The Bayesian Brain: A New Theory for Why Transformers Work

What this episode covers

This episode explores the "black box" problem of large language models, emphasizing the critical need for interpretability due to their complex, inscrutable nature and real-world consequences. It then introduces Gregory Coppola's theory that transformers are formally equivalent to Bayesian networks, providing a detailed explanation of what Bayesian networks are and how they perform probabilistic reasoning. Listeners will learn about the challenges of AI interpretability and a groundbreaking theory that could demystify the inner workings of transformers by linking them to established probabilistic models.

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