演講MP3+雙語文稿:我們?nèi)绾闻c人工智能一起工作

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聽力課堂TED音頻欄目主要包括TED演講的音頻MP3及中英雙語文稿，供各位英語愛好者學(xué)習(xí)使用。本文主要內(nèi)容為演講MP3+雙語文稿:我們?nèi)绾闻c人工智能一起工作，希望你會喜歡！

【演講者及介紹】Matt Beane

是一名技術(shù)管理助理教授，在加州大學(xué)圣巴巴拉分校的項目與麻省理工學(xué)院的數(shù)字經(jīng)濟研究所合作。

【演講主題】我們?nèi)绾螌W(xué)習(xí)與智能機器一起工作

【中英文字幕】

翻譯者 Ruijie Wu 校對者 Chen Yunru

00:13

It’s 6:30 in the morning, and Kristen iswheeling her prostate patient into the OR. She's a resident, a surgeon intraining. It’s her job to learn. Today, she’s really hoping to do some of thenerve-sparing, extremely delicate dissection that can preserve erectilefunction. That'll be up to the attending surgeon, though, but he's not thereyet. She and the team put the patient under, and she leads the initialeight-inch incision in the lower abdomen. Once she’s got that clamped back, shetells the nurse to call the attending. He arrives, gowns up, And from there onin, their four hands are mostly in that patient -- with him guiding but Kristinleading the way. When the prostates out (and, yes, he let Kristen do a littlenerve sparing), he rips off his scrubs. He starts to do paperwork. Kristencloses the patient by 8:15, with a junior resident looking over her shoulder.And she lets him do the final line of sutures. Kristen feels great. Patient’sgoing to be fine, and no doubt she’s a better surgeon than she

清晨六點半，克里斯汀正推著她的前列腺病人進手術(shù)室。她是一名實習(xí)住院外科醫(yī)生，學(xué)習(xí)是她工作的一部分。這天，她非常想?yún)⑴c進行神經(jīng)保留手術(shù)，這要求醫(yī)生有極度精細的切割技巧，以讓病人恢復(fù)勃起的功能。不過，這還要看主治醫(yī)生的意思，但那會兒他并不在手術(shù)室?？死锼雇『推渌中g(shù)人員給病人打了麻醉。首先，她在病人的下腹部切開了一道8英寸的切口，當(dāng)她把切口固定好，便讓護士打電話給主治醫(yī)生。主治醫(yī)生趕到后，穿上手術(shù)服。接著，兩人共同開始手術(shù)，他們四只手都在病人體內(nèi)，主治醫(yī)生負責(zé)指導(dǎo)，克里斯汀則主導(dǎo)了手術(shù)。當(dāng)病人的前列腺被取出后，主治醫(yī)生讓她進行了部分神經(jīng)保留的操作，他脫掉了手術(shù)服，開始處理一些文件。而克里斯汀在一個初級住院醫(yī)生的協(xié)助下于8：15完成了手術(shù)，克里斯汀還讓他給病人做了最后的縫合。克里斯汀感覺好極了，病人很快就會恢復(fù)，而她也無疑比凌晨六點半時的自己更進了一步。

01:34

Now this is extreme work. But Kristin’slearning to do her job the way that most of us do: watching an expert for abit, getting involved in easy, safe parts of the work and progressing toriskier and harder tasks as they guide and decide she’s ready. My whole lifeI’ve been fascinated by this kind of learning. It feels elemental, part of whatmakes us human. It has different names: apprenticeship, coaching, mentorship,on the job training. In surgery, it’s called “see one, do one, teach one.” Butthe process is the same, and it’s been the main path to skill around the globefor thousands of years. Right now, we’re handling AI in a way that blocks thatpath. We’re sacrificing learning in our quest for productivity.

雖然，醫(yī)生的工作挑戰(zhàn)性極高。但克里斯汀的學(xué)習(xí)過程其實和我們的并無分別，通過觀察專家的操作，從簡單、安全的部分開始著手，過渡到風(fēng)險更高、難度更大的工作，其中確保她準(zhǔn)備就緒，并且有專家在一旁指導(dǎo)。我這一生都被這種學(xué)習(xí)過程所吸引。這樣基本的步驟，體現(xiàn)了人之常情，人們?yōu)檫@個過程賦予不同的名字，學(xué)藝、訓(xùn)練、教導(dǎo)和在職培訓(xùn)，在外科手術(shù)中，這被稱為“看、做、教”，但實際步驟是一樣的，這也是千百年來所有人在培養(yǎng)人才時所用的方式。但如今我們應(yīng)用人工智能的方法卻反其道而行之。為了提高效率，我們犧牲了學(xué)習(xí)必經(jīng)的過程。

02:25

I found this first in surgery while I wasat MIT, but now I’ve got evidence it’s happening all over, in very differentindustries and with very different kinds of AI. If we do nothing, millions ofus are going to hit a brick wall as we try to learn to deal with AI. Let’s goback to surgery to see how.

