Google DeepMind

DeepMind Technologies Limited

Trade name	Google DeepMind
Company type	Subsidiary
Industry	Artificial intelligence
Founded	15 November 2010; 14 years ago (2010-11-15)[1][2]
Founders	Demis Hassabis Shane Legg Mustafa Suleyman
Headquarters	London, England[3]
Key people	Demis Hassabis (CEO) Lila Ibrahim (COO)
Products	AlphaGo, AlphaStar, AlphaFold, AlphaZero
Revenue	£1.53 billion (2023)[4]
Operating income	£136 million (2023)[4]
Net income	£113 million (2023)[4]
Owner	Alphabet Inc.[5]
Number of employees	c. 2,600 (2024)[6]
Parent	Deepmind Holdings Limited[7]
Website	deepmind.google

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DeepMind Technologies Limited, also known by its trade name Google DeepMind, is a British-American artificial intelligence research laboratory which serves as a subsidiary of Google. Founded in the UK in 2010, it was acquired by Google in 2014^[8] and merged with Google AI's Google Brain division to become Google DeepMind in April 2023. The company is based in London, with research centres in Canada,^[9] France,^[10] Germany, and the United States.

DeepMind introduced neural Turing machines (neural networks that can access external memory like a conventional Turing machine),^[11] resulting in a computer that loosely resembles short-term memory in the human brain.^[12][13]

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.^[14] A more general program, AlphaZero, beat the most powerful programs playing go, chess and shogi (Japanese chess) after a few days of play against itself using reinforcement learning.^[15]

In 2020, DeepMind made significant advances in the problem of protein folding with AlphaFold.^[16] In July 2022, it was announced that over 200 million predicted protein structures, representing virtually all known proteins, would be released on the AlphaFold database.^[17][18] AlphaFold's database of predictions achieved state of the art records on benchmark tests for protein folding algorithms, although each individual prediction still requires confirmation by experimental tests. AlphaFold3 was released in May 2024, making structural predictions for the interaction of proteins with various molecules. It achieved new standards on various benchmarks, raising the state of the art accuracies from 28 and 52 percent to 65 and 76 percent.