UCL ELLIS

Speaker	David Silver
Affiliation	Google DeepMind, University College London
Date	Thursday, 24 March 2016
Time	13:00-14:00
Location	Zoom
Link	Cruciform B304 - LT1
Event series	Jump Trading/ELLIS CSML Seminar Series
Abstract	Abstract: The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away. NOTE: This will include upcoming result & discussion of the match in Seoul, with the world champion Lee Sedol!
Biography