WEBVTT

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Mark Twain summed up
what I take to be

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one of the fundamental problems
of cognitive science

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with a single witticism.

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He said, "There's something
fascinating about science.

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One gets such wholesale
returns of conjecture

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out of such a trifling
investment in fact."

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(Laughter)

NOTE Paragraph

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Twain meant it as a joke,
of course, but he's right:

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There's something
fascinating about science.

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From a few bones, we infer
the existence of dinosuars.

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From spectral lines,
the composition of nebulae.

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From fruit flies,

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the mechanisms of heredity,

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and from reconstructed images
of blood flowing through the brain,

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or in my case, from the behavior
of very young children,

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we try to say something about
the fundamental mechanisms

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of human cognition.

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In particular, in my lab in the Department
of Brain and Cognitive Sciences at MIT,

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I have spent the past decade
trying to understand the mystery

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of how children learn so much
from so little so quickly.

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Because, it turns out that
the fascinating thing about science

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is also a fascinating
thing about children,

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which, to put a gentler
spin on Mark Twain,

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is precisely their ability
to draw rich, abstract inferences

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rapidly and accurately
from sparse, noisy data.

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I'm going to give you
just two examples today.

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One is about a problem of generalization,

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and the other is about a problem
of causal reasoning.

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And although I'm going to talk
about work in my lab,

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this work is inspired by
and indebted to a field.

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I'm grateful to mentors, colleagues,
and collaborators around the world.

NOTE Paragraph

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Let me start with the problem
of generalization.

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Generalizing from small samples of data
is the bread and butter of science.

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We poll a tiny fraction of the electorate

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and we predict the outcome
of national elections.

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We see how a handful of patients
responds to treatment in a clinical trial,

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and we bring drugs to a national market.

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But this only works if our sample
is randomly drawn from the population.

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If our sample is cherry-picked
in some way --

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say, we poll only urban voters,

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or say, in our clinical trials
for treatments for heart disease,

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we include only men --

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the results may not generalize
to the broader population.

NOTE Paragraph

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So scientists care whether evidence
is randomly sampled or not,

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but what does that have to do with babies?

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Well, babies have to generalize
from small samples of data all the time.

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They see a few rubber ducks
and learn that they float,

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or a few balls and learn that they bounce.

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And they develop expectations
about ducks and balls

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that they're going to extend
to rubber ducks and balls

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for the rest of their lives.

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And the kinds of generalizations
babies have to make about ducks and balls

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they have to make about almost everything:

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shoes and ships and ceiling wax
and cabbages and kings.

NOTE Paragraph

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So do babies care whether
the tiny bit of evidence they see

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is plausibly representative
of a larger population?

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Let's find out.

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I'm going to show you two movies,

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one from each of two conditions
of an experiment,

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and because you're going to see
just two movies,

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you're going to see just two babies,

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and any two babies differ from each other
in innumerable ways.

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But these babies, of course,
here stand in for groups of babies,

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and the differences you're going to see

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represent average group differences
in babies' behavior across conditions.

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In each movie, you're going to see
a baby doing maybe

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just exactly what you might
expect a baby to do,

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and we can hardly make babies
more magical than they already are.

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But to my mind the magical thing,

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and what I want you to pay attention to,

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is the contrast between
these two conditions,

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because the only thing
that differs between these two movies

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is the statistical evidence
the babies are going to observe.

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We're going to show babies
a box of blue and yellow balls,

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and my then-graduate student,
now colleague at Stanford, Hyowon Gweon,

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is going to pull three blue balls
in a row out of this box,

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and when she pulls those balls out,
she's going to squeeze them,

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and the balls are going to squeak.

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And if you're a baby,
that's like a TED Talk.

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It doesn't get better than that.

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(Laughter)

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But the important point is it's really
easy to pull three blue balls in a row

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out of a box of mostly blue balls.

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You could do that with your eyes closed.

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It's plausibly a random sample
from this population.

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And if you can reach into a box at random
and pull out things that squeak,

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then maybe everything in the box squeaks.

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So maybe babies should expect
those yellow balls to squeak as well.

