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Daniel Susskind: 3 myths about the future of work (and why they're not true)
丹尼尔·萨斯堪德: 关于未来工作的三个迷思（以及为何它们不正确）
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“机器会代替人类么？”这个问题萦绕在每个工作者的心头。丹尼尔·萨斯堪德反驳了这个问题和其他三个关于自动化未来的错误观念，他指出我们应该思考另一个问题，即我们应当如何在工作减少甚至没有工作的世界中分配财富？
Daniel Susskind - Economist
Daniel Susskind explores the impact of technology, particularly artificial intelligence, on work and society. Full bio
Daniel Susskind explores the impact of technology, particularly artificial intelligence, on work and society. Full bio
Double-click the English transcript below to play the video.
00:12
Automation anxiety
has been spreading lately,
has been spreading lately,
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如今 关于自动化的焦虑广泛传播
人们开始担心
00:16
a fear that in the future,
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鉴于人工智能和机器人领域
00:18
many jobs will be performed by machines
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不断取得的惊人发展
00:21
rather than human beings,
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在未来 许多工作将由机器完成
00:22
given the remarkable advances
that are unfolding
that are unfolding
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而不是人类自己
00:25
in artificial intelligence and robotics.
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可以明确的是
未来将会出现重大改变
未来将会出现重大改变
00:28
What's clear is that
there will be significant change.
there will be significant change.
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但尚未明确的是
究竟会出现何种改变
究竟会出现何种改变
00:31
What's less clear
is what that change will look like.
is what that change will look like.
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通过研究 我认为 未来
既令人困扰又令人激动
既令人困扰又令人激动
00:34
My research suggests that the future
is both troubling and exciting.
is both troubling and exciting.
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技术性失业的威胁是真实存在的
00:39
The threat of technological
unemployment is real,
unemployment is real,
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00:43
and yet it's a good problem to have.
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但它是一个好问题
00:45
And to explain
how I came to that conclusion,
how I came to that conclusion,
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为了解释我如何得出这个结论
00:48
I want to confront three myths
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我会反驳三个迷思
它们混淆了我们的视线
使我们无法看清自动化的未来
使我们无法看清自动化的未来
00:51
that I think are currently obscuring
our vision of this automated future.
our vision of this automated future.
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00:56
A picture that we see
on our television screens,
on our television screens,
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不管是在电视 书籍
还是实况报道中
还是实况报道中
00:59
in books, in films, in everyday commentary
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我们经常可以看到一个场景
大量机器人走向工作场所
01:01
is one where an army of robots
descends on the workplace
descends on the workplace
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它们只有一个目的
01:05
with one goal in mind:
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就是替代人类工作
01:06
to displace human beings from their work.
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我将这称为终结者迷思
01:09
And I call this the Terminator myth.
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机器的确会代替人类
完成特定的一些任务
完成特定的一些任务
01:11
Yes, machines displace
human beings from particular tasks,
human beings from particular tasks,
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但是他们不只是替代人类
01:15
but they don't just
substitute for human beings.
substitute for human beings.
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01:18
They also complement them in other tasks,
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也会在其他工作上辅助人类
使工作更有价值 更重要
01:20
making that work more valuable
and more important.
and more important.
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01:23
Sometimes they complement
human beings directly,
human beings directly,
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有时他们会直接辅助人类
01:27
making them more productive
or more efficient at a particular task.
or more efficient at a particular task.
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让人们更高效地
完成某项特定的任务
完成某项特定的任务
比如出租车司机使用卫星定位系统
导航到不熟悉的区域
导航到不熟悉的区域
01:31
So a taxi driver can use a satnav system
to navigate on unfamiliar roads.
to navigate on unfamiliar roads.
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建筑师可以使用电脑上的设计软件
01:35
An architect can use
computer-assisted design software
computer-assisted design software
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来帮助自己设计
更宏大更复杂的建筑
更宏大更复杂的建筑
01:39
to design bigger,
more complicated buildings.
more complicated buildings.
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但是技术进步不仅仅
直接帮助人类
直接帮助人类
01:42
But technological progress doesn't
just complement human beings directly.
just complement human beings directly.
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也通过其他两种方式
间接地与人类互补
间接地与人类互补
01:46
It also complements them indirectly,
and it does this in two ways.
and it does this in two ways.
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首先 如果我们把经济
想象成一个蛋糕
想象成一个蛋糕
01:49
The first is if we think
of the economy as a pie,
of the economy as a pie,
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01:52
technological progress
makes the pie bigger.
makes the pie bigger.
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技术进步会使蛋糕变得更大
随着生产力提高
收入和需求都会增加
收入和需求都会增加
01:55
As productivity increases,
incomes rise and demand grows.
incomes rise and demand grows.
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以英国的经济蛋糕为例
01:59
The British pie, for instance,
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现在这个蛋糕的尺寸
是300年前的100多倍
是300年前的100多倍
02:01
is more than a hundred times
the size it was 300 years ago.
the size it was 300 years ago.