我在麻省理工學(xué)院做手術(shù)時第一次發(fā)現(xiàn)了這個現(xiàn)象，但現(xiàn)在我發(fā)現(xiàn)這樣的現(xiàn)象隨處可見，遍布各行各業(yè)，以及各項人工智能的應(yīng)用場景中。如果我們無動于衷，成千上萬的人在學(xué)習(xí)如何掌握人工智能時，將會碰壁。讓我們再用外科手術(shù)作為例子，

02:47

Fast forward six months. It’s 6:30am again,and Kristen is wheeling another prostate patient in, but this time to therobotic OR. The attending leads attaching a four-armed, thousand-pound robot tothe patient. They both rip off their scrubs, head to control consoles 10 or 15feet away, and Kristen just watches. The robot allows the attending to do thewhole procedure himself, so he basically does. He knows she needs practice. Hewants to give her control. But he also knows she’d be slower and make moremistakes, and his patient comes first. So Kristin has no hope of gettinganywhere near those nerves during this rotation. She’ll be lucky if sheoperates more than 15 minutes during a four-hour procedure. And she knows thatwhen she slips up, he’ll tap a touch screen, and she’ll be watching again,feeling like a kid in the corner with a dunce cap.

時間快進六個月，還是凌晨六點半，克里斯汀推著另一個前列腺病人進手術(shù)室。但這一次，是去自動化手術(shù)室。主治醫(yī)生把一個長著四只手、重一千鎊的機器人連接到病人身上，醫(yī)生們都脫掉了手術(shù)服，來到三五米外的控制臺，而克里斯汀只負責(zé)觀察。在機器人的幫助下，主治醫(yī)生獨自便可完成手術(shù)，他也是這么做的，即使他知道克里斯汀需要練習(xí)，他也希望可以給她機會，但是他同樣清楚克里斯汀操作得更慢，還有失誤的風(fēng)險，而病人的安全永遠是第一位的。所以克里斯汀在這次手術(shù)中完全沒有機會碰到病人的神經(jīng)，她能在四個小時的手術(shù)中操刀超過一刻鐘就算是走運了，而且她很清楚，萬一她出現(xiàn)失誤，主治醫(yī)生就會重新操刀，她又不得不回到觀察者的角色，感到非常沮喪和失落。

03:53

Like all the studies of robots and workI’ve done in the last eight years, I started this one with a big, openquestion: How do we learn to work with intelligent machines? To find out, Ispent two and a half years observing dozens of residents and surgeons doingtraditional and robotic surgery, interviewing them and in general hanging outwith the residents as they tried to learn. I covered 18 of the top US teachinghospitals, and the story was the same. Most residents were in Kristen's shoes.They got to “see one” plenty, but the “do one” was barely available. So theycouldn’t struggle, and they weren’t learning.

正如我過去八年做的所有關(guān)于機器人的研究一樣，在這次研究的開始，我也提出了一個宏大的問題：我們要如何與智能機器共存？為了尋找答案，我花了兩年半的時間，觀察了數(shù)位外科醫(yī)生和住院醫(yī)生。他們既做傳統(tǒng)的手術(shù)，也做自動化手術(shù)，我采訪他們，試圖了解他們的學(xué)習(xí)過程。這次研究覆蓋了美國18所頂級的教學(xué)醫(yī)院，研究結(jié)果顯示出相同的趨勢。大部分住院醫(yī)生都和克里斯汀一樣，他們“看”得很多，但“做”的機會卻很少。所以他們難以進步，也無從學(xué)習(xí)。

04:33

This was important news for surgeons, but Ineeded to know how widespread it was: Where else was using AI blocking learningon the job? To find out, I’ve connected with a small but growing group of youngresearchers who’ve done boots-on-the-ground studies of work involving AI invery diverse settings like start-ups, policing, investment banking and onlineeducation. Like me, they spent at least a year and many hundreds of hoursobserving, interviewing and often working side-by-side with the people theystudied. We shared data, and I looked for patterns. No matter the industry, thework, the AI, the story was the same. Organizations were trying harder andharder to get results from AI, and they were peeling learners away from expertwork as they did it. Start-up managers were outsourcing their customer contact.Cops had to learn to deal with crime forecasts without experts support. Juniorbankers were getting cut out of complex analysis, and professors had to buildonline courses without help. And the effect of all of this was the same as insurgery. Learning on the job was getting much harder.

