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  • Every human used to have to hunt or gather to survive. But humans are smart-ly lazy so

    《用不上人類》

  • we made tools to make our work easier. From sticks, to plows to tractors weve gone

    傳統上,人類需要以打獵或聚居維生

  • from everyone needing to make food to, modern agriculture with almost no one needing to

    但人類都是非常聰明(謎之音-懶惰)

  • make foodand yet we still have abundance.

    這使我們發明了各種工具使生活更加便利

  • Of course, it’s not just farming, it’s everything. Weve spent the last several

    由棒子,到犁,到推土機 我們的社會由所有人都要耕作

  • thousand years building tools to reduce physical labor of all kinds. These are mechanical muscles

    轉變成幾乎無人需要從事農業的現代社會

  • stronger, more reliable, and more tireless than human muscles could ever be.

    即便這樣,我們還是有豐富的食物

  • And that's a good thing. Replacing human labor with mechanical muscles frees people to specialize

    這不單止是農業,而是各行各業

  • and that leaves everyone better off even though still doing physical labor. This is how economies

    在過去幾千年,我們發明了無數的工具去減少人力勞動

  • grow and standards of living rise.

    這些就是機械肌肉 更強壯、更可靠、能工作更持久

  • Some people have specialized to be programmers and engineers whose job is to build mechanical

    是人類肌肉永遠無法追上的 而這是一件美好的事情

  • minds. Just as mechanical muscles made human labor less in demand so are mechanical minds

    以機械肌肉替代人類勞動 使我們可以進行工作分工從而提升生產力

  • making human brain labor less in demand.

    (即使我們還是有人專注於幹體力活)

  • This is an economic revolution. You may think we've been here before, but we haven't.

    經濟和生活素質就是這樣提升

  • This time is different.

    有些人專注於程序開發 而這些工程師的工作就是製造機械腦袋

  • ## Physical Labor

    如同機械肌肉能減低人類體力的需求一樣

  • When you think of automation, you probably think of this: giant, custom-built, expensive,

    機械腦袋減低了人類腦力需求

  • efficient but really dumb robots blind to the world and their own work. There were a

    這就是經濟革命 你可能以為我們已經經歷過

  • scary kind of automation but they haven't taken over the world because they're only

    但這次將會不一樣

  • cost effective in narrow situations.

    勞工

  • But they are the old kind of automation, this is the new kind.

    如果我說「自動化」 在你腦海中想的很可能是以下的畫面:

  • Meet Baxter.

    超誇張、特別訂製的、超貴的、超高效的 但同時是超笨的機械人

  • Unlike these things which require skilled operators and technicians and millions of

    它們只活在自己的世界 只會幹被設計能做的事

  • dollars, Baxter has vision and can learn what you want him to do by watching you do it.

    這的而且確是種很可怕的自動化 但世界還未被它們征服

  • And he costs less than the average annual salary of a human worker. Unlike his older

    因為只有很少場合才能用得上它們

  • brothers he isn't pre-programmed for one specific job, he can do whatever work is within the

    但這只是過去式的自動化

  • reach of his arms. Baxter is what might be thought of as a general purpose robot and

    現在式的自動化是這樣的

  • general purpose is a big deal.

    有請,Baxter

  • Think computers, they too started out as highly custom and highly expensive, but when cheap-ish

    有别於過那些需要專業技術員 和數以百萬元去應用的機械人

  • general-purpose computers appeared they quickly became vital to everything.

    Baxter擁有視覺系統 而且可以單靠觀看你怎樣去完成工作中自動學習

  • A general-purpose computer can just as easily calculate change or assign seats on an airplane

    而它的價格比一般人年薪要更便宜

  • or play a game or do anything by just swapping its software. And this huge demand for computers

    和它的前輩不一樣 它並不是預設計成只能做單一工作

  • of all kinds is what makes them both more powerful and cheaper every year.

