Title:
Collision Course: Artificial Intelligence meets Fundamental Interactions
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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