sentence-transformers/parallel-sentences-talks

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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 at a fleeting glimpse.	甚至有個應用程式能用來辨識鳥類，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, and the reason why they were wrong is very important.	他們錯了，而他們為什麼會錯的原因很重要。
They've fallen for the intelligence myth, the belief that machines have to copy the way that human beings think and reason in order to outperform them.	他們陷入了智慧迷思中，相信機器必須要複製人類思考和推理的方式，才能夠表現得比人類好。
When these economists were trying to figure out what tasks machines could not do, they imagined the only way to automate a task was to sit down with a human being, get them to explain to you how it was they performed a task, and then try and capture that explanation in a set of instructions for a machine to follow.	當這些經濟學家在試圖想出機器無法勝任哪些工作任務，他們想像，將工作任務自動化的唯一方式就是和人類坐下來，讓人類解釋他們如何執行工作任務，再試著分析他們的解釋，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, who is my dad and my coauthor, wrote his doctorate in the 1980s on artificial intelligence and the law at Oxford University, and he was part of the vanguard.	我知道這點，因為理查薩斯金，他是我爸爸也是我的共同作者，在八〇年代，在牛津大學寫了一篇關於人工智慧與法律的博士論文，他是先鋒部隊之一。
And with a professor called Phillip Capper and a legal publisher called Butterworths, they produced the world's first commercially available artificial intelligence system in the law.	和一位名叫菲利普卡波的教授，以及一間法律出版社叫做 Butterworths，他們合作製作出了世界上第一個商業用的法律人工智慧系統。
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) I've never been entirely convinced.	（笑聲）我從來沒有被說服。
He published it in the form of two floppy disks, at a time where floppy disks genuinely were floppy, and his approach was the same as the economists': sit down with a lawyer, get her to explain to you how it was she solved a legal problem, and then try and capture that explanation in a set of rules for a machine to follow.	他用兩張軟碟片的形式將之出版，在那個時代，軟碟片真的是軟的，而他的方式就和經濟學家一樣：和一名律師坐下來，讓她向你解釋如何解決法律問題，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, the tasks are called routine, and they could be automated.	在經濟上，如果人類能夠用這種方式解釋自己做的事，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, the tasks are called non-routine, and they're thought to be out reach.	但如果人類無法解釋出怎麼做，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 machines can only perform tasks that are predictable or repetitive, rules-based or well-defined.	想想看，你有多常聽到別人對你說機器能進行的工作任務只有可預測的、重覆性的、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, and she might be able to give you a few rules of thumb, but ultimately she'd struggle.	比如，去問一位醫生如何做醫療診斷，她可能會給你少數經驗法則，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, and so it was thought these tasks would be very hard to automate.	這些東西是很難明確表達的，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, where on earth do we begin in writing a set of instructions for a machine to follow?	如果人類無法解釋他們自己的做法，我們究竟要從何開始寫指令給機器遵循？
Thirty years ago, this view was right, but today it's looking shaky, and in the future it's simply going to be wrong.	三十年前，這個觀點是對的，但現今，它很不穩固，在未來，它將會是錯的。
Advances in processing power, in data storage capability and in algorithm design mean that this routine-nonroutine distinction is diminishingly useful.	處理能力、資料儲存容量，以及演算法設計都在進步，這就表示例行與非例行事務間的區別越來越沒有用了。
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, a team of researchers at Stanford announced they'd developed a system which can tell you whether or not a freckle is cancerous as accurately as leading dermatologists.	今年早些時候，史丹佛的一個研究者團隊宣佈他們發展出了一個系統，它能告訴你一個斑點是否為惡性的，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 through 129,450 past cases, hunting for similarities between those cases and the particular lesion in question.	反之，它進行的是模式辨識演算法，在 129,450 個個案當中，獵尋那些個案與欲探究的損害之間有哪些相似性。
It's performing these tasks in an unhuman way, based on the analysis of more possible cases than any doctor could hope to review in their lifetime.	它是用非人類的方式在進行這些工作任務，且是以大量案例的分析來當依據，案例數多到是醫生一輩子都看不完的。
It didn't matter that that human being, that doctor, couldn't explain how she'd performed the task.	無所謂人類，也就是醫生，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 that these machines aren't built in our image.	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。
As an example, take IBM's Watson, the supercomputer that went on the US quiz show "Jeopardy!" in 2011, and it beat the two human champions at "Jeopardy!"	以 IBM 的「華生」為例，那是台超級電腦，2011 年參加美國的益智節目《危險邊緣》，在節目中它打敗了兩位人類冠軍。
The day after it won, The Wall Street Journal ran a piece by the philosopher John Searle with the title "Watson Doesn't Know It Won on 'Jeopardy!'" Right, and it's brilliant, and it's true.	它獲勝之後的隔天，《華爾街日報》刊了一篇哲學家約翰希爾勒的文章，標題是〈華生不知道它自己贏了《危險邊緣》〉。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。
It didn't call up its parents to say what a good job it had done.	它沒有打電話給它的父母說它的表現多棒。
It didn't go down to the pub for a drink.	它沒有去酒吧喝酒慶祝。
This system wasn't trying to copy the way that those human contestants played, but it didn't matter.	這個系統並沒有試圖複製那些人類參賽者比賽的方式，但那無所謂。
It still outperformed them.	它仍然表現得比人類好。
Resolving the intelligence myth shows us that our limited understanding about human intelligence, about how we think and reason, is far less of a constraint on automation than it was in the past.	解開這個智慧迷思之後，看到的是雖然我們對於人類智慧、對我們如何思考推理的方式了解有限，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, when these machines perform tasks differently to human beings, there's no reason to think that what human beings are currently capable of doing represents any sort of summit in what these machines might be capable of doing in the future.	此外，如我們所見，當這些機器用和人類不同的方式來執行工作任務時，沒有理由認為人類目前能夠做到的事就代表了一種上限，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, what I call the superiority myth.	第三項迷思，我稱之為優越迷思。
