Speaker: Dr. Michael Littman (Brown University)

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 University Professor of Computer Science at Brown University and is doing a rotation as 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 his research. Littman has chaired major conferences in artificial intelligence 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.