TED2015
Laura Schulz: The surprisingly logical minds of babies
Laura Schulz: As sorprendentemente lóxicas mentes dos bebés
Filmed:
Readability: 3.8
1,888,975 views
Como aprenden os bebés tanto de tan pouco, e tan rápido? Nunha charla divertida e chea de experimentos, a científica cognitiva Laura Schulz amosa como os nosos cativos toman decisións cun sentido da lóxica sorprendentemente forte, xa moito antes de que dean falado.
Laura Schulz - Cognitive scientist
Developmental behavior studies spearheaded by Laura Schulz are changing our notions of how children learn. Full bio
Developmental behavior studies spearheaded by Laura Schulz are changing our notions of how children learn. Full bio
Double-click the English transcript below to play the video.
00:12
Mark Twain summed up
what I take to be
what I take to be
0
835
2155
Mark Twain resumiu
o que eu considero que é
o que eu considero que é
00:14
one of the fundamental problems
of cognitive science
of cognitive science
1
2990
3120
un dos problemas fundamentais
da ciencia cognitiva
da ciencia cognitiva
00:18
with a single witticism.
2
6110
1710
cunha sinxela ocorrencia.
00:20
He said, "There's something
fascinating about science.
fascinating about science.
3
8410
3082
Dixo, "A ciencia é fascinante.
00:23
One gets such wholesale
returns of conjecture
returns of conjecture
4
11492
3228
Conséguense cantidades
masivas de conxecturas
masivas de conxecturas
00:26
out of such a trifling
investment in fact."
investment in fact."
5
14720
3204
a partir dun investimento
tan insignificante en feitos.”
tan insignificante en feitos.”
00:29
(Laughter)
6
17924
1585
(Risas)
00:32
Twain meant it as a joke,
of course, but he's right:
of course, but he's right:
7
20199
2604
Twain quería facer unha broma, claro,
pero ten razón:
pero ten razón:
00:34
There's something
fascinating about science.
fascinating about science.
8
22803
2876
A ciencia é fascinante.
00:37
From a few bones, we infer
the existence of dinosuars.
the existence of dinosuars.
9
25679
4261
A partir duns cantos ósos, inferimos
a existencia dos dinosauros.
a existencia dos dinosauros.
00:42
From spectral lines,
the composition of nebulae.
the composition of nebulae.
10
30910
3871
Das liñas espectrais,
a composición das nebulosas.
a composición das nebulosas.
00:47
From fruit flies,
11
35471
2938
A partir das moscas da froita,
00:50
the mechanisms of heredity,
12
38409
2943
os mecanismos da herdanza,
00:53
and from reconstructed images
of blood flowing through the brain,
of blood flowing through the brain,
13
41352
4249
e de imaxes reconstruídas de sangue
fluíndo a través do cerebro,
fluíndo a través do cerebro,
00:57
or in my case, from the behavior
of very young children,
of very young children,
14
45601
4708
ou no meu caso, do comportamento
de nenos moi pequenos,
de nenos moi pequenos,
01:02
we try to say something about
the fundamental mechanisms
the fundamental mechanisms
15
50309
2829
intentamos dicir algo
sobre os mecanismos fundamentais
sobre os mecanismos fundamentais
01:05
of human cognition.
16
53138
1618
da cognición humana.
01:07
In particular, in my lab in the Department
of Brain and Cognitive Sciences at MIT,
of Brain and Cognitive Sciences at MIT,
17
55716
4759
En concreto, no meu laboratorio no Dpto.
de Cerebro e Ciencias Cognitivas, no MIT,
de Cerebro e Ciencias Cognitivas, no MIT,
01:12
I have spent the past decade
trying to understand the mystery
trying to understand the mystery
18
60475
3654
pasei a última década
intentando entender o misterio
intentando entender o misterio
de por que os nenos aprenden tanto,
a partir de tan pouco, e tan rápido.
a partir de tan pouco, e tan rápido.
01:16
of how children learn so much
from so little so quickly.
from so little so quickly.
19
64129
3977
01:20
Because, it turns out that
the fascinating thing about science
the fascinating thing about science
20
68666
2978
Porque resulta que o que a ciencia
ten de fascinante
ten de fascinante
01:23
is also a fascinating
thing about children,
thing about children,
21
71644
3529
téñeno tamén de fascinante os nenos,
01:27
which, to put a gentler
spin on Mark Twain,
spin on Mark Twain,
22
75173
2581
e é, dicíndoo de forma máis suave
ca Mark Twain,
ca Mark Twain,
01:29
is precisely their ability
to draw rich, abstract inferences
to draw rich, abstract inferences
23
77754
4650
precisamente a súa capacidade
de extraer inferencias ricas e abstractas
de extraer inferencias ricas e abstractas
01:34
rapidly and accurately
from sparse, noisy data.
from sparse, noisy data.
24
82404
4661
de forma rápida e precisa a partir
de datos dispersos e confusos.
de datos dispersos e confusos.
01:40
I'm going to give you
just two examples today.
just two examples today.
25
88355
2398
Vou dar só dous exemplos hoxe.
01:42
One is about a problem of generalization,
26
90753
2287
Un deles aborda
un problema de xeneralización,
un problema de xeneralización,
01:45
and the other is about a problem
of causal reasoning.
of causal reasoning.
27
93040
2850
e o outro un de razoamento causal.
01:47
And although I'm going to talk
about work in my lab,
about work in my lab,
28
95890
2525
E aínda que vou falar
do que facemos no meu laboratorio,
do que facemos no meu laboratorio,
01:50
this work is inspired by
and indebted to a field.
and indebted to a field.
29
98415
3460
este traballo está inspirado por un campo
e en débeda con el.
e en débeda con el.
01:53
I'm grateful to mentors, colleagues,
and collaborators around the world.
and collaborators around the world.
30
101875
4283
Estoulles agradecida a mentores,
colegas e colaboradores de todo o mundo.
colegas e colaboradores de todo o mundo.
01:59
Let me start with the problem
of generalization.
of generalization.
31
107308
2974
Quero comezar
co problema de xeneralización.
co problema de xeneralización.
02:02
Generalizing from small samples of data
is the bread and butter of science.
is the bread and butter of science.
32
110652
4133
Xeneralizar a partir de pequenas mostras
de datos é o pan de cada día da ciencia.
de datos é o pan de cada día da ciencia.
02:06
We poll a tiny fraction of the electorate
33
114785
2554
Entrevistamos unha fracción
mínima do electorado
mínima do electorado
02:09
and we predict the outcome
of national elections.
of national elections.
34
117339
2321
e predicimos o resultado
das eleccións nacionais.
das eleccións nacionais.
02:12
We see how a handful of patients
responds to treatment in a clinical trial,
responds to treatment in a clinical trial,
35
120240
3925
Vemos como un puñado de pacientes
responde a tratamento nun ensaio clínico,
responde a tratamento nun ensaio clínico,
02:16
and we bring drugs to a national market.
36
124165
3065
e incorporamos fármacos
ao mercado nacional.
ao mercado nacional.
02:19
But this only works if our sample
is randomly drawn from the population.
is randomly drawn from the population.
