Michael Littman, Brown University & National Science Foundation

Abstract

It is immensely empowering to delegate information processing and automation 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, with your help, could help make it easier for everyone to get computational help with their vexing problems.

[Video]

Speaker bio: Michael L. Littman is University Professor of Computer Science at Brown University, where he studies machine learning and decision-making under uncertainty. He has earned multiple university-level awards for teaching and his research has been recognized with three best-paper awards and three influential paper awards. Littman is a Fellow of the Association for the Advancement of Artificial Intelligence and the Association for Computing Machinery. He is currently serving as Division Director for Information and Intelligent Systems at the National Science Foundation. His book "Code to Joy: Why Everyone Should Learn a Little Programming" (MIT Press) came out earlier this month.