AlphaGo versus Fan Hui was a five-game Go match between European champion Fan Hui, a 2-dan (out of 9 dan possible) professional, and AlphaGo, a computer Go program developed by DeepMind, held at DeepMind's headquarters in London in October 2015.[1] AlphaGo won all five games.[2][3] This was the first time a computer Go program had beaten a professional human player on a full-sized board without handicap.[4] This match was not disclosed to the public until 27 January 2016 to coincide with the publication of a paper in the journal Nature[5] describing the algorithms AlphaGo used.[2]

Fan described the program as "very strong and stable, it seems like a wall. ... I know AlphaGo is a computer, but if no one told me, maybe I would think the player was a little strange, but a very strong player, a real person."[6]

Games

Summary

In this match, DeepMind used AlphaGo's distributed version with 1,202 CPUs and 176 GPUs[5] with Elo rating 3,144.[7] For each game there was a one-hour set time limit for each player followed by three 30-second byo-yomi overtime periods.

Game Date Black White Result Moves
1 5 October 2015 Fan Hui AlphaGo White won 2.5 points 272
2 6 October 2015 AlphaGo Fan Hui Black won by resignation 183
3 7 October 2015 Fan Hui AlphaGo White won by resignation 166
4 8 October 2015 AlphaGo Fan Hui Black won by resignation 165
5 9 October 2015 Fan Hui AlphaGo White won by resignation 214
Result:
AlphaGo 5 – 0 Fan Hui

During this match, AlphaGo and Fan Hui also played another five informal games with shorter time control (each player having just three 30-second byo-yomi) and AlphaGo defeated Fan by three to two.[5]

Game 1

Fan Hui (black) v. AlphaGo (white), 5 October 2015, AlphaGo won by 2.5 points.[5]

First 99 moves
Moves 100–199
Moves 200–272 (234 at ; 250 at )

Game 2

AlphaGo (black) v. Fan Hui (white), 6 October 2015, AlphaGo won by resignation.[5] Although the white stones at the lower-left corner could have been captured if black 135 had been placed at "a", AlphaGo's choice might be safer to win.[8]

First 99 moves
Moves 100–183 (182 at 169)

Game 3

Fan Hui (black) v. AlphaGo (white), 7 October 2015, AlphaGo won by resignation.[5]

First 99 moves
Moves 100–166

Game 4

AlphaGo (black) v. Fan Hui (white), 8 October 2015, AlphaGo won by resignation.[5]

First 99 moves (96 at 10)
Moves 100-165

Game 5

Fan Hui (black) v. AlphaGo (white), 9 October 2015, AlphaGo won by resignation.[5] Black 75 should be placed at 83, and Fan Hui missed the opportunity.[9]

First 99 moves (90 at 15)
Moves 100–199 (151/157/163 at 141, 154/160 at 148)
Moves 200–214

Responses

AlphaGo's victory shocked the Go community.[10][11][12] Lee Sedol commented that AlphaGo reached the top of the amateur level in this match, but had not yet reached the professional level,[13][14] and he could give AlphaGo one or two stones.[15] Ke Jie and Mi Yuting thought that the strength of AlphaGo in this match was equal to that of a candidate for Go professional,[16][17] and extremely close to the professional level,[18] while Shi Yue thought that it already reached the professional level.[19][11] "It was terrifying," said Ke Jie, "that AlphaGo could learn and evolve although its power was still limited then."[20][17][21]

Canadian AI specialist Jonathan Schaeffer, comparing AlphaGo with a "child prodigy" that lacked experience, considered this match "not yet a Deep Blue moment", and said that the real achievement would be "when the program plays a player in the true top echelon".[22]

