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  • Automation anxiety has been spreading lately,

    譯者: Lilian Chiu 審譯者: Chen Chi-An

  • a fear that in the future,

    近期,自動化焦慮一直在散佈,

  • many jobs will be performed by machines

    它是種恐懼,害怕在未來

  • rather than human beings,

    許多工作會由機器來進行,

  • given the remarkable advances that are unfolding

    而不是人類,

  • in artificial intelligence and robotics.

    因為現在已可以看到在人工智慧

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

    和機器人學領域的驚人進步。

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

    很清楚的一點是, 將來會有顯著的改變。

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

    比較不那麼清楚的是, 改變會是什麼樣的。

  • The threat of technological unemployment is real,

    我的研究指出,未來 既讓人困擾又讓人興奮。

  • and yet it's a good problem to have.

    科技造成失業的威脅是真的,

  • And to explain how I came to that conclusion,

    但,能有這種問題也是件好事。

  • I want to confront three myths

    為了解釋我如何得到這個結論,

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

    我想要來正視三項迷思,

  • A picture that we see on our television screens,

    我認為這些迷思 目前遮掩了我們的視線,

  • in books, in films, in everyday commentary

    讓我們看不清自動化的未來。

  • is one where an army of robots descends on the workplace

    我們在電視上、書中、電影中、

  • with one goal in mind:

    每天的評論中所看到的描繪,

  • to displace human beings from their work.

    通常是機器人大軍湧入工作場所,

  • And I call this the Terminator myth.

    心中只有一個目標:

  • Yes, machines displace human beings from particular tasks,

    在工作上取代人類。

  • but they don't just substitute for human beings.

    我稱這個想法為「終結者迷思」。

  • They also complement them in other tasks,

    是的,在特定的工作任務上, 機器會取代人類,

  • making that work more valuable and more important.

    但它們不會就這樣代替人類。

  • Sometimes they complement human beings directly,

    它們在其他工作任務上會補足人類,

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

    讓工作更有價值、更重要。

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

    有時,它們會直接補足人類,

  • An architect can use computer-assisted design software

    讓人類在特定的工作任務上 能更有生產力或更有效率。

  • to design bigger, more complicated buildings.

    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。

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

    建築師可以用電腦輔助的設計軟體

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

    來設計更大、更複雜的建築物。

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

    但科技進步並不只會直接補足人類。

  • technological progress makes the pie bigger.

    它也會用間接方式補足人類, 間接的方式有兩種。

  • As productivity increases, incomes rise and demand grows.

    第一,如果我們把 經濟想成是一塊派,

  • The British pie, for instance,

    科技進步會讓派變更大。

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

    隨著生產力增加, 收入會增加,需求會成長。

  • And so people displaced from tasks in the old pie

    比如,英國的派

  • could find tasks to do in the new pie instead.

    與三百年前相比,現在超過百倍大。

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

    在舊派工作被取代的人,

  • It also changes the ingredients in the pie.

    能在新派中找到工作。

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

    但科技進步並不只會讓派變大。

  • changing how they spread it across existing goods,

    它也會改變派的成分原料。

  • and developing tastes for entirely new goods, too.

    隨時間演進,人會以 不同的方式花費他們的收入,

  • New industries are created,

    改變既有商品花費上的分配,

  • new tasks have to be done

    並也會發展出對於全新商品的品味。

  • and that means often new roles have to be filled.

    新的產業會被創造出來,

  • So again, the British pie:

    有新的工作任務需要被完成,

  • 300 years ago, most people worked on farms,

    那就意味著有新角色要有人扮演。

  • 150 years ago, in factories,

    所以,再回到英國的派:

  • and today, most people work in offices.

    三百年前,大部分的人在農場工作,

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

    一百五十年前,在工廠工作,

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

    現今,大部分的人在辦公室工作。

  • Economists call these effects complementarities,

    再提一次,在老派工作被取代的人,

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

    可能會在新派當中 發現可以做的工作任務。

  • that technological progress helps human beings.

    經濟學家把這些效應稱為互補性,

  • Resolving this Terminator myth

    但那只是個很炫的詞,其實意思就是

  • shows us that there are two forces at play:

    科技進步用不同的方式在協助人類。

  • one, machine substitution that harms workers,

    解開這個終結者迷思之後,

  • but also these complementarities that do the opposite.

