Title:
Collision Course: Artificial Intelligence meets Fundamental Interactions

dc.contributor.author Thaler, Jesse
dc.contributor.corporatename Georgia Institute of Technology. Institute for Data Engineering and Science en_US
dc.contributor.corporatename Massachusetts Institute of Technology. Dept. of Physics en_US
dc.date.accessioned 2020-12-01T21:41:48Z
dc.date.available 2020-12-01T21:41:48Z
dc.date.issued 2020-10-30
dc.description Presented online on October 20, 2020 at 2:00 p.m. en_US
dc.description Jesse Thaler joined the MIT Physics Department in 2010, and is currently an Associate Professor in the Center for Theoretical Physics. He is the inaugural Director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions. He is a theoretical particle physicist who fuses techniques from quantum field theory and machine learning to address outstanding questions in fundamental physics. His current research is focused on maximizing the discovery potential of the Large Hadron Collider (LHC) through new theoretical frameworks and novel data analysis techniques. en_US
dc.description Runtime: 66:08 minutes en_US
dc.description.abstract Modern machine learning has had an outsized impact on many scientific fields, and fundamental physics is no exception. What is special about fundamental physics, though, is the vast amount of theoretical, experimental, and observational knowledge that we already have about many problems in the field. Is it possible to teach a machine to “think like a physicist” and thereby advance physics knowledge from the smallest building blocks of nature to the largest structures in the universe? In this talk, I argue that the answer is “yes”, using the example of particle physics at the Large Hadron Collider to highlight the fascinating synergy between theoretical principles and machine learning architectures. I also argue that by fusing the “deep learning” revolution with the time-tested strategies of “deep thinking” in physics, we can galvanize research innovation in artificial intelligence more broadly. en_US
dc.format.extent 66:08 minutes
dc.identifier.uri http://hdl.handle.net/1853/63946
dc.language.iso en_US en_US
dc.relation.ispartofseries IDEaS-AI Seminar Series en_US
dc.subject Machine learning en_US
dc.subject Particle physics en_US
dc.title Collision Course: Artificial Intelligence meets Fundamental Interactions en_US
dc.type Moving Image
dc.type.genre Lecture
dspace.entity.type Publication
local.contributor.corporatename Institute for Data Engineering and Science
local.relation.ispartofseries IDEaS Seminar Series
relation.isOrgUnitOfPublication 2c237926-6861-4bfb-95dd-03ba605f1f3b
relation.isSeriesOfPublication 315185f2-d0ec-4ea2-8fdc-822ed04da3a8
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