it's very difficult to find a black cat in a dark room,

「在一片漆黑的房間裡，是很難找出一隻黑貓的，

especially when there is no cat.

特別當房間裡根本沒有貓的時候。」

I find this a particularly apt description of science

我覺得將這句話用來形容科學

and how science works --

和科學運作的方式，是非常貼切的。

bumbling around in a dark room, bumping into things,

科學探索就像在漆黑的房間裡亂竄， 然後撞到了某些東西，

trying to figure out what shape this might be,

試圖了解這個東西是什麼形態，

what that might be,

那個東西又可能是什麼。

there are reports of a cat somewhere around,

有報告說一隻貓就在附近，

they may not be reliable, they may be,

這消息可能不是真的，也可能是真的，

and so forth and so on.

就這樣反反覆覆。

Now I know this is different than the way most people

這樣的說法跟大多數人

think about science.

對科學的印象不一樣。

Science, we generally are told,

一般我們對「科學」的理解，

is a very well-ordered mechanism for

就是一套高度秩序化的機制，

understanding the world,

用以解釋世界的種種現象，

for gaining facts, for gaining data,

得到事實和數據。

that it's rule-based,

一切都有規則，

that scientists use this thing called the scientific method

科學家們運用「科學方法」做研究，

and we've been doing this for 14 generations or so now,

至今已有約14代人 (420年)，

and the scientific method is a set of rules

而「科學方法」就是「一套規則，

for getting hard, cold facts out of the data.

用來從數據中得到客觀確鑿的事實。」

I'd like to tell you that's not the case.

這裡我告訴大家，並不是這麼回事。

So there's the scientific method,

「科學方法」是存在的，

but what's really going on is this. (Laughter)

但實際發生的事情是…...(笑聲)

[The Scientific Method vs. Farting Around]

[科學方法 vs 狗屁瞎扯]

And it's going on kind of like that.

實際的狀況大概像這樣：

[... in the dark] (Laughter)

[.....在黑暗中放狗屁](笑聲)

So what is the difference, then,

所以，差別在哪裡呢？

between the way I believe science is pursued

我所相信的科學真諦，

and the way it seems to be perceived?

為何與科學在人們心目中的印象如此不同？

So this difference first came to me in some ways

我第一次意識到兩者的差異，

in my dual role at Columbia University,

是在哥倫比亞大學身兼兩職的時候。

where I'm both a professor and run a laboratory in neuroscience

我當時既當教授， 也主持神經科學的實驗室研究，

where we try to figure out how the brain works.

研究目的是找出腦部運作的原理。

We do this by studying the sense of smell,

我們的實驗室以研究氣味感知

the sense of olfaction, and in the laboratory,

和人類嗅覺為切入點。在實驗室，

it's a great pleasure and fascinating work

這可是非常吸引人而有趣的工作，

and exciting to work with graduate students and post-docs

我很高興能與那些 碩士研究生和博士後共事，

and think up cool experiments to understand how this

一起設計有趣的實驗方法 來去瞭解嗅覺如何運作，

sense of smell works and how the brain might be working,

以及去瞭解大腦相應地如何運作。

and, well, frankly, it's kind of exhilarating.

老實說，這项研究相當振奮我心。

But at the same time, it's my responsibility

但同時我也身兼教職，

to teach a large course to undergraduates on the brain,

我要教本科生關於腦科學的一門大課，

and that's a big subject,

這可是個大工程，

and it takes quite a while to organize that,

我花了很多工夫設計課程內容，

and it's quite challenging and it's quite interesting,

是個很有挑戰性也很有趣的工作。

but I have to say, it's not so exhilarating.

但我得說，設計課程並沒有為我帶來振奮感。

So what was the difference?

為什麼呢？差別在哪？

Well, the course I was and am teaching

那時到現在我一直在教的這門課，

is called Cellular and Molecular Neuroscience - I. (Laughs)

叫做「細胞和分子神經學」——壹。(笑聲)

It's 25 lectures full of all sorts of facts,

25堂課，介紹各種研究結果，

it uses this giant book called "Principles of Neural Science"

教材是這本鴻篇巨制：「神經科學原理」，

by three famous neuroscientists.

由三位著名的神經科學家共同編撰。

This book comes in at 1,414 pages,

全書共1414頁，

it weighs a hefty seven and a half pounds.

重達7.6英磅，

Just to put that in some perspective,

給大家一個概念，

that's the weight of two normal human brains.

這本書的重量相當於兩個正常人類的大腦。

(Laughter)

(笑聲)

So I began to realize, by the end of this course,

於是我開始意識到， 當學生們修完了這門課，

that the students maybe were getting the idea

他們也許會認為，

that we must know everything there is to know about the brain.

