Course Overview:

Duration: 30 min

Conveying Tasks to Computers: How Machine Learning Can Help

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.

Towards Trustworthy LLMs: Understanding Limits, Advancing Capabilities, Ensuring Safety

Instructor:

Michael Littman, PhD

Professor at Brown University

Michael L. Littman is a University Professor of Computer Science at Brown University, studying machine learning and decision making under uncertainty. He has earned multiple university-level awards for teaching, and his research on reinforcement learning, probabilistic planning, and automated crossword-puzzle solving has been recognized with three best-paper awards and three influential paper awards.

Littman is co-director of Brown's Humanity Centered Robotics Initiative and a Fellow of the Association for the Advancement of Artificial Intelligence and the Association for Computing Machinery. He is also a Fellow of the American Association for the Advancement of Science Leshner Leadership Institute for Public Engagement with Science, focusing on Artificial Intelligence. He recently served as Division Director for Information and Intelligent Systems at the National Science Foundation.

Littman is currently Brown University's inaugural Associate Provost for Artificial Intelligence, helping to coordinate AI efforts in research, teaching, operations, policy, and communication across the campus.

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