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機器人
人工智能發展史的15個關鍵時刻
對人工智能的探索始於70多年前，當時的人們認為計算機終將有一日能像人類一樣思考。野心勃勃的預言吸引了大量慷慨投資，但幾十年過去了，成果依舊不甚理想。
不過，在最近25年中，伴隨著科技的進一步發展，人工智能的新形式逐漸呈現，這意味著，我們馬上就能實現那些先鋒者的夢想了。
1943
WW2 triggers fresh thinking
第二次世界大戰激發出新想法
World War Two brought together scientists from many disciplines, including the emerging fields of neuroscience and computing.
二戰將許多不同領域的科學家們聚集到了一起，這些領域中包括神經學和計算機這樣的新興科學。
In Britain, mathematician Alan Turing and neurologist Grey Walter were two of the bright minds who tackled the challenges of intelligent machines.
在英國，數學家阿蘭·圖靈和神經學專家格雷·沃爾特是應對智能機器這一挑戰的兩個智囊。
They traded ideas in an influential dining society called the Ratio Club.
他們在一個名為比例俱樂部的餐飲協會交流各自的想法，該協會在當時十分具有影響力。
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.
沃爾特創造了一些機器人，是世界第一批機器人隊伍中的一部分。圖靈發明了所謂的圖靈測試，設定了智能機器的基準：即智能機器是一種能讓人類誤以為他正在與另一個人交流的計算機。
Grey Walter’s nature-inspired 'tortoise'. It was the world’s first mobile, autonomous robot.格雷·沃爾特以自然為靈感設計的‘tortoise’。這是世界上第一個可移動的自主機器人。
1950
Science fiction steers the conversation
科幻小說引領風潮
In 1950, I Robot was published – a collection of short stories by science fiction writer Isaac Asimov.
1950年，由科幻小說家艾薩克·阿西莫夫撰寫的係列短篇小說《機械公敵》問世。
Asimov was one of several science fiction writers who picked up the idea of machine intelligence, and imagined its future.
阿西莫夫是少數使用了智能機器這一概念並設想了其未來形態的科幻小說家之一。
His work was popular, thought-provoking and visionary, helping to inspire a generation of roboticists and scientists.
他的作品十分受歡迎，其內容發人深省且想象力豐富，啟發了一代機器人專家和科學家。
He is best known for the Three Laws of Robotics, designed to stop our creations turning on us.
他的出名是因為機器人的三大法則，這些法則被設計來阻止機器人的叛變。
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，他對機器人發展的一些設想也十分有先見之明——比如一台能夠儲存所有人類知識的電腦，不管問什麼問題，它都能應答。
Isaac Asimov explain his Three Laws of Robotics to prevent intelligent machines from turning evil.
艾薩克·阿西莫夫解釋，他的機器人三原則用於防止智能機器變邪惡。
1956
A 'top-down' approach
一個“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”這個說法是由一位年輕的電腦科學家約翰·麥卡西在達特茅斯大學的一次夏季峰會上提出的。
Top scientists debated how to tackle AI.
頂級的科學家們對如何處理人工智能這個問題爭論不休。
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，如頗有影響力的學者馬文·明斯基，傾向一個由上至下的方法：預先將一些控製人類行為的準則編到電腦的程序中。
Others preferred a bottom-up approach, such as neural networks that simulated brain cells and learned new behaviors.
其他人則讚成由下至上的處理方式，比如建立一個模擬腦細胞的神經網絡，可以學習新的行為。
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.
過了一段時間後，明斯基的觀點占了上風，他與麥卡西合作，得到了來自美國政府的大量資金支持。政府希望，人工智能可以增強美國在冷戰中的優勢。
Marvin Minsky founded the Artificial Intelligence Laboratory at Massachusetts Institute of Technology (MIT).
馬文·明斯基建立了麻省理工學院的人工智能實驗室。
“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”
“原則上，智力的學習能力或其它特征都能被精確描述，因此，機器便能模擬智力。”——達特茅斯峰會的宗旨，1956年
1968
2001: A Space Odyssey – imagining where AI could lead
《2001：太空漫遊》——關於人工智能發展前景的想象
Minsky influenced science fiction too. He advised Stanley Kubrick on the film 2001: A Space Odyssey, featuring an intelligent computer, HAL 9000.
明斯基也影響了科幻小說。他為斯坦利·庫布裏的電影《2001：太空漫遊》提供了建議，並在其中飾演一個智能電腦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."
