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Seminar Series

December 03

November 12

Panel Discussion: Open Questions in Theory of Learning

Moderator: Tomaso Poggio - Panel: Ila Fiete, Philip Isola, Eran Malach, Haim Sompolinsky

Part Of

4:00P
- 5:30P

Location

46-3002
Singleton Auditorium
Add to Calendar 2024-11-12 16:00:00 2024-11-12 17:30:00 America/New_York Panel Discussion: Open Questions in Theory of Learning Abstract: In a society that is confronting the new age of AI in which LLMs begin to display aspects of human intelligence, understanding the fundamental theory of deep learning and applying it to real systems is a compelling and urgent need.This panel will introduce some new simple foundational results in the theory of supervised learning. It will also discuss open problems in the theory of learning, including problems specific to neuroscience.Panelists:Ila Fiete - Professor of Brain and Cognitive Sciences, MITHaim Sompilinski - Professor of Molecular and Cellular Biology and of Physics, Harvard UniversityEran Malach - Research fellow, Kempner Institute at Harvard UniversityPhilip Isola - Associate Professor, EECS at MIT 46-3002

October 01

September 10

Quest | CBMM Seminar Series - Conveying Tasks to Computers: How Machine Learning Can Help

Michael Littman
Brown University

Part Of

4:00P
- 5:30P

Location

46-3002
Singleton Auditorium
Add to Calendar 2024-09-10 16:00:00 2024-09-10 17:30:00 America/New_York Quest | CBMM Seminar Series - Conveying Tasks to Computers: How Machine Learning Can Help Abstract: It is immensely empowering to delegate information processing work to machines and have them carry out difficult tasks on our behalf. But programming computers is hard. The traditional approach to this problem is to try to fix people: They should work harder to learn to code. In this talk, I argue that a promising alternative is to meet people partway. Specifically, powerful new approaches to machine learning provide ways to infer intent from disparate signals and could help make it easier for everyone to get computational help with their vexing problems.Bio: Michael L. Littman, Ph.D. is a Professor of Computer Science at Brown University and Division Director of Information and Intelligent Systems at the National Science Foundation. He studies machine learning and decision-making under uncertainty and has earned multiple awards for his teaching and research. Littman has chaired major conferences in A.I. and machine learning and is a Fellow of both the Association for the Advancement of Artificial Intelligence and the Association for Computing Machinery. He was selected by the American Association for the Advancement of Science as a Leadership Fellow for Public Engagement with Science in Artificial Intelligence, has a popular YouTube channel and appeared in a national TV commercial in 2016. His book, "Code to Joy: Why Everyone Should Learn a Little Programming" was published in October 2023 by MIT Press. 46-3002