Machine Learning Street Talk (MLST)

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 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

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/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/

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.

TRANSCRIPT:
https://app.rescript.info/public/share/LMreunA-BUpgP-2AkuEvxA7BAFuA-VJNAp2Ut4MkMWk

---

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:* 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 [00:23:41]

---

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

Petar Veličković (GDM)
https://petar-v.com/
Paul Lessard
https://www.linkedin.com/in/paul-roy-lessard/
Bruno Gavranović
https://www.brunogavranovic.com/
Andrew Dudzik (GDM)
https://www.linkedin.com/in/andrew-dudzik-222789142/

---
REFERENCES:

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
https://www.youtube.com/watch?v=bIZB1hIJ4u8
[00:16:45] AlphaGeometry
https://arxiv.org/abs/2401.08312
[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