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Now, those yellow balls
have funny sticks on the end,

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so babies could do other things
with them if they wanted to.

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They could pound them or whack them.

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But let's see what the baby does.

NOTE Paragraph

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(Video) Hyowon Gweon: See this?
(Ball squeaks)

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Did you see that?
(Ball squeaks)

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Cool.

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See this one?

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(Ball squeaks)

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Wow.

NOTE Paragraph

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Laura Schulz: Told you. (Laughs)

NOTE Paragraph

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(Video) HG: See this one?
(Ball squeaks)

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Hey Clara, this one's for you.
You can go ahead and play.

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(Laughter)

NOTE Paragraph

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LS: I don't even have to talk, right?

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All right, it's nice that babies
will generalize properties

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of blue balls to yellow balls,

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and it's impressive that babies
can learn from imitating us,

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but we've known those things about babies
for a very long time.

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The really interesting question

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is what happens when we show babies
exactly the same thing,

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and we can ensure it's exactly the same
because we have a secret compartment

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and we actually pull the balls from there,

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but this time, all we change
is the apparent population

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from which that evidence was drawn.

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This time, we're going to show babies
three blue balls

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pulled out of a box
of mostly yellow balls,

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and guess what?

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You [probably won't] randomly draw
three blue balls in a row

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out of a box of mostly yellow balls.

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That is not plausibly
randomly sampled evidence.

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That evidence suggests that maybe Yowan
was deliberately sampling the blue balls.

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Maybe there's something special
about the blue balls.

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Maybe only the blue balls squeak.

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Let's see what the baby does.

NOTE Paragraph

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(Video) YG: See this?
(Ball squeaks)

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See this toy?
(Ball squeaks)

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Oh, that was cool. See?
(Ball squeaks)

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Now this one's for you to play.
You can go ahead and play.

NOTE Paragraph

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(Baby cries)

NOTE Paragraph

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LS: So you just saw
two 15-month old babies

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do entirely different things

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based only on the probability
of the sample they observed.

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Let me show you the experimental results.

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On the vertical axis, you'll see
the percentage of babies

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who squeezed the ball in each condition,

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and as you'll see, babies are much
more likely to generalize the evidence

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when it's plausibly representative
of the population

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than when the evidence
is clearly cherry-picked.

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And this leads to a fun prediction:

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suppose you pulled just one blue ball
out of the mostly yellow box.

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Now, you just can't pull three blue balls
in a row at random out of a yellow box,

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but you could randomly sample
just one blue ball.

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That's not an improbable sample.

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And if you could reach into
a box at random

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and pull out something that squeaks,
maybe everything in the box squeaks.

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So even though babies are going to see
much less evidence for squeaking,

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and have many fewer actions to imitate

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in this one ball condition than in
the condition you just saw,

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we predicted that babies themselves
would squeeze more,

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and that's exactly what we found.

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So 15-month old babies,
in this respect, like scientists,

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care whether evidence
is randomly sampled or not,

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and they use this to develop
expectations about the world:

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what squeaks and what doesn't,

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what to explore and what to ignore.

NOTE Paragraph

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Let me show you another example now,

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this time about a problem
of causal reasoning.

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And it starts with a problem
of confounded evidence

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that all of us have,

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which is that we are part of the world.

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And this may not seem like a problem
to you, but like most problems,

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it's only a problem when things go wrong.

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Take this baby, for instance.

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Things are going wrong for him.

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He would like to make
this toy go, and he can't.

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I'll show you a few second clip.

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And there's two possibilities, broadly:

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maybe he's doing something wrong,

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or maybe there's something
wrong with the toy.

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So in this next experiment,

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we're going to give babies
just a tiny bit of statistical data

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supporting one hypothesis over the other,

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and we're going to see if babies
can use that to make different decisions

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about what to do.

NOTE Paragraph

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Here's the setup.

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Yowan is going to try to make
the toy go and succeed.

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I am then going to try twice
and fail both times,

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and then Yowan is going
to try again and succeed,

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and this roughly sums up my relationship
to my graduate students

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in technology across the board.

