我与小娜（08）：人工智能的伟大胜利       小娜知道，1月27日，英国《自然》刊登重要文章，题为“Mastering thegame of Go with deep neural networks and tree search”，标志着世界人工智能（AI）历史性的伟大胜利。为什么？

我与小娜（08）：人工智能的伟大胜利

        小娜知道。The game of Go（机器人） has long beenviewed as the most challenging of classic games for artificial intelligenceowing to its enormous search space and the difficulty of evaluating boardpositions and moves. Here we introduce a new approach to computer Go that uses‘value networks’ to evaluate board positions and ‘policy networks’ to selectmoves. These deep neural networks are trained by a novel combination ofsupervised learning from human expert games, and reinforcement learning fromgames of self-play. Without any lookahead search, the neural networks play Goat the level of state-of-the-art Monte Carlo tree search programs that simulatethousands of random games of self-play. We also introduce a new searchalgorithm that combines Monte Carlo simulation with value and policy networks.Using this search algorithm, our program AlphaGo achieved a 99.8% winning rateagainst other Go programs, and defeated the human European Go champion by 5games to 0. This is the first time that a computer program has defeated a humanprofessional player in the full-sized game of Go, a feat previously thought tobe at least a decade away.

        这是《自然》杂志对此文内容的摘要。

详细说明，且听下回分解。

袁萌   2月10日