Automation anxiety has been spreading lately, 自动化焦虑近来一直在蔓延，
a fear that in the future, 担心未来，
many jobs will be performed by machines 许多工作将由机器执行
rather than human beings, 而不是人类，
given the remarkable advances that are unfolding 鉴于正在开展的显着进步
in artificial intelligence and robotics. 在人工智能和机器人领域。
What's clear is that there will be significant change. 显而易见的是会有重大变化。
What's less clear is what that change will look like. 不太清楚的是这种改变会是什么样子。
My research suggests that the future is both troubling and exciting. 我的研究表明，未来既令人困扰又令人兴奋。
The threat of technological unemployment is real, 技术失业的威胁是真实的，
and yet it's a good problem to have. 但它是一个很好的问题。
And to explain how I came to that conclusion, 为了解释我如何得出这个结论，
I want to confront three myths 我想面对三个神话
that I think are currently obscuring our vision of this automated future. 我认为目前正在模糊我们对这个自动化未来的看法。
A picture that we see on our television screens, 我们在电视屏幕上看到的一张照片，
in books, in films, in everyday commentary 在书本，电影，日常评论中
is one where an army of robots descends on the workplace 是机器人大军在工作场所下降的地方
with one goal in mind: 记住一个目标：
to displace human beings from their work. 将人类从工作中取代。
And I call this the Terminator myth. 我称之为终结者的神话。
Yes, machines displace human beings from particular tasks, 是的，机器使人类摆脱特定的任务，
but they don't just substitute for human beings. 但它们不只是替代人类。
They also complement them in other tasks, 他们还在其他任务中补充他们，
making that work more valuable and more important. 使这项工作更有价值，更重要。
Sometimes they complement human beings directly, 有时他们直接补充人类，
making them more productive or more efficient at a particular task. 使他们在特定的任务中更具生产力或更高效。
So a taxi driver can use a satnav system to navigate on unfamiliar roads. 所以出租车司机可以使用卫星导航系统在不熟悉的道路上行驶。
An architect can use computer-assisted design software 建筑师可以使用计算机辅助设计软件
to design bigger, more complicated buildings. 设计更大，更复杂的建筑物。
But technological progress doesn't just complement human beings directly. 但是技术进步并不仅仅是直接补充人类。
It also complements them indirectly, and it does this in two ways. 它也间接地补充了它们，它以两种方式做到了这一点。
The first is if we think of the economy as a pie, 首先，如果我们把经济视为一个馅饼，
technological progress makes the pie bigger. 技术进步使馅饼变大。
As productivity increases, incomes rise and demand grows. 随着生产力的提高，收入增加，需求增长。
The British pie, for instance, 例如，英国派，
is more than a hundred times the size it was 300 years ago. 是300年前的一百倍。
And so people displaced from tasks in the old pie 所以人们从旧派的任务中流离失所
could find tasks to do in the new pie instead. 可以在新的饼图中找到要做的任务。
But technological progress doesn't just make the pie bigger. 但技术进步并不仅仅是让这个馅饼变大。
It also changes the ingredients in the pie. 它也改变了派的成分。
As time passes, people spend their income in different ways, 随着时间的流逝，人们以不同的方式来度过收入，
changing how they spread it across existing goods, 改变它们在现有商品中的传播方式，
and developing tastes for entirely new goods, too. 并为全新的商品开发品味。
New industries are created, 新的行业被创造出来，
new tasks have to be done 新任务必须完成
and that means often new roles have to be filled. 这意味着经常需要填补新角色。
So again, the British pie: 英国派又是如此：
300 years ago, most people worked on farms, 300年前，大多数人在农场工作，
150 years ago, in factories, 150年前，在工厂里，
and today, most people work in offices. 而今天，大多数人都在办公室工作。
And once again, people displaced from tasks in the old bit of pie 再一次，人们从旧时的任务中流离失所
could tumble into tasks in the new bit of pie instead. 反而可能会在新的馅饼中陷入任务。
Economists call these effects complementarities, 经济学家称这些互补效应，
but really that's just a fancy word to capture the different way 但真的这只是一个花哨的词汇来捕捉不同的方式