37
127230
4365
Pero isto soamente funciona se a mostra
se extrae aleatoriamente da poboación.
se extrae aleatoriamente da poboación.
02:23
If our sample is cherry-picked
in some way --
in some way --
38
131595
2735
Se a nosa mostra ten algunha manipulación
--por exemplo,
entrevistamos só votantes urbanos,
entrevistamos só votantes urbanos,
02:26
say, we poll only urban voters,
39
134330
2072
02:28
or say, in our clinical trials
for treatments for heart disease,
for treatments for heart disease,
40
136402
4388
ou nos nosos ensaios clínicos
de tratamentos para doenzas cardíacas
de tratamentos para doenzas cardíacas
02:32
we include only men --
41
140790
1881
incluímos só homes--
02:34
the results may not generalize
to the broader population.
to the broader population.
42
142671
3158
os resultados poden
non ser xeneralizables a toda a poboación.
non ser xeneralizables a toda a poboación.
Por tanto aos científicos impórtalles
se a mostra se recolleu ou non ao chou,
se a mostra se recolleu ou non ao chou,
02:38
So scientists care whether evidence
is randomly sampled or not,
is randomly sampled or not,
43
146479
3581
02:42
but what does that have to do with babies?
44
150060
2015
pero que ten iso que ver cos bebés?
02:44
Well, babies have to generalize
from small samples of data all the time.
from small samples of data all the time.
45
152585
4621
Os bebés teñen que xeneralizar seguido
a partir de pequenas mostras de datos.
a partir de pequenas mostras de datos.
02:49
They see a few rubber ducks
and learn that they float,
and learn that they float,
46
157206
3158
Ven uns poucos parrulos de goma
e aprenden que flotan,
e aprenden que flotan,
02:52
or a few balls and learn that they bounce.
47
160364
3575
ou algunhas pelotas e aprenden que botan.
02:55
And they develop expectations
about ducks and balls
about ducks and balls
48
163939
2951
E desenvolven expectativas
sobre os parrulos e as pelotas
sobre os parrulos e as pelotas
02:58
that they're going to extend
to rubber ducks and balls
to rubber ducks and balls
49
166890
2716
que aplicarán a uns e outras
03:01
for the rest of their lives.
50
169606
1879
o resto das súas vidas.
03:03
And the kinds of generalizations
babies have to make about ducks and balls
babies have to make about ducks and balls
51
171485
3739
E os tipos de xeneralizacións
que deben facer sobre parrulos e pelotas,
que deben facer sobre parrulos e pelotas,
03:07
they have to make about almost everything:
52
175224
2089
deben facelos para case todo:
03:09
shoes and ships and sealing wax
and cabbages and kings.
and cabbages and kings.
53
177313
3917
zapatos e barcos e lacre e verzas e reis.
03:14
So do babies care whether
the tiny bit of evidence they see
the tiny bit of evidence they see
54
182200
2961
Entón aos bebés impórtalles
se o pequeno anaco de proba que ven
se o pequeno anaco de proba que ven
03:17
is plausibly representative
of a larger population?
of a larger population?
55
185161
3692
representa de forma plausíbel
unha poboación maior?
unha poboación maior?
03:21
Let's find out.
56
189763
1900
Descubrámolo.
03:23
I'm going to show you two movies,
57
191663
1723
Vou amosar dous vídeos,
03:25
one from each of two conditions
of an experiment,
of an experiment,
58
193386
2462
un por cada suposto dun experimento,
03:27
and because you're going to see
just two movies,
just two movies,
59
195848
2438
e como só se verán dous vídeos,
03:30
you're going to see just two babies,
60
198286
2136
só se verán dous bebés,
03:32
and any two babies differ from each other
in innumerable ways.
in innumerable ways.
61
200422
3947
e un par calquera de bebés difire
de calquera outro de innumerábeis formas.
de calquera outro de innumerábeis formas.
03:36
But these babies, of course,
here stand in for groups of babies,
here stand in for groups of babies,
62
204369
3051
Pero estes bebés, por suposto,
representan aquí a grupos de bebés,
representan aquí a grupos de bebés,
03:39
and the differences you're going to see
63
207420
1895
e as diferenzas que se van ver
03:41
represent average group differences
in babies' behavior across conditions.
in babies' behavior across conditions.
64
209315
5195
representan as diferenzas grupais medias
no comportamento dos bebés
en cada suposto.
en cada suposto.
03:47
In each movie, you're going to see
a baby doing maybe
a baby doing maybe
65
215160
2583
En cada vídeo verase
un bebé facendo tal vez
un bebé facendo tal vez
03:49
just exactly what you might
expect a baby to do,
expect a baby to do,
66
217743
3460
xusto o que se agardaría que fixese,
03:53
and we can hardly make babies
more magical than they already are.
more magical than they already are.
67
221203
4017
e dificilmente podemos volver
os bebés máis máxicos do que xa son.
os bebés máis máxicos do que xa son.
03:58
But to my mind the magical thing,
68
226090
2010
Pero para min o máxico,
04:00
and what I want you to pay attention to,
69
228100
2089
e ao que quero que se lle preste atención,
04:02
is the contrast between
these two conditions,
these two conditions,
70
230189
3111
é o contraste entre estes dous supostos,
04:05
because the only thing
that differs between these two movies
that differs between these two movies
71
233300
3529
porque o único que difire
entre os dous vídeos
entre os dous vídeos
04:08
is the statistical evidence
the babies are going to observe.
the babies are going to observe.
72
236829
3466
son os datos estatísticos
que os bebés van observar.
que os bebés van observar.
04:13
We're going to show babies
a box of blue and yellow balls,
a box of blue and yellow balls,
73
241425
3183
Imos ensinarlles unha caixa
de bólas azuis e amarelas,
de bólas azuis e amarelas,
04:16
and my then-graduate student,
now colleague at Stanford, Hyowon Gweon,
now colleague at Stanford, Hyowon Gweon,
74
244608
4620
e a que era a miña estudante graduada,
hoxe compañeira en Stanford, Hyowon Gweon,
hoxe compañeira en Stanford, Hyowon Gweon,
04:21
is going to pull three blue balls
in a row out of this box,
in a row out of this box,
75
249228
3077
vai sacar tres bólas azuis
seguidas desta caixa,
seguidas desta caixa,
04:24
and when she pulls those balls out,
she's going to squeeze them,
she's going to squeeze them,
76
252305
3123
e despois de sacalas, vainas apertar,
04:27
and the balls are going to squeak.
77
255428
2113
e as bólas van chiar.
04:29
And if you're a baby,
that's like a TED Talk.
that's like a TED Talk.
78
257541
2763
E se es un bebé,
iso é como un charla TED.
iso é como un charla TED.
Non pode haber nada mellor.
04:32
It doesn't get better than that.
79
260304
1904
(Risas)
04:34
(Laughter)
80
262208
2561
04:38
But the important point is it's really
easy to pull three blue balls in a row
easy to pull three blue balls in a row
81
266968
3659
Pero o importante é que é moi sinxelo
sacar tres bólas azuis seguidas
sacar tres bólas azuis seguidas
04:42
out of a box of mostly blue balls.
