ABOUT THE SPEAKER
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.

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.

More profile about the speaker
Matt Beane | Speaker | TED.com
TED Salon Zebra Technologies

Matt Beane: How do we learn to work with intelligent machines?

马特·比恩: 我们如何学习使用智能机器?

Filmed:
1,770,815 views

几千年来,世界各地的人们的获得技能的途径都是一样的:在专家的指导下进行培训,先完成简单的小任务,然后再进行有风险和难度更大的任务。组织人种学家马特•比恩(Matt Beane)表示,目前我们对待人工智能的态度阻碍了千百年来沿用至今的“学习之路”,追求效率导致我们牺牲了学习的必经过程。那么,我们能做些什么呢?比恩分享了一个愿景:将学习转变为分布式的、机器强化式的指导过程,在充分利用人工智能神奇能力的同时提高自己的技能。
- 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

Double-click the English transcript below to play the video.

00:13
It’s 6:30 in the morning早上,
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清晨六点半,
00:15
and Kristen克里 斯汀 is wheeling续流
her prostate前列腺 patient患者 into the OR.
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克里斯汀正推着
她的前列腺病人进手术室。
00:21
She's a resident居民, a surgeon外科医生 in training训练.
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她是一名实习住院外科医生,
00:24
It’s her job工作 to learn学习.
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学习是她工作的一部分。
00:27
Today今天, she’s really hoping希望 to do
some of the nerve-sparing神经保留,
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这天,她非常想
参与进行神经保留手术,
这要求医生有极度精细的切割技巧,
以让病人恢复勃起的功能。
00:30
extremely非常 delicate精巧 dissection解剖
that can preserve保留 erectile勃起 function功能.
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00:35
That'll那会 be up to the attending出席 surgeon外科医生,
though虽然, but he's not there yet然而.
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不过,这还要看主治医生的意思,
但那会儿他并不在手术室。
00:39
She and the team球队 put the patient患者 under,
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克里斯汀和其他手术人员
给病人打了麻醉。
首先,她在病人的下腹部
切开了一道8英寸的切口,
00:42
and she leads引线 the initial初始 eight-inch8英寸
incision切口 in the lower降低 abdomen腹部.
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00:47
Once一旦 she’s got that clamped夹 紧 back,
she tells告诉 the nurse护士 to call the attending出席.
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当她把切口固定好,
便让护士打电话给主治医生。
00:51
He arrives到达, gowns礼服 up,
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主治医生赶到后,穿上手术服。
00:54
And from there on in, their four hands
are mostly大多 in that patient患者 --
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接着,两人共同开始手术,
他们四只手都在病人体内,
01:00
with him guiding主导
but Kristin克里斯汀 leading领导 the way.
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主治医生负责指导,
克里斯汀则主导了手术。
01:04
When the prostates前列腺 out (and, yes,
he let Kristen克里 斯汀 do a little nerve神经 sparing保守的),
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当病人的前列腺被取出后,主治医生
让她进行了部分神经保留的操作,
01:09
he rips裂口 off his scrubs磨砂.
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他脱掉了手术服,
开始处理一些文件。
01:10
He starts启动 to do paperwork证件.
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01:12
Kristen克里 斯汀 closes关闭 the patient患者 by 8:15,
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而克里斯汀在一个
初级住院医生的协助下
01:18
with a junior初级 resident居民
looking over her shoulder.
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于8:15完成了手术,
克里斯汀还让他
给病人做了最后的缝合。
01:21
And she lets让我们 him do
the final最后 line线 of sutures缝线.
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01:24
Kristen克里 斯汀 feels感觉 great.
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克里斯汀感觉好极了,
01:28
Patient患者’s going to be fine,
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病人很快就会恢复,
而她也无疑比凌晨六点半时的
自己更进了一步。
01:29
and no doubt怀疑 she’s a better surgeon外科医生
than she was at 6:30.
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01:34
Now this is extreme极端 work.
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虽然,医生的工作挑战性极高。
01:37
But Kristin克里斯汀’s learning学习 to do her job工作
the way that most of us do:
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但克里斯汀的学习过程
其实和我们的并无分别,
01:41
watching观看 an expert专家 for a bit,
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通过观察专家的操作,
从简单、安全的部分开始着手,
01:43
getting得到 involved参与 in easy简单,
safe安全 parts部分 of the work
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过渡到风险更高、难度更大的工作,
01:46
and progressing进展 to riskier风险较高
and harder更难 tasks任务
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其中确保她准备就绪,
并且有专家在一旁指导。
01:48
as they guide指南 and decide决定 she’s ready准备.
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01:52
My whole整个 life I’ve已经 been fascinated入迷
by this kind of learning学习.
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我这一生都被这种学习过程所吸引。
