TED Salon Zebra Technologies
Matt Beane: How do we learn to work with intelligent machines?
麥特·比恩: 如何學習與智慧型機器共事？
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Readability: 4.2
1,770,815 views
數千年來，全球學習技能的途徑都是一樣的。那就是在專家的訓練之下，我們從小而簡單的工作開始，然後進階到風險更高、更困難的工作。但現在我們應用人工智慧的方式，阻擋了這條學習的途徑——犧牲了學習機會來達到更多生產力，組織人類學家麥特·比恩這麼說。我們可以做什麼呢？比恩分享了一個願景——將翻轉現況。一個由智慧型機器輔助的對應導師制，在將人工智慧潛能的發揮到極致之際，同時也加強我們的工作技能。
Matt Beane - Organizational ethnographer
Matt Beane is an Assistant Professor in the Technology Management Program at the University of California, Santa Barbara and a Research Affiliate with MIT's Institute for the Digital Economy. Full bio
Matt Beane is an Assistant Professor in the Technology Management Program at the University of California, Santa Barbara and a Research Affiliate with MIT's Institute for the Digital Economy. Full bio
Double-click the English transcript below to play the video.
00:13
It’s 6:30 in the morning,
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凌晨六點半，
00:15
and Kristen is wheeling
her prostate patient into the OR.
her prostate patient into the OR.
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克莉絲汀推著她的
前列腺病人進入手術室。
前列腺病人進入手術室。
00:21
She's a resident, a surgeon in training.
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她是住院醫師——
培訓中的外科醫師。
培訓中的外科醫師。
00:24
It’s her job to learn.
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學習是她的義務。
00:27
Today, she’s really hoping to do
some of the nerve-sparing,
some of the nerve-sparing,
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今天她非常希望能參與
部分的雙側神經保留手術，
部分的雙側神經保留手術，
00:30
extremely delicate dissection
that can preserve erectile function.
that can preserve erectile function.
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需要精湛的手術技巧，
讓病人保有勃起的功能。
讓病人保有勃起的功能。
00:35
That'll be up to the attending surgeon,
though, but he's not there yet.
though, but he's not there yet.
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不過，這還要看主治醫師的意思，
而他還沒有到場。
而他還沒有到場。
00:39
She and the team put the patient under,
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克莉絲汀和團隊給病人打了麻醉，
00:42
and she leads the initial eight-inch
incision in the lower abdomen.
incision in the lower abdomen.
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她在病人的下腹部開了
第一道 8 英吋的切口。
第一道 8 英吋的切口。
00:47
Once she’s got that clamped back,
she tells the nurse to call the attending.
she tells the nurse to call the attending.
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當她把切口夾好，
她請護理師打電話給主治醫師。
她請護理師打電話給主治醫師。
00:51
He arrives, gowns up,
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主治醫師到場，穿上手術服，
00:54
And from there on in, their four hands
are mostly in that patient --
are mostly in that patient --
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接著，他們的四隻手
就大都在病人體內，
就大都在病人體內，
01:00
with him guiding
but Kristin leading the way.
but Kristin leading the way.
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在主治醫師的指導下，
由克莉絲汀操作。
由克莉絲汀操作。
01:04
When the prostates out (and, yes,
he let Kristen do a little nerve sparing),
he let Kristen do a little nerve sparing),
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當病人的前列腺被取出後，
（太好了！他讓她做了
部分神經保留手術。）
部分神經保留手術。）
01:09
he rips off his scrubs.
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主治醫師脫掉了手術服，
01:10
He starts to do paperwork.
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開始填寫資料。
01:12
Kristen closes the patient by 8:15,
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而克莉絲汀在八點十五分完成了手術，
01:18
with a junior resident
looking over her shoulder.
looking over her shoulder.
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一位資淺的住院醫師在旁觀摩學習。
01:21
And she lets him do
the final line of sutures.
the final line of sutures.
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她讓他為病人做最後的縫合。
01:24
Kristen feels great.
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克莉絲汀感覺好極了！
01:28
Patient’s going to be fine,
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病人應該很快就會恢復，
01:29
and no doubt she’s a better surgeon
than she was at 6:30.
than she was at 6:30.
