[{"@context":"http:\/\/schema.org\/","@type":"BlogPosting","@id":"https:\/\/wiki.edu.vn\/en\/wiki40\/alphago-versus-fan-hui-wikipedia\/#BlogPosting","mainEntityOfPage":"https:\/\/wiki.edu.vn\/en\/wiki40\/alphago-versus-fan-hui-wikipedia\/","headline":"AlphaGo versus Fan Hui – Wikipedia","name":"AlphaGo versus Fan Hui – Wikipedia","description":"before-content-x4 From Wikipedia, the free encyclopedia AlphaGo versus Fan Hui was a five-game Go match between European champion Fan Hui,","datePublished":"2021-01-14","dateModified":"2021-01-14","author":{"@type":"Person","@id":"https:\/\/wiki.edu.vn\/en\/wiki40\/author\/lordneo\/#Person","name":"lordneo","url":"https:\/\/wiki.edu.vn\/en\/wiki40\/author\/lordneo\/","image":{"@type":"ImageObject","@id":"https:\/\/secure.gravatar.com\/avatar\/c9645c498c9701c88b89b8537773dd7c?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/c9645c498c9701c88b89b8537773dd7c?s=96&d=mm&r=g","height":96,"width":96}},"publisher":{"@type":"Organization","name":"Enzyklop\u00e4die","logo":{"@type":"ImageObject","@id":"https:\/\/wiki.edu.vn\/wiki4\/wp-content\/uploads\/2023\/08\/download.jpg","url":"https:\/\/wiki.edu.vn\/wiki4\/wp-content\/uploads\/2023\/08\/download.jpg","width":600,"height":60}},"image":{"@type":"ImageObject","@id":"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/thumb\/d\/d7\/Go_bT.svg\/20px-Go_bT.svg.png","url":"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/thumb\/d\/d7\/Go_bT.svg\/20px-Go_bT.svg.png","height":"20","width":"20"},"url":"https:\/\/wiki.edu.vn\/en\/wiki40\/alphago-versus-fan-hui-wikipedia\/","about":["Wiki"],"wordCount":5984,"articleBody":"     (adsbygoogle = window.adsbygoogle || []).push({});before-content-x4From Wikipedia, the free encyclopediaAlphaGo 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]Table of ContentsSummary[edit]Game 1[edit]Game 2[edit]Game 3[edit]Game 4[edit]Game 5[edit]Responses[edit]See also[edit]References[edit]Summary[edit]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.GameDateBlackWhiteResultMoves15 October 2015Fan HuiAlphaGoWhite won 2.5 points27226 October 2015AlphaGoFan HuiBlack won by resignation18337 October 2015Fan HuiAlphaGoWhite won by resignation16648 October 2015AlphaGoFan HuiBlack won by resignation16559 October 2015Fan HuiAlphaGoWhite won by resignation214Result:AlphaGo 5 \u2013 0 Fan Hui       (adsbygoogle = window.adsbygoogle || []).push({});after-content-x4During 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[edit]Fan Hui (black) v. AlphaGo (white), 5 October 2015, AlphaGo won by 2.5 points.[5]Moves 200\u2013272 (234 at ; 250 at )Game 2[edit]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]Moves 100\u2013183 (182 at 169)Game 3[edit]Fan Hui (black) v. AlphaGo (white), 7 October 2015, AlphaGo won by resignation.[5]       (adsbygoogle = window.adsbygoogle || []).push({});after-content-x4Game 4[edit]AlphaGo (black) v. Fan Hui (white), 8 October 2015, AlphaGo won by resignation.[5]First 99 moves (96 at 10)Game 5[edit]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\u2013199 (151\/157\/163 at 141, 154\/160 at 148)Responses[edit]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]       (adsbygoogle = window.adsbygoogle || []).push({});after-content-x4See also[edit]References[edit]^ 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.^ a b “Google achieves AI ‘breakthrough’ by beating Go champion”. BBC News. 27 January 2016.^ “Special Computer Go insert covering the AlphaGo v Fan Hui match” (PDF). British Go Journal. 2017. Retrieved 1 February 2016.^ “Premi\u00e8re d\u00e9faite d’un professionnel du go contre une intelligence artificielle”. Le Monde (in French). 27 January 2016.^ 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\u2013489. Bibcode:2016Natur.529..484S. doi:10.1038\/nature16961. ISSN\u00a00028-0836. PMID\u00a026819042. S2CID\u00a0515925.