    會發現有兩股力量在運作:

  • Now the second myth,

    第一:機器代替,這會傷害到工人,

  • what I call the intelligence myth.

    但也會有第二股力量, 互補性,反而會幫助工人。

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

    再來,第二項迷思,

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

    我稱之為「智慧迷思」。

  • Well, these are all tasks that until very recently,

    以下這些工作任務: 駕駛一台車、做出醫療診斷,

  • leading economists thought couldn't readily be automated.

    及快速一瞥就辨識出 一隻鳥,有何共通性?

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

    這些工作任務都是直到最近

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

    仍被經濟學家認為不能 自動化的工作任務。

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

    然而,現今,所有這些 工作任務都能被自動化。

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

    所有大型汽車製造商都有 無人駕駛汽車的計畫。

  • at a fleeting glimpse.

    外面有數不清的系統 都能夠診斷醫療問題。

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

    甚至有個應用程式能用來辨識鳥類,

  • They were wrong,

    只要快速一瞥。

  • and the reason why they were wrong is very important.

    這並不是經濟學家運氣不好的情況。

  • They've fallen for the intelligence myth,

    他們錯了,

  • the belief that machines have to copy the way

    而他們為什麼會錯的原因很重要。

  • that human beings think and reason

    他們陷入了智慧迷思中,

  • in order to outperform them.

    相信機器必須要複製人類

  • When these economists were trying to figure out

    思考和推理的方式,

  • what tasks machines could not do,

    才能夠表現得比人類好。

  • they imagined the only way to automate a task

    當這些經濟學家在試圖想出

  • was to sit down with a human being,

    機器無法勝任哪些工作任務,

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

    他們想像,將工作任務自動化的

  • and then try and capture that explanation

    唯一方式就是和人類坐下來,

  • in a set of instructions for a machine to follow.

    讓人類解釋他們如何執行工作任務,

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

    再試著分析他們的解釋,

  • I know this because Richard Susskind,

    轉換成一組指令,讓機器照著做。

  • who is my dad and my coauthor,

    在人工智慧領域,這種觀點 曾在某個時點很流行過。

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

    我知道這點,因為理查薩斯金,

  • at Oxford University,

    他是我爸爸也是我的共同作者,

  • and he was part of the vanguard.

    在八〇年代,在牛津大學 寫了一篇關於人工智慧

  • And with a professor called Phillip Capper

    與法律的博士論文,

  • and a legal publisher called Butterworths,

    他是先鋒部隊之一。

  • they produced the world's first commercially available

    和一位名叫菲利普卡波的教授,

  • artificial intelligence system in the law.

    以及一間法律出版社 叫做 Butterworths,

  • This was the home screen design.

    他們合作製作出了 世界上第一個商業用的

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

    法律人工智慧系統。

  • (Laughter)

    這是首頁的畫面設計。

  • I've never been entirely convinced.

    他向我保證,在當時 這是很酷的畫面設計。

  • He published it in the form of two floppy disks,

    (笑聲)

  • at a time where floppy disks genuinely were floppy,

    我從來沒有被說服。

  • and his approach was the same as the economists':

    他用兩張軟碟片的形式將之出版,

  • sit down with a lawyer,

    在那個時代,軟碟片真的是軟的,

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

    而他的方式就和經濟學家一樣:

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

    和一名律師坐下來,

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

    讓她向你解釋如何解決法律問題,

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

    接著就試著把她的解釋 轉成一組指令給機器執行。

  • But if human beings can't explain themselves,

    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 non-routine, and they're thought to be out reach.

    這種工作任務就叫做例行事務, 是可以被自動化的。

  • Today, that routine-nonroutine distinction is widespread.

    但如果人類無法解釋出怎麼做,

  • Think how often you hear people say to you

    這種工作任務叫做非例行事務, 應該是不能自動化的。

  • machines can only perform tasks that are predictable or repetitive,

    現今,將事務區別為例行 與非例行是處處可見的。

  • rules-based or well-defined.

    想想看,你有多常聽到別人對你說

  • Those are all just different words for routine.

    機器能進行的工作任務 只有可預測的、重覆性的、

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

    以規則為基礎的,或定義清楚的。

  • Those are all classic cases of nonroutine tasks.

