Machine Learning Street Talk (MLST)

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)• 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 Evolve00:04:15 AlphaEvolve's Blind Spot: Co-Evolving Problems with Solutions00:09:05 Unknown Unknowns, POET, and Auto-Curricula for AI Science00:14:20 MAP-Elites and Quality-Diversity: Shinka's Evolutionary Architecture00:28:00 UCB Bandits, Mutations and the Vibe Research Vision00:40:00 Scaling Shinka: Meta-Evolution, Democratisation and the Three-Axis Model00:47:10 Applications, ARC-AGI and the Future of Work00:57:00 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 Evolutionhttps://arxiv.org/abs/2509.19349[00:04:15] AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discoveryhttps://arxiv.org/abs/2506.13131[00:06:30] Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agentshttps://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 Problemhttps://arxiv.org/abs/1112.5309[00:10:40] Automated Capability Discovery via Foundation Model Self-Explorationhttps://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<trunc, see ReScript/YT>PDF : https://app.rescript.info/api/sessions/b8a9dcf60623657c/pdf/downloadTranscript: 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 DevelopersRESCRIPT: https://app.rescript.info/public/share/BhX5zP3b0m63srLOQDKBTFTooSzEMh_ARwmDG_h_izkJeremy Howard:https://x.com/jeremyphowardhttps://www.answer.ai/---TIMESTAMPS (fixed):00:00:00 Introduction & GTC Sponsor00:04:30 ULMFiT & The Birth of Fine-Tuning00:12:00 Intuition & The Mechanics of Learning00:18:30 Abstraction Hierarchies & AI Creativity00:23:00 Claude Code & The Interpolation Illusion00:27:30 Coding vs. Software Engineering00:30:00 Cosplaying Intelligence: Dennett vs. Searle00:36:30 Automation, Radiology & Desirable Difficulty00:42:30 Organizational Knowledge & The Slope00:48:00 Vibe Coding as a Slot Machine00:54:00 The Erosion of Control in Software01:01:00 Interactive Programming & REPL Environments01:05:00 The Notebook Debate & Exploratory Science01:17:30 AI Existential Risk & Power Centralization01:24:20 Current Risks, Privacy & Enfeeblement---REFERENCES:Blog Post:[00:03:00] fast.ai Blog: Self-Supervised Learninghttps://www.fast.ai/posts/2020-01-13-self_supervised.html[00:13:30] DeepMind Blog: Gemini Deep Thinkhttps://deepmind.google/blog/accelerating-mathematical-and-scientific-discovery-with-gemini-deep-think/[00:19:30] Modular Blog: Claude C Compiler analysishttps://www.modular.com/blog/the-claude-c-compiler-what-it-reveals-about-the-future-of-software[00:19:45] Anthropic Engineering Blog: Building C Compilerhttps://www.anthropic.com/engineering/building-c-compiler[00:48:00] Cursor Blog: Scaling Agentshttps://cursor.com/blog/scaling-agents[01:05:15] fast.ai Blog: NB Dev Merged Driverhttps://www.fast.ai/posts/2022-08-25-jupyter-git.html[01:17:30] Jeremy Howard: Response to AI Risk Letterhttps://www.normaltech.ai/p/is-avoiding-extinction-from-ai-reallyBook:[00:08:30] M. Chirimuuta: The Brain Abstractedhttps://mitpress.mit.edu/9780262548045/the-brain-abstracted/[00:30:00] Daniel Dennett: Consciousness Explainedhttps://www.amazon.com/Consciousness-Explained-Daniel-C-Dennett/dp/0316180661[00:42:30] Cesar Hidalgo: Infinite Alphabet / Laws of Knowledgehttps://www.amazon.com/Infinite-Alphabet-Laws-Knowledge/dp/0241655676Archive Article:[00:13:45] MLST Archive: Why Creativity Cannot Be Interpolatedhttps://archive.mlst.ai/read/why-creativity-cannot-be-interpolatedResearch Study:[00:24:30] METR Study: AI OS Developmenthttps://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/Paper:[00:24:45] Fred Brooks: No Silver Bullethttps://www.cs.unc.edu/techreports/86-020.pdf[00:30:15] John Searle: Minds, Brains, and Programshttps://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/minds-brains-and-programs/DC644B47A4299C637C89772FACC2706A