that technological progress helps human beings. 技术进步有助于人类。
Resolving this Terminator myth 解决这个终结者的神话
shows us that there are two forces at play: 向我们表明，有两种力量在起作用：
one, machine substitution that harms workers, 一，机器替代危害工人，
but also these complementarities that do the opposite. 而且相反的这些互补性。
Now the second myth, 现在第二个神话，
what I call the intelligence myth. 我称之为情报神话。
What do the tasks of driving a car, making a medical diagnosis 驾驶汽车，做出医疗诊断的任务是什么？
and identifying a bird at a fleeting glimpse have in common? 并在短暂的一瞥中识别一只鸟有共同点？
Well, these are all tasks that until very recently, 那么，这些都是直到最近才完成的任务，
leading economists thought couldn't readily be automated. 领先的经济学家认为不容易实现自动化。
And yet today, all of these tasks can be automated. 而今天，所有这些任务都可以实现自动化。
You know, all major car manufacturers have driverless car programs. 你知道，所有主要的汽车制造商都有无人驾驶的汽车计划。
There's countless systems out there that can diagnose medical problems. 有无数的系统可以诊断医疗问题。
And there's even an app that can identify a bird 甚至有一款应用可以识别一只鸟
at a fleeting glimpse. 在短暂的一瞥。
Now, this wasn't simply a case of bad luck on the part of economists. 现在，这不仅仅是经济学家运气不好的一个例子。
They were wrong, 他们错了，
and the reason why they were wrong is very important. 他们错误的原因非常重要。
They've fallen for the intelligence myth, 他们因智慧神话而陷入困境，
the belief that machines have to copy the way 相信机器必须复制的方式
that human beings think and reason 人类的思考和理性
in order to outperform them. 以超越他们。
When these economists were trying to figure out 这些经济学家试图弄清楚
what tasks machines could not do, 机器无法完成哪些任务，
they imagined the only way to automate a task 他们设想自动执行任务的唯一方法
was to sit down with a human being, 要和一个人坐在一起，
get them to explain to you how it was they performed a task, 让他们向你解释他们是如何执行任务的，
and then try and capture that explanation 然后尝试捕捉那个解释
in a set of instructions for a machine to follow. 在一组机器中遵循的说明。
This view was popular in artificial intelligence at one point, too. 这一观点在人工智能方面也很受欢迎。
I know this because Richard Susskind, 我知道这是因为理查德苏斯金德，
who is my dad and my coauthor, 谁是我的父亲和我的合着者，
wrote his doctorate in the 1980s on artificial intelligence and the law 在20世纪80年代他就博士学位撰写了关于人工智能和法律的博士论文
at Oxford University, 在牛津大学，
and he was part of the vanguard. 他是前锋的一员。
And with a professor called Phillip Capper 还有一位名叫Phillip Capper的教授
and a legal publisher called Butterworths, 还有一个名为Butterworths的合法出版商，
they produced the world's first commercially available 他们生产出世界上第一个商用
artificial intelligence system in the law. 法律中的人工智能系统。
This was the home screen design. 这是主屏幕设计。
He assures me this was a cool screen design at the time. 他向我保证当时这是一个很酷的屏幕设计。
(Laughter) （笑声）
I've never been entirely convinced. 我从来没有完全相信。
He published it in the form of two floppy disks, 他以两张软盘的形式发表了它，
at a time where floppy disks genuinely were floppy, 在软盘真正软盘的时候，
and his approach was the same as the economists': 他的方法和经济学家一样：
sit down with a lawyer, 坐下来与律师，
get her to explain to you how it was she solved a legal problem, 让她向你解释她是如何解决法律问题的，
and then try and capture that explanation in a set of rules for a machine to follow. 然后尝试捕捉一系列机器遵循的规则中的解释。
In economics, if human beings could explain themselves in this way, 在经济学中，如果人类可以这样解释自己，
the tasks are called routine, and they could be automated. 这些任务被称为例程，并且可以自动化。
But if human beings can't explain themselves, 但是，如果人类无法解释自己，
the tasks are called non-routine, and they're thought to be out reach. 这些任务被称为非常规任务，并且他们被认为是超出范围。
Today, that routine-nonroutine distinction is widespread. 今天，这种例行非常规的区分是非常普遍的。
