AlphaGo

Extracted Attributes

• Developer

  DeepMind Technologies

• Model Type

  Hybrid Model (probabilistic and deterministic)

• Game Played

  Go

• Recognition

  Breakthrough of the Year runner-up by Science (2016)

• Parent Company

  Alphabet Inc.

• Primary Algorithms

  Monte Carlo tree search, deep learning, artificial neural networks (policy and value networks), reinforcement learning

• Honorary Ranking (Korea Baduk Association)

  9-dan professional

• Honorary Ranking (Chinese Weiqi Association)

  9-dan professional

Timeline

• AlphaGo became the first computer Go program to defeat a human professional Go player without handicap on a full-sized board, winning 5-0 against Fan Hui. (Source: Provided Summary, DBpedia, Web Search)

  2015-10-01

• AlphaGo began a five-game match against Lee Sedol, a renowned 9-dan professional Go player. (Source: Provided Summary, DBpedia, Web Search)

  2016-03-09

• AlphaGo defeated Lee Sedol with a final score of 4-1 in their five-game match. (Source: Provided Summary, DBpedia, Web Search)

  2016-03-15

• Awarded an honorary 9-dan ranking by the Korea Baduk Association in recognition of its victory over Lee Sedol. (Source: Provided Summary, DBpedia)

  2016-03-01

• Recognized as a Breakthrough of the Year runner-up by Science magazine. (Source: Provided Summary, DBpedia)

  2016-12-22

• AlphaGo Master defeated Ke Jie, the world's top-ranked player at the time, in a three-game match at the Future of Go Summit. (Source: Provided Summary, DBpedia)

  2017-05-23

• Awarded professional 9-dan by the Chinese Weiqi Association following its victory over Ke Jie. (Source: Provided Summary, DBpedia)

  2017-05-01

• DeepMind retired AlphaGo from competitive play after the match against Ke Jie. (Source: Provided Summary, DBpedia)

  2017-05-01

Web Search Results

• AlphaGo versus Lee Sedol - Wikipedia

  AlphaGo is a computer program developed by Google DeepMind to play the board game Go "Go (game)"). AlphaGo's algorithm uses a combination of machine learning and tree search techniques, combined with extensive training, both from human and computer play. The system's neural networks were initially bootstrapped from human game-play expertise. AlphaGo was initially trained to mimic human play by attempting to match the moves of expert players from recorded historical games, using a KGS Go Server [...] AlphaGo is significantly different from previous AI efforts. Instead of using probability algorithms hard-coded by human programmers, AlphaGo uses neural networks to estimate its probability of winning. AlphaGo accesses and analyses the entire online library of Go, including all matches, players, analytics, literature, and games played by AlphaGo against itself and other players. Once set up, AlphaGo is independent of the developer team and evaluates the best pathway to solving Go (i.e., [...] AlphaGo showed anomalies and moves from a broader perspective, which professional Go players described as looking like mistakes at first sight but an intentional strategy in hindsight. As one of the creators of the system explained, AlphaGo does not attempt to maximize its points or its margin of victory, but tries to maximize its probability of winning. If AlphaGo must choose between a scenario where it will win by 20 points with 80 percent probability and another where it will win by 1 and a

• What is AlphaGo, and how did it use reinforcement learning? - Milvus

  AlphaGo is a groundbreaking computer program developed by DeepMind Technologies, a subsidiary of Alphabet Inc., designed to play the board game Go. Go, a strategic game originating from East Asia, is renowned for its complexity and the vast number of potential moves, which far exceeds that of chess. The intricacy of Go has historically made it a challenging game for artificial intelligence to master, and AlphaGo’s success marked a significant milestone in AI development. [...] AlphaGo employed a combination of advanced machine learning techniques, with reinforcement learning being a critical component. Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize a cumulative reward. In the context of AlphaGo, the program was trained to improve its gameplay by playing millions of games against itself and learning from these experiences. [...] The impact of AlphaGo extends beyond the realm of Go. Its innovative use of reinforcement learning has inspired advancements in various domains, including robotics, autonomous systems, and complex decision-making processes. By demonstrating the potential of AI in mastering complex tasks, AlphaGo has paved the way for future developments in artificial intelligence and machine learning. Previous Next

• AlphaGo - Google DeepMind

  We created AlphaGo, an AI system that combines deep neural networks with advanced search algorithms. One neural network — known as the “policy network” — selects the next move to play. The other neural network — the “value network” — predicts the winner of the game. [...] # AlphaGo AlphaGo mastered the ancient game of Go, defeated a Go world champion, and inspired a new era of AI systems. Making history The challenge Our approach The matches Inventing winning moves Technical legacy ## Making history Our artificial intelligence (AI) system, AlphaGo, learned to master the ancient Chinese game of Go — a profoundly complex board game of strategy, creativity, and ingenuity. [...] In October 2015, AlphaGo played its first game against the reigning three-time European Champion, Fan Hui. AlphaGo won the first ever match between an AI system and Go professional, scoring 5-0.

• AI Behind AlphaGo: Machine Learning and Neural Network

  From March 9 to March 15 in 2016, a Go game competition took place between the world’s second-highest ranking professional player, Lee Sedol, and AlphaGo, a computer program created by Google’s DeepMind Company. AlphaGo’s 4-1 victory over Lee Sedol became a significant moment in the history of artificial intelligence. This was the first time that a computer had beaten a human professional at Go. Most major South Korean television networks carried the game. In China, 60 million people watched [...] Go, known as _weiqi_ in China and _igo_ in Japan, is an abstract board game for two players that dates back 3,000 years. It is a board game of abstract strategy played across a 1919 grid. Go starts with an empty board. At each turn, a player places a black or white stone on the board . The general objective of the game is to use the stones to surround more territory than the opponent. Although the rule is very simple, it creates a challenge of depth and nuance. Thus, the board game, Go, has [...] to improve the process of delivering care with digital solutions. AlphaGo uses its computing power to analyze health data and records. This will open up new treatment opportunities to patients and assist physicians in treating patients. The increased efficiency will also reduce costs for insurance companies .

• How would AlphaGo fare against the strongest human player of all ...

  DeepMind's AlphaGo defeated Go opponents using a variety of powerful artificial intelligence approaches. It used neural networks, notably convolutional and long short-term memory networks, which were trained on large datasets of expert Go games. AlphaGo improved its strategies through reinforcement learning by competing against itself in millions of games. [...] DeepMind's AlphaGo defeated Go opponents using a variety of powerful artificial intelligence approaches. It used neural networks, notably convolutional and long short-term memory networks, which were trained on large datasets of expert Go games. AlphaGo improved its strategies through reinforcement learning by competing against itself in millions of games. [...] In 2016, AlphaGo made news for defeating the world champion, displaying its superior strategic thinking and board-game competence. Many experts believe AlphaGo is not just as competent as human players but actually better in many elements of

• Instance Of

  Q3257925

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 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. 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. 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. The lead up and the challenge match with Lee Sedol were documented in a documentary film also titled AlphaGo, 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. 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. After the match between AlphaGo and Ke Jie, DeepMind retired AlphaGo, while continuing AI research in other areas. The self-taught AlphaGo Zero achieved a 100–0 victory against the early competitive version of AlphaGo, and its successor AlphaZero is currently perceived as the world's top player in Go.