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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.
谷歌人工智能軟件在古老的圍棋比賽中擊敗大師級棋手贏得第三局。
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.
在總數(shù)五場的比賽中，谷歌程序AlphaGo連贏三場，擊敗被認為是世界上最好的圍棋選手之一李世石。
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.
這意味著谷歌已經(jīng)獲得比賽的100萬美元獎金，谷歌表示錢將捐贈給慈善機構(gòu)。但這不僅僅是錢的問題，讓谷歌有了炫耀的資本。
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上使用的深層神經(jīng)網(wǎng)絡(luò)，對谷歌的業(yè)務(wù)越來越重要。它可以幫助識別人臉照片，理解向智能手機發(fā)出的指令，選擇互聯(lián)網(wǎng)搜索結(jié)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.
圍棋上戰(zhàn)勝李世石證明了機器學(xué)習(xí)技術(shù)是多么強大。圍棋橫縱19條線，所以在一場比賽中棋子的走法千變?nèi)f化。那就是為什么許多圍棋玩家說，圍棋是直覺游戲，和其它任何事物一樣多。
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.
為了掌握這個游戲，谷歌旗下公司DeepMind開發(fā)了AlphaGo，使用了所謂的強化學(xué)習(xí)。基本上，游戲中它會自己操練成千上萬次，所以它可以決定最有可能勝利的走法。
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.
現(xiàn)在我們知道，人工智能可以教自己成為一流的圍棋高手，專家們想了解電腦能學(xué)習(xí)其它什么東西。
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.
一些研究人員正在測試，人工智能在德克薩斯撲克上，當(dāng)看不得到對手牌時的表現(xiàn)。另一個人工智能正用于標(biāo)準(zhǔn)化測試，例如SATS，所以我們可以看它如何處理不可預(yù)測的問題。
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