3 myths about the future of work (and why they're not true) | Daniel Susskind

翻译人员: YIRAN WANG 校对人员: Yolanda Zhang

Automation anxiety has been spreading lately,

如今 关于自动化的焦虑广泛传播

a fear that in the future,

人们开始担心

many jobs will be performed by machines

鉴于人工智能和机器人领域

rather than human beings,

不断取得的惊人发展

given the remarkable advances that are unfolding

在未来 许多工作将由机器完成

in artificial intelligence and robotics.

而不是人类自己

What's clear is that there will be significant change.

可以明确的是 未来将会出现重大改变

What's less clear is what that change will look like.

但尚未明确的是 究竟会出现何种改变

My research suggests that the future is both troubling and exciting.

通过研究 我认为 未来 既令人困扰又令人激动

The threat of technological unemployment is real,

技术性失业的威胁是真实存在的

and yet it's a good problem to have.

但它是一个好问题

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

我会反驳三个迷思

that I think are currently obscuring our vision of this automated future.

它们混淆了我们的视线 使我们无法看清自动化的未来

A picture that we see on our television screens,

不管是在电视 书籍 还是实况报道中

in books, in films, in everyday commentary

我们经常可以看到一个场景

is one where an army of robots descends on the workplace

大量机器人走向工作场所

with one goal in mind:

它们只有一个目的

to displace human beings from their work.

就是替代人类工作

And I call this the Terminator myth.

我将这称为终结者迷思

Yes, machines displace human beings from particular tasks,

机器的确会代替人类 完成特定的一些任务

but they don't just substitute for human beings.

但是他们不只是替代人类

They also complement them in other tasks,

也会在其他工作上辅助人类

making that work more valuable and more important.

使工作更有价值 更重要

Sometimes they complement human beings directly,

有时他们会直接辅助人类

making them more productive or more efficient at a particular task.

让人们更高效地 完成某项特定的任务

So a taxi driver can use a satnav system to navigate on unfamiliar roads.

比如出租车司机使用卫星定位系统 导航到不熟悉的区域

An architect can use computer-assisted design software

建筑师可以使用电脑上的设计软件

to design bigger, more complicated buildings.

来帮助自己设计 更宏大更复杂的建筑

But technological progress doesn't just complement human beings directly.

但是技术进步不仅仅 直接帮助人类

也通过其他两种方式 间接地与人类互补

It also complements them indirectly, and it does this in two ways.

The first is if we think of the economy as a pie,

首先 如果我们把经济 想象成一个蛋糕

technological progress makes the pie bigger.

技术进步会使蛋糕变得更大

As productivity increases, incomes rise and demand grows.

随着生产力提高 收入和需求都会增加

The British pie, for instance,

以英国的经济蛋糕为例

is more than a hundred times the size it was 300 years ago.

现在这个蛋糕的尺寸 是300年前的100多倍

And so people displaced from tasks in the old pie

因此在旧经济中失去工作的人们

could find tasks to do in the new pie instead.

可以在新经济中找到工作

But technological progress doesn't just make the pie bigger.

但是技术进步不仅仅 让蛋糕变得更大

It also changes the ingredients in the pie.

它也改变了蛋糕的原料

As time passes, people spend their income in different ways,

随着时间推移 人们消费的方式变得不同

changing how they spread it across existing goods,

改变了收入在 现有产品上的分配方式

and developing tastes for entirely new goods, too.

并且发展出对新产品的喜好

New industries are created,

新的行业诞生了

new tasks have to be done

新的任务需要执行

and that means often new roles have to be filled.

这意味着需要填补新的角色

So again, the British pie:

我们再拿英国蛋糕作为例子

300 years ago, most people worked on farms,

300年前 大多数人们在农场工作

150 years ago, in factories,

150年前 大多数人在工厂工作

and today, most people work in offices.

而今天，大多数人在写字楼上班

And once again, people displaced from tasks in the old bit of pie

原有经济蛋糕中被替换的人们

could tumble into tasks in the new bit of pie instead.

能够在新的经济蛋糕中找到工作

Economists call these effects complementarities,

经济学家把这种影响称为互补性

but really that's just a fancy word to capture the different way

但是这只是一种高级叫法

that technological progress helps human beings.