It's often said that those who forget about the helpful side of technological progress, those complementarities from before, are committing something known as the lump of labor fallacy.	常見的說法是，有些人會忘記了科技進步的幫助面，忘記過去的互補性，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 is itself a fallacy, and I call this the lump of labor fallacy fallacy, or LOLFF, for short.	問題是，勞動總合謬誤本身就是個謬誤，我把它稱為「勞動總合謬誤的謬誤」，簡寫為「LOLFF」。
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 who had begun to use a machine to make washers, the small metal discs that fasten on the end of screws.	有件事讓他百思不解，他遇到一個碼頭工人，這個工人開始用機器來製造墊圈，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, that people feel guilty for being unproductive, you know, a little too much time on Facebook or Twitter at work.	通常，我們預期的是相反的反應，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。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, and asked why, he said, "I know I'm doing wrong.	問他原因，他說：「我知道我做錯了。
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 to be divided up between him and his pals, so that if he used this machine to do more, there'd be less left for his pals to do.	在他的認知中，勞動總合是固定的，要由他和他的伙伴來分攤，所以如果他用機器多做一點，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, the price of washers would fall, demand for washers would rise, more washers would have to be made, and there'd be more work for his pals to do.	當這個工人用機器提高生產力，墊圈的價格會下降，對墊圈的需求會提高，就得要做出更多的墊圈，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 to think about the future of all types of work.	現今，在思考有各類工作的未來時，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 between people and machines.	要讓人類與機器瓜分。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, and the lump of work gets bigger and changes.	但它們也會補足人類，勞動總合會變更大並且改變。
But LOLFF.	但，LOLFF。
Here's the mistake: it's right to think that technological progress makes the lump of work to be done bigger.	錯誤是這樣的：認為科技進步會讓要做的勞動總合變大，這點是沒錯的。
Some tasks become more valuable. New tasks have to be done.	有些工作任務變得較有價值。有新工作任務需要完成。
But it's wrong to think that necessarily, human beings will be best placed to perform those tasks.	錯的地方在於，認為安排人類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, but as machines become more capable, it's likely that they'll take on the extra lump of work themselves.	是的，勞動總量可能會變大也會改變，但隨著機器變得更有能力，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, complements machines instead.	科技進步就不是在補足人類了，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, software is going to displace human beings from the driving seat, and these satnav systems, rather than complement human beings, will simply make these driverless cars more efficient, helping the machines instead.	但在未來，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，而單純就是在讓這些無人駕駛汽車更有效率，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, but as machines become more capable, it's possible that any new demand will fall on goods that machines, rather than human beings, are best placed to produce.	經濟的派可能會變更大，但隨著機器更有能力，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。
The economic pie may change, but as machines become more capable, it's possible that they'll be best placed to do the new tasks that have to be done.	經濟的派可能會改變，但隨著機器變得更有能力，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 if they retain the upper hand in all these complemented tasks, but as machines become more capable, that becomes less likely.	人類只有在仍然能支配這些補足性工作任務的情況下才有可能受益，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 shows us that the future of work depends upon this balance between two forces: one, machine substitution that harms workers but also those complementarities that do the opposite.	解開終結者迷思之後，我們知道工作的未來還要仰賴兩股力量間的平衡：第一：機器代替，這會傷害到工人，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 shows us that that first force, machine substitution, is gathering strength.	但解開了智慧迷思之後，我們知道，第一股力量，機器代替，正在聚集實力。
Machines, of course, can't do everything, but they can do far more, encroaching ever deeper into the realm of tasks performed by human beings.	當然，機器並非什麼都能做，但它們能做的很多，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 that what human beings are currently capable of represents any sort of finishing line, that machines are going to draw to a polite stop once they're as capable as us.	此外，沒有理由去認為人類目前已經能做到的事，就表示是某種終點線，等到機器和我們一樣有能力時就會禮貌地在終點線前停下來。
Now, none of this matters so long as those helpful winds of complementarity blow firmly enough, but resolving the superiority myth shows us that that process of task encroachment not only strengthens the force of machine substitution, but it wears down those helpful complementarities too.	這些都無所謂，只要機器和人類在工作上能相得益彰就好。但解開了優越迷思之後，我們了解到，工作任務侵佔的過程不僅是強化了機器代替的那股力量，也會耗損那些有助益的互補性。
Bring these three myths together and I think we can capture a glimpse of that troubling future.	把這三項迷思結合起來，我想，我們就能對讓人困擾的未來有點概念。
Machines continue to become more capable, encroaching ever deeper on tasks performed by human beings, strengthening the force of machine substitution, weakening the force of machine complementarity.	機器持續變得更有能力，比以前更深入人類進行的工作任務，強化機器代替的那股力量，弱化機器互補性的那股力量。
And at some point, that balance falls in favor of machines rather than human beings.	在某個時點，那平衡會變得對機器有利，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, but it is hard to avoid the conclusion that this is our direction of travel.	我刻意用「路」這個字，因為我們還沒有到達那裡，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: how to make the economic pie large enough for everyone to live on.	大部分的人類歷史中，主導的都是這一個經濟問題：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, and if you took the global economic pie and divided it up into equal slices for everyone in the world, everyone would get a few hundred dollars.	回到西元一世紀，如果用全球的派當作例子，將它切成相同的等分，分給全世界的人，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, roughly the same is true.	如果你再向前轉一千年，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。

