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It’s 6:30 morning,
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and Kristen is wheeling
her prostate patient into the OR.
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She's a resident, a surgeon in training.
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It’s her job to learn.
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Today, she’s really hoping to do
some of the nerve-sparing,
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extremely delicate dissection
that can preserve erectile function.
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That'll be up to the attending surgeon,
though, but he's not there yet.
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She and the team put the patient under,
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and she leads the initial eight-inch
incision in the lower abdomen.
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Once she’s got that clamped back,
she tells the nurse to call the attending.
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He arrives, gowns up,
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And from there on in, their four hands
are mostly in that patient --
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with him guiding
but Kristin leading the way.
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When the prostates out (and, yes,
he let Kristen do a little nerve sparing),
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he rips off his scrubs.
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He starts to do paperwork.
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Kristen closes the patient by 8:15,
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with a junior resident
looking over her shoulder.
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And she lets him do
the final line of sutures.
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Kristen feels great.
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Patient’s going to be fine,
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and no doubt she’s a better surgeon
than she was at 6:30.
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Now this is extreme work.
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But Kristin’s learning to do her job
the way that most of us do:
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watching an expert for a bit,
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getting involved in easy,
safe parts of the work
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and progressing to riskier
and harder tasks
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as they guide and decide she’s ready.
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My whole life I’ve been fascinated
by this kind of learning.
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It feels elemental,
part of what makes us human.
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It has different names: apprenticeship,
coaching, mentorship, on the job training.
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In surgery, it’s called
“see one, do one, teach one.”
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But the process is the same,
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and it’s been the main path to skill
around the globe for thousands of years.
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Right now, we’re handling AI
in a way that blocks that path.
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We’re sacrificing learning
in our quest for productivity.
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I found this first in surgery
while I was at MIT,
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but now I’ve got evidence
it’s happening all over,
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in very different industries
and with very different kinds of AI.
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If we do nothing, millions of us
are going to hit a brick wall
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as we try to learn to deal with AI.
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Let’s go back to surgery to see how.
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Fast forward six months.
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It’s 6:30am again, and Kristen
is wheeling another prostate patient in,
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but this time to the robotic OR.
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The attending leads attaching
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a four-armed, thousand-pound
robot to the patient.
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They both rip off their scrubs,
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head to control consoles
10 or 15 feet away,
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and Kristen just watches.
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The robot allows the attending
to do the whole procedure himself,
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so he basically does.
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He knows she needs practice.
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He wants to give her control.
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But he also knows she’d be slower
and make more mistakes,
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and his patient comes first.
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So Kristin has no hope of getting anywhere
near those nerves during this rotation.
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She’ll be lucky if she operates more than
15 minutes during a four-hour procedure.
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And she knows that when she slips up,
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he’ll tap a touch screen,
and she’ll be watching again,
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feeling like a kid in the corner
with a dunce cap.
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Like all the studies of robots and work
I’ve done in the last eight years,
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I started this one
with a big, open question:
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How do we learn to work
with intelligent machines?
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To find out, I spent two and a half years
observing dozens of residents and surgeons
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doing traditional and robotic surgery,
interviewing them
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and in general hanging out
with the residents as they tried to learn.
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I covered 18 of the top
US teaching hospitals,
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and the story was the same.
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Most residents were in Kristen's shoes.
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They got to “see one” plenty,
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but the “do one” was barely available.
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So they couldn’t struggle,
and they weren’t learning.
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This was important news for surgeons, but
I needed to know how widespread it was:
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Where else was using AI
blocking learning on the job?
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To find out, I’ve connected with a small
but growing group of young researchers
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who’ve done boots-on-the-ground studies
of work involving AI
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in very diverse settings
like start-ups, policing,
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investment banking and online education.
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Like me, they spent at least a year
and many hundreds of hours observing,
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interviewing and often working
side-by-side with the people they studied.
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We shared data, and I looked for patterns.
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No matter the industry, the work,
the AI, the story was the same.
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Organizations were trying harder
and harder to get results from AI,
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and they were peeling learners away from
expert work as they did it.
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Start-up managers were outsourcing
their customer contact.
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Cops had to learn to deal with crime
forecasts without experts support.
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Junior bankers were getting
cut out of complex analysis,
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and professors had to build
online courses without help.
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And the effect of all of this
was the same as in surgery.
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Learning on the job
was getting much harder.
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This can’t last.
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McKinsey estimates that between half
a billion and a billion of us
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are going to have to adapt to AI
in our daily work by 2030.
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And we’re assuming
that on-the-job learning
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will be there for us as we try.
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Accenture’s latest workers survey showed
that most workers learned key skills
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on the job, not in formal training.
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So while we talk a lot about its
potential future impact,
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the aspect of AI
that may matter most right now
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is that we’re handling it in a way
that blocks learning on the job
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just when we need it most.
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Now across all our sites,
a small minority found a way to learn.
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They did it by breaking and bending rules.
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Approved methods weren’t working,
so they bent and broke rules
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to get hands-on practice with experts.
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In my setting, residents got involved
in robotic surgery in medical school
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at the expense
of their generalist education.
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And they spent hundreds of extra hours
with simulators and recordings of surgery,
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when you were supposed to learn in the OR.
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And maybe most importantly,
they found ways to struggle
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in live procedures
with limited expert supervision.
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I call all this “shadow learning,”
because it bends the rules
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and learner’s do it out of the limelight.
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And everyone turns a blind eye
because it gets results.
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Remember, these are
the star pupils of the bunch.
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Now, obviously, this is not OK,
and it’s not sustainable.
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No one should have to risk getting fired
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to learn the skills
they need to do their job.
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But we do need to learn from these people.
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They took serious risks to learn.
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They understood they needed to protect
struggle and challenge in their work
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so that they could push themselves
to tackle hard problems
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right near the edge of their capacity.
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They also made sure
there was an expert nearby
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to offer pointers and to backstop
against catastrophe.
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Let’s build this combination
of struggle and expert support
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into each AI implementation.
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Here’s one clear example
I could get of this on the ground.
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Before robots,
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if you were a bomb disposal technician,
you dealt with an IED by walking up to it.
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A junior officer was
hundreds of feet away,
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so could only watch and help
if you decided it was safe
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and invited them downrange.
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Now you sit side-by-side
in a bomb-proof truck.
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You both watched the video feed.
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They control a distant robot,
and you guide the work out loud.
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Trainees learn better than they
did before robots.
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We can scale this to surgery,
start-ups, policing,
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investment banking,
online education and beyond.
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The good news is
we’ve got new tools to do it.
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The internet and the cloud mean we don’t
always need one expert for every trainee,
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for them to be physically near each other
or even to be in the same organization.
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And we can build AI to help:
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to coach learners as they struggle,
to coach experts as they coach
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and to connect those two groups
in smart ways.
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There are people at work
on systems like this,
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but they’ve been mostly focused
on formal training.
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And the deeper crisis
is in on-the-job learning.
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We must do better.
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Today’s problems demand we do better
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to create work that takes full advantage
of AI’s amazing capabilities
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while enhancing our skills as we do it.
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That’s the kind of future
I dreamed of as a kid.
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And the time to create it is now.
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Thank you.
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(Applause)
Hiroshi Uchiyama
The name of trainee varies between "Kristen" and "Kristin" time to time. I am working on Japanese translation based on "Kristin" which sounds more familiar as a female name for me.