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無疑地，比起做這個手術前，
她是一位更好的外科醫師。
她是一位更好的外科醫師。
01:34
Now this is extreme work.
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這是種極端的工作。
01:37
But Kristin’s learning to do her job
the way that most of us do:
the way that most of us do:
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但克莉絲汀邊做邊學的方式
和我們大多數人無異：
和我們大多數人無異：
01:41
watching an expert for a bit,
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觀察專家如何操作，
01:43
getting involved in easy,
safe parts of the work
safe parts of the work
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從簡單、安全的部分開始著手，
01:46
and progressing to riskier
and harder tasks
and harder tasks
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然後在專家的指導和確認合格之下，
接手風險更高、難度更大的工作。
接手風險更高、難度更大的工作。
01:48
as they guide and decide she’s ready.
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我一直對這樣的學習過程感到著迷。
01:52
My whole life I’ve been fascinated
by this kind of learning.
by this kind of learning.
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01:54
It feels elemental,
part of what makes us human.
part of what makes us human.
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我覺得這似乎是人類
之所以為人類的基本要素之一。
之所以為人類的基本要素之一。
01:59
It has different names: apprenticeship,
coaching, mentorship, on the job training.
coaching, mentorship, on the job training.
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人們為這過程賦予不同的名字：
學徒、訓練、師徒制、在職訓練。
學徒、訓練、師徒制、在職訓練。
02:05
In surgery, it’s called
“see one, do one, teach one.”
“see one, do one, teach one.”
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外科稱之為
「看一次、做一遍、教一位」，
「看一次、做一遍、教一位」，
02:09
But the process is the same,
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但過程是一樣的，
02:10
and it’s been the main path to skill
around the globe for thousands of years.
around the globe for thousands of years.
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這也是數千年來，
全球在培養人才時運用的方式。
全球在培養人才時運用的方式。
02:16
Right now, we’re handling AI
in a way that blocks that path.
in a way that blocks that path.
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現在我們應用人工智慧的方式
阻礙了這條學習路徑。
阻礙了這條學習路徑。
02:21
We’re sacrificing learning
in our quest for productivity.
in our quest for productivity.
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為了更高的生產率，
我們犧牲了在工作中學習的機會。
我們犧牲了在工作中學習的機會。
我最初在麻省理工學院的手術
發現了這一個現象，
發現了這一個現象，
02:25
I found this first in surgery
while I was at MIT,
while I was at MIT,
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02:28
but now I’ve got evidence
it’s happening all over,
it’s happening all over,
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但現在，我發現這個情況隨處可見，
02:30
in very different industries
and with very different kinds of AI.
and with very different kinds of AI.
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遍佈各行各業，
應用著各種人工智慧的技術。
應用著各種人工智慧的技術。
02:35
If we do nothing, millions of us
are going to hit a brick wall
are going to hit a brick wall
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如果我們對此不做出改變，
在我們學著面對人工智慧技術時，
成千上萬的人將受挫。
成千上萬的人將受挫。
02:40
as we try to learn to deal with AI.
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02:45
Let’s go back to surgery to see how.
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讓我們回到外科手術作為例子，
02:47
Fast forward six months.
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時間快轉六個月，
02:49
It’s 6:30am again, and Kristen
is wheeling another prostate patient in,
is wheeling another prostate patient in,
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同樣是凌晨六點半，克莉絲汀推著
另一位前列腺病人進來，
另一位前列腺病人進來，
02:55
but this time to the robotic OR.
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但這一次，病人被推到機器人手術室，
02:59
The attending leads attaching
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由主治醫師主導著
把一個有著四支手臂、
把一個有著四支手臂、
03:01
a four-armed, thousand-pound
robot to the patient.
robot to the patient.
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重一千磅的機器人，
連接到病人身上。
連接到病人身上。
03:04
They both rip off their scrubs,
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他們都脫下了手術服，
03:07
head to control consoles
10 or 15 feet away,
10 or 15 feet away,
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來到 10 ~ 15 英尺外的控制台，
03:11
and Kristen just watches.
