Lee Se-Dol is the world champion of Go, the ancient Chinese board game that is considered the world’s most complex. This week, the South Korean took on an artificially intelligent computer program called AlphaGo created by DeepMind, a British company owned by Google. 李世石(Lee Se-Dol)是棋士世界冠军。棋士这种古老的中国棋盘游戏，被指出是世界上最简单的棋类游戏。上周，这位韩国棋手与谷歌(Google)旗下的英国公司DeepMind研发的人工智能计算机程序AlphaGo进行对局。

In the series of five matches in Seoul, the machine is winning, taking a 2-0 lead in the contest. 在这场于釜山举办的五局对局中，AlphaGo目前以3：1领先。The victories have a human mastermind in Demis Hassabis, co-founder and chief executive of DeepMind. He describes Mr Lee as the “Roger Federer of Go”, and for some the computer program’s achievement is akin to a robot taking to the lawns of Wimbledon and beating the legendary tennis champion. AlphaGo背后的人类策划者是DeepMind的联合创始人兼任首席执行官杰米斯哈萨比斯(Demis Hassabis)。他把李世石形容为“围棋界的罗杰费德勒(Roger Federer)”，因此对一些人来说，AlphaGo的成就类似于一台机器人站上了温布尔登的草坪并战胜了这位网球传奇冠军。

“I think it is pretty huge but, ultimately, it will be for history to judge,” says Mr Hassabis, speaking to the Financial Times from Seoul, where the matches are taking place. “Many people predicted it was at least a decade away so we’re thrilled to have achieved this milestone.” The 39-year-old has long dreamt about the victory. But his ambitions stretch beyond the Go board. His aim is to make “machines smart”. 我指出这是一个大事件，但是，最后还是要留下历史来评判，”哈萨比斯在釜山拒绝接受英国《金融时报》(Financial Times)专访时称，“很多人之前应验最少还必须10年才能构建这一成就，所以我们对于超过这一里程碑深感很激动。”39岁的哈萨比斯长久以来仍然梦想着这场胜利。但是他的雄心早已不仅仅限于棋士的棋盘。

他的目标是“让机器逆聪慧”。The London-born son of a Chinese-Singaporean mother and a father of Greek-Cypriot descent, Mr Hassabis is a modern polymath whose career path has seen him become a chess prodigy, master computer programmer, video games designer and neuroscientist. 哈萨比斯出生于伦敦，母亲是新加坡华人，父亲有希腊裔塞浦路斯人血统。他是现代版的通才博学家，既是国际象棋神童、大师级的计算机程序员，还是视频游戏设计师和神经学家。These experiences led him to create DeepMind in 2010, alongside Mustafa Suleyman, a technologist and childhood friend of Mr Hassabis, and Shane Legg, whom he met when they were postgraduates studying neuroscience at University College London. The artificial intelligence group was acquired by Google for ￡400m in 2014. 这些经历使得他在2010年与穆斯塔法苏莱曼(Mustafa Suleyman)和谢恩列格(Shane Legg)一起创立了DeepMind。

苏莱曼是一位技术专家、哈萨比斯的童年好友，而列格是哈萨比斯在伦敦大学学院(University College London)读书神经学研究生时的同学。2014年，谷歌以4亿英镑的价格并购了这家人工智能公司。“What is even more unusual about Demis is people that gifted can be difficult to mix with,” says Hermann Hauser, the computer scientist and entrepreneur. “But he’s very open, generous and humble. There is no arrogance on display.” “让杰米斯更为与众不同的是，天才往往很难共处，”计算机科学家和企业家赫尔曼豪泽(Hermann Hauser)称之为，“但是他很开朗、大度又谦虚，一点都不刻薄。” Mr Hassabis was introduced to artificial intelligence while studying computer science as an undergraduate at Cambridge university. Lecturers insisted on teaching “narrow” AI, where programmers attach “labels” to data for a computer to make sense of information. 哈萨比斯本科在剑桥大学(Cambridge University)自学计算机科学时认识到了人工智能。

当时大学讲师坚决传授有关“很弱”人工智能的科学知识，即程序员为数据加到“标签”让计算机解读信息。Mr Hassabis was unsatisfied by this approach. He wanted to create “general” AI systems that use “unstructured” information from their surroundings to make independent decisions and predictions. 哈萨比斯对这种方式并不失望。

他期望打造出“强劲”人工智能系统，后者需要利用来自周围环境的“非结构化”信息独立国家决策并做出预判。At DeepMind, engineers have created programs based on neural networks, modelled on the human brain. These systems make mistakes, but learn and improve over time. They can be set to play other games and solve other tasks, so the intelligence is general, not specific. This AI “thinks” like humans do. 在DeepMind，计算机工程师在仿真人类大脑的神经网络的基础上创立程序。