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 at a fleeting glimpse.

甚至有個應用程式能用來辨識鳥類，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, and the reason why they were wrong is very important.

他們錯了，而他們為什麼會錯的原因很重要。

They've fallen for the intelligence myth, the belief that machines have to copy the way that human beings think and reason in order to outperform them.

他們陷入了智慧迷思中，相信機器必須要複製人類思考和推理的方式，才能夠表現得比人類好。

When these economists were trying to figure out what tasks machines could not do, they imagined the only way to automate a task was to sit down with a human being, get them to explain to you how it was they performed a task, and then try and capture that explanation in a set of instructions for a machine to follow.

當這些經濟學家在試圖想出機器無法勝任哪些工作任務，他們想像，將工作任務自動化的唯一方式就是和人類坐下來，讓人類解釋他們如何執行工作任務，再試著分析他們的解釋，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, who is my dad and my coauthor, wrote his doctorate in the 1980s on artificial intelligence and the law at Oxford University, and he was part of the vanguard.

我知道這點，因為理查薩斯金，他是我爸爸也是我的共同作者，在八〇年代，在牛津大學寫了一篇關於人工智慧與法律的博士論文，他是先鋒部隊之一。

And with a professor called Phillip Capper and a legal publisher called Butterworths, they produced the world's first commercially available artificial intelligence system in the law.

和一位名叫菲利普卡波的教授，以及一間法律出版社叫做 Butterworths，他們合作製作出了世界上第一個商業用的法律人工智慧系統。

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) I've never been entirely convinced.

（笑聲）我從來沒有被說服。

He published it in the form of two floppy disks, at a time where floppy disks genuinely were floppy, and his approach was the same as the economists': sit down with a lawyer, get her to explain to you how it was she solved a legal problem, and then try and capture that explanation in a set of rules for a machine to follow.

他用兩張軟碟片的形式將之出版，在那個時代，軟碟片真的是軟的，而他的方式就和經濟學家一樣：和一名律師坐下來，讓她向你解釋如何解決法律問題，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, the tasks are called routine, and they could be automated.

在經濟上，如果人類能夠用這種方式解釋自己做的事，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, the tasks are called non-routine, and they're thought to be out reach.