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02:05
And so people displaced
from tasks in the old pie
from tasks in the old pie
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因此在旧经济中失去工作的人们
可以在新经济中找到工作
02:09
could find tasks to do
in the new pie instead.
in the new pie instead.
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02:12
But technological progress
doesn't just make the pie bigger.
doesn't just make the pie bigger.
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但是技术进步不仅仅
让蛋糕变得更大
让蛋糕变得更大
它也改变了蛋糕的原料
02:16
It also changes
the ingredients in the pie.
the ingredients in the pie.
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随着时间推移
人们消费的方式变得不同
人们消费的方式变得不同
02:19
As time passes, people spend
their income in different ways,
their income in different ways,
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改变了收入在
现有产品上的分配方式
现有产品上的分配方式
02:23
changing how they spread it
across existing goods,
across existing goods,
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并且发展出对新产品的喜好
02:25
and developing tastes
for entirely new goods, too.
for entirely new goods, too.
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新的行业诞生了
02:29
New industries are created,
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02:31
new tasks have to be done
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新的任务需要执行
02:32
and that means often
new roles have to be filled.
new roles have to be filled.
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这意味着需要填补新的角色
我们再拿英国蛋糕作为例子
02:35
So again, the British pie:
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02:36
300 years ago,
most people worked on farms,
most people worked on farms,
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300年前 大多数人们在农场工作
02:39
150 years ago, in factories,
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150年前 大多数人在工厂工作
02:42
and today, most people work in offices.
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而今天，大多数人在写字楼上班
02:45
And once again, people displaced
from tasks in the old bit of pie
from tasks in the old bit of pie
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原有经济蛋糕中被替换的人们
02:49
could tumble into tasks
in the new bit of pie instead.
in the new bit of pie instead.
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能够在新的经济蛋糕中找到工作
02:52
Economists call these effects
complementarities,
complementarities,
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经济学家把这种影响称为互补性
02:56
but really that's just a fancy word
to capture the different way
to capture the different way
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但是这只是一种高级叫法
02:59
that technological progress
helps human beings.
helps human beings.
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用以表述技术进步
能够帮助人类
能够帮助人类
对终结者迷思的解析
03:02
Resolving this Terminator myth
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告诉我们有两股力量正在起作用
03:04
shows us that there are
two forces at play:
two forces at play:
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03:07
one, machine substitution
that harms workers,
that harms workers,
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一是机器的替代性会伤害到工人
二是机器的互补性
同时还起到积极的作用
同时还起到积极的作用
03:10
but also these complementarities
that do the opposite.
that do the opposite.
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03:13
Now the second myth,
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接下来是第二个迷思
03:15
what I call the intelligence myth.
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我将其称之为智能迷思
03:18
What do the tasks of driving a car,
making a medical diagnosis
making a medical diagnosis
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以下三种职业
开车 医疗诊断 辨识鸟类
开车 医疗诊断 辨识鸟类
03:23
and identifying a bird
at a fleeting glimpse have in common?
at a fleeting glimpse have in common?
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它们有何共同之处呢
03:27
Well, these are all tasks
that until very recently,
that until very recently,
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不久前 杰出的经济学家都会认为
这些是不能通过自动化完成的任务
03:30
leading economists thought
couldn't readily be automated.
couldn't readily be automated.
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然而如今 这三项任务
都可以实现自动化
都可以实现自动化
03:33
And yet today, all of these tasks
can be automated.
can be automated.
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所有大型汽车生产商
都有无人驾驶程序
都有无人驾驶程序
03:36
You know, all major car manufacturers
have driverless car programs.
have driverless car programs.
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可以诊断医疗问题的系统
也不计其数
也不计其数
03:40
There's countless systems out there
that can diagnose medical problems.
that can diagnose medical problems.
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甚至有一款软件 只需要扫一扫
03:44
And there's even an app
that can identify a bird
that can identify a bird
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03:46
at a fleeting glimpse.
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就可以识别鸟的种类
03:48
Now, this wasn't simply a case of bad luck
on the part of economists.
on the part of economists.
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这并不是因为部分
经济学家运气不好
经济学家运气不好
他们错了
03:53
They were wrong,
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而他们错误的原因非常重要
03:54
and the reason why
they were wrong is very important.
they were wrong is very important.
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因为他们陷入了智能迷思
03:57
They've fallen for the intelligence myth,
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他们认为机器只能通过
03:59
the belief that machines
have to copy the way
have to copy the way
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复制人类思考和推理的方式
04:02
that human beings think and reason
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才能更好地完成工作
04:04
in order to outperform them.
