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Explore automated theory formation and the concept of mathematical interestingness in this seminar lecture by George Tsoukalas from UT Austin's Department of Computer Science and Google DeepMind. Examine how advances in modern learning systems are beginning to demonstrate utility for select problems in research mathematics, with a focus on the broader challenge of developing new theories automatically. Discover the rich historical context of this field, which connects to some of the earliest work in artificial intelligence, particularly the central question of measuring the "interestingness" of mathematical concepts. Learn about recent research using large language models to synthesize interestingness measures that guide theory exploration in elementary number theory from scratch. Gain insights into potential future research directions in this domain through work conducted at UT Austin in collaboration with Rahul Saha, Amitayush Thakur, Sabrina Reguyal, and Swarat Chaudhuri, presented as part of Harvard CMSA's New Technologies in Mathematics Seminar series.
