TED2013
Stuart Firestein: The pursuit of ignorance
斯圖爾特·法爾斯坦: 對無知的追求
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真正的科學工作是什麼樣的？神經科學家斯圖爾特·法爾斯坦打趣說：它看起來不大像科學方法，比較像「在黑暗中……四處放屁」。在這個詼諧的演講裡，法爾斯坦通過科學研究的真實情況揭示了科學的真諦，並提議，我們更應該重視我們所不知道的東西——或者是「高品質的無知」；就像我們重視已掌握的知識一樣。
Stuart Firestein - Neuroscientist
Stuart Firestein teaches students and “citizen scientists” that ignorance is far more important to discovery than knowledge. Full bio
Stuart Firestein teaches students and “citizen scientists” that ignorance is far more important to discovery than knowledge. Full bio
Double-click the English transcript below to play the video.
00:12
There is an ancient proverb that says
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有句古老的諺語這麼說：
00:16
it's very difficult to find a black cat in a dark room,
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「在一片漆黑的房間裡，是很難找出一隻黑貓的，
00:20
especially when there is no cat.
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特別當房間裡根本沒有貓的時候。」
00:22
I find this a particularly apt description of science
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我覺得將這句話用來形容科學
00:26
and how science works --
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和科學運作的方式，是非常貼切的。
00:28
bumbling around in a dark room, bumping into things,
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科學探索就像在漆黑的房間裡亂竄，
然後撞到了某些東西，
然後撞到了某些東西，
00:31
trying to figure out what shape this might be,
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試圖了解這個東西是什麼形態，
00:33
what that might be,
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那個東西又可能是什麼。
00:35
there are reports of a cat somewhere around,
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有報告說一隻貓就在附近，
00:37
they may not be reliable, they may be,
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這消息可能不是真的，也可能是真的，
00:39
and so forth and so on.
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就這樣反反覆覆。
00:41
Now I know this is different than the way most people
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這樣的說法跟大多數人
00:43
think about science.
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對科學的印象不一樣。
00:44
Science, we generally are told,
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一般我們對「科學」的理解，
00:46
is a very well-ordered mechanism for
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就是一套高度秩序化的機制，
00:49
understanding the world,
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用以解釋世界的種種現象，
00:50
for gaining facts, for gaining data,
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得到事實和數據。
00:52
that it's rule-based,
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一切都有規則，
00:54
that scientists use this thing called the scientific method
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科學家們運用「科學方法」做研究，
00:57
and we've been doing this for 14 generations or so now,
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至今已有約14代人 (420年)，
01:00
and the scientific method is a set of rules
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而「科學方法」就是「一套規則，
01:02
for getting hard, cold facts out of the data.
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用來從數據中得到客觀確鑿的事實。」
01:07
I'd like to tell you that's not the case.
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這裡我告訴大家，並不是這麼回事。
01:09
So there's the scientific method,
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「科學方法」是存在的，
01:10
but what's really going on is this. (Laughter)
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但實際發生的事情是…...(笑聲)
01:13
[The Scientific Method vs. Farting Around]
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[科學方法 vs 狗屁瞎扯]
01:14
And it's going on kind of like that.
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實際的狀況大概像這樣：
01:17
[... in the dark] (Laughter)
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[.....在黑暗中放狗屁](笑聲)
01:18
So what is the difference, then,
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所以，差別在哪裡呢？
01:23
between the way I believe science is pursued
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我所相信的科學真諦，
01:27
and the way it seems to be perceived?
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為何與科學在人們心目中的印象如此不同？
01:29
So this difference first came to me in some ways
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我第一次意識到兩者的差異，
01:32
in my dual role at Columbia University,
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是在哥倫比亞大學身兼兩職的時候。
01:34
where I'm both a professor and run a laboratory in neuroscience
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我當時既當教授，
也主持神經科學的實驗室研究，
也主持神經科學的實驗室研究，
01:38
where we try to figure out how the brain works.
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研究目的是找出腦部運作的原理。
01:41
We do this by studying the sense of smell,
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我們的實驗室以研究氣味感知
01:43
the sense of olfaction, and in the laboratory,
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和人類嗅覺為切入點。在實驗室，
01:46
it's a great pleasure and fascinating work
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這可是非常吸引人而有趣的工作，
01:48
and exciting to work with graduate students and post-docs
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我很高興能與那些
碩士研究生和博士後共事，
碩士研究生和博士後共事，
01:51
and think up cool experiments to understand how this
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一起設計有趣的實驗方法
來去瞭解嗅覺如何運作，
來去瞭解嗅覺如何運作，
01:54
sense of smell works and how the brain might be working,
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以及去瞭解大腦相應地如何運作。
01:56
and, well, frankly, it's kind of exhilarating.
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老實說，這项研究相當振奮我心。
01:59
But at the same time, it's my responsibility
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但同時我也身兼教職，
02:02
to teach a large course to undergraduates on the brain,
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我要教本科生關於腦科學的一門大課，
02:05
and that's a big subject,
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這可是個大工程，
02:06
and it takes quite a while to organize that,
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我花了很多工夫設計課程內容，
02:08
and it's quite challenging and it's quite interesting,
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是個很有挑戰性也很有趣的工作。
02:11
but I have to say, it's not so exhilarating.
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但我得說，設計課程並沒有為我帶來振奮感。
02:14
So what was the difference?