用以表述技术进步 能够帮助人类

Resolving this Terminator myth

对终结者迷思的解析

shows us that there are two forces at play:

告诉我们有两股力量正在起作用

one, machine substitution that harms workers,

一是机器的替代性会伤害到工人

but also these complementarities that do the opposite.

二是机器的互补性 同时还起到积极的作用

Now the second myth,

接下来是第二个迷思

what I call the intelligence myth.

我将其称之为智能迷思

What do the tasks of driving a car, making a medical diagnosis

以下三种职业 开车 医疗诊断 辨识鸟类

and identifying a bird at a fleeting glimpse have in common?

它们有何共同之处呢

Well, these are all tasks that until very recently,

不久前 杰出的经济学家都会认为

leading economists thought couldn't readily be automated.

这些是不能通过自动化完成的任务

And yet today, all of these tasks can be automated.

然而如今 这三项任务 都可以实现自动化

You know, all major car manufacturers have driverless car programs.

所有大型汽车生产商 都有无人驾驶程序

There's countless systems out there that can diagnose medical problems.

可以诊断医疗问题的系统 也不计其数

And there's even an app that can identify a bird

甚至有一款软件 只需要扫一扫

at a fleeting glimpse.

就可以识别鸟的种类

Now, this wasn't simply a case of bad luck on the part of economists.

这并不是因为部分 经济学家运气不好

They were wrong,

他们错了

and the reason why they were wrong is very important.

而他们错误的原因非常重要

They've fallen for the intelligence myth,

因为他们陷入了智能迷思

the belief that machines have to copy the way

他们认为机器只能通过

that human beings think and reason

复制人类思考和推理的方式

in order to outperform them.

才能更好地完成工作

When these economists were trying to figure out

当这些经济学家试图找出

机器不能完成哪些任务的时候

what tasks machines could not do,

they imagined the only way to automate a task

他们设想自动化的唯一途径

was to sit down with a human being,

就是找一个人 坐下来

get them to explain to you how it was they performed a task,

让他们向你解释 如何完成这项任务

and then try and capture that explanation

然后尝试和记录这种解释

in a set of instructions for a machine to follow.

使其成为机器可以执行的一套指令

This view was popular in artificial intelligence at one point, too.

这种观点在人工智能 领域曾风靡一时

I know this because Richard Susskind,

我了解它是因为 理查德·萨斯堪德(Richard Susskind)

who is my dad and my coauthor,

他是我的父亲 也是我的合作出书人

wrote his doctorate in the 1980s on artificial intelligence and the law

他曾在牛津大学读书 在1980年代 写下了关于人工智能与法律的

at Oxford University,

博士论文

and he was part of the vanguard.

他算是这个领域的先锋之一

And with a professor called Phillip Capper

他与菲利普·卡普尔(Phillip Capper)教授

and a legal publisher called Butterworths,

和一家法律出版商 巴特沃斯(Butterworths)

they produced the world's first commercially available

一起创造出了世界上第一台商用的

artificial intelligence system in the law.

法律方面的人工智能系统

This was the home screen design.

这是当时的主页面设计

He assures me this was a cool screen design at the time.

他向我保证 这在当时是非常酷的屏幕设计

(Laughter)

（笑声）

我一直对此抱有怀疑

I've never been entirely convinced.

他用两个软磁盘发布了这个系统

He published it in the form of two floppy disks,

at a time where floppy disks genuinely were floppy,

在那时 软磁盘真的是软的

and his approach was the same as the economists':

他的方法与经济学家一样

sit down with a lawyer,

坐下和律师聊天

get her to explain to you how it was she solved a legal problem,

听她解释如何解决法律问题

and then try and capture that explanation in a set of rules for a machine to follow.

然后尝试将这种解释 形成机器可以执行的一系列指令

In economics, if human beings could explain themselves in this way,

在经济学中 如果人类能够 用这种方式解释自己

the tasks are called routine, and they could be automated.

这项任务就称为例行事务 并且可以被自动化

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.

这些工作就是非例行事务 并且机器无法完成

Today, that routine-nonroutine distinction is widespread.