What if life itself is just a really sophisticated computer program that wrote itself into existence?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 History00:03:15 Defining Life: Function as the "Spirit"00:05:45 Von Neumann's Insight: Life is Embodied Computation00:09:15 Physics of Computation: Irreversibility & Fallacies00:15:00 The BFF Experiment: Spontaneous Generation of Code00:23:45 The Mystery: Complexity Growth Without Mutation00:27:00 Symbiogenesis: The Engine of Novelty00:33:15 Mathematical Proof: Blocking Symbiosis Stops Life00:40:15 Evolutionary Implications: It's Symbiogenesis All The Way Down00:44:30 Intelligence as Modeling Others00:46:49 Q&A: Levels of Abstraction & Definitions---REFERENCES:Paper:[00:01:16] Open Problems in Artificial Lifehttps://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 Lifehttps://arxiv.org/abs/2406.19108[00:27:30] On the Origin of Mitosing Cellshttps://pubmed.ncbi.nlm.nih.gov/11541392/[00:42:00] The Major Evolutionary Transitionshttps://www.nature.com/articles/374227a0[00:44:00] The ARC genehttps://www.nih.gov/news-events/news-releases/memory-gene-goes-viralPerson:[00:05:45] Alan Turinghttps://plato.stanford.edu/entries/turing/[00:07:30] John von Neumannhttps://en.wikipedia.org/wiki/John_von_Neumann[00:11:15] Hector Zenilhttps://hectorzenil.net/[00:12:00] Robert Sapolskyhttps://profiles.stanford.edu/robert-sapolsky---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 Symmetries00:00:56 Defining Agency: From Rocks to Planning00:05:25 The Black Box Problem & Counterfactuals00:08:45 Simulated Agency vs. Physical Reality00:12:55 Energy-Based Models & Test-Time Training00:17:30 Bayesian Inference & Free Energy00:20:07 JEPA, Latent Space, & Non-Contrastive Learning00:27:07 Evolution of Intelligence & Modular Brains00:34:00 Scientific Discovery & Automated Experimentation00:38:04 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 Searchhttps://en.wikipedia.org/wiki/Monte_Carlo_tree_searchBook:[00:09:00] The Intentional Stancehttps://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 Algorithmhttps://www.cs.toronto.edu/~hinton/absps/ws.pdf<trunc, see rescript>---RESCRIPT:https://app.rescript.info/public/share/DJlSbJ_Qx080q315tWaqMWn3PixCQsOcM4Kf1IW9_EoPDF: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 Neuroscience00:02:51 Abstraction vs. Idealization: The "Kaleidoscope"00:05:39 Platonism in AI: Discovering or Inventing Patterns?00:09:42 When Simplification Fails: The Reflex Theory00:12:23 Behaviorism and the "Black Box" Trap00:14:20 Haptic Realism: Knowledge Through Interaction00:20:23 Is Nature Protean? The Myth of Converging Truth00:23:23 The Computational Theory of Mind: A Useful Fiction?00:27:25 Biological Constraints: Why Brains Aren't Just Neural Nets00:31:01 Agency, Distal Causes, and Dennett's Stances00:37:13 Searle's Challenge: Causal Powers and Understanding00:41:58 Heidegger's Warning & The Experiment on Children---REFERENCES:Book:[00:01:28] The Brain Abstractedhttps://mitpress.mit.edu/9780262548045/the-brain-abstracted/[00:11:05] The Integrated Action of the Nervous Systemhttps://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<truncated, see ReScript>---RESCRIPT:https://app.rescript.info/public/share/A6cZ1TY35p8ORMmYCWNBI0no9ChU3-Kx7dPXGJURvZ0PDF 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 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 Cow00:02:04 The Necessity of Abstraction00:04:42 Simplicius vs. Ignorantio: The Boxing Match00:06:39 The Kaleidoscope Hypothesis00:08:40 Is the Mind Software?00:13:15 Critique of Causal Patterns00:14:40 Temperature is Not a Thing00:18:24 The Ship of Theseus & Ontology00:23:45 Metaphors Hardening into Reality00:25:41 The Illusion of AGI Inevitability00:27:45 Prediction vs. Understanding00:32:00 Climbing the Mountain vs. The Helicopter00:34:53 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 Chollethttps://fchollet.com/[00:14:41] Cesar Hidalgo, MLST interview.https://www.youtube.com/watch?v=vzpFOJRteeI[00:30:30] Terence Tao's Bloghttps://terrytao.wordpress.com/Book:[00:02:25] The Brain Abstractedhttps://mitpress.mit.edu/9780262548045/the-brain-abstracted/[00:06:00] On