要瞭解大腦， 得先把現有知識全吸收盡才行。

That's clearly not true.

這想法顯然是不對的。

And they must also have this idea, I suppose,

我猜他們一定也有這個想法，

that what scientists do is collect data and collect facts

科學家的工作就只是收集數據和事實，

and stick them in these big books.

再訂到這樣的厚重教科書裡。

And that's not really the case either.

這同樣也不是事實。

When I go to a meeting, after the meeting day is over

我去參加研討會，會議結束之後，

and we collect in the bar over a couple of beers with my colleagues,

我和同事們一起 聚在酒吧裡喝上幾瓶啤酒，

we never talk about what we know.

我們談論的話題， 從來就不是已知的研究成果，

We talk about what we don't know.

而是那些我們還不知道的東西。

We talk about what still has to get done,

我們討論還有什麼問題需要研究，

what's so critical to get done in the lab.

什麼是實驗室下一步的重點工作。

Indeed, this was, I think, best said by Marie Curie

事實上，我認為，居里夫人給出了最好的詮釋：

who said that one never notices what has been done

「不應該只著眼於自己完成了什麼，

but only what remains to be done.

而應該看到還有什麼需要完成。」

This was in a letter to her brother after obtaining

這句話出自居里夫人寫給哥哥的信中，

her second graduate degree, I should say.

那時她剛拿到第二個碩士學位。

I have to point out this has always been one of my favorite pictures of Marie Curie,

我要指出，這一直是 我最喜愛的居里夫人的照片之一。

because I am convinced that that glow behind her

原因是，我確信她身後的光芒

is not a photographic effect. (Laughter)

不是電腦特效。(笑聲)

That's the real thing.

那一定是真的在發光。

It is true that her papers are, to this day,

居里夫人的手稿，直到現在都

stored in a basement room in the Bibliothèque Française

還保存在法國國家圖書館的地下貯藏室裡。

in a concrete room that's lead-lined,

貯藏室的牆壁以水泥砌成， 中間埋鉛以防輻射。

and if you're a scholar and you want access to these notebooks,

如果你以學者的身份申請查閱這些筆記，

you have to put on a full radiation hazmat suit,

就得先穿上全套的輻射防護服，

so it's pretty scary business.

這是頗嚇人的過程。

Nonetheless, this is what I think we were leaving out

不過，我認為她的精神恰恰是

of our courses

我們的課程所欠缺的，

and leaving out of the interaction that we have

也是我們這些科學家 在與大眾互動時所欠缺的，

with the public as scientists, the what-remains-to-be-done.

即「還有什麼需要完成」。

This is the stuff that's exhilarating and interesting.

這是令人振奮和有趣的東西。

It is, if you will, the ignorance.

如果你願意，可以叫它「無知」。

That's what was missing.

這就是我們目前欠缺的。

So I thought, well, maybe I should teach a course

於是我想，或許我應該開一門課

on ignorance,

來討論「無知」，

something I can finally excel at, perhaps, for example.

或許，這才是我真正擅長的。

So I did start teaching this course on ignorance,

於是我真的去開了這門討論「無知」的課，

and it's been quite interesting

得到很有趣的結果。

and I'd like to tell you to go to the website.

我架設了網站，大家可以去看看，

You can find all sorts of information there. It's wide open.

你能在網站裡找到各式各樣的資訊， 它是完全開放的。

And it's been really quite an interesting time for me

我很享受在網站上

to meet up with other scientists who come in and talk

和其他科學家一起切磋

about what it is they don't know.

討論這些未知的、等待探索的領域。

Now I use this word "ignorance," of course,

當然，我現在使用「無知」這個詞，

to be at least in part intentionally provocative,

聽起來好像有些惡意挑釁的意味，

because ignorance has a lot of bad connotations

因為「無知」有很多負面意思，

and I clearly don't mean any of those.

但它們都不是我的本意。

So I don't mean stupidity, I don't mean a callow indifference

我指的不是愚笨，

to fact or reason or data.

也並非是指冷漠看待事實、推理或數據。

The ignorant are clearly unenlightened, unaware,

這種「無知」是未被啟蒙的，沒意識到的，

uninformed, and present company today excepted,

不接收資訊，像今日大家認為的大公司

often occupy elected offices, it seems to me.

裡頭坐滿我們選出的官員，我是這麼想的。

That's another story, perhaps.

這大概又是另一個議題了。

I mean a different kind of ignorance.

