This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at https://notion.com/mlst Why can deep networks discover abstractions that shallow models miss? Statistical physicist Matthieu Wyart joins Tim Scarfe to argue that the answer lies in the hidden hierarchy of data. Language and images are built from parts within parts; depth lets a network recover those coarse-grained variables and escape the curse of dimensionality. The conversation moves from jamming transitions and rough loss surfaces to Chomsky, context-free grammars and machine creativity. Wyart explains why next-token prediction can still recover compositional structure, where current systems fall short of genuine scientific invention, and why predicting latent representations rather than raw tokens could make learning far more sample-efficient. They also examine diffusion models, neural scaling laws and the limits of physics-inspired theory. The final question is on a personal note: if mistakes are the price of leaving the beaten path, how much scientific risk is worth taking? --- TIMESTAMPS: 00:00:00 Can machines learn abstractions from data? 00:02:00 Notion agentic workspace 00:02:49 From statistical physics to machine learning 00:06:40 What physics can explain about learning 00:16:37 From Carnot to Chomsky bulldozer 00:21:21 How deep networks recover hidden hierarchies 00:32:43 Where machine creativity still falls short 00:40:48 How deep nets escape the curse of dimensionality 00:52:19 Why predict latents instead of tokens 01:02:49 The sample-efficiency case for latent prediction 01:08:31 Diffusion, scaling laws and text entropy 01:16:40 The scientists we learn from and the mistakes we make --- REFERENCES: person: [00:00:43] Noam Chomsky https://linguistics.mit.edu/user/chomsky/ tool: [00:02:08] Notion Developer Platform https://www.notion.com/en-gb/blog/introducing-developer-platform paper: [00:04:43] Mastering the game of Go with deep neural networks and tree search https://www.nature.com/articles/nature16961 [00:05:52] Reconciling modern machine-learning practice and the bias-variance trade-off https://arxiv.org/abs/1812.11118 [00:25:54] How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model https://arxiv.org/abs/2307.02129 [00:42:12] Efficient Estimation of Word Representations in Vector Space https://arxiv.org/abs/1301.3781 [00:52:46] Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture https://arxiv.org/abs/2301.08243 [00:52:54] Learn from your own latents and not from tokens: A sample-complexity theory https://arxiv.org/abs/2605.27734 [01:08:31] A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data https://arxiv.org/abs/2402.16991 [01:11:39] Scaling Laws for Neural Language Models https://arxiv.org/abs/2001.08361 [01:12:17] Deriving Neural Scaling Laws from the statistics of natural language https://arxiv.org/abs/2602.07488 [01:13:34] Prediction and Entropy of Printed English https://ieeexplore.ieee.org/document/6773263 --- LINKS: Download PDF transcript: https://app.rescript.info/share/f7644cdaa86c5cc1e41e484e290f2bd4