82
270627
2305
dunha caixa que ten
sobre todo bólas azuis.
sobre todo bólas azuis.
04:44
You could do that with your eyes closed.
83
272932
2060
Poderíase facer cos ollos pechados.
04:46
It's plausibly a random sample
from this population.
from this population.
84
274992
2996
Pódese admitir que é unha
mostra aleatoria desta poboación.
mostra aleatoria desta poboación.
04:49
And if you can reach into a box at random
and pull out things that squeak,
and pull out things that squeak,
85
277988
3732
E se podes meter a man aleatoriamente
nunha caixa e sacar cousas que chían,
nunha caixa e sacar cousas que chían,
04:53
then maybe everything in the box squeaks.
86
281720
2839
ao mellor todo o que hai na caixa chía.
04:56
So maybe babies should expect
those yellow balls to squeak as well.
those yellow balls to squeak as well.
87
284559
3650
Así que tal vez os bebés deberían esperar
que as bólas amarelas chíen tamén.
que as bólas amarelas chíen tamén.
05:00
Now, those yellow balls
have funny sticks on the end,
have funny sticks on the end,
88
288209
2519
As bólas amarelas teñen
divertidos paus nun extremo,
divertidos paus nun extremo,
05:02
so babies could do other things
with them if they wanted to.
with them if they wanted to.
89
290728
2857
que permiten facer con elas
outras cousas se se quere.
outras cousas se se quere.
05:05
They could pound them or whack them.
90
293585
1831
Poderían axitalas ou bater con elas.
05:07
But let's see what the baby does.
91
295416
2586
Pero vexamos qué fai o bebé.
05:12
(Video) Hyowon Gweon: See this?
(Ball squeaks)
(Ball squeaks)
92
300548
3343
(Vídeo) Ves isto? (A bóla chía)
05:16
Did you see that?
(Ball squeaks)
(Ball squeaks)
93
304531
3045
Viches iso? (A bóla chía)
05:20
Cool.
94
308036
3066
Xenial.
05:24
See this one?
95
312706
1950
Ves estoutra?
05:26
(Ball squeaks)
96
314656
1881
(A bóla chía)
Uaau.
05:28
Wow.
97
316537
2653
05:33
Laura Schulz: Told you. (Laughs)
98
321854
2113
Díxenvolo. (Ri)
05:35
(Video) HG: See this one?
(Ball squeaks)
(Ball squeaks)
99
323967
4031
Viches esta? (A bóla chía)
05:39
Hey Clara, this one's for you.
You can go ahead and play.
You can go ahead and play.
100
327998
4619
Clara, agora esta é para ti.
Veña, podes collela e xogar.
Veña, podes collela e xogar.
(Barullo) (Risas)
05:51
(Laughter)
101
339854
4365
05:56
LS: I don't even have to talk, right?
102
344219
2995
LS: Non teño nin que dicir nada, verdade?
05:59
All right, it's nice that babies
will generalize properties
will generalize properties
103
347214
2899
Vale, está ben que os bebés
xeneralicen propiedades
xeneralicen propiedades
das bólas azuis ás bolas amarelas.
06:02
of blue balls to yellow balls,
104
350113
1528
E é impresionante que poidan
aprender imitándonos.
aprender imitándonos.
06:03
and it's impressive that babies
can learn from imitating us,
can learn from imitating us,
105
351641
3096
06:06
but we've known those things about babies
for a very long time.
for a very long time.
106
354737
3669
Pero sabemos iso dos bebés
dende hai moito tempo.
dende hai moito tempo.
06:10
The really interesting question
107
358406
1811
A pregunta realmente interesante é
que ocorre cando lles amosamos
aos bebés exactamente a mesma cousa,
aos bebés exactamente a mesma cousa,
06:12
is what happens when we show babies
exactly the same thing,
exactly the same thing,
108
360217
2852
06:15
and we can ensure it's exactly the same
because we have a secret compartment
because we have a secret compartment
109
363069
3611
podemos asegurar que é a mesma
porque temos un compartimento secreto
porque temos un compartimento secreto
06:18
and we actually pull the balls from there,
110
366680
2110
e en realidade sacamos as bólas del,
06:20
but this time, all we change
is the apparent population
is the apparent population
111
368790
3478
pero esta vez o que cambiamos
foi a poboación aparente
foi a poboación aparente
06:24
from which that evidence was drawn.
112
372268
2902
da que extraemos as mostras.
06:27
This time, we're going to show babies
three blue balls
three blue balls
113
375170
3553
Esta vez amosarémoslles
aos bebés tres bólas azuis
aos bebés tres bólas azuis
06:30
pulled out of a box
of mostly yellow balls,
of mostly yellow balls,
114
378723
3384
sacadas dunha caixa que ten sobre todo
bólas amarelas,
bólas amarelas,
06:34
and guess what?
115
382107
1322
e saben que?
06:35
You [probably won't] randomly draw
three blue balls in a row
three blue balls in a row
116
383429
2840
Non se poden sacar aleatoriamente
tres bólas azuis seguidas
tres bólas azuis seguidas
06:38
out of a box of mostly yellow balls.
117
386269
2484
dunha caixa que ten sobre todo
bólas amarelas.
bólas amarelas.
06:40
That is not plausibly
randomly sampled evidence.
randomly sampled evidence.
118
388753
3747
Esa non é unha mostra aleatoria.
06:44
That evidence suggests that maybe Hyowon
was deliberately sampling the blue balls.
was deliberately sampling the blue balls.
119
392500
5123
Esa proba suxire que ao mellor Hyowon
estivo amosando deliberadamente as azuis.
estivo amosando deliberadamente as azuis.
06:49
Maybe there's something special
about the blue balls.
about the blue balls.
120
397623
2583
Tal vez as bólas azuis teñen algo especial
06:52
Maybe only the blue balls squeak.
121
400846
2976
Tal vez soamente as bólas azuis chían.
06:55
Let's see what the baby does.
122
403822
1895
Vexamos o que fai o bebé.
06:57
(Video) HG: See this?
(Ball squeaks)
(Ball squeaks)
123
405717
2904
(Vídeo) Ves isto?
(A bóla chía)
(A bóla chía)
07:02
See this toy?
(Ball squeaks)
(Ball squeaks)
124
410851
2645
Ves este xoguete?
(A bóla chía)
(A bóla chía)
07:05
Oh, that was cool. See?
(Ball squeaks)
(Ball squeaks)
125
413496
5480
Oh, que xenial. Ves?
(A bóla chía)
(A bóla chía)
07:10
Now this one's for you to play.
You can go ahead and play.
You can go ahead and play.
126
418976
4394
Agora esta é para que xogues ti.
Veña, podes xogar.
Veña, podes xogar.
07:18
(Fussing)
(Laughter)
(Laughter)
127
426074
6347
(Barullo) (Risas)
07:26
LS: So you just saw
two 15-month-old babies
two 15-month-old babies
128
434901
2748
LS: Acabades de ver dous
bebés de 15 meses
bebés de 15 meses
07:29
do entirely different things
129
437649
1942
facendo dúas cousas totalmente diferentes
07:31
based only on the probability
of the sample they observed.
of the sample they observed.