在某個拍攝場景中，HAL接受了BBC的采訪，談到它的任務，並說道它“錯誤率很低，不會犯錯。”
When a mission scientist is interviewed he says he believes HAL may well have genuine emotions.
當任務中的一個科學家接受采訪時，他說自己相信HAL可能還具備真實的情感。
The film mirrored some predictions made by AI researchers at the time, including Minsky, that machines were heading towards human level intelligence very soon.
這部電影反映出了當時包括明斯基在內的人工智能研究者所作出的一些預言，即機器將很快達到與人類同水平的智力。
It also brilliantly captured some of the public’s fears, that artificial intelligences could turn nasty.
它也引起了公眾對人工智能邪惡可能性的擔憂。
Thinking machine HAL 9000’s interview with the BBC.
思考機器HAL9000與BBC進行的采訪。
“In from three to eight years we will have a machine with the general intelligence of an average human being.”
“在3到8年後，我們將會看到一個有著人類平均智力的機器。”
——Marvin Minsky in Life Magazine, 1970.
——1970年，馬文·明斯基登上《生活》雜誌。
1969
Tough problems to crack
需要解決的難題
AI was lagging far behind the lofty predictions made by advocates like Minsky – something made apparent by Shakey the Robot.
與明斯基這種先鋒者所提出的高大上預言相比，AI的發展遠遠落後——這可以從機器人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動機器人，它能通過分析周邊環境來決定自己的行動。
It built a spatial map of what it saw, before moving.
在移動之前，它會將它的所見繪製成一張空間地圖。
But it was painfully slow, even in an area with few obstacles.
但是，即便在一個無障礙的空間裏，它的行動都慢得要命。
Each time it nudged forward, Shakey would have to update its map.
每一次前行，Shakey都需要更新它的地圖。
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.
在它的可視範圍內出現任何移動物體都能輕易地讓它迷惑，有時候，這會使得它停在路上，花一個小時來計劃下一步。
Researchers spent six years developing Shakey. Despite its relative achievements, a powerful critic lay in wait in the UK.
研究者們花了六年時間來改進Shakey。盡管它取得了一些進展，但是英國本土對它的批評依舊此起彼伏。
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世紀70年代初期，人工智能陷入了困境。大量投資有去無回，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.
美國政府對此的批評聲十分強烈，1973年，數學教授詹姆斯·萊特希爾閣下給英國的人工智能開發進展下了“病危診斷”。
His view was that machines would only ever be capable of an "experienced amateur" level of chess.
他的觀點是，機器隻可能具備象棋中“經驗豐富的業餘玩家”的水平。
Common sense reasoning and supposedly simple tasks like face recognition would always be beyond their capability.
常識分析和簡單的任務如臉部識別，都超過了機器的能力範疇。
Funding for the industry was slashed, ushering in what became known as the AI winter.
對AI產業的資助大幅減少，人工智能的開發進入了“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.
約翰·麥卡西被萊特希爾的這份報告給激怒了。他乘飛機趕往英國，並在BBC的特別直播節目中與萊特希爾進行辯論。
“In no part of the field have discoveries made so far produced the major impact that was promised.”
“沒有任何一個領域的發現能和人工智能一樣將預言中的巨大效應變為現實。”
——Professor Sir James Lighthill giving a reality check to AI researchers, 1973.
——1973年，教授詹姆斯·萊特希爾閣下正在給人工智能調查者進行現實核查。
1981
A solution for big business
針對大企業的解決方案
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.
曆史學家所考證的AI冬天結束的時期，是在它的商業價值逐漸得到關注，並吸引到新投資的時候。
The new commercial systems were far less ambitious than early AI.
比起早期的人工智能，新商業係統的野心要小得多。
Instead of trying to create a general intelligence, these ‘expert systems’ focused on much narrower tasks.
這些“專門係統”專注於更細化的任務，而非嚐試創造普通智力。
That meant they only needed to be programmed with the rules of a very particular problem.
意思就是，人工智能隻需根據特定問題的規則進行編程。
The first successful commercial expert system, known as the RI, began operation at the Digital Equipment Corporation helping configure orders for new computer systems.
第一個獲得成功的商業專門係統，即RI，在迪吉多數碼設備公司投入運作，專為新電腦係統安裝指令。
By 1986 it was saving the company an estimated $40million a year.
到了1986年，它每年能為這家公司節省約四千萬美元。
Ken Olsen, founder of Digital Equipment Corporation, was among the first business leaders to realise the commercial benefit of AI.