Think how often you hear people say to you 想想你多久听到别人对你说的话
machines can only perform tasks that are predictable or repetitive, 机器只能执行可预测或重复的任务，
rules-based or well-defined. 基于规则或明确的。
Those are all just different words for routine. 这些都是例行公事的单词。
And go back to those three cases that I mentioned at the start. 回到我在开始时提到的三个案例。
Those are all classic cases of nonroutine tasks. 这些都是非常规任务的典型案例。
Ask a doctor, for instance, how she makes a medical diagnosis, 例如，询问一位医生，她是如何进行医学诊断的，
and she might be able to give you a few rules of thumb, 她可能会给你一些经验法则，
but ultimately she'd struggle. 但最终她会奋斗。
She'd say it requires things like creativity and judgment and intuition. 她会说它需要创造力，判断力和直觉等东西。
And these things are very difficult to articulate, 这些东西非常难以表达，
and so it was thought these tasks would be very hard to automate. 所以有人认为这些任务很难自动化。
If a human being can't explain themselves, 如果一个人不能自己解释，
where on earth do we begin in writing a set of instructions 我们从哪里开始编写一套指令
for a machine to follow? 一台机器要遵循吗？
Thirty years ago, this view was right, 三十年前，这种观点是对的，
but today it's looking shaky, 但今天它看起来不稳定，
and in the future it's simply going to be wrong. 并且在将来它只会是错误的。
Advances in processing power, in data storage capability 处理能力在数据存储能力方面取得进展
and in algorithm design 并在算法设计中
mean that this routine-nonroutine distinction 意味着这种例行非常规的区分
is diminishingly useful. 是有用的。
To see this, go back to the case of making a medical diagnosis. 要看到这一点，请回到进行医疗诊断的情况。
Earlier in the year, 今年早些时候，
a team of researchers at Stanford announced they'd developed a system 斯坦福大学的一个研究小组宣布他们开发了一套系统
which can tell you whether or not a freckle is cancerous 它可以告诉你祛斑是不是癌变
as accurately as leading dermatologists. 与领先的皮肤科医生一样准确。
How does it work? 它是如何工作的？
It's not trying to copy the judgment or the intuition of a doctor. 这并不是要模仿医生的判断或直觉。
It knows or understands nothing about medicine at all. 它完全不知道或不了解药物。
Instead, it's running a pattern recognition algorithm 相反，它运行的是模式识别算法
through 129,450 past cases, 通过129,450起过去的案件，
hunting for similarities between those cases 寻找这些案件之间的相似之处
and the particular lesion in question. 和有问题的特定病变。
It's performing these tasks in an unhuman way, 它以不人道的方式执行这些任务，
based on the analysis of more possible cases 基于对更多可能案例的分析
than any doctor could hope to review in their lifetime. 比任何医生都希望在他们有生之年能够复习。
It didn't matter that that human being, 这个人没有关系，
that doctor, couldn't explain how she'd performed the task. 该医生无法解释她是如何完成这项任务的。
Now, there are those who dwell upon that the fact 现在，有些人详细说明了这一事实
that these machines aren't built in our image. 这些机器不在我们的图像中。
As an example, take IBM's Watson, 作为一个例子，以IBM的沃森，
the supercomputer that went on the US quiz show "Jeopardy!" in 2011, 美国智力竞赛节目中的超级计算机“危险！”在2011年，
and it beat the two human champions at "Jeopardy!" 它在“危险之中”击败了两位人类冠军！
The day after it won, 获胜后的第二天，
The Wall Street Journal ran a piece by the philosopher John Searle “华尔街日报”刊登了哲学家约翰·塞尔的一篇文章
with the title "Watson Doesn't Know It Won on 'Jeopardy!'" 标题为“沃森不知道它赢得'危险'！”
Right, and it's brilliant, and it's true. 好吧，这很棒，而且这是真的。
You know, Watson didn't let out a cry of excitement. 你知道，沃森没有发出激动的呼声。
It didn't call up its parents to say what a good job it had done. 它并没有要求其父母说出它做了什么好的工作。
It didn't go down to the pub for a drink. 它没有去酒吧喝酒。
This system wasn't trying to copy the way that those human contestants played, 这个系统并没有试图模仿那些人类参赛者的方式，
but it didn't matter. 但没关系。
It still outperformed them. 它仍然跑赢他们。
Resolving the intelligence myth 解决智力神话