130
439591
3599
baseadas só na probabilidade
da mostra que observaron.
da mostra que observaron.
07:35
Let me show you the experimental results.
131
443190
2321
Quero ensinar os resultados experimentais.
07:37
On the vertical axis, you'll see
the percentage of babies
the percentage of babies
132
445511
2764
No eixe vertical, pódese ver
a porcentaxe de bebés
a porcentaxe de bebés
07:40
who squeezed the ball in each condition,
133
448275
2530
que apertaron a bóla en cada suposto,
07:42
and as you'll see, babies are much
more likely to generalize the evidence
more likely to generalize the evidence
134
450805
3715
e como se ve, os bebés tenden
moito máis a xeneralizar a mostra
moito máis a xeneralizar a mostra
07:46
when it's plausibly representative
of the population
of the population
135
454520
3135
cando é representativa da poboación
07:49
than when the evidence
is clearly cherry-picked.
is clearly cherry-picked.
136
457655
3738
ca cando está claramente manipulada.
07:53
And this leads to a fun prediction:
137
461393
2415
E isto lévanos a unha predición curiosa:
07:55
Suppose you pulled just one blue ball
out of the mostly yellow box.
out of the mostly yellow box.
138
463808
4868
supoñamos que sacamos só unha bóla azul
da caixa que ten sobre todo
bólas amarelas.
bólas amarelas.
08:00
You [probably won't] pull three blue balls
in a row at random out of a yellow box,
in a row at random out of a yellow box,
139
468896
3869
Non se poderían sacar aleatoriamente
3 bólas azuis seguidas dunha caixa amarela
3 bólas azuis seguidas dunha caixa amarela
08:04
but you could randomly sample
just one blue ball.
just one blue ball.
140
472765
2455
pero poderíase sacar soamente unha.
08:07
That's not an improbable sample.
141
475220
1970
Non é unha mostra improbable.
08:09
And if you could reach into
a box at random
a box at random
142
477190
2224
E se se puidese meter a man
ao chou nunha caixa
ao chou nunha caixa
08:11
and pull out something that squeaks,
maybe everything in the box squeaks.
maybe everything in the box squeaks.
143
479414
3987
e sacar algo que chía,
tal vez todo o da caixa chíe.
tal vez todo o da caixa chíe.
08:15
So even though babies are going to see
much less evidence for squeaking,
much less evidence for squeaking,
144
483875
4445
Entón, aínda que os bebés van observar
moita menos probas para chíos,
moita menos probas para chíos,
08:20
and have many fewer actions to imitate
145
488320
2242
e contan con moitas menos
accións que imitar
accións que imitar
08:22
in this one ball condition than in
the condition you just saw,
the condition you just saw,
146
490562
3343
neste suposto dunha única bóla
ca no que vimos antes,
ca no que vimos antes,
08:25
we predicted that babies themselves
would squeeze more,
would squeeze more,
147
493905
3892
predicimos que os bebés por si sós
apertarían a bóla máis veces,
apertarían a bóla máis veces,
08:29
and that's exactly what we found.
148
497797
2894
e iso é exactamente o que atopamos.
08:32
So 15-month-old babies,
in this respect, like scientists,
in this respect, like scientists,
149
500691
4411
Así que aos bebés de 15 meses,
neste sentido, como científicos,
neste sentido, como científicos,
08:37
care whether evidence
is randomly sampled or not,
is randomly sampled or not,
150
505102
3088
impórtalles se a proba é
unha mostra representativa ou non,
unha mostra representativa ou non,
08:40
and they use this to develop
expectations about the world:
expectations about the world:
151
508190
3507
e usan isto para desenvolver
expectativas sobre o mundo:
expectativas sobre o mundo:
08:43
what squeaks and what doesn't,
152
511697
2182
qué chía e qué non,
08:45
what to explore and what to ignore.
153
513879
3145
qué explorar e qué ignorar.
08:50
Let me show you another example now,
154
518384
2066
Agora quero amosar outro exemplo,
08:52
this time about a problem
of causal reasoning.
of causal reasoning.
155
520450
2730
esta vez sobre un problema
de razoamento causal.
de razoamento causal.
E comeza cun problema de proba confusa
08:55
And it starts with a problem
of confounded evidence
of confounded evidence
156
523180
2439
08:57
that all of us have,
157
525619
1672
que todos temos:
08:59
which is that we are part of the world.
158
527291
2020
o feito de que formamos parte do mundo.
09:01
And this might not seem like a problem
to you, but like most problems,
to you, but like most problems,
159
529311
3436
Isto pode non parecer un problema,
pero como a maior parte deles,
pero como a maior parte deles,
09:04
it's only a problem when things go wrong.
160
532747
2337
maniféstase só cando as cousas van mal.
09:07
Take this baby, for instance.
161
535464
1811
Velaquí este bebé, por exemplo.
09:09
Things are going wrong for him.
162
537275
1705
As cousas están indo mal para el.
09:10
He would like to make
this toy go, and he can't.
this toy go, and he can't.
163
538980
2271
Gustaríalle facer funcionar
o seu xoguete, e non pode.
o seu xoguete, e non pode.
09:13
I'll show you a few-second clip.
164
541251
2529
Amosarei un vídeo duns poucos segundos.
09:21
And there's two possibilities, broadly:
165
549340
1920
En xeral, hai dúas posibilidades:
09:23
Maybe he's doing something wrong,
166
551260
2634
ou el está facendo algo mal,
09:25
or maybe there's something
wrong with the toy.
wrong with the toy.
167
553894
4216
ou algo non funciona no xoguete.
09:30
So in this next experiment,
168
558110
2111
Así que no seguinte experimento,
darémoslles aos bebés só
unha mínima porción de datos estatísticos
unha mínima porción de datos estatísticos
09:32
we're going to give babies
just a tiny bit of statistical data
just a tiny bit of statistical data
169
560221
3297
09:35
supporting one hypothesis over the other,
170
563518
2582
que apoian unha das hipóteses
sobre a outra,
sobre a outra,
09:38
and we're going to see if babies
can use that to make different decisions
can use that to make different decisions
171
566100
3455
e veremos se os bebés poden usar iso
para tomar decisións diferentes
para tomar decisións diferentes
09:41
about what to do.
172
569555
1834
sobre qué facer.
09:43
Here's the setup.
173
571389
2022
Velaquí o plan.
09:46
Hyowon is going to try to make
the toy go and succeed.
the toy go and succeed.
174
574071
3030
Hyowon vai intentar que o xoguete
funcione, e conségueo.
funcione, e conségueo.
09:49
I am then going to try twice
and fail both times,
and fail both times,
175
577101
3320
Entón eu vou intentalo dúas veces
e fracasar as dúas,
e fracasar as dúas,
09:52
and then Hyowon is going
to try again and succeed,
to try again and succeed,
176
580421
3112
despois Hyowon vai intentalo
outra vez e conseguilo,
outra vez e conseguilo,
09:55
and this roughly sums up my relationship
to my graduate students
to my graduate students
177
583533
3172
o que resume en xeral a miña relación
cos meus estudantes de posgrao
cos meus estudantes de posgrao
09:58
in technology across the board.