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為什麼呢？差別在哪？
02:16
Well, the course I was and am teaching
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那時到現在我一直在教的這門課，
02:18
is called Cellular and Molecular Neuroscience - I. (Laughs)
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叫做「細胞和分子神經學」——壹。(笑聲)
02:24
It's 25 lectures full of all sorts of facts,
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25堂課，介紹各種研究結果，
02:29
it uses this giant book called "Principles of Neural Science"
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教材是這本鴻篇巨制：「神經科學原理」，
02:33
by three famous neuroscientists.
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由三位著名的神經科學家共同編撰。
02:36
This book comes in at 1,414 pages,
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全書共1414頁，
02:39
it weighs a hefty seven and a half pounds.
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重達7.6英磅，
02:42
Just to put that in some perspective,
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給大家一個概念，
02:44
that's the weight of two normal human brains.
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這本書的重量相當於兩個正常人類的大腦。
02:47
(Laughter)
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(笑聲)
02:51
So I began to realize, by the end of this course,
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於是我開始意識到，
當學生們修完了這門課，
當學生們修完了這門課，
02:54
that the students maybe were getting the idea
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他們也許會認為，
02:56
that we must know everything there is to know about the brain.
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要瞭解大腦，
得先把現有知識全吸收盡才行。
得先把現有知識全吸收盡才行。
02:59
That's clearly not true.
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這想法顯然是不對的。
03:01
And they must also have this idea, I suppose,
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我猜他們一定也有這個想法，
03:04
that what scientists do is collect data and collect facts
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科學家的工作就只是收集數據和事實，
03:07
and stick them in these big books.
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再訂到這樣的厚重教科書裡。
03:09
And that's not really the case either.
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這同樣也不是事實。
03:11
When I go to a meeting, after the meeting day is over
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我去參加研討會，會議結束之後，
03:14
and we collect in the bar over a couple of beers with my colleagues,
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我和同事們一起
聚在酒吧裡喝上幾瓶啤酒，
聚在酒吧裡喝上幾瓶啤酒，
03:17
we never talk about what we know.
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我們談論的話題，
從來就不是已知的研究成果，
從來就不是已知的研究成果，
03:19
We talk about what we don't know.
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而是那些我們還不知道的東西。
03:21
We talk about what still has to get done,
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我們討論還有什麼問題需要研究，
03:24
what's so critical to get done in the lab.
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什麼是實驗室下一步的重點工作。
03:26
Indeed, this was, I think, best said by Marie Curie
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事實上，我認為，居里夫人給出了最好的詮釋：
03:29
who said that one never notices what has been done
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「不應該只著眼於自己完成了什麼，
03:31
but only what remains to be done.
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而應該看到還有什麼需要完成。」
03:33
This was in a letter to her brother after obtaining
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這句話出自居里夫人寫給哥哥的信中，
03:35
her second graduate degree, I should say.
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那時她剛拿到第二個碩士學位。
03:39
I have to point out this has always been one of my favorite pictures of Marie Curie,
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我要指出，這一直是
我最喜愛的居里夫人的照片之一。
我最喜愛的居里夫人的照片之一。
03:42
because I am convinced that that glow behind her
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原因是，我確信她身後的光芒
03:44
is not a photographic effect. (Laughter)
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不是電腦特效。(笑聲)
03:47
That's the real thing.
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那一定是真的在發光。
03:48
It is true that her papers are, to this day,
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居里夫人的手稿，直到現在都
03:53
stored in a basement room in the Bibliothèque Française
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還保存在法國國家圖書館的地下貯藏室裡。
03:56
in a concrete room that's lead-lined,
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貯藏室的牆壁以水泥砌成，
中間埋鉛以防輻射。
中間埋鉛以防輻射。
03:58
and if you're a scholar and you want access to these notebooks,
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如果你以學者的身份申請查閱這些筆記，
04:01
you have to put on a full radiation hazmat suit,
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就得先穿上全套的輻射防護服，
04:03
so it's pretty scary business.
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這是頗嚇人的過程。
04:06
Nonetheless, this is what I think we were leaving out
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不過，我認為她的精神恰恰是
04:08
of our courses
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我們的課程所欠缺的，
04:10
and leaving out of the interaction that we have
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也是我們這些科學家
在與大眾互動時所欠缺的，
在與大眾互動時所欠缺的，
04:13
with the public as scientists, the what-remains-to-be-done.
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即「還有什麼需要完成」。
04:16
This is the stuff that's exhilarating and interesting.
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這是令人振奮和有趣的東西。
04:18
It is, if you will, the ignorance.
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如果你願意，可以叫它「無知」。
04:21
That's what was missing.
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這就是我們目前欠缺的。
04:22
So I thought, well, maybe I should teach a course
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於是我想，或許我應該開一門課
04:25
on ignorance,
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來討論「無知」，
04:27
something I can finally excel at, perhaps, for example.
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或許，這才是我真正擅長的。
04:31
So I did start teaching this course on ignorance,
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於是我真的去開了這門討論「無知」的課，
04:33
and it's been quite interesting
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得到很有趣的結果。
04:34
and I'd like to tell you to go to the website.
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我架設了網站，大家可以去看看，
04:36
You can find all sorts of information there. It's wide open.
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你能在網站裡找到各式各樣的資訊，
它是完全開放的。
它是完全開放的。
04:39
And it's been really quite an interesting time for me
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我很享受在網站上
04:43
to meet up with other scientists who come in and talk
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和其他科學家一起切磋
04:45
about what it is they don't know.
