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Go match between AI and human AlphaGo versus Lee Sedol4 1Seoul South Korea 9 15 March 2016Game oneAlphaGo W RGame twoAlphaGo B RGame threeAlphaGo W RGame fourLee Sedol W RGame fiveAlphaGo W R AlphaGo versus Lee Sedol also known as the Google DeepMind Challenge Match was a five game Go match between top Go player Lee Sedol and AlphaGo a computer Go program developed by Google DeepMind played in Seoul South Korea between 9 and 15 March 2016 AlphaGo won all but the fourth game 91 1 93 all games were won by resignation 91 2 93 The match has been compared with the historic chess match between Deep Blue and Garry Kasparov in 1997 The winner of the match was slated to win 1 million Since AlphaGo won Google DeepMind stated that the prize will be donated to charities including UNICEF and Go organisations 91 3 93 Lee received 170 000 150 000 for participating in the five games and an additional 20 000 for winning one game 91 4 93 After the match The Korea Baduk Association awarded AlphaGo the highest Go grandmaster rank an honorary 9 dan It was given in recognition of AlphaGo s sincere efforts to master Go 91 5 93 This match was chosen by Science as one of the runners up for Breakthrough of the Year on 22 December 2016 91 6 93 Contents 1 Background 1 1 Difficult challenge in artificial intelligence 1 2 Match against Fan Hui 1 3 Preparation 2 Players 2 1 AlphaGo 2 2 Lee Sedol 3 Games 3 1 Summary 3 2 Game 1 3 3 Game 2 3 4 Game 3 3 5 Game 4 3 6 Game 5 4 Coverage 5 Responses 5 1 AI community 5 2 Go community 5 3 Government 6 Documentary film 7 See also 8 References 9 External links 9 1 Official match commentary 9 2 SGF files Background Edit Difficult challenge in artificial intelligence Edit Main article Computer Go External videoMachine trains self to beat humans at world s hardest game Retro Report 2 51 Retro Report 91 7 93 Go is a complex board game that requires intuition creative and strategic thinking 91 8 93 91 9 93 It has long been considered a difficult challenge in the field of artificial intelligence AI and is considerably more difficult 91 10 93 to solve than chess Many in the field of artificial intelligence consider Go to require more elements that mimic human thought than chess 91 11 93 Mathematician I J Good wrote in 1965 91 12 93 Go on a computer In order to program a computer to play a reasonable game of Go rather than merely a legal game it is necessary to formalise the principles of good strategy or to design a learning program The principles are more qualitative and mysterious than in chess and depend more on judgement So I think it will be even more difficult to program a computer to play a reasonable game of Go than of chess Prior to 2015 91 13 93 the best Go programs only managed to reach amateur dan level 91 14 93 On the small 9 9 board the computer fared better and some programs managed to win a fraction of their 9 9 games against professional players Prior to AlphaGo some researchers had claimed that computers would never defeat top humans at Go 91 15 93 Elon Musk an early investor of Deepmind said in 2016 that experts in the field thought AI was 10 years away from achieving a victory against a top professional Go player 91 16 93 The match AlphaGo versus Lee Sedol is comparable to the 1997 chess match when Garry Kasparov lost to IBM computer Deep Blue Kasparov s loss to Deep Blue is considered the moment a computer became better than humans at chess 91 17 93 AlphaGo is significantly different from previous AI efforts Instead of using probability algorithms hard coded by human programmers AlphaGo uses neural networks to estimate its probability of winning AlphaGo accesses and analyses the entire online library of Go including all matches players analytics and literature as well as games played by AlphaGo against itself and other players Once setup AlphaGo is independent of the developer team and evaluates the best pathway to solving Go i e winning the game By using neural networks and Monte Carlo tree search AlphaGo calculates colossal numbers of likely and unlikely probabilities many moves into the future 91 citation needed 93 Related research results are being applied to fields such as cognitive science pattern recognition and machine learning 91 18 93 58 8202 150 8202 Match against Fan Hui Edit Main article AlphaGo versus Fan Hui Fan Hui vs AlphaGo Game 5 AlphaGo defeated European champion