Laura Schulz: The surprisingly logical minds of babies

譯者: Kitty Lau 審譯者: Jack Ricardo

Mark Twain summed up what I take to be

馬克．吐溫用一句妙語概括了， 我認為是認知科學的一個最根本問題。

one of the fundamental problems of cognitive science

with a single witticism.

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

他說:「科學的有趣之處在於，

One gets such wholesale returns of conjecture

一個人可從微不足道的事得出了偉大的猜想。」

out of such a trifling investment in fact."

(Laughter)

(笑聲)

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

馬克．吐溫當然只是開玩笑，但他是對的。

There's something fascinating about science.

科學有其有趣之處。

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

從幾塊骨頭，我們推測了恐龍的存在;

From spectral lines, the composition of nebulae.

從譜線得出了星雲的成份;

From fruit flies,

從果蠅得出了遺傳的機制;

the mechanisms of heredity,

and from reconstructed images of blood flowing through the brain,

以及從血液流入大腦的重建影像，

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

在我的研究則是從幼兒的行為中，

we try to say something about the fundamental mechanisms

我們嘗試解釋人類認知的基本機制。

of human cognition.

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

我在麻省理工大腦及認知科學系實驗室中，

I have spent the past decade trying to understand the mystery

花了過去十年研究一個謎團，

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

就是兒童如何從零開始， 快速地學到那麼多的東西。

Because, it turns out that the fascinating thing about science

科學令人著迷之處，

is also a fascinating thing about children,

亦正是孩子令人著迷的地方。

which, to put a gentler spin on Mark Twain,

回應馬克．吐溫的話，

is precisely their ability to draw rich, abstract inferences

那就是孩子從零碎和離亂的訊息中， 能夠得出豐富而抽象的推論的能力。

rapidly and accurately from sparse, noisy data.

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

我將舉出兩個例子:

One is about a problem of generalization,

一個是關於廣義化的問題，

and the other is about a problem of causal reasoning.

另一個則是關於因果推理的。

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

雖然我將會談及我實驗室的研究，

this work is inspired by and indebted to a field.

但這個研究的靈感是來自一個領域，

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

一個我要感謝世界各地的導師、 同事和工作夥伴付出的領域。

Let me start with the problem of generalization.

讓我先談談廣義化的問題。

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

歸納數據樣本在科學上是不可或缺的，

We poll a tiny fraction of the electorate

如我們調查一部分的選民，

and we predict the outcome of national elections.

然後預測國家大選的結果。

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

我們觀察一小撮病人在臨床試驗中的反應，

and we bring drugs to a national market.

然後把藥物帶入市場，

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

但只有在整個人口中隨機抽樣才可行。

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

當我們刻意挑選樣本，

say, we poll only urban voters,

如我們只調查城市中的選民，

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

又或在治療心臟病的臨床試驗中，

we include only men --

我們只研究男性，

the results may not generalize to the broader population.

這樣的結果便不能代表整個人口。

So scientists care whether evidence is randomly sampled or not,

因此科學家著緊抽樣的方法是否隨機。

but what does that have to do with babies?

但這又跟嬰兒有甚麼關係?

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

嬰兒在任何時候都要歸納數據樣本，

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

當他們看到幾隻橡皮鴨， 並知道它們浮在水面。

or a few balls and learn that they bounce.

又或見到幾個皮球， 並知道它們能彈跳。

And they develop expectations about ducks and balls

從中他們建立對橡膠鴨和皮球的概念，

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

並將這概念延伸至日後會見到的 所有橡膠鴨和皮球。

for the rest of their lives.

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

嬰兒對橡膠鴨和皮球的這種概括，

they have to make about almost everything:

他們會運用在每一件事上:

shoes and ships and sealing wax and cabbages and kings.

鞋子、船、封蠟、捲心菜和皇帝。

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

因此嬰兒留意這些細節能否代表整體。

is plausibly representative of a larger population?

Let's find out.

