本文摘要：The development of computer programs that can beat humans at games has a long history — from the mastery of noughts and crosses in the 1950s to Deep Blue’s celebrated defeat of world chess champion Garry Kasparov in 1997. 需要在游戏中打败人类高手的计算机程序具有历史悠久的发展历史——从上世纪50年代掌控“井字棋”制胜之道，到1997年“深蓝”(Deep Blue；IBM研发的计算机——译者录）打败国际象棋世界冠军加里卡斯帕罗夫(Garry Kasparov)。

The development of computer programs that can beat humans at games has a long history — from the mastery of noughts and crosses in the 1950s to Deep Blue’s celebrated defeat of world chess champion Garry Kasparov in 1997. 需要在游戏中打败人类高手的计算机程序具有历史悠久的发展历史——从上世纪50年代掌控“井字棋”制胜之道，到1997年“深蓝”(Deep Blue；IBM研发的计算机——译者录）打败国际象棋世界冠军加里卡斯帕罗夫(Garry Kasparov)。In recent years, however, the pace of advance has quickened. Data-crunching devices routinely notch up previously unthinkable victories. Computers can triumph in quiz games, as IBM’s Watson proved when it won the TV show Jeopardy in 2011. They also mimic human aptitudes with ever greater facility. For instance, machines play arcade games simply by observing the movement of objects on the screen. 然而，近年来变革速度减缓了。需要运算海量数据的设备常常获得以往不可想象的胜利。计算机需要在智力竞赛中取得胜利，IBM的“沃森”(Watson)在2011年夺得电视节目《危险性边缘》(Jeopardy)就是相比较。

它们还能以更加强劲的“悟性”仿效人的天赋。例如，机器通过观察屏幕上物体的运动，就能学会玩游戏街机游戏。Even so, the triumph of the AlphaGo computer over the South Korean world champion Lee Se-dol in the first of a five-match series in the ancient Chinese board game of Go marks more than just a new notch on the computerised honours board. Mr Lee had been confident of victory and proclaimed himself “shocked” by his defeat. 即便如此，AlphaGo电脑在古老的中国棋盘游戏——棋士的对垒中打败韩国九段棋手李世石(Lee Sedol)，在五局“人机对战”中首战告捷，不仅标志着电脑荣誉板上的一个新的档次。

赛前对胜利信心满满的李世石，在败给后坦白“愤慨”。Go is a little like a version of chess, only vastly more complicated. Indeed the possible moves within a game exceed the number of atoms within the universe. This is a challenge that would defeat traditional programmes. Indeed it can only be mastered by computers assembled into neural networks that teach themselves through observation and practice — abilities that remain at the frontiers of computer science. 棋士类似于国际象棋的变体，只是复杂程度低得多。的确，其棋局的变数比宇宙中的原子数量还要多。

这个挑战不会惨败传统的程序。事实上，只有多台计算机构成神经网络，通过观察和实践中来“自学”（这些能力仍正处于计算机科学的前沿），才能匹敌这种高难度挑战。

Demis Hassabis and his team at DeepMind, the UK-based artificial intelligence (AI) arm of Alphabet, deserve credit for the speed at which they have mastered this undertaking. True, AlphaGo, a formidable piece of IT, could be described as a computerised sledgehammer aimed at a recreational nut. Its victory, however, is a reminder of how fast the world is overcoming the obstacles in the way of AI, and its deployment in the world about us. 杰米斯哈萨比斯(Demis Hassabis)以及他在DeepMind（Alphabet旗下英国人工智能部门）的团队以如此慢的速度掌控棋士制胜之道，这一点有一点赞许。到底，作为一件具备强劲能力的信息技术设备，AlphaGo可以被形容为一把计算机化的大锤，其用途是敲开一个玩乐的坚果。

然而，它的胜利警告世人，世界正在较慢攻下人工智能及其实际部署所面对的障碍。That is largely due to the huge amount of cash being poured into AI research by US and Chinese companies. These are poaching some of the brightest computer scientists from universities, giving them the capacity and tools to pursue their heart’s desire. 这在相当大程度上得益于美国和中国企业对人工智能研究的极大投放。这些企业从高校挤到一些最杰出的计算机科学家，并获取资源和工具，让这些科学家专门从事内心渴求的研究。

According to a recent survey, half of the world’s AI experts believe human level machine intelligence will be achieved by 2040. This opens up huge possibilities for the enrichment of mankind, from tackling climate change and treating disease to labour-saving devices. It also raises ethical questions every bit as profound as those posed by genetics. AI experts talk about the possibility of the human brain being reverse-engineered. Physicist Stephen Hawking last year warned that unless we take care, board games might be the least of it: AI could ultimately “outsmart us all”. 根据最近的一项调查，全球半数人工智能专家坚信，人类水平的机器智能到2040年就能沦为现实。这为促进人类福祉打开极大可能性——从应付气候变化、化疗疾病，到节省劳动力的设备。

这也引起种种道德问题，其深刻性丝毫远不如遗传学所包含的道德问题。人工智能专家谈及人脑被“逆向工程”的可能性。物理学家史蒂芬霍金(Stephen Hawking)去年曾警告，除非我们小心，否则棋盘游戏有可能是最无关紧要的问题：人工智能最后有可能“比我们所有人更聪明”。

One does not have to believe in some future tech dystopia to believe that governments and wider society should take the implications of these developments seriously. Google, Facebook and other companies rushing into AI point out that they are establishing ethics panels to consider appropriate uses for these technologies. These are unlikely to be immune from commercial interests or indeed from the gung-ho enthusiasm of the researchers. 人们不一定非要坚信未来将经常出现某种科技“敌托邦”才不会指出，政府和整个社会应当认真对待这些发展的潜在影响。竞相进占人工智能领域的谷歌(Google)、Facebook等公司认为，他们正在正式成立伦理小组以考量这些技术的必要用途。这些小组不太可能对商业利益以及研究人员的热忱无动于衷。

Some external scrutiny akin to that supplied in the case of genetics by the UK’s Human Fertilisation and Embryology Authority is needed to protect the public from developments that may threaten more than the amour-propre of a South Korean Go champion. Granted, there may yet be no evidence that computers will ever shrug off their human masters but we should still treat these developments with the humility and caution they deserve. 必须展开一些外部监督，类似于遗传学领域的英国人类受精卵和胚胎学管理局(HFEA)，以维护公众免遭涉及发展的威胁，这些威胁所涉及的不只是韩国棋士高手的自尊心。当然，目前或许还没证据指出计算机有朝一日将踢开他们的人类主人，但我们仍应当对这些发展给与理应的顺服和谨慎。

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