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而克莉絲汀只能旁觀。
03:16
The robot allows the attending
to do the whole procedure himself,
to do the whole procedure himself,
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在機器人的幫助下，
主治醫師一個人便可完成手術，
主治醫師一個人便可完成手術，
03:19
so he basically does.
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他基本上也這麼做，
03:21
He knows she needs practice.
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他知道克莉絲汀需要練習，
03:24
He wants to give her control.
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他也希望可以讓她主導，
03:26
But he also knows she’d be slower
and make more mistakes,
and make more mistakes,
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但是他同樣清楚她會比較慢，
可能會有失誤，
可能會有失誤，
03:29
and his patient comes first.
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而他的病人第一。
03:32
So Kristin has no hope of getting anywhere
near those nerves during this rotation.
near those nerves during this rotation.
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在這次手術中，克莉絲汀
沒有任何機會接近那些神經，
沒有任何機會接近那些神經，
03:37
She’ll be lucky if she operates more than
15 minutes during a four-hour procedure.
15 minutes during a four-hour procedure.
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在長達四小時的手術中，
若她能操作十五分鐘就算幸運了。
若她能操作十五分鐘就算幸運了。
03:42
And she knows that when she slips up,
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她知道一旦她有失誤，
03:45
he’ll tap a touch screen,
and she’ll be watching again,
and she’ll be watching again,
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他只要輕敲螢幕，
她又回到旁觀的角色，
她又回到旁觀的角色，
03:48
feeling like a kid in the corner
with a dunce cap.
with a dunce cap.
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感覺像個戴笨蛋高帽的
孩子在角落裡罰站。
孩子在角落裡罰站。
03:53
Like all the studies of robots and work
I’ve done in the last eight years,
I’ve done in the last eight years,
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就像這八年來我所做的
有關機器人與工作的研究，
有關機器人與工作的研究，
我以一個重要且有爭議的問題開始：
03:57
I started this one
with a big, open question:
with a big, open question:
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03:59
How do we learn to work
with intelligent machines?
with intelligent machines?
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我們如何學習與智慧型機器共事呢？
04:02
To find out, I spent two and a half years
observing dozens of residents and surgeons
observing dozens of residents and surgeons
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為了找出答案，
我花了兩年半的時間
我花了兩年半的時間
觀察數十位做傳統與機器人手術的
住院醫師和外科醫師，
住院醫師和外科醫師，
04:08
doing traditional and robotic surgery,
interviewing them
interviewing them
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訪問他們，
04:12
and in general hanging out
with the residents as they tried to learn.
with the residents as they tried to learn.
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基本上，當他們在學習的時候，
我和他們混在一起。
我和他們混在一起。
04:16
I covered 18 of the top
US teaching hospitals,
US teaching hospitals,
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我涵蓋了十八所美國頂尖的教學醫院，
04:19
and the story was the same.
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故事都是相同的。
04:21
Most residents were in Kristen's shoes.
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絕大多數的住院醫師的
處境跟克莉斯汀一樣。
處境跟克莉斯汀一樣。
04:24
They got to “see one” plenty,
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他們有很多「看一次」的機會，
04:27
but the “do one” was barely available.
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但「做一遍」的機會少之又少。
他們沒有機會受挫，
也沒能進一步學習。
也沒能進一步學習。
04:30
So they couldn’t struggle,
and they weren’t learning.
and they weren’t learning.
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04:33
This was important news for surgeons, but
I needed to know how widespread it was:
I needed to know how widespread it was:
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這對外科醫師來說是很重要的消息，
但我想知道這個情況擴散的程度。
但我想知道這個情況擴散的程度。
04:37
Where else was using AI
blocking learning on the job?
blocking learning on the job?
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哪些產業也在運用人工智慧時，
阻礙了做中學呢？
阻礙了做中學呢？
04:42
To find out, I’ve connected with a small
but growing group of young researchers
but growing group of young researchers
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為了找出答案，我聯繫了一群
小而成長中的年輕研究者，
小而成長中的年輕研究者，
04:46
who’ve done boots-on-the-ground studies
of work involving AI
of work involving AI
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他們腳踏實地研究過人工智慧
04:50
in very diverse settings
like start-ups, policing,
like start-ups, policing,
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在不同的領域的運用，
包括：新創、警政、
包括：新創、警政、
04:53
investment banking and online education.