shows us that our limited understanding about human intelligence, 向我们表明，我们对人类智力的理解有限，
about how we think and reason, 关于我们如何思考和理智，
is far less of a constraint on automation than it was in the past. 对自动化的限制远不如以往。
What's more, as we've seen, 更重要的是，正如我们所见，
when these machines perform tasks differently to human beings, 当这些机器对人类执行不同的任务时，
there's no reason to think 没有理由去思考
that what human beings are currently capable of doing 人类目前有能力做什么
represents any sort of summit 代表任何形式的峰会
in what these machines might be capable of doing in the future. 这些机器将来可能会做什么。
Now the third myth, 现在第三个神话，
what I call the superiority myth. 我称之为优越感的神话。
It's often said that those who forget 人们常说那些忘记的人
about the helpful side of technological progress, 关于技术进步的有益方面，
those complementarities from before, 那些来自以前的互补性，
are committing something known as the lump of labor fallacy. 正在犯下所谓的劳工谬误。
Now, the problem is the lump of labor fallacy 现在，问题是劳动力谬误
is itself a fallacy, 本身就是一个谬论，
and I call this the lump of labor fallacy fallacy, 我把这称为劳动谬误的谬误，
or LOLFF, for short. 或简称LOLFF。
Let me explain. 让我解释。
The lump of labor fallacy is a very old idea. 劳动力谬误是一个非常古老的想法。
It was a British economist, David Schloss, who gave it this name in 1892. 这是一位英国经济学家大卫·施洛斯（David Schloss），他在1892年给出了这个名字。
He was puzzled to come across a dock worker 他不明白遇到码头工人
who had begun to use a machine to make washers, 谁开始使用一台机器来制造洗衣机，
the small metal discs that fasten on the end of screws. 紧固在螺钉末端的小金属盘。
And this dock worker felt guilty for being more productive. 这位码头工人对提高生产力感到内疚。
Now, most of the time, we expect the opposite, 现在，大多数时候，我们预期的是相反的，
that people feel guilty for being unproductive, 人们因为没有生产力而感到内疚，
you know, a little too much time on Facebook or Twitter at work. 你知道，在工作中在Facebook或Twitter上花费太多时间。
But this worker felt guilty for being more productive, 但这名工人对提高生产力感到内疚，
and asked why, he said, "I know I'm doing wrong. 问他为什么，他说，“我知道我做错了。
I'm taking away the work of another man." 我正在拿走另一个男人的工作。“
In his mind, there was some fixed lump of work 在他看来，有一些固定的工作
to be divided up between him and his pals, 在他和他的朋友之间被分开，
so that if he used this machine to do more, 所以如果他用这台机器做更多，
there'd be less left for his pals to do. 他的好朋友可以做更少的事情。
Schloss saw the mistake. Schloss看到了这个错误。
The lump of work wasn't fixed. 工作量并不固定。
As this worker used the machine and became more productive, 当这名工人使用机器并提高了生产力时，
the price of washers would fall, demand for washers would rise, 洗衣机的价格会下降，洗衣机的需求会增加，
more washers would have to be made, 更多的洗衣机将不得不被制造，
and there'd be more work for his pals to do. 他的朋友们会做更多的工作。
The lump of work would get bigger. 工作量会变大。
Schloss called this "the lump of labor fallacy." 施洛斯称之为“劳动谬误的一团”。
And today you hear people talk about the lump of labor fallacy 今天，你听到人们谈论劳动力谬误
to think about the future of all types of work. 思考各种工作的未来。
There's no fixed lump of work out there to be divided up 没有固定的工作要分开
between people and machines. 人与机器之间。
Yes, machines substitute for human beings, making the original lump of work smaller, 是的，机器替代了人类，使得原来的工作量变小了，
but they also complement human beings, 但他们也是对人类的补充，
and the lump of work gets bigger and changes. 而且工作量越来越大，变化越来越大。
But LOLFF. 但是LOLFF。
Here's the mistake: 这是错误的：
it's right to think that technological progress 认为技术进步是正确的
makes the lump of work to be done bigger. 使工作做得更大。
Some tasks become more valuable. New tasks have to be done. 有些任务变得更有价值。新任务必须完成。
But it's wrong to think that necessarily, 但认为这是错误的，
human beings will be best placed to perform those tasks. 人类将最适合执行这些任务。
