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What the AI behind Alphango can teach us about being human

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By Cade Metz for the Wired

AJA HUANG DIPS his hand into a wooden bowl of polished black stones and, without looking, thumbs one between his middle and index finger. Peering through wire-rim glasses, he places the black stone on the board, in a mostly empty zone, just below and to the left of a single white stone. In Go parlance it is a “shoulder hit,” in from the side, far away from most of the game’s other action.

Across the table, Lee Sedol, the best Go player of the past decade, freezes. He looks at the 37 stones fanned out across the board, then stands up and leaves.

In the commentary room, about 50 feet away, Michael Redmond is watching the game via closed-circuit. Redmond, the only Western Go player to reach the rank of nine dan, the game’s uppermost designation, literally does a double take. He is just as shocked as Lee. “I don’t really know if it’s a good move or a bad move,” Redmond says to the nearly 2 million people following the game online.

“I thought it was a mistake,” says the other English-language commentator, Chris Garlock, vice president of communications for the American Go Association.

A few minutes later, Lee walks back into the match room. He sits down but doesn’t touch his bowl of white stones. A minute goes by, then another—15 in all, a significant chunk of the initial two hours the players are allowed each game in the tournament. Finally, Lee plucks out a stone and places it on the board, just above the black one Huang played.

Huang’s move was just the 37th in the game, but Lee never recovers from the blow. Four hours and 20 minutes later, he resigns, defeated.

But Huang was not the true winner of this game of Go. He was only following orders—conveyed on a flatscreen monitor to his left, which was connected to a nearby control room here at the Four Seasons Hotel in Seoul and itself networked into hundreds of computers inside Google data centers scattered throughout the world. Huang was just the hands; the mind behind the game was an artificial intelligence named AlphaGo, and it was beating one of the best players of perhaps the most complex game ever devised by humans.

In the same room, another Go expert watches—three-time European champion Fan Hui. At first, Move 37 confuses him too. But he has a history with AlphaGo. He is, more than any other human being, its sparring partner. Over five months, Fan played hundreds of games with the machine, allowing its creators to see where it faltered. Fan lost time and again, but he’s come to understand AlphaGo—as much as anyone ever could. That shoulder hit, Fan thinks, it wasn’t a human move. But after 10 seconds of pondering it, he understands. “So beautiful,” he says. “So beautiful.”

In this best-of-five series, AlphaGo now led Lee—and, by proxy, humanity—two games to none. Move 37 showed that AlphaGo wasn’t just regurgitating years of programming or cranking through a brute-force predictive algorithm. It was the moment AlphaGo proved itunderstands, or at least appears to mimic understanding in a way that is indistinguishable from the real thing. From where Lee sat, AlphaGo displayed what Go players might describe as intuition, the ability to play a beautiful game not just like a person but in a way no person could.

But don’t weep for Lee Sedol in his defeat, or for humanity. Lee isn’t a martyr, and Move 37 wasn’t the moment where the machines began their inexorable rise to power over our lesser minds. Quite the opposite: Move 37 was the moment machines and humanity finally began to evolve together.

WHEN DAVID SILVER was a 15-year-old tournament chess player from Suffolk, on the east coast of England, Demis Hassabis was the kid no one could beat. Hassabis was a bona fide prodigy, the child of a Chinese-Singaporean mother and Greek-Cypriot father in London, and at one point the second-highest-rated under-14 chess player in the world. He would come out to the provincial tournaments to stay limber and earn a few extra quid. “I knew Demis before he knew me,” says Silver, the researcher who led the creation of AlphaGo. “I would see him turn up in my town, win the competition, and leave.”

They met properly as undergraduates at Cambridge studying computational neuroscience—an effort to understand the human mind and how machines might, one day, become a little bit intelligent themselves. But what they really bonded over was gaming, on boards and on computers.

Chess is a metaphor for war, but it’s really just a single battle. Go is like a global battlespace.

This was 1998, so naturally, after they graduated Hassabis and Silver started a videogame company. Hassabis often played Go with a coworker, and, piqued by his colleague’s interest, Silver began learning on his own.“It became almost like a badge of honor if you could beat Demis at anything,” Silver says. “And I knew that Demis was just starting to get interested in the game.”

They joined a local Go club and played against two- and three-dan players, the equivalent of karate black belts. And there was something more: They couldn’t stop thinking about how this was the one game of intellect that machines had never cracked. In 1995 a computer program called Chinook beat one of the world’s best players at checkers. Two years later, IBM’s Deep Blue supercomputer toppled world chess champion Garry Kasparov. In the years that followed, machines triumphed at Scrabble, Othello, even TV’s Jeopardy! In game-theory terms, Go is a perfect information game like chess and checkers—no elements of chance, no information hidden. Typically those are easy for computers to master. But Go wouldn’t fall.

The thing is, Go looks pretty simple. Created in China more than 3,000 years ago, it pits two players against each other across a 19-by-19 grid. The players take turns putting stones at intersections—black versus white—trying to enclose territory or wall off swaths of their opponent’s color. People say chess is a metaphor for war, but it’s really more a metaphor for a single battle. Go is like a global battlespace, or geopolitics. A move in one corner of the grid can ripple everywhere else. Advantage ebbs and flows. In a game of chess, a player typically has about 35 possible moves to choose from in a given turn. In Go, the number is closer to 200. Over an entire game, that’s a whole other level of complexity. As Hassabis and Silver like to say, the number of possible positions on a Go board exceeds the number of atoms in the universe.

Lee Sedol, seated at right, lost three games in a row to AlphaGo.GEORDIE WOOD

Reporters packed into the press center at the Seoul Four Seasons.GEORDIE WOOD

The upshot is that, unlike in chess, players—whether human or machine—can’t look ahead to the ultimate outcome of each potential move. The top players play by intuition, not raw calculation. “Good positions look good,” Hassabis says. “It seems to follow some kind of aesthetic. That’s why it has been such a fascinating game for thousands of years.”

In 2005, Hassabis and Silver’s game company folded and they went their separate ways. At the University of Alberta, Silver studied a nascent form of AI called reinforcement learning, a way for machines to learn on their own by performing tasks over and over again and tracking which decisions bring the most reward. Hassabis enrolled at University College London and got his PhD in cognitive neuroscience.

In 2010 they found each other again. Hassabis cofounded an AI company in London called DeepMind; Silver joined him. Their ambitions were grandiose: create general artificial intelligence, AI that really thinks. But they had to start somewhere.

That starting point was, of course, games. They’re actually a good test for artificial intelligence. By definition, games are constrained. They’re little bottled universes where, unlike in real life, you can objectively judge success and failure, victory and defeat. DeepMind set out to combine reinforcement learning with deep learning, a newish approach to finding patterns in enormous data sets. To figure out if it was working, the researchers taught their fledgling AI to play Space Invaders and Breakout.

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