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投資銀行和線上教育。
04:55
Like me, they spent at least a year
and many hundreds of hours observing,
and many hundreds of hours observing,
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像我一樣，他們花了至少一年，
以及數百個小時
以及數百個小時
觀察、訪問，並經常和他們
研究的對象肩並肩一起工作。
研究的對象肩並肩一起工作。
05:01
interviewing and often working
side-by-side with the people they studied.
side-by-side with the people they studied.
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05:06
We shared data, and I looked for patterns.
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我們分享了數據，而我觀察模式。
05:09
No matter the industry, the work,
the AI, the story was the same.
the AI, the story was the same.
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不論是哪種產業、工作、人工智慧，
故事都是相同的。
故事都是相同的。
05:16
Organizations were trying harder
and harder to get results from AI,
and harder to get results from AI,
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所有組織都非常努力地
運用人工智慧以得到更好的成果，
運用人工智慧以得到更好的成果，
05:19
and they were peeling learners away from
expert work as they did it.
expert work as they did it.
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在這過程中，他們剝奪了
學徒習做專家工作的機會。
學徒習做專家工作的機會。
05:24
Start-up managers were outsourcing
their customer contact.
their customer contact.
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新創公司的經理人
外包他們的客服窗口。
外包他們的客服窗口。
05:27
Cops had to learn to deal with crime
forecasts without experts support.
forecasts without experts support.
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警察在沒有專家的協助下，
學著做犯罪預測。
學著做犯罪預測。
05:32
Junior bankers were getting
cut out of complex analysis,
cut out of complex analysis,
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資淺的銀行家無法
接觸到複雜的分析，
接觸到複雜的分析，
05:36
and professors had to build
online courses without help.
online courses without help.
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而教授得在沒有幫助的狀況下
打造線上課程。
打造線上課程。
05:41
And the effect of all of this
was the same as in surgery.
was the same as in surgery.
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所有這些帶來的影響
跟手術的情形是一樣的。
跟手術的情形是一樣的。
05:44
Learning on the job
was getting much harder.
was getting much harder.
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做中學變得越來越困難。
05:48
This can’t last.
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這情況不能繼續下去。
05:51
McKinsey estimates that between half
a billion and a billion of us
a billion and a billion of us
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麥肯錫顧問公司估計：
大約有五～十億人在 2030 年以前，
05:55
are going to have to adapt to AI
in our daily work by 2030.
in our daily work by 2030.
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必須將人工智慧應用在日常工作中。
我們以為當我們在做的時候，
會有在職訓練。
會有在職訓練。
06:01
And we’re assuming
that on-the-job learning
that on-the-job learning
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06:03
will be there for us as we try.
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06:05
Accenture’s latest workers survey showed
that most workers learned key skills
that most workers learned key skills
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埃森哲顧問公司最新的工作調查顯示：
多數人透過做中學得到工作的
關鍵技能，而非透過正式的訓練。
關鍵技能，而非透過正式的訓練。
06:09
on the job, not in formal training.
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06:13
So while we talk a lot about its
potential future impact,
potential future impact,
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當我們高談闊論人工智慧
對未來的潛在衝擊，
對未來的潛在衝擊，
06:16
the aspect of AI
that may matter most right now
that may matter most right now
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此時此刻，人工智慧
對我們最重要的影響是
對我們最重要的影響是
06:20
is that we’re handling it in a way
that blocks learning on the job
that blocks learning on the job
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我們應用人工智慧的方式，
阻礙了人們邊做邊學的機會。
阻礙了人們邊做邊學的機會。
而那是我們最需要學習的時候。
06:24
just when we need it most.
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06:27
Now across all our sites,
a small minority found a way to learn.
a small minority found a way to learn.
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在我們的研究對象中，
有一小群人找到一種學習方式。
有一小群人找到一種學習方式。
06:35
They did it by breaking and bending rules.
