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

譯者: Lilian Chiu 審譯者: Chen Chi-An

Automation anxiety has been spreading lately,

近期，自動化焦慮一直在散佈，

a fear that in the future,

它是種恐懼，害怕在未來

many jobs will be performed by machines

許多工作會由機器來進行，

rather than human beings,

而不是人類，

given the remarkable advances that are unfolding

因為現在已可以看到在人工智慧

in artificial intelligence and robotics.

和機器人學領域的驚人進步。

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

很清楚的一點是， 將來會有顯著的改變。

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

比較不那麼清楚的是， 改變會是什麼樣的。

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

我的研究指出，未來 既讓人困擾又讓人興奮。

The threat of technological unemployment is real,

科技造成失業的威脅是真的，

and yet it's a good problem to have.

但，能有這種問題也是件好事。

And to explain how I came to that conclusion,

為了解釋我如何得到這個結論，

I want to confront three myths

我想要來正視三項迷思，

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

我認為這些迷思 目前遮掩了我們的視線，

讓我們看不清自動化的未來。

A picture that we see on our television screens,

我們在電視上、書中、電影中、

in books, in films, in everyday commentary

每天的評論中所看到的描繪，

is one where an army of robots descends on the workplace

通常是機器人大軍湧入工作場所，

with one goal in mind:

心中只有一個目標：

to displace human beings from their work.

在工作上取代人類。

And I call this the Terminator myth.

我稱這個想法為「終結者迷思」。

Yes, machines displace human beings from particular tasks,

是的，在特定的工作任務上， 機器會取代人類，

but they don't just substitute for human beings.

但它們不會就這樣代替人類。

They also complement them in other tasks,

它們在其他工作任務上會補足人類，

making that work more valuable and more important.

讓工作更有價值、更重要。

Sometimes they complement human beings directly,

有時，它們會直接補足人類，

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

讓人類在特定的工作任務上 能更有生產力或更有效率。

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

計程車司機在不熟悉的路上 可以用衛星導航系統來協助導航。

An architect can use computer-assisted design software

建築師可以用電腦輔助的設計軟體

to design bigger, more complicated buildings.

來設計更大、更複雜的建築物。

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

但科技進步並不只會直接補足人類。

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

它也會用間接方式補足人類， 間接的方式有兩種。

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

第一，如果我們把 經濟想成是一塊派，

technological progress makes the pie bigger.

科技進步會讓派變更大。

As productivity increases, incomes rise and demand grows.

隨著生產力增加， 收入會增加，需求會成長。

The British pie, for instance,

比如，英國的派

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

與三百年前相比，現在超過百倍大。

And so people displaced from tasks in the old pie

在舊派工作被取代的人，

could find tasks to do in the new pie instead.

能在新派中找到工作。

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

但科技進步並不只會讓派變大。

It also changes the ingredients in the pie.

它也會改變派的成分原料。

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

隨時間演進，人會以 不同的方式花費他們的收入，

changing how they spread it across existing goods,

改變既有商品花費上的分配，

and developing tastes for entirely new goods, too.

並也會發展出對於全新商品的品味。

New industries are created,

新的產業會被創造出來，

new tasks have to be done

有新的工作任務需要被完成，

and that means often new roles have to be filled.

那就意味著有新角色要有人扮演。

So again, the British pie:

所以，再回到英國的派：

300 years ago, most people worked on farms,

三百年前，大部分的人在農場工作，

150 years ago, in factories,

一百五十年前，在工廠工作，

and today, most people work in offices.

現今，大部分的人在辦公室工作。

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

再提一次，在老派工作被取代的人，

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

可能會在新派當中 發現可以做的工作任務。

Economists call these effects complementarities,

經濟學家把這些效應稱為互補性，

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

但那只是個很炫的詞，其實意思就是

that technological progress helps human beings.

科技進步用不同的方式在協助人類。

Resolving this Terminator myth

解開這個終結者迷思之後，

shows us that there are two forces at play:

會發現有兩股力量在運作：

one, machine substitution that harms workers,

第一：機器代替，這會傷害到工人，

but also these complementarities that do the opposite.

但也會有第二股力量， 互補性，反而會幫助工人。

Now the second myth,

再來，第二項迷思，

what I call the intelligence myth.

我稱之為「智慧迷思」。

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

以下這些工作任務： 駕駛一台車、做出醫療診斷，

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

及快速一瞥就辨識出 一隻鳥，有何共通性？

Well, these are all tasks that until very recently,

這些工作任務都是直到最近

leading economists thought couldn't readily be automated.