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当这些经济学家试图找出
04:06
When these economists
were trying to figure out
were trying to figure out
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机器不能完成哪些任务的时候
04:08
what tasks machines could not do,
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他们设想自动化的唯一途径
04:10
they imagined the only way
to automate a task
to automate a task
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就是找一个人 坐下来
04:12
was to sit down with a human being,
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让他们向你解释
如何完成这项任务
如何完成这项任务
04:14
get them to explain to you
how it was they performed a task,
how it was they performed a task,
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然后尝试和记录这种解释
04:17
and then try and capture that explanation
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使其成为机器可以执行的一套指令
04:20
in a set of instructions
for a machine to follow.
for a machine to follow.
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这种观点在人工智能
领域曾风靡一时
领域曾风靡一时
04:23
This view was popular in artificial
intelligence at one point, too.
intelligence at one point, too.
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我了解它是因为
理查德·萨斯堪德(Richard Susskind)
理查德·萨斯堪德(Richard Susskind)
04:27
I know this because Richard Susskind,
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04:29
who is my dad and my coauthor,
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他是我的父亲
也是我的合作出书人
也是我的合作出书人
04:32
wrote his doctorate in the 1980s
on artificial intelligence and the law
on artificial intelligence and the law
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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
写下了关于人工智能与法律的
博士论文
04:36
at Oxford University,
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04:38
and he was part of the vanguard.
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他算是这个领域的先锋之一
他与菲利普·卡普尔(Phillip Capper)教授
04:39
And with a professor called Phillip Capper
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和一家法律出版商
巴特沃斯(Butterworths)
巴特沃斯(Butterworths)
04:42
and a legal publisher called Butterworths,
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一起创造出了世界上第一台商用的
04:44
they produced the world's first
commercially available
commercially available
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04:50
artificial intelligence system in the law.
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法律方面的人工智能系统
这是当时的主页面设计
04:52
This was the home screen design.
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04:55
He assures me this was
a cool screen design at the time.
a cool screen design at the time.
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他向我保证
这在当时是非常酷的屏幕设计
这在当时是非常酷的屏幕设计
（笑声）
04:58
(Laughter)
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我一直对此抱有怀疑
04:59
I've never been entirely convinced.
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他用两个软磁盘发布了这个系统
05:01
He published it
in the form of two floppy disks,
in the form of two floppy disks,
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在那时 软磁盘真的是软的
05:03
at a time where floppy disks
genuinely were floppy,
genuinely were floppy,
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他的方法与经济学家一样
05:07
and his approach was the same
as the economists':
as the economists':
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坐下和律师聊天
05:09
sit down with a lawyer,
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听她解释如何解决法律问题
05:10
get her to explain to you
how it was she solved a legal problem,
how it was she solved a legal problem,
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然后尝试将这种解释
形成机器可以执行的一系列指令
形成机器可以执行的一系列指令
05:14
and then try and capture that explanation
in a set of rules for a machine to follow.
in a set of rules for a machine to follow.
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在经济学中 如果人类能够
用这种方式解释自己
用这种方式解释自己
05:19
In economics, if human beings
could explain themselves in this way,
could explain themselves in this way,
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这项任务就称为例行事务
并且可以被自动化
并且可以被自动化
05:23
the tasks are called routine,
and they could be automated.
and they could be automated.
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但是如果人类无法解释自己
05:26
But if human beings
can't explain themselves,
can't explain themselves,
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这些工作就是非例行事务
并且机器无法完成
并且机器无法完成
05:28
the tasks are called non-routine,
and they're thought to be out reach.
and they're thought to be out reach.
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如今 例行和非例行的界限非常广泛
05:33
Today, that routine-nonroutine
distinction is widespread.
distinction is widespread.
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你是不是经常听见人们对你说
05:36
Think how often you hear people say to you
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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
那些以规则为基础的
05:38
machines can only perform tasks
that are predictable or repetitive,
that are predictable or repetitive,
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05:41
rules-based or well-defined.
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或是定义清晰的工作
这些都只是例行工作的不同叫法
05:43
Those are all just
different words for routine.
different words for routine.
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重新回到我开始提到的三个工作
05:46
And go back to those three cases
that I mentioned at the start.
that I mentioned at the start.
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这些都是典型的非例行工作
05:50
Those are all classic cases
of nonroutine tasks.
of nonroutine tasks.
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如果你问医生如何做出医疗诊断
05:53
Ask a doctor, for instance,
how she makes a medical diagnosis,
how she makes a medical diagnosis,
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她可能会告诉你一些经验之谈
05:56
and she might be able
to give you a few rules of thumb,
to give you a few rules of thumb,
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但是最后她会耸耸肩
05:59
but ultimately she'd struggle.
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告诉你这需要想象力 判断力和直觉
06:00
She'd say it requires things like
creativity and judgment and intuition.
creativity and judgment and intuition.
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这些只可意会不可言传
06:05
And these things are
very difficult to articulate,
very difficult to articulate,
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因此人们认为
这些任务难以实现自动化
这些任务难以实现自动化
06:08
and so it was thought these tasks
would be very hard to automate.
would be very hard to automate.
