892
机器人
人工智能发展史的15个关键时刻
对人工智能的探索始于70多年前，当时的人们认为计算机终将有一日能像人类一样思考。野心勃勃的预言吸引了大量慷慨投资，但几十年过去了，成果依旧不甚理想。
不过，在最近25年中，伴随着科技的进一步发展，人工智能的新形式逐渐呈现，这意味着，我们马上就能实现那些先锋者的梦想了。
1943
WW2 triggers fresh thinking
第二次世界大战激发出新想法
World War Two brought together scientists from many disciplines, including the emerging fields of neuroscience and computing.
二战将许多不同领域的科学家们聚集到了一起，这些领域中包括神经学和计算机这样的新兴科学。
In Britain, mathematician Alan Turing and neurologist Grey Walter were two of the bright minds who tackled the challenges of intelligent machines.
在英国，数学家阿兰·图灵和神经学专家格雷·沃尔特是应对智能机器这一挑战的两个智囊。
They traded ideas in an influential dining society called the Ratio Club.
他们在一个名为比例俱乐部的餐饮协会交流各自的想法，该协会在当时十分具有影响力。
Walter built some of the first ever robots. Turing went on to invent the so-called Turing Test, which set the bar for an intelligent machine: a computer that could fool someone into thinking they were talking to another person.
沃尔特创造了一些机器人，是世界第一批机器人队伍中的一部分。图灵发明了所谓的图灵测试，设定了智能机器的基准：即智能机器是一种能让人类误以为他正在与另一个人交流的计算机。
Grey Walter’s nature-inspired 'tortoise'. It was the world’s first mobile, autonomous robot.格雷·沃尔特以自然为灵感设计的‘tortoise’。这是世界上第一个可移动的自主机器人。
1950
Science fiction steers the conversation
科幻小说引领风潮
In 1950, I Robot was published – a collection of short stories by science fiction writer Isaac Asimov.
1950年，由科幻小说家艾萨克·阿西莫夫撰写的系列短篇小说《机械公敌》问世。
Asimov was one of several science fiction writers who picked up the idea of machine intelligence, and imagined its future.
阿西莫夫是少数使用了智能机器这一概念并设想了其未来形态的科幻小说家之一。
His work was popular, thought-provoking and visionary, helping to inspire a generation of roboticists and scientists.
他的作品十分受欢迎，其内容发人深省且想象力丰富，启发了一代机器人专家和科学家。
He is best known for the Three Laws of Robotics, designed to stop our creations turning on us.
他的出名是因为机器人的三大法则，这些法则被设计来阻止机器人的叛变。
But he also imagined developments that seem remarkably prescient – such as a computer capable of storing all human knowledge that anyone can ask any question.
但是，他对机器人发展的一些设想也十分有先见之明——比如一台能够储存所有人类知识的电脑，不管问什么问题，它都能应答。
Isaac Asimov explain his Three Laws of Robotics to prevent intelligent machines from turning evil.
艾萨克·阿西莫夫解释，他的机器人三原则用于防止智能机器变邪恶。
1956
A 'top-down' approach
一个“由上往下”的方法
The term 'artificial intelligence' was coined for a summer conference at Dartmouth University, organised by a young computer scientist, John McCarthy.
“人工智能”这个说法是由一位年轻的电脑科学家约翰·麦卡西在达特茅斯大学的一次夏季峰会上提出的。
Top scientists debated how to tackle AI.
顶级的科学家们对如何处理人工智能这个问题争论不休。
Some, like influential academic Marvin Minsky, favoured a top-down approach: pre-programming a computer with the rules that govern human behaviour.
其中一些人，如颇有影响力的学者马文·明斯基，倾向一个由上至下的方法：预先将一些控制人类行为的准则编到电脑的程序中。
Others preferred a bottom-up approach, such as neural networks that simulated brain cells and learned new behaviors.
其他人则赞成由下至上的处理方式，比如建立一个模拟脑细胞的神经网络，可以学习新的行为。
Over time Minsky's views dominated, and together with McCarthy he won substantial funding from the US government, who hoped AI might give them the upper hand in the Cold War.
过了一段时间后，明斯基的观点占了上风，他与麦卡西合作，得到了来自美国政府的大量资金支持。政府希望，人工智能可以增强美国在冷战中的优势。
Marvin Minsky founded the Artificial Intelligence Laboratory at Massachusetts Institute of Technology (MIT).