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討論這些未知的、等待探索的領域。
04:46
Now I use this word "ignorance," of course,
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當然，我現在使用「無知」這個詞，
04:48
to be at least in part intentionally provocative,
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聽起來好像有些惡意挑釁的意味，
04:51
because ignorance has a lot of bad connotations
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因為「無知」有很多負面意思，
04:54
and I clearly don't mean any of those.
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但它們都不是我的本意。
04:56
So I don't mean stupidity, I don't mean a callow indifference
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我指的不是愚笨，
04:59
to fact or reason or data.
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也並非是指冷漠看待事實、推理或數據。
05:02
The ignorant are clearly unenlightened, unaware,
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這種「無知」是未被啟蒙的，沒意識到的，
05:05
uninformed, and present company today excepted,
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不接收資訊，像今日大家認為的大公司
05:08
often occupy elected offices, it seems to me.
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裡頭坐滿我們選出的官員，我是這麼想的。
05:11
That's another story, perhaps.
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這大概又是另一個議題了。
05:13
I mean a different kind of ignorance.
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我所指的「無知」是另一種意義的無知。
05:15
I mean a kind of ignorance that's less pejorative,
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它不包含那麼多的負面意義，
05:17
a kind of ignorance that comes from a communal gap in our knowledge,
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而是說我們在知識上共同的差距，
05:20
something that's just not there to be known
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一些我們還沒有瞭解的東西，
05:22
or isn't known well enough yet or we can't make predictions from,
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或者瞭解得還不夠的東西，
或者我們無法預知的東西。
或者我們無法預知的東西。
05:25
the kind of ignorance that's maybe best summed up
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用一言以蔽之，
05:27
in a statement by James Clerk Maxwell,
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這句話是詹姆士‧克拉克‧麥斯威爾說的，
05:29
perhaps the greatest physicist between Newton and Einstein,
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他大概是牛頓和愛因斯坦之間
最偉大的物理學家，
最偉大的物理學家，
05:33
who said, "Thoroughly conscious ignorance
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他說過：「完全自覺自醒的無知
05:35
is the prelude to every real advance in science."
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是每一次科學的實質性進步的前奏。」
05:38
I think it's a wonderful idea:
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我認為他提出了很棒的看法：
05:39
thoroughly conscious ignorance.
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「完全自覺自醒的無知」
05:42
So that's the kind of ignorance that I want to talk about today,
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也是我今天要探討的「無知」。
05:44
but of course the first thing we have to clear up
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不過首先我們得弄清楚
05:46
is what are we going to do with all those facts?
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該如何對待現有的研究成果？
05:48
So it is true that science piles up at an alarming rate.
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各式各樣的科學研究成果
以驚人的速率被提出，
以驚人的速率被提出，
05:52
We all have this sense that science is this mountain of facts,
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讓我們覺得科學似乎
就等於這座研究成果堆成的高山。
就等於這座研究成果堆成的高山。
05:55
this accumulation model of science, as many have called it,
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科學的這種積累模式，就象很多人說的，
05:59
and it seems impregnable, it seems impossible.
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它似乎堅不可摧，也似乎不可企及
06:01
How can you ever know all of this?
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一個人怎麼能完全瞭解這裡頭所有的知識？
06:02
And indeed, the scientific literature grows at an alarming rate.
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事實上，科學文獻在以驚人的速度增長。
06:06
In 2006, there were 1.3 million papers published.
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2006年發表的科學論文總計130萬篇，
06:10
There's about a two-and-a-half-percent yearly growth rate,
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年增長率約2.5%。
06:12
and so last year we saw over one and a half million papers being published.
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去年，我們看到有150萬篇論文發表，
06:17
Divide that by the number of minutes in a year,
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這個數值除以一年的總分鐘數，
06:19
and you wind up with three new papers per minute.
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意味著每分鐘就有三篇論文發表。
06:22
So I've been up here a little over 10 minutes,
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我站在這裡超過十分鐘了，
06:23
I've already lost three papers.
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已經錯過了三篇論文沒讀
(*講者計算有誤 他會錯過三十篇)
(*講者計算有誤 他會錯過三十篇)
06:25
I have to get out of here actually. I have to go read.
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我得離開這裡，趕緊去讀那些論文呢。
06:28
So what do we do about this? Well, the fact is
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我們拿這些研究成果怎麼辦呢？事實上，
06:32
that what scientists do about it is a kind of a controlled neglect, if you will.
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科學家的工作也是
某種程度的控制下的忽視。
某種程度的控制下的忽視。
06:36
We just don't worry about it, in a way.
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可以說，我們根本不去操這份心。
06:39
The facts are important. You have to know a lot of stuff
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研究成果固然重要，你要知道很多東西，
06:41
to be a scientist. That's true.
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才能成為科學家，這點沒錯。
06:43
But knowing a lot of stuff doesn't make you a scientist.
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但知識淵博並不能使你成為科學家。
06:46
You need to know a lot of stuff to be a lawyer
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要作律師也得掌握很多知識，
06:48
or an accountant or an electrician or a carpenter.
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作會計師、電工、木匠亦然。
06:52
But in science, knowing a lot of stuff is not the point.
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在科學領域裡，知識淵博並不是重點。
06:56
Knowing a lot of stuff is there to help you get
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知道的多是為了讓你
06:59
to more ignorance.
