What Can be Automated: A Viewpoint from Learning and Evolution
Event details
| Date | 01.05.2017 |
| Hour | 16:15 › 17:15 |
| Speaker |
Prof. Leslie Valiant, Harvard University Bio: 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, learning, and parallel computation. He also has interests in computational neuroscience, evolution and artificial intelligence and is the author of two books, Circuits of the Mind, and Probably Approximately Correct. 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). Please see his website for more information. |
| Location | |
| Category | Conferences - Seminars |
IC Distinguished Lecture
With machine learning technology we are now able to automate many tasks that humans learn to perform through experience rather than through step-by-step instruction. Without such a learning capability we are limited to automating tasks for which a step-by-step sequence of instructions is known. In this talk we shall ask whether it is possible to circumscribe the set of tasks that we can expect to effectively automate. The discussion will start from the hypothesis that all the information that resides in living organisms was initially acquired either through learning by an individual or through evolution. Then any unified theory of evolution and learning should be able to characterize the capabilities that humans and other living organisms can potentially acquire and perform. Characterizing these capabilities would tell us about the nature of humans, but would also inform us about feasible targets for automation. We shall discuss where we are with such a unified theory.
With machine learning technology we are now able to automate many tasks that humans learn to perform through experience rather than through step-by-step instruction. Without such a learning capability we are limited to automating tasks for which a step-by-step sequence of instructions is known. In this talk we shall ask whether it is possible to circumscribe the set of tasks that we can expect to effectively automate. The discussion will start from the hypothesis that all the information that resides in living organisms was initially acquired either through learning by an individual or through evolution. Then any unified theory of evolution and learning should be able to characterize the capabilities that humans and other living organisms can potentially acquire and perform. Characterizing these capabilities would tell us about the nature of humans, but would also inform us about feasible targets for automation. We shall discuss where we are with such a unified theory.
Practical information
- General public
- Free
- This event is internal
Organizer
- Prof. Nisheeth Vishnoi