马文·明斯基建立了麻省理工学院的人工智能实验室。
“Every aspect of learning or other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
Mission statement of the Dartmouth Conference, 1956”
“原则上，智力的学习能力或其它特征都能被精确描述，因此，机器便能模拟智力。”——达特茅斯峰会的宗旨，1956年
1968
2001: A Space Odyssey – imagining where AI could lead
《2001：太空漫游》——关于人工智能发展前景的想象
Minsky influenced science fiction too. He advised Stanley Kubrick on the film 2001: A Space Odyssey, featuring an intelligent computer, HAL 9000.
明斯基也影响了科幻小说。他为斯坦利·库布里的电影《2001：太空漫游》提供了建议，并在其中饰演一个智能电脑HAL9000。
During one scene, HAL is interviewed on the BBC talking about the mission and says that he is "fool-proof and incapable of error."
在某个拍摄场景中，HAL接受了BBC的采访，谈到它的任务，并说道它“错误率很低，不会犯错。”
When a mission scientist is interviewed he says he believes HAL may well have genuine emotions.
当任务中的一个科学家接受采访时，他说自己相信HAL可能还具备真实的情感。
The film mirrored some predictions made by AI researchers at the time, including Minsky, that machines were heading towards human level intelligence very soon.
这部电影反映出了当时包括明斯基在内的人工智能研究者所作出的一些预言，即机器将很快达到与人类同水平的智力。
It also brilliantly captured some of the public’s fears, that artificial intelligences could turn nasty.
它也引起了公众对人工智能邪恶可能性的担忧。
Thinking machine HAL 9000’s interview with the BBC.
思考机器HAL9000与BBC进行的采访。
“In from three to eight years we will have a machine with the general intelligence of an average human being.”
“在3到8年后，我们将会看到一个有着人类平均智力的机器。”
——Marvin Minsky in Life Magazine, 1970.
——1970年，马文·明斯基登上《生活》杂志。
1969
Tough problems to crack
需要解决的难题
AI was lagging far behind the lofty predictions made by advocates like Minsky – something made apparent by Shakey the Robot.
与明斯基这种先锋者所提出的高大上预言相比，AI的发展远远落后——这可以从机器人Shakey身上看出。
Shakey was the first general-purpose mobile robot able to make decisions about its own actions by reasoning about its surroundings.
Shakey是第一代通用型可移动机器人，它能通过分析周边环境来决定自己的行动。
It built a spatial map of what it saw, before moving.
在移动之前，它会将它的所见绘制成一张空间地图。
But it was painfully slow, even in an area with few obstacles.
但是，即便在一个无障碍的空间里，它的行动都慢得要命。
Each time it nudged forward, Shakey would have to update its map.
每一次前行，Shakey都需要更新它的地图。
A moving object in its field of view could easily bewilder it, sometimes stopping it in its tracks for an hour while it planned its next move.
在它的可视范围内出现任何移动物体都能轻易地让它迷惑，有时候，这会使得它停在路上，花一个小时来计划下一步。
Researchers spent six years developing Shakey. Despite its relative achievements, a powerful critic lay in wait in the UK.
研究者们花了六年时间来改进Shakey。尽管它取得了一些进展，但是英国本土对它的批评依旧此起彼伏。
1973
The AI winter
人工智能的冬天
By the early 1970s AI was in trouble. Millions had been spent, with little to show for it.
20世纪70年代初期，人工智能陷入了困境。大量投资有去无回，收效甚微。
There was strong criticism from the US Congress and, in 1973, leading mathematician Professor Sir James Lighthill gave a damning health report on the state of AI in the UK.
美国政府对此的批评声十分强烈，1973年，数学教授詹姆斯·莱特希尔阁下给英国的人工智能开发进展下了“病危诊断”。
His view was that machines would only ever be capable of an "experienced amateur" level of chess.
他的观点是，机器只可能具备象棋中“经验丰富的业余玩家”的水平。
Common sense reasoning and supposedly simple tasks like face recognition would always be beyond their capability.
常识分析和简单的任务如脸部识别，都超过了机器的能力范畴。
Funding for the industry was slashed, ushering in what became known as the AI winter.