178
586705
2835
no que ten que ver coa tecnoloxía.
10:02
But the important point here is
it provides a little bit of evidence
it provides a little bit of evidence
179
590030
3292
Pero o importante aquí é
que proporciona algunha proba
que proporciona algunha proba
10:05
that the problem isn't with the toy,
it's with the person.
it's with the person.
180
593322
3668
de que o problema non é o xoguete,
senón a persoa.
senón a persoa.
10:08
Some people can make this toy go,
181
596990
2350
Algunhas poden facer
que o xoguete funcione,
que o xoguete funcione,
10:11
and some can't.
182
599340
959
e outras non.
10:12
Now, when the baby gets the toy,
he's going to have a choice.
he's going to have a choice.
183
600799
3413
Agora, cando o bebé consegue o xoguete,
vai ter unha elección.
vai ter unha elección.
10:16
His mom is right there,
184
604212
2188
Súa nai está xusto alí,
polo que pode ir e darlle o xoguete
e cambiar a persoa,
e cambiar a persoa,
10:18
so he can go ahead and hand off the toy
and change the person,
and change the person,
185
606400
3315
10:21
but there's also going to be
another toy at the end of that cloth,
another toy at the end of that cloth,
186
609715
3158
pero tamén vai haber outro xoguete
no bordo desa tea,
no bordo desa tea,
10:24
and he can pull the cloth towards him
and change the toy.
and change the toy.
187
612873
3552
así que pode tirar da tea cara a el
e cambiar o xoguete.
e cambiar o xoguete.
10:28
So let's see what the baby does.
188
616425
2090
Vexamos logo qué fai o bebé.
10:30
(Video) HG: Two, three. Go!
(Music)
(Music)
189
618515
4183
(Vídeo) HG: Dous, tres. Xa!
(Música)
(Música)
10:34
LS: One, two, three, go!
190
622698
3131
LS: Un, dous, tres. Xa!
10:37
Arthur, I'm going to try again.
One, two, three, go!
One, two, three, go!
191
625829
7382
Arthur, vou intentalo outra vez.
Un, dous, tres. Xa!
Un, dous, tres. Xa!
10:45
YG: Arthur, let me try again, okay?
192
633677
2600
HG: Arthur, déixame probar outra vez, si?
10:48
One, two, three, go!
(Music)
(Music)
193
636277
4550
Un, dous, tres. Xa! (Música)
10:53
Look at that. Remember these toys?
194
641583
1883
Mira. Acórdaste destes xoguetes?
10:55
See these toys? Yeah, I'm going
to put this one over here,
to put this one over here,
195
643466
3264
Ves estes xoguetes?
Si, vou poñer este por aquí,
Si, vou poñer este por aquí,
e a ti vouche dar este.
10:58
and I'm going to give this one to you.
196
646730
2062
11:00
You can go ahead and play.
197
648792
2335
Veña, xa podes xogar.
11:23
LS: Okay, Laura, but of course,
babies love their mommies.
babies love their mommies.
198
671213
4737
LS: Vale, Laura, pero claro,
os bebés quérenlles ás súas mamás.
os bebés quérenlles ás súas mamás.
11:27
Of course babies give toys
to their mommies
to their mommies
199
675950
2182
Normal que lles dean os xoguetes a ela
11:30
when they can't make them work.
200
678132
2030
cando non conseguen que funcionen.
11:32
So again, the really important question
is what happens when we change
is what happens when we change
201
680162
3593
De novo, a pregunta realmente importante
é que ocorre cando cambiamos
é que ocorre cando cambiamos
11:35
the statistical data ever so slightly.
202
683755
3154
os datos estatísticos só levemente.
11:38
This time, babies are going to see the toy
work and fail in exactly the same order,
work and fail in exactly the same order,
203
686909
4087
Agora, os bebés van ver o xoguete
funcionar e fallar xusto na mesma orde,
funcionar e fallar xusto na mesma orde,
11:42
but we're changing
the distribution of evidence.
the distribution of evidence.
204
690996
2415
pero imos cambiar a distribución da proba.
11:45
This time, Hyowon is going to succeed
once and fail once, and so am I.
once and fail once, and so am I.
205
693411
4411
Agora, Hyowon vai conseguilo unha vez
e fracasar outra, e eu tamén.
e fracasar outra, e eu tamén.
11:49
And this suggests it doesn't matter
who tries this toy, the toy is broken.
who tries this toy, the toy is broken.
206
697822
5637
O que suxire que non importa
quen proba este xoguete, está roto.
quen proba este xoguete, está roto.
Non funciona nunca.
11:55
It doesn't work all the time.
207
703459
1886
De novo, o bebé
vai ter que tomar unha decisión.
vai ter que tomar unha decisión.
11:57
Again, the baby's going to have a choice.
208
705345
1965
A súa nai está xusto ao lado,
así que pode cambiar a persoa,
así que pode cambiar a persoa,
11:59
Her mom is right next to her,
so she can change the person,
so she can change the person,
209
707310
3396
12:02
and there's going to be another toy
at the end of the cloth.
at the end of the cloth.
210
710706
3204
e haberá outro xoguete ao final da tea.
Vexamos que fai.
12:05
Let's watch what she does.
211
713910
1378
12:07
(Video) HG: Two, three, go!
(Music)
(Music)
212
715288
4348
HG: Dous, tres, xa!
(Música)
(Música)
12:11
Let me try one more time.
One, two, three, go!
One, two, three, go!
213
719636
4984
Déixame probar outra vez.
Un, dous, tres, xa!
Un, dous, tres, xa!
12:17
Hmm.
214
725460
1697
Umm.
12:19
LS: Let me try, Clara.
215
727950
2692
LS: Déixame probar a min, Clara.
12:22
One, two, three, go!
216
730642
3945
Un, dous, tres, xa!
Umm, déixame probar outra vez.
12:27
Hmm, let me try again.
217
735265
1935
12:29
One, two, three, go!
(Music)
(Music)
218
737200
5670
Un, dos, tres, xa!
(Música)
(Música)
12:35
HG: I'm going
to put this one over here,
to put this one over here,
219
743009
2233
HG: Vou poñer este por aquí,
12:37
and I'm going to give this one to you.
220
745242
2001
e vouche dar este a ti.
12:39
You can go ahead and play.
221
747243
3597
Veña, xa podes xogar.
12:58
(Applause)
222
766376
4897
(Aplausos)
13:04
LS: Let me show you
the experimental results.
the experimental results.
223
772993
2392
LS: Amosarei agora
os resultados experimentais.
os resultados experimentais.
13:07
On the vertical axis,
you'll see the distribution
you'll see the distribution
224
775385
2475
No eixe vertical, vese a distribución
13:09
of children's choices in each condition,
225
777860
2577
das eleccións dos nenos
baixo cada suposto,
baixo cada suposto,
13:12
and you'll see that the distribution
of the choices children make
of the choices children make
226
780437
4551
e vese que a distribución
das eleccións que fan
das eleccións que fan
13:16
depends on the evidence they observe.
