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  2. AlphaDev - Wikipedia

    en.wikipedia.org/wiki/AlphaDev

    AlphaDev is an artificial intelligence system developed by Google DeepMind to discover enhanced computer science algorithms using reinforcement learning.AlphaDev is based on AlphaZero, a system that mastered the games of chess, shogi and go by self-play.

  3. AlphaGo Zero - Wikipedia

    en.wikipedia.org/wiki/AlphaGo_Zero

    Unlike earlier versions of AlphaGo, Zero only perceived the board's stones, rather than having some rare human-programmed edge cases to help recognize unusual Go board positions. The AI engaged in reinforcement learning, playing against itself until it could anticipate its own moves and how those moves would affect the game's outcome. [10]

  4. AlphaGo - Wikipedia

    en.wikipedia.org/wiki/AlphaGo

    Decommissioned AlphaGo backend rack. Go is considered much more difficult for computers to win than other games such as chess, because its strategic and aesthetic nature makes it hard to directly construct an evaluation function, and its much larger branching factor makes it prohibitively difficult to use traditional AI methods such as alpha–beta pruning, tree traversal and heuristic search.

  5. David Silver (computer scientist) - Wikipedia

    en.wikipedia.org/wiki/David_Silver_(computer...

    He studied at Christ's College, Cambridge, [3] graduating in 1997 with the Addison-Wesley award, and having befriended Demis Hassabis whilst at Cambridge. [4] Silver returned to academia in 2004 at the University of Alberta to study for a PhD on reinforcement learning, [5] where he co-introduced the algorithms used in the first master-level 9×9 Go programs and graduated in 2009.

  6. MuZero - Wikipedia

    en.wikipedia.org/wiki/MuZero

    MuZero (MZ) is a combination of the high-performance planning of the AlphaZero (AZ) algorithm with approaches to model-free reinforcement learning. The combination allows for more efficient training in classical planning regimes, such as Go, while also handling domains with much more complex inputs at each stage, such as visual video games.

  7. Computer Go - Wikipedia

    en.wikipedia.org/wiki/Computer_Go

    AlphaGo won the final match two days later. [27] [28] With this victory, AlphaGo became the first program to beat a 9 dan human professional in a game without handicaps on a full-sized board. In May 2017, AlphaGo beat Ke Jie, who at the time was ranked top in the world, [29] [30] in a three-game match during the Future of Go Summit. [31]

  8. AlphaGo (film) - Wikipedia

    en.wikipedia.org/wiki/AlphaGo_(film)

    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. [4] 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".

  9. Google DeepMind - Wikipedia

    en.wikipedia.org/wiki/Google_DeepMind

    A new reinforcement learning algorithm incorporated lookahead search inside the training loop. [64] AlphaGo Zero employed around 15 people and millions in computing resources. [65] Ultimately, it needed much less computing power than AlphaGo, running on four specialized AI processors (Google TPUs), instead of AlphaGo's 48. [66]