Editing Deep Blue (chess computer) (section)

===Computer science===
Computer scientists such as Deep Blue developer Campbell believed that playing chess was a good measurement for the effectiveness of artificial intelligence, and by beating a world champion chess player, IBM showed that they had made significant progress.<ref name=":0">{{Cite news |last=Greenemeier |first=Larry |title=20 Years after Deep Blue: How AI Has Advanced Since Conquering Chess |language=en |work=Scientific American |url=https://www.scientificamerican.com/article/20-years-after-deep-blue-how-ai-has-advanced-since-conquering-chess/ |access-date=29 June 2018 |date=2 June 2017 |archive-date=30 June 2018 |archive-url=https://web.archive.org/web/20180630025017/https://www.scientificamerican.com/article/20-years-after-deep-blue-how-ai-has-advanced-since-conquering-chess/ |url-status=live }}</ref> Deep Blue is also responsible for the popularity of using games as a display medium for artificial intelligence, as in the cases of [[IBM Watson]] or [[AlphaGo]].<ref>{{cite journal | doi=10.3390/ai3020021 | doi-access=free | title=Shifting Perspectives on AI Evaluation: The Increasing Role of Ethics in Cooperation | year=2022 | last1=Barbierato | first1=Enrico | last2=Zamponi | first2=Maria Enrica | journal=AI | volume=3 | issue=2 | pages=331–352 | hdl=10807/259716 | hdl-access=free }}</ref>

While Deep Blue, with its capability of evaluating 200&nbsp;million positions per second,<ref>{{cite news|last=Strogatz|first=Steven|date=26 December 2018|title=One Giant Step for a Chess-Playing Machine|language=en-US|work=The New York Times|url=https://www.nytimes.com/2018/12/26/science/chess-artificial-intelligence.html|access-date=4 January 2022|issn=0362-4331|archive-date=4 January 2022|archive-url=https://web.archive.org/web/20220104004951/https://www.nytimes.com/2018/12/26/science/chess-artificial-intelligence.html|url-status=live}}</ref> was the first computer to face a world chess champion in a formal match,<ref name=":0" /> it was a then-state-of-the-art [[expert system]], relying upon rules and variables defined and fine-tuned by chess masters and computer scientists. In contrast, current chess engines such as [[Leela Chess Zero]] typically use [[reinforcement learning|reinforcement]] [[machine learning]] systems that train a [[artificial neural network|neural network]] to play, developing its own internal logic rather than relying upon rules defined by human experts.<ref name=":1" />

In a November 2006 match between Deep Fritz and world chess champion [[Vladimir Kramnik]], the program ran on a computer system containing a dual-core [[List of Intel Xeon processors (Core-based)#"Woodcrest" (65 nm)|Intel Xeon 5160 CPU]], capable of evaluating only 8&nbsp;million positions per second, but searching to an average depth of 17 to 18 [[Ply (game theory)|plies]] (half-moves) in the [[Chess middlegame|middlegame]] thanks to [[heuristic]]s; it won 4–2.<ref>{{cite news |last=Schulz |first=André |title=Das letzte Match Mensch gegen Maschine? |trans-title=The last man vs machine match? |url=https://en.chessbase.com/post/the-last-man-vs-machine-match- |language=de |newspaper=Der Spiegel |translator-last=ChessBase Chess News |date=23 November 2006 |url-status=live |archive-url=https://web.archive.org/web/20121016172318/http://www.chessbase.com/newsdetail.asp?newsid=3504 |archive-date=16 October 2012 |access-date=18 August 2021}}</ref><ref>{{Cite news |date=5 December 2006 |title=Chess champion loses to computer |work=[[BBC News]] |url=http://news.bbc.co.uk/2/hi/europe/6212076.stm |url-status=live |access-date=4 May 2008 |archive-url=https://web.archive.org/web/20071231112514/http://news.bbc.co.uk/2/hi/europe/6212076.stm |archive-date=31 December 2007}}</ref>