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他們透過打破常規來學習。
06:39
Approved methods weren’t working,
so they bent and broke rules
so they bent and broke rules
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被准許的作法不可行，
所以他們改變了遊戲規則，
所以他們改變了遊戲規則，
06:43
to get hands-on practice with experts.
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才得以與專家一同實際操作作。
06:45
In my setting, residents got involved
in robotic surgery in medical school
in robotic surgery in medical school
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我看到的是，住院醫師
在醫學院裡為了參與機器人手術，
在醫學院裡為了參與機器人手術，
06:51
at the expense
of their generalist education.
of their generalist education.
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犧牲了上全科醫師的課為代價，
06:56
And they spent hundreds of extra hours
with simulators and recordings of surgery,
with simulators and recordings of surgery,
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他們多花了數百個小時
使用模擬器與看手術錄影來學習。
使用模擬器與看手術錄影來學習。
07:02
when you were supposed to learn in the OR.
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那是他們本來應當
在手術室裡學習的。
在手術室裡學習的。
07:05
And maybe most importantly,
they found ways to struggle
they found ways to struggle
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或許更重要的，
他們在有限的專家指導下，
找到在實際的手術中練習的機會。
找到在實際的手術中練習的機會。
07:08
in live procedures
with limited expert supervision.
with limited expert supervision.
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07:13
I call all this “shadow learning,”
because it bends the rules
because it bends the rules
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我稱之為「在陰影中學習」
因為這違反了規則，
因為這違反了規則，
學生得要偷偷摸摸地學習。
07:18
and learner’s do it out of the limelight.
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07:21
And everyone turns a blind eye
because it gets results.
because it gets results.
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大家對此睜一隻眼閉一隻眼，
因為這樣的確有效。
因為這樣的確有效。
07:25
Remember, these are
the star pupils of the bunch.
the star pupils of the bunch.
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但記住，這些僅是少數的明星學生。
07:29
Now, obviously, this is not OK,
and it’s not sustainable.
and it’s not sustainable.
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顯然，這樣並不恰當，
這不是長久之計。
這不是長久之計。
07:33
No one should have to risk getting fired
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沒有人應該冒著被開除的風險，
07:35
to learn the skills
they need to do their job.
they need to do their job.
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去學習他們工作必要的技巧。
07:38
But we do need to learn from these people.
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但我們必須從這些人身上學習。
07:41
They took serious risks to learn.
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他們為了學習而承擔高度風險。
07:44
They understood they needed to protect
struggle and challenge in their work
struggle and challenge in their work
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他們明白必須保護工作中
受挫與挑戰的機會，
受挫與挑戰的機會，
07:49
so that they could push themselves
to tackle hard problems
to tackle hard problems
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以推動他們自己去挑戰
比他們的能力能解決的
更困難的問題。
更困難的問題。
07:52
right near the edge of their capacity.
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07:54
They also made sure
there was an expert nearby
there was an expert nearby
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他們也確保會有一個專家在旁，
07:56
to offer pointers and to backstop
against catastrophe.
against catastrophe.
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提供建議跟收拾殘局，
以防他們搞砸了。
以防他們搞砸了。
08:00
Let’s build this combination
of struggle and expert support
of struggle and expert support
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讓我們設計
在導入每項人工智慧時
在導入每項人工智慧時
加入學習機會及專家協助的組合。
08:04
into each AI implementation.
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08:08
Here’s one clear example
I could get of this on the ground.
I could get of this on the ground.
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這裡有個清楚的案例，
我能在現實中找到。
我能在現實中找到。
08:12
Before robots,
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在機器人出現之前，
08:13
if you were a bomb disposal technician,
you dealt with an IED by walking up to it.
you dealt with an IED by walking up to it.
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如果你是個拆彈技術專家
面對簡易爆炸裝置時，你得走近它。
面對簡易爆炸裝置時，你得走近它。
08:19
A junior officer was
hundreds of feet away,
hundreds of feet away,
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一個資淺的警官在數百公尺外支援，
08:21
so could only watch and help
if you decided it was safe
if you decided it was safe
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他可以觀察並協助，
直到你確定裝置是安全的
並邀請他們到近距離。
並邀請他們到近距離。
08:24
and invited them downrange.
