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Video: Toward a General AI-Agent Architecture

Richard S. Sutton from DeepMind Alberta

In this video from NeurIPS 2019, Rich Sutton from DeepMind Alberta presents: Toward a General AI-Agent Architecture.

In practice, I work primarily in reinforcement learning as an approach to artificial intelligence. I am exploring ways to represent a broad range of human knowledge in an empirical form–that is, in a form directly in terms of experience–and in ways of reducing the dependence on manual encoding of world state and knowledge.

Richard S. Sutton is a distinguished research scientist at DeepMind in Edmonton and a professor in the Department of Computing Science at the University of Alberta. Prior to joining DeepMind in 2017 and 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. He is co-author of the textbook Reinforcement Learning: An Introduction from MIT Press. He is also a fellow of the Royal Society of Canada, the Association for the Advancement of Artificial Intelligence, the Alberta Machine Intelligence Institute, and CIFAR. His research interests center on the learning problems facing a decision-maker interacting with its environment, which he sees as central to intelligence. He has additional interests in animal learning psychology, in connectionist networks, and generally in systems that continually improve their representations and models of the world. His scientific publications have been cited more than 70,000 times. He is also a libertarian, a chess player, and a cancer survivor.

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