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Google's AlphaGo Zero Dominates in Chess

posted by martyb on Thursday December 07 2017, @07:40PM   
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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Original Submission

• Re:Welcome, new Watson! (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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  [SIG] 10/28/2017: Soylent Upgrade v14 [soylentnews.org]

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