这样基本的步骤,体现了人之常情,
01:54
It feels感觉 elemental元素,
part部分 of what makes品牌 us human人的.
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01:59
It has different不同 names: apprenticeship学徒,
coaching教练, mentorship导师, on the job工作 training训练.
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人们为这个过程赋予不同的名字,
学艺、训练、教导和在职培训,
02:05
In surgery手术, it’s called
“see one, do one, teach one.”
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在外科手术中,
这被称为“看、做、教”,
02:09
But the process处理 is the same相同,
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但实际步骤是一样的,
这也是千百年来所有人
在培养人才时所用的方式。
02:10
and it’s been the main主要 path路径 to skill技能
around the globe地球 for thousands数千 of years年份.
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02:16
Right now, we’re回覆 handling处理 AIAI
in a way that blocks that path路径.
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但如今我们应用人工智能的
方法却反其道而行之。
02:21
We’re回覆 sacrificing牺牲 learning学习
in our quest寻求 for productivity生产率.
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为了提高效率,我们
牺牲了学习必经的过程。
02:25
I found发现 this first in surgery手术
while I was at MITMIT,
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我在麻省理工学院做手术时
第一次发现了这个现象,
但现在我发现这样的现象随处可见,
02:28
but now I’ve已经 got evidence证据
it’s happening事件 all over,
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遍布各行各业,
以及各项人工智能的应用场景中。
02:30
in very different不同 industries行业
and with very different不同 kinds of AIAI.
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02:35
If we do nothing, millions百万 of us
are going to hit击中 a brick wall
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如果我们无动于衷,成千上万的人
在学习如何掌握人工智能时,
将会碰壁。
02:40
as we try to learn学习 to deal合同 with AIAI.
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02:45
Let’s go back to surgery手术 to see how.
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让我们再用外科手术作为例子,
02:47
Fast快速 forward前锋 six months个月.
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时间快进六个月,
还是凌晨六点半,克里斯汀推着
另一个前列腺病人进手术室。
02:49
It’s 6:30am again, and Kristen克里 斯汀
is wheeling续流 another另一个 prostate前列腺 patient患者 in,
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02:55
but this time to the robotic机器人 OR.
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但这一次,是去自动化手术室。
02:59
The attending出席 leads引线 attaching附上
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主治医生把一个
长着四只手、重一千镑的
机器人连接到病人身上,
03:01
a four-armed四武装, thousand-pound千磅
robot机器人 to the patient患者.
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03:04
They both rip安息 off their scrubs磨砂,
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医生们都脱掉了手术服,
来到三五米外的控制台,
03:07
head to control控制 consoles游戏机
10 or 15 feet away,
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03:11
and Kristen克里 斯汀 just watches手表.
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而克里斯汀只负责观察。
03:16
The robot机器人 allows允许 the attending出席
to do the whole整个 procedure程序 himself他自己,
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在机器人的帮助下,
主治医生独自便可完成手术,
他也是这么做的,
03:19
so he basically基本上 does.
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03:21
He knows知道 she needs需求 practice实践.
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即使他知道克里斯汀需要练习,
他也希望可以给她机会,
03:24
He wants to give her control控制.
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03:26
But he also knows知道 she’d be slower比较慢
and make more mistakes错误,
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但是他同样清楚克里斯汀
操作得更慢,还有失误的风险,
03:29
and his patient患者 comes first.
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而病人的安全永远是第一位的。
03:32
So Kristin克里斯汀 has no hope希望 of getting得到 anywhere随地
near those nerves神经 during this rotation回转.
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所以克里斯汀在这次手术中
完全没有机会碰到病人的神经,
03:37
She’ll be lucky幸运 if she operates操作 more than
15 minutes分钟 during a four-hour四小时 procedure程序.
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她能在四个小时的手术中
操刀超过一刻钟就算是走运了,
03:42
And she knows知道 that when she slips卡瓦 up,
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而且她很清楚,万一她出现失误,
03:45
he’ll tap龙头 a touch触摸 screen屏幕,
and she’ll be watching观看 again,
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主治医生就会重新操刀,
她又不得不回到观察者的角色,
03:48
feeling感觉 like a kid孩子 in the corner
with a dunce傻瓜 cap.
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感到非常沮丧和失落。
03:53
Like all the studies学习 of robots机器人 and work
I’ve已经 doneDONE in the last eight years年份,
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正如我过去八年做的
所有关于机器人的研究一样,
