Difference between revisions of "Timeline of AlphaGo"

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This is a '''timeline of {{w|AlphaGo}}''', a {{w|computer program}} developed by {{w|DeepMind}} that plays the {{w|board game}} [[w:Go (game)|Go]].  
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This is a '''timeline of {{w|AlphaGo}}''', a {{w|computer program}} developed by {{w|DeepMind}} that plays the {{w|board game}} [[w:Go (game)|Go]].
  
 
==Big picture==
 
==Big picture==
  
 
{| class="wikitable"
 
{| class="wikitable"
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! Time period !! Development summary
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! Time period !! Development summary
 
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| 2014 || The AlphaGo research project is formed.
 
| 2014 || The AlphaGo research project is formed.
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|}
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==Full timeline==
 
==Full timeline==
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! Year !! Month and date !! Event type !! Details
 
! Year !! Month and date !! Event type !! Details
 
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| 3000 BP || || Prelude || The game of Go originates in {{w|China}}. The rules of the game are simple: players take turns to place black or white stones on a board, trying to capture the opponent's stones or surround empty space to make points of territory.<ref name="Alphago's Games"/>  
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| 3000 BP || || Prelude || The game of Go originates in {{w|China}}. The rules of the game are simple: players take turns to place black or white stones on a board, trying to capture the opponent's stones or surround empty space to make points of territory.<ref name="Alphago's Games"/>
 
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| 1997 || May || Prelude || {{w|IBM}}'s computer [[w:Deep Blue (chess computer)|Deep Blue]] beats world chess champion {{w|Garry Kasparov}}.<ref>{{cite web |title=Deep Blue, IBM's supercomputer, defeats chess champion Garry Kasparov in 1997 |url=https://www.nydailynews.com/news/world/kasparov-deep-blues-losingchess-champ-rooke-article-1.762264 |website=nydailynews.com |accessdate=6 June 2019}}</ref>
 
| 1997 || May || Prelude || {{w|IBM}}'s computer [[w:Deep Blue (chess computer)|Deep Blue]] beats world chess champion {{w|Garry Kasparov}}.<ref>{{cite web |title=Deep Blue, IBM's supercomputer, defeats chess champion Garry Kasparov in 1997 |url=https://www.nydailynews.com/news/world/kasparov-deep-blues-losingchess-champ-rooke-article-1.762264 |website=nydailynews.com |accessdate=6 June 2019}}</ref>
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| 2014 || || Founding || The AlphaGo research project is formed to test how well a {{w|neural network}} using deep learning can compete at Go.<ref>{{cite book |last1=Ciaburro |first1=Giuseppe |title=Keras Reinforcement Learning Projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents |url=https://books.google.com.ar/books?id=dv1wDwAAQBAJ&pg=PA233&lpg=PA233&dq=alphago+project+2014&source=bl&ots=8a5wqOA5GT&sig=ACfU3U3N6H4pdbtSLIjxti8Xg-m0mXlvIg&hl=en&sa=X&ved=2ahUKEwjxio-HuNPiAhU6FLkGHaY-BfAQ6AEwEXoECAkQAQ#v=onepage&q=alphago%20project%202014&f=false}}</ref>
 
| 2014 || || Founding || The AlphaGo research project is formed to test how well a {{w|neural network}} using deep learning can compete at Go.<ref>{{cite book |last1=Ciaburro |first1=Giuseppe |title=Keras Reinforcement Learning Projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents |url=https://books.google.com.ar/books?id=dv1wDwAAQBAJ&pg=PA233&lpg=PA233&dq=alphago+project+2014&source=bl&ots=8a5wqOA5GT&sig=ACfU3U3N6H4pdbtSLIjxti8Xg-m0mXlvIg&hl=en&sa=X&ved=2ahUKEwjxio-HuNPiAhU6FLkGHaY-BfAQ6AEwEXoECAkQAQ#v=onepage&q=alphago%20project%202014&f=false}}</ref>
 
|-
 
|-
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| 2015 || October || Achievement || {{w|AlphaGo versus Fan Hui}} is held at DeepMind's headquarters in {{w|London}}.<ref name=MetzWired2016>{{Cite web|title = In Major AI Breakthrough, Google System Secretly Beats Top Player at the Ancient Game of Go|url = https://www.wired.com/2016/01/in-a-huge-breakthrough-googles-ai-beats-a-top-player-at-the-game-of-go/|website = WIRED|access-date = 1 February 2016|language = en-US|date =27 January 2016|last = Metz|first = Cade}}</ref> The distributed version of AlphaGo defeates the [[w:European Go Championship|European Go champion]] {{w|Fan Hui}}, a [[w:Go ranks and ratings|2-dan]] (out of 9 dan possible) professional, five to zero.<ref name="bbcgo">{{cite news |url=https://www.bbc.com/news/technology-35420579 |title=Google achieves AI 'breakthrough' by beating Go champion |date=27 January 2016 |work={{w|BBC News}}}}</ref><ref>{{cite web|url = http://www.britgo.org/files/2016/deepmind/BGJ174-AlphaGo.pdf|title = Special Computer Go insert covering the AlphaGo v Fan Hui match|access-date = 1 February 2016|website = |publisher = British Go Journal|last = |first = |month = |year = 2017}}</ref> AlphaGo wins all the five games.<ref name="bbcgo">{{cite news |url=https://www.bbc.com/news/technology-35420579 |title=Google achieves AI 'breakthrough' by beating Go champion |date=27 January 2016 |work={{w|BBC News}}}}</ref><ref>{{Cite web|url = http://www.britgo.org/files/2016/deepmind/BGJ174-AlphaGo.pdf|title = Special Computer Go insert covering the AlphaGo v Fan Hui match|date = |access-date = 1 February 2016|website = |publisher = British Go Journal|last = |first = |month = |orig-year = 2017}}</ref> This is the first time a computer Go program beats a professional human player on a full-sized board without handicap.<ref name="lemondego">{{cite news |url=http://www.lemonde.fr/pixels/article/2016/01/27/premiere-defaite-d-un-professionnel-du-go-contre-une-intelligence-artificielle_4854886_4408996.html |title=Première défaite d’un professionnel du go contre une intelligence artificielle |date=27 January 2016 |work={{w|Le Monde}} |language=fr}}</ref>  
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| 2015 || October || Achievement || {{w|AlphaGo versus Fan Hui}} is held at DeepMind's headquarters in {{w|London}}.<ref name=MetzWired2016>{{Cite web|title = In Major AI Breakthrough, Google System Secretly Beats Top Player at the Ancient Game of Go|url = https://www.wired.com/2016/01/in-a-huge-breakthrough-googles-ai-beats-a-top-player-at-the-game-of-go/|website = WIRED|access-date = 1 February 2016|language = en-US|date =27 January 2016|last = Metz|first = Cade}}</ref> The distributed version of AlphaGo defeates the [[w:European Go Championship|European Go champion]] {{w|Fan Hui}}, a [[w:Go ranks and ratings|2-dan]] (out of 9 dan possible) professional, five to zero.<ref name="bbcgo">{{cite news |url=https://www.bbc.com/news/technology-35420579 |title=Google achieves AI 'breakthrough' by beating Go champion |date=27 January 2016 |work={{w|BBC News}}}}</ref><ref>{{cite web|url = http://www.britgo.org/files/2016/deepmind/BGJ174-AlphaGo.pdf|title = Special Computer Go insert covering the AlphaGo v Fan Hui match|access-date = 1 February 2016|website = |publisher = British Go Journal|last = |first = |month = |year = 2017}}</ref><ref name="bbcgo">{{cite news |url=https://www.bbc.com/news/technology-35420579 |title=Google achieves AI 'breakthrough' by beating Go champion |date=27 January 2016 |work={{w|BBC News}}}}</ref><ref>{{Cite web|url = http://www.britgo.org/files/2016/deepmind/BGJ174-AlphaGo.pdf|title = Special Computer Go insert covering the AlphaGo v Fan Hui match|date = |access-date = 1 February 2016|website = |publisher = British Go Journal|last = |first = |month = |orig-year = 2017}}</ref> This is the first time a computer Go program beats a professional human player on a full-sized board without handicap.<ref name="lemondego">{{cite news |url=http://www.lemonde.fr/pixels/article/2016/01/27/premiere-defaite-d-un-professionnel-du-go-contre-une-intelligence-artificielle_4854886_4408996.html |title=Première défaite d’un professionnel du go contre une intelligence artificielle |date=27 January 2016 |work={{w|Le Monde}} |language=fr}}</ref>
 
