About this episode

In this episode, Prof. Andrew Saxe discusses the intersection of artificial and biological learning systems.

What does it really mean to understand how something learns, whether it's a child, a brain, or a neural network? In today’s episode, we sit down with Prof. Andrew Saxe, Professor of Theoretical Neuroscience & Machine Learning at University College London, to explore how mathematics can help us understand learning processes in all kinds of networks. We dive into the two-way street between how and what we learn, examining the way learnable content and the learning process shape each other. Our conversation anchors itself in Prof. Saxe’s early projects on language development in infants, current interests in knowledge generalization, and future aims for experimentalist-theorist collaborations, and we can’t wait for you to give it a listen! In this episode, we cover: (1:13) Motivations behind studying the learning process (5:24) When did neuroscience and psychology come into the picture? (8:06) Projects and people that defined his trajectory (12:33) Simple mathematical models give rise to complex dynamics. Why focus on simplicity? (15:16) Current and future focus points of the lab? (in the era of LLMs…) (20:27) Sparse reward problems in learning processes (21:46) How hierarchy…

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