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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.

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

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  • (Score: 2) by HiThere on Thursday October 19 2017, @11:38PM

    by HiThere (866) Subscriber Badge on Thursday October 19 2017, @11:38PM (#584992) Journal

    Welll.... it's also true that different people mean different things when they say "the Singularity". I don't think human level AI will necessarily be able to pass the Turing test...and I know a number of people who couldn't. The Turing test depends too heavily on the judges to have much significance.

    FWIW, it's already true that in some areas AI's are superhuman, but they aren't yet generalized enough. And I don't think an actual "general AI" is even possible. And I don't think humans are "general intelligence". Humans have a lot of areas where they are smart, and a lot of areas where they can be trained to be smart, but I don't believe that we cover the spectrum of possible intelligences. To be specific, it's my belief that in areas that require dealing simultaneously with more than seven variables people are essentially untrainable. Possibly seven is slightly too low a number, there may be some people who can be trained to handle that, but that's my current estimate. However, switch it to 17 and I doubt there would be many who would claim to be able to handle it...and they'd all be obviously deluded. So we're just talking about (by analogy) maximum stack depth.

    Once you get rid of the idea that a general intelligence is even possible you start noticing that in a lot of places it isn't even desirable. It's better to have communicating simpler processes. And we don't know just how much can be done at that level even using the tools that already exist.

    So as I said, I expect the Singularity in the period 2030-2035. And it won't look like any of the predictions.

    Put not your faith in princes.
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