仍被經濟學家認為不能 自動化的工作任務。

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

然而，現今，所有這些 工作任務都能被自動化。

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

所有大型汽車製造商都有 無人駕駛汽車的計畫。

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

外面有數不清的系統 都能夠診斷醫療問題。

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

甚至有個應用程式能用來辨識鳥類，

at a fleeting glimpse.

只要快速一瞥。

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

這並不是經濟學家運氣不好的情況。

They were wrong,

他們錯了，

and the reason why they were wrong is very important.

而他們為什麼會錯的原因很重要。

They've fallen for the intelligence myth,

他們陷入了智慧迷思中，

the belief that machines have to copy the way

相信機器必須要複製人類

that human beings think and reason

思考和推理的方式，

in order to outperform them.

才能夠表現得比人類好。

When these economists were trying to figure out

當這些經濟學家在試圖想出

what tasks machines could not do,

機器無法勝任哪些工作任務，

they imagined the only way to automate a task

他們想像，將工作任務自動化的

was to sit down with a human being,

唯一方式就是和人類坐下來，

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

讓人類解釋他們如何執行工作任務，

and then try and capture that explanation

再試著分析他們的解釋，

in a set of instructions for a machine to follow.

轉換成一組指令，讓機器照著做。

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

在人工智慧領域，這種觀點 曾在某個時點很流行過。

I know this because Richard Susskind,

我知道這點，因為理查薩斯金，

who is my dad and my coauthor,

他是我爸爸也是我的共同作者，

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

在八〇年代，在牛津大學 寫了一篇關於人工智慧

at Oxford University,

與法律的博士論文，

and he was part of the vanguard.

他是先鋒部隊之一。

And with a professor called Phillip Capper

和一位名叫菲利普卡波的教授，

and a legal publisher called Butterworths,

以及一間法律出版社 叫做 Butterworths，

they produced the world's first commercially available

他們合作製作出了 世界上第一個商業用的

artificial intelligence system in the law.

法律人工智慧系統。

This was the home screen design.

這是首頁的畫面設計。

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

他向我保證，在當時 這是很酷的畫面設計。

(Laughter)

（笑聲）

I've never been entirely convinced.

我從來沒有被說服。

He published it in the form of two floppy disks,

他用兩張軟碟片的形式將之出版，

at a time where floppy disks genuinely were floppy,

在那個時代，軟碟片真的是軟的，

and his approach was the same as the economists':

而他的方式就和經濟學家一樣：

sit down with a lawyer,

和一名律師坐下來，

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

讓她向你解釋如何解決法律問題，

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

接著就試著把她的解釋 轉成一組指令給機器執行。

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

在經濟上，如果人類能夠用 這種方式解釋自己做的事，

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

這種工作任務就叫做例行事務， 是可以被自動化的。

But if human beings can't explain themselves,

但如果人類無法解釋出怎麼做，

the tasks are called non-routine, and they're thought to be out reach.

這種工作任務叫做非例行事務， 應該是不能自動化的。

Today, that routine-nonroutine distinction is widespread.

現今，將事務區別為例行 與非例行是處處可見的。

Think how often you hear people say to you

想想看，你有多常聽到別人對你說

machines can only perform tasks that are predictable or repetitive,

機器能進行的工作任務 只有可預測的、重覆性的、

rules-based or well-defined.

以規則為基礎的，或定義清楚的。

Those are all just different words for routine.

那些詞只是例行事務的不同說法。

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

回到我一開始提到的三個案例。

Those are all classic cases of nonroutine tasks.

那些案例是典型的非例行事務。

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

比如，去問一位醫生 如何做醫療診斷，

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

她可能會給你少數經驗法則，

but ultimately she'd struggle.

但最終，她會很掙扎。

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

她會說，你還需要創意、 判斷，以及直覺才行。

And these things are very difficult to articulate,

這些東西是很難明確表達的，

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

所以這些工作任務就會 被認為很難自動化。

If a human being can't explain themselves,

如果人類無法解釋他們自己的做法，

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

我們究竟要從何開始寫指令

for a machine to follow?

給機器遵循？

Thirty years ago, this view was right,

三十年前，這個觀點是對的，

but today it's looking shaky,

但現今，它很不穩固，

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

在未來，它將會是錯的。

Advances in processing power, in data storage capability

處理能力、資料儲存容量，

and in algorithm design

以及演算法設計都在進步，

mean that this routine-nonroutine distinction

這就表示例行與非例行事務間的區別

is diminishingly useful.