我們一起看看吧。

I'm going to show you two movies,

我將讓你看兩段短片，

one from each of two conditions of an experiment,

這兩段短片分別代表實驗的兩個情況。

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

由於你將看到兩段短片，

you're going to see just two babies,

你只會看到兩個嬰兒，

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

而這兩個嬰兒在很多地方都是不同的。

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

但這兩個嬰兒將代表更大的群組，

and the differences you're going to see

你將看到的不同之處則代表 嬰兒行為中的平均差異。

represent average group differences in babies' behavior across conditions.

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

在每一段短片中你會見到嬰兒 在做些他們正常會做的事。

just exactly what you might expect a baby to do,

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

嬰兒本身已是十分神奇的，

But to my mind the magical thing,

但對我來說他們的神奇之處，

and what I want you to pay attention to,

也是我想你們留意的地方，

is the contrast between these two conditions,

就是這兩種情況之間的分別。

because the only thing that differs between these two movies

因為這兩段短片唯一不同的地方，

is the statistical evidence the babies are going to observe.

正是嬰兒將要觀察的資料。

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

我們把一些藍色和黃色的球給嬰兒看。

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

權孝媛當時是我的學生， 現在則是史丹佛大學的同事。

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

她將拿出三個藍色的球，

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

而當她拿出這些球時， 她會把球擠一下，

and the balls are going to squeak.

讓這些球發出吱吱聲。

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

這對於嬰兒來說就像TED一樣，

It doesn't get better than that.

是件很美好的事。

(Laughter)

（笑聲）

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

從一個裝滿藍色球的箱中， 抽出三個藍色球是件很容易的事。

out of a box of mostly blue balls.

You could do that with your eyes closed.

你閉上眼睛也能做到，

It's plausibly a random sample from this population.

這就像隨機抽樣。

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

因此當你可以在箱中隨機地抽出 能吱吱叫的物件時，

then maybe everything in the box squeaks.

也許箱中所有物件都能吱吱叫，

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

所以嬰兒可能會假設黃色球也能吱吱叫。

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

但這些黃色球都有一根棒，

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

所以嬰兒可用它們做些不同的事，

They could pound them or whack them.

他們可以拍打或搖動這些球。

But let's see what the baby does.

就讓我們看看這嬰兒會做甚麼。

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

(影片) 權孝媛: 看看這個。 (球發出吱吱聲)

Did you see that? (Ball squeaks)

看到這個嗎? (球發出吱吱聲)

Cool.

很酷吧!

See this one?

看看這個。

(Ball squeaks)

(球發出吱吱聲)

Wow.

哇!

Laura Schulz: Told you. (Laughs)

羅拉·舒爾茨: 早就說了。 (笑聲)

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

(影片) 孝媛: 看到這個嗎? (球發出吱吱聲)

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

克拉拉, 這個是給你的, 你拿去玩吧。

(Laughter)

(笑聲)

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

羅拉: 我不用解釋, 對吧?

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

嬰兒把藍色球的特性套用到黃色球上。

of blue balls to yellow balls,

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

嬰兒從模仿我們中學習，這是很神奇的，

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

但我們早就知道嬰兒能這樣做。

The really interesting question

有趣的地方是當把一樣的東西給嬰兒看時， 甚麼事會發生。

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

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

我們能肯定這是完全一樣的， 因我們有個秘密的空間，

and we actually pull the balls from there,

從中我們抽出這些球。

but this time, all we change is the apparent population

但這次我們改變了抽樣的母體。

from which that evidence was drawn.

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

這次我們在一個裝滿黃色球的箱中， 抽出三個藍色球給嬰兒看。

pulled out of a box of mostly yellow balls,

and guess what?

想想甚麼事會發生?

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

你大概不能隨機地在裝滿黃色球的箱中， 連續抽出三個藍色球，

out of a box of mostly yellow balls.

That is not plausibly randomly sampled evidence.

因此這很可能不是隨機抽樣。

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

這反映了孝媛可能是刻意抽出藍色球，

Maybe there's something special about the blue balls.

可能這些藍色球是特別的，

Maybe only the blue balls squeak.

可能只有藍色球能吱吱叫。

Let's see what the baby does.

