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posted by CoolHand on Saturday January 30 2016, @12:27AM   Printer-friendly
from the going-deep dept.

Researchers from Google subsidiary DeepMind have published an article in Nature detailing AlphaGo, a Go-playing program that achieved a 99.8% win rate (494 of 495 games) against other Go algorithms, and has also defeated European Go champion Fan Hui 5-to-0. The researchers claim that defeating a human professional in full-sized Go was a feat expected to be achieved "at least a decade away" (other statements suggest 5-10 years). The Register details the complexity of the problem:

Go presents a particularly difficult scenario for computers, as the possible number of moves in a given match (opening at around 2.08 x 10170 and decreasing with successive moves) is so large as to be practically impossible to compute and analyze in a reasonable amount of time.

While previous efforts have shown machines capable of breaking down a Go board and playing competitively, the programs were only able to compete with humans of a moderate skill level and well short of the top meat-based players. To get around this, the DeepMind team said it combined a Monte Carlo Tree Search method with neural network and machine learning techniques to develop a system capable of analyzing the board and learning from top players to better predict and select moves. The result, the researchers said, is a system that can select the best move to make against a human player relying not just on computational muscle, but with patterns learned and selected from a neural network.

"During the match against [European Champion] Fan Hui, AlphaGo evaluated thousands of times fewer positions than Deep Blue did in its chess match against Kasparov; compensating by selecting those positions more intelligently, using the policy network, and evaluating them more precisely, using the value network – an approach that is perhaps closer to how humans play," the researchers said. "Furthermore, while Deep Blue relied on a handcrafted evaluation function, the neural networks of AlphaGo are trained directly from gameplay purely through general-purpose supervised and reinforcement methods."

The AlphaGo program can win against other algorithms even after giving itself a four-move handicap. AlphaGo will play five matches against the top human player Lee Sedol in March.

Google and Facebook teams have been engaged in a rivalry to produce an effective human champion-level Go algorithm/system in recent years. Facebook's CEO Mark Zuckerberg hailed his company's AI Research progress a day before the Google DeepMind announcement, and an arXiv paper from Facebook researchers was updated to reflect their algorithm's third-place win... in a monthly bot tournament.

Mastering the game of Go with deep neural networks and tree search (DOI: 10.1038/nature16961)

Previously: Google's DeepMind AI Project Mimics Human Memory and Programming Skills


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  • (Score: 0) by Anonymous Coward on Saturday January 30 2016, @03:16AM

    by Anonymous Coward on Saturday January 30 2016, @03:16AM (#296830)

    Dart, Rust, Tcl, Nock, Hoon, Squeak, Pike? ...JavaScript?

    It's easier to ask if there are any programming languages with good names.

  • (Score: 0) by Anonymous Coward on Saturday January 30 2016, @08:29AM

    by Anonymous Coward on Saturday January 30 2016, @08:29AM (#296941)

    It's easier to ask if there are any programming languages with good names.

    BASIC. Really, it tells you right from the start what is is, and if you expand the acronym, it tells you in more detail.

    However, is there a good language with a good name?