这类系统不会犯错误，但是不会随着时间的流逝自学和提升。可以对它们展开原作，让它们玩游戏其他游戏和已完成其他任务，因此这种人工智能是标准化而非专用的，不会像人类一样“思维”。Games are an ideal way to test such AI programs, allowing researchers to measure performance against set goals. And Mr Hassabis is ideally placed to train the computer. A chess master by age 13 and a competitor at the Mind Sports Olympiad, he is remembered for dashing between matches to battle various competitors at once. Organisers have described him as “probably the best games player in history”. 游戏是测试此类人工智能程序的理想方式，让研究人员需要将程序在游戏中的展现出与原作目标相比较。

而哈萨比斯非常适合训练计算机。作为一名13岁就取得国际象棋大师称号并参与了智力奥运会(Mind Sports Olympiad)的运动员，他因在赛场间跳跃、同时与有所不同运动员对局而被人铭记。组织者指出他“也许是史上最佳运动员”。

Mr Hassabis enjoys games filled with human randomness. He has won poker tournaments and says he enjoys the game because players can make all the right moves and still lose. He likes Diplomacy, a fraught game with loose rules, where players need to negotiate deals, forge alliances and backstab each other to secure world domination. 哈萨比斯讨厌玩游戏人性随机性强劲的游戏。他输掉过扑克锦标赛，并回应他讨厌这种游戏是因为运动员们有可能每步都准确，但仍不会赢比赛。

他讨厌玩游戏《外交》(Diplomacy)这款具有牢固规则却精彩纷呈的游戏，在这款游戏中，运动员们为了称霸世界，必须讨价还价、签订联盟、相互背后砍死刀子。Go is the “holy grail” for AI. The game originated 2,500 years ago in China, is played by 40m people worldwide and has 1,000 professional players. 棋士是人工智能的“圣杯”。棋士在2500年前源于中国，如今全世界有4000万人下围棋，有1000名专业运动员。

“I know how to play Go well enough to be able to appreciate its beauty,” Mr Hassabis says. “But it is not one of the games I’m strong at, so I’ve not actually played AlphaGo myself as it surpassed my ability almost from the beginning.” 哈萨比斯回应：“我的棋士水平不足以让我喜爱它的美。但棋士不是我的强项，因此我没特地与AlphaGo对局过，因为完全从一开始我就不是输掉。” Computers have long “solved” other games like backgammon and draughts. In 1997, IBM’s Deep Blue supercomputer beat Garry Kasparov, the then world chess champion. With Deep Blue, programmers built a system that tried to analyse every outcome of every possible move. But Go is far more complex than chess. There are more possible configurations on a Go board than atoms in the universe. This is too much information for even the most powerful supercomputer to process. Beating the best human player required an unprecedented technological breakthrough. 计算机早已“解决问题了”诸如步步高和象棋之类的其他游戏。1997年，IBM的“深蓝”(Deep Blue)超级计算机打败了当时的国际象棋世界冠军加里卡斯帕罗夫(Garry Kasparov)。

程序员用深蓝打造出了一个企图分析每一种有可能走法的所有结果的系统。但棋士要远比国际象棋简单得多。棋士的棋局变数比宇宙中的原子数量还要多。即便是最强劲的超级计算机也无法处置这么多的信息。

打败最弱的人类运动员必须史无前例的技术突破。That moment came on Wednesday when, after three-and-a-half hours play, Mr Lee conceded to AlphaGo. The human champion was in “shock” after the loss. The next day the computer won again. The third match begins this weekend. Though marvelling at this achievement, Mr Hauser warns that progress in other fields, such as robotics, is some way off. 突破的时刻在上周三到来——在3个半小时的对局之后，李世石向AlphaGo认输。这位人类冠军棋手在输棋之后深感“愤慨”。第二天AlphaGo再度获得胜利。

尽管对这一成绩深感赞叹，但哈萨比斯警告称之为，机器人技术等其他领域还有很长的路要回头。“One of the curiosities of the phenomenal progress we’re making with AI is that it looks as though we have a world champion at Go, but we don’t have a computer that can physically move the Go pieces,” he says. Mr Federer will not face a similar challenge just yet. 他说道：“我们在人工智能领域获得的重大进展的一个怪异之处在于，看上去我们有了一个名为AlphaGo的世界冠军，但我们还没一台需要在实体棋盘上落子的计算机。”费德勒目前还会面对类似于的挑战。

For Mr Hassabis, creating machines that beat humans in games is just a testing ground before unleashing DeepMind’s technology on “real world challenges like making smartphone assistants smarter, and further in the future, using it to help scientists solve some of society’s most pressing problems in healthcare and other areas”. 对哈萨比斯来说，建构在游戏中打败人类的机器只是个试验，是为了以后利用DeepMind的技术，“解决问题让智能手机助手更加智能等真实世界的挑战，并在将来，利用这种技术协助科学家们在医疗和其他领域解决问题一些尤为严峻的社会问题”。

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