這一現(xiàn)象對外科醫(yī)生來說十分重要，但我想知道，這樣的現(xiàn)象有多普遍？還有哪些領(lǐng)域也是這樣，人工智能阻礙了人們的學(xué)習(xí)？為了找到答案，我聯(lián)系了一個年輕但正迅速成長的研究團隊。他們在不同領(lǐng)域都做了一些關(guān)于人工智能的實地研究，包括初創(chuàng)公司、監(jiān)管治安部門、投資銀行和在線教育等。和我一樣，他們花了至少一年的時間，用了數(shù)百個小時進行觀察采訪研究對象，甚至和他們一起生活、工作。我們共享了數(shù)據(jù)，我想從中尋找出規(guī)律。不管在什么行業(yè)，利用何種人工智能，結(jié)果都非常相似。企業(yè)、機構(gòu)都卯足了勁，想從人工智能中獲益，而這一行為導(dǎo)致學(xué)習(xí)者從專業(yè)工作中脫離出來。初創(chuàng)公司的管理者把聯(lián)系消費者的工作外包出去，警察在沒有專家的支持下去做犯罪預(yù)測工作，初級銀行家被排除在復(fù)雜分析之外，教授也要獨自開始做在線課程。而這些種種帶來的后果和上述外科例子是一樣的，在工作中學(xué)習(xí)變得越來越難，

05:48

This can’t last. McKinsey estimates thatbetween half a billion and a billion of us are going to have to adapt to AI inour daily work by 2030. And we’re assuming that on-the-job learning will bethere for us as we try. Accenture’s latest workers survey showed that mostworkers learned key skills on the job, not in formal training. So while we talka lot about its potential future impact, the aspect of AI that may matter mostright now is that we’re handling it in a way that blocks learning on the jobjust when we need it most.

這樣的情況需要得到改善。據(jù)麥肯錫估計，到2030年，我們中有5億到10億人，將不得不在日常工作中適應(yīng)人工智能。而我們卻以為在職學(xué)習(xí)機制將一直存在，在我們想要學(xué)習(xí)的時候就唾手可得。埃森哲最新的員工調(diào)查顯示，多數(shù)員工在工作時才能真正掌握技能，而不是在培訓(xùn)中。我們一直在關(guān)注人工智能對未來潛在的影響，但卻忘了它在目前最大的影響，就是它阻礙了我們學(xué)習(xí)的步伐，而學(xué)習(xí)恰恰是我們目前最需要的東西。

06:27

Now across all our sites, a small minorityfound a way to learn. They did it by breaking and bending rules. Approvedmethods weren’t working, so they bent and broke rules to get hands-on practicewith experts. In my setting, residents got involved in robotic surgery inmedical school at the expense of their generalist education. And they spenthundreds of extra hours with simulators and recordings of surgery, when youwere supposed to learn in the OR. And maybe most importantly, they found waysto struggle in live procedures with limited expert supervision. I call all this“shadow learning,” because it bends the rules and learner’s do it out of thelimelight. And everyone turns a blind eye because it gets results. Remember, theseare the star pupils of the bunch.

現(xiàn)在有一個小群體找到了學(xué)習(xí)的方法，通過改變和突破規(guī)則。因為現(xiàn)有的方法不奏效，所以他們要改變和突破規(guī)則，來獲取和專家一起學(xué)習(xí)的機會。在我經(jīng)歷的環(huán)境里，住院醫(yī)生在醫(yī)學(xué)院時可以參與到自動化手術(shù)中，犧牲他們的通識教育課程，他們花了數(shù)百個小時研究模擬器和手術(shù)記錄，雖然他們更應(yīng)該在手術(shù)室里實操。最重要的是，他們找到了奮斗的方法，在有限的專家指導(dǎo)下進行現(xiàn)場操作。我稱之為“影子學(xué)習(xí)”，因為它修改了規(guī)則，讓學(xué)習(xí)者在聚光燈之外學(xué)習(xí)，而所有人都對此睜一只眼閉一只眼，因為這樣的學(xué)習(xí)的確有效。記住，這樣學(xué)習(xí)的學(xué)生都是學(xué)霸。

