在一系列非官方的在線對弈中，谷歌的人工智能旗手AlphaGo的升級版在與世界圍棋頂級棋手的對弈中，創(chuàng)下了60-0的紀(jì)錄，就連世界排名第一的柯潔亦被其輕松擊敗。

2016年3月，AlphaGo曾擊敗韓國的李世石。

AlphaGo利用在線賬號Magister和Master，通過計(jì)算機(jī)進(jìn)行了比賽——這兩個(gè)名字似乎也預(yù)示了最終的結(jié)果。《華爾街日報(bào)》稱，人工智能的策略不合乎傳統(tǒng)棋理，令人無法預(yù)測，它的一些著法要經(jīng)過很多手之后才能展顯現(xiàn)其完整的意圖。它的棋路也讓它的人類對手開始深深地反思，這也折射出了計(jì)算機(jī)智能所帶來的更廣泛的問題。

被人工智能擊敗的九段棋手古力在微博中表示：“AlphaGo的出現(xiàn)已經(jīng)徹底顛覆了我們棋手對局勢原有的掌控、判斷。那我不禁要問，多年后的某一天，當(dāng)你發(fā)現(xiàn)自己曾經(jīng)的意識、認(rèn)知、抉擇都是錯(cuò)誤的時(shí)候，你會一如即往的錯(cuò)下去，還是否定自己，給自己重新開始的機(jī)會呢？”

另外一位來自以色列的棋手阿里·賈巴林稱，他偶然碰到了剛剛被AlphaGo擊敗的柯潔。據(jù)賈巴林表示，柯潔“有點(diǎn)被震撼到了”……只是反復(fù)說“它太厲害了”。

可以預(yù)料，開發(fā)團(tuán)隊(duì)DeepMind的官方聲明并沒有這么焦慮。聲明中強(qiáng)調(diào)希望人工智能能夠與人類“在相互啟發(fā)的氣氛中共同領(lǐng)會圍棋的奧妙”——并且承諾將很快安排AlphaGo下一階段的正式比賽。

這種樂觀情緒恐怕無法在現(xiàn)實(shí)社會的經(jīng)濟(jì)領(lǐng)域引起共鳴，例如上周被人工智能取代的日本保險(xiǎn)行業(yè)從業(yè)者。

就像工業(yè)機(jī)器人對工廠工人的影響一樣，如今，決策程序似乎也對正白領(lǐng)工人產(chǎn)生了同樣的影響。雖然AlphaGo掌握的是一種游戲，但開發(fā)它采用的機(jī)器學(xué)習(xí)技術(shù)，將有助于制造能解決更加復(fù)雜的現(xiàn)實(shí)問題的人工智能，從而接管更多的人類工作。（財(cái)富中文網(wǎng)）

作者：David z.Morris

譯者：劉進(jìn)龍/汪皓

In a series of unofficial online games, an updated version of Google’s AlphaGo artificial intelligence has compiled a 60-0 record against some of the game’s premier players. Among the defeated, according to the Wall Street Journal, were China’s Ke Jie, reigning world Go champion.

The run follows AlphaGo’s defeat of South Korea’s Lee Se-dol in March of 2016, in a more official setting and using a previous version of the program.

The games were played by the computer through online accounts dubbed Magister and Master—names that proved prophetic. As described by the Journal, the AI’s strategies were unconventional and unpredictable, including moves that only revealed their full implications many turns later. That pushed its human opponents into deep reflections that mirror the broader questions posed by computer intelligence.

“AlphaGo has completely subverted the control and judgment of us Go players,” wrote Gu Li, a grandmaster defeated by the program, in an online post. “When you find your previous awareness, cognition and choices are all wrong, will you keep going along the wrong path or reject yourself?”

Another Go player, Ali Jabarin, described running into Ke Jie after he had been defeated by the program. According to Jabarin, Jie was “a bit shocked . . . just repeating ‘it’s too strong’.”

Predictably, the official statement from developers DeepMind is less angsty, emphasizing the potential for AI to “explore the profound mysteries of the game further in this spirit of mutual enlightenment”—and promising that AlphaGo will take its next-level game to more official venues soon.

That optimism might not resonate with the Go players’ real-economy counterparts—for instance, the group of Japanese insurance workers supplanted by an artificial intelligence this week.

What industrial robots have been to factory jobs, it seems, decision-making programs are becoming for white-collar workers. And though AlphaGo has mastered a game, the machine learning techniques behind its development will help build AIs to solve ever more complex real-world problems—and take ever more human jobs.