如今 例行和非例行的界限非常广泛

Think how often you hear people say to you

你是不是经常听见人们对你说

machines can only perform tasks that are predictable or repetitive,

机器只能执行可预测和重复性的工作 那些以规则为基础的

rules-based or well-defined.

或是定义清晰的工作

Those are all just different words for routine.

这些都只是例行工作的不同叫法

And go back to those three cases that I mentioned at the start.

重新回到我开始提到的三个工作

Those are all classic cases of nonroutine tasks.

这些都是典型的非例行工作

Ask a doctor, for instance, how she makes a medical diagnosis,

如果你问医生如何做出医疗诊断

and she might be able to give you a few rules of thumb,

她可能会告诉你一些经验之谈

but ultimately she'd struggle.

但是最后她会耸耸肩

She'd say it requires things like creativity and judgment and intuition.

告诉你这需要想象力 判断力和直觉

And these things are very difficult to articulate,

这些只可意会不可言传

and so it was thought these tasks would be very hard to automate.

因此人们认为 这些任务难以实现自动化

If a human being can't explain themselves,

如果人无法解释自己

where on earth do we begin in writing a set of instructions

我们要从哪儿开始写一串指令

for a machine to follow?

然后让机器去执行呢

Thirty years ago, this view was right,

30年前 这个观点曾是正确的

but today it's looking shaky,

但是如今却站不住脚

and in the future it's simply going to be wrong.

在未来它会变成错误的

Advances in processing power, in data storage capability

在数据处理 数据存储 和算法设计方面

and in algorithm design

人们已经取得了进步

mean that this routine-nonroutine distinction

这意味着例行和非例行工作的界限

is diminishingly useful.

不再那么有价值

To see this, go back to the case of making a medical diagnosis.

为了印证这一点 我们重新回到医疗诊断的例子

Earlier in the year,

今年早些时候

a team of researchers at Stanford announced they'd developed a system

斯坦福大学的一组研究人员宣布

which can tell you whether or not a freckle is cancerous

他们开发了一个系统 可以判断雀斑是否癌变

判断结果与皮肤科医生 给出的结果一样准确

as accurately as leading dermatologists.

How does it work?

这个系统是如何工作的呢

It's not trying to copy the judgment or the intuition of a doctor.

它并未尝试复制医生的判断或直觉

It knows or understands nothing about medicine at all.

它对医学一无所知

相反 它执行一种模式识别的算法

Instead, it's running a pattern recognition algorithm

through 129,450 past cases,

根据过去的12.9万个案例

hunting for similarities between those cases

它会寻找与过去病例的相似之处

and the particular lesion in question.

以及该病例中的特定组织损伤

It's performing these tasks in an unhuman way,

它以一种非人类的方式 执行这些任务

based on the analysis of more possible cases

以分析更多可能的例子为基础

than any doctor could hope to review in their lifetime.

这些病历的数量 可能是任何医生一生都无法看完的

It didn't matter that that human being,

即使人类医生无法解释

that doctor, couldn't explain how she'd performed the task.

自己如何完成某项工作 但这也没关系

Now, there are those who dwell upon that the fact

如今有一些人总是专注于

这些机器与我们不同

that these machines aren't built in our image.

As an example, take IBM's Watson,

举例来说 IBM公司的 超级电脑沃森(Watson)

the supercomputer that went on the US quiz show "Jeopardy!" in 2011,

在2011年参加了 美国智力问答节目《危险边缘》

and it beat the two human champions at "Jeopardy!"

它击败了两位人类冠军

The day after it won,

赢得比赛的第二天

华尔街日报刊登了哲学家 约翰·希尔勒(John Searle)的一篇文章

The Wall Street Journal ran a piece by the philosopher John Searle

with the title "Watson Doesn't Know It Won on 'Jeopardy!'"

题目为“沃森不知道自己赢了”

Right, and it's brilliant, and it's true.

说的没错 非常精确也是事实

You know, Watson didn't let out a cry of excitement.

沃森不会激动地大喊

它不会打电话告诉父母自己多么棒

It didn't call up its parents to say what a good job it had done.

更不会去酒吧喝一杯

It didn't go down to the pub for a drink.

This system wasn't trying to copy the way that those human contestants played,

这个系统没有试图模仿 人类选手的参赛方式

but it didn't matter.

但这没关系

It still outperformed them.