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But the important point here is
it provides a little bit of evidence

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that the problem isn't with the toy,
it's with the person.

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Some people can make this toy go,

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and some can't.

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Now, when the baby gets the toy,
he's going to have a choice.

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His mom is right there,

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so he can go ahead and hand off the toy
and change the person,

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but there's also going to be
another toy at the end of that cloth,

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and he can pull the cloth towards him
and change the toy.

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So let's see what the baby does.

NOTE Paragraph

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(Video) YG: Two, three. Go!

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(Video) LS: One, two, three, go!

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Arthur, I'm going to try again.
One, two, three, go!

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(Video) YG: Arthur,
let me try again, okay?

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One, two, three, go!

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Look at that. Remember these toys?

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See these toys? Yeah, I'm going
to put this one over here,

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and I'm going to give this one to you.

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You can go ahead and play.

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LS: Okay, Laura, but of course,
babies love their mommies.

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Of course babies give toys
to their mommies

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when they can't make them work.

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So again, the really important question
is what happens when we change

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the statistical data ever so slightly.

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This time, babies are going to see the toy
work and fail in exactly the same order,

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but we're changing
the distribution of evidence.

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This time, Yowan is going to succeed
once and fail once, and so am I.

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And this suggests it doesn't matter
who tries this toy, the toy is broken.

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It doesn't work all the time.

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Again, the baby's going to have a choice.

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Her mom is right next to her,
so she can change the person,

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and there's going to be another toy
at the end of the cloth.

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Let's watch what she does.

NOTE Paragraph

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(Video) YG: Two, three, go!

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Let me try one more time.
One, two, three, go!

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Hmm.

NOTE Paragraph

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(Video) LS: Let me try, Clara.

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One, two, three, go!

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Hmm, let me try again.

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One, two, three, go!

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(Video) YG: I'm going
to put this one over here,

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and I'm going to give this one to you.

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You can go ahead and play.

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(Applause)

NOTE Paragraph

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LS: Let me show you
the experimental results.

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On the vertical axis,
you'll see the distribution

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of children's choices in each condition,

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and you'll see that the distribution
of the choices children make

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depends on the evidence they observe.

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So in the second year of life,

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babies can use a tiny bit
of statistical data

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to decide between two
fundamentally different strategies

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for acting on the world:

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asking for help and exploring.

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I've just shown you
two laboratory experiments

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out of literally hundreds in the field
that make similar points,

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because the really critical point

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is that children's ability
to make rich inferences from sparse data

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underlies all the species-specific
cultural learning that we do.

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Children learn about new tools
from just a few examples.

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They learn new causal relationships
from just a few examples.

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They even learn new words,
in this case in American sign language.

NOTE Paragraph

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I want to close with just two points.

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If you've been following my world,
the field of brain and cognitive sciences,

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for the past few years,

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three big ideas will have come
to your attention.

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The first is that this is
the era of the brain.

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And indeed, there have been
staggering discoveries in neuroscience:

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localizing, functionally specialized
regions of cortex,

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turning mouse brains transparent,

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activating neurons with light.

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A second big idea

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is that this is the era of big data
and machine learning,

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and machine learning promises
to revolutionize our understanding

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of everything from social networks
to epidemiology.

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And maybe, as it tackles problems
of seen understanding

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and natural language processing

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to tell us something
about human cognition.

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And the final big idea you'll have heard

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is that maybe it's a good idea we're going
to know so much about brains

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and have so much access to big data,

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because left to our own devices,

00:14:57.504 --> 00:15:01.335
humans are fallible, we take shortcuts,

00:15:01.335 --> 00:15:04.772
we err, we make mistakes,

00:15:04.772 --> 00:15:08.246
we're biased, and in innumerable ways,

00:15:09.326 --> 00:15:11.425
we get the world wrong.

00:15:12.843 --> 00:15:15.792
I think these are all important stories,

00:15:15.792 --> 00:15:19.577
and they have a lot to tell us
about what it means to be human,

00:15:19.577 --> 00:15:23.106
but I want you to note that today
I told you a very different story.