对AI产业的资助大幅减少，人工智能的开发进入了“冬天”。
John McCarthy was incensed by the Lighthill Report. He flew to the UK and debated its findings with Lighthill on a BBC Television live special.
约翰·麦卡西被莱特希尔的这份报告给激怒了。他乘飞机赶往英国，并在BBC的特别直播节目中与莱特希尔进行辩论。
“In no part of the field have discoveries made so far produced the major impact that was promised.”
“没有任何一个领域的发现能和人工智能一样将预言中的巨大效应变为现实。”
——Professor Sir James Lighthill giving a reality check to AI researchers, 1973.
——1973年，教授詹姆斯·莱特希尔阁下正在给人工智能调查者进行现实核查。
1981
A solution for big business
针对大企业的解决方案
The moment that historians pinpoint as the end of the AI winter was when AI's commercial value started to be realised, attracting new investment.
历史学家所考证的AI冬天结束的时期，是在它的商业价值逐渐得到关注，并吸引到新投资的时候。
The new commercial systems were far less ambitious than early AI.
比起早期的人工智能，新商业系统的野心要小得多。
Instead of trying to create a general intelligence, these ‘expert systems’ focused on much narrower tasks.
这些“专门系统”专注于更细化的任务，而非尝试创造普通智力。
That meant they only needed to be programmed with the rules of a very particular problem.
意思就是，人工智能只需根据特定问题的规则进行编程。
The first successful commercial expert system, known as the RI, began operation at the Digital Equipment Corporation helping configure orders for new computer systems.
第一个获得成功的商业专门系统，即RI，在迪吉多数码设备公司投入运作，专为新电脑系统安装指令。
By 1986 it was saving the company an estimated $40million a year.
到了1986年，它每年能为这家公司节省约四千万美元。
Ken Olsen, founder of Digital Equipment Corporation, was among the first business leaders to realise the commercial benefit of AI.
肯·奥森，迪吉多数码设备公司的创始人，他是意识到AI商业价值的第一批商业领袖之一。
1990
Back to nature for 'bottom-up' inspiration
回归到“由下至上”的灵感
Expert systems couldn't crack the problem of imitating biology.
专门系统并不能攻破生物模仿的问题。
Then AI scientist Rodney Brooks published a new paper: Elephants Don’t Play Chess.
人工智能科学家罗德尼·布洛克斯发表了一份新论文：《大象不下棋》。
Brooks was inspired by advances in neuroscience, which had started to explain the mysteries of human cognition.
那时的神经系统科学开始能够解开人类意识的神秘面纱，而这一进展让布洛克斯大受启发。
Vision, for example, needed different 'modules' in the brain to work together to recognize patterns, with no central control.
比如说，在没有中央控制的情况下，视觉需要大脑中多个“模块”协调作用来辨识图案。
Brooks argued that the top-down approach of pre-programming a computer with the rules of intelligent behaviour was wrong.
布鲁克斯称，用智能行为准则来对电脑进行预编程这一从上至下的方法是错误的。
He helped drive a revival of the bottom-up approach to AI, including the long unfashionable field of neural networks.
在他的助力下，由下至上控制人工智能的方法得到复兴，也让过时已久的类神经网路科学再次回潮。
Rodney Brooks became director of the MIT Artfificial Intelligence Laboratory, a post once held by Marvin Minsky.
罗德尼·布鲁克斯成为了麻省理工学院人工智能实验室的主管，这一职位的前任中就有马文·明斯基。
1997
Man vs machine: Fight of the 20th Century
人类对战机器：20世纪的战争
Supporters of top-down AI still had their champions: supercomputers like Deep Blue, which in 1997 took on world chess champion Garry Kasparov.
从上至下理论的拥护者依旧拿着一个好牌：像深蓝这样的超级电脑。深蓝在1997年打败了世界象棋冠军加里·卡斯帕罗夫。
The IBM-built machine was, on paper, far superior to Kasparov - capable of evaluating up to 200 million positions a second.
根据书面文件的说法，这台IBM制造的机器远远优于卡斯帕罗夫——它能在一秒钟内评估将近20亿个走法。
But could it think strategically? The answer was a resounding yes.
但是它有战略性的思维吗？答案是个大写的“有”。
The supercomputer won the contest, dubbed 'the brain's last stand', with such flair that Kasparov believed a human being had to be behind the controls.