And this is the superiority myth. 这就是优越感的神话。
Yes, the lump of work might get bigger and change, 是的，这些工作可能会变得更大和变化，
but as machines become more capable, 但随着机器变得更有能力，
it's likely that they'll take on the extra lump of work themselves. 他们很可能会自己承担额外的工作。
Technological progress, rather than complement human beings, 技术进步，而不是补充人类，
complements machines instead. 而是补充机器。
To see this, go back to the task of driving a car. 要看到这一点，请回到驾驶汽车的任务。
Today, satnav systems directly complement human beings. 今天，卫星导航系统直接补充人类。
They make some human beings better drivers. 他们让一些人成为更好的司机。
But in the future, 但在未来，
software is going to displace human beings from the driving seat, 软件将从驾驶座位上取代人，
and these satnav systems, rather than complement human beings, 而这些卫星系统，而不是补充人类，
will simply make these driverless cars more efficient, 只会让这些无人驾驶汽车更加高效，
helping the machines instead. 改为帮助机器。
Or go to those indirect complementarities that I mentioned as well. 或者转到我提到的那些间接的互补性。
The economic pie may get larger, 经济派可能会变得更大，
but as machines become more capable, 但随着机器变得更有能力，
it's possible that any new demand will fall on goods that machines, 任何新的需求都有可能落在机器，
rather than human beings, are best placed to produce. 而不是人类，最适合生产。
The economic pie may change, 经济派可能会改变，
but as machines become more capable, 但随着机器变得更有能力，
it's possible that they'll be best placed to do the new tasks that have to be done. 有可能他们将最适合完成必须完成的新任务。
In short, demand for tasks isn't demand for human labor. 总之，对任务的需求不是对人力劳动的需求。
Human beings only stand to benefit 人类只会受益
if they retain the upper hand in all these complemented tasks, 如果他们在所有这些补充任务中保持优势，
but as machines become more capable, that becomes less likely. 但随着机器变得更有能力，这变得不太可能。
So what do these three myths tell us then? 那么这三个神话告诉我们什么呢？
Well, resolving the Terminator myth 那么，解决终结者的神话
shows us that the future of work depends upon this balance between two forces: 向我们表明，未来的工作取决于两种力量之间的这种平衡：
one, machine substitution that harms workers 一，机器替代危害工人
but also those complementarities that do the opposite. 还有那些相反的互补性。
And until now, this balance has fallen in favor of human beings. 直到现在，这种平衡已经下降到了人类。
But resolving the intelligence myth 但解决智力神话
shows us that that first force, machine substitution, 向我们表明，第一种力量，机器替代，
is gathering strength. 正在聚集力量。
Machines, of course, can't do everything, 机器当然不能做任何事情，
but they can do far more, 但他们可以做得更多，
encroaching ever deeper into the realm of tasks performed by human beings. 越来越深入地侵入人类所执行的任务领域。
What's more, there's no reason to think 更重要的是，没有理由去思考
that what human beings are currently capable of 人类目前能够做什么
represents any sort of finishing line, 代表任何形式的终点线，
that machines are going to draw to a polite stop 那些机器将会被吸引到一个礼貌的站点
once they're as capable as us. 一旦他们像我们一样有能力。
Now, none of this matters 现在，这些都不重要
so long as those helpful winds of complementarity 只要那些有益的互补性的风
blow firmly enough, 吹得够紧，
but resolving the superiority myth 但解决优越性的神话
shows us that that process of task encroachment 向我们表明，任务侵占的过程
not only strengthens the force of machine substitution, 不仅增强了机器替代的力量，
but it wears down those helpful complementarities too. 但它也破坏了这些有益的互补性。
Bring these three myths together 把这三个神话结合在一起
and I think we can capture a glimpse of that troubling future. 我想我们可以看到这个令人不安的未来。
Machines continue to become more capable, 机器继续变得更有能力，
encroaching ever deeper on tasks performed by human beings, 越来越多地侵入人类所执行的任务，
strengthening the force of machine substitution, 加强机器替代的力量，
weakening the force of machine complementarity. 削弱机器互补的力量。