在这次研究的开始,
我也提出了一个宏大的问题:
03:57
I started开始 this one
with a big, open打开 question:
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我们要如何与智能机器共存?
03:59
How do we learn学习 to work
with intelligent智能 machines?
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04:02
To find out, I spent花费 two and a half years年份
observing观察 dozens许多 of residents居民 and surgeons外科医生
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为了寻找答案,我花了两年半的时间,
观察了数位外科医生和住院医生。
04:08
doing traditional传统 and robotic机器人 surgery手术,
interviewing面试 them
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他们既做传统的手术,
也做自动化手术,
我采访他们,试图了解他们的学习过程。
04:12
and in general一般 hanging out
with the residents居民 as they tried试着 to learn学习.
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04:16
I covered覆盖 18 of the top最佳
US teaching教学 hospitals医院,
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这次研究覆盖了
美国18所顶级的教学医院,
研究结果显示出相同的趋势。
04:19
and the story故事 was the same相同.
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04:21
Most residents居民 were in Kristen's克里斯汀的 shoes.
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大部分住院医生都和克里斯汀一样,
04:24
They got to “see one” plenty丰富,
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他们“看”得很多,
04:27
but the “do one” was barely仅仅 available可得到.
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但“做”的机会却很少。
04:30
So they couldncouldn’t struggle斗争,
and they weren间没有’t learning学习.
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所以他们难以进步,也无从学习。
04:33
This was important重要 news新闻 for surgeons外科医生, but
I needed需要 to know how widespread广泛 it was:
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这一现象对外科医生来说十分重要,
但我想知道,这样的现象有多普遍?
04:37
Where else其他 was using运用 AIAI
blocking闭塞 learning学习 on the job工作?
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还有哪些领域也是这样,
人工智能阻碍了人们的学习?
04:42
To find out, I’ve已经 connected连接的 with a small
but growing生长 group of young年轻 researchers研究人员
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为了找到答案,我联系了一个
年轻但正迅速成长的研究团队。
04:46
who’ve已经 doneDONE boots-on-the-ground现场引导 studies学习
of work involving涉及 AIAI
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他们在不同领域都做了一些
关于人工智能的实地研究,
包括初创公司、监管治安部门、
04:50
in very diverse多种 settings设置
like start-ups创业, policing治安,
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投资银行和在线教育等。
04:53
investment投资 banking银行业 and online线上 education教育.
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和我一样,他们花了至少一年的时间,
用了数百个小时进行观察
04:55
Like me, they spent花费 at least最小 a year
and many许多 hundreds数以百计 of hours小时 observing观察,
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05:01
interviewing面试 and often经常 working加工
side-by-side并排侧 with the people they studied研究.
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采访研究对象,甚至
和他们一起生活、工作。
05:06
We shared共享 data数据, and I looked看着 for patterns模式.
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我们共享了数据,
我想从中寻找出规律。
05:09
No matter the industry行业, the work,
the AIAI, the story故事 was the same相同.
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不管在什么行业,利用何种
人工智能,结果都非常相似。
05:16
Organizations组织 were trying harder更难
and harder更难 to get results结果 from AIAI,
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企业、机构都卯足了劲,
想从人工智能中获益,
而这一行为导致学习者
从专业工作中脱离出来。
05:19
and they were peeling去皮 learners学习者 away from
expert专家 work as they did it.
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05:24
Start-up启动时间 managers经理 were outsourcing外包
their customer顾客 contact联系.
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初创公司的管理者把
联系消费者的工作外包出去,
05:27
Cops警察 had to learn学习 to deal合同 with crime犯罪
forecasts预测 without experts专家 support支持.
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警察在没有专家的支持下
去做犯罪预测工作,
05:32
Junior初级 bankers银行家 were getting得到
cut out of complex复杂 analysis分析,
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初级银行家被排除在复杂分析之外,
05:36
and professors教授 had to build建立
online线上 courses培训班 without help.
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教授也要独自开始做在线课程。
05:41
And the effect影响 of all of this
was the same相同 as in surgery手术.
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而这些种种带来的后果
和上述外科例子是一样的,
在工作中学习变得越来越难,
05:44
Learning学习 on the job工作
was getting得到 much harder更难.
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05:48
This can’t last.
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这样的情况需要得到改善。
05:51
McKinsey麦肯锡 estimates估计 that between之间 half
a billion十亿 and a billion十亿 of us
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据麦肯锡估计,到2030年,
