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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 takyon on Thursday October 19 2017, @08:09PM

    by takyon (881) Subscriber Badge <reversethis-{gro ... s} {ta} {noykat}> on Thursday October 19 2017, @08:09PM (#584829) Journal

    I agree. Google's TPUs, Intel's Nervana, Nvidia's Tesla, etc. are just accelerators for 8-bit machine learning. They are capable of increasing the performance (and perf/Watt) of machine learning tasks by a lot compared to previous chips, but even if they got a hundred times better and passed the Turing test it couldn't be called real intelligence.

    If real "strong AI" is going to come from any hardware, it will likely be using neuromorphic designs such as IBM's TrueNorth or this thing []. What's more, these designs use so little power that they could be scaled up to 3D without the same overheating issues, allowing more artificial "synapses" and even greater resemblance to the human brain. If that approach stalls out, you simply need to connect several of them with a high-speed interconnect.

    We're not far off from making that happen. We're reaching the limits of Moore's law (as far as we know) and 3D NAND chips are commonplace. I wouldn't be surprised if it "strong AI" is about to be created [] or already has been created, and corporations or the govt/military are keeping it under wraps to continue development while avoiding IP issues and the inevitable public/ethics debates. I doubt it would be hard to find comrades for a domestic terrorist group hell bent on destroying the technology by any means necessary.

    [SIG] 10/28/2017: Soylent Upgrade v14 []
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