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如果人无法解释自己
06:11
If a human being can't explain themselves,
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我们要从哪儿开始写一串指令
06:13
where on earth do we begin
in writing a set of instructions
in writing a set of instructions
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然后让机器去执行呢
06:16
for a machine to follow?
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06:18
Thirty years ago, this view was right,
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30年前 这个观点曾是正确的
但是如今却站不住脚
06:21
but today it's looking shaky,
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在未来它会变成错误的
06:23
and in the future
it's simply going to be wrong.
it's simply going to be wrong.
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在数据处理 数据存储
和算法设计方面
和算法设计方面
06:25
Advances in processing power,
in data storage capability
in data storage capability
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人们已经取得了进步
06:28
and in algorithm design
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这意味着例行和非例行工作的界限
06:30
mean that this
routine-nonroutine distinction
routine-nonroutine distinction
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不再那么有价值
06:33
is diminishingly useful.
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为了印证这一点
我们重新回到医疗诊断的例子
我们重新回到医疗诊断的例子
06:34
To see this, go back to the case
of making a medical diagnosis.
of making a medical diagnosis.
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今年早些时候
06:38
Earlier in the year,
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斯坦福大学的一组研究人员宣布
06:39
a team of researchers at Stanford
announced they'd developed a system
announced they'd developed a system
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06:42
which can tell you
whether or not a freckle is cancerous
whether or not a freckle is cancerous
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他们开发了一个系统
可以判断雀斑是否癌变
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
给出的结果一样准确
06:46
as accurately as leading dermatologists.
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06:49
How does it work?
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这个系统是如何工作的呢
它并未尝试复制医生的判断或直觉
06:50
It's not trying to copy the judgment
or the intuition of a doctor.
or the intuition of a doctor.
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它对医学一无所知
06:55
It knows or understands
nothing about medicine at all.
nothing about medicine at all.
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相反 它执行一种模式识别的算法
06:59
Instead, it's running
a pattern recognition algorithm
a pattern recognition algorithm
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根据过去的12.9万个案例
07:01
through 129,450 past cases,
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它会寻找与过去病例的相似之处
07:06
hunting for similarities
between those cases
between those cases
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以及该病例中的特定组织损伤
07:09
and the particular lesion in question.
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07:12
It's performing these tasks
in an unhuman way,
in an unhuman way,
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它以一种非人类的方式
执行这些任务
执行这些任务
以分析更多可能的例子为基础
07:15
based on the analysis
of more possible cases
of more possible cases
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这些病历的数量
可能是任何医生一生都无法看完的
可能是任何医生一生都无法看完的
07:17
than any doctor could hope
to review in their lifetime.
to review in their lifetime.
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即使人类医生无法解释
07:20
It didn't matter that that human being,
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07:22
that doctor, couldn't explain
how she'd performed the task.
how she'd performed the task.
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自己如何完成某项工作
但这也没关系
但这也没关系
07:25
Now, there are those
who dwell upon that the fact
who dwell upon that the fact
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如今有一些人总是专注于
这些机器与我们不同
07:28
that these machines
aren't built in our image.
aren't built in our image.
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举例来说 IBM公司的
超级电脑沃森(Watson)
超级电脑沃森(Watson)
07:30
As an example, take IBM's Watson,
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在2011年参加了
美国智力问答节目《危险边缘》
美国智力问答节目《危险边缘》
07:32
the supercomputer that went
on the US quiz show "Jeopardy!" in 2011,
on the US quiz show "Jeopardy!" in 2011,
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它击败了两位人类冠军
07:37
and it beat the two
human champions at "Jeopardy!"
human champions at "Jeopardy!"
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赢得比赛的第二天
07:40
The day after it won,
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华尔街日报刊登了哲学家
约翰·希尔勒(John Searle)的一篇文章
约翰·希尔勒(John Searle)的一篇文章
07:42
The Wall Street Journal ran a piece
by the philosopher John Searle
by the philosopher John Searle
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题目为“沃森不知道自己赢了”
07:45
with the title "Watson
Doesn't Know It Won on 'Jeopardy!'"
Doesn't Know It Won on 'Jeopardy!'"
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说的没错 非常精确也是事实
07:48
Right, and it's brilliant, and it's true.
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沃森不会激动地大喊
07:50
You know, Watson didn't
let out a cry of excitement.
let out a cry of excitement.
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它不会打电话告诉父母自己多么棒
07:53
It didn't call up its parents
to say what a good job it had done.
to say what a good job it had done.
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更不会去酒吧喝一杯
07:56
It didn't go down to the pub for a drink.
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这个系统没有试图模仿
人类选手的参赛方式
人类选手的参赛方式
07:58
This system wasn't trying to copy the way
that those human contestants played,
that those human contestants played,
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但这没关系
08:03
but it didn't matter.
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它仍然赢了人类
08:04
It still outperformed them.
