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AlphaGo
AlphaGo is a computer program that plays the board game Go. It was developed by the London-based DeepMind Technologies, an acquired subsidiary of Google. 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 ne ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AlphaGo Versus Lee Sedol
AlphaGo versus Lee Sedol, also known as the DeepMind Challenge Match, was a five-game Go (game), Go match between top Go player Lee Sedol and AlphaGo, a computer Go program developed by DeepMind, played in Seoul, South Korea between the 9th 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 versus Garry Kasparov, 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 would be donated to charities, including UNICEF, and List of Go organizations, 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 Go ranks and ratings, 9 dan". It was given in recognition of AlphaGo's "sincere efforts" t ... [...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 ''Nature'' in 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 previous 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, as 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 deep reinforcement learning models (such as Deep Q-Network impleme ... [...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 Limited, trading as Google DeepMind or simply DeepMind, is a British–American artificial intelligence research laboratory which serves as a subsidiary of Alphabet Inc. Founded in the UK in 2010, it was acquired by Google in 2014 and merged with Google AI's Google Brain division to become Google DeepMind in April 2023. The company is headquartered in London, with research centres in the United States, Canada, France, Germany, and Switzerland. DeepMind introduced neural Turing machines (neural networks that can access external memory like a conventional Turing machine), resulting in a computer that loosely resembles short-term memory in the human brain. DeepMind has created neural network models to play video games and board games. It 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, AlphaZer ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Google DeepMind
DeepMind Technologies Limited, trading as Google DeepMind or simply DeepMind, is a British–American artificial intelligence research laboratory which serves as a subsidiary of Alphabet Inc. Founded in the UK in 2010, it was acquired by Google in 2014 and merged with Google AI's Google Brain division to become Google DeepMind in April 2023. The company is headquartered in London, with research centres in the United States, Canada, France, Germany, and Switzerland. DeepMind introduced neural Turing machines (neural networks that can access external memory like a conventional Turing machine), resulting in a computer that loosely resembles short-term memory in the human brain. DeepMind has created neural network models to play video games and board games. It 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, ... [...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 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lee Sedol
Lee Sedol (; born 2 March 1983), or Lee Se-dol, is a South Korean former professional Go player of 9 dan rank. As of February 2016, he ranked second in international titles (18), behind only Lee Chang-ho (21). His nickname is "The Strong Stone" ("Ssen-dol"). In March 2016, he played a notable series of matches against the program AlphaGo that ended in Lee losing 1–4. Lee announced his retirement from professional play in November 2019, stating he could never be the top overall player of Go due to the increasing dominance of AI, which he called "an entity that cannot be defeated". Lee shared in a 2024 interview, "losing to AI, in a sense, meant my entire world was collapsing. ... I could no longer enjoy the game. So I retired." Biography Lee was born in South Korea in 1983. He is known as 'Bigeumdo Boy' because he was born and grew up on Bigeumdo Island. He studied at the Korea Baduk Association. He is the fifth-youngest (12 years 4 months) to become a profession ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Go (game)
# Go is an abstract strategy game, abstract strategy board game for two players in which the aim is to fence off 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 Game piece (board game), playing pieces are called ''Go equipment#Stones, stones''. One player uses the white stones and the other black stones. The players take turns placing their stones on the vacant intersections (''points'') on the #Boards, board. Once placed, stones may not be moved, but ''captured stones'' are immediately removed from the board. A single stone (or connected group of stones) is ''captured'' when surrounded by the opponent's stones on all Orthogona ... [...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 o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AlphaGo (film)
''AlphaGo'' is a 2017 documentary directed by Greg Kohs about the Google DeepMind Challenge Match with top-ranked Go player Lee Sedol. Premise The film presents how AlphaGo, a computer program developed by DeepMind Technologies, mastered the game of Go through artificial intelligence. Its competence was tested by Lee Sedol, a South Korean world champion. Release ''AlphaGo'' was released in New York City on September 29, 2017, and Los Angeles the next month. Reception Critical response ''AlphaGo'' earned positive reviews. On Rotten Tomatoes, the film has an approval rating of 100%, with an average score of 8/10, based on 10 reviews. Charlotte O'Sullivan of ''Evening Standard'' gave the film 4 stars out of five, calling it a "gripping, emotional documentary, which gets us thinking, about thinking, in a whole new way". Accolades Winner * Denver International Film Festival (2017) - Maysles Brothers Award, Best documentary * New Media Film Festival (2018) - Best T ... [...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, 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) and applications outside of games. 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-out ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
