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Google DeepMind researchers have made their old AlphaGo program obsolete:
The old AlphaGo relied on a computationally intensive Monte Carlo tree search to play through Go scenarios. The nodes and branches created a much larger tree than AlphaGo practically needed to play. A combination of reinforcement learning and human-supervised learning was used to build "value" and "policy" neural networks that used the search tree to execute gameplay strategies. The software learned from 30 million moves played in human-on-human games, and benefited from various bodges and tricks to learn to win. For instance, it was trained from master-level human players, rather than picking it up from scratch.
AlphaGo Zero did start from scratch with no experts guiding it. And it is much more efficient: it only uses a single computer and four of Google's custom TPU1 chips to play matches, compared to AlphaGo's several machines and 48 TPUs. Since Zero didn't rely on human gameplay, and a smaller number of matches, its Monte Carlo tree search is smaller. The self-play algorithm also combined both the value and policy neural networks into one, and was trained on 64 GPUs and 19 CPUs over a few days by playing nearly five million games against itself. In comparison, AlphaGo needed months of training and used 1,920 CPUs and 280 GPUs to beat Lee Sedol.
Though self-play AlphaGo Zero even discovered for itself, without human intervention, classic moves in the theory of Go, such as fuseki opening tactics, and what's called life and death. More details can be found in Nature, or from the paper directly here. Stanford computer science academic Bharath Ramsundar has a summary of the more technical points, here.
Go is an abstract strategy board game for two players, in which the aim is to surround more territory than the opponent.
Previously: Google's New TPUs are Now Much Faster -- will be Made Available to Researchers
Google's AlphaGo Wins Again and Retires From Competition
(Score: 2, Touché) by Anonymous Coward on Thursday October 19 2017, @03:47PM (3 children)
First you say novel strategies would be useful, then that they "won't be very useful". Which is it?
(Score: 2) by looorg on Thursday October 19 2017, @04:11PM (2 children)
It clearly depends on the strategy. It would have been awesome if they had actually mentioned what it was. For all we know their new strategy is completely worthless for human players. Then what is it good for? When AlphaGo-1 tries to play AlphaGo-2 and they try and trick eachother?
(Score: 2) by HiThere on Thursday October 19 2017, @07:13PM (1 child)
Why should you expect it to be useful for human players? Perhaps it's a strategy that's only useful when you're playing against something better than any human player. Not that I expect this to be true, but your basic criterion seems to need justification.
Javascript is what you use to allow unknown third parties to run software you have no idea about on your computer.
(Score: 2) by takyon on Thursday October 19 2017, @08:13PM
You should note that aside from Human vs. AlphaGo, there have also been Human + AlphaGo vs. Human + AlphaGo [futurism.com] matches. Go players have been able to use computer software for assistance [wikipedia.org] for years, it's just going to get a lot more useful (to the point of being "cheaty").
Not responding to any particular post, just throwing it out there.
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