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Google's artificial intelligence software has won its third straight match against a grandmaster of an ancient board game called Go.
谷歌人工智能软件在古老的围棋比赛中击败大师级棋手赢得第三局。
Google's program, AlphaGo, won the first three games in a series of five against Lee Sedol, who's considered one of the world's best Go players.
在总数五场的比赛中，谷歌程序AlphaGo连赢三场，击败被认为是世界上最好的围棋选手之一李世石。
This means Google has secured the $1 million in prize money from the competition, which it says it will donate to charity. But this was about more than money and bragging rights for Google.
这意味着谷歌已经获得比赛的100万美元奖金，谷歌表示钱将捐赠给慈善机构。但这不仅仅是钱的问题，让谷歌有了炫耀的资本。
Deep neural networks, like the ones used in AlphaGo, are becoming increasingly important to Google's business. It helps identify faces in photos, understands commands spoken into smartphones, chooses Internet search results and more.
像在AlphaGo上使用的深层神经网络，对谷歌的业务越来越重要。它可以帮助识别人脸照片，理解向智能手机发出的指令，选择互联网搜索结果等。
And the Go victory over Sedol is a testament to how powerful its machine-learning techniques are. Go is played on a 19-by-19 board, so there are a huge number of possible moves during a match. That's why a lot of Go players say it's a game of intuition as much as anything else.
围棋上战胜李世石证明了机器学习技术是多么强大。围棋横纵19条线，所以在一场比赛中棋子的走法千变万化。那就是为什么许多围棋玩家说，围棋是直觉游戏，和其它任何事物一样多。
To master the game, DeepMind, the Google-owned company that developed AlphaGo, used something it called reinforcement learning. Basically, it made the game practice Go by playing thousands and thousands of matches against itself so it could determine the moves most likely to lead to victory.
为了掌握这个游戏，谷歌旗下公司DeepMind开发了AlphaGo，使用了所谓的强化学习。基本上，游戏中它会自己操练成千上万次，所以它可以决定最有可能胜利的走法。
So now that we know an AI can teach itself to be a top-notch Go player, experts want to see what other things computers can learn.
现在我们知道，人工智能可以教自己成为一流的围棋高手，专家们想了解电脑能学习其它什么东西。
Some researchers are testing how AI fairs in Texas Hold'em poker to see what it does when it can't see its opponents cards. Another AI is working on standardized testing, like the SATS, so we can see how it processes less predictable questions.
一些研究人员正在测试，人工智能在德克萨斯扑克上，当看不得到对手牌时的表现。另一个人工智能正用于标准化测试，例如SATS，所以我们可以看它如何处理不可预测的问题。
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