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It's 6:30 in the morning,
and Kristen is wheeling her prostate patient into the OR.
She's a resident, a surgeon in training.
It's her job to learn.
Today, she's really hoping to do some of the nerve-sparing,
extremely delicate dissection that can preserve erectile function.
That'll be up to the attending surgeon, though, but he's not there yet.
She and the team put the patient under,
and she leads the initial eight-inch incision in the lower abdomen.
Once she's got that clamped back, she tells the nurse to call the attending.
He arrives, gowns up,
And from there on in, their four hands are mostly in that patient --
with him guiding but Kristin leading the way.
When the prostates out (and, yes, he let Kristen do a little nerve sparing),
he rips off his scrubs.
He starts to do paperwork.
Kristen closes 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's going to be fine,
and no doubt she's a better surgeon than she was at 6:30.
Now this is extreme work.
But Kristin's learning to do her job the way that most of us do:
watching an expert for a bit,
getting involved in easy, safe parts of the work
and progressing to riskier and harder tasks
as they guide and decide she's ready.
My whole life I've been fascinated by this kind of learning.
It feels elemental, part of what makes us human.
It has different names: apprenticeship, coaching, mentorship, on the job training.
In surgery, it's called “see one, do one, teach one.”
But the process is the same,
and it's been the main path to skill around the globe for thousands of years.
Right now, we're handling AI in a way that blocks that path.
We're sacrificing learning in our quest for productivity.
I found this first in surgery while I was at MIT,
but now I've got evidence it's happening all over,
in very different industries and with very different kinds of AI.
If we do nothing, millions of us are going to hit a brick wall
as we try to learn to deal with AI.
Let's go back to surgery to see how.
Fast forward six months.
It's 6:30am again, and Kristen is wheeling another prostate patient in,
but this time to the robotic OR.
The attending leads attaching
a four-armed, thousand-pound robot to the patient.
They both rip off their scrubs,
head to control consoles 10 or 15 feet away,
and Kristen just watches.
The robot allows the attending to do the whole procedure himself,
so he basically does.
He knows she needs practice.
He wants to give her control.
But he also knows she'd be slower and make more mistakes,
and his patient comes first.
So Kristin has no hope of getting anywhere near those nerves during this rotation.
She'll be lucky if she operates more than 15 minutes during a four-hour procedure.
And she knows that when 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.
Like all the studies of robots and work I've done in the last eight years,
I started this one with a big, open question:
How do we learn to work with intelligent machines?
To find out, I spent two and a half years observing dozens of residents and surgeons
doing traditional and robotic surgery, interviewing them
and in general hanging out with the residents as they tried to learn.
I covered 18 of the top US teaching hospitals,
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 they couldn't struggle, and they weren't learning.
This was important news for surgeons, but I needed to know how widespread it was:
Where else was using AI blocking learning on the job?
To find out, I've connected with a small but growing group of young researchers
who've done boots-on-the-ground studies of work involving AI
in very diverse settings like start-ups, policing,
investment banking and online education.
Like me, they spent at least a year and many hundreds of hours observing,
interviewing and often working side-by-side with the people they studied.
We shared data, and I looked for patterns.
No matter the industry, the work, the AI, the story was the same.
Organizations were trying harder and harder to get results from AI,
and they were peeling learners away from expert work as they did it.
Start-up managers were outsourcing their customer contact.
Cops had to learn to deal with crime forecasts without experts support.
Junior bankers were getting cut out of complex analysis,
and professors had to build online courses without help.
And the effect of all of this was the same as in surgery.
Learning on the job was getting much harder.
This can't last.
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.
And we're assuming that on-the-job learning
will be there for us as we try.
Accenture's latest workers survey showed that most workers learned key skills
on the job, not in formal training.
So while we talk a lot about its potential future impact,
the aspect of AI that may matter most right now
is that we're handling it in a way that blocks learning on the job
just when we need it most.
Now across all our sites, a small minority found a way to learn.
They did it by breaking and bending rules.
Approved methods weren't working, so they bent and broke rules
to get hands-on practice with experts.
In my setting, residents got involved in robotic surgery in medical school
at the expense of their generalist education.
And they spent hundreds of extra hours with simulators and recordings of surgery,
when you were supposed to learn in the OR.
And maybe most importantly, they found ways to 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 the limelight.
And everyone turns a blind eye because it gets results.
Remember, these are the star pupils of the bunch.
Now, obviously, this is not OK, and it's not sustainable.
No one should have to risk getting fired
to learn the skills they need to do their job.
But we do need to learn from these people.
They took serious risks to learn.
They understood they needed to protect struggle and challenge in their work
so that they could push themselves to tackle hard problems
right near the edge of their capacity.
They also made sure there was an expert nearby
to offer pointers and to backstop against catastrophe.
Let's build this combination of struggle and expert support
into each AI implementation.
Here's one clear example I could get of this on the ground.
Before robots,
if you were a bomb disposal technician, you dealt with an IED by walking up to it.
A junior officer was hundreds of feet away,
so could only watch and help if you decided it was safe
and invited them downrange.
Now you sit side-by-side in a bomb-proof truck.
You both watched the video 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 to surgery, 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 mean we don't always need one expert for every trainee,
for them to be physically near each other or even to be in the same organization.
And we can build AI to help:
to coach learners as they struggle, to coach experts as they coach
and to connect those two groups in smart ways.
There are people at work on systems like this,
but they've been mostly focused on formal training.
And the deeper crisis is in on-the-job learning.
We must do better.
Today's problems demand we do better
to create work that takes full advantage of AI's amazing capabilities
while enhancing our skills as we do it.
That's the kind of future I dreamed of as a kid.
And the time to create it is now.
Thank you.
(Applause)
コツ:単語をクリックしてすぐ意味を調べられます!

読み込み中…

【TED】How do we learn to work with intelligent machines? | Matt Beane

1763 タグ追加 保存
林宜悉 2019 年 2 月 22 日 に公開
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