我们中有5亿到10亿人,
将不得不在日常工作中
适应人工智能。
05:55
are going to have to adapt适应 to AIAI
in our daily日常 work by 2030.
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06:01
And we’re回覆 assuming假设
that on-the-job在工作中 learning学习
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而我们却以为
在职学习机制将一直存在,
在我们想要学习的时候就唾手可得。
06:03
will be there for us as we try.
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06:05
Accenture埃森哲’s latest最新 workers工人 survey调查 showed显示
that most workers工人 learned学到了 key skills技能
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埃森哲最新的员工调查显示,
多数员工在工作时才能真正掌握技能,
06:09
on the job工作, not in formal正式 training训练.
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而不是在培训中。
06:13
So while we talk a lot about its
potential潜在 future未来 impact碰撞,
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我们一直在关注
人工智能对未来潜在的影响,
但却忘了它在目前最大的影响,
06:16
the aspect方面 of AIAI
that may可能 matter most right now
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就是它阻碍了我们学习的步伐,
06:20
is that we’re回覆 handling处理 it in a way
that blocks learning学习 on the job工作
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06:24
just when we need it most.
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而学习恰恰是
我们目前最需要的东西。
06:27
Now across横过 all our sites网站,
a small minority少数民族 found发现 a way to learn学习.
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现在有一个小群体
找到了学习的方法,
06:35
They did it by breaking破坏 and bending弯曲 rules规则.
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通过改变和突破规则。
06:39
Approved批准 methods方法 weren间没有’t working加工,
so they bent弯曲 and broke打破 rules规则
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因为现有的方法不奏效,
所以他们要改变和突破规则,
06:43
to get hands-on动手 practice实践 with experts专家.
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来获取和专家一起学习的机会。
在我经历的环境里,住院医生
在医学院时可以参与到自动化手术中,
06:45
In my setting设置, residents居民 got involved参与
in robotic机器人 surgery手术 in medical school学校
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06:51
at the expense费用
of their generalist通才 education教育.
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牺牲他们的通识教育课程,
06:56
And they spent花费 hundreds数以百计 of extra额外 hours小时
with simulators模拟器 and recordings录音 of surgery手术,
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他们花了数百个小时
研究模拟器和手术记录,
虽然他们更应该在手术室里实操。
07:02
when you were supposed应该 to learn学习 in the OR.
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07:05
And maybe most importantly重要的,
they found发现 ways方法 to struggle斗争
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最重要的是,
他们找到了奋斗的方法,
在有限的专家指导下进行现场操作。
07:08
in live生活 procedures程序
with limited有限 expert专家 supervision监督.
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07:13
I call all this “shadow阴影 learning学习,”
because it bends弯曲 the rules规则
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我称之为“影子学习”,
因为它修改了规则,
让学习者在聚光灯之外学习,
07:18
and learner学习者’s do it out of the limelight聚光灯.
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07:21
And everyone大家 turns a blind eye
because it gets得到 results结果.
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而所有人都对此睁一只眼闭一只眼,
因为这样的学习的确有效。
07:25
Remember记得, these are
the star pupils学生 of the bunch.
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记住,这样学习的学生都是学霸。
07:29
Now, obviously明显, this is not OK,
and it’s not sustainable可持续发展.
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显然,这样的方式并不对,
也并不可持续,
07:33
No one should have to risk风险 getting得到 fired解雇
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没有人应该要冒着被开除的风险,
去学习应掌握的技能,
07:35
to learn学习 the skills技能
they need to do their job工作.
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07:38
But we do need to learn学习 from these people.
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但我们可能真的要向这些人学习。
07:41
They took serious严重 risks风险 to learn学习.
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他们为了学习不惜冒着巨大的风险,
07:44
They understood了解 they needed需要 to protect保护
struggle斗争 and challenge挑战 in their work
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他们明白需要保护那些工作中
遇到的困难和挑战,
而强迫自己去解决难题,
07:49
so that they could push themselves他们自己
to tackle滑车 hard problems问题
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不断挑战自己的极限。
07:52
right near the edge边缘 of their capacity容量.
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07:54
They also made制作 sure
there was an expert专家 nearby附近
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他们也保证身边有
足够的专家资源指导他们,
07:56
to offer提供 pointers指针 and to backstop逆止