Fan Hui a 2 dan professional 5 0 in October 2015 the first time an AI had beaten a human professional player at the game on a full sized board without a handicap 91 19 93 91 20 93 Some commentators stressed the gulf between Fan and Lee who is ranked 9 dan professional 91 21 93 Computer programs Zen and Crazy Stone have previously defeated human players ranked 9 dan professional with handicaps of four or five stones 91 22 93 91 23 93 Canadian AI specialist Jonathan Schaeffer commenting after the win against Fan compared AlphaGo with a child prodigy that lacked experience and considered the real achievement will be when the program plays a player in the true top echelon He then believed that Lee would win the match in March 2016 91 20 93 Hajin Lee a professional Go player and the International Go Federation s secretary general commented that she was very excited at the prospect of an AI challenging Lee and thought the two players had an equal chance of winning 91 20 93 In the aftermath of his match against AlphaGo Fan Hui noted that the game had taught him to be a better player and to see things he had not previously seen By March 2016 Wired reported that his ranking had risen from 633 in the world to around 300 91 24 93 Preparation Edit Go experts found errors in AlphaGo s play against Fan in particular relating to a lack of awareness of the entire board Before the game against Lee it was unknown how much the program had improved its game since its October match 91 21 93 91 25 93 AlphaGo s original training dataset started with games of strong amateur players from internet Go servers after which AlphaGo trained by playing against itself for tens of millions of games 91 26 93 91 27 93 Players Edit AlphaGo Edit Main article AlphaGo AlphaGo logo AlphaGo is a computer program developed by Google DeepMind to play the board game Go AlphaGo s algorithm uses a combination of machine learning and tree search techniques combined with extensive training both from human and computer play The system s neural networks were initially bootstrapped from human game play expertise AlphaGo was initially trained to mimic human play by attempting to match the moves of expert players from recorded historical games using a KGS Go Server database of around 30 million moves from 160 000 games by KGS 6 to 9 dan human players 91 13 93 91 28 93 Once it had reached a certain degree of proficiency it was trained further by being set to play large numbers of games against other instances of itself using reinforcement learning to improve its play 91 29 93 The system does not use a database of moves to play As one of the creators of AlphaGo explained 91 30 93 Although we have programmed this machine to play we have no idea what moves it will come up with Its moves are an emergent phenomenon from the training We just create the data sets and the training algorithms But the moves it then comes up with are out of our hands and much better than we as Go players could come up with In the match against Lee AlphaGo used about the same computing power as it had in the match against Fan Hui 91 31 93 where it used 1 202 CPUs and 176 GPUs 91 13 93 The Economist reported that it used 1 920 CPUs and 280 GPUs 91 32 93 Google has also stated that its proprietary tensor processing units were used in the match against Lee Sedol 91 33 93 Lee Sedol Edit Main article Lee Sedol Lee Sedol in 2012 Lee Sedol is a professional Go player of 9 dan rank 91 34 93 and is one of the strongest players in the history of Go He started his career in 1996 promoted to professional dan rank at the age of 12 winning 18 international titles since then 91 35 93 He is a national hero in his native South Korea known for his unconventional and creative play 91 36 93 Lee Sedol initially predicted he would defeat AlphaGo in a landslide 91 36 93 Some weeks before the match he won the Korean Myungin title a major championship 91 37 93 Games Edit The match was a five game match with one million US dollars as the grand prize 91 3 93 using Chinese rules with a 7 5 point komi 91 4 93 For each game there was a two hour set time limit for each player followed by three 60 second byo yomi overtime periods 91 4 93 Each game started at 13 00 KST 04 00 GMT 91 38 93 The match was played at the Four Seasons Hotel in Seoul South Korea in March 2016 and was video streamed live with commentary the English language commentary was done by Michael Redmond 