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| 2015 || October || Achievement || The original AlphaGo becomes the first {{w|computer Go}} program to beat a human {{w|professional Go player}} without [[w:Go handicaps|handicaps]] on a full-sized 19&times;19 board.<ref name="googlego">{{cite web |url=http://googleresearch.blogspot.com/2016/01/alphago-mastering-ancient-game-of-go.html|title=Research Blog: AlphaGo: Mastering the ancient game of Go with Machine Learning |date=27 January 2016 |work=Google Research Blog}}</ref><ref name="bbcgo" />  
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| 2015 || October || Achievement || The original AlphaGo becomes the first {{w|computer Go}} program to beat a human {{w|professional Go player}} without [[w:Go handicaps|handicaps]] on a full-sized 19&times;19 board.<ref name="googlego">{{cite web |url=http://googleresearch.blogspot.com/2016/01/alphago-mastering-ancient-game-of-go.html|title=Research Blog: AlphaGo: Mastering the ancient game of Go with Machine Learning |date=27 January 2016 |work=Google Research Blog}}</ref><ref name="bbcgo" />
 
|-
 
|-
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| 2015 || October || Game series release || Fan Hui versus AlphaGo is released in v13 version. It consists of five games.<ref name="Alphago's Games"/>
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| 2015 || October || Game series release || Fan Hui versus AlphaGo game play is released in version 13. It consists of five games.<ref name="Alphago's Games"/>
 
|-
 
|-
 
| 2016 || January 28 || Research || AlphaGo's team publishes an article in the journal ''[[w:Nature (journal)|Nature]]'', describing the technical details behind the {{w|reinforcement learning}} approach used in AlphaGo.<ref name="The story of AlphaGo so far">{{cite web |title=The story of AlphaGo so far |url=https://deepmind.com/research/alphago/ |website=deepmind.com |accessdate=23 May 2019}}</ref>
 
| 2016 || January 28 || Research || AlphaGo's team publishes an article in the journal ''[[w:Nature (journal)|Nature]]'', describing the technical details behind the {{w|reinforcement learning}} approach used in AlphaGo.<ref name="The story of AlphaGo so far">{{cite web |title=The story of AlphaGo so far |url=https://deepmind.com/research/alphago/ |website=deepmind.com |accessdate=23 May 2019}}</ref>
 
|-
 
|-
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| 2016 || February || Game series release || AlphaGo versus AlphaGo is released in v18 version. It consists of three games.<ref name="Alphago's Games"/>
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| 2016 || February || Game series release || AlphaGo versus AlphaGo is released in version 18. It consists of three games.<ref name="Alphago's Games"/>
 
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| 2016 || March || Achievement || {{w|AlphaGo versus Lee Sedol}} takes place as a five-game [[w:Go (game)|Go]] match between 18-time world champion {{w|Lee Sedol}} and {{w|AlphaGo}}. Played in {{w|Seoul}}, South Korea between 9 and 15 March 2016, AlphaGo wins all but the fourth game.<ref name="BBC News 13 March 2016">{{cite web | title= Artificial intelligence: Go master Lee Se-dol wins against AlphaGo program |url= https://www.bbc.co.uk/news/technology-35797102| author=<!--Staff writer; no by-line.-->|date= 13 March 2016| website= {{w|BBC News Online}} | accessdate= 13 March 2016 }}</ref> This is the first time a computer Go program beats a [[w:Go ranks and ratings|9-dan]] professional without handicaps.<ref name="leesedolwin">{{cite web |url=https://www.youtube.com/watch?v=vFr3K2DORc8&t=1h57m |title=Match 1 – Google DeepMind Challenge Match: Lee Sedol vs AlphaGo |date=8 March 2016}}</ref><ref>{{cite web |title=DeepMind’s AlphaGo Zero Becomes Go Champion Without Human Input |url=https://futureoflife.org/2017/10/18/deepminds-alphago-zero-becomes-go-champion-without-human-assistance/?cn-reloaded=1 |website=futureoflife.org |accessdate=5 April 2019}}</ref><ref>{{cite web |title=AI: How big a deal is Google's latest AlphaGo breakthrough? |url=https://www.techrepublic.com/article/ai-how-big-a-deal-is-googles-latest-alphago-breakthrough/ |website=techrepublic.com |accessdate=5 April 2019}}</ref><ref name="AlphaGov">{{cite web |title=AlphaGo |url=https://www.britgo.org/alphago |website=britgo.org |accessdate=5 April 2019}}</ref> The match is watched by over 200 million people worldwide.<ref name="AlphaGo China"/>  
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| 2016 || March || Achievement || {{w|AlphaGo versus Lee Sedol}} takes place as a five-game [[w:Go (game)|Go]] match between 18-time world champion {{w|Lee Sedol}} and {{w|AlphaGo}}. Played in {{w|Seoul}}, South Korea between 9 and 15 March 2016, AlphaGo wins all but the fourth game.<ref name="BBC News 13 March 2016">{{cite web | title= Artificial intelligence: Go master Lee Se-dol wins against AlphaGo program |url= https://www.bbc.co.uk/news/technology-35797102| author=<!--Staff writer; no by-line.-->|date= 13 March 2016| website= {{w|BBC News Online}} | accessdate= 13 March 2016 }}</ref> This is the first time a computer Go program beats a [[w:Go ranks and ratings|9-dan]] professional without handicaps.<ref name="leesedolwin">{{cite web |url=https://www.youtube.com/watch?v=vFr3K2DORc8&t=1h57m |title=Match 1 – Google DeepMind Challenge Match: Lee Sedol vs AlphaGo |date=8 March 2016}}</ref><ref>{{cite web |title=DeepMind’s AlphaGo Zero Becomes Go Champion Without Human Input |url=https://futureoflife.org/2017/10/18/deepminds-alphago-zero-becomes-go-champion-without-human-assistance/?cn-reloaded=1 |website=futureoflife.org |accessdate=5 April 2019}}</ref><ref>{{cite web |title=AI: How big a deal is Google's latest AlphaGo breakthrough? |url=https://www.techrepublic.com/article/ai-how-big-a-deal-is-googles-latest-alphago-breakthrough/ |website=techrepublic.com |accessdate=5 April 2019}}</ref><ref name="AlphaGov">{{cite web |title=AlphaGo |url=https://www.britgo.org/alphago |website=britgo.org |accessdate=5 April 2019}}</ref> The match is watched by over 200 million people worldwide.<ref name="AlphaGo China"/>
 
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| 2016 || March || Game series release || Lee Sedol versus Alphago is released on v18 version. It consists of five games.<ref name="Alphago's Games"/>
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| 2016 || March || Game series release || Lee Sedol versus AlphaGo game play is released on version 18. It consists of five games.<ref name="Alphago's Games"/>
 
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| 2016 || May || Hardware development || Google unveils its own proprietary hardware "{{w|tensor processing unit}}s". The company states having already deployed this hardware in multiple internal projects, including the AlphaGo match against {{w|Lee Sedol}}.<ref>{{cite news|last1=McMillan|first1=Robert|title=Google Isn’t Playing Games With New Chip|url=https://www.wsj.com/articles/google-isnt-playing-games-with-new-chip-1463597820|accessdate=21 May 2019|work={{w|Wall Street Journal}}|date=18 May 2016}}</ref><ref>{{cite web|url=https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chip.html|title=Google supercharges machine learning tasks with TPU custom chip|last=Jouppi|first=Norm|date=May 18, 2016|website=Google Cloud Platform Blog|language=en-US|access-date=21 May 2019}}</ref>
 