一起看看這嬰兒會做甚麼。

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

(影片) 孝媛: 看看這個。 (球發出吱吱聲)

See this toy? (Ball squeaks)

看到這個玩具嗎? (球發出吱吱聲)

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

哇, 這很酷, 看到嗎? (球發出吱吱聲)

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

這個是給你的, 你拿去玩吧。

(Fussing) (Laughter)

(不耐煩的) (笑聲)

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

羅拉: 你剛剛看到兩個15月大的嬰兒，

do entirely different things

按他們觀察到樣本出現的機率， 而做出完全不同的事。

based only on the probability of the sample they observed.

Let me show you the experimental results.

一起看看實驗的結果，

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

垂直軸代表在每一個情況中， 有多少百分比的嬰兒擠壓球。

who squeezed the ball in each condition,

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

你可看見嬰兒在樣本和整體一致時，

when it's plausibly representative of the population

比刻意挑選的樣本，

than when the evidence is clearly cherry-picked.

較會歸納他們看到的特徵。

And this leads to a fun prediction:

因此這帶出一個有趣的預測。

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

假設你在一個裝滿黃色球的箱中 只拿出一個藍色球，

You [probably won't] pull three blue balls in a row at random out of a yellow box,

當然你很難隨機地連續抽出三個藍色球，

but you could randomly sample just one blue ball.

但你可以只用一個藍色球作樣本，

That's not an improbable sample.

這不一定是個不可行的樣本。

And if you could reach into a box at random

當你隨機抽出一個會吱吱叫的東西時，

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

可能箱中所有的東西都會吱吱叫，

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

因此雖然嬰兒會看到較少吱吱叫的例子，

and have many fewer actions to imitate

而且在只抽出一個球的情況下， 他們會有較少的動作去模仿，

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

we predicted that babies themselves would squeeze more,

但我們預計會有更多嬰兒擠壓球。

and that's exactly what we found.

這正是我們發現的結果。

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

因此15月大的嬰兒在這方面就像科學家，

care whether evidence is randomly sampled or not,

他們留意抽樣的方法是否隨機，

and they use this to develop expectations about the world:

並以此建立對事物的概念:

what squeaks and what doesn't,

甚麼會吱吱叫而甚麼不會，

what to explore and what to ignore.

甚麼需要探索而甚麼可忽略。

Let me show you another example now,

現在讓我給你們看看另一個例子，

this time about a problem of causal reasoning.

這次是關於因果推理的。

And it starts with a problem of confounded evidence

每人都要面對這個問題，

that all of us have,

which is that we are part of the world.

因為我們都是這世界的一部份。

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

這看似不是一個問題， 但和其他問題一樣，

it's only a problem when things go wrong.

事情會出狀況。

Take this baby, for instance.

以這個嬰兒為例，

Things are going wrong for him.

所有事都出了問題，

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

他想開動這個玩具，但他做不到。

I'll show you a few-second clip.

我會讓你看一段幾秒的影片。

And there's two possibilities, broadly:

這有兩個可能的原因，

Maybe he's doing something wrong,

可能是他做錯了一些事，

or maybe there's something wrong with the toy.

又或是那個玩具有些問題。

So in this next experiment,

因此在這個實驗中，

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

我們會給嬰兒們少許資料。

supporting one hypothesis over the other,

這些資料會傾向支持其中一個可能性，

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

我們將研究這些嬰兒能否運用這些資料，

about what to do.

而作出不同的決定。

Here's the setup.

這個實驗是這樣的:

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

孝媛嘗試開動那個玩具並成功了，

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

而我的兩次嘗試都失敗了，

and then Hyowon is going to try again and succeed,

之後孝媛再嘗試，並再次成功了。

and this roughly sums up my relationship to my graduate students

這就像我和我的學生在使用新科技的情況。

in technology across the board.

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

重要的是這提供了少許的資料，

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

這反映玩具並沒有問題，而是人的問題。

Some people can make this toy go,

有些人可以開動這玩具，

and some can't.

有些人則不能。

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

當這嬰兒拿到玩具時，他要作一個選擇。

His mom is right there,

他的母親在旁，

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

所以他可以把玩具交給母親， 換另一人嘗試。

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

同時在毛巾上有另一個玩具，

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

所以他也可以把玩具拉向自己， 換另一個玩具。

So let's see what the baby does.