227
784988
2787
depende da proba que observan.
13:19
So in the second year of life,
228
787775
1857
No segundo ano de idade,
os bebés poden usar unha fracción
mínima de datos estatísticos
mínima de datos estatísticos
13:21
babies can use a tiny bit
of statistical data
of statistical data
229
789632
2577
13:24
to decide between two
fundamentally different strategies
fundamentally different strategies
230
792209
3367
para decidir entre dúas estratexias
fundamentalmente diferentes
fundamentalmente diferentes
13:27
for acting in the world:
231
795576
1881
para actuar no mundo:
13:29
asking for help and exploring.
232
797457
2743
pedir axuda e explorar.
13:33
I've just shown you
two laboratory experiments
two laboratory experiments
233
801700
3434
Acabo de amosar
dous experimentos de laboratorio
dous experimentos de laboratorio
13:37
out of literally hundreds in the field
that make similar points,
that make similar points,
234
805134
3691
dos literalmente centos neste campo
que chegan a conclusións similares,
que chegan a conclusións similares,
13:40
because the really critical point
235
808825
2392
porque o auténtico punto clave
13:43
is that children's ability
to make rich inferences from sparse data
to make rich inferences from sparse data
236
811217
5108
é que a capacidade dos nenos
para facer ricas inferencias
partindo de datos dispersos
partindo de datos dispersos
13:48
underlies all the species-specific
cultural learning that we do.
cultural learning that we do.
237
816325
5341
serve de base a toda a nosa aprendizaxe
cultural específica como especie.
cultural específica como especie.
13:53
Children learn about new tools
from just a few examples.
from just a few examples.
238
821666
4597
Os nenos aprenden sobre novas ferramentas
a partir duns poucos exemplos.
a partir duns poucos exemplos.
13:58
They learn new causal relationships
from just a few examples.
from just a few examples.
239
826263
4717
Aprenden novas relacións causais
a partir duns poucos exemplos.
a partir duns poucos exemplos.
14:03
They even learn new words,
in this case in American Sign Language.
in this case in American Sign Language.
240
831928
4871
Incluso aprenden palabras novas ,
neste caso en lingua de signos americana.
neste caso en lingua de signos americana.
14:08
I want to close with just two points.
241
836799
2311
Quero concluír con só dúas cousas.
14:12
If you've been following my world,
the field of brain and cognitive sciences,
the field of brain and cognitive sciences,
242
840050
3688
A quen seguise o meu campo
(o do cerebro e as ciencias cognitivas)
(o do cerebro e as ciencias cognitivas)
14:15
for the past few years,
243
843738
1927
durante os últimos anos,
chamaríanlle a atención
tres grandes ideas.
tres grandes ideas.
14:17
three big ideas will have come
to your attention.
to your attention.
244
845665
2415
14:20
The first is that this is
the era of the brain.
the era of the brain.
245
848080
3436
A primeira é que esta é a era do cerebro.
14:23
And indeed, there have been
staggering discoveries in neuroscience:
staggering discoveries in neuroscience:
246
851516
3669
E por suposto, houbo descubrimentos
impresionantes en neurociencia:
impresionantes en neurociencia:
14:27
localizing functionally specialized
regions of cortex,
regions of cortex,
247
855185
3436
localizar rexións do córtex
funcionalmente especializadas,
funcionalmente especializadas,
14:30
turning mouse brains transparent,
248
858621
2601
facer transparentes os cerebros de ratos,
14:33
activating neurons with light.
249
861222
3776
activar neuronas con luz.
14:36
A second big idea
250
864998
1996
Unha segunda grande idea
14:38
is that this is the era of big data
and machine learning,
and machine learning,
251
866994
4104
é que esta é a era dos datos masivos
e da aprendizaxe automática,
e da aprendizaxe automática,
14:43
and machine learning promises
to revolutionize our understanding
to revolutionize our understanding
252
871098
3141
e a aprendizaxe automática promete
revolucionar a nosa comprensión
revolucionar a nosa comprensión
14:46
of everything from social networks
to epidemiology.
to epidemiology.
253
874239
4667
de todo, dende as redes sociais
ata a epidemioloxía.
ata a epidemioloxía.
E tal vez, á vez que afronta problemas
de comprensión do contexto
de comprensión do contexto
14:50
And maybe, as it tackles problems
of scene understanding
of scene understanding
254
878906
2693
14:53
and natural language processing,
255
881599
1993
e de procesamento da linguaxe natural,
14:55
to tell us something
about human cognition.
about human cognition.
256
883592
3324
poida desvelarnos algo
sobre a cognición humana.
sobre a cognición humana.
14:59
And the final big idea you'll have heard
257
887756
1937
E a gran idea final que escoitarían
15:01
is that maybe it's a good idea we're going
to know so much about brains
to know so much about brains
258
889693
3387
é que pode ser boa idea
saber tanto sobre os cerebros
saber tanto sobre os cerebros
15:05
and have so much access to big data,
259
893080
1917
e ter tanto acceso a datos masivos,
15:06
because left to our own devices,
260
894997
2507
porque pola nosa conta,
15:09
humans are fallible, we take shortcuts,
261
897504
3831
os humanos somos falíbeis,
buscamos atallos,
buscamos atallos,
15:13
we err, we make mistakes,
262
901335
3437
erramos, temos fallos,
15:16
we're biased, and in innumerable ways,
263
904772
3684
non somos neutrais,
e de formas innumerables,
e de formas innumerables,
15:20
we get the world wrong.
264
908456
2969
chegamos a ideas falsas sobre o mundo.
15:24
I think these are all important stories,
265
912843
2949
Eu creo que todas estas
son noticias importantes,
son noticias importantes,
15:27
and they have a lot to tell us
about what it means to be human,
about what it means to be human,
266
915792
3785
e que teñen moito que contarnos
sobre qué significa ser humano,
sobre qué significa ser humano,
15:31
but I want you to note that today
I told you a very different story.
I told you a very different story.
267
919577
3529
pero gustaríame destacar
que hoxe tratei unha noticia moi distinta.
que hoxe tratei unha noticia moi distinta.
15:35
It's a story about minds and not brains,
268
923966
3807
Unha noticia sobre mentes,
non sobre cerebros,
non sobre cerebros,
15:39
and in particular, it's a story
about the kinds of computations
about the kinds of computations
269
927773
3006
e en particular,
sobre o tipo de computación
sobre o tipo de computación
que só as mentes humanas poden realizar,
15:42
that uniquely human minds can perform,
270
930779
2590
15:45
which involve rich, structured knowledge
and the ability to learn
and the ability to learn
271
933369
3944
que implican coñecementos ricos
e estruturados e capacidade de aprender
e estruturados e capacidade de aprender
15:49
from small amounts of data,
the evidence of just a few examples.
the evidence of just a few examples.
272
937313
5268
a partir de pequenas cantidades de datos,
coa proba de só uns poucos exemplos.
coa proba de só uns poucos exemplos.