    而是只要手能觸及的,它都可以幹

  • Baxter today is the computer in the 1980s. He’s not the apex but the beginning. Even

    Baxter可以說是一台「通用性」機械人

  • if Baxter is slow his hourly cost is pennies worth of electricity while his meat-based

    而「通用性」是非常關鍵

  • competition costs minimum wage. A tenth the speed is still cost effective when it's a

    以電腦做例子,一開始的時候都是價格高昂 且只能處理特定一種運算

  • hundred times cheaper. And while Baxtor isn't as smart as some of the other things we will

    但當便宜又通用的電腦出現 一下子全世界都圍著它轉

  • talk about, he's smart enough to take over many low-skill jobs.

    一台通用性電腦可以用來做找續計算

  • And we've already seen how dumber robots than Baxter can replace jobs. In new supermarkets

    又或者飛機航班指定座位 又或者用來玩遊戲

  • what used to be 30 humans is now one human overseeing 30 cashier robots.

    甚至是任何的任務 只需要換個軟件就行了

  • Or the hundreds of thousand baristas employed world-wide? There’s a barista robot coming

    就是這般強大的電腦需求 促使它們一年比一年便宜,且性能節節提升

  • for them. Sure maybe your guy makes your double-mocha-whatever just perfect and you’d never trust anyone

    今天的Baxter就是1980年的電腦 它不是科技的尖端,而是開始

  • else -- but millions of people don’t care and just want a decent cup of coffee. Oh and

    即使Baxter今天速度還是很慢 但他的工作成本就是幾毛錢的電費

  • by the way this robot is actually a giant network of robots that remembers who you are

    而它的人肉版對手 - 還得付最低工資

  • and how you like your coffee no matter where you are. Pretty convenient.

    即使速度慢十倍,但成本便宜一百倍 性價比還是非常的高

  • We think of technological change as the fancy new expensive stuff, but the real change comes

    雖然Baxter還未至於聰明到 可以締代所有即將要提及的行業

  • from last decade's stuff getting cheaper and faster. That's what's happening to robots

    但已經足夠聰明去替代很多低技術含量的行業

  • now. And because their mechanical minds are capable of decision making they are out-competing

    在我們身邊已經有很多更笨的機械人 替代了大量工作崗位的例子

  • humans for jobs in a way no pure mechanical muscle ever could.

    在新式大超市,從前需要三十個收銀員的地方 現在一個人管理三十台自助收銀機

  • ## Luddite Horses

    再看看世界各地顧用的幾十萬位咖啡烹調師

  • Imagine a pair of horses in the early 1900s talking about technology. One worries all

    咖啡烹調機械人也即將普及

  • these new mechanical muscles will make horses unnecessary.

    也許你們能烹調一杯完美的雙份摩卡加什麼的 而且永遠不會相信别人的手藝

  • The other reminds him that everything so far has made their lives easier -- remember all

    但數以百萬計的消費者根本不關心 只要有杯靚咖啡喝就行

  • that farm work? Remember running coast-to-coast delivering mail? Remember riding into battle?

    順帶一提,這台機械人是已經聯網的

  • All terrible. These city jobs are pretty cushy -- and with so many humans in the cities there

    它能記住你是誰和你的口味 無論你身在何方。很方便吧?

  • are more jobs for horses than ever.

    很多人以為科技改革都是指那些浮誇的東東

  • Even if this car thingy takes off you might say, there will be new jobs for horses we

    但真正改變的在過去十數年間的 電腦變得又快又便宜

  • can't imagine.

    機械人科技發展也是一樣的

  • But you, dear viewer, from beyond 2000 know what happened -- there are still working horses,

    因為它們的機械腦袋已經發展到擁有決策的功能

  • but nothing like before. The horse population peaked in 1915 -- from that point on it was

    這使它們能夠與人類爭長短 有别於之前看到的純機動機械人

  • nothing but down.

