AlphaGo and AI Progress - Future of Life Institute

AlphaGo and AI Progress - Future of Life Institute 
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 Published: March 8, 2016 Author: a guest blogger Contents 

 Tomorrow, March 9, DeepMind’s AlphaGo begins its quest to beat the reigning world champion of Go, Lee Se-dol . In anticipation of the event, we’re pleased to feature this excellent overview of the impact of AlphaGo on the AI field, written by Miles Brundage . Don’t forget to tune in to Youtube March 9-15 for the full tournament! 

 Introduction 

 AlphaGo’s victory over Fan Hui has gotten a lot of press attention, and relevant experts in AI and Go have generally agreed that it is a significant milestone. For example, Jon Diamond, President of the British Go Association, called the victory a “large, sudden jump in strength,” and AI researchers Francesca Rossi, Stuart Russell, and Bart Selman called it “important,” “impressive,” and “significant,” respectively.

 How large/sudden and important/impressive/significant was AlphaGo’s victory? Here, I’ll try to at least partially answer this by putting it in a larger context of recent computer Go history, AI progress in general, and technological forecasting. In short, it’s an impressive achievement, but considering it in this larger context should cause us to at least slightly decrease our assessment of its size/suddenness/significance in isolation. Still, it is an enlightening episode in AI history in other ways, and merits some additional commentary/analysis beyond the brief snippets of praise in the news so far. So in addition to comparing the reality to the hype, I’ll try to distill some general lessons from AlphaGo’s first victory about the pace/nature of AI progress and how we should think about its upcoming match against Lee Sedol.

 What happened 

 AlphaGo, a system designed by a team of 15-20 people [1] at Google DeepMind, beat Fan Hui, three-time European Go champion, in 5 out of 5 formal games of Go. Hui also won 2 out of 5 informal games with less time per move (for more interesting details often unreported in press accounts, see also the relevant Nature paper ). The program is stronger at Go than all previous Go engines (more on the question of how much stronger below).

 How it was done 

 AlphaGo was developed by a relatively large team (compared to those associated with other computer Go programs), using significant computing resources (more on this below). The program combines neural networks and Monte Carlo tree search (MCTS) in a novel way, and was trained in multiple phases involving both supervised learning and self-play. Notably from the perspective of evaluating its relation to AI progress, it was not trained end-to-end (though according to Demis Hassabis at AAAI 2016, they may try to do this in the future). It also used some hand-crafted features for the MCTS component (another point often missed by observers). The claimed contributions of the relevant paper are the ideas of value and policy networks, and 

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