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解析智能迷思告诉我们
08:06
Resolving the intelligence myth
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对人类的智力
对我们如何思考推理
对我们如何思考推理
08:08
shows us that our limited understanding
about human intelligence,
about human intelligence,
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08:11
about how we think and reason,
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我们的理解还很有限
如今 这种认知局限
对自动化的限制远小于以前
对自动化的限制远小于以前
08:13
is far less of a constraint
on automation than it was in the past.
on automation than it was in the past.
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此外 如我们所见
08:16
What's more, as we've seen,
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当这些机器以不同于
人类的方式执行任务时
人类的方式执行任务时
08:18
when these machines
perform tasks differently to human beings,
perform tasks differently to human beings,
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我们没有理由认为
08:21
there's no reason to think
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人类现在能够完成的事情与未来机器
08:23
that what human beings
are currently capable of doing
are currently capable of doing
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能够胜任的任务相比
08:25
represents any sort of summit
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08:27
in what these machines
might be capable of doing in the future.
might be capable of doing in the future.
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仍能代表着某种意义的巅峰
08:31
Now the third myth,
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现在我们来看第三个迷思
我将它称为优越性迷思
08:32
what I call the superiority myth.
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我们常说 那些忘记
08:34
It's often said that those who forget
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08:37
about the helpful side
of technological progress,
of technological progress,
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科技进步有用之处的人
和那些忘记之前
机器辅助人类的人
机器辅助人类的人
08:39
those complementarities from before,
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都犯了劳动合成谬误
08:42
are committing something
known as the lump of labor fallacy.
known as the lump of labor fallacy.
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08:45
Now, the problem is
the lump of labor fallacy
the lump of labor fallacy
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问题在于 劳动合成谬误
本身就是一个谬误
08:48
is itself a fallacy,
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我把它叫做劳动合成谬误的谬误
08:49
and I call this the lump
of labor fallacy fallacy,
of labor fallacy fallacy,
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或者简单称为LOLFF
08:52
or LOLFF, for short.
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08:56
Let me explain.
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让我来解释一下
劳动力合成谬误
是一个非常老的概念
是一个非常老的概念
08:57
The lump of labor fallacy
is a very old idea.
is a very old idea.
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它由英国经济学家大卫·施劳斯
(David Schloss)于1892年提出
(David Schloss)于1892年提出
08:59
It was a British economist, David Schloss,
who gave it this name in 1892.
who gave it this name in 1892.
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他当时碰到一个码头工人
09:03
He was puzzled
to come across a dock worker
to come across a dock worker
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09:06
who had begun to use
a machine to make washers,
a machine to make washers,
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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
用来扣住螺丝底部
09:09
the small metal discs
that fasten on the end of screws.
that fasten on the end of screws.
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09:13
And this dock worker
felt guilty for being more productive.
felt guilty for being more productive.
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这个码头工人因生产力更高
而怀有负罪感
而怀有负罪感
09:17
Now, most of the time,
we expect the opposite,
we expect the opposite,
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如今 在大多数情况下
我们的表现则相反
我们的表现则相反
人们会因效率低下而感到惭愧
09:19
that people feel guilty
for being unproductive,
for being unproductive,
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比如在上班时
多看了会儿Facebook或Twitter
多看了会儿Facebook或Twitter
09:22
you know, a little too much time
on Facebook or Twitter at work.
on Facebook or Twitter at work.
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但是这个工人因为效率高而内疚
09:25
But this worker felt guilty
for being more productive,
for being more productive,
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问及原因 他是这么说的
我知道我做的不对
我知道我做的不对
09:27
and asked why, he said,
"I know I'm doing wrong.
"I know I'm doing wrong.
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我抢了其他工人的工作
09:29
I'm taking away the work of another man."
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09:32
In his mind, there was
some fixed lump of work
some fixed lump of work
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在他看来
工作的总量是固定的
工作的总量是固定的
由他和他的伙伴共同分担
09:35
to be divided up between him and his pals,
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因此 如果他用这台机器
做了更多活儿
做了更多活儿
09:37
so that if he used
this machine to do more,
this machine to do more,
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他伙伴能分到的活儿就更少
09:40
there'd be less left for his pals to do.
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施劳斯看到了其中的错误
09:42
Schloss saw the mistake.
200
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工作的总量并不是固定的
09:43
The lump of work wasn't fixed.
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当这个工人使用机器提高生产力
09:45
As this worker used the machine
and became more productive,
and became more productive,
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垫圈的价格将会下降
对垫圈的需求会增加
对垫圈的需求会增加
09:48
the price of washers would fall,
demand for washers would rise,
demand for washers would rise,
203
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2976
于是就需要生产更多的垫圈
09:51
more washers would have to be made,
204
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他的伙伴也可以做更多工作
09:53
and there'd be more work
for his pals to do.
for his pals to do.
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工作的总量将会变大
09:55
The lump of work would get bigger.
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施劳斯将此称为劳动合成谬误
09:57
Schloss called this
"the lump of labor fallacy."
