab the food once she moves. This escalates into an arms race of deception and counter-deception—proof that apes can think about what others are thinking. *Language Is the Human Superpower* Other animals learn by watching each other's actions. Humans can share what's happening *inside our minds*. You can describe a dream, plan a hunt with five other people, or warn someone about a snake you saw yesterday. This ability to share mental simulations is what lets knowledge accumulate across generations—and it's arguably the "singularity that already happened." *Does ChatGPT Think?* ChatGPT clearly has *a model* (it wouldn't work otherwise), but it doesn't have a *world model* in the way brains do. A real world model means you can form a hypothesis, test it, and update your beliefs based on what happens. GPT learns only from its training data—it can't run experiments or reject information it knows to be false. Understanding how the brain evolved isn't just about the past. It gives us clues about: - What's actually different between human intelligence and AI - Why we're so easily fooled by status games and tribal thinking - What features we might want to build into—or leave out of—future AI systems Get Max's book: https://www.amazon.com/Brief-History-Intelligence-Humans-Breakthroughs/dp/0063286343 Rescript: https://app.rescript.info/public/share/R234b7AXyDXZusqQ_43KMGsUSvJ2TpSz2I3emnI6j9A --- TIMESTAMPS: 00:00:00 Introduction: Outsider's Advantage & Neocortex Theories 00:11:34 Perception as Inference: The Filling-In Machine 00:19:11 Understanding, Recognition & Generative Models 00:36:39 How Mice Plan: Vicarious Trial & Error 00:46:15 Evolution of Self: The Layer 4 Mystery 00:58:31 Ancient Minds & The Social Brain: Machiavellian Apes 01:19:36 AI Alignment, Instrumental Convergence & Status Games 01:33:07 Metacognition & The IQ Paradox 01:48:40 Does GPT Have Theory of Mind? 02:00:40 Memes, Language Singularity & Brain Size Myths 02:16:44 Communication, Language & The Cyborg Future 02:44:25 Shared Fictions, World Models & The Reality Gap --- REFERENCES:Person: [00:00:05] Karl Friston (UCL) https://www.youtube.com/watch?v=PNYWi996Beg [00:00:06] Jeff Hawkins https://www.youtube.com/watch?v=6VQILbDqaI4 [00:12:19] Hermann von Helmholtz https://plato.stanford.edu/entries/hermann-helmholtz/ [00:38:34] David Redish (U. Minnesota) https://redishlab.umn.edu/ [01:10:19] Robin Dunbar https://www.psy.ox.ac.uk/people/robin-dunbar [01:15:04] Emil Menzel https://www.sciencedirect.com/bookseries/behavior-of-nonhuman-primates/vol/5/suppl/C [01:19:49] Nick Bostrom https://nickbostrom.com/ [02:28:25] Noam Chomsky https://linguistics.mit.edu/user/chomsky/ [03:01:22] Judea Pearl https://samueli.ucla.edu/people/judea-pearl/ Concept/Framework: [00:05:04] Active Inference https://www.youtube.com/watch?v=KkR24ieh5Ow Paper: [00:35:59] Predictions not commands [Rick A Adams] https://pubmed.ncbi.nlm.nih.gov/23129312/ Book: [01:25:42] The Elephant in the Brain https://www.amazon.com/Elephant-Brain-Hidden-Motives-Everyday/dp/0190495995 [01:28:27] The Status Game https://www.goodreads.com/book/show/58642436-the-status-game [02:00:40] The Selfish Gene https://amazon.com/dp/0198788606 [02:14:25] The Language Game https://www.amazon.com/Language-Game-Improvisation-Created-Changed/dp/1541674987 [02:54:40] The Evolution of Language https://www.amazon.com/Evolution-Language-Approaches/dp/052167736X [03:09:37] The Three-Body Problem https://amazon.com/dp/0765377063

César Hidalgo has spent years trying to answer a deceptively simple question: What is knowledge, and why is it so hard to move around? We all have this intuition that knowledge is just... information. Write it down in a book, upload it to GitHub, train an AI on it—done. But César argues that's completely wrong. Knowledge isn't a thing you can copy and paste. It's more like a living organism that needs the right environment, the right people, and constant exercise to survive. Guest: César Hidalgo, Director of the Center for Collective Learning The Big Ideas 1. Knowledge Follows Laws (Like Physics) Just as temperature and gravity follow predictable rules, so does knowledge. César outlines three laws: - Time: How knowledge grows (fast at first, then it plateaus) - Space: How knowledge spreads (it's way harder than you think) - Value: How we can measure a country's "knowledge potential" 2. You Can't Download Expertise The most memorable stories in this conversation prove that knowledge is embodied—it lives in people, teams, and organizations, not in manuals. 