Machine Learning Street Talk (MLST)

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.

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*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 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*
*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 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

1. Knowledge Follows Laws (Like Physics)
2. You Can't Download Expertise
3. Why Big Companies Fail to Adapt
4. The "Infinite Alphabet" of Economies

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