against反对 catastrophe灾难.
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在必要的时候出来提供支持。
08:00
Let’s build建立 this combination组合
of struggle斗争 and expert专家 support支持
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让我们把努力和专家支持结合起来,
08:04
into each AIAI implementation履行.
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将其应用到人工智能中。
08:08
Here’s one clear明确 example
I could get of this on the ground地面.
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我这里有一个具体的例子,
08:12
Before robots机器人,
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在有机器人之前,
如果你是一个拆弹专家,
你经常要直接处理简单易爆装置,
08:13
if you were a bomb炸弹 disposal处置 technician技术员,
you dealt处理 with an IEDIED by walking步行 up to it.
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08:19
A junior初级 officer was
hundreds数以百计 of feet away,
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一个年轻的警官就在你几百米之外,
他只能观察你,并且在
你觉得安全的时候才能提供帮助,
08:21
so could only watch and help
if you decided决定 it was safe安全
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才能接近装置。
08:24
and invited邀请 them downrange下行范围.
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08:27
Now you sit side-by-side并排侧
in a bomb-proof防弹 truck卡车.
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现在你们并排坐在防弹卡车里,
一起看着视频,
08:31
You both watched看着 the video视频 feed饲料.
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他们远程控制着机器人,
而你大声地指挥工作,
08:32
They control控制 a distant遥远 robot机器人,
and you guide指南 the work out loud.
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这样一来,他们反而可以
有更好的机会学习。
08:37
Trainees学员 learn学习 better than they
did before robots机器人.
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08:41
We can scale规模 this to surgery手术,
start-ups创业, policing治安,
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我们可以把这种方式应用到
外科手术、初创企业、治安系统、
08:45
investment投资 banking银行业,
online线上 education教育 and beyond.
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投资银行和在线教育等等行业中。
08:48
The good news新闻 is
we’ve已经 got new tools工具 to do it.
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好消息是,我们有了
更好的工具辅助学习,
08:51
The internet互联网 and the cloud mean we don不要’t
always need one expert专家 for every一切 trainee实习生,
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网络和云技术的发展意味着我们不再
需要专家进行一对一、面对面的教学,
专家和学习者甚至
不需要在同一个机构中。
08:56
for them to be physically物理 near each other
or even to be in the same相同 organization组织.
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09:01
And we can build建立 AIAI to help:
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我们可以利用人工智能来辅助学习,
09:05
to coach教练 learners学习者 as they struggle斗争,
to coach教练 experts专家 as they coach教练
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在学习者奋斗的过程中指导他们,
还可以指导专家进行更有效的教学,
09:10
and to connect those two groups
in smart聪明 ways方法.
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将两者以更智慧的方式联系起来。
09:15
There are people at work
on systems系统 like this,
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现在已经有在职人员
有这样的教学系统,
09:18
but they’ve已经 been mostly大多 focused重点
on formal正式 training训练.
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但是他们也仅仅是
关注入职培训,
09:21
And the deeper更深 crisis危机
is in on-the-job在工作中 learning学习.
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更大的危机其实出现在
在职培训当中。
09:24
We must必须 do better.
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我们必须要做得更好,
现在出现的问题
要求我们要做得更好,
09:26
Today今天’s problems问题 demand需求 we do better
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09:29
to create创建 work that takes full充分 advantage优点
of AIAI’s amazing惊人 capabilities功能
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来创造价值,来更好地利用
人工智能带来的便利,
09:35
while enhancing提高 our skills技能 as we do it.
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同时也让我们的技术变得更加成熟。
09:38
That’s the kind of future未来
I dreamed梦见 of as a kid孩子.
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这才是我小时候梦想的未来,
09:41
And the time to create创建 it is now.
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而现在正是去开创
这一未来的最佳时机。
09:44
Thank you.
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谢谢。
(掌声)
09:45
(Applause掌声)
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Translated by Ruijie Wu
Reviewed by Chen Yunru

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ABOUT THE SPEAKER
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.

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.

More profile about the speaker
Matt Beane | Speaker | TED.com

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