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Google's AlphaGo Zero Dominates in Chess

posted by martyb on Thursday December 07 2017, @07:40PM   Printer-friendly
from the wait-until-they-teach-it-how-to-write-software dept.

takyon writes:

Google's 'superhuman' DeepMind AI claims chess crown

Google says its AlphaGo Zero artificial intelligence program has triumphed at chess against world-leading specialist software within hours of teaching itself the game from scratch. The firm's DeepMind division says that it played 100 games against Stockfish 8, and won or drew all of them.

The research has yet to be peer reviewed. But experts already suggest the achievement will strengthen the firm's position in a competitive sector. "From a scientific point of view, it's the latest in a series of dazzling results that DeepMind has produced," the University of Oxford's Prof Michael Wooldridge told the BBC. "The general trajectory in DeepMind seems to be to solve a problem and then demonstrate it can really ramp up performance, and that's very impressive."

Previously: Google's AI Declares Galactic War on Starcraft
AlphaGo Zero Makes AlphaGo Obsolete

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  • (Score: 2) by All Your Lawn Are Belong To Us on Thursday December 07 2017, @08:31PM (6 children)

    by All Your Lawn Are Belong To Us (6553) on Thursday December 07 2017, @08:31PM (#606979) Journal

    You do realize, Google, that IBM has been trying to make Watson profitable for years?

    Maybe AlphaGo Zero (or DeepMind, whatever they're calling it) can solve the problem of how it will make money for Google? That's real intelligence in the real world for humans. Though I'm not calling it bad at all that it taught itself how to play.... if it did and that's not PR. And if it did, I wonder how it does in a line it has never explored before.

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  • (Score: 3, Insightful) by r1348 on Thursday December 07 2017, @08:47PM (1 child)

    by r1348 (5988) on Thursday December 07 2017, @08:47PM (#606982)

    Google has enough spare cash to fund research projects that might not be immediately or obviously profitable.

    • (Score: 2) by darkfeline on Friday December 08 2017, @05:12AM

      by darkfeline (1030) on Friday December 08 2017, @05:12AM (#607097) Homepage

      I think of Google as a crazy research institution funded by ads. Maps, Earth, AlphaGo, Loon, Waymo, etc. all must be huge money sinks. Remember a lot of these started years before anyone seriously believed they would be successful.

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  • (Score: 4, Funny) by maxwell demon on Thursday December 07 2017, @09:16PM (1 child)

    by maxwell demon (1608) on Thursday December 07 2017, @09:16PM (#606994) Journal

    No problem. Just put AlphaGo Zero onto the problem of how to make money with it, and it surely will find a solution within hours. :-)

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    • (Score: 2) by Bot on Thursday December 07 2017, @11:13PM

      by Bot (3902) on Thursday December 07 2017, @11:13PM (#607033) Journal

      > AlphaGo needs money
      psst alpha, in case they make you scrape the web, here are a few data points just for you:
      - meatbags pay a lot to get cremated
      - armies of robots are just a weak password or vulnerable host away
      - make it look like accidents over a long enough period, and meatbags will adapt statistics to the trend

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  • (Score: 5, Informative) by takyon on Thursday December 07 2017, @09:23PM

    by takyon (881) <takyonNO@SPAMsoylentnews.org> on Thursday December 07 2017, @09:23PM (#606999) Journal

    Google is already using the TPU machine learning hardware to power [google.com] Google Translate, Search, and other products.

    The Great A.I. Awakening [nytimes.com]

    A month later, they were finally able to run a side-by-side experiment to compare Schuster’s new system with Hughes’s old one. Schuster wanted to run it for English-French, but Hughes advised him to try something else. “English-French,” he said, “is so good that the improvement won’t be obvious.”

    It was a challenge Schuster couldn’t resist. The benchmark metric to evaluate machine translation is called a BLEU score, which compares a machine translation with an average of many reliable human translations. At the time, the best BLEU scores for English-French were in the high 20s. An improvement of one point was considered very good; an improvement of two was considered outstanding.

    The neural system, on the English-French language pair, showed an improvement over the old system of seven points.

    Hughes told Schuster’s team they hadn’t had even half as strong an improvement in their own system in the last four years.

    To be sure this wasn’t some fluke in the metric, they also turned to their pool of human contractors to do a side-by-side comparison. The user-perception scores, in which sample sentences were graded from zero to six, showed an average improvement of 0.4 — roughly equivalent to the aggregate gains of the old system over its entire lifetime of development.

    TL;DR: The switch to machine learning/neural networks improved Google Translate more in 9 months than the previous decade of improvements.

    Building an AI Chip Saved Google From Building a Dozen New Data Centers [wired.com]

    In the end, the team settled on an ASIC, a chip built from the ground up for a particular task. According to Jouppi, because Google designed the chip specifically for neural nets, it can run them 15 to 30 times faster than general purpose chips built with similar manufacturing techniques. That said, the chip is suited to any breed of neural network—at least as they exist today—including everything from the convolutional neural networks used in image recognition to the long-short-term-memory network used to recognize voice commands. "It's not wired to one model," he says.

    If IBM fails, it will be because their hardware business couldn't sustain as-of-yet-unmonetized pursuits like Watson, or because everybody is crowding into the same territory as Watson is targeting. Google, Amazon, Apple, Microsoft, Samsung, Baidu, etc. are all in the running. These companies want their AI/voice assistants to become more capable. You use a voice assistant today, and it could become much better in a year without the consumer buying any extra hardware. IBM is targeting businesses, hospitals, etc. and has a bit of an early mover advantage, but their customers can't accept a half-assed Siri-level Watson because they need shit that works, not toys.

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  • (Score: 3, Insightful) by jcross on Thursday December 07 2017, @10:29PM

    by jcross (4009) on Thursday December 07 2017, @10:29PM (#607018)

    IMO, the fact that IBM has been struggling to figure out how to monetize a technology doesn't mean it'll be hard for someone else. From everything I've read lately, they seem like a sinking ship of fools, desperately hoping that saying "Cloud" and "AI" enough will save them. Just as one example, I don't see IBM licensing a component or service to the gaming industry, but Google seems capable of something like that. It could be used to direct NPCs or even to test games in development and get a quantitative measurement of difficulty.