Learned Ignorancehttps://www.amazon.com/Nicholas-Cusa-learned-ignorance-translation/dp/0938060236[00:24:15] Science and the Modern Worldhttps://amazon.com/dp/0684836394<truncated, see ReScript>RESCRIPT:https://app.rescript.info/public/share/CYy0ex2M2kvcVRdMnSUky5O7H7hB7v2u_nVhoUiuKD4PDF Transcript: https://app.rescript.info/api/public/sessions/6c44c41e1e0fa6dd/pdf 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.Rescript: https://app.rescript.info/public/share/D-b494t8DIV-KRGYONJghvg-aelMmxSDjKthjGdYqsE---TIMESTAMPS:00:00:00 Introduction & The Bayesian Brain00:01:25 Bayesian Inference & Information Processing00:05:17 The Brain Metaphor: From Levers to Computers00:10:13 Micro vs. Macro Causation & Instrumentalism00:16:59 The Active Inference Community & AutoGrad00:22:54 Object-Centered Models & The Grounding Problem00:35:50 Scaling Bayesian Inference & Architecture Design00:48:05 The Cat in the Warehouse: Solving Generalization00:58:17 Alignment via Belief Exchange01:05:24 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 Modelinghttps://arxiv.org/abs/2312.00752[00:27:36] xLSTM: Extended Long Short-Term Memoryhttps://arxiv.org/abs/2405.04517[00:41:12] 3D Gaussian Splattinghttps://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/[01:07:09] Lenia: Biology of Artificial Lifehttps://arxiv.org/abs/1812.05433[01:08:20] Growing Neural Cellular Automatahttps://distill.pub/2020/growing-ca/[01:14:05] DreamCoderhttps://arxiv.org/abs/2006.08381[01:14:58] The Genomic Bottleneckhttps://www.nature.com/articles/s41467-019-11786-6Person:[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**Chimps Are Machiavellian**Language Is the Human Superpower**Does ChatGPT Think?*(truncated description, more on rescript)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 systemsGet Max's book:https://www.amazon.com/Brief-History-Intelligence-Humans-Breakthroughs/dp/0063286343Rescript: https://app.rescript.info/public/share/R234b7AXyDXZusqQ_43KMGsUSvJ2TpSz2I3emnI6j9A---TIMESTAMPS:00:00:00 Introduction: Outsider's Advantage & Neocortex Theories00:11:34 Perception as Inference: The Filling-In Machine00:19:11 Understanding, Recognition & Generative Models00:36:39 How Mice Plan: Vicarious Trial & Error00:46:15 Evolution of Self: The Layer 4 Mystery00:58:31 Ancient Minds & The Social Brain: Machiavellian Apes01:19:36 AI Alignment, Instrumental Convergence & Status Games01:33:07 Metacognition & The IQ Paradox01:48:40 Does GPT Have Theory of Mind?02:00:40 Memes, Language Singularity & Brain Size Myths02:16:44 Communication, Language & The Cyborg Future02:44:25 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 Hawkinshttps://www.youtube.com/watch?v=6VQILbDqaI4[00:12:19] Hermann von Helmholtzhttps://plato.stanford.edu/entries/hermann-helmholtz/[00:38:34] David Redish (U. Minnesota)https://redishlab.umn.edu/[01:10:19] Robin Dunbarhttps://www.psy.ox.ac.uk/people/robin-dunbar[01:15:04] Emil Menzelhttps://www.sciencedirect.com/bookseries/behavior-of-nonhuman-primates/vol/5/suppl/C[01:19:49] Nick Bostromhttps://nickbostrom.com/[02:28:25] Noam Chomskyhttps://linguistics.mit.edu/user/chomsky/[03:01:22] Judea Pearlhttps://samueli.ucla.edu/people/judea-pearl/Concept/Framework:[00:05:04] Active Inferencehttps://www.youtube.com/watch?v=KkR24ieh5OwPaper:[00:35:59] Predictions not commands [Rick A Adams]https://pubmed.ncbi.nlm.nih.gov/23129312/Book:[01:25:42] The Elephant in the Brainhttps://www.amazon.com/Elephant-Brain-Hidden-Motives-Everyday/dp/0190495995[01:28:27] The Status Gamehttps://www.goodreads.com/book/show/58642436-the-status-game[02:00:40] The Selfish Genehttps://amazon.com/dp/0198788606[02:14:25] The Language Gamehttps://www.amazon.com/Language-Game-Improvisation-Created-Changed/dp/1541674987[02:54:40] The Evolution of Languagehttps://www.amazon.com/Evolution-Language-Approaches/dp/052167736X[03:09:37] The Three-Body Problemhttps://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 Learning1. Knowledge Follows Laws (Like Physics)2. You