越來越沒有用了。

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

要了解這點，我們 回到醫療診斷的案例。

Earlier in the year,

今年早些時候，

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

史丹佛的一個研究者團隊 宣佈他們發展出了一個系統，

which can tell you whether or not a freckle is cancerous

它能告訴你一個斑點是否為惡性的，

as accurately as leading dermatologists.

正確率不輸給頂尖皮膚科醫生。

How does it work?

它怎麼做到的？

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

它並不是嘗試複製 醫生的判斷或是直覺。

It knows or understands nothing about medicine at all.

它對於醫學是一竅不通。

Instead, it's running a pattern recognition algorithm

反之，它進行的是模式辨識演算法，

through 129,450 past cases,

在 129,450 個個案當中，

hunting for similarities between those cases

獵尋那些個案與欲探究的損害

and the particular lesion in question.

之間有哪些相似性。

It's performing these tasks in an unhuman way,

它是用非人類的方式 在進行這些工作任務，

based on the analysis of more possible cases

且是以大量案例的分析來當依據，

than any doctor could hope to review in their lifetime.

案例數多到是醫生 一輩子都看不完的。

It didn't matter that that human being,

無所謂人類，也就是醫生，

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

是否能解釋她如何進行此工作任務。

Now, there are those who dwell upon that the fact

有些人老是會想著

這些機器被建立時 沒有依循我們的形象。

that these machines aren't built in our image.

As an example, take IBM's Watson,

以 IBM 的「華生 」為例，

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

那是台超級電腦，2011 年參加 美國的益智節目《危險邊緣》，

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

在節目中它打敗了兩位人類冠軍。

The day after it won,

它獲勝之後的隔天，

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

《華爾街日報》刊了一篇 哲學家約翰希爾勒的文章，

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

標題是〈華生不知道 它自己贏了《危險邊緣》 〉。

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

是的，這篇文章很聰明也沒說錯。

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

華生並沒有興奮地放聲大叫。

它沒有打電話給它的父母 說它的表現多棒。

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

它沒有去酒吧喝酒慶祝。

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,

這個系統並沒有試圖複製 那些人類參賽者比賽的方式，

but it didn't matter.

但那無所謂。

It still outperformed them.

它仍然表現得比人類好。

Resolving the intelligence myth

解開這個智慧迷思之後，

shows us that our limited understanding about human intelligence,

看到的是雖然我們對於 人類智慧、對我們如何

about how we think and reason,

思考推理的方式了解有限，

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

但這個限制對於自動化的影響 已經遠比過去小很多。

What's more, as we've seen,

此外，如我們所見，

when these machines perform tasks differently to human beings,

當這些機器用和人類不同的 方式來執行工作任務時，

there's no reason to think

沒有理由認為

that what human beings are currently capable of doing

人類目前能夠做到的事

represents any sort of summit

就代表了一種上限，

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

在未來機器能夠達成的事 都不可能超過這個上限。

Now the third myth,

第三項迷思，

what I call the superiority myth.

我稱之為優越迷思。

It's often said that those who forget

常見的說法是，有些人會

about the helpful side of technological progress,

忘記了科技進步的幫助面，

those complementarities from before,

忘記過去的互補性，

are committing something known as the lump of labor fallacy.

這些人所犯的，就是 所謂的「勞動總合謬誤」。

Now, the problem is the lump of labor fallacy

問題是，勞動總合謬誤本身

is itself a fallacy,

就是個謬誤，

and I call this the lump of labor fallacy fallacy,

我把它稱為 「勞動總合謬誤的謬誤」，

or LOLFF, for short.

簡寫為「LOLFF」。

Let me explain.

讓我解釋一下。

The lump of labor fallacy is a very old idea.

勞動總合謬誤是個很古老的想法。

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

這個名稱是 1892 年由英國 經濟學家大衛許洛斯取的。

He was puzzled to come across a dock worker

有件事讓他百思不解， 他遇到一個碼頭工人，

who had begun to use a machine to make washers,

這個工人開始用機器來製造墊圈，

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

墊圈是小型的金屬圓盤， 固定在螺絲底端。

And this dock worker felt guilty for being more productive.