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更好地去探索「無知」。
07:01
So knowledge is a big subject, but I would say
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我要說，知識是個重要的議題，
07:03
ignorance is a bigger one.
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但「無知」更為重要。
07:06
So this leads us to maybe think about, a little bit
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這或許能讓我們想到，多多少少
07:08
about, some of the models of science that we tend to use,
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想到一些常用來類比科學的模型。
07:11
and I'd like to disabuse you of some of them.
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我要糾正你們對這些模型的錯誤看法。
07:13
So one of them, a popular one, is that scientists
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當中一個很受歡迎的理論是，
07:15
are patiently putting the pieces of a puzzle together
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科學家們將一片片拼圖耐心組合，
07:18
to reveal some grand scheme or another.
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去揭示一個又一個重大的發現。
07:20
This is clearly not true. For one, with puzzles,
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這顯然不是那麼回事。首先，說到拼圖，
07:23
the manufacturer has guaranteed that there's a solution.
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廠家能保證你一定能做出完整的圖案。
07:27
We don't have any such guarantee.
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而我們對科學研究卻沒法打保票。
07:28
Indeed, there are many of us who aren't so sure about the manufacturer.
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事實上，我們中的很多人對廠家也不太有信心。
07:31
(Laughter)
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(笑聲)
07:34
So I think the puzzle model doesn't work.
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所以我認為拼圖模型是說不通的。
07:36
Another popular model is that science is busy unraveling things
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另一個受歡迎的模型是，
科學就是忙著解開層層謎題，
科學就是忙著解開層層謎題，
07:40
the way you unravel the peels of an onion.
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就像剝洋蔥一樣。
07:42
So peel by peel, you take away the layers of the onion
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一層接著一層，你剝開洋蔥的皮，
07:45
to get at some fundamental kernel of truth.
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最後得到核心真相。
07:47
I don't think that's the way it works either.
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我也不認為科學是這樣運作的。
07:49
Another one, a kind of popular one, is the iceberg idea,
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另一種理論，也蠻有名的，就是冰山模型：
07:52
that we only see the tip of the iceberg but underneath
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我們所見只是冰山一角，
07:55
is where most of the iceberg is hidden.
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水面之下隱藏的冰山才占絕大部分。
07:57
But all of these models are based on the idea of a large body of facts
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這些模型都基於同一個理念，
即存在一個龐大的知識體系，
即存在一個龐大的知識體系，
08:01
that we can somehow or another get completed.
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我們能夠通過這樣那樣的方法使之完善。
08:03
We can chip away at this iceberg and figure out what it is,
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我們可以鏟開冰山，去研究它究竟是怎麼回事，
08:06
or we could just wait for it to melt, I suppose, these days,
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或者以現今的氣候，等它融化就好。
08:09
but one way or another we could get to the whole iceberg. Right?
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但不論如何我們都能看透冰山的全貌，對吧？
08:12
Or make it manageable. But I don't think that's the case.
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或讓它變得可控。但我不這麼認為。
08:15
I think what really happens in science
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我認為科學真正的模型
08:17
is a model more like the magic well,
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更接近一座魔法水井，
08:19
where no matter how many buckets you take out,
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不論你從井中打了多少桶水，
08:21
there's always another bucket of water to be had,
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都還能再打出一桶。
08:23
or my particularly favorite one,
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還有一個我特別鍾愛的模型，
08:25
with the effect and everything, the ripples on a pond.
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考慮到種種影響和元素，科學就像是池塘裡的漣漪。
08:28
So if you think of knowledge being this ever-expanding ripple on a pond,
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如果把知識比作池塘裡不斷漾開的漣漪，
08:31
the important thing to realize is that our ignorance,
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那麼重要的是要意識到我們的「無知」，
08:34
the circumference of this knowledge, also grows with knowledge.
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就像漣漪的圓周長一樣，
隨著知識的擴大而不斷擴展。
隨著知識的擴大而不斷擴展。
08:38
So the knowledge generates ignorance.
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知識產生「無知」。
08:41
This is really well said, I thought, by George Bernard Shaw.
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蕭伯納說過一句很棒的話，
08:43
This is actually part of a toast that he delivered
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他在慶祝愛因斯坦工作成績的晚宴上
08:46
to celebrate Einstein at a dinner celebrating Einstein's work,
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為愛因斯坦致祝酒詞，
08:50
in which he claims that science
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他認為，與其說科學在解決問題，
08:51
just creates more questions than it answers.
["Science is always wrong. It never solves a problem without creating 10 more."]
["Science is always wrong. It never solves a problem without creating 10 more."]
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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。]
[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。]
08:53
I find that kind of glorious, and I think he's precisely right,
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我覺得這真是至理名言了。
他說的一點沒錯。
他說的一點沒錯。
08:57
plus it's a kind of job security.
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這也說明了我們永遠不會失業。
09:00
As it turns out, he kind of cribbed that
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後來發現，這可能是借鑒了
09:02
from the philosopher Immanuel Kant
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哲學家康德的理念。
09:04
who a hundred years earlier had come up with this idea
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早在一百年前，
康德就提出了「問題相生」的概念，
康德就提出了「問題相生」的概念，
09:07
of question propagation, that every answer begets more questions.
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每個答案都會帶來更多的問題。
09:11
I love that term, "question propagation,"
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我喜歡「 問題相生」這個術語，
09:13
this idea of questions propagating out there.