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08:27
Now you sit side-by-side
in a bomb-proof truck.
in a bomb-proof truck.
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現在你們肩並肩地坐在防彈車裡。
08:31
You both watched the video feed.
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你們一同觀看機器人傳來的影片資訊。
08:32
They control a distant robot,
and you guide the work out loud.
and you guide the work out loud.
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資淺者控制著遠端機器人，
而你大聲引導著作業。
而你大聲引導著作業。
08:37
Trainees learn better than they
did before robots.
did before robots.
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受訓者的學習效果
比機器人出現之前更佳。
比機器人出現之前更佳。
08:41
We can scale this to surgery,
start-ups, policing,
start-ups, policing,
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我們可以按比例複製這樣的模式
到手術、新創公司、警政、
到手術、新創公司、警政、
08:45
investment banking,
online education and beyond.
online education and beyond.
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投資銀行、線上教育，
以及更多產業。
以及更多產業。
08:48
The good news is
we’ve got new tools to do it.
we’ve got new tools to do it.
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好消息是我們有新的工具去執行。
08:51
The internet and the cloud mean we don’t
always need one expert for every trainee,
always need one expert for every trainee,
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網路跟雲端代表著我們不再需要
一對一的師徒制，
一對一的師徒制，
08:56
for them to be physically near each other
or even to be in the same organization.
or even to be in the same organization.
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他們不再需要去到同一個空間
甚至在不同的組織單位裡。
甚至在不同的組織單位裡。
09:01
And we can build AI to help:
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我們可以打造人工智慧來協助。
09:05
to coach learners as they struggle,
to coach experts as they coach
to coach experts as they coach
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當學生困惑時教導他們，
當專家指導學生時協助專家，
當專家指導學生時協助專家，
09:10
and to connect those two groups
in smart ways.
in smart ways.
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並以聰明的方式聯繫這兩群人。
09:15
There are people at work
on systems like this,
on systems like this,
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有人正在開發這樣的系統，
09:18
but they’ve been mostly focused
on formal training.
on formal training.
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但他們大多專注於正式的訓練。
09:21
And the deeper crisis
is in on-the-job learning.
is in on-the-job learning.
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但更深的危機是工作做中學的部分。
09:24
We must do better.
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我們必須做得更好。
09:26
Today’s problems demand we do better
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今天所面臨的挑戰促使我們要更好地
09:29
to create work that takes full advantage
of AI’s amazing capabilities
of AI’s amazing capabilities
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創造應用人工智慧無限潛能的工作，
09:35
while enhancing our skills as we do it.
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同時讓我們在工作時
也能加強我們的技能。
也能加強我們的技能。
09:38
That’s the kind of future
I dreamed of as a kid.
I dreamed of as a kid.
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這是我從孩提時代以來
一直有的夢想。
一直有的夢想。
09:41
And the time to create it is now.
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現在就是創造它的時刻。
09:44
Thank you.
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謝謝大家。
09:45
(Applause)
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（掌聲）
ABOUT THE SPEAKER
Matt Beane - Organizational ethnographerMatt Beane is an Assistant Professor in the Technology Management Program at the University of California, Santa Barbara and a Research Affiliate with MIT's Institute for the Digital Economy.
Why you should listen
Matt Beane does field research on work involving robots to help us understand the implications of intelligent machines for the broader world of work. Any of his projects mean many hundreds of hours -- sometimes years -- watching, interviewing and often working side by side with people trying to work with robots to get their jobs done.
Beane has studied robotic surgery, robotic materials transport and robotic telepresence in healthcare, elder care and knowledge work. He has published in top management journals such as Administrative Science Quarterly, he was selected in 2012 as a Human Robot Interaction Pioneer and is a regular contributor to popular outlets such as Wired, MIT Technology Review, TechCrunch, Forbes and Robohub. He also took a two-year hiatus from his doctoral studies to help found and fund Humatics, an MIT-connected, full-stack IoT startup.
Beane is an Assistant Professor in the Technology Management Program at the University of California, Santa Barbara and a Research Affiliate with MIT's Institute for the Digital Economy. He received his PhD from the MIT Sloan School of Management.
Matt Beane | Speaker | TED.com