And at some point, that balance falls in favor of machines 在某种程度上，这种平衡有利于机器
rather than human beings. 而不是人类。
This is the path we're currently on. 这是我们目前的路径。
I say "path" deliberately, because I don't think we're there yet, 我故意说“路径”，因为我认为我们还没到，
but it is hard to avoid the conclusion that this is our direction of travel. 但很难避免这个结论，即这是我们的旅行方向。
That's the troubling part. 这是令人不安的部分。
Let me say now why I think actually this is a good problem to have. 现在让我说，为什么我认为这是一个很好的问题。
For most of human history, one economic problem has dominated: 对于大多数人类历史来说，一个经济问题主导着：
how to make the economic pie large enough for everyone to live on. 如何让经济派大到足以让每个人都能活下去。
Go back to the turn of the first century AD, 回到公元一世纪之交，
and if you took the global economic pie 如果你拿走了全球经济派
and divided it up into equal slices for everyone in the world, 并将其分成世界上每个人的平等片段，
everyone would get a few hundred dollars. 每个人都会得到几百美元。
Almost everyone lived on or around the poverty line. 几乎每个人都生活在贫困线上或附近。
And if you roll forward a thousand years, 如果你向前推进一千年，
roughly the same is true. 大致相同也是如此。
But in the last few hundred years, economic growth has taken off. 但在过去的几百年里，经济增长已经起飞。
Those economic pies have exploded in size. 这些经济派的规模已经爆炸。
Global GDP per head, 全球人均GDP，
the value of those individual slices of the pie today, 今天这些馅饼的单个切片的价值，
they're about 10,150 dollars. 他们大约是10,150美元。
If economic growth continues at two percent, 如果经济增长率保持在2％
our children will be twice as rich as us. 我们的孩子将会像我们一样富有。
If it continues at a more measly one percent, 如果它继续下降一个百分点，
our grandchildren will be twice as rich as us. 我们的孙辈将是我们的两倍。
By and large, we've solved that traditional economic problem. 总的来说，我们解决了这个传统的经济问题。
Now, technological unemployment, if it does happen, 现在，技术失业如果真的发生了，
in a strange way will be a symptom of that success, 以一种奇怪的方式将是成功的症状，
will have solved one problem -- how to make the pie bigger -- 将解决一个问题 - 如何使馅饼变大 -
but replaced it with another -- 但换成另一个 -
how to make sure that everyone gets a slice. 如何确保每个人都能分得一杯羹。
As other economists have noted, solving this problem won't be easy. 正如其他经济学家指出的那样，解决这个问题并不容易。
Today, for most people, 今天，对于大多数人来说，
their job is their seat at the economic dinner table, 他们的工作是他们在经济餐桌上的座位，
and in a world with less work or even without work, 在一个工作较少甚至没有工作的世界里，
it won't be clear how they get their slice. 他们如何得到他们的切片并不清楚。
There's a great deal of discussion, for instance, 例如，有很多讨论，
about various forms of universal basic income 关于各种形式的普遍基本收入
as one possible approach, 作为一种可能的方法，
and there's trials underway 而且正在进行试验
in the United States and in Finland and in Kenya. 在美国，芬兰和肯尼亚。
And this is the collective challenge that's right in front of us, 这就是我们面前的集体挑战，
to figure out how this material prosperity generated by our economic system 弄清楚我们的经济体系如何产生这种物质繁荣
can be enjoyed by everyone 可以被大家享用
in a world in which our traditional mechanism 在一个我们传统机制的世界里
for slicing up the pie, 为了馅饼的切片，
the work that people do, 人们所做的工作，
withers away and perhaps disappears. 消失，也许消失。
Solving this problem is going to require us to think in very different ways. 解决这个问题需要我们以非常不同的方式进行思考。
There's going to be a lot of disagreement about what ought to be done, 对于应该做什么会有很多不同意见，
but it's important to remember that this is a far better problem to have 但要记住这是一个更好的问题
than the one that haunted our ancestors for centuries: 比几个世纪以来困扰我们祖先的人还要多：
how to make that pie big enough in the first place. 如何使馅饼足够大。
Thank you very much. 非常感谢你。
(Applause) （掌声）