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這個「問題會衍生問題」的概念。
09:16
So I'd say the model we want to take is not
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所以我要說，我們想採用的模型，並不是
09:17
that we start out kind of ignorant and we get some facts together
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要從無知開始，共同找到一些現象，
09:21
and then we gain knowledge.
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然後獲得獲得某種知識。
09:23
It's rather kind of the other way around, really.
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實際情況正好相反。
09:25
What do we use this knowledge for?
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現有的知識有什麼用？
09:27
What are we using this collection of facts for?
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至今收集到的事實有什麼用？
09:30
We're using it to make better ignorance,
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我們要用它們來得到更好的「無知」，
09:33
to come up with, if you will, higher-quality ignorance.
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得到「高品質的無知」。
09:36
Because, you know, there's low-quality ignorance
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因為有低品質的無知，
09:38
and there's high-quality ignorance. It's not all the same.
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相對也有高品質的，兩者並不相同。
09:40
Scientists argue about this all the time.
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科學家們總是為此爭論。
09:42
Sometimes we call them bull sessions.
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有時我們稱它為鬥牛大會，
09:44
Sometimes we call them grant proposals.
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有時我們稱它為申請研究基金。
09:46
But nonetheless, it's what the argument is about.
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無論是哪個，我們爭論的點都是相同的，
09:50
It's the ignorance. It's the what we don't know.
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那就是「無知」，什麼是我們不知道的，
09:52
It's what makes a good question.
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怎樣才是一個好問題。
09:54
So how do we think about these questions?
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我們又怎麼看待這些問題呢？
09:56
I'm going to show you a graph that shows up
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給大家看一張圖，
09:58
quite a bit on happy hour posters in various science departments.
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它經常被各個科學部門用來做聚會的海報。
10:02
This graph asks the relationship between what you know
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這個圖表探討「你知道什麼」和「你瞭解多少」
10:06
and how much you know about it.
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兩者之間的關係。
10:08
So what you know, you can know anywhere from nothing to everything, of course,
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「你知道什麼」，你可以從一無所知到無所不知；
10:12
and how much you know about it can be anywhere
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「你瞭解多少」，則可以從只瞭解一點點
10:13
from a little to a lot.
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到瞭解很多。
10:16
So let's put a point on the graph. There's an undergraduate.
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讓我們在這張圖表上畫一個點，這是一名大學生。
10:20
Doesn't know much but they have a lot of interest.
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瞭解程度不高，但有很多的興趣。
10:22
They're interested in almost everything.
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他們幾乎對什麼事都感興趣。
10:24
Now you look at a master's student, a little further along in their education,
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現在來看一個碩士生，
因為他受教育的時間更長，
因為他受教育的時間更長，
10:28
and you see they know a bit more,
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所以他們瞭解程度更高，
10:29
but it's been narrowed somewhat.
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但知識面變窄了。
10:31
And finally you get your Ph.D., where it turns out
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接下來終於你拿到博士學位了，結果…
10:34
you know a tremendous amount about almost nothing. (Laughter)
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瞭解很深，但知識面近乎為零。（笑聲）
10:39
What's really disturbing is the trend line that goes through that
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令人困擾的是穿越這些點的趨勢線，
10:42
because, of course, when it dips below the zero axis, there,
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因為當它達到零以下，這個地方，
10:46
it gets into a negative area.
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它就進入了負值區域，
10:48
That's where you find people like me, I'm afraid.
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恐怕我這樣的人都在那兒了。
10:51
So the important thing here is that this can all be changed.
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不過，重要的是這都可以改變。
10:55
This whole view can be changed
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整個觀點可以變得截然不同，
10:57
by just changing the label on the x-axis.
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只要把 X 軸的標籤改掉就好了。
11:00
So instead of how much you know about it,
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我們把「你瞭解多少」的標籤
11:02
we could say, "What can you ask about it?"
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換成「你能問出什麼」。
11:05
So yes, you do need to know a lot of stuff as a scientist,
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當然，作為一名科學家確實需要知識淵博，
11:08
but the purpose of knowing a lot of stuff
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但吸收大量知識的目的
11:11
is not just to know a lot of stuff. That just makes you a geek, right?
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並不在於獲得各種知識，以致成為技客。
11:13
Knowing a lot of stuff, the purpose is
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吸收大量知識是為了
11:15
to be able to ask lots of questions,
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能提出很多問題，
11:17
to be able to frame thoughtful, interesting questions,
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能架構出深思熟慮的、有趣的問題，
11:20
because that's where the real work is.
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而才是真正的科學工作。
11:22
Let me give you a quick idea of a couple of these sorts of questions.
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我給大家舉兩個例子。
11:24
I'm a neuroscientist, so how would we come up
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我是一名神經科學家，
在神經學這個領域，
在神經學這個領域，
11:27
with a question in neuroscience?
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我們是如何提出問題的呢？
11:28
Because it's not always quite so straightforward.
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情況並不是總是直截了當的。
11:31
So, for example, we could say, well what is it that the brain does?
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比如，我們可以問，大腦到底起什麼作用？
11:33
Well, one thing the brain does, it moves us around.
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大腦的一項功能是指揮身體行動，
11:35
We walk around on two legs.
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讓我們以雙腳行走。
11:37
That seems kind of simple, somehow or another.
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這似乎太簡單了。
11:39
I mean, virtually everybody over 10 months of age
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幾乎每個年齡超過10個月的人
11:42
walks around on two legs, right?