| 2016 || May || Hardware development || Google unveils its own proprietary hardware "{{w|tensor processing unit}}s". The company states having already deployed this hardware in multiple internal projects, including the AlphaGo match against {{w|Lee Sedol}}.<ref>{{cite news|last1=McMillan|first1=Robert|title=Google Isn’t Playing Games With New Chip|url=https://www.wsj.com/articles/google-isnt-playing-games-with-new-chip-1463597820|accessdate=21 May 2019|work={{w|Wall Street Journal}}|date=18 May 2016}}</ref><ref>{{cite web|url=https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chip.html|title=Google supercharges machine learning tasks with TPU custom chip|last=Jouppi|first=Norm|date=May 18, 2016|website=Google Cloud Platform Blog|language=en-US|access-date=21 May 2019}}</ref>
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| 2016 || October 12 || Research || DeepMind publishes a paper on {{w|differentiable neural computers}}, which demonstrates that models that can simultaneously learn like neural networks as well as memorize data like computers.<ref name="DeepMind’s work in 2016: a round-up">{{cite web |title=DeepMind’s work in 2016: a round-up |url=https://deepmind.com/blog/deepmind-round-up-2016/ |website=deepmind.com |accessdate=23 May 2019}}</ref><ref>{{cite journal |last1=Graves |first1=Alex |last2=Wayne |first2=Greg |last3=Reynolds |first3=Malcolm |last4=Harley |first4=Tim |last5=Danihelka |first5=Ivo |last6=Grabska-Barwińska |first6=Agnieszka |last7=Gómez Colmenarejo |first7=Sergio |last8=Grefenstette |first8=Edward |last9=Ramalho |first9=Tiago |last10=Agapiou |first10=John |last11=Puigdomènech Badia |first11=Adrià |last12=Moritz Hermann |first12=Karl |last13=Zwols |first13=Yori |last14=Ostrovski |first14=Georg |last15=Cain |first15=Adam |last16=King |first16=Helen |last17=Summerfield |first17=Christopher |last18=Blunsom |first18=Phil |last19=Kavukcuoglu |first19=Koray |last20=Hassabis |first20=Demis |title=Hybrid computing using a neural network with dynamic external memory |journal=Nature |url=https://www.nature.com/articles/nature20101 |accessdate=23 May 2019}}</ref>
 
| 2016 || October 12 || Research || DeepMind publishes a paper on {{w|differentiable neural computers}}, which demonstrates that models that can simultaneously learn like neural networks as well as memorize data like computers.<ref name="DeepMind’s work in 2016: a round-up">{{cite web |title=DeepMind’s work in 2016: a round-up |url=https://deepmind.com/blog/deepmind-round-up-2016/ |website=deepmind.com |accessdate=23 May 2019}}</ref><ref>{{cite journal |last1=Graves |first1=Alex |last2=Wayne |first2=Greg |last3=Reynolds |first3=Malcolm |last4=Harley |first4=Tim |last5=Danihelka |first5=Ivo |last6=Grabska-Barwińska |first6=Agnieszka |last7=Gómez Colmenarejo |first7=Sergio |last8=Grefenstette |first8=Edward |last9=Ramalho |first9=Tiago |last10=Agapiou |first10=John |last11=Puigdomènech Badia |first11=Adrià |last12=Moritz Hermann |first12=Karl |last13=Zwols |first13=Yori |last14=Ostrovski |first14=Georg |last15=Cain |first15=Adam |last16=King |first16=Helen |last17=Summerfield |first17=Christopher |last18=Blunsom |first18=Phil |last19=Kavukcuoglu |first19=Koray |last20=Hassabis |first20=Demis |title=Hybrid computing using a neural network with dynamic external memory |journal=Nature |url=https://www.nature.com/articles/nature20101 |accessdate=23 May 2019}}</ref>
 
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| 2016 || December || Game series release || Alphago Online Series on Tygem and Fox is released on [[w:Master (software)|Alphago Master]] version. It consists of sixty games.<ref name="Alphago's Games"/>
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| 2016 || December || Game series release || AlphaGo Online Series on Tygem and Fox game play is released on [[w:Master (software)|AlphaGo Master]] version. It consists of sixty games.<ref name="Alphago's Games"/>
 
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| 2017 || January || Achievement || Under the pseudonym "Master", AlphaGo plays several of the world's top players in a series of online matches, winning all 60 of its completed contests.<ref>{{cite web |title=AN IMPROVED ALPHAGO WINS ITS FIRST GAME AGAINST THE WORLD'S TOP GO PLAYER |url=https://www.wired.com/2017/05/revamped-alphago-wins-first-game-chinese-go-grandmaster/ |website=wired.com |accessdate=5 April 2019}}</ref><ref>{{cite web |title=DeepMind’s AI beats world's best Go player in latest face-off |url=https://www.newscientist.com/article/2132086-deepminds-ai-beats-worlds-best-go-player-in-latest-face-off/ |website=newscientist.com |accessdate=5 April 2019}}</ref><ref name="Alphago's Games"/>
 
| 2017 || January || Achievement || Under the pseudonym "Master", AlphaGo plays several of the world's top players in a series of online matches, winning all 60 of its completed contests.<ref>{{cite web |title=AN IMPROVED ALPHAGO WINS ITS FIRST GAME AGAINST THE WORLD'S TOP GO PLAYER |url=https://www.wired.com/2017/05/revamped-alphago-wins-first-game-chinese-go-grandmaster/ |website=wired.com |accessdate=5 April 2019}}</ref><ref>{{cite web |title=DeepMind’s AI beats world's best Go player in latest face-off |url=https://www.newscientist.com/article/2132086-deepminds-ai-beats-worlds-best-go-player-in-latest-face-off/ |website=newscientist.com |accessdate=5 April 2019}}</ref><ref name="Alphago's Games"/>
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| 2017 || May || International meeting || The {{w|Future of Go Summit}} is held by the {{w|Chinese Go Association}}, Sport Bureau of Zhejiang Province and {{w|Google}} in {{w|Wuzhen}}, {{w|Zhejiang}}, the permanent host of the {{w|World Internet Conference}}. It features five [[w:Go (game)|Go]] games involving {{w|AlphaGo}} and top Chinese Go players,<ref>{{cite web|url=https://deepmind.com/blog/exploring-mysteries-alphago/|title=Exploring the mysteries of Go with AlphaGo and China's top players|publisher={{w|DeepMind}}|date=10 April 2017|accessdate=21 May 2019}}</ref> as well as a forum on the future of {{w|artificial intelligence}}.<ref>{{cite web|url=https://www.wired.co.uk/article/deepmind-go-alphago-china-may-2017|title=DeepMind's AlphaGo is back..and this time it's taking on five humans at once|publisher={{w|Wired.com}}|date=10 April 2017|accessdate=21 May 2019}}</ref><ref name="AlphaGo China">{{Cite web|url=https://deepmind.com/research/alphago/alphago-china/|title=AlphaGo China {{!}} DeepMind|website=DeepMind|access-date=21 May 2019}}</ref><ref name="Alphago's Games"/>
 
| 2017 || May || International meeting || The {{w|Future of Go Summit}} is held by the {{w|Chinese Go Association}}, Sport Bureau of Zhejiang Province and {{w|Google}} in {{w|Wuzhen}}, {{w|Zhejiang}}, the permanent host of the {{w|World Internet Conference}}. It features five [[w:Go (game)|Go]] games involving {{w|AlphaGo}} and top Chinese Go players,<ref>{{cite web|url=https://deepmind.com/blog/exploring-mysteries-alphago/|title=Exploring the mysteries of Go with AlphaGo and China's top players|publisher={{w|DeepMind}}|date=10 April 2017|accessdate=21 May 2019}}</ref> as well as a forum on the future of {{w|artificial intelligence}}.<ref>{{cite web|url=https://www.wired.co.uk/article/deepmind-go-alphago-china-may-2017|title=DeepMind's AlphaGo is back..and this time it's taking on five humans at once|publisher={{w|Wired.com}}|date=10 April 2017|accessdate=21 May 2019}}</ref><ref name="AlphaGo China">{{Cite web|url=https://deepmind.com/research/alphago/alphago-china/|title=AlphaGo China {{!}} DeepMind|website=DeepMind|access-date=21 May 2019}}</ref><ref name="Alphago's Games"/>
 
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| 2017 || May || Game series release || Future of Go Summit is released on Alphago Master version. It consists of five games.<ref name="Alphago's Games"/>  
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| 2017 || May || Game series release || Future of Go Summit game play is released on AlphaGo Master version. It consists of five games.<ref name="Alphago's Games"/>
 
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| 2017 || May || Game series release || Alphago versus Alphago Batch 1 to 5 are released on Alphago Master version. Each series consisis of ten games.<ref name="Alphago's Games"/>
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| 2017 || May || Game series release || AlphaGo versus AlphaGo Batch 1 to 5 game plays are released on AlphaGo Master version. Each series consisis of ten games.<ref name="Alphago's Games"/>
 
