It's 6:30 in the morning, and Kristen is wheeling her prostate patient into the OR.
清晨六点半，克里斯汀正推着她的前列腺病人进手术室。
She's a resident, a surgeon in training. It's her job to learn.
她是一名实习住院外科医生，学习是她工作的一部分。
Today, she's really hoping to do some of the nerve-sparing, extremely delicate dissection that can preserve erectile function.
今天，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。
That'll be up to the attending surgeon, though, but he's not there yet.
不过，这还要看主治医生的意思，但那会儿他并不在手术室。
She and the team put the patient under, and she leads the initial eight-inch incision in the lower abdomen.
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。
Once she's got that clamped back, she tells the nurse to call the attending. He arrives, gowns up.
当她把切口固定好，便让护士打电话给主治医生。主治医生赶到后，穿上手术服。
And from there on in, their four hands are mostly in that patient -- with him guiding but Kristin leading the way.
接着，两人共同开始手术，他们四只手都在病人体内，主治医生负责指导，克里斯汀则主导了手术。
When the prostates out (and, yes, he let Kristen do a little nerve sparing), he rips off his scrubs.
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，他脱掉了手术服。
He starts to do paperwork. Kristen closes the patient by 8:15, with a junior resident looking over her shoulder.
他开始处理一些文件。而克里斯汀在一个初级住院医生的协助下于8:15完成了手术。
And she lets him do the final line of sutures. Kristen feels great.
克里斯汀还让他给病人做了最后的缝合。克里斯汀感觉好极了。
Patient's going to be fine, and no doubt she's a better surgeon than she was at 6:30.
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。
Now this is extreme work. But Kristin's learning to do her job the way that most of us do:
虽然医生的工作挑战性极高，但克里斯汀的学习过程其实和我们的并无分别，
watching an expert for a bit, getting involved in easy, safe parts of the work
通过观察专家的操作，从简单、安全的部分开始着手，
and progressing to riskier and harder tasks as they guide and decide she's ready.
过渡到风险更高、难度更大的工作，其中确保她准备就绪并且有专家在一旁指导。
My whole life I've been fascinated by this kind of learning.
我这一生都被这种学习过程所吸引。
It feels elemental, part of what makes us human.
这样基本的步骤体现了人之常情。
It has different names: apprenticeship, coaching, mentorship, on the job training.
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。
In surgery, it's called "see one, do one, teach one."
在外科手术中，这被称为“看、做、教”。
But the process is the same, and it's been the main path to skill around the globe for thousands of years.
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。
Right now, we're handling AI in a way that blocks that path. We're sacrificing learning in our quest for productivity.
但如今我们应用人工智能的方法却反其道而行之。为了提高效率，我们牺牲了学习必经的过程。
I found this first in surgery while I was at MIT,
我在麻省理工学院做手术时第一次发现了这个现象，
but now I've got evidence it's happening all over, in very different industries and with very different kinds of AI.
但现在我发现这样的现象随处可见，遍布各行各业以及各项人工智能的应用场景中。
If we do nothing, millions of us are going to hit a brick wall as we try to learn to deal with AI.
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。
Let's go back to surgery to see how.
让我们再用外科手术作为例子。
Fast forward six months.
时间快进六个月。
It's 6:30am again, and Kristen is wheeling another prostate patient in, but this time to the robotic OR.
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，但这一次是去自动化手术室。
The attending leads attaching a four-armed, thousand-pound robot to the patient.
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。
They both rip off their scrubs, head to control consoles 10 or 15 feet away, and Kristen just watches.
医生们都脱掉了手术服，来到三五米外的控制台，而克里斯汀只负责观察。
The robot allows the attending to do the whole procedure himself, so he basically does.
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。
He knows she needs practice. He wants to give her control.
即使他知道克里斯汀需要练习。他也希望可以给她机会。
But he also knows she'd be slower and make more mistakes, and his patient comes first.