但如果人類無法解釋出怎麼做，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 machines can only perform tasks that are predictable or repetitive, rules-based or well-defined.

想想看，你有多常聽到別人對你說機器能進行的工作任務只有可預測的、重覆性的、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, and she might be able to give you a few rules of thumb, but ultimately she'd struggle.

比如，去問一位醫生如何做醫療診斷，她可能會給你少數經驗法則，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, and so it was thought these tasks would be very hard to automate.

這些東西是很難明確表達的，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, where on earth do we begin in writing a set of instructions for a machine to follow?

如果人類無法解釋他們自己的做法，我們究竟要從何開始寫指令給機器遵循？

Thirty years ago, this view was right, but today it's looking shaky, and in the future it's simply going to be wrong.

三十年前，這個觀點是對的，但現今，它很不穩固，在未來，它將會是錯的。

Advances in processing power, in data storage capability and in algorithm design mean that this routine-nonroutine distinction is diminishingly useful.

處理能力、資料儲存容量，以及演算法設計都在進步，這就表示例行與非例行事務間的區別越來越沒有用了。

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, a team of researchers at Stanford announced they'd developed a system which can tell you whether or not a freckle is cancerous as accurately as leading dermatologists.

今年早些時候，史丹佛的一個研究者團隊宣佈他們發展出了一個系統，它能告訴你一個斑點是否為惡性的，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 through 129,450 past cases, hunting for similarities between those cases and the particular lesion in question.

反之，它進行的是模式辨識演算法，在 129,450 個個案當中，獵尋那些個案與欲探究的損害之間有哪些相似性。

It's performing these tasks in an unhuman way, based on the analysis of more possible cases than any doctor could hope to review in their lifetime.

它是用非人類的方式在進行這些工作任務，且是以大量案例的分析來當依據，案例數多到是醫生一輩子都看不完的。

It didn't matter that that human being, that doctor, couldn't explain how she'd performed the task.

無所謂人類，也就是醫生，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 that these machines aren't built in our image.

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。

As an example, take IBM's Watson, the supercomputer that went on the US quiz show "Jeopardy!" in 2011, and it beat the two human champions at "Jeopardy!"

以 IBM 的「華生」為例，那是台超級電腦，2011 年參加美國的益智節目《危險邊緣》，在節目中它打敗了兩位人類冠軍。

The day after it won, The Wall Street Journal ran a piece by the philosopher John Searle with the title "Watson Doesn't Know It Won on 'Jeopardy!'" Right, and it's brilliant, and it's true.

它獲勝之後的隔天，《華爾街日報》刊了一篇哲學家約翰希爾勒的文章，標題是〈華生不知道它自己贏了《危險邊緣》〉。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。

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

它沒有打電話給它的父母說它的表現多棒。

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

它沒有去酒吧喝酒慶祝。

This system wasn't trying to copy the way that those human contestants played, but it didn't matter.

這個系統並沒有試圖複製那些人類參賽者比賽的方式，但那無所謂。

It still outperformed them.

它仍然表現得比人類好。

Resolving the intelligence myth shows us that our limited understanding about human intelligence, about how we think and reason, is far less of a constraint on automation than it was in the past.

解開這個智慧迷思之後，看到的是雖然我們對於人類智慧、對我們如何思考推理的方式了解有限，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, when these machines perform tasks differently to human beings, there's no reason to think that what human beings are currently capable of doing represents any sort of summit in what these machines might be capable of doing in the future.

此外，如我們所見，當這些機器用和人類不同的方式來執行工作任務時，沒有理由認為人類目前能夠做到的事就代表了一種上限，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, what I call the superiority myth.

第三項迷思，我稱之為優越迷思。

It's often said that those who forget about the helpful side of technological progress, those complementarities from before, are committing something known as the lump of labor fallacy.

常見的說法是，有些人會忘記了科技進步的幫助面，忘記過去的互補性，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 is itself a fallacy, and I call this the lump of labor fallacy fallacy, or LOLFF, for short.

問題是，勞動總合謬誤本身就是個謬誤，我把它稱為「勞動總合謬誤的謬誤」，簡寫為「LOLFF」。

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 who had begun to use a machine to make washers, the small metal discs that fasten on the end of screws.

有件事讓他百思不解，他遇到一個碼頭工人，這個工人開始用機器來製造墊圈，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, that people feel guilty for being unproductive, you know, a little too much time on Facebook or Twitter at work.

通常，我們預期的是相反的反應，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。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, and asked why, he said, "I know I'm doing wrong.