我所指的「無知」是另一種意義的無知。

I mean a kind of ignorance that's less pejorative,

它不包含那麼多的負面意義，

a kind of ignorance that comes from a communal gap in our knowledge,

而是說我們在知識上共同的差距，

something that's just not there to be known

一些我們還沒有瞭解的東西，

or isn't known well enough yet or we can't make predictions from,

或者瞭解得還不夠的東西， 或者我們無法預知的東西。

the kind of ignorance that's maybe best summed up

用一言以蔽之，

in a statement by James Clerk Maxwell,

這句話是詹姆士‧克拉克‧麥斯威爾說的，

perhaps the greatest physicist between Newton and Einstein,

他大概是牛頓和愛因斯坦之間 最偉大的物理學家，

who said, "Thoroughly conscious ignorance

他說過：「完全自覺自醒的無知

is the prelude to every real advance in science."

是每一次科學的實質性進步的前奏。」

I think it's a wonderful idea:

我認為他提出了很棒的看法：

thoroughly conscious ignorance.

「完全自覺自醒的無知」

So that's the kind of ignorance that I want to talk about today,

也是我今天要探討的「無知」。

but of course the first thing we have to clear up

不過首先我們得弄清楚

is what are we going to do with all those facts?

該如何對待現有的研究成果？

So it is true that science piles up at an alarming rate.

各式各樣的科學研究成果 以驚人的速率被提出，

We all have this sense that science is this mountain of facts,

讓我們覺得科學似乎 就等於這座研究成果堆成的高山。

this accumulation model of science, as many have called it,

科學的這種積累模式，就象很多人說的，

and it seems impregnable, it seems impossible.

它似乎堅不可摧，也似乎不可企及

How can you ever know all of this?

一個人怎麼能完全瞭解這裡頭所有的知識？

And indeed, the scientific literature grows at an alarming rate.

事實上，科學文獻在以驚人的速度增長。

In 2006, there were 1.3 million papers published.

2006年發表的科學論文總計130萬篇，

There's about a two-and-a-half-percent yearly growth rate,

年增長率約2.5%。

and so last year we saw over one and a half million papers being published.

去年，我們看到有150萬篇論文發表，

Divide that by the number of minutes in a year,

這個數值除以一年的總分鐘數，

and you wind up with three new papers per minute.

意味著每分鐘就有三篇論文發表。

So I've been up here a little over 10 minutes,

我站在這裡超過十分鐘了，

I've already lost three papers.

已經錯過了三篇論文沒讀 (*講者計算有誤 他會錯過三十篇)

I have to get out of here actually. I have to go read.

我得離開這裡，趕緊去讀那些論文呢。

So what do we do about this? Well, the fact is

我們拿這些研究成果怎麼辦呢？事實上，

that what scientists do about it is a kind of a controlled neglect, if you will.

科學家的工作也是 某種程度的控制下的忽視。

We just don't worry about it, in a way.

可以說，我們根本不去操這份心。

The facts are important. You have to know a lot of stuff

研究成果固然重要，你要知道很多東西，

to be a scientist. That's true.

才能成為科學家，這點沒錯。

But knowing a lot of stuff doesn't make you a scientist.

但知識淵博並不能使你成為科學家。

You need to know a lot of stuff to be a lawyer

要作律師也得掌握很多知識，

or an accountant or an electrician or a carpenter.

作會計師、電工、木匠亦然。

But in science, knowing a lot of stuff is not the point.

在科學領域裡，知識淵博並不是重點。

Knowing a lot of stuff is there to help you get

知道的多是為了讓你

to more ignorance.

更好地去探索「無知」。

So knowledge is a big subject, but I would say

我要說，知識是個重要的議題，

ignorance is a bigger one.

但「無知」更為重要。

So this leads us to maybe think about, a little bit

這或許能讓我們想到，多多少少

about, some of the models of science that we tend to use,

想到一些常用來類比科學的模型。

and I'd like to disabuse you of some of them.

我要糾正你們對這些模型的錯誤看法。

So one of them, a popular one, is that scientists

當中一個很受歡迎的理論是，

are patiently putting the pieces of a puzzle together

科學家們將一片片拼圖耐心組合，

to reveal some grand scheme or another.

去揭示一個又一個重大的發現。

This is clearly not true. For one, with puzzles,

這顯然不是那麼回事。首先，說到拼圖，

the manufacturer has guaranteed that there's a solution.

廠家能保證你一定能做出完整的圖案。

We don't have any such guarantee.

而我們對科學研究卻沒法打保票。

Indeed, there are many of us who aren't so sure about the manufacturer.

事實上，我們中的很多人對廠家也不太有信心。

(Laughter)

(笑聲)

So I think the puzzle model doesn't work.

所以我認為拼圖模型是說不通的。

Another popular model is that science is busy unraveling things

另一個受歡迎的模型是， 科學就是忙著解開層層謎題，

the way you unravel the peels of an onion.

就像剝洋蔥一樣。

So peel by peel, you take away the layers of the onion

一層接著一層，你剝開洋蔥的皮，

to get at some fundamental kernel of truth.

