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DTSTART;TZID=America/New_York:20240320T110000
DTEND;TZID=America/New_York:20240320T121500
DTSTAMP:20260717T100828
CREATED:20240313T144657Z
LAST-MODIFIED:20240318T143700Z
UID:10002909-1710932400-1710936900@cmsa.fas.harvard.edu
SUMMARY:AQFT Lecture Series
DESCRIPTION:AQFT Lecture Series \nSpeaker: Stephen D. Miller (Rutgers University) \nTitle: What 4-graviton scattering amplitudes had to say about the unitary dual \nAbstract: I’ll give an update on the problem of describing all unitary representations of a Lie group\, including joint work with Michael Green and Pierre Vanhove that used intuition from string theory to show the unitarity of the “next to minimal” representation of E8\, and more recent work with Joe Hundley and Jeff Adams\, Marc van Leeuwen\, and David Vogan.
URL:https://cmsa.fas.harvard.edu/event/aqft-lecture-series-32024/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:AQFT Lecture Series,Colloquia & Seminar
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DTSTART;TZID=America/New_York:20240320T140000
DTEND;TZID=America/New_York:20240320T150000
DTSTAMP:20260717T100829
CREATED:20240130T215041Z
LAST-MODIFIED:20240321T140550Z
UID:10001519-1710943200-1710946800@cmsa.fas.harvard.edu
SUMMARY:Solving olympiad geometry without human demonstrations
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Trieu H. Trinh\, Google Deepmind and NYU Dept. of Computer Science \nTitle: Solving olympiad geometry without human demonstrations \nAbstract: Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning\, owing to their reputed difficulty among the world’s best talents in pre-university mathematics. Current machine-learning approaches\, however\, are not applicable to most mathematical domains owing to the high cost of translating human proofs into machine-verifiable format. The problem is even worse for geometry because of its unique translation challenges\, resulting in severe scarcity of training data. We propose AlphaGeometry\, a theorem prover for Euclidean plane geometry that sidesteps the need for human demonstrations by synthesizing millions of theorems and proofs across different levels of complexity. AlphaGeometry is a neuro-symbolic system that uses a neural language model\, trained from scratch on our large-scale synthetic data\, to guide a symbolic deduction engine through infinite branching points in challenging problems. On a test set of 30 latest olympiad-level problems\, AlphaGeometry solves 25\, outperforming the previous best method that only solves ten problems and approaching the performance of an average International Mathematical Olympiad (IMO) gold medallist. Notably\, AlphaGeometry produces human-readable proofs\, solves all geometry problems in the IMO 2000 and 2015 under human expert evaluation and discovers a generalized version of a translated IMO theorem in 2004. \n 
URL:https://cmsa.fas.harvard.edu/event/nt-32024/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-03.20.2024.png
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