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[ChouGoku]

Google has taken a brilliant and unexpected step toward building an AI with more humanlike intuition, developing a computer capable of beating even expert human players at the fiendishly complicated board game Go.

The objective of Go, a game invented in China more than 2,500 years ago, is fairly simple: players must alternately place black and white “stones” on a grid of 19 horizontal and 19 vertical lines with the aim of surrounding the opponent’s pieces, and avoiding having one’s own pieces surrounded. Mastering Go, however, requires endless practice, as well as a finely tuned knack of recognizing subtle patterns in the arrangement of the pieces spread across the board.

Google’s team has shown that the skills needed to master Go are not so uniquely human after all. Their computer program, called AlphaGo, beat the European Go champion, Fan Hui, five games to zero. And this March it will take on one of the world’s best players, Lee Sedol, in a tournament to be held in Seoul, South Korea.

Hassabis also said the techniques used to create AlphaGo would lend themselves to his team’s effort to develop a general AI. “Ultimately we want to apply these techniques to important real-world problems,” he said. “Because the methods we used were general purpose, our hope is that one day they could be extended to help address some of society’s most pressing problems, from medical diagnostics to climate modeling” (see “Could AI Solve the World’s Biggest Problems?”).

Hassabis said the first way the technology might be applied at Google would involve the development of better software personal assistants. Such an assistant might learn a user’s preferences from his online behavior, and make more intuitive recommendations about products or events, he suggested.

Go is far more challenging for computers than, say, chess for two reasons: the number of potential moves each turn is far higher, and there is no simple way to measure material advantage. A player must therefore learn to recognize abstract patterns in hundreds of pieces placed across the board. And even experts often struggle to explain why a particular position seems advantageous or problematic.

Just a couple of years ago, in fact, most Go players and game programmers believed the game was so complex that it would take several decades before computers might reach the standard of a human expert player.

AlphaGo was developed by a team known as Google DeepMind, a group created after Google acquired a small AI U.K. startup called DeepMind in 2014. The researchers built AlphaGo using an extremely popular and successful machine-learning method known as deep learning combined with another simulation technique for modeling potential moves. Deep learning involves training a large simulated neural network to respond to patterns in data. It has proven very useful for image and audio processing, and many large tech companies are exploring new ways to apply the technique.

Two deep-learning networks were used in AlphaGo: one network learned to predict the next move, and the other learned to predict the outcome from different arrangements on the board. The two networks were combined using a more conventional AI algorithm to look ahead in the game for possible moves. A scientific paper written by researchers from Google that describes the work appears in the journal Nature today.

“The game of Go has an enormous search space, which is intractable to brute-force search,” says David Silver, another Google researcher who led the effort. “The key to AlphaGo is to reduce that search space to something more manageable. This approach makes AlphaGo much more humanlike than previous approaches.”

Seeing AI master Go may also lead to some existential angst. During the press briefing announcing the news, Hassabis was faced with questions about the long-term risks of the AI systems Google is developing. He said that the company was taking steps to mitigate those risks by collaborating with academics, by organizing conferences, and by working with an internal ethics board.

Full article at the source

As another human talent begins to bite the dust faster than expected, I'm pretty excited of what comes of this.

 

[BronsonLee]

AVA NOOOO

 

[Dicer]

Posted several times already, but still neat...

 

[blackflag]

Destroy it now before it's too late for us.

 

[Cornballer]

http://www.neogaf.com/forum/showthread.php?t=1176311

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