最後得到核心真相。

I don't think that's the way it works either.

我也不認為科學是這樣運作的。

Another one, a kind of popular one, is the iceberg idea,

另一種理論，也蠻有名的，就是冰山模型：

that we only see the tip of the iceberg but underneath

我們所見只是冰山一角，

is where most of the iceberg is hidden.

水面之下隱藏的冰山才占絕大部分。

But all of these models are based on the idea of a large body of facts

這些模型都基於同一個理念， 即存在一個龐大的知識體系，

that we can somehow or another get completed.

我們能夠通過這樣那樣的方法使之完善。

We can chip away at this iceberg and figure out what it is,

我們可以鏟開冰山，去研究它究竟是怎麼回事，

or we could just wait for it to melt, I suppose, these days,

或者以現今的氣候，等它融化就好。

but one way or another we could get to the whole iceberg. Right?

但不論如何我們都能看透冰山的全貌，對吧？

Or make it manageable. But I don't think that's the case.

或讓它變得可控。但我不這麼認為。

I think what really happens in science

我認為科學真正的模型

is a model more like the magic well,

更接近一座魔法水井，

where no matter how many buckets you take out,

不論你從井中打了多少桶水，

there's always another bucket of water to be had,

都還能再打出一桶。

or my particularly favorite one,

還有一個我特別鍾愛的模型，

with the effect and everything, the ripples on a pond.

考慮到種種影響和元素，科學就像是池塘裡的漣漪。

So if you think of knowledge being this ever-expanding ripple on a pond,

如果把知識比作池塘裡不斷漾開的漣漪，

the important thing to realize is that our ignorance,

那麼重要的是要意識到我們的「無知」，

the circumference of this knowledge, also grows with knowledge.

就像漣漪的圓周長一樣， 隨著知識的擴大而不斷擴展。

So the knowledge generates ignorance.

知識產生「無知」。

This is really well said, I thought, by George Bernard Shaw.

蕭伯納說過一句很棒的話，

This is actually part of a toast that he delivered

他在慶祝愛因斯坦工作成績的晚宴上

to celebrate Einstein at a dinner celebrating Einstein's work,

為愛因斯坦致祝酒詞，

in which he claims that science

他認為，與其說科學在解決問題，

just creates more questions than it answers. ["Science is always wrong. It never solves a problem without creating 10 more."]

不如說是在製造問題。 [科學總是錯的。每當解決了一個問題，它總是製造出十個新的問題。]

I find that kind of glorious, and I think he's precisely right,

我覺得這真是至理名言了。 他說的一點沒錯。

plus it's a kind of job security.

這也說明了我們永遠不會失業。

As it turns out, he kind of cribbed that

後來發現，這可能是借鑒了

from the philosopher Immanuel Kant

哲學家康德的理念。

who a hundred years earlier had come up with this idea

早在一百年前， 康德就提出了「問題相生」的概念，

of question propagation, that every answer begets more questions.

每個答案都會帶來更多的問題。

I love that term, "question propagation,"

我喜歡「 問題相生」這個術語，

this idea of questions propagating out there.

這個「問題會衍生問題」的概念。

So I'd say the model we want to take is not

所以我要說，我們想採用的模型，並不是

that we start out kind of ignorant and we get some facts together

要從無知開始，共同找到一些現象，

and then we gain knowledge.

然後獲得獲得某種知識。

It's rather kind of the other way around, really.

實際情況正好相反。

What do we use this knowledge for?

現有的知識有什麼用？

What are we using this collection of facts for?

至今收集到的事實有什麼用？

We're using it to make better ignorance,

我們要用它們來得到更好的「無知」，

to come up with, if you will, higher-quality ignorance.

得到「高品質的無知」。

Because, you know, there's low-quality ignorance

因為有低品質的無知，

and there's high-quality ignorance. It's not all the same.

相對也有高品質的，兩者並不相同。

Scientists argue about this all the time.

科學家們總是為此爭論。

Sometimes we call them bull sessions.

有時我們稱它為鬥牛大會，

Sometimes we call them grant proposals.

有時我們稱它為申請研究基金。

But nonetheless, it's what the argument is about.

無論是哪個，我們爭論的點都是相同的，

It's the ignorance. It's the what we don't know.

那就是「無知」，什麼是我們不知道的，

It's what makes a good question.

怎樣才是一個好問題。

So how do we think about these questions?

我們又怎麼看待這些問題呢？

I'm going to show you a graph that shows up

給大家看一張圖，

quite a bit on happy hour posters in various science departments.

它經常被各個科學部門用來做聚會的海報。

This graph asks the relationship between what you know

這個圖表探討「你知道什麼」和「你瞭解多少」

and how much you know about it.

兩者之間的關係。