你的工作還有多久會被機器取代?

The world is widely considered to be on the cusp of a fourth industrial revolution – one where machines will be able to do many of the jobs currently performed by humans, and perhaps even do them better. It is a future that promises greater efficiency and cheaper services, but one that also could herald widespread job losses.

很多人認爲，世界即將迎來第四次工業革命——這一次，機器可以完成很多由人類負責的工作，甚至比人類做得更好。未來的世界可以實現更高的效率，享受更廉價的服務，但失業也將變得更加普遍。

It raises a troubling question for all of us – when will a machine be able to do my job?

這便引發了一個令人不安的問題——機器什麼時候能夠取代你的工作？

There are no certain answers, but some of the world's top artificial intelligence researchers are trying to find out.

目前還沒有確切答案，但一些全球頂尖的人工智能研究人員希望找到答案。

Katja Grace, a research associate at the University of Oxford's Future of Humanity Institute, and her colleagues from the AI Impacts project and the Machine Intelligence Research Institute, have surveyed 352 scientists and compiled their answers into predictions about how long it may take for machines to outperform humans on various tasks.

牛津大學人類未來研究院（Future of Humanity Institute）助理研究員卡特佳·格蕾絲（Katja Grace）與來自人工智能影響項目（AI Impacts）和機器智能研究院（Machine Intelligence Research Institute）的同事，對352名科學家展開了調查，用他們的答案來預測機器還要多久能在各種任務上超越人類。

Many of the world's leading experts on machine learning were among those they contacted, including Yann LeCun, director of AI research at Facebook, Mustafa Suleyman from Google's DeepMind and Zoubin Ghahramani, director of Uber's AI labs.

他們聯繫了很多全球頂尖的機器學習專家，其中包括Facebook人工智能研究總監嚴·勒坤（Yann LeCun）、谷歌DeepMind的穆斯塔法·蘇萊曼（Mustafa Suleyman）和Uber人工智能實驗室的左斌·加赫拉瑪尼（Zoubin Ghahramani）。

The good news is that many of us will probably be safe in our jobs for some time to come. The researchers predict there is a 50% chance that machines will be capable of taking over all human jobs in 120 years.

好消息是，很多人的工作在未來一段時間內可能都是安全的。研究人員預計，機器有50%的概率能在未來120年取代所有人的工作。

"One of the biggest surprises was the overall lateness of the predictions," says Grace. "I expected the amazing progress in machine learning in recent years,plus the fact that we were only talking to machine learning researchers, to make the estimates earlier."

“最令人意外的是，這些預測的時點都很晚，”格蕾絲說。“我原本預計，由於機器學習最近幾年進步神速，加上我們的調查對象都是機器學習研究人員，所以時點應該早一點。”

So what does this mean for the coming years and decades?

那麼，這對未來幾年、幾十年究竟意味着什麼？

In-creasing unemployment?

失業增加？

The survey suggest machines could also be folding laundry by 2021. So, if you work at a laundromat, is it time to throw in the towel? Perhaps not.

這項調查表明，到2021年，機器可以把洗好的衣服疊起來。所以，如果你在洗衣店工作，是不是就該投降了？恐怕不是。

Machines that can fold clothes do already exist: roboticists at the University of California, Berkeley, have already developed a robot that can neatly fold towels, jeans and t-shirts.

能疊衣服的機器已經存在：加州大學伯克利分校的機器人學家已經開發了一種能夠熟練疊好毛巾、牛仔褲和T恤衫的機器人。

Admittedly, it took the robot nearly 19 minutes to pick up, inspect and fold a single towel in 2010, but by 2012, it could fold a pair of jeans in five minutes and a t-shirt in a little over six minutes. Perhaps most excitingly, though, the robot can even take on the tedious task of pairing socks.

但必須承認，要讓機器人撿起、查看、疊好一件衣服，2010年大約要花19分鐘，但到2012年，只需要6分多鐘就能疊好一條牛仔褲和一件T恤衫。但最令人驚訝的或許在於，機器人可以完成襪子配對這種乏味的工作。

But despite this progress, it could be some time before robots like this are able to replace humans.

然而，儘管取得了這種進步，這樣的機器人想要真正取代人類仍然需要一段時間。

"I am a bit sceptical of some of the timelines given for tasks that involve physical manipulation," says Jeremy Wyatt, professor of robotics and artificial intelligence at the University of Birmingham.

“我對某些需要實際操作的任務被機器取代的時間表持懷疑態度。”伯明翰大學機器人和人工智能教授傑里米·懷亞特（Jeremy Wyatt）說。

"It is one thing doing it in the lab, and quite another having a robot that can do a job reliably in the real world better than a human."

“在實驗室裏是一回事，但要讓機器人在現實世界中比人類做得更好卻是另一回事。”

Manipulating physical objects in the real world – figuring out what to manipulate, and how, in a random, changing environment – is an incredibly complex job for a machine. Tasks that don't involve physical manipulation are easier to teach.

對機器來說，在現實世界中操縱物體是一個無比複雜的任務，需要搞清楚操作的對象，還要了解如何在一個隨機變化的環境中進行操作。不需要實際操作的任務反而更容易掌握。

Robot mobility – things like self-driving cars and autonomous deliveries – are probably at the stage the internet was in the early 1990s, Wyatt says. "Moving things around in the world is probably 10 years further behind that."

