Invited Talk: Logic, Probability, Knowledge, and Learning
One purpose of learning is to accumulate knowledge, which then becomes an input to enable further learning. I will examine this idea first in the context of logic and then in the context of probability. The idea becomes particularly powerful with probabilistic formalisms that draw on the expressive power of first-order logic, although there is still a long way to go before the potential of cumulative learning is fulfilled.
Stuart Russell is a Professor of Computer Science at the University of California at Berkeley, holder of the Smith-Zadeh Chair in Engineering, and Director of the Center for Human-Compatible AI. He is a recipient of the IJCAI Computers and Thought Award and from 2012 to 2014 held the Chaire Blaise Pascal in Paris. He is an Honorary Fellow of Wadham College, Oxford, an Andrew Carnegie Fellow, and a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science. His book “Artificial Intelligence: A Modern Approach” (with Peter Norvig) is the standard text in AI, used in 1500 universities in 135 countries. His research covers a wide range of topics in artificial intelligence, with an emphasis on the long-term future of artificial intelligence and its relation to humanity. He has developed a new global seismic monitoring system for the nuclear-test-ban treaty and is currently working to ban lethal autonomous weapons.
Sun 15 NovDisplayed time zone: Central Time (US & Canada) change
11:00 - 12:20
|Invited Talk: Logic, Probability, Knowledge, and Learning|
I: Stuart Russell University of California, Berkeley
|Training Neural Networks to Do Logic, with Logic|
Paul Tarau University of North Texas
|Break: Ask Me Anything|