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Google DeepMind Challenge Match
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 Breakthroug ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Seoul
Seoul (; ; ), officially known as the Seoul Special City, is the capital and largest metropolis of South Korea.Before 1972, Seoul was the ''de jure'' capital of the Democratic People's Republic of Korea (North Korea) as stated iArticle 103 of the 1948 constitution. According to the 2020 census, Seoul has a population of 9.9 million people, and forms the heart of the Seoul Capital Area with the surrounding Incheon metropolis and Gyeonggi province. Considered to be a global city and rated as an Alpha – City by Globalization and World Cities Research Network (GaWC), Seoul was the world's fourth largest metropolitan economy in 2014, following Tokyo, New York City and Los Angeles. Seoul was rated Asia's most livable city with the second highest quality of life globally by Arcadis in 2015, with a GDP per capita (PPP) of around $40,000. With major technology hubs centered in Gangnam and Digital Media City, the Seoul Capital Area is home to the headquarters of 15 ''Fo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Chess
Chess is a board game for two players, called White and Black, each controlling an army of chess pieces in their color, with the objective to checkmate the opponent's king. It is sometimes called international chess or Western chess to distinguish it from related games, such as xiangqi (Chinese chess) and shogi (Japanese chess). The recorded history of chess goes back at least to the emergence of a similar game, chaturanga, in seventh-century India. The rules of chess as we know them today emerged in Europe at the end of the 15th century, with standardization and universal acceptance by the end of the 19th century. Today, chess is one of the world's most popular games, played by millions of people worldwide. Chess is an abstract strategy game that involves no hidden information and no use of dice or cards. It is played on a chessboard with 64 squares arranged in an eight-by-eight grid. At the start, each player controls sixteen pieces: one king, one queen, two rooks, t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hajin Lee
Lee Ha-jin ( ko, 이하진; born 21 June 1988; known as ''Haylee'' on YouTube) is a retired professional Go player from South Korea. From July 2014 to 2016, she served as Secretary General of the International Go Federation. She is now a software development engineer with an MBA. Early life Lee was born and grew up in Daejeon, South Korea. She is the elder daughter of an engineer father and an accountant mother, both of whom are avid Go players. Lee naturally picked up an interest in Go at a young age, and her parents began teaching her the game when she was 5 years old. As her talents won increasing recognition, her parents sent Lee to a professional Go school in Seoul, to live at the master’s house with him, his family, and several fellow students. Lee was 9 years old, and from that time on would take a break from her Go studies only for a few days each year to visit her family. Career Lee debuted as a professional player in 2004, at the age of 16, passing the intense c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Jonathan Schaeffer
Jonathan Herbert Schaeffer (born 1957) is a Canadian researcher and professor at the University of Alberta and the former Canada Research Chair in Artificial Intelligence. He led the team that wrote Chinook, the world's strongest American checkers player, after some relatively good results in writing computer chess programs. He is involved in the University of Alberta GAMES group developing computer poker systems. Schaeffer is also a member of the research group that created Polaris, a program designed to play the Texas Hold'em variant of poker. He is a Founder of Onlea, which produces online learning experiences. Early life Born in Toronto, Ontario, he received a Bachelor of Science degree in 1979 from the University of Toronto. He received a Master of Mathematics degree in 1980 and a Ph.D. in 1986 from the University of Waterloo. Schaeffer reached national master strength in chess while in his early 20s, but has played little competitive chess since that time. Draughts: C ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Crazy Stone (software)
Crazy Stone (Champion Go on iOS and Android platforms) is a Go playing engine, developed by Rémi Coulom, a French computer scientist. It is one of the first computer Go programs to utilize a modern variant of the Monte Carlo tree search. It is part of the Computer Go effort. In January 2012 Crazy Stone was rated as 5 dan on KGS, in March 2014 as 6 dan.Crazy Stone's profile . Retrieved 2 February 2010 History Coulom began writing Crazy Stone in July 2005, and at the outset incorporated the Monte Carlo algorithm in its design. Early versions were initially available to download as freeware from his website, albeit no longer.[...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 ob ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
European Go Championship
The European Go Championship or Congress (EGC) is the annual and main event of many organised by the European Go Federation for players of the board game Go. It consists of a 2-week open competition, one round per day, making a total of 10 rounds with a champion ultimately emerging - the player with the most (or best) wins. The congress has taken place in a different European city each year, since the first contest in 1983. During these two weeks, the best Go players in Europe fight for the title of European Champion. Entry in recent years has ranged from a low of 290 to a high of 718 players. History The first European Go Championship was held in 1938. The current annual series begun in 1957, with the first championship held in Cuxhaven, Germany. Germany has been quite dominant at the championships. In 1961 the 5th European Go Championship was held in August in Baden, where Japanese professional players Kensaku Segoe and Utaro Hashimoto gave exhibitions. In 1976 European G ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pattern Recognition
Pattern recognition is the automated recognition of patterns and regularities in data. It has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use of machine learning, due to the increased availability of big data and a new abundance of processing power. These activities can be viewed as two facets of the same field of application, and they have undergone substantial development over the past few decades. Pattern recognition systems are commonly trained from labeled "training" data. When no labeled data are available, other algorithms can be used to discover previously unknown patterns. KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition focuses more on the s ... [...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 to b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
