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What Does Machine Learning Have to Offer Mathematics?

The interaction of machine learning with math has attracted a lot of attention, because mathematics is in some respects a closed world with well-defined rules (like chess, and unlike poetry-writing) but also a domain where success is ultimately judged by human assessments of ingenuity and importance, not rigid criteria (like poetry-writing, and unlike chess). Can machines prove theorems? Can they have mathematical ideas? Jordan Ellenberg will talk about his joint work with researchers from DeepMind, which used novel techniques in machine learning to make progress in a problem in combinatorics, and will try to chart some near-term ways that machine learning may affect mathematical practice.

Speaker: Jordan Ellenberg, University of Wisconsin - Madison

Register at weblink to attend in person.  Lecture will be available on YouTube later (see weblink)

Monday, 07/22/24


Website: Click to Visit



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Calvin Laboratory

UC Berkeley
Berkeley, CA 94720