Since the 1990s computers have continually gotten better at beating us at our own games like chess, checkers, poker and Jeopardy!. But there is one game at which expert human players continue to dominate machines: Go. The more than 2,500-year-old board game, in which two players use black and white stones to try to capture more territory than their opponent, is extremely complex, which has made it difficult for computers to master. But it seems as though human supremacy in Go may have finally ended—researchers at Google DeepMind announced today that they’ve created a sophisticated artificial intelligence (AI) program—a combination of deep neural networks and a search technique—that has beaten a Go champion for the first time in history.
Last October in London the DeepMind team invited the European Go champion, Fan Hui, to play against their program,
AlphaGo. The match was private, witnessed by just a few spectators. Hui and AlphaGo played a full-size game on a 19 by 19 grid board. AlphaGo had already been tested against state-of-the-art Go programs, like Crazy Stone and Zen, and had won all but one of 495 contests. But playing against a human expert is a much greater challenge than playing other computers because, well, the pros are still so much better—they have years of experience with the game and a certain intuition about how to play it. So when AlphaGo won the game 5–0, it was a big deal.
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Many predicted that computers would not beat a champion Go player for at least another 10 years. “This leap in performance is just completely unexpected and unprecedented,” says David Fotland, a software engineer at Amazon and also creator of a computer Go program who was not involved in the study.
To understand what the DeepMind researchers did to create such an impressive program you first have to appreciate why Go is such a difficult game for computers to play well. First, Go has a ridiculous number of possible moves and outcomes—according to the researchers, there are more possible positions in Go than the number of atoms in the universe. One of the study authors, Demis Hassabis, co-founder of DeepMind, made a comparison to chess, saying that in a game of chess there is an average 20 possible moves per turn whereas for Go in each turn an average 200 moves are possible. This means that if a computer were to search all the possible moves and outcomes in Go, it would take an enormous amount of computing power to do so, one that some say may not even be possible.
Another reason computers have a hard time with Go, explains Jonathan Schaeffer, a computer scientist at the University of Alberta, is that Go players need a large pool of knowledge—past experiences with the game—to draw from. “With chess, you can put in a small amount of knowledge and you can build a strong game-playing program,” says Schaeffer, who wasn’t involved in the study, “in Go you can’t.” That is because in chess a computer can follow preprogrammed rules, but using this strategy for Go is not workable, because the game is largely about patterns rather than a set of logical rules that could be written down.
The DeepMind team’s system addresses both the massive search and lack of knowledge problems. In a new study in published in this week’s Nature, they describe combining a search technique and deep learning to overcome these obstacles. (Scientific American is part of Nature Publishing Group.)
To solve the knowledge problem, they used what are called deep neural networks—in this case two 13-layer-deep networks that consist of millions of connections, akin to neural connections in the human brain. The researchers trained these networks with two methods: For one network, they showed the computer more than 30 million moves from games played by human experts (this helped the system learn how the best players win); and for both of the networks, the researchers had the computer play thousands of games with itself so it could discover new strategies and learn the game on its own. These two training strategies allowed the computer to recognize patterns in the game and identify what moves gave it the best chance of winning.
For the intractable search problem, the researchers exploited a special search technique called the Monte Carlo Tree Search. This method, which has been around for years and is used in other computer game programs, essentially allows the system to use statistics as a shortcut to determine the best move, rather than playing out each and every possible outcome of a given move (which in Go would take forever).
The search technique and deep-learning tools used by the DeepMind team are not new. Many computer Go programs already use the Monte Carlo Tree Search and neural networks have been employed as well. But what makes DeepMind’s AlphaGo so advanced is the way they put together these tools, along with the high performance of the deep neural networks. “The main novelty is in how they’ve combined these different ingredients together—they’ve innovated in doing that,” says Yoshua Bengio, a computer scientist at the University of Montreal who was not involved in the work. Schaeffer says he is impressed with the results: “This is a simpler, more comprehensive approach than what people have done in the past, and it’s more elegant,” he says, “I see this as a huge leap forward.” And it’s exactly what gave AlphaGo the edge over Fan Hui in their match—the computer won 5–0.
And although not everyone may care that a computer beat a champion Go player, this advance is important in other fields as well. The researchers who built the system with “general purpose methods” instead of creating something that is specifically made to only play Go, intend to “ultimately apply these techniques to important real-world problems,” Hassabis says, “Our hope is that one day they could be extended to help us address some of society’s toughest and most pressing problems, from climate modeling to complex disease analysis.” Yoshua Bengio says that another possible important application is in computerized dialogue, and Schaeffer says that in the future these programs might be able to come up with answers to abstract social issues that can be expressed as games, like national politics or international climate negotiations.
But AlphaGo first has a more immediate problem: how to beat the world’s best Go player, Lee Sedol. This March the two will play each other in Seoul, South Korea. And although AlphaGo played well against Fan Hui, Schaeffer and Fotland still predict Sedol will win the match. “I think the pro will win,” Fotland says, “But I think the pro will be shocked at how strong the program is.” For now, at least some people are still placing their bets on humans.