VAEs Are Energy-Based Models? [Dr. Jeff Beck]
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.
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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
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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
<trunc, see rescript>
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RESCRIPT:
https://app.rescript.info/public/share/DJlSbJ_Qx080q315tWaqMWn3PixCQsOcM4Kf1IW9_Eo
PDF:
https://app.rescript.info/api/public/sessions/0efec296b9b6e905/pdf
Bayesian Brain, Scientific Method, and Models [Dr. Jeff Beck]
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
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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
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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