    馬版盧德運動

  • There isn’t a rule of economics that says better technology makes more, better jobs

    想像一下在20世紀初時 兩匹馬在討論科技改變

  • for horses. It sounds shockingly dumb to even say that out loud, but swap horses for humans

    其中一匹擔心這些新發明的機械肌肉會使馬匹都要失業

  • and suddenly people think it sounds about right.

    另一匹則說,到目前為止的改變都使牠生活更輕鬆

  • As mechanical muscles pushed horses out of the economy, mechanical minds will do the

    還記得我們要耕田 要東西岸穿梭跑送快遞

  • same to humans. Not immediately, not everywhere, but in large enough numbers and soon enough

    還要上戰場的日子嗎?多悲哀啊…

  • that it's going to be a huge problem if we are not prepared. And we are not prepared.

    今天的城市新工作舒服多了

  • You, like the second horse, may look at the state of technology now and think it can’t

    而且一個城市人口這麼多 想必需要更多新工作給馬匹幹

  • possibly replace your job. But technology gets better, cheaper, and faster at a rate

    即使是你說的那個叫「汽車」的普及了

  • biology can’t match.

    肯定也有其他新工作等著我們的

  • Just as the car was the beginning of the end for the horse so now does the car show us

    但親愛的觀眾,來自21世紀的你 都知道故事的結局

  • the shape of things to come.

    工作用的馬還是有的 但和上個世紀的情況完全拉不上邊

  • ## The Shape Of Things to Come

    世上馬匹數目在1915年到達高峰 但自此之後就一直滑坡

  • Self-driving cars aren't the future: they're here and they work. Self-driving cars have

    經濟學並沒有「科技改進會為馬匹帶來更好的工作」這一規定

  • traveled hundreds of thousands of miles up and down the California coast and through

    單單是說出口都覺得自己是個白痴

  • cities -- all without human intervention.

    若將「馬」換成「人類」 突然有些人就覺得這句話很乎合邏輯

  • The question is not if they'll replaces cars, but how quickly. They don’t need to be perfect,

    如同機械肌肉為馬匹帶來的經濟衝擊 機械腦袋也會為人類帶來同樣的影響

  • they just need to be better than us. Humans drivers, by the way, kill 40,000 people a

    我不是說全球性立刻會出現這種現像

  • year with cars just in the United States. Given that self-driving cars don’t blink,

    但如果我們毫無準備的話 這將會是個大問題

  • don’t text while driving, don’t get sleepy or stupid, it easy to see them being better

    而我們的確是毫無準備

  • than humans because they already are.

    你就像第二匹馬,根據觀察現今科技的情況 得出無可能取締自已的工作這一結論

  • Now to describe self-driving cars as cars at all is like calling the first cars mechanical

    但科技改變一日千里 並以生物無可能追上的速度更新換代

  • horses. Cars in all their forms are so much more than horses that using the name limits

    如同汽車發明是馬匹終結的開端

  • your thinking about what they can even do. Lets call self-driving cars what they really

    今天的汽車預示了未來的日子

  • are:

    汽車

  • Autos: the solution to the transport-objects-from-point-A-to-point-B problem. Traditional cars happen to be human

    自動駕駛汽車(自駕車)並不是科幻電影情節 而是今天就存在的東西

  • sized to transport humans but tiny autos can work in wear houses and gigantic autos can

    自駕車已經在加州開行了幾千萬公里

  • work in pit mines. Moving stuff around is who knows how many jobs but the transportation

    攀山越嶺並經得起城市穿梭的考驗 當中完全不經人手

  • industry in the United States employs about three million people. Extrapolating world-wide

    問題不是在於傳統汽車會否被取代 而是多快被取代

  • that’s something like 70 million jobs at a minimum.

    自駕車技術不需要做到完美 只需要比人類駕駛汽車好就可以

  • These jobs are over.