它仍然赢了人类

Resolving the intelligence myth

解析智能迷思告诉我们

shows us that our limited understanding about human intelligence,

对人类的智力 对我们如何思考推理

about how we think and reason,

我们的理解还很有限

is far less of a constraint on automation than it was in the past.

如今 这种认知局限 对自动化的限制远小于以前

What's more, as we've seen,

此外 如我们所见

when these machines perform tasks differently to human beings,

当这些机器以不同于 人类的方式执行任务时

there's no reason to think

我们没有理由认为

人类现在能够完成的事情与未来机器

that what human beings are currently capable of doing

represents any sort of summit

能够胜任的任务相比

in what these machines might be capable of doing in the future.

仍能代表着某种意义的巅峰

Now the third myth,

现在我们来看第三个迷思

what I call the superiority myth.

我将它称为优越性迷思

It's often said that those who forget

我们常说 那些忘记

about the helpful side of technological progress,

科技进步有用之处的人

those complementarities from before,

和那些忘记之前 机器辅助人类的人

都犯了劳动合成谬误

are committing something known as the lump of labor fallacy.

Now, the problem is the lump of labor fallacy

问题在于 劳动合成谬误

本身就是一个谬误

is itself a fallacy,

and I call this the lump of labor fallacy fallacy,

我把它叫做劳动合成谬误的谬误

or LOLFF, for short.

或者简单称为LOLFF

Let me explain.

让我来解释一下

The lump of labor fallacy is a very old idea.

劳动力合成谬误 是一个非常老的概念

It was a British economist, David Schloss, who gave it this name in 1892.

它由英国经济学家大卫·施劳斯 (David Schloss)于1892年提出

He was puzzled to come across a dock worker

他当时碰到一个码头工人

who had begun to use a machine to make washers,

这个工人使用机器来生产垫圈

就是那种小的金属圆盘 用来扣住螺丝底部

the small metal discs that fasten on the end of screws.

And this dock worker felt guilty for being more productive.

这个码头工人因生产力更高 而怀有负罪感

Now, most of the time, we expect the opposite,

如今 在大多数情况下 我们的表现则相反

that people feel guilty for being unproductive,

人们会因效率低下而感到惭愧

比如在上班时 多看了会儿Facebook或Twitter

you know, a little too much time on Facebook or Twitter at work.

但是这个工人因为效率高而内疚

But this worker felt guilty for being more productive,

问及原因 他是这么说的 我知道我做的不对

and asked why, he said, "I know I'm doing wrong.

I'm taking away the work of another man."

我抢了其他工人的工作

In his mind, there was some fixed lump of work

在他看来 工作的总量是固定的

to be divided up between him and his pals,

由他和他的伙伴共同分担

so that if he used this machine to do more,

因此 如果他用这台机器 做了更多活儿

他伙伴能分到的活儿就更少

there'd be less left for his pals to do.

施劳斯看到了其中的错误

Schloss saw the mistake.

The lump of work wasn't fixed.

工作的总量并不是固定的

As this worker used the machine and became more productive,

当这个工人使用机器提高生产力

the price of washers would fall, demand for washers would rise,

垫圈的价格将会下降 对垫圈的需求会增加

more washers would have to be made,

于是就需要生产更多的垫圈

and there'd be more work for his pals to do.

他的伙伴也可以做更多工作

The lump of work would get bigger.

工作的总量将会变大

Schloss called this "the lump of labor fallacy."

施劳斯将此称为劳动合成谬误

And today you hear people talk about the lump of labor fallacy

现如今 你听到人们用这种错误方式

思考未来各种类型的工作

to think about the future of all types of work.

需要在人和机器之间 分配的工作量

There's no fixed lump of work out there to be divided up

并不是固定的

between people and machines.

是的 机器会代替人类 使原来的工作总量变少

Yes, machines substitute for human beings, making the original lump of work smaller,

but they also complement human beings,

但是他们也与人类互补

and the lump of work gets bigger and changes.

工作量因此变多 并且类型也会发生改变

But LOLFF.

但是LOLFF 即劳动合成谬误

Here's the mistake:

存在着一个问题

it's right to think that technological progress

认为技术进步使得工作更多

这种想法是正确的

makes the lump of work to be done bigger.

