Leslie Valiant was educated at King’s College, Cambridge; Imperial College, London; and at Warwick University where he received his Ph.D. in computer science in 1974. He is currently T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in the School of Engineering and Applied Sciences at Harvard University, where he has taught since 1982. Before coming to Harvard he had taught at Carnegie Mellon University, Leeds University, and the University of Edinburgh.
His work has ranged over several areas of theoretical computer science, particularly complexity theory, computational learning, and parallel computation. He also has interests in computational neuroscience, evolution and artificial intelligence.
He received the Nevanlinna Prize at the International Congress of
Mathematicians in 1986, the Knuth Award in 1997, the European
Association for Theoretical Computer Science EATCS Award in 2008, and
the 2010 A. M. Turing Award. He is a Fellow of the Royal Society
(London) and a member of the National Academy of Sciences (USA).
Machine Learning and Beyond
Machine learning is a highly effective technology that has found broad applications in science and technology. Behind it is a mathematical science that first defines the goals that need to be achieved if learning is to be successful. It goes on to study the most effective ways of achieving these goals, and also to characterize cases where effective learning is impossible. However, central as this study may be for cognition, it does not account for all of cognition. The question we ask here is whether one can build on the success of machine learning to address the broader goals of artificial intelligence. We regard reasoning as the other main component, and suggest that the central challenge is to unify learning and reasoning into a single framework.