这台超级电脑赢得了胜利，这场对弈被称为“头脑的背水一战”。卡斯帕罗夫认为，一台电脑能拥有如此资质，必定是有人类在背后操纵。
Some hailed this as the moment that AI came of age. But for others, this simply showed brute force at work on a highly specialised problem with clear rules.
有些人将这一时刻赞为“人工智能的成熟”。而对于其他人来说，这仅仅显示了我们在某一有着明确规则的、高度专业化的问题上付出了大量精力。
Deep Blue "thinks like God" according to Gary Kasparov.
据加里·卡斯帕罗夫所说，深蓝的“思维就像是上帝”。
2002
The first robot for the home
第一个家用机器人
Rodney Brook's spin-off company, iRobot, created the first commercially successful robot for the home – an autonomous vacuum cleaner called Roomba.
罗德尼·布鲁克的衍生公司iRobot创造了第一个在商业上取得成功的家用机器人——一个叫鲁姆巴的自动吸尘器。
Cleaning the carpet was a far cry from the early AI pioneers' ambitions.
清扫地毯与早期人工智能先驱者的野心相差甚远。
But Roomba was a big achievement. Its few layers of behaviour-generating systems were far simpler than Shakey the Robot's algorithms, and were more like Grey Walter’s robots over half a century before.
但鲁姆巴已经是一个很大的成就了。它的行为生成系统层次与机器人Shakey的算法相比要简单很多，与一个世纪以前格雷·瓦尔特创造的机器人更相像些。
Despite relatively simple sensors and minimal processing power, the device had enough intelligence to reliably and efficiently clean a home.
尽管它的感应器相对比较简单，处理能力较小，但是它的智力足以让它有效地清理房屋了。
Roomba ushered in a new era of autonomous robots, focused on specific tasks.
鲁姆巴带领人们走进了自动化机器人（专注于特定任务）的新时代。
The Roomba vacuum has cleaned up commercially – over 10 million units have been bought across the world.
鲁姆巴吸尘器在商业上也取得了很大成功——在全球售出约一千万台。
2005
War machines
战争机器
Having seen their dreams of AI in the Cold War come to nothing, the US military was now getting back on board with this new approach.
冷战期间，美国军队对人工智能的梦想泡汤了，而现在，他们又凭借这一新手段卷土重来。
They began to invest in autonomous robots. BigDog, made by Boston Dynamics, was one of the first.
他们开始投资于自动化机器人的研发。由波士顿动力公司制造的BigDog，便是第一批产物之一。
Built to serve as a robotic pack animal in terrain too rough for conventional vehicles, it has never actually seen active service.
它的制造意图是用于在普通汽车难以行进的区域进行活动，不过并没得到频繁使用。
iRobot also became a big player in this field. Their bomb disposal robot, PackBot, marries user control with intelligent capabilities such as explosives sniffing.
IRobot也成了这一领域的主要参与者之一。他们的拆弹机器人PackBot结合人工操控，能够完成嗅探爆炸物之类的智能任务。
Over 2000 PackBots have been deployed in Iraq and Afghanistan.
超过2000台PackBots机器人被部署在伊拉克和阿富汗。
The legs of BigDog contain a number of sensors that enable each limb to move autonomously when it walks over rough terrain.
BigDog的四肢装有多个感应器，使其每条腿在恶劣地形上都能自主活动。
2008
Starting to crack the big problems
向大数据领域进军
In November 2008, a small feature appeared on the new Apple iPhone – a Google app with speech recognition.
2008年11月，新苹果手机上出现了一个小功能——带有语音识别功能的谷歌APP。
It seemed simple. But this heralded a major breakthrough. Despite speech recognition being one of AI's key goals, decades of investment had never lifted it above 80% accuracy.
这看似简单，却预示着一个重大突破的到来。尽管语音识别是人工智能的关键目标之一，但是几十年以来的投入从未将它的准确率提高到80%以上。
Google pioneered a new approach: thousands of powerful computers, running parallel neural networks, learning to spot patterns in the vast volumes of data streaming in from Google's many users.
谷歌在一项新手段上取得了领先：上千台强大的电脑同时运行类神经网路，学习在谷歌众多用户带来的大型数据信息流中辨析模式。
At first it was still fairly inaccurate but, after years of learning and improvements, Google now claims it is 92% accurate.