一起看看嬰兒會怎樣做。

(Video) HG: Two, three. Go! (Music)

(影片) 孝媛: 二、三、開始! (音樂)

LS: One, two, three, go!

羅拉: 一、二、三、開始!

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

亞瑟，讓我再試一次， 一、二、三、開始!

YG: Arthur, let me try again, okay?

孝媛: 亞瑟，讓我再試吧，好嗎?

One, two, three, go! (Music)

一、二、三、開始! (音樂)

Look at that. Remember these toys?

看看這裡，記得這些玩具嗎?

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

看到嗎? 對，我會把這個放在這裡，

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

把另一個給你。

You can go ahead and play.

你拿去玩吧。

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

羅拉: 你或許會說嬰兒都愛他們的母親，

Of course babies give toys to their mommies

因此當玩具出現問題時， 嬰兒自然會把它交給母親。

when they can't make them work.

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

因此，問題在於當我們稍微改變資料時， 甚麼事會發生。

the statistical data ever so slightly.

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

這次，嬰兒將看到這玩具 按同一次序成功運作和失敗，

but we're changing the distribution of evidence.

但我們改變了資料的分佈。

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

這次孝媛和我各有一次成功和一次失敗，

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

這代表誰人嘗試都沒有分別， 那件玩具是壞的，

It doesn't work all the time.

它不是每次都能運作的。

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

同樣地，嬰兒要作出一個選擇，

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

她的母親在旁，所以她可換另一人嘗試，

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

同時另一個玩具就在毛巾上。

Let's watch what she does.

看看她會怎樣做。

(Video) HG: Two, three, go! (Music)

(影片) 孝媛: 二、三、開始! (音樂)

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

讓我再試一次， 一、二、三、開始!

Hmm.

嗯...

LS: Let me try, Clara.

羅拉: 讓我試試吧，克拉拉。

One, two, three, go!

一、二、三、開始!

Hmm, let me try again.

嗯...讓我再試試。

One, two, three, go! (Music)

一、二、三、開始! (音樂)

HG: I'm going to put this one over here,

孝媛: 我把這個放在這裡，

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

這個則交給你，

You can go ahead and play.

你拿去玩吧。

(Applause)

(掌聲)

LS: Let me show you the experimental results.

羅拉: 看看這個實驗的結果，

On the vertical axis, you'll see the distribution

在垂直軸上，你會看到在每種情況下， 嬰兒作出不同選擇的分佈。

of children's choices in each condition,

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

你會發現他們作的選擇是 基於他們觀察到的資料。

depends on the evidence they observe.

So in the second year of life,

因此當他們兩歲時，

babies can use a tiny bit of statistical data

嬰兒已經可以運用細微的資料，

to decide between two fundamentally different strategies

在兩個完全不同的選項中作出決定:

for acting in the world:

asking for help and exploring.

尋求幫忙或自行探索。

I've just shown you two laboratory experiments

我剛才讓你們看了兩個實驗，

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

在這領域中有數千個得出相同結果的實驗。

because the really critical point

當中反映的重點是，

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

兒童擁有充分解讀零碎資訊的能力，

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

這超出了所有文化的學習方式。

Children learn about new tools from just a few examples.

孩子從少數的例子便能學到新技能，

They learn new causal relationships from just a few examples.

他們從少數的例子便能領略到新的因果關係，

They even learn new words, in this case in American Sign Language.

他們甚至能學到新的生字，如美國手語。

I want to close with just two points.

我會提出兩個重點作總結。

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

如果你近年有留意大腦和認知科學領域，

for the past few years,

three big ideas will have come to your attention.

你會聽到三個重要的概念。

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

第一，現在是大腦的時代。

And indeed, there have been staggering discoveries in neuroscience:

的確，神經科學近來有不少驚人的發現，

localizing functionally specialized regions of cortex,

例如標記了大腦皮層負責不同功能的位置、

turning mouse brains transparent,

製造出透明的老鼠大腦、

activating neurons with light.