    順帶一提,單在美國 人類司機每年就因駕車殺死四萬人

  • The usual argument is that unions will prevent it. But history is filled with workers who

    自駕車不會眨眼、不會滑手機 不會犯睏、不會犯傻

  • fought technology that would replace them and the workers always loose. Economics always

    很顯現易見它們都比人類來駕駛汽車好 因為這個就是今天的事實

  • wins and there are huge incentives across wildly diverse industries to adopt autos.

    我們將自駕車形容為「汽車」 就像將第一代汽車形容為「機械馬」一樣

  • For many transportation companies, the humans are about a third of their total costs. That's

    現今各式汽車絕對不是馬能夠相比 用字不當會使我們無法突破思考空間

  • just the straight salary costs. Humans sleeping in their long haul trucks costs time and money.

    我們必須更正自駕車這個用字

  • Accidents cost money. Carelessness costs money. If you think insurance companies will be against

    自動交通工具(Autos): 一個能將物件由A移到B的解決方案

  • it, guess what? Their perfect driver is one who pays their small premium but never gets

    傳統汽車都是依人類大小去設計 用來解決人類移動問題

  • into an accident.

    但微型Autos能夠在倉庫裏工作 巨大Autos則能在礦場中作業

  • The autos are coming and they're the first place where most people will really see the

    天曉得有多少人的工作就是將東西搬來搬去

  • robots changing society. But there are many other places in the economy where the same

    而在美國運輸業顧用了約三百萬人

  • thing is happening, just less visibly.

    將數字外推,全世界則 最起碼有七千萬人顧用於運輸業

  • So it goes with autos, so it goes for everything.

    這些工作崗位即將成為過去式

  • ## Intellectual Labor

    一般反方的意見是,工會會阻止這樣的事情發生

  • ### White Collar Work

    但歷史告訴我們 工人與科技搞抗爭的結果只有一個

  • It's easy to look at Autos and Baxters and think: technology has always gotten rid of

    工人必輸,經纃必勝

  • low-skill jobs we don't want people doing anyway. They'll get more skilled and do better

    而全球各行各業都有很大的誘因去引入Autos

  • educated jobs -- like they've always done.

    在一般運輸企業中,人力成本約佔三份一

  • Even ignoring the problem of pushing a hundred-million additional people through higher education,

    而且這只是工資部份

  • white-collar work is no safe haven either. If your job is sitting in front of a screen

    工人在長途貨車中睡覺既花時間又花錢

  • and typing and clicking -- like maybe you're supposed to be doing right now -- the bots

    出意外又要花錢,一不小心又要花錢

  • are coming for you too, buddy.

    如果你覺得保險公司一定會搞抗爭

  • Software bots are both intangible and way faster and cheaper than physical robots. Given

    但「完美司機」是一位會付保費而又永不闖禍的顧客

  • that white collar workers are, from a companies perspective, both more expensive and more

    Autos普及是勢不可擋

  • numerous -- the incentive to automate their work is greater than low skilled work.

    這將是大眾最能直接感知的機械化轉變

  • And that's just what automation engineers are for. These are skilled programmers whose

    經纃上的各個領域 都如同Autos的故事一樣悄悄展開

  • entire job is to replace your job with a software bot.

    Autos的故事,到處都在發生

  • You may think even the world's smartest automation engineer could never make a bot to do your

    未來轉變

  • job -- and you may be right -- but the cutting edge of programming isn't super-smart programmers

    看著Autos和Baxter,我們會以為科技 只會帶走那些我們其實都不想幹的工作而已

  • writing bots it's super-smart programmers writing bots that teach themselves how to

    而事實上,它們會演進並取代更高級的工作 如同過往的科技改革一樣

  • do things the programmer could never teach them to do.

    我們暫且先跳過強逼了上億人進高等教育這個議題

  • How that works is well beyond the scope of this video, but the bottom line is there are

    白領工種亦不能幸免於難

  • limited ways to show a bot a bunch of stuff to do, show the bot a bunch of correctly done

    如果你的工作是在屏幕前打打字點點滑鼠

  • stuff, and it can figure out how to do the job to be done.

