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posted by martyb on Thursday October 19 2017, @02:39PM   Printer-friendly
from the Zeroing-in-on-AI dept.

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.


Previously: Google's New TPUs are Now Much Faster -- will be Made Available to Researchers
Google's AlphaGo Wins Again and Retires From Competition

Original Submission

  • (Score: 2) by rylyeh on Thursday October 19 2017, @10:19PM

    by rylyeh (6726) Subscriber Badge <{kadath} {at} {}> on Thursday October 19 2017, @10:19PM (#584943)

    Absolutely. Go is a zero-sum game of 'Perfect' information as defined in game theory [URL:]. That's what makes it a great way to test ML.
    Non-zero-sum games of imperfect information more closely represent the class of problems like self-driving, emergency response, diagnosis and system analysis.
    Although I hope it's not needed, artificially aware AI (with a meaningful point of view) may be necessary to solve some of them.

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