以及利用光線啟動神經元。

A second big idea

第二個重要的概念是，

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

現在是大數據和機器學習的時代，

and machine learning promises to revolutionize our understanding

而機器學習能徹底改變我們對任何事的理解，

of everything from social networks to epidemiology.

從社交網站到流行病學。

And maybe, as it tackles problems of scene understanding

當機器學習能理解埸合和處理自然語言時，

and natural language processing,

to tell us something about human cognition.

也許我們能藉此了解人類的認知。

And the final big idea you'll have heard

最後一個你會聽過的重要概念是，

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

我們將對大腦有很深入的認識， 並能掌握大數據，而這很可能是件好事。

and have so much access to big data,

because left to our own devices,

因為相比機器而言，

humans are fallible, we take shortcuts,

人類易犯錯誤，我們會走捷徑，

we err, we make mistakes,

我們會做錯，

we're biased, and in innumerable ways,

我們在很多方面都有偏見，

we get the world wrong.

我們會有錯誤的理解。

I think these are all important stories,

我認為這都是重要的，

and they have a lot to tell us about what it means to be human,

因為這反映了人類的特質，

but I want you to note that today I told you a very different story.

但我今天想帶出事情的另一面。

It's a story about minds and not brains,

這是關於思維而非大腦的，

and in particular, it's a story about the kinds of computations

尤其是人類獨有的運算能力，

that uniquely human minds can perform,

which involve rich, structured knowledge and the ability to learn

這牽涉了豐富、有條理的知識，

以及從少量的數據和例子中學習的能力。

from small amounts of data, the evidence of just a few examples.

And fundamentally, it's a story about how starting as very small children

再者，這是關於我們如何從幼童，

and continuing out all the way to the greatest accomplishments

一路發展至成為文化中偉大的成就，

of our culture,

we get the world right.

我們能正確地理解這個世界。

Folks, human minds do not only learn from small amounts of data.

大家， 人腦不只是懂得從少量的數據中學習。

Human minds think of altogether new ideas.

人腦能想到新的主意。

Human minds generate research and discovery,

人腦能創造出研究和發明。

and human minds generate art and literature and poetry and theater,

人腦能創作藝術、文學、寫詩和戲劇。

and human minds take care of other humans:

人腦可照顧其他人，

our old, our young, our sick.

包括年老的、年輕的、患病的，

We even heal them.

我們甚至能治癒他們。

In the years to come, we're going to see technological innovations

在未來，我們將會看到 超乎現在能想像的科技發展，

beyond anything I can even envision,

but we are very unlikely

但在我或你們的一生中， 我們不太可能目睹比得上嬰兒運算能力的機器。

to see anything even approximating the computational power of a human child

in my lifetime or in yours.

If we invest in these most powerful learners and their development,

假如我們投資在最厲害的學習者和其發展身上，

in babies and children

在嬰兒和兒童身上、

and mothers and fathers

在母親和父親身上、

and caregivers and teachers

在照顧者和老師身上，

the ways we invest in our other most powerful and elegant forms

如同我們投資在最厲害的科技、工程和設計上時，

of technology, engineering and design,

we will not just be dreaming of a better future,

我們不只是夢想有個更好的將來，

we will be planning for one.

而是在計劃一個更好的將來。

Thank you very much.

謝謝。

(Applause)

(掌聲)

Chris Anderson: Laura, thank you. I do actually have a question for you.

克里斯·安德森: 羅拉, 謝謝你， 我其實想問你一個問題。

First of all, the research is insane.

首先，這項研究真是太瘋狂了。

I mean, who would design an experiment like that? (Laughter)

我的意思是，有誰會想到這些實驗? (笑聲)

I've seen that a couple of times,

我見過很多類似的實驗，

and I still don't honestly believe that that can truly be happening,

但我仍然覺得難以置信，

but other people have done similar experiments; it checks out.

儘管很多人做了類似的實驗，而事實的確如此。

The babies really are that genius.

這些嬰兒根本是天才。

LS: You know, they look really impressive in our experiments,

羅拉: 在實驗中這看似很神奇，

but think about what they look like in real life, right?

但想想在現實生活中是怎樣的，對嗎?

It starts out as a baby.