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| 2017 || May 24 || Achievement || AlphaGo defeats Chinese Go world champion {{w|Ke Jie}} in a second, decisive win of a three-part series taking place in China.<ref>{{cite web |last1=Russell |first1=Jon |title=Google’s AlphaGo AI wins three-match series against the world’s best Go player |url=https://techcrunch.com/2017/05/24/alphago-beats-planets-best-human-go-player-ke-jie/ |website=techcrunch.com |accessdate=31 May 2019}}</ref><ref>{{cite web |title=Google AI defeats human Go champion |url=https://www.bbc.com/news/technology-40042581 |website=bbc.com |accessdate=31 May 2019}}</ref>  
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| 2017 || May 24 || Achievement || AlphaGo defeats Chinese Go world champion {{w|Ke Jie}} in a second, decisive win of a three-part series taking place in China.<ref>{{cite web |last1=Russell |first1=Jon |title=Google’s AlphaGo AI wins three-match series against the world’s best Go player |url=https://techcrunch.com/2017/05/24/alphago-beats-planets-best-human-go-player-ke-jie/ |website=techcrunch.com |accessdate=31 May 2019}}</ref><ref>{{cite web |title=Google AI defeats human Go champion |url=https://www.bbc.com/news/technology-40042581 |website=bbc.com |accessdate=31 May 2019}}</ref>
 
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| 2017 || July || Game series release || WeiQi TV-5 Extra Games is released on Alphago Master version. It consists of five games.<ref name="Alphago's Games"/>
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| 2017 || July || Game series release || WeiQi TV-5 Extra Games game play is released on AlphaGo Master version. It consists of five games.<ref name="Alphago's Games"/>
 
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| 2017 || September 29 || || The AlphaGo documentary film is released.<ref>{{cite web|url=https://www.rottentomatoes.com/m/alphago/ |title=AlphaGo (2017) |publisher=Rotten Tomatoes |date= |accessdate=23 May 2019}}</ref>  
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| 2017 || September 29 || || The AlphaGo documentary film is released.<ref>{{cite web|url=https://www.rottentomatoes.com/m/alphago/ |title=AlphaGo (2017) |publisher=Rotten Tomatoes |date= |accessdate=23 May 2019}}</ref>
 
|-
 
|-
 
| 2017 || October 19 || Research || AlphaGo's team publishes an article in the journal ''[[w:Nature (journal)|Nature]]'', introducing {{w|AlphaGo Zero}}, a version created without using data from human games, and stronger than any previous version.<ref name="Nature2017">{{cite journal |first1=David |last1=Silver|first2= Julian|last2= Schrittwieser|first3= Karen|last3= Simonyan|first4= Ioannis|last4= Antonoglou|first5= Aja|last5= Huang|first6=Arthur|last6= Guez|first7= Thomas|last7= Hubert|first8= Lucas|last8= Baker|first9= Matthew|last9= Lai|first10= Adrian|last10= Bolton|first11= Yutian|last11= Chen|first12= Timothy|last12= Lillicrap|first13=Hui|last13= Fan|first14= Laurent|last14= Sifre|first15= George van den|last15= Driessche|first16= Thore|last16= Graepel|first17= Demis|last17=Hassabis|url=https://www.nature.com/nature/journal/v550/n7676/full/nature24270.html|title=Mastering the game of Go without human knowledge|journal=[[w:Nature (journal)|Nature]]|issn= 0028-0836|pages=354–359|volume =550|issue =7676|doi =10.1038/nature24270|pmid=29052630|date=19 October 2017|accessdate=10 December 2017}}</ref><ref name="Alphago's Games"/>
 
| 2017 || October 19 || Research || AlphaGo's team publishes an article in the journal ''[[w:Nature (journal)|Nature]]'', introducing {{w|AlphaGo Zero}}, a version created without using data from human games, and stronger than any previous version.<ref name="Nature2017">{{cite journal |first1=David |last1=Silver|first2= Julian|last2= Schrittwieser|first3= Karen|last3= Simonyan|first4= Ioannis|last4= Antonoglou|first5= Aja|last5= Huang|first6=Arthur|last6= Guez|first7= Thomas|last7= Hubert|first8= Lucas|last8= Baker|first9= Matthew|last9= Lai|first10= Adrian|last10= Bolton|first11= Yutian|last11= Chen|first12= Timothy|last12= Lillicrap|first13=Hui|last13= Fan|first14= Laurent|last14= Sifre|first15= George van den|last15= Driessche|first16= Thore|last16= Graepel|first17= Demis|last17=Hassabis|url=https://www.nature.com/nature/journal/v550/n7676/full/nature24270.html|title=Mastering the game of Go without human knowledge|journal=[[w:Nature (journal)|Nature]]|issn= 0028-0836|pages=354–359|volume =550|issue =7676|doi =10.1038/nature24270|pmid=29052630|date=19 October 2017|accessdate=10 December 2017}}</ref><ref name="Alphago's Games"/>
Line 69: Line 70:
 
| 2017 || October 20 || Recognition || The DeepMind team behind AlphaGo is awarded the inaugural {{w|Marvin Minsky Medal}} by the {{w|International Joint Conference On Artificial Intelligence}} (IJCAI) in {{w|Stockholm}}, for outstanding achievements in the field of AI.<ref>{{cite web |last1=Gorey |first1=Colm |title=DeepMind team behind AlphaGo wins inaugural ‘Nobel Prize for AI’ |url=https://www.siliconrepublic.com/machines/deepmind-alphago-marvin-minsky-award |website=siliconrepublic.com |accessdate=1 June 2019}}</ref><ref>{{cite web |title=DeepMind team behind AlphaGo wins inaugural ‘Nobel Prize for AI’ |url=http://www.lionra.ie/feed-items/deepmind-team-behind-alphago-wins-inaugural-nobel-prize-for-ai/ |website=lionra.ie |accessdate=1 June 2019}}</ref><ref>{{cite web |title=IJCAI 2018 Kicks Off; DeepMind AlphaGo Wins Marvin Minsky Medal |url=https://medium.com/syncedreview/ijcai-2018-kicks-off-deepmind-alphago-wins-marvin-minsky-medal-56ff073f2c38 |website=medium.com |accessdate=1 June 2019}}</ref><ref>{{cite web |title=DeepMind wins the Minsky medal for AlphaGo |url=https://www.celi.it/en/blog/2017/10/deepmind-alphago-team-receive-inaugural-ijcai-marvin-minsky-medal/ |website=celi.it |accessdate=1 June 2019}}</ref>
 
| 2017 || October 20 || Recognition || The DeepMind team behind AlphaGo is awarded the inaugural {{w|Marvin Minsky Medal}} by the {{w|International Joint Conference On Artificial Intelligence}} (IJCAI) in {{w|Stockholm}}, for outstanding achievements in the field of AI.<ref>{{cite web |last1=Gorey |first1=Colm |title=DeepMind team behind AlphaGo wins inaugural ‘Nobel Prize for AI’ |url=https://www.siliconrepublic.com/machines/deepmind-alphago-marvin-minsky-award |website=siliconrepublic.com |accessdate=1 June 2019}}</ref><ref>{{cite web |title=DeepMind team behind AlphaGo wins inaugural ‘Nobel Prize for AI’ |url=http://www.lionra.ie/feed-items/deepmind-team-behind-alphago-wins-inaugural-nobel-prize-for-ai/ |website=lionra.ie |accessdate=1 June 2019}}</ref><ref>{{cite web |title=IJCAI 2018 Kicks Off; DeepMind AlphaGo Wins Marvin Minsky Medal |url=https://medium.com/syncedreview/ijcai-2018-kicks-off-deepmind-alphago-wins-marvin-minsky-medal-56ff073f2c38 |website=medium.com |accessdate=1 June 2019}}</ref><ref>{{cite web |title=DeepMind wins the Minsky medal for AlphaGo |url=https://www.celi.it/en/blog/2017/10/deepmind-alphago-team-receive-inaugural-ijcai-marvin-minsky-medal/ |website=celi.it |accessdate=1 June 2019}}</ref>
 
