Global InformationAlphaGo information
 | |
| Developer(s) | DeepMind |
|---|---|
| Type | Computer Go software |
| Website | deepmind.com/research/highlighted-research/alphago |
| Part of a series on |
| Artificial intelligence |
|---|
 |
AlphaGo is a computer program that plays the board game Go.[1] It was developed by the London-based DeepMind Technologies,[2] an acquired subsidiary of Google (now Alphabet Inc.). Subsequent versions of AlphaGo became increasingly powerful, including a version that competed under the name Master.[3] After retiring from competitive play, AlphaGo Master was succeeded by an even more powerful version known as AlphaGo Zero, which was completely self-taught without learning from human games. AlphaGo Zero was then generalized into a program known as AlphaZero, which played additional games, including chess and shogi. AlphaZero has in turn been succeeded by a program known as MuZero which learns without being taught the rules.
AlphaGo and its successors use a Monte Carlo tree search algorithm to find its moves based on knowledge previously acquired by machine learning, specifically by an artificial neural network (a deep learning method) by extensive training, both from human and computer play.[4] A neural network is trained to identify the best moves and the winning percentages of these moves. This neural network improves the strength of the tree search, resulting in stronger move selection in the next iteration.
In October 2015, in a match against Fan Hui, the original AlphaGo became the first computer Go program to beat a human professional Go player without handicap on a full-sized 19×19 board.[5][6] In March 2016, it beat Lee Sedol in a five-game match, the first time a computer Go program has beaten a 9-dan professional without handicap.[7] Although it lost to Lee Sedol in the fourth game, Lee resigned in the final game, giving a final score of 4 games to 1 in favour of AlphaGo. In recognition of the victory, AlphaGo was awarded an honorary 9-dan by the Korea Baduk Association.[8] The lead up and the challenge match with Lee Sedol were documented in a documentary film also titled AlphaGo,[9] directed by Greg Kohs. The win by AlphaGo was chosen by Science as one of the Breakthrough of the Year runners-up on 22 December 2016.[10]
At the 2017 Future of Go Summit, the Master version of AlphaGo beat Ke Jie, the number one ranked player in the world at the time, in a three-game match, after which AlphaGo was awarded professional 9-dan by the Chinese Weiqi Association.[11]
After the match between AlphaGo and Ke Jie, DeepMind retired AlphaGo, while continuing AI research in other areas.[12] The self-taught AlphaGo Zero achieved a 100–0 victory against the early competitive version of AlphaGo, and its successor AlphaZero was perceived as the world's top player in Go by the end of the 2010s.[13][14]
- ^ "Artificial intelligence: Google's AlphaGo beats Go master Lee Se-dol". BBC News. 12 March 2016. Archived from the original on 26 August 2016. Retrieved 17 March 2016.
- ^ "DeepMind AlphaGO". DeepMind Artificial Intelligence AlphaGo. Archived from the original on 14 September 2019. Retrieved 16 September 2019.
- ^ "AlphaGo | DeepMind". DeepMind. Archived from the original on 28 May 2017. Retrieved 28 May 2017.
- ^ Cite error: The named reference
DeepMindnature2016was invoked but never defined (see the help page). - ^ "Research Blog: AlphaGo: Mastering the ancient game of Go with Machine Learning". Google Research Blog. 27 January 2016. Archived from the original on 30 January 2016. Retrieved 28 January 2016.
- ^ Cite error: The named reference
bbcgowas invoked but never defined (see the help page). - ^ "Match 1 – Google DeepMind Challenge Match: Lee Sedol vs AlphaGo". YouTube. 8 March 2016. Archived from the original on 29 March 2017. Retrieved 9 March 2016.
- ^ "Google's AlphaGo gets 'divine' Go ranking". The Straits Times. straitstimes.com. 15 March 2016. Archived from the original on 7 October 2016. Retrieved 9 December 2017.
- ^ "AlphaGo Movie". AlphaGo Movie. Archived from the original on 3 January 2018. Retrieved 14 October 2017.
- ^ "From AI to protein folding: Our Breakthrough runners-up". Science. 22 December 2016. Archived from the original on 17 June 2022. Retrieved 29 December 2016.
- ^ "中国围棋协会授予AlphaGo职业九段 并颁发证书" (in Chinese). Sohu.com. 27 May 2017. Archived from the original on 3 June 2017. Retrieved 9 December 2017.
- ^ Metz, Cade (27 May 2017). "After Win in China, AlphaGo's Designers Explore New AI". Wired.
- ^ "AlphaZero Crushes Stockfish In New 1,000-Game Match". 17 April 2019. Archived from the original on 12 November 2020. Retrieved 22 April 2021.
- ^ 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 (7 December 2018). "A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play". Science. 362 (6419): 1140–1144. Bibcode:2018Sci...362.1140S. doi:10.1126/science.aar6404. PMID 30523106. S2CID 54457125. Archived from the original on 3 June 2022. Retrieved 30 June 2022.