問題詳情

AlphaGo’s victories against legendary Go player Lee Sedol in March 2016 markeda major milestone in AI research. The complex Chinese board game had long beenconsidered impossible for computers to crack, but DeepMind used machine learning andneural networks to give AlphaGo the ability to evaluate and execute strategy at aworld-class level.But you don’t put some of the most intelligent people in the world to work onartificial intelligence just to beat board games. DeepMind’s work has major implicationsfor the field of AI, and the deep-learning technology it uses has the potential torevolutionize everything from the way you use your phone to the way you drive yourcar—or the way your car drives you.DeepMind founder Demis Hassabis said that DeepMind is only interested in gamesthat lie on the main track of its research. “It’s to the extent that they’re useful as atestbed, a platform for trying to write our algorithmic ideas and testing out how far theyscale and how well they do, and it’s just a very efficient way of doing that. Ultimatelywe want to apply this to big real-world problems.”These problems could be anything where human decision-making could benefitfrom faster learning and more efficient data processing. Machine-learning techniquesand deep neural networks are already in wide use at Google, for example, in its searchand self-driving car programs. The lessons of AlphaGo could yield incrementalimprovements in any of these areas.
41. AlphaGo is a
(A) player who plays Go
(B) smart phone
(C) computer program
(D) self-driving car

參考答案

答案：[無官方正解]
難度：適中0.5
統計：A(0),B(0),C(0),D(0),E(0)

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