一开始，它还不够准确，但是通过几年的学习和改善之后，谷歌称自己已达到了92%的准确度。
According to Google, its speech recognition technology had an 8% word error rate as of 2015.
据谷歌的说法，到2015年为止，其语音识别技术的错误率仅为8%。
“Artificial intelligence would be the ultimate version of Google. It would understand exactly what you wanted, and it would give you the right thing.”
“人工智能将是谷歌的终极形态。它会完全理解你所想要的东西，并给予你相应的物件。”
——Google co-founder Larry Page, 2000
——谷歌的联合创始人拉里·佩吉，2000年。
2010
Dance bots
跳舞机器人
At the same time as massive mainframes were changing the way AI was done, new technology meant smaller computers could also pack a bigger punch.
与此同时，随着大量主机改变了人工智能的实现方式，新科技的出现意味着更小的电脑也能完成大型任务。
These new computers enabled humanoid robots, like the NAO robot, which could do things predecessors like Shakey had found almost impossible.
这些新型电脑让NAO机器人这样的类人机器人能够完成当初Shakey根本无法完成的事情。
NAO robots used lots of the technology pioneered over the previous decade, such as learning enabled by neural networks.
NAO机器人用到了许多领先过去十年的技术，比如通过类神经网路进行学习。
At Shanghai's 2010 World Expo, some of the extraordinary capabilities of these robots went on display, as 20 of them danced in perfect harmony for eight minutes.
在2010年上海世界博览会上，这些机器人的特别能力得到展示，20个机器人一同有序地跳舞长达八分钟。
How close we are to enabling robots to learn with mathematician Marcus Du Sautoy？
距离让机器人向数学家马库斯·杜·索托伊学习还有多远？
2011
Man vs machine: Fight of the 21st Century
人类对战机器：21世纪的战争
In 2011, IBM's Watson took on the human brain on US quiz show Jeopardy.
2011年，IBM的电脑系统Watson在美国智力问答节目Jeopardy上战胜了人类选手。
This was a far greater challenge for the machine than chess.
这比起象棋那场比赛的挑战性要大得多。
Watson had to answer riddles and complex questions.
Watson得回答谜语和复杂的问题。
Its makers used a myriad of AI techniques, including neural networks, and trained the machine for more than three years to recognise patterns in questions and answers.
它的制造者使用了大量的人工智能技术，包括类神经网路，并训练它三年多，让它能够在问答之间发现模式和规律。
Watson trounced its opposition – the two best performers of all time on the show. The victory went viral and was hailed as a triumph for AI.
Watson给了它的对手——两位节目有史以来表现最佳的参赛者——狠狠一击。这场胜利被大肆报道，并被称为人工智能的凯旋。
Watson is now used in medicine. It mines vast sets of data to find facts relevant to a patient’s history and makes recommendations to doctors.
Watson现在被用于医疗事业。它记忆大量数据，从中找出与病人病史相关的内容，并向医生提供治疗建议。
2014
Are machines intelligent now?
那么，现在的机器智能了吗？
Sixty-four years after Turing published his idea of a test that would prove machine intelligence, a chatbot called Eugene Goostman finally passed.
在图灵推出机器智力测试的六十四年后，终于有机器人通过测试了——一个名叫Eugene Goostman的聊天机器人。
But very few AI experts saw this a watershed moment. Eugene Goostman was seen as 'taught for the test', using tricks to fool the judges.
但没有几个人工智能专家将之视为一个转折点。Eugene Goostman被认为是经过了“与测试相关的培训”，利用技巧蒙骗了评判。
It was other developments in 2014 that really showed how far AI had come in 70 years.
2014年出现的另一些发展才真正显示了70年来人工智能的进步所在。
From Google's billion dollar investment in driverless cars, to Skype's launch of real-time voice translation, intelligent machines were now becoming an everyday reality that would change all of our lives.
从谷歌投资了十亿美元的无人驾驶汽车到Skype的实时语音翻译，智能机器现已逐渐成为现实日常的一部分，为我们的生活带来巨大变化。
Across four states in America it is legal for driverless cars to take to the road.
无人驾驶汽车在美国的四个州都可以合法上路。
最后更新：2017-10-08 01:59:04