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AlphaGo versus Fan Hui information


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]

  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. ^ 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

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