肯·奧森，迪吉多數碼設備公司的創始人，他是意識到AI商業價值的第一批商業領袖之一。
1990
Back to nature for 'bottom-up' inspiration
回歸到“由下至上”的靈感
Expert systems couldn't crack the problem of imitating biology.
專門係統並不能攻破生物模仿的問題。
Then AI scientist Rodney Brooks published a new paper: Elephants Don’t Play Chess.
人工智能科學家羅德尼·布洛克斯發表了一份新論文：《大象不下棋》。
Brooks was inspired by advances in neuroscience, which had started to explain the mysteries of human cognition.
那時的神經係統科學開始能夠解開人類意識的神秘麵紗，而這一進展讓布洛克斯大受啟發。
Vision, for example, needed different 'modules' in the brain to work together to recognize patterns, with no central control.
比如說，在沒有中央控製的情況下，視覺需要大腦中多個“模塊”協調作用來辨識圖案。
Brooks argued that the top-down approach of pre-programming a computer with the rules of intelligent behaviour was wrong.
布魯克斯稱，用智能行為準則來對電腦進行預編程這一從上至下的方法是錯誤的。
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，由下至上控製人工智能的方法得到複興，也讓過時已久的類神經網路科學再次回潮。
Rodney Brooks became director of the MIT Artfificial Intelligence Laboratory, a post once held by Marvin Minsky.
羅德尼·布魯克斯成為了麻省理工學院人工智能實驗室的主管，這一職位的前任中就有馬文·明斯基。
1997
Man vs machine: Fight of the 20th Century
人類對戰機器：20世紀的戰爭
Supporters of top-down AI still had their champions: supercomputers like Deep Blue, which in 1997 took on world chess champion Garry Kasparov.
從上至下理論的擁護者依舊拿著一個好牌：像深藍這樣的超級電腦。深藍在1997年打敗了世界象棋冠軍加裏·卡斯帕羅夫。
The IBM-built machine was, on paper, far superior to Kasparov - capable of evaluating up to 200 million positions a second.
根據書麵文件的說法，這台IBM製造的機器遠遠優於卡斯帕羅夫——它能在一秒鍾內評估將近20億個走法。
But could it think strategically? The answer was a resounding yes.
但是它有戰略性的思維嗎？答案是個大寫的“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.
這台超級電腦贏得了勝利，這場對弈被稱為“頭腦的背水一戰”。卡斯帕羅夫認為，一台電腦能擁有如此資質，必定是有人類在背後操縱。
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.
有些人將這一時刻讚為“人工智能的成熟”。而對於其他人來說，這僅僅顯示了我們在某一有著明確規則的、高度專業化的問題上付出了大量精力。
Deep Blue "thinks like God" according to Gary Kasparov.
據加裏·卡斯帕羅夫所說，深藍的“思維就像是上帝”。
2002
The first robot for the home
第一個家用機器人
Rodney Brook's spin-off company, iRobot, created the first commercially successful robot for the home – an autonomous vacuum cleaner called Roomba.
羅德尼·布魯克的衍生公司iRobot創造了第一個在商業上取得成功的家用機器人——一個叫魯姆巴的自動吸塵器。
Cleaning the carpet was a far cry from the early AI pioneers' ambitions.
清掃地毯與早期人工智能先驅者的野心相差甚遠。
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.
但魯姆巴已經是一個很大的成就了。它的行為生成係統層次與機器人Shakey的算法相比要簡單很多，與一個世紀以前格雷·瓦爾特創造的機器人更相像些。
Despite relatively simple sensors and minimal processing power, the device had enough intelligence to reliably and efficiently clean a home.
盡管它的感應器相對比較簡單，處理能力較小，但是它的智力足以讓它有效地清理房屋了。
Roomba ushered in a new era of autonomous robots, focused on specific tasks.
魯姆巴帶領人們走進了自動化機器人（專注於特定任務）的新時代。
The Roomba vacuum has cleaned up commercially – over 10 million units have been bought across the world.
魯姆巴吸塵器在商業上也取得了很大成功——在全球售出約一千萬台。
2005
War machines
戰爭機器
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.
冷戰期間，美國軍隊對人工智能的夢想泡湯了，而現在，他們又憑借這一新手段卷土重來。
They began to invest in autonomous robots. BigDog, made by Boston Dynamics, was one of the first.
他們開始投資於自動化機器人的研發。由波士頓動力公司製造的BigDog，便是第一批產物之一。
Built to serve as a robotic pack animal in terrain too rough for conventional vehicles, it has never actually seen active service.
