Timeline of AlphaGo
|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.|
|2016||AlphaGo victory in March becomes a major milestone in artificial intelligence research, 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.|
|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.|
|1997||May||Prelude||IBM's computer Deep Blue beats world chess champion Garry Kasparov.|
|2010||Prelude||DeepMind is founded to create general-purpose artificial intelligence that can learn on its own.|
|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.|
|2013||March||Prelude||Software Crazy Stone beats Japanese Go professional Yoshio Ishida (9p) at four-stones handicap.|
|2014||Founding||The AlphaGo research project is formed to test how well a neural network using deep learning can compete at Go.|
|2015||October||Achievement||AlphaGo versus Fan Hui is held at DeepMind's headquarters in London. The distributed version of AlphaGo defeates the European Go champion Fan Hui, a 2-dan (out of 9 dan possible) professional, five to zero. This is the first time a computer Go program beats a professional human player on a full-sized board without handicap.|
|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.|
|2015||October||Game series release||Fan Hui versus AlphaGo game play is released in version 13. It consists of five games.|
|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.|
|2016||February||Game series release||AlphaGo versus AlphaGo is released in version 18. It consists of three games.|
|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. This is the first time a computer Go program beats a 9-dan professional without handicaps. The match is watched by over 200 million people worldwide.|
|2016||March||Game series release||Lee Sedol versus AlphaGo game play is released on version 18. It consists of five games.|
|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.|
|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.|
|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.|
|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.|
|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, as well as a forum on the future of artificial intelligence.|
|2017||May||Game series release||Future of Go Summit game play is released on AlphaGo Master version. It consists of five games.|
|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.|
|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.|
|2017||July||Game series release||WeiQi TV-5 Extra Games game play is released on AlphaGo Master version. It consists of five games.|
|2017||September 29||The AlphaGo documentary film is released.|
|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.|
|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.|
|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, the author of chess engine Sjeng and Go engine Leela.|
|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.|
|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.|
|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.|
|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.|
|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.|
|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.|
|2017||December||Software release||DeepMind releases the AlphaGo teaching tool on its website, to analyze winning rates of different Go openings as calculated by AlphaGo Master. 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.|
|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).|
|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.|
|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.|
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