    (如同可能你現在可能應該在幹的事)

  • Even with just a goal and no example of how to do it the bots can still learn. Take the

    我告訴你,機械人已經盯上了你

  • stock market which, in many ways, is no longer a human endeavor. It's mostly bots that taught

    軟件機械人遠遠比物理機械人無形、快速、便宜

  • themselves to trade stocks, trading stocks with other bots that taught themselves.

    在公司角度中 白領工人更往往是最昂貴的開支

  • Again: it's not bots that are executing orders based on what their human controllers want,

    相對於低技術工作 公司絕對會更希望把白領工作自動化

  • it's bots making the decisions of what to buy and sell on their own.

    這就是自動化工程師的工作 他們都是編寫程序為生

  • As a result the floor of the New York Stock exchange isn't filled with traders doing their

    工作就是製造各種軟件機械人 以取締你的工作為目標

  • day jobs anymore, it's largely a TV set.

    你可能以為世界最聰明的自動化工程師 都絕不能夠發明出替代你的工作的機械人

  • So bots have learned the market and bots have learned to write. If you've picked up a newspaper

    可能你是對的,但最尖端的編程科技並不限於此

  • lately you've probably already read a story written by a bot. There are companies that

    而是由超聰明程序員編寫出 一套會自我學習的機械人

  • are teaching bots to write anything: Sports stories, TPS reports, even say, those quarterly

    而且能習得無法由程序員直接傳授的智能

  • reports that you write at work.

    詳細的說明並不是本片的重點

  • Paper work, decision making, writing -- a lot of human work falls into that category

    基本來說,就是先為軟件編寫一堆特定算法

  • and the demand for human metal labor is these areas is on the way down. But surely the professions

    然後向它展示哪些工作算是「完成」

  • are safe from bots? Yes?

    之後軟件就能自行推算出 應該要怎樣做才能把工作完成

  • ## Professions

    即使只單單定義了目標而不教授方法 也不防礙軟件自行學習

  • When you think 'lawyer' it's easy to think of trials. But the bulk of lawyering is actually

    看下股票市場交易,在各種義意上 都已經不算是人類的工作

  • drafting legal documents predicting the likely outcome and impact of lawsuits, and something

    市場上都是交易軟件,自行學習如何做交易 並與其他同樣的軟件做交易

  • called 'discovery' which is where boxes of paperwork gets dumped on the lawyers and they

    結果就是,紐約交易所裏 充斥的不再是交易員,而是大屏幕

  • need to find the pattern or the one out-of-place transaction among it all.

    軟件已經看透交易市場 甚至能編寫文章

  • This can all be bot work. Discovery, in particular, is already not a human job in many firms.

    若你最近有讀報,很可能已經 閱讀過一些由軟件編成的稿件

  • Not because there isn't paperwork to go through, there's more of it than ever, but because

    世上有專門的軟件公司 開發出能編寫任何文件的軟件

  • clever research bots sift through millions of emails and memos and accounts in hours

    由運動新聞、到軟件測試報告

  • not weeks -- crushing human researchers in terms of not just cost and time but, most

    到甚至乎你工作上要寫的季度報告

  • importantly, accuracy. Bots don't get sleeping reading through a million emails.

    文書工作、決策工作、寫文章 很多行業都是屬於這一類的

  • But that's the simple stuff: IBM has a bot named Watson: you may have seen him on TV

    而這方面的人力腦力需求正在下跌當中

  • destroy humans at Jeopardybut that was just a fun side project for him.

    當然,專業行業不會這樣就被自動化了吧。 你說呢?

  • Watson's day-job is to be the best doctor in the world: to understand what people say

    專業機械人

  • in their own words and give back accurate diagnoses. And he's already doing that at

    當提到律師這個字,多半馬上聯想到是上法庭

  • Slone-Kettering, giving guidance on lung cancer treatments.

    但其實大部份的工作是在「發掘」階段:

  • Just as Auto don’t need to