15:56
And fundamentally, it's a story
about how starting as very small children
about how starting as very small children
273
944301
4299
E fundamentalmente, é unha noticia
sobre como dende meniños
sobre como dende meniños
16:00
and continuing out all the way
to the greatest accomplishments
to the greatest accomplishments
274
948600
4180
e continuando todo o camiño
ata os máis grandes logros
ata os máis grandes logros
16:04
of our culture,
275
952780
3843
da nosa cultura,
16:08
we get the world right.
276
956623
1997
conseguimos entender ben o mundo.
16:12
Folks, human minds do not only learn
from small amounts of data.
from small amounts of data.
277
960433
5267
Amigos, as mentes humanas non aprenden só
a partir de pequenas cantidades de datos
a partir de pequenas cantidades de datos
16:18
Human minds think
of altogether new ideas.
of altogether new ideas.
278
966285
2101
As mentes humanas pensan
ideas totalmente novas.
ideas totalmente novas.
16:20
Human minds generate
research and discovery,
research and discovery,
279
968746
3041
As mentes humanas xeran
investigación e descubrimento,
investigación e descubrimento,
16:23
and human minds generate
art and literature and poetry and theater,
art and literature and poetry and theater,
280
971787
5273
e as mentes humanas xeran
arte e literatura e poesía e teatro,
arte e literatura e poesía e teatro,
16:29
and human minds take care of other humans:
281
977070
3760
e as mentes humanas
coidan doutros seres humanos:
coidan doutros seres humanos:
16:32
our old, our young, our sick.
282
980830
3427
os nosos maiores, a nosa mocidade,
os nosos enfermos.
os nosos enfermos.
16:36
We even heal them.
283
984517
2367
Incluso os curamos.
16:39
In the years to come, we're going
to see technological innovations
to see technological innovations
284
987564
3103
Nos próximos anos,
imos ver innovacións tecnolóxicas
imos ver innovacións tecnolóxicas
16:42
beyond anything I can even envision,
285
990667
3797
máis alá do que podo concibir,
16:46
but we are very unlikely
286
994464
2150
pero hai moi poucas probabilidades
16:48
to see anything even approximating
the computational power of a human child
the computational power of a human child
287
996614
5709
de que vexamos algo
que se aproxime sequera
que se aproxime sequera
ao poder computacional dun neno humano,
16:54
in my lifetime or in yours.
288
1002323
4298
no resto da miña vida ou da vosa.
16:58
If we invest in these most powerful
learners and their development,
learners and their development,
289
1006621
5047
Se investimos nestes potentísimos
aprendices e no seu desenvolvemento,
aprendices e no seu desenvolvemento,
17:03
in babies and children
290
1011668
2917
en bebés e cativos,
17:06
and mothers and fathers
291
1014585
1826
e nais e pais
17:08
and caregivers and teachers
292
1016411
2699
e coidadores e profesores
17:11
the ways we invest in our other
most powerful and elegant forms
most powerful and elegant forms
293
1019110
4170
do xeito que investimos nas nosas
outras poderosísimas e elegantes formas
outras poderosísimas e elegantes formas
17:15
of technology, engineering and design,
294
1023280
3218
de tecnoloxía, enxeñaría e deseño,
17:18
we will not just be dreaming
of a better future,
of a better future,
295
1026498
2939
non estaremos simplemente
soñando cun mellor futuro,
soñando cun mellor futuro,
estaremos planificándoo.
17:21
we will be planning for one.
296
1029437
2127
17:23
Thank you very much.
297
1031564
2345
Moitísimas grazas.
(Aplausos)
17:25
(Applause)
298
1033909
3421
17:29
Chris Anderson: Laura, thank you.
I do actually have a question for you.
I do actually have a question for you.
299
1037810
4426
Chris Anderson: Grazas, Laura.
Quería facerche unha pregunta.
Quería facerche unha pregunta.
17:34
First of all, the research is insane.
300
1042236
2359
Antes de nada,
esta investigación é de tolos.
esta investigación é de tolos.
17:36
I mean, who would design
an experiment like that? (Laughter)
an experiment like that? (Laughter)
301
1044595
3725
Quen deseñaría
un experimento coma ese? (Risas)
un experimento coma ese? (Risas)
17:41
I've seen that a couple of times,
302
1049150
1790
Vino unhas cantas veces,
17:42
and I still don't honestly believe
that that can truly be happening,
that that can truly be happening,
303
1050940
3222
e sigo sen acabar de crer
que poida estar ocorrendo de verdade,
que poida estar ocorrendo de verdade,
17:46
but other people have done
similar experiments; it checks out.
similar experiments; it checks out.
304
1054162
3158
pero outras persoas fixeron
experimentos similares; está comprobado.
experimentos similares; está comprobado.
17:49
The babies really are that genius.
305
1057320
1633
Os bebés son realmente xenios.
17:50
LS: You know, they look really impressive
in our experiments,
in our experiments,
306
1058953
3007
LS: Parecen realmente impresionantes
nos nosos experimentos,
nos nosos experimentos,
17:53
but think about what they
look like in real life, right?
look like in real life, right?
307
1061960
2652
pero pensa no que fan na vida real, non?
Todo comeza cun bebé.
17:56
It starts out as a baby.
308
1064612
1150
Dezaoito meses despois, estache falando,
17:57
Eighteen months later,
it's talking to you,
it's talking to you,
309
1065762
2007
e as primeiras palabras dos bebés
non van de pelotas e parrulos,
non van de pelotas e parrulos,
17:59
and babies' first words aren't just
things like balls and ducks,
things like balls and ducks,
310
1067769
3041
18:02
they're things like "all gone,"
which refer to disappearance,
which refer to disappearance,
311
1070810
2881
son cousas como “non ta”
que se refire á desaparición,
que se refire á desaparición,
ou “uh oh”, para referirse
a accións involuntarias.
a accións involuntarias.
18:05
or "uh-oh," which refer
to unintentional actions.
to unintentional actions.
312
1073691
2283
18:07
It has to be that powerful.
313
1075974
1562
Ten que ser así de poderoso.
Ten que ser moito máis poderoso
que o que ensinei.
que o que ensinei.
18:09
It has to be much more powerful
than anything I showed you.
than anything I showed you.
314
1077536
2775
Están descifrando o mundo enteiro.
18:12
They're figuring out the entire world.
315
1080311
1974
Un neno de catro anos
pode falarche sobre case todo.
pode falarche sobre case todo.
18:14
A four-year-old can talk to you
about almost anything.
about almost anything.
316
1082285
3144
(Aplausos)
18:17
(Applause)
317
1085429
1601
18:19
CA: And if I understand you right,
the other key point you're making is,
the other key point you're making is,
318
1087030
3414
CA: E se entendo ben,
o outro punto clave que destacas é
o outro punto clave que destacas é
18:22
we've been through these years
where there's all this talk
where there's all this talk
319
1090444
2754
que durante estes anos
tivemos todo este debate
tivemos todo este debate
18:25
of how quirky and buggy our minds are,
320
1093198
1932
sobre o peculiares e confusas
que son as nosas mentes,
que son as nosas mentes,
18:27
that behavioral economics
and the whole theories behind that
and the whole theories behind that
321
1095130
2867
coa economía condutual
e teorías enteiras detrás
e teorías enteiras detrás
18:29
that we're not rational agents.