|-
 
|-
−
| 2017 || October 25 || Software release || {{w|Leela Zero}}, a [[w:Free and open-source software|free and open-source]] {{w|computer Go}} software, is released. It is developed by Belgian programmer {{w|Gian-Carlo Pascutto}},<ref>{{cite web|url=http://www.xinhuanet.com/english/2018-04/09/c_137097436.htm|title=Feature: One man's Go program looks to remake AlphaGo Zero - and beyond|publisher={{w|Xinhuanet}}|date=9 April 2018|accessdate=5 April 2019}}</ref><ref>{{cite web|url=http://sports.xinhuanet.com/c/2018-02/05/c_1122370241.htm|title=围棋AI"丽拉"获赞接近职业棋手水准，它的作者竟是一个不太会下棋的程序员|publisher={{w|Xinhuanet}}|date=5 February 2018|accessdate=5 April 2019|language=zh}}</ref><ref name="Xinhuanet20180408">{{cite web|url=http://www.xinhuanet.com/sports/2018-04/08/c_1122648768.htm|title=更开放，更共享，比利时围棋AI“丽拉·元”重塑“阿尔法元”|publisher={{w|Xinhuanet}}|date=8 April 2018|accessdate=5 April 2019|language=zh}}</ref> the author of chess engine [[w:Sjeng (software)|Sjeng]] and Go engine [[w:Leela (software)|Leela]].<ref name="baduknews">{{cite web|url=http://baduknews.com/news/view.php?idx=236|title=프로 수준급 인공지능 바둑 프로그램 ‘릴라(Leela)’ 무료 공개|publisher=Baduk News|language=Korean|date=23 February 2017|accessdate=5 April 2019}}</ref><ref>{{cite web|url=https://www.cyberoro.com/board/oro_view.oro?bd_div=1&bd_num=16436|title=릴라의 출현과 온라인 대국의 비극적인 종말...|publisher=Cyberoro|date=3 March 2017|accessdate=5 April 2019|language=Korean}}</ref>  
+
| 2017 || October 25 || Software release || {{w|Leela Zero}}, a [[w:Free and open-source software|free and open-source]] {{w|computer Go}} software, is released. It is developed by Belgian programmer {{w|Gian-Carlo Pascutto}},<ref>{{cite web|url=http://www.xinhuanet.com/english/2018-04/09/c_137097436.htm|title=Feature: One man's Go program looks to remake AlphaGo Zero - and beyond|publisher={{w|Xinhuanet}}|date=9 April 2018|accessdate=5 April 2019}}</ref><ref>{{cite web|url=http://sports.xinhuanet.com/c/2018-02/05/c_1122370241.htm|title=围棋AI"丽拉"获赞接近职业棋手水准，它的作者竟是一个不太会下棋的程序员|publisher={{w|Xinhuanet}}|date=5 February 2018|accessdate=5 April 2019|language=zh}}</ref><ref name="Xinhuanet20180408">{{cite web|url=http://www.xinhuanet.com/sports/2018-04/08/c_1122648768.htm|title=更开放，更共享，比利时围棋AI“丽拉·元”重塑“阿尔法元”|publisher={{w|Xinhuanet}}|date=8 April 2018|accessdate=5 April 2019|language=zh}}</ref> the author of chess engine [[w:Sjeng (software)|Sjeng]] and Go engine [[w:Leela (software)|Leela]].<ref name="baduknews">{{cite web|url=http://baduknews.com/news/view.php?idx=236|title=프로 수준급 인공지능 바둑 프로그램 ‘릴라(Leela)’ 무료 공개|publisher=Baduk News|language=Korean|date=23 February 2017|accessdate=5 April 2019}}</ref><ref>{{cite web|url=https://www.cyberoro.com/board/oro_view.oro?bd_div=1&bd_num=16436|title=릴라의 출현과 온라인 대국의 비극적인 종말...|publisher=Cyberoro|date=3 March 2017|accessdate=5 April 2019|language=Korean}}</ref>
 
|-
 
|-
 
| 2017 || October || || It is announced that {{w|AlphaGo Zero}}, armed with just the rules, has in 40 days become even better at Go than the original AlphaGo, without the help of game records.<ref name="AlphaGov"/>
 
| 2017 || October || || It is announced that {{w|AlphaGo Zero}}, armed with just the rules, has in 40 days become even better at Go than the original AlphaGo, without the help of game records.<ref name="AlphaGov"/>
 
|-
 
|-
−
| 2017 || October || Game series release || AlphaGo Zero (20 Blocks) vs AlphaGo Lee is released on Alphago Zero version. It consists of twenty games.<ref name="Alphago's Games"/>  
+
| 2017 || October || Game series release || AlphaGo Zero (20 Blocks) vs AlphaGo Lee game play is released on AlphaGo Zero version. It consists of twenty games.<ref name="Alphago's Games"/>
 
|-
 
|-
−
| 2017 || October || Game series release || AlphaGo Zero vs AlphaGo Zero - 20 Blocks is released on Alphago Zero version. It consists of twenty games.<ref name="Alphago's Games"/>  
+
| 2017 || October || Game series release || AlphaGo Zero vs AlphaGo Zero - 20 Blocks game play is released on AlphaGo Zero version. It consists of twenty games.<ref name="Alphago's Games"/>
 
|-
 
|-
−
| 2017 || October || Game series release || AlphaGo Zero vs AlphaGo Zero - 40 Blocks is released on Alphago Zero version. It consists of twenty games.<ref name="Alphago's Games"/>  
+
| 2017 || October || Game series release || AlphaGo Zero vs AlphaGo Zero - 40 Blocks game play is released on AlphaGo Zero version. It consists of twenty games.<ref name="Alphago's Games"/>
 
|-
 
|-
−
| 2017 || October || Game series release || AlphaGo Zero (40 Blocks) vs AlphaGo Master is released in Alphago Zero version. It consists of twenty games.<ref name="Alphago's Games">{{cite web |title=Alphago's Games |url=https://www.alphago-games.com/ |website=alphago-games.com |accessdate=21 May 2019}}</ref>
+
| 2017 || October || Game series release || AlphaGo Zero (40 Blocks) vs AlphaGo Master game play is released in AlphaGo Zero version. It consists of twenty games.<ref name="Alphago's Games">{{cite web |title=AlphaGo's Games |url=https://www.alphago-games.com/ |website=alphago-games.com |accessdate=21 May 2019}}</ref>
 
|-
 
|-
 
| 2017 || December 5 || Software release || The DeepMind team releases a preprint on {{w|arXiv}}, introducing AlphaZero, a program using generalized AlphaGo Zero's approach, which achieved within 24 hours a superhuman level of play in {{w|chess}}, {{w|shogi}}, and [[w:Go (game)|Go]], defeating world-champion programs, [[w:Stockfish (chess)|Stockfish]], [[w:Elmo (shogi engine)|Elmo]], and 3-day version of AlphaGo Zero in each case.<ref name="preprint"/>
 
| 2017 || December 5 || Software release || The DeepMind team releases a preprint on {{w|arXiv}}, introducing AlphaZero, a program using generalized AlphaGo Zero's approach, which achieved within 24 hours a superhuman level of play in {{w|chess}}, {{w|shogi}}, and [[w:Go (game)|Go]], defeating world-champion programs, [[w:Stockfish (chess)|Stockfish]], [[w:Elmo (shogi engine)|Elmo]], and 3-day version of AlphaGo Zero in each case.<ref name="preprint"/>
Line 85: Line 86:
 
| 2017 || December || Software release || DeepMind releases the AlphaGo teaching tool on its website,<ref>{{cite web|url=https://alphagoteach.deepmind.com/|title=AlphaGo teaching tool|publisher={{w|DeepMind}}}}</ref> to analyze winning rates of different {{w|Go opening}}s as calculated by {{w|AlphaGo Master}}.<ref name="sina20171211">{{cite web|url=http://sports.sina.com.cn/go/2017-12-11/doc-ifypnsip8212788.shtml|title=AlphaGo教学工具上线 樊麾：使用Master版本|publisher={{w|Sina.com.cn}}|date=11 December 2017|accessdate=11 December 2017|language=Chinese}}</ref> The teaching tool collects 6,000 Go openings from 230,000 human games each analyzed with 10,000,000 simulations by AlphaGo Master. Many of the openings include human move suggestions.<ref name="sina20171211"/><ref name="Alphago's Games"/>
 
