AlphaGo背后的人脑

 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)是围棋世界冠军。围棋这种古老的中国棋盘游戏，被认为是世界上最复杂的棋类游戏。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展开对弈。
In the series of five matches in Seoul, the machine is winning, taking a 2-0 lead in the contest.
在这场于首尔举行的五局对弈中，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 1 to a robot taking to the lawns of Wimbledon and beating the legendary 2 tennis champion.
AlphaGo背后的人类策划者是DeepMind的共同创始人兼首席执行官杰米斯?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。
AlphaGo
“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 3.” 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”.
我认为这是一个大事件，但是，最终还是要留给历史来评判，”哈萨比斯在首尔接受英国《金融时报》(Financial Times)采访时称，“很多人之前预言至少还需要10年才能实现这一成就，所以我们对于达到这一里程碑感到很兴奋。”39岁的哈萨比斯长久以来一直梦想着这场胜利。但是他的雄心已经不仅限于围棋的棋盘。他的目标是“让机器变聪明”。
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 4, 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 5 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)一起创建了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)读神经学研究生时的同学。2014年，谷歌以4亿英镑的价格收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 6. There is no arrogance 7 on display.”
“让杰米斯更加与众不同的是，天才往往很难相处，”计算机科学家和企业家赫尔曼?豪泽(Hermann Hauser)称，“但是他很开朗、大度又谦逊，一点都不傲慢。”
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)学习计算机科学时接触到了人工智能。当时大学讲师坚持传授有关“弱”人工智能的知识，即程序员为数据添加“标签”让计算机理解信息。
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满意。他希望打造“强”人工智能系统，后者能够利用来自周围环境的“非结构化”信息独立决策并作出预判。
At DeepMind, engineers have created programs based on neural 8 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，计算机工程师在模拟人类大脑的神经网络的基础上创建程序。这类系统会犯错误，但是会随着时间的推移学习和提高。可以对它们进行设定，让它们玩其他游戏和完成其他任务，因此这种人工智能是通用而非专用的，会像人类一样“思考”。
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，让研究人员能够将程序在游戏中的表现与设定目标相比较。而哈萨比斯非常适合训练计算机。作为一名13岁就获得国际象棋大师称号并参加了智力奥运会(Mind Sports Olympiad)的选手，他因在赛场间奔跑、同时与不同选手对弈而被人铭记。组织者认为他“或许是史上最佳选手”。
Mr Hassabis enjoys games filled with human randomness 9. He has won poker 10 tournaments and says he enjoys the game because players can make all the right moves and still lose. He likes Diplomacy 11, a fraught 12 game with loose rules, where players need to negotiate deals, forge alliances and backstab each other to secure world domination.
哈萨比斯喜欢玩人性随机性强的游戏。他赢过扑克锦标赛，并表示他喜欢这种游戏是因为选手们可能每步都正确，但仍会输掉比赛。他喜欢玩《外交》(Diplomacy)这款有着松散规则却精彩纷呈的游戏，在这款游戏中，选手们为了称霸世界，需要讨价还价、缔结联盟、互相背后捅刀子。
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.
围棋是人工智能的“圣杯”。围棋在2500年前起源于中国，如今全世界有4000万人下围棋，有1000名专业选手。
“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.”
哈萨比斯表示：“我的围棋水平足以让我欣赏它的美。但围棋不是我的强项，因此我没有亲自与AlphaGo对弈过，因为几乎从一开始我就不是对手。”
Computers have long “solved” other games like backgammon and draughts 13. 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 14 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 15 technological 16 breakthrough. 
计算机早就“解决了”诸如步步高和跳棋之类的其他游戏。1997年，IBM的“深蓝”(Deep Blue)超级计算机击败了当时的国际象棋世界冠军加里?卡斯帕罗夫(Garry Kasparov)。程序员用深蓝打造了一个试图分析每一种可能走法的所有结果的系统。但围棋要远比国际象棋复杂得多。围棋的棋局变数比宇宙中的原子数量还要多。即便是最强大的超级计算机也无法处理这么多的信息。击败最强的人类选手需要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 17 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来临——在3个半小时的对弈之后，李世石向AlphaGo认输。这位人类冠军棋手在输棋之后感到“震惊”。第二天AlphaGo再次获胜。尽管对这一成绩感到惊叹，但哈萨比斯警告称，机器人技术等其他领域还有很长的路要走。
“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 18 move the Go pieces,” he says. Mr Federer will not face a similar challenge just yet. 
他说：“我们在人工智能领域取得的重大进展的一个古怪之处在于，看起来我们有了一个名叫AlphaGo的世界冠军，但我们还没有一台能够在实体棋盘上落子的计算机。”费德勒目前还不会面临类似的挑战。
For Mr Hassabis, creating machines that beat humans in games is just a testing ground before unleashing 19 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，创造在游戏中击败人类的机器只是个试验，是为了以后利用DeepMind的技术，“解决让智能手机助手更智能等真实世界的挑战，并在将来，利用这种技术帮助科学家们在医疗和其他领域解决一些最为紧迫的社会问题”。