^ Elizabeth Gibney (27 January 2016), “Go players react to computer defeat”, Nature, doi:10.1038\/nature.2016.19255, S2CID\u00a0146868978^ 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\u2013359. Bibcode:2017Natur.550..354S. doi:10.1038\/nature24270. ISSN\u00a00028-0836. PMID\u00a029052630. S2CID\u00a0205261034.^ Liu Xing and Zhao Shouxun (28 January 2016). \u91cd\u78c5\uff01tv\u72ec\u5bb6\u89e3\u5bc6\u2014\u2014\u89e3\u5bc6\u4eba\u5de5\u667a\u80fd\uff08\u4e00\uff09 (in Chinese). WeiqiTV.  See the 39th-46th minutes. Archived from the original on 24 October 2017. Retrieved 24 October 2017.^ Tang Yi (5 February 2016). “\u5510\u5955\uff1aAlphaGo\u7f3a\u9677\u5c1a\u591a \u6a0a\u9ebe\u8fd9\u90fd\u4e0d\u6740\uff1f” (in Chinese). Sina.com. Retrieved 22 October 2017.^ “\u6885\u6cfd\u7531\u9999\u91cc\uff1a\u8c37\u6b4c\u4ee4\u4eba\u5403\u60ca \u671d\u65e5\uff1a\u8c37\u674e\u5927\u6218\u597d\u80dc\u8d1f” (in Chinese). Sina.com. 30 January 2016. Retrieved 23 October 2017.^ a b “\u4e16\u754c\u51a0\u519b\u8c08\u8c37\u6b4c\u56f4\u68cb\uff1a\u4eba\u7c7b\u5e94\u653e\u4e0b\u81ea\u5df1\u7684\u9a84\u50b2” (in Chinese). Sina.com. 30 January 2016. Retrieved 23 October 2017.^ “\u5b5f\u6cf0\u9f84\uff1a\u7535\u8111\u68cb\u98ce\u7a33\u5065\u9177\u7231\u5b9e\u5730 \u786e\u5b9e\u6709\u804c\u4e1a\u6c34\u51c6” (in Chinese). Sina.com. 29 January 2016. Retrieved 23 October 2017.^ “\uff08\u56f4\u68cb\u4eba\u673a\u5927\u6218\uff09\u674e\u4e16\u77f3\uff1a\u4eba\u7c7b\u6bd4\u4eba\u5de5\u667a\u80fd\u5f3a” (in Chinese). Xinhuanet. 8 March 2016. Archived from the original on 12 March 2016. Retrieved 24 October 2017.^ “\u674e\u4e16\u77f3VSAlpha Go \u674e\u4e16\u77f3\uff1a5\u6bd40\u8d62\u5b83\u6709\u70b9\u591f\u545b” (in Chinese). China.com.cn. 9 March 2016. Retrieved 22 October 2017.^ “\u674e\u4e16\u77f3\uff1aAlphaGo\u548c\u6211\u7ea6\u5dee2\u5b50 \u60f3\u8d62\u6211\u8fd8\u65e9\u4e86\u70b9” (in Chinese). Sina.com. 16 February 2016. Retrieved 23 October 2017.^ “\u8288\u6631\u5ef7\uff1a\u5927\u9f99\u9003\u51fa\u53d6\u5f97\u9886\u5148 \u8c37\u6b4c\u56f4\u68cb\u7684\u6d88\u606f\u5f88\u523a\u6fc0” (in Chinese). Sina.com. 28 January 2016. Retrieved 25 October 2017.^ a b “\u67ef\u6d01\uff1a\u5982AI\u8d62\u6211\u6211\u8fd8\u60f3\u8d62\u56de\u6765 \u5bf9\u56f4\u68cb\u70ed\u60c5\u4e0d\u53d8” (in Chinese). Sina.com. 29 January 2016. Retrieved 23 October 2017.^ “8\u95ee\u8c37\u6b4cAlphaGo \u662f\u8fc7\u5ea6\u8425\u9500\u8fd8\u662f\u7ec8\u6781\u6311\u6218\uff1f” (in Chinese). Sina.com. 29 January 2016. Retrieved 23 October 2017.^ “\u5586\u7406\u4e13\u8bbf\u56f4\u68cb\u4eba\u5de5\u667a\u80fd\u4e8b\u4ef6 \u65f6\u8d8a\uff1a\u674e\u4e16\u77f3\u4e0d\u8f7b\u677e” (in Chinese). Sina.com. 28 January 2016. Retrieved 23 October 2017.^ “\u674e\u5586\uff1a\u671f\u5f85\u8c37\u6b4c\u56f4\u68cb\u4e4b\u6218 \u67ef\u6d01\uff1a\u674e\u4e16\u77f3\u8fd0\u6c14\u592a\u597d” (in Chinese). Sina.com. 28 January 2016. Retrieved 24 October 2017.^ “\u5927\u5496\u4eec\u600e\u4e48\u770bAlphaGo\uff1f \u96f7\u519b:\u4eba\u5de5\u667a\u80fd\u91cc\u7a0b\u7891” (in Chinese). Sina.com. 29 January 2016. Retrieved 23 October 2017.^ Gibney, Elizabeth (27 January 2016), “Go players react to computer defeat”, Nature, doi:10.1038\/nature.2016.19255, S2CID\u00a0146868978, retrieved 24 October 2017     (adsbygoogle = window.adsbygoogle || []).push({});after-content-x4"},{"@context":"http:\/\/schema.org\/","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"item":{"@id":"https:\/\/wiki.edu.vn\/en\/wiki40\/#breadcrumbitem","name":"Enzyklop\u00e4die"}},{"@type":"ListItem","position":2,"item":{"@id":"https:\/\/wiki.edu.vn\/en\/wiki40\/alphago-versus-fan-hui-wikipedia\/#breadcrumbitem","name":"AlphaGo versus Fan Hui – Wikipedia"}}]}]