A Triangle of Influence: Bringing Together Physics, Pure Mathematics, and Computer Science

Recent advances in machine learning have begun creating new bridges to physics and mathematics that have traditionally existed between the latter two. Given this progress, I will speculate about where we are and where things might be headed, including through the recently launched NSF AI Institute for Artificial Intelligence and Fundamental Interactions. Specifically, I’ll survey well-known machine learning results in supervised learning, reinforcement learning, and generative models, and explain cases where these techniques are already impacting physics and math. In more detail, I will explain some remarkable similarities between neural networks and quantum field theory that might point towards a theoretical understanding of deep learning, and also how an AI agent’s ability to unknot headphones might provide useful in cracking a foundational problem in topology.