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Game theory as an engine for large-scale data analysis - Google DeepMind

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Modern AI systems approach tasks like recognising objects in images and predicting the 3D structure of proteins as a diligent student would prepare for an exam. By training on many example problems, they minimise their mistakes over time until they achieve success. But this is a solitary endeavour and only one of the known forms of learning. Learning also takes place by interacting and playing with others. It’s rare that a single individual can solve extremely complex problems alone. By allowing problem solving to take on these game-like qualities, previous DeepMind efforts have trained AI agents to play Capture the Flag and achieve Grandmaster level at Starcraft. This made us wonder if such a perspective modeled on game theory could help solve other fundamental machine learning problems. Today at ICLR 2021 (the International Conference on Learning Representations), we presented “EigenGame: PCA as a Nash Equilibrium,” which received an Outstanding Paper Award. Our research explored a n

Game theory as an engine for large-scale data analysis — Google DeepMind Skip to main content May 6, 2021 Research Game theory as an engine for large-scale data analysis Brian McWilliams, Ian Gemp, Claire Vernade Share EigenGame maps out a new approach to solve fundamental ML problems Modern AI systems approach tasks like recognising objects in images and predicting the 3D structure of proteins as a diligent student would prepare for an exam. By training on many example problems, they minimise their mistakes over time until they achieve success. But this is a solitary endeavour and only one of

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