懷亞特認爲，機器人的移動性——包括無人駕駛汽車和自動化配送等——大概就像20世紀90年代初的互聯網。“四處移動東西可能還要再等10年。”

Your friendly robot assistant

機器人好助手

While towel folders are safe for now, perhaps there is reason for truck drivers and retailers to consider their roles over the coming two decades. The researchers predict that AI could be driving trucks by 2027 and doing retail jobs by 2031.

疊毛巾工人現在很安全，但卡車駕駛員和零售店員的確有理由在未來20年考慮自己的職業去向。研究人員預計，到2027年，人工智能便可駕駛卡車，到2031年可以勝任零售工作。

The stereotypical retail assistant job – a friendly human to help you find a pair of jeans in a shop, and tell you how they look -is a role that requires complex physical and communication skills, and is probably safe for the moment.

傳統的零售助手工作——幫你在店裏找到某條牛仔褲，並告訴你試穿效果的友好店員——需要掌握複雜的身體技能和溝通技巧。目前來看，這項工作可能是安全的。

But as more people shop online, AI in the form of bots and algorithms may be replacing other roles in retail far earlier than we might think, says Wyatt. "Look at how many transactions we now do online that are largely automated – it is a significant proportion. And they are already using a reasonable amount of AI."

但懷亞特表示，隨着越來越多的人在網上購物，以聊天機器人和算法形式存在的人工智能想要取代零售行業的其他職位，或許遠比我們想象得更加容易。“看看我們目前在網上進行的交易有多少是主要由自動化程序完成的——很大一部分都是。他們已經在使用一定數量的人工智能。”

Fear not, fellow humans

人類別害怕

Perhaps the hardest jobs for machines to perform are those that take years of training for humans to excel at. These often involve intuitive decision making, complex physical environments or abstract thinking – all things computers struggle with.

機器最難勝任的，或許是那些就連人類都需要通過多年的訓練才能熟練掌握的任務。這通常牽扯直覺決策、複雜的物理環境或者抽象思維——這些都是電腦難以勝任的。

The experts predict robots will not be taking over as surgeons until around 2053, while it could take 43 years before machines are competing with mathematicians for space in top academic journals.

專家預計，機器人大約要到2053年左右才能取代外科醫生，要在頂尖學術刊物上與數學家競爭可能要等待43年。

They also predict AI computers could be churning out New York Times bestselling novels by 2049.

他們還預計，到2049年，人工智能創作的小說就有可能登上《紐約時報》暢銷書單。

In reality, machines are already dipping their digital fingers into this field too.

事實上，機器已經開始染指這一領域。

Google has been training its AI on romantic novels and news articles in an attempt to help it write more creatively, and an AI bot called Benjamin can write short sci-fi film scripts – even if they don't entirely make sense. Then there is the work of Automated Insights, which has created algorithms that churn out millions of personalised news, finance and sports articles for companies like Reuters and the Associated Press.

谷歌一直在訓練該公司的人工智能程序創作愛情小說和新聞報道，希望它能更有創造力。而一個名叫本傑明（Benjamin）的人工智能機器人也可以撰寫短篇科幻電影劇本——即便有的內容完全說不通。此外還有Automated Insights的作品，他們開發的算法已經爲路透社和美聯社生成了數百萬條個性化新聞、理財和體育文章。

Adam Smith, chief operating officer at Automated Insights, says this technology is intended to complement, rather than replace, human expertise. "Automated journalism is creating content that would not have existed before, but humans still need to add context to those stories."

Automated Insights首席運營官亞當·史密斯（Adam Smith）表示，這項技術是爲了對人類的工作進行補充，而不是取代人類。“自動化新聞創作的是之前並不存在的內容，但人類仍然需要爲這些報道添加背景信息。”

These stories, however, are produced according to a formula, where information is pulled out of large data sets and plugged in to templates. Producing bestselling fiction – rich in word play and with compelling twists in narrative – is still probably three decades away. Attempts by to use machines to play with language in creative ways usually result in nonsense.

這些報道都是根據既定模式製作的，從龐大的數據集中提取信息之後，再添加到模板裏。而要創作文字優美、情節誘人的暢銷小說，仍然要等到30年後。想讓機器進行語言創作，最終結果往往只是東施效顰。

"The challenge will be getting AI to produce material that is acceptable to our human tastes," says Wyatt. He says "We find anything that is even slightly below human-level performance to be unacceptable. Take chatbots – they are not that far from human level performance... but we are so sensitive to any imperfections that they often seem laughably bad."

懷亞特表示，當前的挑戰是讓人工智能製作出能夠被人類接受的材料。他說：“只要略低於人類的水平，就無法被我們接受。以聊天機器人爲例——它們與人類的表現相差不遠……但我們對它們的任何缺陷所包含的可笑錯誤非常敏感。”

Grace believes the survey should serve as a reminder that the world is on the cusp of radical change: "I don't think there are any tasks humans can do that AI will be technically unable to carry out."

格蕾絲認爲，這項調查可以提醒人們，整個世界將迎來一場鉅變：“從技術上講，我不認爲有什麼任務是人類能做到，人工智能卻做不到的。”

But she believes some roles may never be replaced by machines. A minister in a church, for example, might never be replaced by a robot if the churchgoers want a person to be in the role.

但她認爲，某些職位可能永遠不會被機器取代。例如，只要去做禮拜的人希望牧師由人來擔任，這項工作或許就永遠不會被機器人取代。

"There will still be tasks that can only be conducted by a human because we will care that they are," she says.

“仍然有一些工作只能由人來負責，因爲我們在乎他們的身份，”她說。