Editing Artificial intelligence (section)

=== Games ===
{{Main|Game artificial intelligence}}

[[Game AI|Game playing]] programs have been used since the 1950s to demonstrate and test AI's most advanced techniques.<ref>{{Cite magazine |last1=Grant |first1=Eugene F. |last2=Lardner |first2=Rex |date=1952-07-25 |title=The Talk of the Town – It |url=https://www.newyorker.com/magazine/1952/08/02/it |access-date=2024-01-28 |magazine=The New Yorker |issn=0028-792X |archive-date=16 February 2020 |archive-url=https://web.archive.org/web/20200216034025/https://www.newyorker.com/magazine/1952/08/02/it |url-status=live }}</ref> [[IBM Deep Blue|Deep Blue]] became the first computer chess-playing system to beat a reigning world chess champion, [[Garry Kasparov]], on 11 May 1997.<ref>{{Cite web |last=Anderson |first=Mark Robert |date=2017-05-11 |title=Twenty years on from Deep Blue vs Kasparov: how a chess match started the big data revolution |url=http://theconversation.com/twenty-years-on-from-deep-blue-vs-kasparov-how-a-chess-match-started-the-big-data-revolution-76882 |access-date=2024-01-28 |website=The Conversation |archive-date=17 September 2024 |archive-url=https://web.archive.org/web/20240917000827/https://theconversation.com/twenty-years-on-from-deep-blue-vs-kasparov-how-a-chess-match-started-the-big-data-revolution-76882 |url-status=live }}</ref> In 2011, in a ''[[Jeopardy!]]'' [[quiz show]] exhibition match, [[IBM]]'s [[question answering system]], [[Watson (artificial intelligence software)|Watson]], defeated the two greatest ''Jeopardy!'' champions, [[Brad Rutter]] and [[Ken Jennings]], by a significant margin.<ref>{{Cite news |last=Markoff |first=John |date=2011-02-16 |title=Computer Wins on 'Jeopardy!': Trivial, It's Not |url=https://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html |url-access=subscription |access-date=2024-01-28 |work=The New York Times |issn=0362-4331 |archive-date=22 October 2014 |archive-url=https://web.archive.org/web/20141022023202/http://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html |url-status=live }}</ref> In March 2016, [[AlphaGo]] won 4 out of 5 games of [[Go (game)|Go]] in a match with Go champion [[Lee Sedol]], becoming the first [[computer Go]]-playing system to beat a professional Go player without [[Go handicaps|handicaps]]. Then, in 2017, it [[AlphaGo versus Ke Jie|defeated Ke Jie]], who was the best Go player in the world.<ref>{{Cite web |last=Byford |first=Sam |date=2017-05-27 |title=AlphaGo retires from competitive Go after defeating world number one 3–0 |url=https://www.theverge.com/2017/5/27/15704088/alphago-ke-jie-game-3-result-retires-future |access-date=2024-01-28 |website=The Verge |archive-date=7 June 2017 |archive-url=https://web.archive.org/web/20170607184301/https://www.theverge.com/2017/5/27/15704088/alphago-ke-jie-game-3-result-retires-future |url-status=live }}</ref> Other programs handle [[Imperfect information|imperfect-information]] games, such as the [[poker]]-playing program [[Pluribus (poker bot)|Pluribus]].<ref>{{Cite journal |last1=Brown |first1=Noam |last2=Sandholm |first2=Tuomas |date=2019-08-30 |title=Superhuman AI for multiplayer poker |url=https://www.science.org/doi/10.1126/science.aay2400 |journal=Science |volume=365 |issue=6456 |pages=885–890 |bibcode=2019Sci...365..885B |doi=10.1126/science.aay2400 |issn=0036-8075 |pmid=31296650}}</ref> [[DeepMind]] developed increasingly generalistic [[reinforcement learning]] models, such as with [[MuZero]], which could be trained to play chess, Go, or [[Atari]] games.<ref>{{Cite web |date=2020-12-23 |title=MuZero: Mastering Go, chess, shogi and Atari without rules |url=https://deepmind.google/discover/blog/muzero-mastering-go-chess-shogi-and-atari-without-rules |access-date=2024-01-28 |website=Google DeepMind}}</ref> In 2019, DeepMind's AlphaStar achieved grandmaster level in [[StarCraft II]], a particularly challenging real-time strategy game that involves incomplete knowledge of what happens on the map.<ref>{{Cite news |last=Sample |first=Ian |date=2019-10-30 |title=AI becomes grandmaster in 'fiendishly complex' StarCraft II |url=https://www.theguardian.com/technology/2019/oct/30/ai-becomes-grandmaster-in-fiendishly-complex-starcraft-ii |access-date=2024-01-28 |work=The Guardian |issn=0261-3077 |archive-date=29 December 2020 |archive-url=https://web.archive.org/web/20201229185547/https://www.theguardian.com/technology/2019/oct/30/ai-becomes-grandmaster-in-fiendishly-complex-starcraft-ii |url-status=live }}</ref> In 2021, an AI agent competed in a PlayStation [[Gran Turismo (series)|Gran Turismo]] competition, winning against four of the world's best Gran Turismo drivers using deep reinforcement learning.<ref>{{Cite journal |last1=Wurman |first1=P. R. |last2=Barrett |first2=S. |last3=Kawamoto |first3=K. |date=2022 |title=Outracing champion Gran Turismo drivers with deep reinforcement learning |journal=Nature |volume=602 |issue=7896 |pages=223–228 |bibcode=2022Natur.602..223W |doi=10.1038/s41586-021-04357-7 |pmid=35140384|url=https://www.researchsquare.com/article/rs-795954/latest.pdf }}</ref> In 2024, Google DeepMind introduced SIMA, a type of AI capable of autonomously playing nine previously unseen [[open-world]] video games by observing screen output, as well as executing short, specific tasks in response to natural language instructions.<ref>{{Cite web |last=Wilkins |first=Alex |date=13 March 2024 |title=Google AI learns to play open-world video games by watching them |url=https://www.newscientist.com/article/2422101-google-ai-learns-to-play-open-world-video-games-by-watching-them |access-date=2024-07-21 |website=New Scientist |archive-date=26 July 2024 |archive-url=https://web.archive.org/web/20240726182946/https://www.newscientist.com/article/2422101-google-ai-learns-to-play-open-world-video-games-by-watching-them/ |url-status=live }}</ref>