Location: Hugh Dempster Building (6245 Agronomy Rd.), Room 110
Speaker: Richard S. Sutton, Professor, University of Alberta
Title: The Future of AI
Host: Mark Schmidt, UBC Computer Science
Abstract: When mankind finally comes to understand the principles of intelligence and how they can be embodied in machines, it will be the most important discovery of our age, perhaps of any age. In recent years, with progress in deep learning and other areas, this great scientific prize has begun to appear almost within reach. Artificial superintelligences are not imminent, but they may well occur within our lifetimes. The consequences, benefits, and dangers for humanity have become popular topics in the press, at public policy meetings (e.g., Davos) and at scientific meetings; luminaries such as Stephen Hawking and Elon Musk have weighed in. Is it all hyperbole and fear mongering, or are there genuine scientific advances underlying the current excitement? In this talk, I try to provide some perspective on these issues, informed and undoubtedly biased by my 38 years of research in AI. I seek to contribute to the conversation in two ways: 1) by seeing current developments as part of the longest trend in AI---towards cheaper computation and thus a greater role for search, learning, and all things meta, and 2) by sketching one possible path to AI (the one I am currently treading) and what it might look like for it to succeed.
Bio: Richard S. Sutton is a professor and iCORE chair in the department of computing science at the University of Alberta. He is a fellow of the Association for the Advancement of Artificial Intelligence and co-author of the textbook Reinforcement Learning: An Introduction from MIT Press. Before joining the University of Alberta in 2003, he worked in industry at AT&T and GTE Labs, and in academia at the University of Massachusetts. He received a PhD in computer science from the University of Massachusetts in 1984 and a BA in psychology from Stanford University in 1978. Rich's research interests center on the learning problems facing a decision-maker interacting with its environment, which he sees as central to artificial intelligence. He is also interested in animal learning psychology, in connectionist networks, and generally in systems that continually improve their representations and models of the world.