See also

References

  1. ^ Metz, Cade (27 January 2016). "In Major AI Breakthrough, Google System Secretly Beats Top Player at the Ancient Game of Go". WIRED. Retrieved 1 February 2016.
  2. ^ a b "Google achieves AI 'breakthrough' by beating Go champion". BBC News. 27 January 2016.
  3. ^ "Special Computer Go insert covering the AlphaGo v Fan Hui match" (PDF). British Go Journal. 2017. Retrieved 1 February 2016.
  4. ^ "Première défaite d'un professionnel du go contre une intelligence artificielle". Le Monde (in French). 27 January 2016.
  5. ^ a b c d e f g h Silver, David; Huang, Aja; Maddison, Chris J.; Guez, Arthur; Sifre, Laurent; Driessche, George van den; Schrittwieser, Julian; Antonoglou, Ioannis; Panneershelvam, Veda; Lanctot, Marc; Dieleman, Sander; Grewe, Dominik; Nham, John; Kalchbrenner, Nal; Sutskever, Ilya; Lillicrap, Timothy; Leach, Madeleine; Kavukcuoglu, Koray; Graepel, Thore; Hassabis, Demis (28 January 2016). "Mastering the game of Go with deep neural networks and tree search". Nature. 529 (7587): 484–489. Bibcode:2016Natur.529..484S. doi:10.1038/nature16961. ISSN 0028-0836. PMID 26819042. S2CID 515925.Closed access icon
  6. ^ Elizabeth Gibney (27 January 2016), "Go players react to computer defeat", Nature, doi:10.1038/nature.2016.19255, S2CID 146868978
  7. ^ Silver, David; Schrittwieser, Julian; Simonyan, Karen; Antonoglou, Ioannis; Huang, Aja; Guez, Arthur; Hubert, Thomas; Baker, Lucas; Lai, Matthew; Bolton, Adrian; Chen, Yutian; Lillicrap, Timothy; Fan, Hui; Sifre, Laurent; Driessche, George van den; Graepel, Thore; Hassabis, Demis (19 October 2017). "Mastering the game of Go without human knowledge" (PDF). Nature. 550 (7676): 354–359. Bibcode:2017Natur.550..354S. doi:10.1038/nature24270. ISSN 0028-0836. PMID 29052630. S2CID 205261034.Closed access icon
  8. ^ Liu Xing and Zhao Shouxun (28 January 2016). 重磅！tv独家解密——解密人工智能（一） (in Chinese). WeiqiTV. See the 39th-46th minutes. Archived from the original on 24 October 2017. Retrieved 24 October 2017.
  9. ^ Tang Yi (5 February 2016). "唐奕：AlphaGo缺陷尚多 樊麾这都不杀？" (in Chinese). Sina.com. Retrieved 22 October 2017.
  10. ^ "梅泽由香里：谷歌令人吃惊 朝日：谷李大战好胜负" (in Chinese). Sina.com. 30 January 2016. Retrieved 23 October 2017.
  11. ^ a b "世界冠军谈谷歌围棋：人类应放下自己的骄傲" (in Chinese). Sina.com. 30 January 2016. Retrieved 23 October 2017.
  12. ^ "孟泰龄：电脑棋风稳健酷爱实地 确实有职业水准" (in Chinese). Sina.com. 29 January 2016. Retrieved 23 October 2017.
  13. ^ "（围棋人机大战）李世石：人类比人工智能强" (in Chinese). Xinhuanet. 8 March 2016. Archived from the original on 12 March 2016. Retrieved 24 October 2017.
  14. ^ "李世石VSAlpha Go 李世石：5比0赢它有点够呛" (in Chinese). China.com.cn. 9 March 2016. Retrieved 22 October 2017.
  15. ^ "李世石：AlphaGo和我约差2子 想赢我还早了点" (in Chinese). Sina.com. 16 February 2016. Retrieved 23 October 2017.
  16. ^ "芈昱廷：大龙逃出取得领先 谷歌围棋的消息很刺激" (in Chinese). Sina.com. 28 January 2016. Retrieved 25 October 2017.
  17. ^ a b "柯洁：如AI赢我我还想赢回来 对围棋热情不变" (in Chinese). Sina.com. 29 January 2016. Retrieved 23 October 2017.
  18. ^ "8问谷歌AlphaGo 是过度营销还是终极挑战？" (in Chinese). Sina.com. 29 January 2016. Retrieved 23 October 2017.
  19. ^ "喆理专访围棋人工智能事件 时越：李世石不轻松" (in Chinese). Sina.com. 28 January 2016. Retrieved 23 October 2017.
  20. ^ "李喆：期待谷歌围棋之战 柯洁：李世石运气太好" (in Chinese). Sina.com. 28 January 2016. Retrieved 24 October 2017.
  21. ^ "大咖们怎么看AlphaGo？ 雷军:人工智能里程碑" (in Chinese). Sina.com. 29 January 2016. Retrieved 23 October 2017.
  22. ^ Gibney, Elizabeth (27 January 2016), "Go players react to computer defeat", Nature, doi:10.1038/nature.2016.19255, S2CID 146868978, retrieved 24 October 2017
Categories