但是他同样清楚克里斯汀操作得更慢，还有失误的风险，而病人的安全永远是第一位的。
So Kristin has no hope of getting anywhere near those nerves during this rotation.
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。
She'll be lucky if she operates more than 15 minutes during a four-hour procedure.
她能在四个小时的手术中操刀超过一刻钟就算是走运了。
And she knows that when she slips up, he'll tap a touch screen,
而且她很清楚，万一她出现失误，主治医生就会重新操刀，
and she'll be watching again, feeling like a kid in the corner with a dunce cap.
她又不得不回到观察者的角色，感到非常沮丧和失落。
Like all the studies of robots and work I've done in the last eight years,
正如我过去八年做的所有关于机器人的研究一样，
I started this one with a big, open question: How do we learn to work with intelligent machines?
在这次研究的开始，我也提出了一个宏大的问题：我们要如何与智能机器共存？
To find out, I spent two and a half years observing dozens of residents and surgeons doing traditional and robotic surgery,
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，他们既做传统的手术，也做自动化手术，
interviewing them and in general hanging out with the residents as they tried to learn.
我采访他们，试图了解他们的学习过程。
I covered 18 of the top US teaching hospitals, and the story was the same.
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。
Most residents were in Kristen's shoes. They got to "see one" plenty, but the "do one" was barely available.
大部分住院医生都和克里斯汀一样。他们“看”得很多，但“做”的机会却很少。
So they couldn't struggle, and they weren't learning.
所以他们难以进步，也无从学习。
This was important news for surgeons, but I needed to know how widespread it was:
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，
Where else was using AI blocking learning on the job?
还有哪些领域也是这样，人工智能阻碍了人们的学习？
To find out, I've connected with a small but growing group of young researchers
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，
who've done boots-on-the-ground studies of work involving AI in very diverse settings
他们在不同领域都做了一些关于人工智能的实地研究，
like start-ups, policing, investment banking and online education.
包括初创公司、监管治安部门、投资银行和在线教育等。
Like me, they spent at least a year and many hundreds of hours observing,
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，
interviewing and often working side-by-side with the people they studied.
采访研究对象，甚至和他们一起生活、工作。
We shared data, and I looked for patterns.
我们共享了数据，我想从中寻找出规律。
No matter the industry, the work, the AI, the story was the same.
不管在什么行业，利用何种人工智能，结果都非常相似。

Organizations were trying harder and harder to get results from AI,
企业、机构都卯足了劲，想从人工智能中获益，
and they were peeling learners away from expert work as they did it.
而这一行为导致学习者从专业工作中脱离出来。
Start-up managers were outsourcing their customer contact.
初创公司的管理者把联系消费者的工作外包出去。
Cops had to learn to deal with crime forecasts without experts support.
警察在没有专家的支持下去做犯罪预测工作。
Junior bankers were getting cut out of complex analysis, and professors had to build online courses without help.
初级银行家被排除在复杂分析之外，教授也要独自开始做在线课程。
And the effect of all of this was the same as in surgery.
而这些种种带来的后果和上述外科例子是一样的。
Learning on the job was getting much harder. This can't last.
在工作中学习变得越来越难，这样的情况需要得到改善。
McKinsey estimates that between half a billion and a billion of us are going to have to adapt to AI in our daily work by 2030.
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。
And we're assuming that on-the-job learning will be there for us as we try.
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。
Accenture's latest workers survey showed that most workers learned key skills on the job, not in formal training.
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，而不是在培训中。
So while we talk a lot about its potential future impact, the aspect of AI that may matter most right now
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，
is that we're handling it in a way that blocks learning on the job just when we need it most.
就是它阻碍了我们学习的步伐，而学习恰恰是我们目前最需要的东西。
Now across all our sites, a small minority found a way to learn. They did it by breaking and bending rules.