| 2017 || December || Software release || DeepMind releases the AlphaGo teaching tool on its website,<ref>{{cite web|url=https://alphagoteach.deepmind.com/|title=AlphaGo teaching tool|publisher={{w|DeepMind}}}}</ref> to analyze winning rates of different {{w|Go opening}}s as calculated by {{w|AlphaGo Master}}.<ref name="sina20171211">{{cite web|url=http://sports.sina.com.cn/go/2017-12-11/doc-ifypnsip8212788.shtml|title=AlphaGo教学工具上线 樊麾：使用Master版本|publisher={{w|Sina.com.cn}}|date=11 December 2017|accessdate=11 December 2017|language=Chinese}}</ref> The teaching tool collects 6,000 Go openings from 230,000 human games each analyzed with 10,000,000 simulations by AlphaGo Master. Many of the openings include human move suggestions.<ref name="sina20171211"/><ref name="Alphago's Games"/>
 
|-
 
|-
−
| 2017 || December || Achievement || AlphaZero beats the 3-day version of {{w|AlphaGo Zero}} by winning 60 games to 40, and with 8 hours of training it outperformes {{w|AlphaGo Lee}} on an [[w:Elo rating system|Elo scale]]. AlphaZero also defeats a top chess program ([[w:Stockfish (chess)|Stockfish]]) and a top Shōgi program ([[w:Elmo (shogi engine)|Elmo]]).<ref name="preprint">{{Cite web|first1=David|last1= Silver|first2=Thomas|last2= Hubert|first3= Julian|last3=Schrittwieser|first4= Ioannis|last4=Antonoglou |first5= Matthew|last5= Lai|first6= Arthur|last6= Guez|first7= Marc|last7= Lanctot|first8= Laurent|last8= Sifre|first9= Dharshan|last9= Kumaran|first10= Thore|last10= Graepel|first11= Timothy|last11= Lillicrap|first12= Karen|last12= Simonyan|first13=Demis |last13=Hassabis|title=Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm|class=cs.AI|date=5 December 2017|url=https://arxiv.org/abs/1712.01815}}</ref>  
+
| 2017 || December || Achievement || AlphaZero beats the 3-day version of {{w|AlphaGo Zero}} by winning 60 games to 40, and with 8 hours of training it outperformes {{w|AlphaGo Lee}} on an [[w:Elo rating system|Elo scale]]. AlphaZero also defeats a top chess program ([[w:Stockfish (chess)|Stockfish]]) and a top Shōgi program ([[w:Elmo (shogi engine)|Elmo]]).<ref name="preprint">{{Cite web|first1=David|last1= Silver|first2=Thomas|last2= Hubert|first3= Julian|last3=Schrittwieser|first4= Ioannis|last4=Antonoglou |first5= Matthew|last5= Lai|first6= Arthur|last6= Guez|first7= Marc|last7= Lanctot|first8= Laurent|last8= Sifre|first9= Dharshan|last9= Kumaran|first10= Thore|last10= Graepel|first11= Timothy|last11= Lillicrap|first12= Karen|last12= Simonyan|first13=Demis |last13=Hassabis|title=Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm|class=cs.AI|date=5 December 2017|url=https://arxiv.org/abs/1712.01815}}</ref>
 
|-
 
|-
 
| 2018 || April || Research || A paper published in ''[[w:Nature (journal)|Nature]]'' cites AlphaGo's approach as the basis for a new means of computing potential pharmaceutical drug molecules.<ref>{{cite web|url=https://www.theengineer.co.uk/go-make-drugs/|title=Go and make some drugs The Engineer|website=www.theengineer.co.uk|language=en-UK|access-date=21 May 2019}}</ref><ref>{{cite journal |last1=Segler |first1=Marwin H. S. |last2=Preuss |first2=Mike |last3=Waller |first3=Mark P. |title=Planning chemical syntheses with deep neural networks and symbolic AI |url=https://www.nature.com/articles/nature25978}}</ref>
 
| 2018 || April || Research || A paper published in ''[[w:Nature (journal)|Nature]]'' cites AlphaGo's approach as the basis for a new means of computing potential pharmaceutical drug molecules.<ref>{{cite web|url=https://www.theengineer.co.uk/go-make-drugs/|title=Go and make some drugs The Engineer|website=www.theengineer.co.uk|language=en-UK|access-date=21 May 2019}}</ref><ref>{{cite journal |last1=Segler |first1=Marwin H. S. |last2=Preuss |first2=Mike |last3=Waller |first3=Mark P. |title=Planning chemical syntheses with deep neural networks and symbolic AI |url=https://www.nature.com/articles/nature25978}}</ref>
Line 92: Line 93:
 
|-
 
|-
 
|}
 
|}
 +
 +
== Numerical and visual data ==
 +
 +
=== Mentions on Google Scholar ===
 +
 +
The following table summarizes per-year mentions on Google Scholar as of May 13, 2021.
 +
 +
{| class="sortable wikitable"
 +
! Year
 +
! DeepMind
 +
! AlphaGo 
 +
! AlphaZero
 +
|-
 +
| 2010 || 97 || 0 || 0
 +
|-
 +
| 2011 || 83 || 0 || 0
 +
|-
 +
| 2012 || 109 || 0 || 0
 +
|-
 +
| 2013 || 111 || 0 || 0
 +
|-
 +
| 2014 || 251 || 135 || 0
 +
|-
 +
| 2015 || 479 || 111 || 0
 +
|-
 +
| 2016 || 1,450 || 2,190 || 0
 +
|-
 +
| 2017 || 2,430 || 2,980 || 56
 +
|-
 +
| 2018 || 3,800 || 3,730 || 480
 +
|-
 +
| 2019 || 4,740 || 4,000 || 833
 +
|-
 +
| 2020 || 5,520 || 4,120 || 937
 +
|-
 +
|}
 +
 +
[[File:AlphaZero google sch.png|thumb|center|700px]]
 +
 +
=== Google trends ===
 +
 +
The comparative chart below shows Google trends data for AlphaGo (computer program) and AlphaGo Zero from October 2015 to January 2021, when the screenshot was taken.<ref>{{cite web |title=AlphaGo |url=https://trends.google.com/trends/explore?date=2015-10-01%202021-01-06&q=%2Fg%2F11c3w4kywq,%2Fg%2F11f1jww7v6 |website=trends.google.com |access-date=6 January 2021}}</ref>
 +
 +
[[File:AlphaGo gtrends.jpeg|thumb|center|700px]]
 +
 +
=== Google Ngram Viewer ===
 +
The chart below shows {{w|Google Ngram Viewer}} data for {{w|AlphaGo}}, from 2015 to 2019.<ref>{{cite web |title=AlphaGo |url=https://books.google.com/ngrams/graph?content=AlphaGo+&year_start=2015&year_end=2019&corpus=26&smoothing=1&case_insensitive=true |website=books.google.com |access-date=13 January 2021}}</ref>
 +
 +
[[File:AlphaGo ngram.jpeg|thumb|center|700px]]
 +
 +
=== Wikipedia Views ===
 +
 +
The chart below shows pageviews of the English Wikipedia article {{w|AlpgaGo}}, created in January 2016.<ref>{{cite web |title=Wikipedia Views: results |url=https://wikipediaviews.org/displayviewsformultiplemonths.php?page=AlphaGo&allmonths=allmonths-api&language=en&drilldown=all |website=wikipediaviews.org |access-date=25 January 2021}}</ref>
 +
 +
[[File:Alphago wv.png|thumb|center|500px]]
  
 
==Meta information on the timeline==
 
==Meta information on the timeline==

Latest revision as of 18:01, 9 June 2023

This is a timeline of AlphaGo, a computer program developed by DeepMind that plays the board game Go.