"the lump of labor fallacy."
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10:00
And today you hear people talk
about the lump of labor fallacy
about the lump of labor fallacy
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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
10:03
to think about the future
of all types of work.
of all types of work.
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需要在人和机器之间
分配的工作量
分配的工作量
10:05
There's no fixed lump of work
out there to be divided up
out there to be divided up
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并不是固定的
10:08
between people and machines.
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是的 机器会代替人类
使原来的工作总量变少
使原来的工作总量变少
10:09
Yes, machines substitute for human beings,
making the original lump of work smaller,
making the original lump of work smaller,
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但是他们也与人类互补
10:14
but they also complement human beings,
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工作量因此变多
并且类型也会发生改变
并且类型也会发生改变
10:16
and the lump of work
gets bigger and changes.
gets bigger and changes.
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2096
10:19
But LOLFF.
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但是LOLFF 即劳动合成谬误
存在着一个问题
10:21
Here's the mistake:
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认为技术进步使得工作更多
10:22
it's right to think
that technological progress
that technological progress
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这种想法是正确的
10:25
makes the lump of work to be done bigger.
218
613040
1976
一些工作变得更有价值
新的任务需要完成
新的任务需要完成
10:27
Some tasks become more valuable.
New tasks have to be done.
New tasks have to be done.
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但是认为人类会是
10:30
But it's wrong to think that necessarily,
220
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完成这些任务的最好人选的
想法并不正确
想法并不正确
10:32
human beings will be best placed
to perform those tasks.
to perform those tasks.
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这就是优越性迷思
10:35
And this is the superiority myth.
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没错 工作会变多 也会发生变化
10:37
Yes, the lump of work
might get bigger and change,
might get bigger and change,
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3416
但是机器的能力也会变强
10:41
but as machines become more capable,
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1976
很可能机器会从事
多出来的那部分工作
多出来的那部分工作
10:43
it's likely that they'll take on
the extra lump of work themselves.
the extra lump of work themselves.
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技术进步没有利于人类
10:46
Technological progress,
rather than complement human beings,
rather than complement human beings,
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而是利于机器
10:50
complements machines instead.
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10:52
To see this, go back
to the task of driving a car.
to the task of driving a car.
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关于这一点
我们回到开车这件事上
我们回到开车这件事上
如今 卫星定位系统
可以直接辅助人类
可以直接辅助人类
10:55
Today, satnav systems
directly complement human beings.
directly complement human beings.
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它帮助人们成为更好的司机
11:00
They make some
human beings better drivers.
human beings better drivers.
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11:02
But in the future,
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但是在未来
软件将会代替
驾驶座椅上的人类
驾驶座椅上的人类
11:04
software is going to displace
human beings from the driving seat,
human beings from the driving seat,
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这些卫星定位系统
不再辅助人类
不再辅助人类
11:07
and these satnav systems,
rather than complement human beings,
rather than complement human beings,
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而是使无人驾驶变得更加高效
11:10
will simply make these
driverless cars more efficient,
driverless cars more efficient,
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从而帮衬了机器
11:12
helping the machines instead.
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再回到我之前提到的
那些机器间接互补性的例子
那些机器间接互补性的例子
11:14
Or go to those indirect complementarities
that I mentioned as well.
that I mentioned as well.
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经济蛋糕会变得更大
11:18
The economic pie may get larger,
237
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但是随着机器的能力变得更强
11:20
but as machines become more capable,
238
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很可能最适合
应对新需求的一方
应对新需求的一方
11:22
it's possible that any new demand
will fall on goods that machines,
will fall on goods that machines,
239
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3143
是机器而不是人类自己
11:25
rather than human beings,
are best placed to produce.
are best placed to produce.
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经济蛋糕可能会发生改变
11:27
The economic pie may change,
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1896
但是随着机器能力变强
11:29
but as machines become more capable,
242
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1896
很可能它们才是
最适合完成新工作的一方
最适合完成新工作的一方
11:31
it's possible that they'll be best placed
to do the new tasks that have to be done.
to do the new tasks that have to be done.
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简单来说 对工作的需求
并不一定需要人力来完成
并不一定需要人力来完成
11:36
In short, demand for tasks
isn't demand for human labor.
isn't demand for human labor.
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人们只关心利益
11:40
Human beings only stand to benefit
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1936
是否能够在这些工作中
保持有利地位
保持有利地位
11:42
if they retain the upper hand
in all these complemented tasks,
in all these complemented tasks,
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但是随着机器的能力变强
这将越来越难实现
这将越来越难实现
11:46
but as machines become more capable,
that becomes less likely.
that becomes less likely.
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11:50
So what do these three myths tell us then?