9 dan professional and Chris Garlock 91 39 93 91 40 93 91 41 93 Aja Huang a DeepMind team member and amateur 6 dan Go player placed stones on the Go board for AlphaGo which ran through the Google Cloud Platform with its server located in the United States 91 42 93 Summary Edit Game Date Black White Result Moves 1 9 March 2016 Lee Sedol AlphaGo Lee Sedol resigned 186 Game 1 2 10 March 2016 AlphaGo Lee Sedol Lee Sedol resigned 211 Game 2 3 12 March 2016 Lee Sedol AlphaGo Lee Sedol resigned 176 Game 3 4 13 March 2016 AlphaGo Lee Sedol AlphaGo resigned 180 Game 4 5 15 March 2016 Lee Sedol note 1 AlphaGo Lee Sedol resigned 280 Game 5 Result AlphaGo 4 1 Lee Sedol note 1 For Game Five under the official rules it was intended that the colour assignments would be done at random 91 43 93 However during the press conference after the fourth match Lee requested since I won with white I really do hope that in the fifth match I could win with black because winning with black is much more valuable 91 44 93 Hassabis agreed to allow Sedol to play with black Game 1 Edit AlphaGo white won the first game Lee appeared to be in control throughout much of the match but AlphaGo gained the advantage in the final 20 minutes and Lee resigned 91 45 93 Lee stated afterwards that he had made a critical error at the beginning of the match he said that the computer s strategy in the early part of the game was excellent and that the AI had made one unusual move that no human Go player would have made 91 45 93 David Ormerod commenting on the game at Go Game Guru described Lee s seventh stone as a strange move to test AlphaGo s strength in the opening characterising the move as a mistake and AlphaGo s response as accurate and efficient He described AlphaGo s position as favourable in the first part of the game considering that Lee started to come back with move 81 before making questionable moves at 119 and 123 followed by a losing move at 129 91 46 93 Professional Go player Cho Hanseung commented that AlphaGo s game had greatly improved from when it beat Fan Hui in October 2015 91 46 93 Michael Redmond described the computer s game as being more aggressive than against Fan 91 47 93 According to 9 dan Go grandmaster Kim Seong ryong Lee seemed stunned by AlphaGo s strong play on the 102nd stone 91 48 93 After watching AlphaGo make the game s 102nd move Lee mulled over his options for more than 10 minutes 91 48 93 First 99 moves Moves 100 186 Game 2 Edit AlphaGo black won the second game Lee stated afterwards that AlphaGo played a nearly perfect game 91 49 93 from very beginning of the game I did not feel like there was a point that I was leading 91 50 93 One of the creators of AlphaGo Demis Hassabis said that the system was confident of victory from the midway point of the game even though the professional commentators could not tell which player was ahead 91 50 93 Michael Redmond 9p noted that AlphaGo s 19th stone move 37 was creative and unique It was a move that no human would ve ever made 91 30 93 Lee took an unusually long time to respond to the move 91 30 93 An Younggil 8p called AlphaGo s move 37 a rare and intriguing shoulder hit but said Lee s counter was exquisite He stated that control passed between the players several times before the endgame and especially praised AlphaGo s moves 151 157 and 159 calling them brilliant 91 51 93 AlphaGo showed anomalies and moves from a broader perspective which professional Go players described as looking like mistakes at the first sight but an intentional strategy in hindsight 91 52 93 As one of the creators of the system explained AlphaGo does not attempt to maximize its points or its margin of victory but tries to maximize its probability of winning 91 30 93 91 53 93 If AlphaGo must choose between a scenario where it will win by 20 points with 80 percent probability and another where it will win by 1 and a half points with 99 percent probability it will choose the latter even if it must give up points to achieve it 91 30 93 In particular move 167 by AlphaGo seemed to give Lee a fighting chance and was declared to look like an obvious mistake by commentators An Younggil stated So when AlphaGo plays a slack looking move we may regard it as a mistake but perhaps it should more accurately be viewed as a declaration of victory 91 54 93 First 99 moves, wikipedia, wiki, book, books, library,

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