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Provably beneficial AI

Seminar

Speaker(s)

Stuart Russell, University of California, Berkeley

Date and Time

March 13, 2017 11:30 AM - 1:00 PM

Availability

RSVP

RSVP required by 5PM March 11.

Location

CISAC Central Conference Room
Encina Hall, 2nd Floor
616 Serra St
Stanford, CA 94305

Abstract: Should we be concerned about long-term risks from superintelligent AI?

If so, what can we do about it?  While some in the mainstream AI community dismiss these concerns, I will argue instead that a fundamental reorientation of the field is required. Instead of building systems that optimize arbitrary objectives, we need to learn how to build systems that will, in fact, be beneficial for us.  I will show that it is useful to imbue systems with explicit uncertainty concerning the true objectives of the humans they are designed to help.

About the Speaker: Stuart Russell received his B.A. with first-class honors in physics from Oxford University in 1982 and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is Professor (and formerly Chair) of Electrical Engineering and Computer Sciences and holder of the Smith-Zadeh Chair in Engineering. He is also an Adjunct Professor of Neurological Surgery at UC San Francisco and Vice-Chair of the World Economic Forum's Council on AI and Robotics. He is a recipient of the Presidential Young Investigator Award of the National Science Foundation, the IJCAI Computers and Thought Award, the World Technology Award (Policy category), the Mitchell Prize of the American Statistical Association and the International Society for Bayesian Analysis, and Outstanding Educator Awards from both ACM and AAAI. In 1998, he gave the Forsythe Memorial Lectures at Stanford University and from 2012 to 2014 he held the Chaire Blaise Pascal in Paris. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science. His research covers a wide range of topics in artificial intelligence including machine learning, probabilistic reasoning, knowledge representation, planning, real-time decision making, multitarget tracking, computer vision, computational physiology, global seismic monitoring, and philosophical foundations. His books include The Use of Knowledge in Analogy and Induction, Do the Right Thing: Studies in Limited Rationality (with Eric Wefald), and Artificial Intelligence: A Modern Approach (with Peter Norvig).

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