|
AlphaGo
AlphaGo is a computer program that plays the board game Go. It was developed by DeepMind Technologies a subsidiary of Google (now Alphabet Inc.). Subsequent versions of AlphaGo became increasingly powerful, including a version that competed under the name Master. 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. A neural network is ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Alphago Logo Reversed
AlphaGo is a computer program that plays the board game Go. It was developed by DeepMind Technologies a subsidiary of Google (now Alphabet Inc.). Subsequent versions of AlphaGo became increasingly powerful, including a version that competed under the name Master. 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. A neural network i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AlphaGo Versus Lee Sedol
AlphaGo versus Lee Sedol, also known as the Google DeepMind Challenge Match, was a five-game Go match between top Go player Lee Sedol and AlphaGo, a computer Go program developed by Google DeepMind, played in Seoul, South Korea between 9 and 15 March 2016. AlphaGo won all but the fourth game; all games were won by resignation. The match has been compared with the historic chess match between Deep Blue and Garry Kasparov in 1997. The winner of the match was slated to win $1 million. Since AlphaGo won, Google DeepMind stated that the prize will be donated to charities, including UNICEF, and Go organisations. Lee received $170,000 ($150,000 for participating in the five games and an additional $20,000 for winning one game). After the match, The Korea Baduk Association awarded AlphaGo the highest Go grandmaster rank – an "honorary 9 dan". It was given in recognition of AlphaGo's "sincere efforts" to master Go. This match was chosen by ''Science'' as one of the Breakthrough of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AlphaGo Zero
AlphaGo Zero is a version of DeepMind's Go software AlphaGo. AlphaGo's team published an article in the journal ''Nature'' on 19 October 2017, introducing AlphaGo Zero, a version created without using data from human games, and stronger than any previous version. By playing games against itself, AlphaGo Zero surpassed the strength of AlphaGo Lee in three days by winning 100 games to 0, reached the level of AlphaGo Master in 21 days, and exceeded all the old versions in 40 days. Training artificial intelligence (AI) without datasets derived from human experts has significant implications for the development of AI with superhuman skills because expert data is "often expensive, unreliable or simply unavailable." Demis Hassabis, the co-founder and CEO of DeepMind, said that AlphaGo Zero was so powerful because it was "no longer constrained by the limits of human knowledge". Furthermore, AlphaGo Zero performed better than standard reinforcement deep learning models (such as DQN imple ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Future Of Go Summit
The Future of Go Summit () was held in May 2017 by the Chinese Go Association, Sport Bureau of Zhejiang Province and Google in Wuzhen, Zhejiang, the permanent host of the World Internet Conference. It featured five Go games involving AlphaGo and top Chinese Go players, as well as a forum on the future of AI. It was Google’s biggest public event in partnership with the Chinese government since Google China's search engine was moved out of mainland China to Hong Kong due to the government censorship in 2010. It was seen as a charm offensive launched by Google toward Chinese officials, being part of effort to reopen China's market. The version of AlphaGo used in this Summit was AlphaGo Master, using four TPUs on a single machine with Elo rating 4,858. DeepMind claimed that this version was 3-stones stronger in games of self-play against itself than the version used in AlphaGo v. Lee Sedol. After winning its three-game match against Chinese grandmaster Ke Jie, the world's top ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computer Go
Computer Go is the field of artificial intelligence (AI) dedicated to creating a computer program that plays the traditional board game Go. The field is sharply divided into two eras. Before 2015, the programs of the era were weak. The best efforts of the 1980s and 1990s produced only AIs that could be defeated by beginners, and AIs of the early 2000s were intermediate level at best. Professionals could defeat these programs even given handicaps of 10+ stones in favor of the AI. Many of the algorithms such as alpha-beta minimax that performed well as AIs for checkers and chess fell apart on Go's 19x19 board, as there were too many branching possibilities to consider. Creation of a human professional quality program with the techniques and hardware of the time was out of reach. Some AI researchers speculated that the problem was unsolvable without creation of human-like AI. The application of Monte Carlo tree search to Go algorithms provided a notable improvement in the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
DeepMind
DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research laboratory founded in 2010. DeepMind was acquired by Google in 2014 and became a wholly owned subsidiary of Alphabet Inc, after Google's restructuring in 2015. The company is based in London, with research centres in Canada, France, and the United States. DeepMind has created a neural network that learns how to play video games in a fashion similar to that of humans, as well as a Neural Turing machine, or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain. DeepMind made headlines in 2016 after its AlphaGo program beat a human professional Go player Lee Sedol, a world champion, in a five-game match, which was the subject of a documentary film. A more general program, AlphaZero, beat the most powerful programs playing go, chess and shogi (Japanese c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lee Sedol