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都能以雙腳行走，對吧？
11:44
So that maybe is not that interesting.
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所以說這個問題沒什麼意思。
11:45
So instead maybe we want to choose something a little more complicated to look at.
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所以我們可能會選擇
提出一些更複雜些的問題去研究。
提出一些更複雜些的問題去研究。
11:48
How about the visual system?
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視覺系統怎麼樣？
11:51
There it is, the visual system.
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好，就選視覺系統了。
11:53
I mean, we love our visual systems. We do all kinds of cool stuff.
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我們喜歡視覺系統，可以搞很酷的研究。
11:56
Indeed, there are over 12,000 neuroscientists
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事實上，有超過一萬兩千名神經學家
11:59
who work on the visual system,
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以視覺系統為研究對象，
12:01
from the retina to the visual cortex,
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從視網膜到視覺皮層，
12:03
in an attempt to understand not just the visual system
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這些研究不僅僅是局限在視覺系統，
12:06
but to also understand how general principles
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還包括如何通過視覺系統研究去瞭解
12:09
of how the brain might work.
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大腦是如何運作的普遍原理。
12:11
But now here's the thing:
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但目前的情況是：
12:12
Our technology has actually been pretty good
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我們現在擁有很好的
12:15
at replicating what the visual system does.
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複製視覺系統的技術。
12:17
We have TV, we have movies,
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我們有電視，我們有電影，
12:20
we have animation, we have photography,
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我們有動畫，我們有攝影，
12:23
we have pattern recognition, all of these sorts of things.
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我們有模型識別技術，
很多其他的這一類技術。
很多其他的這一類技術。
12:26
They work differently than our visual systems in some cases,
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有些視覺技術的工作原理
和視覺系統不大一樣。
和視覺系統不大一樣。
12:29
but nonetheless we've been pretty good at
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儘管如此，我們現有的視覺技術
12:30
making a technology work like our visual system.
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已經與視覺系統非常近似了。
12:34
Somehow or another, a hundred years of robotics,
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但是，機器人技術的發展已經有一百年了，
12:37
you never saw a robot walk on two legs,
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你還沒見過一個用兩條腿走路的機器人。
12:39
because robots don't walk on two legs
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因為機器人不是用兩條腿走路的，
12:41
because it's not such an easy thing to do.
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這可不是一件易事。
12:43
A hundred years of robotics,
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一百年的機器人技術發展，
12:45
and we can't get a robot that can move more than a couple steps one way or the other.
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我們甚至不能讓機器人走上一兩步。
12:48
You ask them to go up an inclined plane, and they fall over.
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你讓機器人走個斜面試試，它們肯定會摔倒。
12:51
Turn around, and they fall over. It's a serious problem.
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讓它們轉身，它們也會摔倒。
這是個科技上的難題。
這是個科技上的難題。
12:53
So what is it that's the most difficult thing for a brain to do?
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那麼，對大腦來說，
什麼是最難完成的任務呢？
什麼是最難完成的任務呢？
12:57
What ought we to be studying?
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我們必需要研究的是什麼？
12:58
Perhaps it ought to be walking on two legs, or the motor system.
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或許是研究以雙腳走路，或動力系統。
13:02
I'll give you an example from my own lab,
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我給你們舉個我自己實驗室的例子，
13:04
my own particularly smelly question,
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我的實驗小組研究嗅覺系統，
13:06
since we work on the sense of smell.
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於是設法找出嗅覺方面的問題。
13:08
But here's a diagram of five molecules
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這張圖裡有五個分子，
13:11
and sort of a chemical notation.
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和它們的化學式。
13:13
These are just plain old molecules, but if you sniff those molecules
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這都是些最普通的分子了，但如果你
13:16
up these two little holes in the front of your face,
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用你臉上這兩個小洞洞
來聞聞那些分子的話，
來聞聞那些分子的話，
13:18
you will have in your mind the distinct impression of a rose.
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你的腦海中會出現
一朵玫瑰的鮮明印象。
一朵玫瑰的鮮明印象。
13:22
If there's a real rose there, those molecules will be the ones,
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如果說真的有玫瑰的話，
那些分子就是「玫瑰」。
那些分子就是「玫瑰」。
13:24
but even if there's no rose there,
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但即使沒有玫瑰，
13:26
you'll have the memory of a molecule.
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你也會有關於這些分子的記憶。
13:27
How do we turn molecules into perceptions?
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我們怎麼將這些分子轉化為知覺？
13:30
What's the process by which that could happen?
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會發生什麼樣的轉變過程？
13:32
Here's another example: two very simple molecules, again in this kind of chemical notation.
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再舉一個例子，這是兩個簡單的分子化學式。
13:36
It might be easier to visualize them this way,
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或許這樣看比較容易想像，
13:38
so the gray circles are carbon atoms, the white ones
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灰色圓圈代表碳原子，
白色圓圈代表氫原子，
白色圓圈代表氫原子，
13:41
are hydrogen atoms and the red ones are oxygen atoms.
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紅色圓圈代表氧原子。
13:44
Now these two molecules differ by only one carbon atom
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那麼這兩個分子式的差別
就在於一個碳原子
就在於一個碳原子
13:48
and two little hydrogen atoms that ride along with it,
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和兩個與之相連的氫原子，
13:51
and yet one of them, heptyl acetate,
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其中一個分子叫乙酸庚酯
13:53
has the distinct odor of a pear,
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帶著特殊的梨的氣味。
13:55
and hexyl acetate is unmistakably banana.