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那么这三个迷思告诉了我们什么呢
解析终结者迷思
11:52
Well, resolving the Terminator myth
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告诉我们未来的工作
取决于两种力量的平衡
取决于两种力量的平衡
11:54
shows us that the future of work depends
upon this balance between two forces:
upon this balance between two forces:
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一是机器的替代性会伤害工人
11:58
one, machine substitution
that harms workers
that harms workers
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3136
二是其互补性也会有利于工人
12:01
but also those complementarities
that do the opposite.
that do the opposite.
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截至目前
这种平衡在向人类一方倾斜
这种平衡在向人类一方倾斜
12:04
And until now, this balance
has fallen in favor of human beings.
has fallen in favor of human beings.
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12:09
But resolving the intelligence myth
254
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1736
但是对智能迷思的分析
告诉我们 机器对人类的替代
12:10
shows us that that first force,
machine substitution,
machine substitution,
255
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正在蓄势待发
12:13
is gathering strength.
256
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1296
机器并不能做所有事
12:14
Machines, of course, can't do everything,
257
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1976
但是它们可以做的更多
12:16
but they can do far more,
258
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更深入地干涉人类工作的领域
12:18
encroaching ever deeper into the realm
of tasks performed by human beings.
of tasks performed by human beings.
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此外 我们也没理由相信
12:22
What's more, there's no reason to think
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1896
人们现在能做的事情
12:24
that what human beings
are currently capable of
are currently capable of
261
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2216
代表着某种终结
12:26
represents any sort of finishing line,
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1856
当机器与我们同样能干时
12:28
that machines are going
to draw to a polite stop
to draw to a polite stop
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2256
它们会带来某种和平的结局
12:30
once they're as capable as us.
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只要机器对我们的互补
12:32
Now, none of this matters
265
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依然确实地存在
12:34
so long as those helpful
winds of complementarity
winds of complementarity
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那么这些担忧都不重要
12:37
blow firmly enough,
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通过解析优越性迷思
12:38
but resolving the superiority myth
268
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1936
展现出工作侵蚀的过程
12:40
shows us that that process
of task encroachment
of task encroachment
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这不止加强了机器的替代性
12:44
not only strengthens
the force of machine substitution,
the force of machine substitution,
270
752000
3936
也削弱了那些有益的互补性
12:47
but it wears down
those helpful complementarities too.
those helpful complementarities too.
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3336
将这三个迷思放到一起
12:51
Bring these three myths together
272
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1936
我认为我们能够看到令人担忧的未来
12:53
and I think we can capture a glimpse
of that troubling future.
of that troubling future.
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2936
机器的能力会继续变强
12:56
Machines continue to become more capable,
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764240
2016
更深入地占据更多人类从事的工作
12:58
encroaching ever deeper
on tasks performed by human beings,
on tasks performed by human beings,
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3656
加强机器的替代性
13:01
strengthening the force
of machine substitution,
of machine substitution,
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同时削弱机器的互补性
13:04
weakening the force
of machine complementarity.
of machine complementarity.
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到某一点时
这个平衡会倾向于机器
这个平衡会倾向于机器
13:08
And at some point, that balance
falls in favor of machines
falls in favor of machines
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而不再是人类
13:12
rather than human beings.
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这是我们现在所面临的道路
13:14
This is the path we're currently on.
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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
因为我不认为我们已经到达这一点
13:16
I say "path" deliberately,
because I don't think we're there yet,
because I don't think we're there yet,
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13:19
but it is hard to avoid the conclusion
that this is our direction of travel.
that this is our direction of travel.
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但是我们无法避免它
这就是我们前进的方向
这就是我们前进的方向
13:24
That's the troubling part.
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这是麻烦的部分
我现在来说一下
为什么我认为这是一个好问题
为什么我认为这是一个好问题
13:26
Let me say now why I think actually
this is a good problem to have.
this is a good problem to have.
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13:30
For most of human history,
one economic problem has dominated:
one economic problem has dominated:
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在大部分的人类历史中
一个经济问题最为重要
一个经济问题最为重要
如何让经济蛋糕足够大
使每个人都可以生存
使每个人都可以生存
13:34
how to make the economic pie
large enough for everyone to live on.
large enough for everyone to live on.
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回到公元1世纪
13:38
Go back to the turn
of the first century AD,
of the first century AD,
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如果你将世界经济这块蛋糕
13:40
and if you took the global economic pie
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等分给世界上每一个人
13:42
and divided it up into equal slices
for everyone in the world,
for everyone in the world,
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人均将得到几百美元
13:45
everyone would get a few hundred dollars.
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基本上大家都生活在贫困线水平
13:47
Almost everyone lived
on or around the poverty line.
on or around the poverty line.
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13:51
And if you roll forward a thousand years,
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如果再前进一千年
大概也还是这个情况
13:53
roughly the same is true.
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13:55
But in the last few hundred years,
economic growth has taken off.
economic growth has taken off.
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但是在最近几百年
经济开始起飞
经济开始起飞
经济蛋糕呈爆炸性增长
13:59
Those economic pies have exploded in size.