Lee Sedol ( ko, 이세돌; born 2 March 1983), or Lee Se-dol, is a former South Korean professional Go player of 9 dan rank. As of February 2016, he ranked second in international titles (18), behind only Lee Chang-ho (21). He is the fifth-youngest (12 years 4 months) to become a professional Go player in South Korean history behind Cho Hun-hyun (9 years 7 months), Lee Chang-ho (11 years 1 months), Cho Hye-yeon (11 years 10 months) and Choi Cheol-han (12 years 2 months). His nickname is "The Strong Stone" ("Ssen-dol"). In March 2016, he played a notable series of matches against AlphaGo that ended in 1–4. On 19 November 2019, Lee announced his retirement from professional play, stating that he could never be the top overall player of Go due to the increasing dominance of AI. Lee referred to them as being "an entity that cannot be defeated". Biography Lee was born in South Korea in 1983 and studied at the Korea Baduk Association. He ranks second in international ti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AlphaGo Master
Master is a version of DeepMind's Go software AlphaGo, named after the account name (originally Magister/Magist) used online, which won 60 straight online games against human professional Go players from 29 December 2016 to 4 January 2017. This version was also used in the Future of Go Summit in May 2017. It used four TPUs on a single machine with Elo rating 4,858. DeepMind claimed that AlphaGo Master was 3-stone stronger than the version used in AlphaGo v. Lee Sedol. DeepMind released a version of AlphaGo Master in December 2017 that serves as a teaching tool analyzing the win rates of 6,000 Go openings from 230,000 human games. Matches Online games The software was first used to play games against professional players on 29 December 2016 on the Tygem Go server, under the account name 'Magister' (shown as 'Magist' at the server's Chinese version). The account name was changed to 'Master' on 30 December. After playing 30 games on Tygem, it was moved to the FoxGo server on 1 Janua ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Go (game)
Go is an abstract strategy board game for two players in which the aim is to surround more territory than the opponent. The game was invented in China more than 2,500 years ago and is believed to be the oldest board game continuously played to the present day. A 2016 survey by the International Go Federation's 75 member nations found that there are over 46 million people worldwide who know how to play Go and over 20 million current players, the majority of whom live in East Asia. The playing pieces are called stones. One player uses the white stones and the other, black. The players take turns placing the stones on the vacant intersections (''points'') of a board. Once placed on the board, stones may not be moved, but stones are removed from the board if the stone (or group of stones) is surrounded by opposing stones on all orthogonally adjacent points, in which case the stone or group is ''captured''. The game proceeds until neither player wishes to make another move. Wh ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ke Jie
Ke Jie () is a Chinese professional Go player of 9 dan rank. He was born on August 2, 1997 in Liandu District, Lishui City, Zhejiang Province. Career 2008–15: Early Career and Bailing Cup Breakthrough Ke Jie started to learn how to play Go in 2003 when he was 5 years old and won his first national championship in 2007. He became a professional Go player in 2008 when he was 10 years old and was promoted to 9 dan in 2015. In January 2015, Ke won his first world title when he won the 2nd Bailing Cup, defeating Qiu Jun 3-2 in the finals. 2015–16: Two International Titles and Chinese No.1 In December 2015, he defeated Shi Yue in the 20th Samsung Cup finals to win another world title. In January 2016, Ke won the 2nd MLily Cup, defeating world renowned Go player Lee Sedol in the fifth round. According to South Korean 9 dan professionals commenting on the final game, the result hinged on a half-point ko and the peculiarities of Chinese scoring rules; however, others have ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fan Hui
Fan Hui (; born 27 December 1981) is a Chinese-born French Go player. Becoming a professional Go player in 1996, Fan moved to France in 2000 and became the coach of the French national Go team in 2005. He was the winner of the European Go Championship in 2013, 2014 and 2015. As of 2015, he is ranked as a 2 dan professional. He additionally won the 2016 European Professional Go Championship. AlphaGo vs Fan Hui In October 2015, Fan was defeated by the Google DeepMind AI program AlphaGo 5–0, the first time an AI has beaten a human professional player at the game without a handicap. Fan described the program as "very strong and stable, it seems like a wall. ... I know AlphaGo is a computer, but if no one told me, maybe I would think the player was a little strange, but a very strong player, a real person." After his defeat, Fan Hui was hired to advise the AlphaGo team and provided a "sanity check" on Go theory. He served as a judge for the AlphaGo versus Lee Sedol match and obs ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Monte Carlo Tree Search
In computer science, Monte Carlo tree search (MCTS) is a heuristic search algorithm for some kinds of decision processes, most notably those employed in software that plays board games. In that context MCTS is used to solve the game tree. MCTS was combined with neural networks in 2016 and has been used in multiple board games like Chess, Shogi, Checkers, Backgammon, Contract Bridge, Computer Go, Scrabble, and Clobber as well as in turn-based-strategy video games (such as Total War: Rome II's implementation in the high level campaign AI). History Monte Carlo method The Monte Carlo method, which uses random sampling for deterministic problems which are difficult or impossible to solve using other approaches, dates back to the 1940s. In his 1987 PhD thesis, Bruce Abramson combined minimax search with an ''expected-outcome model'' based on random game playouts to the end, instead of the usual static evaluation function. Abramson said the expected-outcome model "is shown t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