07:29

Now, obviously, this is not OK, and it’snot sustainable. No one should have to risk getting fired to learn the skillsthey need to do their job. But we do need to learn from these people. They tookserious risks to learn. They understood they needed to protect struggle andchallenge in their work so that they could push themselves to tackle hardproblems right near the edge of their capacity. They also made sure there wasan expert nearby to offer pointers and to backstop against catastrophe. Let’sbuild this combination of struggle and expert support into each AIimplementation.

顯然，這樣的方式并不對，也并不可持續(xù)，沒有人應(yīng)該要冒著被開除的風(fēng)險，去學(xué)習(xí)應(yīng)掌握的技能，但我們可能真的要向這些人學(xué)習(xí)。他們?yōu)榱藢W(xué)習(xí)不惜冒著巨大的風(fēng)險，他們明白需要保護那些工作中遇到的困難和挑戰(zhàn)，而強迫自己去解決難題，不斷挑戰(zhàn)自己的極限。他們也保證身邊有足夠的專家資源指導(dǎo)他們，在必要的時候出來提供支持。讓我們把努力和專家支持結(jié)合起來，將其應(yīng)用到人工智能中。

08:08

Here’s one clear example I could get ofthis on the ground. Before robots, if you were a bomb disposal technician, youdealt with an IED by walking up to it. A junior officer was hundreds of feetaway, so could only watch and help if you decided it was safe and invited themdownrange. Now you sit side-by-side in a bomb-proof truck. You both watched thevideo feed. They control a distant robot, and you guide the work out loud.Trainees learn better than they did before robots. We can scale this tosurgery, start-ups, policing, investment banking, online education and beyond.The good news is we’ve got new tools to do it. The internet and the cloud meanwe don’t always need one expert for every trainee, for them to be physicallynear each other or even to be in the same organization. And we can build AI tohelp: to coach learners as they struggle, to coach experts as they coach and toconnect those two groups in smart ways.

我這里有一個具體的例子，在有機器人之前，如果你是一個拆彈專家，你經(jīng)常要直接處理簡單易爆裝置，一個年輕的警官就在你幾百米之外，他只能觀察你，并且在你覺得安全的時候才能提供幫助，才能接近裝置?，F(xiàn)在你們并排坐在防彈卡車?yán)?，一起看著視頻，他們遠程控制著機器人，而你大聲地指揮工作，這樣一來，他們反而可以有更好的機會學(xué)習(xí)。我們可以把這種方式應(yīng)用到外科手術(shù)、初創(chuàng)企業(yè)、治安系統(tǒng)、投資銀行和在線教育等等行業(yè)中。好消息是，我們有了更好的工具輔助學(xué)習(xí)，網(wǎng)絡(luò)和云技術(shù)的發(fā)展意味著我們不再需要專家進行一對一、面對面的教學(xué)，專家和學(xué)習(xí)者甚至不需要在同一個機構(gòu)中。我們可以利用人工智能來輔助學(xué)習(xí)，在學(xué)習(xí)者奮斗的過程中指導(dǎo)他們，還可以指導(dǎo)專家進行更有效的教學(xué)，將兩者以更智慧的方式聯(lián)系起來。

09:15

There are people at work on systems likethis, but they’ve been mostly focused on formal training. And the deeper crisisis in on-the-job learning. We must do better. Today’s problems demand we dobetter to create work that takes full advantage of AI’s amazing capabilitieswhile enhancing our skills as we do it. That’s the kind of future I dreamed ofas a kid. And the time to create it is now.

現(xiàn)在已經(jīng)有在職人員有這樣的教學(xué)系統(tǒng)，但是他們也僅僅是關(guān)注入職培訓(xùn)，更大的危機其實出現(xiàn)在在職培訓(xùn)當(dāng)中。我們必須要做得更好，現(xiàn)在出現(xiàn)的問題要求我們要做得更好，來創(chuàng)造價值，來更好地利用人工智能帶來的便利，同時也讓我們的技術(shù)變得更加成熟。這才是我小時候夢想的未來，而現(xiàn)在正是去開創(chuàng)這一未來的最佳時機。

09:44

Thank you.

謝謝。

09:45

(Applause)

（掌聲）