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(另一個是)醋酸己酯，卻有一種明顯的香蕉氣味。
13:59
So there are two really interesting questions here, it seems to me.
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這裡我發現兩個有趣的問題
14:02
One is, how can a simple little molecule like that
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其一，如此一個簡單的小分子
14:05
create a perception in your brain that's so clear
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是如何在你的腦海裡
建立起如此清晰的認識
建立起如此清晰的認識
14:07
as a pear or a banana?
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讓你輕鬆辨別出一顆梨，或一條香蕉？
14:09
And secondly, how the hell can we tell the difference
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其二，為什麼我們能辨別出兩者的差異
14:12
between two molecules that differ by a single carbon atom?
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兩個分子僅僅只有一個碳原子鍵的不同而已。
14:16
I mean, that's remarkable to me,
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這是對我意義重大的發現，
14:18
clearly the best chemical detector on the face of the planet.
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地球上最精密的化學探測器，
顯然長在我們臉上。
顯然長在我們臉上。
14:21
And you don't even think about it, do you?
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你甚至從來都沒想過這些，對吧？
14:24
So this is a favorite quote of mine that takes us
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讓我用我喜愛的名言拉回主題
14:27
back to the ignorance and the idea of questions.
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「無知」和「提出問題」
14:28
I like to quote because I think dead people
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我喜愛引用名人名言，因為我覺得
14:30
shouldn't be excluded from the conversation.
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死者也應該參與這樣的討論。
14:33
And I also think it's important to realize that
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而我也認為有必要彰顯出
14:35
the conversation's been going on for a while, by the way.
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這個討論已經存在好一段時間了。
14:37
So Erwin Schrodinger, a great quantum physicist
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薛定諤，偉大的量子物理學家，
14:40
and, I think, philosopher, points out how you have to
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我覺得他也是哲學家，他指出你必須
14:43
"abide by ignorance for an indefinite period" of time.
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「保持無知，以面對浩瀚無垠的時間」
14:46
And it's this abiding by ignorance
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而我們要學習的課題，
14:48
that I think we have to learn how to do.
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就是怎麼「保持無知」。
14:50
This is a tricky thing. This is not such an easy business.
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這是個棘手的問題，並非易事。
14:53
I guess it comes down to our education system,
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我想得從我們的教育系統探討起，
14:55
so I'm going to talk a little bit about ignorance and education,
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這裡我談一點「無知」和教育間的關係，
14:57
because I think that's where it really has to play out.
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因為我認為必需教導「無知」的概念。
14:59
So for one, let's face it,
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首先，讓我們面對現實，
15:02
in the age of Google and Wikipedia,
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這是個 Google 和維基百科的時代，
15:05
the business model of the university
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大學的運營模式，
15:07
and probably secondary schools is simply going to have to change.
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甚至是我們的中學，
真的都需要一些實質的改變。
真的都需要一些實質的改變。
15:10
We just can't sell facts for a living anymore.
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我們真的不能光靠販賣「事實」為生了。
15:12
They're available with a click of the mouse,
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學生們動一動滑鼠就能得資訊，
15:14
or if you want to, you could probably just ask the wall
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如果你想，大概敲牆問一問也行。
15:17
one of these days, wherever they're going to hide the things
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現今社會中，不管你把東西藏在哪裡，
15:18
that tell us all this stuff.
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科技都能讓你無所遁形。
15:20
So what do we have to do? We have to give our students
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那我們得做什麼？我們得告訴我們的學生，
15:23
a taste for the boundaries, for what's outside that circumference,
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探索邊界的滋味，漣漪之外有什麼，
15:27
for what's outside the facts, what's just beyond the facts.
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事實之外是什麼，事實背後有什麼。
15:31
How do we do that?
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我們應該怎麼做？
15:33
Well, one of the problems, of course,
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當然，我們一定會遇到的困難之一
15:35
turns out to be testing.
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就是考試。
15:37
We currently have an educational system
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我們目前的教育體系
15:39
which is very efficient but is very efficient at a rather bad thing.
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很高效，但效率的指向並不好。
15:43
So in second grade, all the kids are interested in science,
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所有上二年級的孩子都對科學感興趣，
15:46
the girls and the boys.
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1263
無論女孩還是男孩，
15:47
They like to take stuff apart. They have great curiosity.
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都喜歡拆解東西來研究，好奇心強烈，
15:51
They like to investigate things. They go to science museums.
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喜歡做調查，參觀科學博物館，
15:54
They like to play around. They're in second grade.
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喜歡四處玩耍。這就是二年級生的情況，
16:00
They're interested.
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他們對什麼都感興趣。
16:01
But by 11th or 12th grade, fewer than 10 percent
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但到了高中二年級或三年級，只剩不到10%的學生
16:04
of them have any interest in science whatsoever,
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還對科學抱持興趣，
16:07
let alone a desire to go into science as a career.
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更別提想從事科學方面的工作了。
16:10
So we have this remarkably efficient system
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2982
我們有個極其高效的系統
16:13
for beating any interest in science out of everybody's head.
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3973
來打擊孩子們對於科學的興致。
16:17
Is this what we want?
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這是我們想要的嗎？
16:19
I think this comes from what a teacher colleague of mine
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2342
我的一位大學老師同事把這
16:22
calls "the bulimic method of education."