這個碼頭工人對於自己的 高生產力有罪惡感。

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

通常，我們預期的是相反的反應，

that people feel guilty for being unproductive,

生產力不高才會讓人感到罪惡，

你知道的，工作時 花太多時間滑臉書或推特。

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

但這個工人對於 太有生產力感到罪惡，

But this worker felt guilty for being more productive,

問他原因，他說：「我知道我做錯了。

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

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

我搶走了另一個人的工作。」

In his mind, there was some fixed lump of work

在他的認知中，勞動總合是固定的，

to be divided up between him and his pals,

要由他和他的伙伴來分攤，

so that if he used this machine to do more,

所以如果他用機器多做一點，

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

他伙伴能做的就變少了。

Schloss saw the mistake.

許洛斯看到了這個錯誤。

The lump of work wasn't fixed.

勞動總合並不是固定的。

As this worker used the machine and became more productive,

當這個工人用機器提高生產力，

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

墊圈的價格會下降， 對墊圈的需求會提高，

more washers would have to be made,

就得要做出更多的墊圈，

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

他的伙伴反而會有更多要做。

The lump of work would get bigger.

勞動總合變更大了。

Schloss called this "the lump of labor fallacy."

許洛斯稱之為「勞動總合謬誤」。

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

現今，在思考有各類工作的未來時，

to think about the future of all types of work.

會聽到人們談到勞動總合謬誤。

沒有固定的勞動總合

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

要讓人類與機器瓜分。

between people and machines.

是的，機器會取代人類， 讓原本的勞動總合變少，

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

but they also complement human beings,

但它們也會補足人類，

and the lump of work gets bigger and changes.

勞動總合會變更大並且改變。

But LOLFF.

但，LOLFF。

Here's the mistake:

錯誤是這樣的：

it's right to think that technological progress

認為科技進步會讓 要做的勞動總合變大，

makes the lump of work to be done bigger.

這點是沒錯的。

有些工作任務變得較有價值。 有新工作任務需要完成。

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

But it's wrong to think that necessarily,

錯的地方在於，認為安排人類

human beings will be best placed to perform those tasks.

來做那些工作任務一定是最好的。

And this is the superiority myth.

這就是優越迷思。

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

是的，勞動總量可能 會變大也會改變，

but as machines become more capable,

但隨著機器變得更有能力，

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

很有可能它們會自己去接下 那些額外的勞動總量。

Technological progress, rather than complement human beings,

科技進步就不是在補足人類了，

complements machines instead.

反而是補足機器。

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

可以回頭看駕駛汽車的 工作任務來了解這點。

Today, satnav systems directly complement human beings.

現今，衛星導航系統直接補足人類。

They make some human beings better drivers.

它讓一些人類變成更好的駕駛。

But in the future,

但在未來，

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

軟體會取代坐在駕駛座上的人類，

這些衛星導航系統 就不是在補足人類了，

and these satnav systems, rather than complement human beings,

而單純就是在讓這些 無人駕駛汽車更有效率，

will simply make these driverless cars more efficient,

helping the machines instead.

改而協助機器。

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

或也可以回到 我剛提過的間接互補性。

The economic pie may get larger,

經濟的派可能會變更大，

but as machines become more capable,

但隨著機器更有能力，

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

有可能所有符合新需求的商品都適合

由機器而不是由人類來製造。

rather than human beings, are best placed to produce.

The economic pie may change,

經濟的派可能會改變，

but as machines become more capable,

但隨著機器變得更有能力，

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

有可能它們最適合運用在 新工作任務中，那些必須解決的事。

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

簡言之，對工作任務的需求 並非對人類勞動力的需求。

Human beings only stand to benefit

人類只有在仍然能支配

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

這些補足性工作任務的 情況下才有可能受益，

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

但隨著機器變得更有能力， 那就更不可能發生。

So what do these three myths tell us then?

所以，這三項迷思告訴我們什麼？

Well, resolving the Terminator myth

解開終結者迷思之後，

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

我們知道工作的未來還要 仰賴兩股力量間的平衡：

one, machine substitution that harms workers

第一：機器代替，這會傷害到工人，

but also those complementarities that do the opposite.

但也會有第二股力量， 互補性，反而會幫助工人。

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

直到目前，這平衡是對人類有利的。

But resolving the intelligence myth

但解開了智慧迷思之後，

shows us that that first force, machine substitution,

我們知道，第一股力量，機器代替，

is gathering strength.

正在聚集實力。

Machines, of course, can't do everything,

當然，機器並非什麼都能做，

but they can do far more,

但它們能做的很多，

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

能更深進入到人類所進行之 工作任務的領域中。

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

此外，沒有理由去認為

that what human beings are currently capable of

人類目前已經能做到的事，

represents any sort of finishing line,

就表示是某種終點線，

that machines are going to draw to a polite stop

等到機器和我們一樣有能力時

once they're as capable as us.