一出世時，他只是個嬰兒，

Eighteen months later, it's talking to you,

但18個月後他開始說話，

and babies' first words aren't just things like balls and ducks,

而嬰兒最初說的話不只是物件， 如皮球和鴨子，

they're things like "all gone," which refer to disappearance,

他們更能表達「不見了」的概念，

or "uh-oh," which refer to unintentional actions.

又或是以「哎喲」表達無心之失。

It has to be that powerful.

這必須是那麼厲害的，

It has to be much more powerful than anything I showed you.

這必須比我剛才展示的還要厲害。

They're figuring out the entire world.

嬰兒在弄清楚整個世界，

A four-year-old can talk to you about almost anything.

一個四歲的小孩幾乎懂得說所有東西。

(Applause)

(掌聲)

CA: And if I understand you right, the other key point you're making is,

克里斯: 如果我沒錯的話， 你想指出的另一個重點是，

we've been through these years where there's all this talk

這些年來，我們都聽說 我們的腦袋是不可信和會出錯的，

of how quirky and buggy our minds are,

that behavioral economics and the whole theories behind that

行為經濟學和其他新理論都指出我們不是理性的。

that we're not rational agents.

You're really saying that the bigger story is how extraordinary,

但你指出了我們的腦袋是非凡的，

and there really is genius there that is underappreciated.

我們一直忽略了我們的腦袋是多麼神奇。

LS: One of my favorite quotes in psychology

羅拉: 我最喜愛的心理學名言之一，

comes from the social psychologist Solomon Asch,

來自社會心理學家所羅門·阿希，

and he said the fundamental task of psychology is to remove

他說心理學首要的任務是 去除那些毋需証明的事物的面紗。

the veil of self-evidence from things.

There are orders of magnitude more decisions you make every day

每天你作出大大小小的決定去理解這個世界。

that get the world right.

You know about objects and their properties.

你知道不同物件及其特性，

You know them when they're occluded. You know them in the dark.

即使被覆蓋和在黑暗中你也知道。

You can walk through rooms.

你能在空間中行走。

You can figure out what other people are thinking. You can talk to them.

你能猜到別人在想甚麼，你能和別人交談。

你能探索空間，你明白數字。

You can navigate space. You know about numbers.

你明白因果關係， 你懂得分辨是非。

You know causal relationships. You know about moral reasoning.

你毫不費力便能做到， 所以我們不會察覺，

You do this effortlessly, so we don't see it,

但這就是我們理解這個世界的方法，

but that is how we get the world right, and it's a remarkable

這是個神奇而又難以理解的成就。

and very difficult-to-understand accomplishment.

克里斯: 我相信在坐有人認為科技正急速發展，

CA: I suspect there are people in the audience who have

this view of accelerating technological power

他們可能不認同你說電腦 不能做到三歲小孩能做到的事。

who might dispute your statement that never in our lifetimes

will a computer do what a three-year-old child can do,

但可以肯定的是，無論在甚麼場合，

but what's clear is that in any scenario,

our machines have so much to learn from our toddlers.

嬰兒有很多地方值得我們的機器學習。

LS: I think so. You'll have some machine learning folks up here.

羅拉: 我同意。有些人認同機器學習。

I mean, you should never bet against babies or chimpanzees

我的意思是，你不應將嬰兒和黑猩猩跟科技比較，

or technology as a matter of practice,

but it's not just a difference in quantity,

因為這不是數量上的不同，

it's a difference in kind.

而是性質上的不同。

We have incredibly powerful computers,

我們有十分厲害的電腦，

and they do do amazingly sophisticated things,

它們能做到複雜的事情，

often with very big amounts of data.

和處理大量的資料。

Human minds do, I think, something quite different,

我認為人類的腦袋做的事是不同的，

and I think it's the structured, hierarchical nature of human knowledge

人類的知識是有系統和條理分明的，

that remains a real challenge.

這對機器仍然是一個挑戰。

CA: Laura Schulz, wonderful food for thought. Thank you so much.

克里斯: 勞拉·舒爾茨，十分精彩。謝謝。

LS: Thank you. (Applause)

羅拉: 謝謝。 (掌聲)