它的製造意圖是用於在普通汽車難以行進的區域進行活動，不過並沒得到頻繁使用。
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也成了這一領域的主要參與者之一。他們的拆彈機器人PackBot結合人工操控，能夠完成嗅探爆炸物之類的智能任務。
Over 2000 PackBots have been deployed in Iraq and Afghanistan.
超過2000台PackBots機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的四肢裝有多個感應器，使其每條腿在惡劣地形上都能自主活動。
2008
Starting to crack the big problems
向大數據領域進軍
In November 2008, a small feature appeared on the new Apple iPhone – a Google app with speech recognition.
2008年11月，新蘋果手機上出現了一個小功能——帶有語音識別功能的穀歌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.
這看似簡單，卻預示著一個重大突破的到來。盡管語音識別是人工智能的關鍵目標之一，但是幾十年以來的投入從未將它的準確率提高到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.
穀歌在一項新手段上取得了領先：上千台強大的電腦同時運行類神經網路，學習在穀歌眾多用戶帶來的大型數據信息流中辨析模式。
At first it was still fairly inaccurate but, after years of learning and improvements, Google now claims it is 92% accurate.
一開始，它還不夠準確，但是通過幾年的學習和改善之後，穀歌稱自己已達到了92%的準確度。
According to Google, its speech recognition technology had an 8% word error rate as of 2015.
據穀歌的說法，到2015年為止，其語音識別技術的錯誤率僅為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.”
“人工智能將是穀歌的終極形態。它會完全理解你所想要的東西，並給予你相應的物件。”
——Google co-founder Larry Page, 2000
——穀歌的聯合創始人拉裏·佩吉，2000年。
2010
Dance bots
跳舞機器人
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.
與此同時，隨著大量主機改變了人工智能的實現方式，新科技的出現意味著更小的電腦也能完成大型任務。
These new computers enabled humanoid robots, like the NAO robot, which could do things predecessors like Shakey had found almost impossible.
這些新型電腦讓NAO機器人這樣的類人機器人能夠完成當初Shakey根本無法完成的事情。
NAO robots used lots of the technology pioneered over the previous decade, such as learning enabled by neural networks.
NAO機器人用到了許多領先過去十年的技術，比如通過類神經網路進行學習。
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年上海世界博覽會上，這些機器人的特別能力得到展示，20個機器人一同有序地跳舞長達八分鍾。
How close we are to enabling robots to learn with mathematician Marcus Du Sautoy？
距離讓機器人向數學家馬庫斯·杜·索托伊學習還有多遠？
2011
Man vs machine: Fight of the 21st Century
人類對戰機器：21世紀的戰爭
In 2011, IBM's Watson took on the human brain on US quiz show Jeopardy.
2011年，IBM的電腦係統Watson在美國智力問答節目Jeopardy上戰勝了人類選手。
This was a far greater challenge for the machine than chess.
這比起象棋那場比賽的挑戰性要大得多。
Watson had to answer riddles and complex questions.
Watson得回答謎語和複雜的問題。
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.
它的製造者使用了大量的人工智能技術，包括類神經網路，並訓練它三年多，讓它能夠在問答之間發現模式和規律。
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給了它的對手——兩位節目有史以來表現最佳的參賽者——狠狠一擊。這場勝利被大肆報道，並被稱為人工智能的凱旋。
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現在被用於醫療事業。它記憶大量數據，從中找出與病人病史相關的內容，並向醫生提供治療建議。
2014
Are machines intelligent now?
那麼，現在的機器智能了嗎？
Sixty-four years after Turing published his idea of a test that would prove machine intelligence, a chatbot called Eugene Goostman finally passed.
在圖靈推出機器智力測試的六十四年後，終於有機器人通過測試了——一個名叫Eugene Goostman的聊天機器人。
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.
但沒有幾個人工智能專家將之視為一個轉折點。Eugene Goostman被認為是經過了“與測試相關的培訓”，利用技巧蒙騙了評判。
It was other developments in 2014 that really showed how far AI had come in 70 years.
2014年出現的另一些發展才真正顯示了70年來人工智能的進步所在。
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.
從穀歌投資了十億美元的無人駕駛汽車到Skype的實時語音翻譯，智能機器現已逐漸成為現實日常的一部分，為我們的生活帶來巨大變化。
Across four states in America it is legal for driverless cars to take to the road.
無人駕駛汽車在美國的四個州都可以合法上路。
最後更新：2017-10-08 01:59:04