一些工作变得更有价值 新的任务需要完成

Some tasks become more valuable. New tasks have to be done.

But it's wrong to think that necessarily,

但是认为人类会是

human beings will be best placed to perform those tasks.

完成这些任务的最好人选的 想法并不正确

And this is the superiority myth.

这就是优越性迷思

Yes, the lump of work might get bigger and change,

没错 工作会变多 也会发生变化

但是机器的能力也会变强

but as machines become more capable,

很可能机器会从事 多出来的那部分工作

it's likely that they'll take on the extra lump of work themselves.

Technological progress, rather than complement human beings,

技术进步没有利于人类

complements machines instead.

而是利于机器

To see this, go back to the task of driving a car.

关于这一点 我们回到开车这件事上

Today, satnav systems directly complement human beings.

如今 卫星定位系统 可以直接辅助人类

They make some human beings better drivers.

它帮助人们成为更好的司机

But in the future,

但是在未来

软件将会代替 驾驶座椅上的人类

software is going to displace human beings from the driving seat,

and these satnav systems, rather than complement human beings,

这些卫星定位系统 不再辅助人类

而是使无人驾驶变得更加高效

will simply make these driverless cars more efficient,

helping the machines instead.

从而帮衬了机器

Or go to those indirect complementarities that I mentioned as well.

再回到我之前提到的 那些机器间接互补性的例子

The economic pie may get larger,

经济蛋糕会变得更大

but as machines become more capable,

但是随着机器的能力变得更强

很可能最适合 应对新需求的一方

it's possible that any new demand will fall on goods that machines,

rather than human beings, are best placed to produce.

是机器而不是人类自己

The economic pie may change,

经济蛋糕可能会发生改变

but as machines become more capable,

但是随着机器能力变强

it's possible that they'll be best placed to do the new tasks that have to be done.

很可能它们才是 最适合完成新工作的一方

In short, demand for tasks isn't demand for human labor.

简单来说 对工作的需求 并不一定需要人力来完成

Human beings only stand to benefit

人们只关心利益

if they retain the upper hand in all these complemented tasks,

是否能够在这些工作中 保持有利地位

but as machines become more capable, that becomes less likely.

但是随着机器的能力变强 这将越来越难实现

So what do these three myths tell us then?

那么这三个迷思告诉了我们什么呢

Well, resolving the Terminator myth

解析终结者迷思

shows us that the future of work depends upon this balance between two forces:

告诉我们未来的工作 取决于两种力量的平衡

one, machine substitution that harms workers

一是机器的替代性会伤害工人

but also those complementarities that do the opposite.

二是其互补性也会有利于工人

截至目前 这种平衡在向人类一方倾斜

And until now, this balance has fallen in favor of human beings.

But resolving the intelligence myth

但是对智能迷思的分析

shows us that that first force, machine substitution,

告诉我们 机器对人类的替代

is gathering strength.

正在蓄势待发

Machines, of course, can't do everything,

机器并不能做所有事

but they can do far more,

但是它们可以做的更多

更深入地干涉人类工作的领域

encroaching ever deeper into the realm of tasks performed by human beings.

What's more, there's no reason to think

此外 我们也没理由相信

that what human beings are currently capable of

人们现在能做的事情

represents any sort of finishing line,

代表着某种终结

当机器与我们同样能干时

that machines are going to draw to a polite stop

once they're as capable as us.

它们会带来某种和平的结局

Now, none of this matters

只要机器对我们的互补

so long as those helpful winds of complementarity

依然确实地存在

blow firmly enough,

那么这些担忧都不重要

but resolving the superiority myth

通过解析优越性迷思

shows us that that process of task encroachment

展现出工作侵蚀的过程

这不止加强了机器的替代性

not only strengthens the force of machine substitution,

but it wears down those helpful complementarities too.

也削弱了那些有益的互补性

Bring these three myths together

将这三个迷思放到一起

and I think we can capture a glimpse of that troubling future.

我认为我们能够看到令人担忧的未来

机器的能力会继续变强

Machines continue to become more capable,

encroaching ever deeper on tasks performed by human beings,

更深入地占据更多人类从事的工作

strengthening the force of machine substitution,

加强机器的替代性

weakening the force of machine complementarity.