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叫做「填鴨式教育」
16:24
You know. You can imagine what it is.
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大家都知道，能想像出那是什麼情形。
16:26
We just jam a whole bunch of facts down their throats over here
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2948
我們只是在把一大堆事實
塞進他們的喉嚨裡，
塞進他們的喉嚨裡，
16:29
and then they puke it up on an exam over here
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然後在考試的時候讓他們吐出來，
16:31
and everybody goes home with no added intellectual heft whatsoever.
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沒有一個學生真正帶著知識回家。
16:36
This can't possibly continue to go on.
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2081
我們不能這樣繼續下去了。
16:38
So what do we do? Well the geneticists, I have to say,
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2334
那我們該怎麼辦？我得說，遺傳學家
16:40
have an interesting maxim they live by.
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1983
他們中流傳著很有趣的格言。
16:42
Geneticists always say, you always get what you screen for.
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遺傳學家總說：「你總能得到想要篩選出來的結果。」
16:47
And that's meant as a warning.
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我們可以把這句話當成警告。
16:50
So we always will get what we screen for,
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2319
我們總能得到想要篩選出來的結果。
16:52
and part of what we screen for is in our testing methods.
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3455
我們想要篩選出來的結果
部分存在於考試方法中。
部分存在於考試方法中。
16:56
Well, we hear a lot about testing and evaluation,
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3243
我們已經聽過太多的測試呀，評估呀，
16:59
and we have to think carefully when we're testing
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當我們實際去測試時，我們得想清楚
17:01
whether we're evaluating or whether we're weeding,
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是在做評估還是要做淘汰，
17:04
whether we're weeding people out,
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1459
是否在做淘汰，
17:06
whether we're making some cut.
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3134
是否在做精簡。
17:09
Evaluation is one thing. You hear a lot about evaluation
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2641
評估是一回事。近來在教育學的文獻中，
17:12
in the literature these days, in the educational literature,
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2910
有許多關於做評估的，
17:14
but evaluation really amounts to feedback and it amounts
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但評估其實意味著回饋，
17:17
to an opportunity for trial and error.
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意味著給試驗和犯錯提供機會。
17:20
It amounts to a chance to work over a longer period of time
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4494
它意味著在更長的期間裡，
17:24
with this kind of feedback.
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利用這些回饋的機會。
17:26
That's different than weeding, and usually, I have to tell you,
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這跟淘汰是不同的。我要告訴大家，通常
17:29
when people talk about evaluation, evaluating students,
395
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2726
當人們談到評估，評估學生，
17:32
evaluating teachers, evaluating schools,
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2787
評估老師，評估學校，
17:34
evaluating programs, that they're really talking about weeding.
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4161
評估專案，他們真正的意思是淘汰。
17:39
And that's a bad thing, because then you will get what you select for,
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這就不是什麼好事了。
因為你會得到你想選擇的，
因為你會得到你想選擇的，
17:43
which is what we've gotten so far.
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1958
這也是我們的現狀。
17:45
So I'd say what we need is a test that says, "What is x?"
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我認為我們需要這樣的測驗，問「什麼是X」
17:48
and the answers are "I don't know, because no one does,"
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3092
回答則是「我不知道，因為沒人知道。」
17:51
or "What's the question?" Even better.
402
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1741
或「問題是什麼？」這樣更好。
17:53
Or, "You know what, I'll look it up, I'll ask someone,
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2390
或「知道嗎？我會查一下，我會去問問別人。
17:55
I'll phone someone. I'll find out."
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2700
我會打幾個電話。我會找出答案。」
17:58
Because that's what we want people to do,
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而這才是我們希望人們去做的，
18:00
and that's how you evaluate them.
406
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這才是做評估的方式。
18:01
And maybe for the advanced placement classes,
407
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1943
對一些優等生班，
18:03
it could be, "Here's the answer. What's the next question?"
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答案可能是：「這是答案，下一個問題是什麼？」
18:07
That's the one I like in particular.
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這是我特別喜歡的一個問題。
18:08
So let me end with a quote from William Butler Yeats,
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請讓我以葉慈的話來結束我的演講。
18:10
who said "Education is not about filling buckets;
411
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3167
他說：「教育並不是注滿水桶，
18:14
it is lighting fires."
412
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而是點燃火種。」
18:16
So I'd say, let's get out the matches.
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讓我們拿出火柴吧！
18:20
Thank you.
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謝謝大家。
18:21
(Applause)
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(掌聲)
18:24
Thank you. (Applause)
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謝謝。(掌聲)
ABOUT THE SPEAKER
Stuart Firestein - NeuroscientistStuart Firestein teaches students and “citizen scientists” that ignorance is far more important to discovery than knowledge.
Why you should listen
You’d think that a scientist who studies how the human brain receives and perceives information would be inherently interested in what we know. But Stuart Firestein says he’s far more intrigued by what we don’t. “Answers create questions,” he says. “We may commonly think that we begin with ignorance and we gain knowledge [but] the more critical step in the process is the reverse of that.”
Firestein, who chairs the biological sciences department at Columbia University, teaches a course about how ignorance drives science. In it -- and in his 2012 book on the topic -- he challenges the idea that knowledge and the accumulation of data create certainty. Facts are fleeting, he says; their real purpose is to lead us to ask better questions.
Stuart Firestein | Speaker | TED.com