Similarly, human-computer centaur teams are likely to be characterised by a constant tug of war between the humans and the computers,instead of settling down to a lifelong partnership. Teams made exclusively of humans - such as Sherlock Holmes and Dr Watson - usually develop permanent hierarchies and routines that last decades. But a human detective who teams up with IBM's Watson computer system (which became famous after winning the US TV quiz show Jeopardy in 2011) will find that every routine is an invitation for disruption, and every hierarchy an invitation for revolution. Yesterday's sidekick might morph into tomorrow’s superintendent, and all protocols and manuals will have to be rewritten every year.
同样地，人类与计算机相结合的半人马团队中，人类和计算机之间可能会发生一场持久战，而不是建立终身伙伴关系。完全由人类组成的团队——如夏洛克·福尔摩斯和华生医生——通常会发展出能维持几十年的永久等级制度和惯例。但是，一个与IBM的华生计算机系统（因2011年在美国电视智力竞赛节目《危险》中赢得大奖而名声大噪）合作的人类侦探会发现，每一道例行程序都是在招致干扰，每一个等级都可能引发革命。昨天的助手明天可能会摇身一变成为主管，所有的协议和手册每年都要重写。
同样，“半人马”组合很有可能变成一场人类与计算机之间不断的拔河角力，而不是稳定的终身伙伴关系。完全由人类组成的团队（比如福尔摩斯和华生），常常会形成长期的阶层和惯例，并能够延续数十年。然而，如果侦探IBM的超级计算机系统“沃森”合作（该人工智能系统 2011年在电视益智抢答节目《危险边缘》（Jeopardy!）中获胜，会发现所有的阶层都可能被打破，所有的惯例也都可能被干扰。昨天的搭档，明天可能就成了你的主管；所有的规章和守则也都必须每年重写。【林俊宏】
A closer look at the world of chess might indicate where things are heading in the long run. It is true that for several years after Deep Blue defeated Kasparov, human-computer cooperation flourished in chess. Yet in recent years computers have become so good at playing chess that their human collaborators lost their value, and might soon become utterly irrelevant.
仔细观察国际象棋，可能会发现它的长远发展方向。的确，在“深蓝”击败卡斯帕罗夫后的几年里，人机合作在国际象棋领域蓬勃发展。然而，近年来，计算机越来越擅长下棋，人类合作者失去了价值，可能很快就会变得完全无关紧要。
仔细观察国际棋坛的动态，或许可以预估未来世界将走向何方。“深蓝”战胜卡斯帕罗夫之后的几年间，人机合作是国际棋坛的热门形式。但近几年来，计算机已经变得非常擅长下棋，以至于人类合作者失去了他们的价值，而且可能很快就会变得完全无关紧要。【林俊宏】
On 7 December 2017 a critical milestone was reached, not when a computer defeated a human at chess—that's old news—but when Google's AlphaZero program defeated the Stockfish 8 program. Stockfish 8 was the world's computer chess champion for 2016. It had access to centuries of accumulated human experience in chess, as well as to decades of computer experience. It was able to calculate 70 million chess positions per second. In contrast, AlphaZero performed only 80,000 such calculations per second, and its human creators never taught it any chess strategies - not even standard openings. Rather, AlphaZero used the latest machine-learning principles to self-learn chess by playing against itself. Nevertheless, out of a hundred games the novice AlphaZero played against Stockfish, AlphaZero won twenty-eight and tied seventy-two. It didn't lose even once. Since AlphaZero learned nothing from any human, many of its winning moves and strategies seemed unconventional to human eyes. They may well be considered creative, if not downright genius.
2017年12月7日是一个关键的里程碑时刻，不是因为计算机在国际象棋比赛中击败了人类（这已经过时了），而是因为谷歌的AlphaZero程序击败了Stockfish 8程序。Stockfish 8是2016年计算机象棋世界冠军。它获得了人类几个世纪以来积累的国际象棋经验，以及数十年的计算机经验，每秒能够计算出7000万个象棋位置。相比之下，AlphaZero每秒只能进行8万次这样的计算，它的人类发明者从未教过它任何象棋策略，甚至连标准的开局策略都没有教。相反，AlphaZero使用最新的机器学习原理，通过与自己较量来自学国际象棋。然而，在新手AlphaZero与Stockfish之间进行的一百场比赛中，AlphaZero赢了二十八场，其余七十二场与Stockfish打成平局，一次也没输。由于AlphaZero从没有从任何人身上学习，因此在人类看来，它的许多致胜的招数和策略都是不合常规的。即使这些招数和策略不是天才所为，也可以认为是极具创造力的。
2017年12月7日，这是围棋具有里程碑意义的一天，但这一天并不是计算机击败人脑（那已经是旧闻了），而是谷歌的AlphaZero程序击败了Stockfish8程序。Stockfish8是2016年的全球计算机国际象棋冠军，运用的是几百年来累积的人类国际象棋经验，再加上几十年的计算机象棋经验，每秒计算7000万次走法。相较之下，AlphaZero每秒只计算8万次走法，而且写程序的时候完全没教它任何国际象棋规则，它连基本的起手下法都不会！AlphaZero完全是运用最新的机器学习原理，不断和自己下棋，就这样自学了国际象棋。虽然如此，在AlphaZero与Stockfish8的100场比赛中，AlphaZero赢28场、平72场，完全未尝败绩。AlphaZero完全没向任何人类学习任何东西，许多获胜走法和策略对人类来说完全是打破常规的，可以说是创意十足，甚至是天纵英才。【林俊宏】