    那些詞只是例行事務的不同說法。

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

    回到我一開始提到的三個案例。

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

    那些案例是典型的非例行事務。

  • but ultimately she'd struggle.

    比如,去問一位醫生 如何做醫療診斷,

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

    她可能會給你少數經驗法則,

  • And these things are very difficult to articulate,

    但最終,她會很掙扎。

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

    她會說,你還需要創意、 判斷,以及直覺才行。

  • If a human being can't explain themselves,

    這些東西是很難明確表達的,

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

    所以這些工作任務就會 被認為很難自動化。

  • for a machine to follow?

    如果人類無法解釋他們自己的做法,

  • Thirty years ago, this view was right,

    我們究竟要從何開始寫指令

  • but today it's looking shaky,

    給機器遵循?

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

    三十年前,這個觀點是對的,

  • Advances in processing power, in data storage capability

    但現今,它很不穩固,

  • and in algorithm design

    在未來,它將會是錯的。

  • mean that this routine-nonroutine distinction

    處理能力、資料儲存容量,

  • is diminishingly useful.

    以及演算法設計都在進步,

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

    這就表示例行與非例行事務間的區別

  • Earlier in the year,

    越來越沒有用了。

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

    要了解這點,我們 回到醫療診斷的案例。

  • which can tell you whether or not a freckle is cancerous

    今年早些時候,

  • as accurately as leading dermatologists.

    史丹佛的一個研究者團隊 宣佈他們發展出了一個系統,

  • How does it work?

    它能告訴你一個斑點是否為惡性的,

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

    正確率不輸給頂尖皮膚科醫生。

  • It knows or understands nothing about medicine at all.

    它怎麼做到的?

  • Instead, it's running a pattern recognition algorithm

    它並不是嘗試複製 醫生的判斷或是直覺。

  • through 129,450 past cases,

    它對於醫學是一竅不通。

  • hunting for similarities between those cases

    反之,它進行的是模式辨識演算法,

  • and the particular lesion in question.

    在 129,450 個個案當中,

  • It's performing these tasks in an unhuman way,

    獵尋那些個案與欲探究的損害

  • based on the analysis of more possible cases

    之間有哪些相似性。

  • 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,

  • It didn't matter that that human being,

    且是以大量案例的分析來當依據,

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

    案例數多到是醫生 一輩子都看不完的。

  • Now, there are those who dwell upon that the fact

    無所謂人類,也就是醫生,

  • that these machines aren't built in our image.

    是否能解釋她如何進行此工作任務。

  • As an example, take IBM's Watson,

    有些人老是會想著

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

    這些機器被建立時 沒有依循我們的形象。

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

    以 IBM 的「華生 」為例,

  • The day after it won,

    那是台超級電腦,2011 年參加 美國的益智節目《危險邊緣》,

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

    在節目中它打敗了兩位人類冠軍。

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

    它獲勝之後的隔天,

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

    《華爾街日報》刊了一篇 哲學家約翰希爾勒的文章,

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

    標題是〈華生不知道 它自己贏了《危險邊緣》 〉。

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

    是的,這篇文章很聰明也沒說錯。

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

    華生並沒有興奮地放聲大叫。

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

    它沒有打電話給它的父母 說它的表現多棒。

  • but it didn't matter.

    它沒有去酒吧喝酒慶祝。

  • It still outperformed them.

    這個系統並沒有試圖複製 那些人類參賽者比賽的方式,

  • Resolving the intelligence myth

    但那無所謂。

  • shows us that our limited understanding about human intelligence,

    它仍然表現得比人類好。

  • about how we think and reason,

    解開這個智慧迷思之後,

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

    看到的是雖然我們對於 人類智慧、對我們如何

  • What's more, as we've seen,

    思考推理的方式了解有限,

  • when these machines perform tasks differently to human beings,

    但這個限制對於自動化的影響 已經遠比過去小很多。

  • there's no reason to think

    此外,如我們所見,

  • that what human beings are currently capable of doing

    當這些機器用和人類不同的 方式來執行工作任務時,

  • represents any sort of summit

    沒有理由認為

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

    人類目前能夠做到的事

  • Now the third myth,

    就代表了一種上限,

  • what I call the superiority myth.