00:15:23.966 --> 00:15:27.773
It's a story about minds and not brains,

00:15:27.773 --> 00:15:30.779
and in particular, it's a story
about the kinds of computations

00:15:30.779 --> 00:15:33.369
that uniquely human minds can perform,

00:15:33.369 --> 00:15:37.313
which involve rich, structured knowledge
and the ability to learn

00:15:37.313 --> 00:15:42.581
from small amounts of data,
the evidence of just a few examples.

00:15:44.301 --> 00:15:48.600
And fundamentally, it's a story
about how starting as very small children

00:15:48.600 --> 00:15:52.780
and continuing out all the way
to the greatest accomplishments

00:15:52.780 --> 00:15:55.223
of our culture,

00:15:56.623 --> 00:15:58.620
we get the world right.

NOTE Paragraph

00:16:00.433 --> 00:16:05.700
Folks, human minds do not only learn
from small amounts of data.

00:16:06.285 --> 00:16:08.386
Human minds think
of altogether new ideas.

00:16:08.746 --> 00:16:11.437
Human minds generate
research and discovery,

00:16:11.787 --> 00:16:16.320
and human minds generate
art and literature and poetry and theater,

00:16:17.070 --> 00:16:20.600
and human minds take care of other humans:

00:16:21.330 --> 00:16:24.257
our old, our young, our sick.

00:16:24.517 --> 00:16:26.884
We even heal them.

00:16:27.624 --> 00:16:30.667
In the years to come, we're going
to see technological innovation

00:16:30.667 --> 00:16:33.284
beyond anything I can even envision,

00:16:34.594 --> 00:16:36.614
but we are very unlikely

00:16:36.614 --> 00:16:42.323
to see anything even approximating
the computational power of a human child

00:16:42.323 --> 00:16:46.621
in my lifetime or in yours.

00:16:46.621 --> 00:16:51.668
If we invest in these most powerful
learners and their development,

00:16:51.668 --> 00:16:54.585
in babies and children

00:16:54.585 --> 00:16:56.411
and mothers and fathers

00:16:56.411 --> 00:16:59.110
and caregivers and teachers

00:16:59.110 --> 00:17:03.280
the ways we invest in our other
most powerful and elegant forms

00:17:03.280 --> 00:17:05.848
of technology, engineering, and design,

00:17:06.488 --> 00:17:09.437
we will not just be dreaming
of a better future,

00:17:09.437 --> 00:17:11.564
we will be planning for one.

NOTE Paragraph

00:17:11.564 --> 00:17:13.909
Thank you very much.

NOTE Paragraph

00:17:13.909 --> 00:17:17.810
(Applause)

NOTE Paragraph

00:17:17.810 --> 00:17:22.236
Chris Anderson: Laura, thank you.
I do actually have a question for you.

00:17:22.236 --> 00:17:24.595
First of all, the research is insane.

00:17:24.595 --> 00:17:28.320
I mean, who would design
an experiment like that? (Laughter)

00:17:29.150 --> 00:17:30.940
I've seen that a couple of times,

00:17:30.940 --> 00:17:34.162
and I still don't honestly believe
that that can truly be happening,

00:17:34.162 --> 00:17:37.320
but other people have done
similar experiments. It checks out.

00:17:37.320 --> 00:17:38.953
They really are that genius.

NOTE Paragraph

00:17:38.953 --> 00:17:41.960
LS: You know, they look really impressive
in our experiments,

00:17:41.960 --> 00:17:44.592
but think about what they
look like in real life, right?

00:17:44.592 --> 00:17:45.762
It starts out as a baby.

00:17:45.762 --> 00:17:47.769
Eighteen months later,
it's talking to you,

00:17:47.769 --> 00:17:50.810
and babies' first words aren't just
things like balls and ducks,

00:17:50.810 --> 00:17:53.691
they're things like "all gone,"
which refer to disappearance,

00:17:53.691 --> 00:17:55.974
or "uh oh," which refer
to unintentional actions.

00:17:55.974 --> 00:17:57.536
It has to be that powerful.

00:17:57.536 --> 00:18:00.311
It has to be much more powerful
than anything I showed you.