問他原因，他說：「我知道我做錯了。

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 to be divided up between him and his pals, so that if he used this machine to do more, there'd be less left for his pals to do.

在他的認知中，勞動總合是固定的，要由他和他的伙伴來分攤，所以如果他用機器多做一點，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, the price of washers would fall, demand for washers would rise, more washers would have to be made, and there'd be more work for his pals to do.

當這個工人用機器提高生產力，墊圈的價格會下降，對墊圈的需求會提高，就得要做出更多的墊圈，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 to think about the future of all types of work.

現今，在思考有各類工作的未來時，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 between people and machines.

要讓人類與機器瓜分。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, and the lump of work gets bigger and changes.

但它們也會補足人類，勞動總合會變更大並且改變。

But LOLFF.

但，LOLFF。

Here's the mistake: it's right to think that technological progress makes the lump of work to be done bigger.

錯誤是這樣的：認為科技進步會讓要做的勞動總合變大，這點是沒錯的。

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

有些工作任務變得較有價值。有新工作任務需要完成。

But it's wrong to think that necessarily, human beings will be best placed to perform those tasks.

錯的地方在於，認為安排人類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, but as machines become more capable, it's likely that they'll take on the extra lump of work themselves.

是的，勞動總量可能會變大也會改變，但隨著機器變得更有能力，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, complements machines instead.

科技進步就不是在補足人類了，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, software is going to displace human beings from the driving seat, and these satnav systems, rather than complement human beings, will simply make these driverless cars more efficient, helping the machines instead.

但在未來，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，而單純就是在讓這些無人駕駛汽車更有效率，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, but as machines become more capable, it's possible that any new demand will fall on goods that machines, rather than human beings, are best placed to produce.

經濟的派可能會變更大，但隨著機器更有能力，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。

The economic pie may change, but as machines become more capable, it's possible that they'll be best placed to do the new tasks that have to be done.

經濟的派可能會改變，但隨著機器變得更有能力，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 if they retain the upper hand in all these complemented tasks, but as machines become more capable, that becomes less likely.

人類只有在仍然能支配這些補足性工作任務的情況下才有可能受益，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 shows us that the future of work depends upon this balance between two forces: one, machine substitution that harms workers but also those complementarities that do the opposite.

解開終結者迷思之後，我們知道工作的未來還要仰賴兩股力量間的平衡：第一：機器代替，這會傷害到工人，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 shows us that that first force, machine substitution, is gathering strength.

但解開了智慧迷思之後，我們知道，第一股力量，機器代替，正在聚集實力。

Machines, of course, can't do everything, but they can do far more, encroaching ever deeper into the realm of tasks performed by human beings.

當然，機器並非什麼都能做，但它們能做的很多，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 that what human beings are currently capable of represents any sort of finishing line, that machines are going to draw to a polite stop once they're as capable as us.

此外，沒有理由去認為人類目前已經能做到的事，就表示是某種終點線，等到機器和我們一樣有能力時就會禮貌地在終點線前停下來。

Now, none of this matters so long as those helpful winds of complementarity blow firmly enough, but resolving the superiority myth shows us that that process of task encroachment not only strengthens the force of machine substitution, but it wears down those helpful complementarities too.

這些都無所謂，只要機器和人類在工作上能相得益彰就好。但解開了優越迷思之後，我們了解到，工作任務侵佔的過程不僅是強化了機器代替的那股力量，也會耗損那些有助益的互補性。

Bring these three myths together and I think we can capture a glimpse of that troubling future.

把這三項迷思結合起來，我想，我們就能對讓人困擾的未來有點概念。

Machines continue to become more capable, encroaching ever deeper on tasks performed by human beings, strengthening the force of machine substitution, weakening the force of machine complementarity.

機器持續變得更有能力，比以前更深入人類進行的工作任務，強化機器代替的那股力量，弱化機器互補性的那股力量。

And at some point, that balance falls in favor of machines rather than human beings.

在某個時點，那平衡會變得對機器有利，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, but it is hard to avoid the conclusion that this is our direction of travel.

我刻意用「路」這個字，因為我們還沒有到達那裡，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: how to make the economic pie large enough for everyone to live on.

大部分的人類歷史中，主導的都是這一個經濟問題：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, and if you took the global economic pie and divided it up into equal slices for everyone in the world, everyone would get a few hundred dollars.

回到西元一世紀，如果用全球的派當作例子，將它切成相同的等分，分給全世界的人，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, roughly the same is true.

如果你再向前轉一千年，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。