就會禮貌地在終點線前停下來。

Now, none of this matters

這些都無所謂，

so long as those helpful winds of complementarity

只要機器和人類在工作上 能相得益彰就好。

blow firmly enough,

but resolving the superiority myth

但解開了優越迷思之後，

shows us that that process of task encroachment

我們了解到，工作任務侵佔的過程

not only strengthens the force of machine substitution,

不僅是強化了機器代替的那股力量，

but it wears down those helpful complementarities too.

也會耗損那些有助益的互補性。

Bring these three myths together

把這三項迷思結合起來，

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

我想，我們就能對 讓人困擾的未來有點概念。

Machines continue to become more capable,

機器持續變得更有能力，

encroaching ever deeper on tasks performed by human beings,

比以前更深入人類進行的工作任務，

strengthening the force of machine substitution,

強化機器代替的那股力量，

weakening the force of machine complementarity.

弱化機器互補性的那股力量。

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

在某個時點，那平衡 會變得對機器有利，

rather than human beings.

而非人類。

This is the path we're currently on.

我們目前就在這條路上。

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

我刻意用「路」這個字， 因為我們還沒有到達那裡，

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

但無可避免，結論會是： 這就是我們行進的方向。

That's the troubling part.

那是讓人困擾的部分。

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

現在讓我說明為什麼我認為 有這個問題是件好事。

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

大部分的人類歷史中， 主導的都是這一個經濟問題：

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

如何讓經濟的派夠大， 確保每個人都得以維生。

Go back to the turn of the first century AD,

回到西元一世紀，

and if you took the global economic pie

如果用全球的派當作例子，

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

將它切成相同的等分， 分給全世界的人，

everyone would get a few hundred dollars.

每個人可能得到幾百美元。

Almost everyone lived on or around the poverty line.

幾乎每個人都是在 貧窮水平線上下過生活。

And if you roll forward a thousand years,

如果你再向前轉一千年，

roughly the same is true.

大致上也是一樣的。

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

但在過去幾百年間，經濟成長起飛。

Those economic pies have exploded in size.

這些經濟的派在尺寸上都爆增。

Global GDP per head,

全球的人均生產總值，

the value of those individual slices of the pie today,

也就是現今每個人分到的那片派，

they're about 10,150 dollars.

價值約 10,150 美元。

If economic growth continues at two percent,

如果經濟成長率維持 2%，

our children will be twice as rich as us.

我們的孩子會比我們富有兩倍。

If it continues at a more measly one percent,

如果成長率低一點，維持在 1%，

our grandchildren will be twice as rich as us.

我們的孫子會比我們富有兩倍。

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

總的來說，我們解決了 傳統的經濟問題。

Now, technological unemployment, if it does happen,

如果真的因為科技進步而造成失業，

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

從一種奇怪的角度來看， 那會是一種成功的象徵，

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

它能夠解決一個問題 ──如何讓派變大──

but replaced it with another --

但卻用另一個問題取代它──

how to make sure that everyone gets a slice.

如何確保每個人得到一片派。

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

如其他經濟學家注意到的， 解決這個問題並不容易。

Today, for most people,

現今，對大部分人而言，

their job is their seat at the economic dinner table,

他們的工作就是在 經濟晚餐餐桌上的席位，

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

在一個更少或甚至沒工作的世界裡，

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

沒人知道他們如何得到自己的那片派。

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

比如，有很多的討論都是

about various forms of universal basic income

關於全體基本收入的各種形式，

as one possible approach,

這是種可能的方式，

and there's trials underway

且在美國、芬蘭，

in the United States and in Finland and in Kenya.

及肯亞都有試驗正在進行中。

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

這是我們要面臨的集體挑戰，

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

要想出我們的經濟體制 所產生出的物質繁榮要如何

can be enjoyed by everyone

讓每個人都享受到，

in a world in which our traditional mechanism

而且在這個世界中，

我們的傳統切派機制，

for slicing up the pie,

the work that people do,

瓜分人們所做的工作的機制，

withers away and perhaps disappears.

在衰弱且也許在消失中。

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

若要解決這個問題，我們 得要用很不同的方式思考。

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

對於該做什麼事， 必定會有很多異議，

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

但很重要的是要記住， 有這個問題其實算好事，

than the one that haunted our ancestors for centuries:

比我們的祖先煩惱了 幾世紀的問題要好多了，

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

他們煩惱的是： 一開始要如何讓派變大。

Thank you very much.

非常謝謝各位。

(Applause)

（掌聲）