AlphaGo versus Fan Hui

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

• AlphaGo versus Lee Sedol
• AlphaGo versus Ke Jie

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. "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. 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.
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.
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. "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. "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