同时削弱机器的互补性

And at some point, that balance falls in favor of machines

到某一点时 这个平衡会倾向于机器

rather than human beings.

而不再是人类

This is the path we're currently on.

这是我们现在所面临的道路

I say "path" deliberately, because I don't think we're there yet,

我特意用了道路这个词 因为我不认为我们已经到达这一点

but it is hard to avoid the conclusion that this is our direction of travel.

但是我们无法避免它 这就是我们前进的方向

That's the troubling part.

这是麻烦的部分

Let me say now why I think actually this is a good problem to have.

我现在来说一下 为什么我认为这是一个好问题

For most of human history, one economic problem has dominated:

在大部分的人类历史中 一个经济问题最为重要

how to make the economic pie large enough for everyone to live on.

如何让经济蛋糕足够大 使每个人都可以生存

Go back to the turn of the first century AD,

回到公元1世纪

and if you took the global economic pie

如果你将世界经济这块蛋糕

and divided it up into equal slices for everyone in the world,

等分给世界上每一个人

everyone would get a few hundred dollars.

人均将得到几百美元

Almost everyone lived on or around the poverty line.

基本上大家都生活在贫困线水平

And if you roll forward a thousand years,

如果再前进一千年

roughly the same is true.

大概也还是这个情况

But in the last few hundred years, economic growth has taken off.

但是在最近几百年 经济开始起飞

Those economic pies have exploded in size.

经济蛋糕呈爆炸性增长

Global GDP per head,

全球平均GDP

the value of those individual slices of the pie today,

也就是如今个人分到的蛋糕

they're about 10,150 dollars.

大约是10150美元

如果经济继续增长2%

If economic growth continues at two percent,

our children will be twice as rich as us.

我们下一代的富有程度 会是我们的二倍

If it continues at a more measly one percent,

如果经济增长只有可怜的1%

our grandchildren will be twice as rich as us.

我们孙辈的富有程度 会是我们的二倍

By and large, we've solved that traditional economic problem.

由此 我们解决了传统的经济问题

Now, technological unemployment, if it does happen,

那么 技术性失业 如果真的以某种方式发生

in a strange way will be a symptom of that success,

它将是经济增长成功的一种表现

will have solved one problem -- how to make the pie bigger --

它解决了一个问题 那就是如何让蛋糕变得更大

but replaced it with another --

但是也带来了另一个问题

how to make sure that everyone gets a slice.

如何确保每个人都能分一杯羹

As other economists have noted, solving this problem won't be easy.

我们的经济学家曾指出 解决这些问题并不容易

Today, for most people,

如今 对大部分人来说

他们的工作就是 他们分得经济蛋糕的方式

their job is their seat at the economic dinner table,

and in a world with less work or even without work,

这个世界的工作越来越少 或是甚至没有工作

人们如何分得蛋糕 仍不得而知

it won't be clear how they get their slice.

There's a great deal of discussion, for instance,

可能解决该问题的方法之一

about various forms of universal basic income

是提供全民基本收入

as one possible approach,

关于其形式有各种讨论

and there's trials underway

在美国 芬兰和肯尼亚

in the United States and in Finland and in Kenya.

也正在进行一些尝试

And this is the collective challenge that's right in front of us,

这是我们共同面对的挑战

to figure out how this material prosperity generated by our economic system

那就是 在这个仍然用 传统方式分配所得的

can be enjoyed by everyone

世界中

in a world in which our traditional mechanism

当人们做的工作越来越少

for slicing up the pie,

也许彻底消失

the work that people do,

如何让经济系统带来的

withers away and perhaps disappears.

物质繁荣能够被每个人享有

Solving this problem is going to require us to think in very different ways.

解决这个问题 需要我们用不同的方法思考

There's going to be a lot of disagreement about what ought to be done,

对于需要做些什么 将会有很多反对意见

but it's important to remember that this is a far better problem to have

但重要的是明确一点 相比如何让经济蛋糕变大

than the one that haunted our ancestors for centuries:

这个曾困扰我们祖先 长达几个世纪的问题

how to make that pie big enough in the first place.

我们面临的是一个 要好得多的问题

Thank you very much.

非常感谢

(Applause)

（掌声）