3. Why Big Companies Fail to Adapt César explains "architectural innovation"—the idea that small changes (like shipping books directly to customers) can require a completely different organizational structure. 4. The "Infinite Alphabet" of Economies Every skill, every industry, every capability is like a letter in an alphabet. César's research shows you can actually predict which countries will grow by counting their "letters." If you think AI can just "copy" human knowledge, or that development is just about throwing money at poor countries, or that writing things down preserves them forever—this conversation will change your mind. Knowledge is fragile, specific, and collective. It decays fast if you don't use it. The Infinite Alphabet [César A. Hidalgo] https://www.penguin.co.uk/books/458054/the-infinite-alphabet-by-hidalgo-cesar-a/9780241655672 https://x.com/cesifoti Rescript link. https://app.rescript.info/public/share/eaBHbEo9xamwbwpxzcVVm4NQjMh7lsOQKeWwNxmw0JQ --- TIMESTAMPS: 00:00:00 The Three Laws of Knowledge 00:02:28 Rival vs. Non-Rival: The Economics of Ideas 00:05:43 Why You Can't Just 'Download' Knowledge 00:08:11 The Detective Novel Analogy 00:11:54 Collective Learning & Organizational Networks 00:16:27 Architectural Innovation: Amazon vs. Barnes & Noble 00:19:15 The First Law: Learning Curves 00:23:05 The Samuel Slater Story: Treason & Memory 00:28:31 Physics of Knowledge: Joule's Cannon 00:32:33 Extensive vs. Intensive Properties 00:35:45 Knowledge Decay: Ise Temple & Polaroid 00:41:20 Absorptive Capacity: Sony & Donetsk 00:47:08 Disruptive Innovation & S-Curves 00:51:23 Team Size & The Cost of Innovation 00:57:13 Geography of Knowledge: Vespa's Origin 01:04:34 Migration, Diversity & 'Planet China' 01:12:02 Institutions vs. Knowledge: The China Story 01:21:27 Economic Complexity & The Infinite Alphabet 01:32:27 Do LLMs Have Knowledge? --- REFERENCES: Book: [00:47:45] The Innovator's Dilemma (Christensen) https://www.amazon.com/Innovators-Dilemma-Revolutionary-Change-Business/dp/0062060244 [00:55:15] Why Greatness Cannot Be Planned https://amazon.com/dp/3319155237 [01:35:00] Why Information Grows https://amazon.com/dp/0465048994 Paper: [00:03:15] Endogenous Technological Change (Romer, 1990) https://web.stanford.edu/~klenow/Romer_1990.pdf [00:03:30] A Model of Growth Through Creative Destruction (Aghion & Howitt, 1992) https://dash.harvard.edu/server/api/core/bitstreams/7312037d-2b2d-6bd4-e053-0100007fdf3b/content [00:14:55] Organizational Learning: From Experience to Knowledge (Argote & Miron-Spektor, 2011) https://www.researchgate.net/publication/228754233_Organizational_Learning_From_Experience_to_Knowledge [00:17:05] Architectural Innovation (Henderson & Clark, 1990) https://www.researchgate.net/publication/200465578_Architectural_Innovation_The_Reconfiguration_of_Existing_Product_Technologies_and_the_Failure_of_Established_Firms [00:19:45] The Learning Curve Equation (Thurstone, 1916) https://dn790007.ca.archive.org/0/items/learningcurveequ00thurrich/learningcurveequ00thurrich.pdf [00:21:30] Factors Affecting the Cost of Airplanes (Wright, 1936) https://pdodds.w3.uvm.edu/research/papers/others/1936/wright1936a.pdf [00:52:45] Are Ideas Getting Harder to Find? (Bloom et al.) https://web.stanford.edu/~chadj/IdeaPF.pdf [01:33:00] LLMs/ Emergence https://arxiv.org/abs/2506.11135 Person: [00:25:30] Samuel Slater https://en.wikipedia.org/wiki/Samuel_Slater [00:42:05] Masaru Ibuka (Sony) https://www.sony.com/en/SonyInfo/CorporateInfo/History/SonyHistory/1-02.html [01:01:45] Corradino D'Ascanio https://link.springer.com/chapter/10.1007/978-3-319-09858-6_38#:~:text=6%20Conclusions,%2C%20comfort%2C%20and%20technical%20performance. [01:16:00] Chen Chunxian https://thebhc.org/sites/default/files/tzeng.pdf Event/Place:

This is a lively, no-holds-barred debate about whether AI can truly be intelligent, conscious, or understand anything at all — and what happens when (or if) machines become smarter than us. Dr. Mike Israetel is a sports scientist, entrepreneur, and co-founder of RP Strength (a fitness company). He describes himself as a "dilettante" in AI but brings a fascinating outsider's perspective. Jared Feather (IFBB Pro bodybuilder and exercise physiologist) The Big Questions: 1. When is superintelligence coming? 2. Does AI actually understand anything? 3. The Simulation Debate (The Spiciest Part) Tim says a simulation of fire doesn't get hot. They go back and forth on whether you could upload your mind to a computer — Mike says yes, Tim says absolutely not. 4. Will AI kill us all? (The Doomer Debate) Mike thinks the "AI will exterminate humanity" crowd has it backwards. His argument: any system smart enough to wage war is smart enough to realize cooperation is the winning strategy. Super-intelligent AI would want to *study* us, not destroy us. He uses the raccoon analogy to explain what agency really means. 5. What happens to human jobs and purpose? 6. Do we need suffering? In a surprisingly emotional moment, Tim asks if suffering gives life meaning. Mike's answer? "Fuck no. Desperately" Mikes channel: https://www.youtube.com/channel/UCfQgsKhHjSyRLOp9mnffqVg RESCRIPT INTERACTIVE PLAYER: https://app.rescript.info/public/share/GVMUXHCqctPkXH8WcYtufFG7FQcdJew_RL_MLgMKU1U --- TIMESTAMPS: 00:00:00 Introduction & Workout Demo 00:04:15 ASI Timelines & Definitions 00:10:24 The Embodiment Debate 00:18:28 Neutrinos & Abstract Knowledge 00:25:56 Can AI Learn From YouTube? 00:31:25 Diversity of Intelligence 00:36:00 AI Slop & Understanding 00:45:18 The Simulation Argument: Fire & Water 00:58:36 Consciousness & Zombies 01:04:30 Do Reasoning Models Actually Reason? 01:12:00 The Live Learning Problem 01:19:15 Superintelligence & Benevolence 01:28:59 What is True Agency? 01:37:20 Game Theory & The "Kill All Humans" Fallacy 01:48:05 Regulation & The China Factor 01:55:52 Mind Uploading & The Future of Love 02:04:41 Economics of ASI: Will We Be Useless? 02:13:35 The Matrix & The Value of Suffering 02:17:30 Transhumanism & Inequality 02:21:28 Debrief: AI Medical Advice & Final Thoughts --- REFERENCES: Paper: [00:10:45] Alchemy and Artificial Intelligence (Dreyfus) https://www.rand.org/content/dam/rand/pubs/papers/2006/P3244.pdf [00:10:55] The Chinese Room Argument (John Searle) https://home.csulb.edu/~cwallis/382/readings/482/searle.minds.brains.programs.bbs.1980.pdf [00:11:05] The Symbol Grounding Problem (Stephen Harnad) https://arxiv.org/html/cs/9906002 [00:23:00] Attention Is All You Need https://arxiv.org/abs/1706.03762 [00:45:00] GPT-4 Technical Report https://arxiv.org/abs/2303.08774 [01:45:00] Anthropic Agentic Misalignment Paper https://www.anthropic.com/research/agentic-misalignment [02:17:45] Retatrutide https://pubmed.ncbi.nlm.nih.gov/37366315/ Organization: [00:15:50] CERN https://home.cern/ [01:05:00] METR Long Horizon Evaluations https://evaluations.metr.org/ MLST Episode: [00:23:10] MLST: Llion Jones - Inventors' Remorse https://www.youtube.com/watch?v=DtePicx_kFY [00:50:30] MLST: Blaise Agüera y Arcas Interview https://www.youtube.com/watch?v=rMSEqJ_4EBk [01:10:00] MLST: David Krakauer https://www.youtube.com/watch?v=dY46YsGWMIc Event: [00:23:40] ARC Prize/Challenge https://arcprize.org/ Book: [00:24:45] The Brain Abstracted https://www.amazon.com/Brain-Abstracted-Simplification-Philosophy-Neuroscience/dp/0262548046 [00:47:55] Pamela McCorduck https://www.amazon.com/Machines-Who-Think-Artificial-Intelligence/dp/1568812051 [01:23:15] The Singularity Is Nearer (Ray Kurzweil) https://www.amazon.com/Singularity-Nearer-Ray-Kurzweil-ebook/dp/B08Y6FYJVY [01:27:35] A Fire Upon The Deep (Vernor Vinge) https://www.amazon.com/Fire-Upon-Deep-S-F-MASTERWORKS-ebook/dp/B00AVUMIZE/ [02:04:50] Deep Utopia (Nick Bostrom) https://www.amazon.com/Deep-Utopia-Meaning-Solved-World/dp/1646871642 [02:05:00] Technofeudalism (Yanis Varoufakis) https://www.amazon.com/Technofeudalism-Killed-Capitalism-Yanis-Varoufakis/dp/1685891241 Visual Context Needed: [00:29:40] AT-AT Walker (Star Wars) https://starwars.fandom.com/wiki/All_Terrain_Armored_Transport Person: [00:33:15] Andrej Karpathy https://karpathy.ai/ Video: [01:40:00] Mike Israetel vs Liron Shapira AI Doom Debate https://www.youtube.com/watch?v=RaDWSPMdM4o Company: [02:26:30] Examine.com https://examine.com/