现在有一个小群体找到了学习的方法，通过改变和突破规则。
Approved methods weren't working, so they bent and broke rules to get hands-on practice with experts.
因为现有的方法不奏效，所以他们要改变和突破规则，来获取和专家一起学习的机会。
In my setting, residents got involved in robotic surgery in medical school at the expense of their generalist education.
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，牺牲他们的通识教育课程。
And they spent hundreds of extra hours with simulators and recordings of surgery, when you were supposed to learn in the OR.
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。
And maybe most importantly, they found ways to struggle in live procedures with limited expert supervision.
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。
I call all this "shadow learning," because it bends the rules and learner's do it out of the limelight.
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。
And everyone turns a blind eye because it gets results. Remember, these are the star pupils of the bunch.
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。记住，这样学习的学生都是学霸。
Now, obviously, this is not OK, and it's not sustainable.
显然，这样的方式并不对，也并不可持续。
No one should have to risk getting fired to learn the skills they need to do their job.
没有人应该要冒着被开除的风险去学习应掌握的技能。
But we do need to learn from these people. They took serious risks to learn.
但我们可能真的要向这些人学习。他们为了学习不惜冒着巨大的风险。
They understood they needed to protect struggle and challenge in their work
他们明白需要保护那些工作中遇到的困难和挑战，
so that they could push themselves to tackle hard problems right near the edge of their capacity.
而强迫自己去解决难题，不断挑战自己的极限。
They also made sure there was an expert nearby to offer pointers and to backstop against catastrophe.
他们也保证身边有足够的专家资源指导他们，在必要的时候出来提供支持。
Let's build this combination of struggle and expert support into each AI implementation.
让我们把努力和专家支持结合起来，将其应用到人工智能中。
Here's one clear example I could get of this on the ground.
我这里有一个具体的例子。
Before robots, if you were a bomb disposal technician, you dealt with an IED by walking up to it.
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。
A junior officer was hundreds of feet away,
一个年轻的警官就在你几百米之外，
so could only watch and help if you decided it was safe and invited them downrange.
他只能观察你，并且在你觉得安全的时候才能提供帮助，才能接近装置。
Now you sit side-by-side in a bomb-proof truck. You both watched the video feed.
现在你们并排坐在防弹卡车里，一起看着视频。
They control a distant robot, and you guide the work out loud.
他们远程控制着机器人，而你大声地指挥工作。
Trainees learn better than they did before robots.
这样一来，他们反而可以有更好的机会学习。
We can scale this to surgery, start-ups, policing, investment banking, online education and beyond.
我们可以把这种方式应用到外科手术、初创企业、治安系统、投资银行和在线教育等等行业中。
The good news is we've got new tools to do it.
好消息是，我们有了更好的工具辅助学习。
The internet and the cloud mean we don't always need one expert for every trainee,
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、
for them to be physically near each other or even to be in the same organization.
面对面的教学，专家和学习者甚至不需要在同一个机构中。
And we can build AI to help: to coach learners as they struggle,
我们可以利用人工智能来辅助学习，在学习者奋斗的过程中指导他们，
to coach experts as they coach and to connect those two groups in smart ways.
还可以指导专家进行更有效的教学，将两者以更智慧的方式联系起来。
There are people at work on systems like this, but they've been mostly focused on formal training.
现在已经有在职人员有这样的教学系统，但是他们也仅仅是关注入职培训。
And the deeper crisis is in on-the-job learning. We must do better.
更大的危机其实出现在在职培训当中。我们必须要做得更好。
Today's problems demand we do better to create work
现在出现的问题要求我们要做得更好来创造价值，
that takes full advantage of AI's amazing capabilities while enhancing our skills as we do it.
来更好地利用人工智能带来的便利，同时也让我们的技术变得更加成熟。
That's the kind of future I dreamed of as a kid. And the time to create it is now. Thank you.
这才是我小时候梦想的未来。而现在正是去开创这一未来的最佳时机。谢谢。