Big picture

Time period Development summary
2014 The AlphaGo research project is formed.
Since 2015 AlphaGo starts defeating human professional Go players. Earlier, computers were only known to have played Go at the “amateur” level.[1]
2016 AlphaGo victory in March becomes a major milestone in artificial intelligence research,[2] with Go having being previously been regarded as a hard problem in machine learning expected to be out of reach for the technology of the time.[2][3][4]


Full timeline

Year Month and date Event type Details
3000 BP Prelude The game of Go originates in China. The rules of the game are simple: players take turns to place black or white stones on a board, trying to capture the opponent's stones or surround empty space to make points of territory.[5]
1997 May Prelude IBM's computer Deep Blue beats world chess champion Garry Kasparov.[6]
2010 Prelude DeepMind is founded to create general-purpose artificial intelligence that can learn on its own.[7]
2012 March Prelude Software program Zen, running on a four PC cluster, beats Japanese 9p professional Masaki Takemiya two times at five and four stones handicap.[8]
2013 March Prelude Software Crazy Stone beats Japanese Go professional Yoshio Ishida (9p) at four-stones handicap.[9]
2014 Founding The AlphaGo research project is formed to test how well a neural network using deep learning can compete at Go.[10]
2015 October Achievement AlphaGo versus Fan Hui is held at DeepMind's headquarters in London.[11] The distributed version of AlphaGo defeates the European Go champion Fan Hui, a 2-dan (out of 9 dan possible) professional, five to zero.[12][13][12][14] This is the first time a computer Go program beats a professional human player on a full-sized board without handicap.[15]
2015 October Achievement The original AlphaGo becomes the first computer Go program to beat a human professional Go player without handicaps on a full-sized 19×19 board.[16][12]
2015 October Game series release Fan Hui versus AlphaGo game play is released in version 13. It consists of five games.[5]
2016 January 28 Research AlphaGo's team publishes an article in the journal Nature, describing the technical details behind the reinforcement learning approach used in AlphaGo.[17]
2016 February Game series release AlphaGo versus AlphaGo is released in version 18. It consists of three games.[5]
2016 March Achievement AlphaGo versus Lee Sedol takes place as a five-game Go match between 18-time world champion Lee Sedol and AlphaGo. Played in Seoul, South Korea between 9 and 15 March 2016, AlphaGo wins all but the fourth game.[18] This is the first time a computer Go program beats a 9-dan professional without handicaps.[19][20][21][22] The match is watched by over 200 million people worldwide.[23]
2016 March Game series release Lee Sedol versus AlphaGo game play is released on version 18. It consists of five games.[5]
2016 May Hardware development Google unveils its own proprietary hardware "tensor processing units". The company states having already deployed this hardware in multiple internal projects, including the AlphaGo match against Lee Sedol.[24][25]
2016 October 12 Research DeepMind publishes a paper on differentiable neural computers, which demonstrates that models that can simultaneously learn like neural networks as well as memorize data like computers.[26][27]
2016 December Game series release AlphaGo Online Series on Tygem and Fox game play is released on AlphaGo Master version. It consists of sixty games.[5]
2017 January Achievement Under the pseudonym "Master", AlphaGo plays several of the world's top players in a series of online matches, winning all 60 of its completed contests.[28][29][5]
2017 May International meeting The Future of Go Summit is held by the Chinese Go Association, Sport Bureau of Zhejiang Province and Google in Wuzhen, Zhejiang, the permanent host of the World Internet Conference. It features five Go games involving AlphaGo and top Chinese Go players,[30] as well as a forum on the future of artificial intelligence.[31][23][5]
2017 May Game series release Future of Go Summit game play is released on AlphaGo Master version. It consists of five games.[5]
2017 May Game series release AlphaGo versus AlphaGo Batch 1 to 5 game plays are released on AlphaGo Master version. Each series consisis of ten games.[5]
2017 May 24 Achievement AlphaGo defeats Chinese Go world champion Ke Jie in a second, decisive win of a three-part series taking place in China.[32][33]
2017 July Game series release WeiQi TV-5 Extra Games game play is released on AlphaGo Master version. It consists of five games.[5]
2017 September 29 The AlphaGo documentary film is released.[34]
2017 October 19 Research AlphaGo's team publishes an article in the journal Nature, introducing AlphaGo Zero, a version created without using data from human games, and stronger than any previous version.[35][5]
2017 October 20 Recognition The DeepMind team behind AlphaGo is awarded the inaugural Marvin Minsky Medal by the International Joint Conference On Artificial Intelligence (IJCAI) in Stockholm, for outstanding achievements in the field of AI.[36][37][38][39]
2017 October 25 Software release Leela Zero, a free and open-source computer Go software, is released. It is developed by Belgian programmer Gian-Carlo Pascutto,[40][41][42] the author of chess engine Sjeng and Go engine Leela.[43][44]
2017 October It is announced that AlphaGo Zero, armed with just the rules, has in 40 days become even better at Go than the original AlphaGo, without the help of game records.[22]
2017 October Game series release AlphaGo Zero (20 Blocks) vs AlphaGo Lee game play is released on AlphaGo Zero version. It consists of twenty games.[5]
2017 October Game series release AlphaGo Zero vs AlphaGo Zero - 20 Blocks game play is released on AlphaGo Zero version. It consists of twenty games.[5]
2017 October Game series release AlphaGo Zero vs AlphaGo Zero - 40 Blocks game play is released on AlphaGo Zero version. It consists of twenty games.[5]
2017 October Game series release AlphaGo Zero (40 Blocks) vs AlphaGo Master game play is released in AlphaGo Zero version. It consists of twenty games.[5]
2017 December 5 Software release The DeepMind team releases a preprint on arXiv, introducing AlphaZero, a program using generalized AlphaGo Zero's approach, which achieved within 24 hours a superhuman level of play in chess, shogi, and Go, defeating world-champion programs, Stockfish, Elmo, and 3-day version of AlphaGo Zero in each case.[45]
2017 December Software release DeepMind releases the AlphaGo teaching tool on its website,[46] to analyze winning rates of different Go openings as calculated by AlphaGo Master.[47] The teaching tool collects 6,000 Go openings from 230,000 human games each analyzed with 10,000,000 simulations by AlphaGo Master. Many of the openings include human move suggestions.[47][5]
2017 December Achievement AlphaZero beats the 3-day version of AlphaGo Zero by winning 60 games to 40, and with 8 hours of training it outperformes AlphaGo Lee on an Elo scale. AlphaZero also defeats a top chess program (Stockfish) and a top Shōgi program (Elmo).[45]
2018 April Research A paper published in Nature cites AlphaGo's approach as the basis for a new means of computing potential pharmaceutical drug molecules.[48][49]
2018 December Upgrade A paper is published in Science describing AlphaZero, a new version having been able to teach itself to play three different board games (chess, Go, and shogi) in just three days, with no human intervention.[50][51][52][53]

Numerical and visual data

Mentions on Google Scholar

The following table summarizes per-year mentions on Google Scholar as of May 13, 2021.

Year DeepMind AlphaGo AlphaZero
2010 97 0 0
2011 83 0 0
2012 109 0 0
2013 111 0 0
2014 251 135 0
2015 479 111 0
2016 1,450 2,190 0
2017 2,430 2,980 56
2018 3,800 3,730 480
2019 4,740 4,000 833
2020 5,520 4,120 937
AlphaZero google sch.png

Google trends

The comparative chart below shows Google trends data for AlphaGo (computer program) and AlphaGo Zero from October 2015 to January 2021, when the screenshot was taken.[54]

AlphaGo gtrends.jpeg

Google Ngram Viewer

The chart below shows Google Ngram Viewer data for AlphaGo, from 2015 to 2019.[55]

AlphaGo ngram.jpeg

Wikipedia Views

The chart below shows pageviews of the English Wikipedia article AlpgaGo, created in January 2016.[56]

Alphago wv.png

Meta information on the timeline

How the timeline was built

The initial version of the timeline was written by User:Sebastian.

Funding information for this timeline is available.

