AlphaGo

Origin

Developed by a team led by David SilverDavid Silver, prop. n.: A British computer scientist who led the reinforcement-learning research behind AlphaGo and AlphaZero, programs that taught themselves to play board games better than any human. and Aja Huang under Demis HassabisDemis Hassabis, prop. n.: A British AI researcher and former chess prodigy who co-founded DeepMind, led the teams behind AlphaGo and AlphaFold, and shared the 2024 Nobel Prize in Chemistry. and described in Nature on 27 January 2016 in 'Mastering the game of Go with deep neural networks and tree search'. Go's enormous number of possible positions, and the difficulty of judging them, had made it a long-standing grand challenge for AI.

Usage note

Lee Sedol's single win came in game 4, after his unexpected move 78. He retired from professional play in 2019, citing the rise of programs he said could not be beaten. See Move 37Move 37, prop. n.: A surprising move played by the AI program AlphaGo against champion Lee Sedol in 2016. Experts first thought it a mistake; it proved brilliant, and the phrase now stands for a creative, unhuman idea from AI..

Related

Sources

1. Silver, D. et al. (2016). Mastering the Game of Go with Deep Neural Networks and Tree Search. Nature 529, 484–489.
2. Silver, D. et al. (2017). Mastering the Game of Go without Human Knowledge. Nature 550, 354–359.

Mentioned in

Demis HassabisDemis Hassabis, prop. n.: A British AI researcher and former chess prodigy who co-founded DeepMind, led the teams behind AlphaGo and AlphaFold, and shared the 2024 Nobel Prize in Chemistry., reinforcement learningreinforcement learning, n.: A way of teaching a computer program by trial and error: it tries actions, earns points for good results and penalties for bad ones, and gradually learns which choices pay off over time., AlphaFoldAlphaFold, prop. n.: A DeepMind AI system that predicts the 3D shape of proteins from their chemical sequence, often about as accurately as laboratory experiments. It cracked a problem biologists had worked on for 50 years and won a Nobel Prize., AlphaGo ZeroAlphaGo Zero, prop. n.: A program from DeepMind that taught itself to play the board game Go better than any human in 2017, starting from nothing but the rules and learning only by playing millions of games against itself., AlphaZeroAlphaZero, prop. n.: A 2017 DeepMind program that taught itself chess, shogi and Go from nothing but the rules by playing against itself, and within hours of training beat the strongest existing programs., David SilverDavid Silver, prop. n.: A British computer scientist who led the reinforcement-learning research behind AlphaGo and AlphaZero, programs that taught themselves to play board games better than any human., Deep BlueDeep Blue, prop. n.: IBM's chess computer that beat world champion Garry Kasparov in 1997, the first time a machine defeated a reigning world champion in a match under standard tournament conditions., deep learning revolutiondeep learning revolution, n.: The rapid takeover of AI by many-layered neural networks from about 2012, when they suddenly began beating older methods at recognizing images and speech, leading to today's AI systems., deep reinforcement learningdeep reinforcement learning, n.: Reinforcement learning in which a large neural network does the learning, letting an AI learn tasks such as video games or robot control directly from raw inputs like screen pixels or camera images., DeepMindDeepMind, prop. n.: A London artificial intelligence company, bought by Google in 2014, famous for programs that mastered the game of Go and predicted the shapes of proteins. In 2023 it merged into Google DeepMind., Elo ratingElo rating, n.: A number that ranks players — or AI models — from the results of head-to-head contests, rising when you beat a strong opponent and falling when you lose to a weak one., game treegame tree, n.: A branching diagram of every way a game could unfold: each point is a position, each branch a possible move, and the tips of the branches are wins, losses or draws., Monte Carlo tree searchMonte Carlo tree search, n.: A way for a game-playing program to choose a move by simulating many possible continuations, focusing on the most promising lines, and picking the move that worked out best on average., Move 37Move 37, prop. n.: A surprising move played by the AI program AlphaGo against champion Lee Sedol in 2016. Experts first thought it a mistake; it proved brilliant, and the phrase now stands for a creative, unhuman idea from AI., narrow AInarrow AI, n.: AI built to do one specific job, or a small set of related jobs, such as playing chess or spotting spam, without being able to turn its skill to unrelated problems., neuro-symbolic AIneuro-symbolic AI, n.: AI that combines neural networks, which learn patterns from data, with symbolic methods, which follow explicit rules and logic, aiming for systems that both learn flexibly and reason reliably., policypolicy, n.: An AI agent's strategy: the rule it follows to pick an action in each situation. Training in reinforcement learning means improving this rule until it earns high rewards., test-time computetest-time compute, n.: The computing effort an AI model spends while answering a question, as opposed to while being trained. Letting a model think longer or try many answers can make it markedly smarter., value functionvalue function, n.: An estimate of how promising a situation is for an AI agent, judged by the rewards it is likely to collect from that point on, the way a chess player sizes up a board., evaluation functionevaluation function, n.: In a game-playing program, a formula that looks at a board position and gives it a score saying how good it is, without playing the game out to the end., Gerald TesauroGerald Tesauro, prop. n.: An American IBM researcher whose program TD-Gammon learned backgammon purely by playing against itself and by the early 1990s rivaled the best human players, a landmark for machines that learn by trial and error., iterated amplificationiterated amplification, n.: A proposed training scheme in which a person, helped by many copies of an AI, solves harder problems than either could alone; a new AI then learns to imitate that team, and the cycle repeats., LibratusLibratus, prop. n.: A poker-playing AI from Carnegie Mellon University that in 2017 decisively beat four top professionals at two-player no-limit Texas hold'em. Its successor, Pluribus, beat professionals at six-player poker in 2019., Volodymyr MnihVolodymyr Mnih, prop. n.: A DeepMind researcher who led the 2013–2015 work in which one neural network learned to play dozens of Atari video games from screen pixels, launching deep reinforcement learning.