On a chilly Thursday in Seoul, Lee faced AlphaGo in the second of five games. He had been beaten the day before, but once the initial shock had worn off he was determined to come back with a 4-1 win.  Just after move 37, AlphaGoâs turn, the room suddenly went silent. AlphaGoâs black stone had been placed in an unusual spot on the fifth line, rendering both commentators speechless. âI thought it was a mistake,â one of them said, with a disbelieving laugh. âAlmost no human pro would ever have played this.â  The tactic jolted Lee so much he actually stood up and left the room for a few minutes. As viewers watched in confusion, AlphaGo went on to win game two decisively, with Lee playing shakily after the surprise of move 37.  âThe move was so different than what we thought of as normal, it was eye-opening. It turned it into an even fight,â said Michael Redmond, an American-born Go player with the same 9-dan ranking as Lee. When Silver and his team looked at AlphaGoâs internal analyses after the game, they found the probability of this move was one in 10,000, which explains Leeâs surprise.  âIt was thrilling, particularly the style, elegance and beauty of the game. This is why we play Go,â the match commentator said in the post-game press conference. âI found it incredibly moving, and halfway through the game I felt a glimmer of intelligence there.â Although the pride of the Go community had been bruised, some believe this powerful young entrant can infuse the game with new creative strategies. âGo players spend a lot of time thinking about the perfect play and how far away they are from that perfection. Having a program to train against could help them all improve,â Hassabis said.  Now that AlphaGo is âsuperhumanâ, whatâs next? âIt would be pretty trivial for it to master any other board game, including chess. And we might actually run that experiment soon just to show that thatâs possible,â Hassabis told me. Other games that DeepMind may take on include the strategy game StarCraft and perhaps poker.  Silver independently worked with a PhD student at UCL to build an algorithm that learnt how to play Texas Hold âEm (a variant of poker) with no instructions, and still beat humans. âThere are other computer programs out there, but they are trained in how to play poker,â Silver explains. â[Our program] just teaches and plays itself, and we ended up with a superhuman poker player.â  The learning principles behind AlphaGo will also be applied across Googleâs products â several, such as Google Search, Photos and Translate, are already powered by AI, and could be super-charged.  It could even be used by professional Go players as a form of extreme training. In October, Chinese-born French Go player Fan Hui was the first professional to be soundly beaten by AlphaGo in a 5-0 match, although he is far less skilled than Lee.  Since his loss, Fan has been training against AlphaGo in DeepMindâs offices â and his world ranking has jumped from 600 to 300 in the last three months alone. âHe just won the European pro Go championship in February, and he won with a full score; he beat absolutely everybody,â Hassabis says, with a trace of pride. Fan is on the best form heâs ever been, and he attributes it to AlphaGo.
Earlier this year, 100 million people watched a Google-owned computer beat a (human) champion at Go, the worldâs most complicated board game. So how did the machine triumph, and what are the implications for the struggle between man and machine?