Can't Download Expertise3. Why Big Companies Fail to Adapt4. The "Infinite Alphabet" of EconomiesIf 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/9780241655672https://x.com/cesifotiRescript link. https://app.rescript.info/public/share/eaBHbEo9xamwbwpxzcVVm4NQjMh7lsOQKeWwNxmw0JQ---TIMESTAMPS:00:00:00 The Three Laws of Knowledge00:02:28 Rival vs. Non-Rival: The Economics of Ideas00:05:43 Why You Can't Just 'Download' Knowledge00:08:11 The Detective Novel Analogy00:11:54 Collective Learning & Organizational Networks00:16:27 Architectural Innovation: Amazon vs. Barnes & Noble00:19:15 The First Law: Learning Curves00:23:05 The Samuel Slater Story: Treason & Memory00:28:31 Physics of Knowledge: Joule's Cannon00:32:33 Extensive vs. Intensive Properties00:35:45 Knowledge Decay: Ise Temple & Polaroid00:41:20 Absorptive Capacity: Sony & Donetsk00:47:08 Disruptive Innovation & S-Curves00:51:23 Team Size & The Cost of Innovation00:57:13 Geography of Knowledge: Vespa's Origin01:04:34 Migration, Diversity & 'Planet China'01:12:02 Institutions vs. Knowledge: The China Story01:21:27 Economic Complexity & The Infinite Alphabet01:32:27 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 Plannedhttps://amazon.com/dp/3319155237[01:35:00] Why Information Growshttps://amazon.com/dp/0465048994Paper:[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/ Emergencehttps://arxiv.org/abs/2506.11135Person:[00:25:30] Samuel Slaterhttps://en.wikipedia.org/wiki/Samuel_Slater[00:42:05] Masaru Ibuka (Sony)https://www.sony.com/en/SonyInfo/CorporateInfo/History/SonyHistory/1-02.html

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)4. Will AI kill us all? (The Doomer Debate)5. What happens to human jobs and purpose?6. Do we need suffering?Mikes channel: https://www.youtube.com/channel/UCfQgsKhHjSyRLOp9mnffqVgRESCRIPT INTERACTIVE PLAYER: https://app.rescript.info/public/share/GVMUXHCqctPkXH8WcYtufFG7FQcdJew_RL_MLgMKU1U---TIMESTAMPS:00:00:00 Introduction & Workout Demo00:04:15 ASI Timelines & Definitions00:10:24 The Embodiment Debate00:18:28 Neutrinos & Abstract Knowledge00:25:56 Can AI Learn From YouTube?00:31:25 Diversity of Intelligence00:36:00 AI Slop & Understanding00:45:18 The Simulation Argument: Fire & Water00:58:36 Consciousness & Zombies01:04:30 Do Reasoning Models Actually Reason?01:12:00 The Live Learning Problem01:19:15 Superintelligence & Benevolence01:28:59 What is True Agency?01:37:20 Game Theory & The "Kill All Humans" Fallacy01:48:05 Regulation & The China Factor01:55:52 Mind Uploading & The Future of Love02:04:41 Economics of ASI: Will We Be Useless?02:13:35 The Matrix & The Value of Suffering02:17:30 Transhumanism & Inequality02:21:28 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 Needhttps://arxiv.org/abs/1706.03762[00:45:00] GPT-4 Technical Reporthttps://arxiv.org/abs/2303.08774[01:45:00] Anthropic Agentic Misalignment Paperhttps://www.anthropic.com/research/agentic-misalignment[02:17:45] Retatrutidehttps://pubmed.ncbi.nlm.nih.gov/37366315/Organization:[00:15:50] CERNhttps://home.cern/[01:05:00] METR Long Horizon Evaluationshttps://evaluations.metr.org/MLST Episode:[00:23:10] MLST: Llion Jones - Inventors' Remorsehttps://www.youtube.com/watch?v=DtePicx_kFY[00:50:30] MLST: Blaise Agüera y Arcas Interviewhttps://www.youtube.com/watch?v=rMSEqJ_4EBk[01:10:00] MLST: David Krakauerhttps://www.youtube.com/watch?v=dY46YsGWMIcEvent:[00:23:40] ARC Prize/Challengehttps://arcprize.org/Book:[00:24:45] The Brain Abstractedhttps://www.amazon.com/Brain-Abstracted-Simplification-Philosophy-Neuroscience/dp/0262548046[00:47:55] Pamela McCorduckhttps://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/1685891241Visual Context Needed:[00:29:40] AT-AT Walker (Star Wars)https://starwars.fandom.com/wiki/All_Terrain_Armored_TransportPerson:[00:33:15] Andrej Karpathyhttps://karpathy.ai/Video:[01:40:00] Mike Israetel vs Liron Shapira AI Doom Debatehttps://www.youtube.com/watch?v=RaDWSPMdM4oCompany:[02:26:30] Examine.comhttps://examine.com/