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.