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.

　　很多人认为，世界即将迎来第四次工业革命——这一次，机器可以完成很多由人类负责的工作，甚至比人类做得更好。未来的世界可以实现更高的效率，享受更廉价的服务，但失业也将变得更加普遍。

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

　　这便引发了一个令人不安的问题——机器什么时候能够取代你的工作?

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

　　目前还没有确切答案，但一些全球顶 尖的人工智能研究人员希望找到答案。

　　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.

　　牛津大学人类未来研究院(Future of Humanity Institute)助理研究员卡特佳·格蕾丝(Katja Grace)与来自人工智能影响项目(AI Impacts)和机器智能研究院(Machine Intelligence Research Institute)的同事，对352名科学家展开了调查，用他们的答案来预测机器还要多久能在各种任务上超越人类。

　　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.

　　他们联系了很多全球顶 尖的机器学习专家，其中包括Facebook人工智能研究总监严·勒坤(Yann LeCun)、谷歌DeepMind的穆斯塔法·苏莱曼(Mustafa Suleyman)和Uber人工智能实验室的左斌·加赫拉玛尼(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，很多人的工作在未来一段时间内可能都是安全的。研究人员预计，机器有50%的概率能在未来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，这些预测的时点都很晚，”格蕾丝说。“我原本预计，由于机器学习最近几年进步神速，加上我们的调查对象都是机器学习研究人员，所以时点应该早一点。”

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

　　那么，这对未来几年、几十年究竟意味着什么?

　　In-creasing unemployment?

　　失业增加?

　　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.

　　这项调查表明，到2021年，机器可以把洗好的衣服叠起来。所以，如果你在洗衣店工作，是不是就该投降了?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.

　　能叠衣服的机器已经存在：加州大学伯克利分校的机器人学家已经开发了一种能够熟练叠好毛巾、牛仔裤和T恤衫的机器人。

　　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.

　　但必须承认，要让机器人捡起、查看、叠好一件衣服，2010年大约要花19分钟，但到2012年，只需要6分多钟就能叠好一条牛仔裤和一件T恤衫。但最令人惊讶的或许在于，机器人可以完成袜子配对这种乏味的工作。

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

　　然而，尽管取得了这种进步，这样的机器人想要真正取代人类仍然需要一段时间。

　　"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.

　　“我对某些需要实际操作的任务被机器取代的时间表持怀疑态度。”伯明翰大学机器人和人工智能教授杰里米·怀亚特(Jeremy Wyatt)说。

　　"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."

　　“在实验室里是一回事，但要让机器人在现实世界中比人类做得更好却是另一回事。”

　　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.

　　对机器来说，在现实世界中操纵物体是一个无比复杂的任务，需要搞清楚操作的对象，还要了解如何在一个随机变化的环境中进行操作。不需要实际操作的任务反而更容易掌握。

　　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."

　　怀亚特认为，机器人的移动性——包括无人驾驶汽车和自动化配送等——大概就像20世纪90年代初的互联网。“四处移动东西可能还要再等10年。”

　　Your friendly robot assistant

　　机器人好助手

　　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.

　　叠毛巾工人现在很安全，但卡车驾驶员和零售店员的确有理由在未来20年考虑自己的职业去向。研究人员预计，到2027年，人工智能便可驾驶卡车，到2031年可以胜任零售工作。

　　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.

　　传统的零售助手工作——帮你在店里找到某条牛仔裤，并告诉你试穿效果的友好店员——需要掌握复杂的身体技能和沟通技巧。目前来看，这项工作可能是安全的。

　　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."

　　但怀亚特表示，随着越来越多的人在网上购物，以聊天机器人和算法形式存在的人工智能想要取代零售行业的其他职位，或许远比我们想象得更加容易。“看看我们目前在网上进行的交易有多少是主要由自动化程序完成的——很大一部分都是。他们已经在使用一定数量的人工智能。”

　　Fear not, fellow humans

　　人类别害怕

　　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.

　　机器最难胜任的，或许是那些就连人类都需要通过多年的训练才能熟练掌握的任务。这通常牵扯直觉决策、复杂的物理环境或者抽象思维——这些都是电脑难以胜任的。

　　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.

　　专家预计，机器人大约要到2053年左右才能取代外科医生，要在顶 尖学术刊物上与数学家竞争可能要等待43年。

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

　　他们还预计，到2049年，人工智能创作的小说就有可能登上《纽约时报》畅销书单。

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

　　事实上，机器已经开始染指这一领域。

　　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.

　　谷歌一直在训练该公司的人工智能程序创作爱情小说和新闻报道，希望它能更有创造力。而一个名叫本杰明(Benjamin)的人工智能机器人也可以撰写短篇科幻电影剧本——即便有的内容完全说不通。此外还有Automated Insights的作品，他们开发的算法已经为路透社和美联社生成了数百万条个性化新闻、理财和体育文章。

　　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 席运营官亚当·史密斯(Adam Smith)表示，这项技术是为了对人类的工作进行补充，而不是取代人类。“自动化新闻创作的是之前并不存在的内容，但人类仍然需要为这些报道添加背景信息。”

　　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.

　　这些报道都是根据既定模式制作的，从庞大的数据集中提取信息之后，再添加到模板里。而要创作文字优美、情节诱人的畅销小说，仍然要等到30年后。想让机器进行语言创作，最终结果往往只是东施效颦。

　　"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."

　　怀亚特表示，当前的挑战是让人工智能制作出能够被人类接受的材料。他说：“只要略低于人类的水平，就无法被我们接受。以聊天机器人为例——它们与人类的表现相差不远……但我们对它们的任何缺陷所包含的可笑错误非常敏感。”

　　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."

　　格蕾丝认为，这项调查可以提醒人们，整个世界将迎来一场巨变：“从技术上讲，我不认为有什么任务是人类能做到，人工智能却做不到的。”

　　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.

　　但她认为，某些职位可能永远不会被机器取代。例如，只要去做礼拜的人希望牧师由人来担任，这项工作或许就永远不会被机器人取代。

　　"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来负责，因为我们在乎他们的身份，”她说。

　　口译： 翻译资格考试二级口译模拟题

　　笔译： 翻译资格考试二级笔译模拟题

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