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全球平均GDP
14:01
Global GDP per head,
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也就是如今个人分到的蛋糕
14:03
the value of those individual
slices of the pie today,
slices of the pie today,
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大约是10150美元
14:07
they're about 10,150 dollars.
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如果经济继续增长2%
14:10
If economic growth continues
at two percent,
at two percent,
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我们下一代的富有程度
会是我们的二倍
会是我们的二倍
14:12
our children will be twice as rich as us.
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如果经济增长只有可怜的1%
14:14
If it continues
at a more measly one percent,
at a more measly one percent,
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我们孙辈的富有程度
会是我们的二倍
会是我们的二倍
14:17
our grandchildren
will be twice as rich as us.
will be twice as rich as us.
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由此 我们解决了传统的经济问题
14:19
By and large, we've solved
that traditional economic problem.
that traditional economic problem.
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14:24
Now, technological unemployment,
if it does happen,
if it does happen,
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那么 技术性失业
如果真的以某种方式发生
如果真的以某种方式发生
它将是经济增长成功的一种表现
14:27
in a strange way will be
a symptom of that success,
a symptom of that success,
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它解决了一个问题
那就是如何让蛋糕变得更大
那就是如何让蛋糕变得更大
14:30
will have solved one problem --
how to make the pie bigger --
how to make the pie bigger --
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但是也带来了另一个问题
14:34
but replaced it with another --
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如何确保每个人都能分一杯羹
14:36
how to make sure
that everyone gets a slice.
that everyone gets a slice.
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14:39
As other economists have noted,
solving this problem won't be easy.
solving this problem won't be easy.
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我们的经济学家曾指出
解决这些问题并不容易
解决这些问题并不容易
如今 对大部分人来说
14:43
Today, for most people,
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他们的工作就是
他们分得经济蛋糕的方式
他们分得经济蛋糕的方式
14:45
their job is their seat
at the economic dinner table,
at the economic dinner table,
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这个世界的工作越来越少
或是甚至没有工作
或是甚至没有工作
14:47
and in a world with less work
or even without work,
or even without work,
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人们如何分得蛋糕
仍不得而知
仍不得而知
14:50
it won't be clear
how they get their slice.
how they get their slice.
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可能解决该问题的方法之一
14:52
There's a great deal
of discussion, for instance,
of discussion, for instance,
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是提供全民基本收入
14:54
about various forms
of universal basic income
of universal basic income
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关于其形式有各种讨论
14:57
as one possible approach,
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在美国 芬兰和肯尼亚
14:58
and there's trials underway
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15:00
in the United States
and in Finland and in Kenya.
and in Finland and in Kenya.
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也正在进行一些尝试
15:03
And this is the collective challenge
that's right in front of us,
that's right in front of us,
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这是我们共同面对的挑战
那就是 在这个仍然用
传统方式分配所得的
传统方式分配所得的
15:06
to figure out how this material prosperity
generated by our economic system
generated by our economic system
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世界中
15:11
can be enjoyed by everyone
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15:13
in a world in which
our traditional mechanism
our traditional mechanism
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当人们做的工作越来越少
15:15
for slicing up the pie,
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也许彻底消失
如何让经济系统带来的
15:17
the work that people do,
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物质繁荣能够被每个人享有
15:19
withers away and perhaps disappears.
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15:22
Solving this problem is going to require
us to think in very different ways.
us to think in very different ways.
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解决这个问题
需要我们用不同的方法思考
需要我们用不同的方法思考
15:27
There's going to be a lot of disagreement
about what ought to be done,
about what ought to be done,
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对于需要做些什么
将会有很多反对意见
将会有很多反对意见
但重要的是明确一点
相比如何让经济蛋糕变大
相比如何让经济蛋糕变大
15:31
but it's important to remember
that this is a far better problem to have
that this is a far better problem to have
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这个曾困扰我们祖先
长达几个世纪的问题
长达几个世纪的问题
15:35
than the one that haunted
our ancestors for centuries:
our ancestors for centuries:
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我们面临的是一个
要好得多的问题
要好得多的问题
15:37
how to make that pie
big enough in the first place.
big enough in the first place.
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非常感谢
15:41
Thank you very much.
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（掌声）
15:42
(Applause)
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Translated by Yiran Wang
ABOUT THE SPEAKER
Daniel Susskind - EconomistDaniel Susskind explores the impact of technology, particularly artificial intelligence, on work and society.
Why you should listen
Daniel Susskind is the co-author, with Richard Susskind, of the best-selling book, The Future of the Professions, and a Fellow in Economics at Balliol College, Oxford University. He is currently finishing his latest book, on the future of work. Previously, he worked in the British Government -- as a policy adviser in the Prime Minister's Strategy Unit, as a policy analyst in the Policy Unit in 10 Downing Street, and as a senior policy adviser in the Cabinet Office. Susskind received a doctorate in economics from Oxford University and was a Kennedy Scholar at Harvard University.
Daniel Susskind | Speaker | TED.com