People are using AI for mental health advice and life decisions, but there's no oversight and no safety ratings. We grade models on speed and smarts... but not on whether they're safe to use. Why isn't that just as important? Featuring Andrew Gordon and Nora Petrova from Prolific, discussing AI evaluation, benchmarks, and why human preference matters. 🎙️ Full episode: https://youtu.be/rqiC9a2z8Io #AIShorts #AISafety #MachineLearning

We often think of Large Language Models (LLMs) as all-knowing, but as the team reveals, they still struggle with the logic of a second-grader. Why can’t ChatGPT reliably add large numbers? Why does it "hallucinate" the laws of physics? The answer lies in the architecture. This episode explores how *Category Theory*—an ultra-abstract branch of mathematics—could provide the "Periodic Table" for neural networks, turning the "alchemy" of modern AI into a rigorous science. In this deep-dive exploration, *Andrew Dudzik*, *Petar Velichkovich*, *Taco Cohen*, *Bruno Gavranović*, and *Paul Lessard* join host *Tim Scarfe* to discuss the fundamental limitations of today’s AI and the radical mathematical framework that might fix them. --- Key Insights in This Episode: * *The "Addition" Problem:* *Andrew Dudzik* explains why LLMs don't actually "know" math—they just recognize patterns. When you change a single digit in a long string of numbers, the pattern breaks because the model lacks the internal "machinery" to perform a simple carry operation. * *Beyond Alchemy:* *Tim Scarfe* argues that deep learning is currently in its "alchemy" phase—we have powerful results, but we lack a unifying theory. Category Theory is proposed as the framework to move AI from trial-and-error to principled engineering. [00:13:49] * *Algebra with Colors:* To make Category Theory accessible, the guests use brilliant analogies—like thinking of matrices as *magnets with colors* that only snap together when the types match. This "partial compositionality" is the secret to building more complex internal reasoning. [00:09:17] * *Synthetic vs. Analytic Math:* *Paul Lessard* breaks down the philosophical shift needed in AI research: moving from "Analytic" math (what things are made of) to "Synthetic" math (how things behave and relate to one another). [00:23:41] * *The 4D Carry:* In a mind-blowing conclusion, the team discusses how simple algorithmic tasks, like "carrying the one" in addition, actually relate to complex geometric structures like *Hopf Fibrations*. [00:39:30] --- Why This Matters for AGI If we want AI to solve the world's hardest scientific problems, it can't just be a "stochastic parrot." It needs to internalize the rules of logic and computation. By imbuing neural networks with categorical priors, researchers are attempting to build a future where AI doesn't just predict the next word—it understands the underlying structure of the universe. --- TIMESTAMPS: 00:00:00 The Failure of LLM Addition & Physics 00:01:26 Tool Use vs Intrinsic Model Quality 00:03:07 Efficiency Gains via Internalization 00:04:28 Geometric Deep Learning & Equivariance 00:07:05 Limitations of Group Theory 00:09:17 Category Theory: Algebra with Colors 00:11:25 The Systematic Guide of Lego-like Math 00:13:49 The Alchemy Analogy & Unifying Theory 00:15:33 Information Destruction & Reasoning 00:18:00 Pathfinding & Monoids in Computation 00:20:15 System 2 Reasoning & Error Awareness 00:23:31 Analytic vs Synthetic Mathematics 00:25:52 Morphisms & Weight Tying Basics 00:26:48 2-Categories & Weight Sharing Theory 00:28:55 Higher Categories & Emergence 00:31:41 Compositionality & Recursive Folds 00:34:05 Syntax vs Semantics in Network Design 00:36:14 Homomorphisms & Multi-Sorted Syntax 00:39:30 The Carrying Problem & Hopf Fibrations --- REFERENCES: Company: Model: [00:01:05] Veo https://deepmind.google/models/veo/ [00:01:10] Genie https://deepmind.google/blog/genie-3-a-new-frontier-for-world-models/ Paper: [00:04:30] Geometric Deep Learning Blueprint https://arxiv.org/abs/2104.13478 [00:16:45] AlphaGeometry https://deepmind.google/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/ [00:16:55] AlphaCode https://arxiv.org/abs/2203.07814 [00:17:05] FunSearch https://www.nature.com/articles/s41586-023-06924-6 [00:37:00] Attention Is All You Need https://arxiv.org/abs/1706.03762 [00:43:00] Categorical Deep Learning https://arxiv.org/abs/2402.15332 Thanks to ly3xqhl8g9 on our Discord server for the draft show review!