Thursday, February 4, 2021, 9:00 am — Videoconference / Virtual Event (see link below)
Abstract: 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.
Hosted by: Akio Tomiya
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