Feedback and comments

Feedback for the timeline can be provided at the following places:

  • FIXME

What the timeline is still missing

Timeline update strategy

See also

External links

References

  1. ↑ "Why DeepMind AlphaGo Zero is a game changer for AI research". hub.packtpub.com. Retrieved 5 June 2019. 
  2. ↑ 2.0 2.1 Steven Borowiec; Tracey Lien (12 March 2016). "AlphaGo beats human Go champ in milestone for artificial intelligence". Los Angeles Times. Retrieved 13 March 2016. 
  3. ↑ Connor, Steve (27 January 2016). "A computer has beaten a professional at the world's most complex board game". The Independent. Retrieved 21 May 2019. 
  4. ↑ "Google's AI beats human champion at Go". CBC News. 27 January 2016. Retrieved 21 May 2019. 
  5. ↑ 5.00 5.01 5.02 5.03 5.04 5.05 5.06 5.07 5.08 5.09 5.10 5.11 5.12 5.13 5.14 5.15 "AlphaGo's Games". alphago-games.com. Retrieved 21 May 2019. 
  6. ↑ "Deep Blue, IBM's supercomputer, defeats chess champion Garry Kasparov in 1997". nydailynews.com. Retrieved 6 June 2019. 
  7. ↑ "What we learned in Seoul with AlphaGo". blog.google. Retrieved 21 May 2019. 
  8. ↑ "Zen computer Go program beats Takemiya Masaki with just 4 stones!". Go Game Guru. Archived from the original on 1 February 2016. Retrieved 28 January 2016. 
  9. ↑ "「アマ六段の力。天才かも」囲碁棋士、コンピューターに敗れる　初の公式戦". MSN Sankei News. Archived from the original on 24 March 2013. Retrieved 27 March 2013. 
  10. ↑ Ciaburro, Giuseppe. Keras Reinforcement Learning Projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents. 
  11. ↑ 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. 
  12. ↑ 12.0 12.1 12.2 "Google achieves AI 'breakthrough' by beating Go champion". BBC News. 27 January 2016. 
  13. ↑ "Special Computer Go insert covering the AlphaGo v Fan Hui match" (PDF). British Go Journal. 2017. Retrieved 1 February 2016. 
  14. ↑ "Special Computer Go insert covering the AlphaGo v Fan Hui match" (PDF). British Go Journal. Retrieved 1 February 2016. 
  15. ↑ "Première défaite d'un professionnel du go contre une intelligence artificielle". Le Monde (in français). 27 January 2016. 
  16. ↑ "Research Blog: AlphaGo: Mastering the ancient game of Go with Machine Learning". Google Research Blog. 27 January 2016. 
  17. ↑ "The story of AlphaGo so far". deepmind.com. Retrieved 23 May 2019. 
  18. ↑ "Artificial intelligence: Go master Lee Se-dol wins against AlphaGo program". BBC News Online. 13 March 2016. Retrieved 13 March 2016. 
  19. ↑ "Match 1 – Google DeepMind Challenge Match: Lee Sedol vs AlphaGo". 8 March 2016. 
  20. ↑ "DeepMind's AlphaGo Zero Becomes Go Champion Without Human Input". futureoflife.org. Retrieved 5 April 2019. 
  21. ↑ "AI: How big a deal is Google's latest AlphaGo breakthrough?". techrepublic.com. Retrieved 5 April 2019. 
  22. ↑ 22.0 22.1 "AlphaGo". britgo.org. Retrieved 5 April 2019. 
  23. ↑ 23.0 23.1 "AlphaGo China | DeepMind". DeepMind. Retrieved 21 May 2019. 
  24. ↑ McMillan, Robert (18 May 2016). "Google Isn't Playing Games With New Chip". Wall Street Journal. Retrieved 21 May 2019. 
  25. ↑ Jouppi, Norm (May 18, 2016). "Google supercharges machine learning tasks with TPU custom chip". Google Cloud Platform Blog. Retrieved 21 May 2019. 
  26. ↑ "DeepMind's work in 2016: a round-up". deepmind.com. Retrieved 23 May 2019. 
  27. ↑ Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Gómez Colmenarejo, Sergio; Grefenstette, Edward; Ramalho, Tiago; Agapiou, John; Puigdomènech Badia, Adrià; Moritz Hermann, Karl; Zwols, Yori; Ostrovski, Georg; Cain, Adam; King, Helen; Summerfield, Christopher; Blunsom, Phil; Kavukcuoglu, Koray; Hassabis, Demis. "Hybrid computing using a neural network with dynamic external memory". Nature. Retrieved 23 May 2019. 
  28. ↑ "AN IMPROVED ALPHAGO WINS ITS FIRST GAME AGAINST THE WORLD'S TOP GO PLAYER". wired.com. Retrieved 5 April 2019. 
  29. ↑ "DeepMind's AI beats world's best Go player in latest face-off". newscientist.com. Retrieved 5 April 2019. 
  30. ↑ "Exploring the mysteries of Go with AlphaGo and China's top players". DeepMind. 10 April 2017. Retrieved 21 May 2019. 
  31. ↑ "DeepMind's AlphaGo is back..and this time it's taking on five humans at once". Wired.com. 10 April 2017. Retrieved 21 May 2019. 
  32. ↑ Russell, Jon. "Google's AlphaGo AI wins three-match series against the world's best Go player". techcrunch.com. Retrieved 31 May 2019. 
  33. ↑ "Google AI defeats human Go champion". bbc.com. Retrieved 31 May 2019. 
  34. ↑ "AlphaGo (2017)". Rotten Tomatoes. Retrieved 23 May 2019. 
  35. ↑ 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". Nature. 550 (7676): 354–359. ISSN 0028-0836. PMID 29052630. doi:10.1038/nature24270. Retrieved 10 December 2017. 
  36. ↑ Gorey, Colm. "DeepMind team behind AlphaGo wins inaugural 'Nobel Prize for AI'". siliconrepublic.com. Retrieved 1 June 2019. 
  37. ↑ "DeepMind team behind AlphaGo wins inaugural 'Nobel Prize for AI'". lionra.ie. Retrieved 1 June 2019. 
  38. ↑ "IJCAI 2018 Kicks Off; DeepMind AlphaGo Wins Marvin Minsky Medal". medium.com. Retrieved 1 June 2019. 
  39. ↑ "DeepMind wins the Minsky medal for AlphaGo". celi.it. Retrieved 1 June 2019. 
  40. ↑ "Feature: One man's Go program looks to remake AlphaGo Zero - and beyond". Xinhuanet. 9 April 2018. Retrieved 5 April 2019. 
  41. ↑ "围棋AI"丽拉"获赞接近职业棋手水准，它的作者竟是一个不太会下棋的程序员" (in 中文). Xinhuanet. 5 February 2018. Retrieved 5 April 2019. 
  42. ↑ "更开放，更共享，比利时围棋AI"丽拉·元"重塑"阿尔法元"" (in 中文). Xinhuanet. 8 April 2018. Retrieved 5 April 2019. 
  43. ↑ "프로 수준급 인공지능 바둑 프로그램 '릴라(Leela)' 무료 공개" (in Korean). Baduk News. 23 February 2017. Retrieved 5 April 2019. 
  44. ↑ "릴라의 출현과 온라인 대국의 비극적인 종말..." (in Korean). Cyberoro. 3 March 2017. Retrieved 5 April 2019. 
  45. ↑ 45.0 45.1 Silver, David; Hubert, Thomas; Schrittwieser, Julian; Antonoglou, Ioannis; Lai, Matthew; Guez, Arthur; Lanctot, Marc; Sifre, Laurent; Kumaran, Dharshan; Graepel, Thore; Lillicrap, Timothy; Simonyan, Karen; Hassabis, Demis (5 December 2017). "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm". 
  46. ↑ "AlphaGo teaching tool". DeepMind. 
  47. ↑ 47.0 47.1 "AlphaGo教学工具上线 樊麾：使用Master版本" (in Chinese). Sina.com.cn. 11 December 2017. Retrieved 11 December 2017. 
  48. ↑ "Go and make some drugs The Engineer". www.theengineer.co.uk. Retrieved 21 May 2019. 
  49. ↑ Segler, Marwin H. S.; Preuss, Mike; Waller, Mark P. "Planning chemical syntheses with deep neural networks and symbolic AI". 
  50. ↑ Silver, David; Hubert, Thomas; Schrittwieser, Julian; Antonoglou, Ioannis; Lai, Matthew; Guez, Arthur; Lanctot, Marc. "A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play". 
  51. ↑ "Move over AlphaGo: AlphaZero taught itself to play three different games". arstechnica.com. Retrieved 5 June 2019. 
  52. ↑ Yirka, Bob. "AlphaZero AI system able to teach itself how to play games, play at highest levels". techxplore.com. Retrieved 5 June 2019. 
  53. ↑ "Google's New AI Is a Master of Games, but How Does It Compare to the Human Mind?". smithsonianmag.com. Retrieved 5 June 2019. 
  54. ↑ "AlphaGo". trends.google.com. Retrieved 6 January 2021. 
  55. ↑ "AlphaGo". books.google.com. Retrieved 13 January 2021. 
  56. ↑ "Wikipedia Views: results". wikipediaviews.org. Retrieved 25 January 2021. 
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