00:18:00.311 --> 00:18:02.285
They're figuring out the entire world.

00:18:02.285 --> 00:18:05.429
A four-year old can talk to you
about almost anything.

00:18:05.429 --> 00:18:07.030
(Applause)

NOTE Paragraph

00:18:07.030 --> 00:18:10.444
CA: And if I understand you right,
the other key point you're making is,

00:18:10.444 --> 00:18:13.198
we've been through these years
where there's all this talk

00:18:13.198 --> 00:18:15.130
of how quirky and buggy our minds are,

00:18:15.130 --> 00:18:17.997
that behavioral economics
and the whole theories behind that

00:18:17.997 --> 00:18:19.600
that we're not rational agents.

00:18:19.600 --> 00:18:23.816
You're really saying that the biggest
story is how extraordinary,

00:18:23.816 --> 00:18:28.760
and there really is genius there
that is underappreciated.

NOTE Paragraph

00:18:28.760 --> 00:18:30.830
LS: One of my favorite
quotes in psychology

00:18:30.830 --> 00:18:33.120
comes from the social
psychologist Solomon Asch,

00:18:33.120 --> 00:18:35.927
and he said the fundamental task
of psychology is to remove

00:18:35.927 --> 00:18:38.553
the veil of self-evidence from things.

00:18:38.553 --> 00:18:43.104
There are a million orders of magnitude
more decisions you make every day

00:18:43.104 --> 00:18:44.451
that get the world right.

00:18:44.451 --> 00:18:46.583
You know about objects
and their properties.

00:18:46.583 --> 00:18:49.612
You know them when they're occluded.
You know them in the dark.

00:18:49.612 --> 00:18:50.920
You can walk through rooms.

00:18:50.920 --> 00:18:54.452
You can figure out what other people
are thinking. You can talk to them.

00:18:54.452 --> 00:18:56.682
You can navigate space.
You know about numbers.

00:18:56.682 --> 00:18:59.704
You know causal relationships.
You know about moral reasoning.

00:18:59.704 --> 00:19:02.060
You do this effortlessly,
so we don't see it,

00:19:02.060 --> 00:19:04.972
but that is how we get the world right,
and it's a remarkable

00:19:04.972 --> 00:19:07.290
and very difficult-to-understand
accomplishment.

NOTE Paragraph

00:19:07.290 --> 00:19:09.918
CA: I suspect there are people
in the audience who have

00:19:09.918 --> 00:19:12.156
this view of accelerating
technological power

00:19:12.156 --> 00:19:15.114
who might dispute your statement
that never in our lifetimes

00:19:15.114 --> 00:19:17.732
will a computer do what
a three-year old child can do,

00:19:17.732 --> 00:19:20.980
but what's clear is that in any scenario,

00:19:20.980 --> 00:19:24.750
our machines have so much to learn
from our toddlers.

00:19:26.230 --> 00:19:29.446
LS: I think so. You'll have some
machine learning folks up here.

00:19:29.446 --> 00:19:33.649
I mean, you should never bet
against babies or chimpanzees

00:19:33.649 --> 00:19:37.294
or technology as a matter of practice,

00:19:37.294 --> 00:19:41.822
but it's not just
a difference in quantity,

00:19:41.822 --> 00:19:43.586
it's a difference in kind. Right?

00:19:43.586 --> 00:19:45.746
We have incredibly powerful computers,

00:19:45.746 --> 00:19:48.137
and they do do amazingly
sophisticated things,

00:19:48.137 --> 00:19:51.341
often with very big amounts of data.

00:19:51.341 --> 00:19:53.948
Human minds do, I think,
something quite different,

00:19:53.948 --> 00:19:57.843
and I think it's the structured,
hierarchical nature of human knowledge

00:19:57.843 --> 00:19:59.875
that remains a real challenge.

NOTE Paragraph

00:19:59.875 --> 00:20:02.936
CA: Laura Schulz, wonderful
food for thought. Thank you so much.

NOTE Paragraph

00:20:02.936 --> 00:20:05.858
LS: Thank you.
(Applause)