    在未來機器能夠達成的事 都不可能超過這個上限。

  • It's often said that those who forget

    第三項迷思,

  • about the helpful side of technological progress,

    我稱之為優越迷思。

  • those complementarities from before,

    常見的說法是,有些人會

  • are committing something known as the lump of labor fallacy.

    忘記了科技進步的幫助面,

  • Now, the problem is the lump of labor fallacy

    忘記過去的互補性,

  • is itself a fallacy,

    這些人所犯的,就是 所謂的「勞動總合謬誤」。

  • and I call this the lump of labor fallacy fallacy,

    問題是,勞動總合謬誤本身

  • or LOLFF, for short.

    就是個謬誤,

  • Let me explain.

    我把它稱為 「勞動總合謬誤的謬誤」,

  • The lump of labor fallacy is a very old idea.

    簡寫為「LOLFF」。

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

    讓我解釋一下。

  • He was puzzled to come across a dock worker

    勞動總合謬誤是個很古老的想法。

  • who had begun to use a machine to make washers,

    這個名稱是 1892 年由英國 經濟學家大衛許洛斯取的。

  • 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,

  • And this dock worker felt guilty for being more productive.

    這個工人開始用機器來製造墊圈,

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

    墊圈是小型的金屬圓盤, 固定在螺絲底端。

  • that people feel guilty for being unproductive,

    這個碼頭工人對於自己的 高生產力有罪惡感。

  • 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,

  • But this worker felt guilty for being more productive,

    生產力不高才會讓人感到罪惡,

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

    你知道的,工作時 花太多時間滑臉書或推特。

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

    但這個工人對於 太有生產力感到罪惡,

  • In his mind, there was some fixed lump of work

    問他原因,他說:「我知道我做錯了。

  • to be divided up between him and his pals,

    我搶走了另一個人的工作。」

  • so that if he used this machine to do more,

    在他的認知中,勞動總合是固定的,

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

    要由他和他的伙伴來分攤,

  • Schloss saw the mistake.

    所以如果他用機器多做一點,

  • The lump of work wasn't fixed.

    他伙伴能做的就變少了。

  • As this worker used the machine and became more productive,

    許洛斯看到了這個錯誤。

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

    勞動總合並不是固定的。

  • more washers would have to be made,

    當這個工人用機器提高生產力,

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

    墊圈的價格會下降, 對墊圈的需求會提高,

  • The lump of work would get bigger.

    就得要做出更多的墊圈,

  • Schloss called this "the lump of labor fallacy."

    他的伙伴反而會有更多要做。

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

    勞動總合變更大了。

  • to think about the future of all types of work.

    許洛斯稱之為「勞動總合謬誤」。

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

    現今,在思考有各類工作的未來時,

  • between people and machines.

    會聽到人們談到勞動總合謬誤。

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

    沒有固定的勞動總合

  • but they also complement human beings,

    要讓人類與機器瓜分。

  • and the lump of work gets bigger and changes.

    是的,機器會取代人類, 讓原本的勞動總合變少,

  • But LOLFF.

    但它們也會補足人類,

  • Here's the mistake:

    勞動總合會變更大並且改變。

  • it's right to think that technological progress

    但,LOLFF。

  • makes the lump of work to be done bigger.

    錯誤是這樣的:

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

    認為科技進步會讓 要做的勞動總合變大,

  • But it's wrong to think that necessarily,

    這點是沒錯的。

  • human beings will be best placed to perform those tasks.

    有些工作任務變得較有價值。 有新工作任務需要完成。

  • And this is the superiority myth.

    錯的地方在於,認為安排人類

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

    來做那些工作任務一定是最好的。

  • but as machines become more capable,

    這就是優越迷思。

  • 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,

  • Technological progress, rather than complement human beings,

    但隨著機器變得更有能力,

  • complements machines instead.

    很有可能它們會自己去接下 那些額外的勞動總量。

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

    科技進步就不是在補足人類了,

  • Today, satnav systems directly complement human beings.

    反而是補足機器。

  • They make some human beings better drivers.

    可以回頭看駕駛汽車的 工作任務來了解這點。

  • But in the future,

    現今,衛星導航系統直接補足人類。

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

    它讓一些人類變成更好的駕駛。

  • and these satnav systems, rather than complement human beings,

    但在未來,