322
1097997
1603
de que non somos axentes racionais.
18:31
You're really saying that the bigger
story is how extraordinary,
story is how extraordinary,
323
1099600
4216
E ti estás a dicir que este fenómeno
é extraordinario,
é extraordinario,
18:35
and there really is genius there
that is underappreciated.
that is underappreciated.
324
1103816
4944
e que en realidade hai xenialidade
que está subestimada.
que está subestimada.
18:40
LS: One of my favorite
quotes in psychology
quotes in psychology
325
1108760
2070
Unha das miñas citas favoritas
en psicoloxía
en psicoloxía
18:42
comes from the social
psychologist Solomon Asch,
psychologist Solomon Asch,
326
1110830
2290
é do psicólogo social Solomon Asch,
18:45
and he said the fundamental task
of psychology is to remove
of psychology is to remove
327
1113120
2807
que dixo que
“o cometido fundamental da psicoloxía
“o cometido fundamental da psicoloxía
é eliminar
o veo de autoevidencia das cousas”.
o veo de autoevidencia das cousas”.
18:47
the veil of self-evidence from things.
328
1115927
2626
18:50
There are orders of magnitude
more decisions you make every day
more decisions you make every day
329
1118553
4551
Hai millóns de decisións
que se toman a diario
que se toman a diario
18:55
that get the world right.
330
1123104
1347
que interpretan ben o mundo.
18:56
You know about objects
and their properties.
and their properties.
331
1124451
2132
Coñecemos os obxectos
e as súas propiedades.
e as súas propiedades.
18:58
You know them when they're occluded.
You know them in the dark.
You know them in the dark.
332
1126583
3029
Recoñecémolos cando están ocultos.
Recoñecémolos na escuridade.
Recoñecémolos na escuridade.
19:01
You can walk through rooms.
333
1129612
1308
Camiñamos por cuartos.
Podemos percibir o que pensan outros.
Podemos falarlles.
Podemos falarlles.
19:02
You can figure out what other people
are thinking. You can talk to them.
are thinking. You can talk to them.
334
1130920
3532
Podemos navegar no espazo.
Coñecemos os números.
Coñecemos os números.
19:06
You can navigate space.
You know about numbers.
You know about numbers.
335
1134452
2230
Entendemos as relacións causais.
Entendemos o razoamento moral.
Entendemos o razoamento moral.
19:08
You know causal relationships.
You know about moral reasoning.
You know about moral reasoning.
336
1136682
3022
E todo isto sen esforzo ningún,
por iso non nos decatamos,
por iso non nos decatamos,
19:11
You do this effortlessly,
so we don't see it,
so we don't see it,
337
1139704
2356
pero así interpretamos ben o mundo,
19:14
but that is how we get the world right,
and it's a remarkable
and it's a remarkable
338
1142060
2912
e moi difícil de entender.
19:16
and very difficult-to-understand
accomplishment.
accomplishment.
339
1144972
2318
CA: Imaxino que hai persoas no público
que comparten
que comparten
19:19
CA: I suspect there are people
in the audience who have
in the audience who have
340
1147290
2628
esa visión do crecente poder tecnolóxico
19:21
this view of accelerating
technological power
technological power
341
1149918
2238
que poderían cuestionar a túa afirmación
de que nunca nas nosas vidas
de que nunca nas nosas vidas
19:24
who might dispute your statement
that never in our lifetimes
that never in our lifetimes
342
1152156
2958
un ordenador fará
o que un neno de tres anos pode facer,
o que un neno de tres anos pode facer,
19:27
will a computer do what
a three-year-old child can do,
a three-year-old child can do,
343
1155114
2618
pero está claro que en calquera situación,
19:29
but what's clear is that in any scenario,
344
1157732
3248
as nosas máquinas teñen
moito que aprender dos nosos cativos.
moito que aprender dos nosos cativos.
19:32
our machines have so much to learn
from our toddlers.
from our toddlers.
345
1160980
3770
19:38
LS: I think so. You'll have some
machine learning folks up here.
machine learning folks up here.
346
1166230
3216
LS: Eu tamén o creo. Aquí haberá
partidarios da aprendizaxe automática.
partidarios da aprendizaxe automática.
19:41
I mean, you should never bet
against babies or chimpanzees
against babies or chimpanzees
347
1169446
4203
Nunca deberías apostar
contra os bebés ou os chimpancés
contra os bebés ou os chimpancés
19:45
or technology as a matter of practice,
348
1173649
3645
ou da tecnoloxía, en principio.
19:49
but it's not just
a difference in quantity,
a difference in quantity,
349
1177294
4528
pero non se trata só
dunha diferenza de cantidade,
dunha diferenza de cantidade,
19:53
it's a difference in kind.
350
1181822
1764
é unha diferenza cualitativa.
19:55
We have incredibly powerful computers,
351
1183586
2160
Temos ordenadores incriblemente potentes,
19:57
and they do do amazingly
sophisticated things,
sophisticated things,
352
1185746
2391
que fan cousas incriblemente sofisticadas,
20:00
often with very big amounts of data.
353
1188137
3204
por veces con enormes cantidades de datos.
As mentes humanas fan, para min,
algo bastante diferente,
algo bastante diferente,
20:03
Human minds do, I think,
something quite different,
something quite different,
354
1191341
2607
20:05
and I think it's the structured,
hierarchical nature of human knowledge
hierarchical nature of human knowledge
355
1193948
3895
e creo que é a natureza estruturada
e xerarquizada do coñecemento humano
e xerarquizada do coñecemento humano
20:09
that remains a real challenge.
356
1197843
2032
o que permanece como
un verdadeiro desafío.
un verdadeiro desafío.
20:11
CA: Laura Schulz, wonderful
food for thought. Thank you so much.
food for thought. Thank you so much.
357
1199875
3061
CA: Laura Schulz, un gran tema
para reflexionar. Moitas grazas.
para reflexionar. Moitas grazas.
20:14
LS: Thank you.
(Applause)
(Applause)
358
1202936
2922
Grazas
(Aplausos)
(Aplausos)
ABOUT THE SPEAKER
Laura Schulz - Cognitive scientistDevelopmental behavior studies spearheaded by Laura Schulz are changing our notions of how children learn.
Why you should listen
MIT Early Childhood Cognition Lab lead investigator Laura Schulz studies learning in early childhood. Her research bridges computational models of cognitive development and behavioral studies in order to understand the origins of inquiry and discovery.
Working in play labs, children’s museums, and a recently-launched citizen science website, Schultz is reshaping how we view young children’s perceptions of the world around them. Some of the surprising results of her research: before the age of four, children expect hidden causes when events happen probabilistically, use simple experiments to distinguish causal hypotheses, and trade off learning from instruction and exploration.
Laura Schulz | Speaker | TED.com