TED2013
Stuart Firestein: The pursuit of ignorance
斯达特•菲尔斯坦: 对无知的追求
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真正的科学研究是什么样的？作为神经学家，斯达特•菲尔斯坦开玩笑说，科学研究不想人们提到的那些科研模式，而更像”在黑暗里......四处排气。“在这个诙谐的演讲里，菲尔斯坦形象地描述了科学研究，并提议，我们更应该重视我们所不知道的东西——或者说”高质量的无知“——就像我们重视我们掌握的知识一样。
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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有句古老的谚语说
00:16
it's very difficult to find a black cat in a dark room,
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在暗室里很难找到黑猫，
00:20
especially when there is no cat.
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特别是里面根本没有猫的时候。
00:22
I find this a particularly apt description of science
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我认为用这句话来描述科学
00:26
and how science works --
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和科研方法特别恰当。
00:28
bumbling around in a dark room, bumping into things,
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在暗室里胡乱摸索，磕磕碰碰，
00:31
trying to figure out what shape this might be,
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想弄清楚不同东西的形状，
00:33
what that might be,
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猜想这些东西是什么。
00:35
there are reports of a cat somewhere around,
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有报道说有只猫在暗室的某处了，
00:37
they may not be reliable, they may be,
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也许这个报道不可靠，也许可靠。
00:39
and so forth and so on.
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等等，等等。
00:41
Now I know this is different than the way most people
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我知道我现在所说的和大多数人
00:43
think about science.
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对科学的看法是不同的。
00:44
Science, we generally are told,
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我们对科学的普遍认识是
00:46
is a very well-ordered mechanism for
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它是一个
00:49
understanding the world,
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用来了解世界、
00:50
for gaining facts, for gaining data,
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获得事实和数据的非常有序有理的机制。
00:52
that it's rule-based,
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就是说科学是以各种原理为基础的。
00:54
that scientists use this thing called the scientific method
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这些原理被称为科学方法。
00:57
and we've been doing this for 14 generations or so now,
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我们这样做科学研究已经大约14个世代了，
01:00
and the scientific method is a set of rules
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科研方法被认为是一系列
01:02
for getting hard, cold facts out of the data.
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用来从数据中得到事实真相的准则。
01:07
I'd like to tell you that's not the case.
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我现在想告诉你，我们并不是这样搞科学的。
01:09
So there's the scientific method,
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确实是有一些科研方法，
01:10
but what's really going on is this. (Laughter)
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但是我们真正在做的就象是这样。（笑声）
01:13
[The Scientific Method vs. Farting Around]
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[科研方法Vs.四处排气]
01:14
And it's going on kind of like that.
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真的是有点象
01:17
[... in the dark] (Laughter)
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[...在黑暗中四处排气]（笑声）
01:18
So what is the difference, then,
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那么，我所相信的
01:23
between the way I believe science is pursued
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对科学的追求方式
01:27
and the way it seems to be perceived?
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和传统意义上的科学追求有什么不同呢？
01:29
So this difference first came to me in some ways
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我第一次认识到这个差别是
01:32
in my dual role at Columbia University,
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在哥伦比亚大学担任两份工作的时候。
01:34
where I'm both a professor and run a laboratory in neuroscience
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在哥大，我是一名神经科学教授，同时也要掌管运作一个实验室。
01:38
where we try to figure out how the brain works.
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我们的实验室研究大脑如何运作。
01:41
We do this by studying the sense of smell,
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我们通过研究嗅觉
01:43
the sense of olfaction, and in the laboratory,
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来做到这一点。
01:46
it's a great pleasure and fascinating work
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这是一个令人愉悦和振奋的工作。
01:48
and exciting to work with graduate students and post-docs
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我很高兴能与我的研究生和博士后们一起工作，
01:51
and think up cool experiments to understand how this
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想出一些很酷的实验来研究
01:54
sense of smell works and how the brain might be working,
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嗅觉系统甚至是大脑是如何运作的。
01:56
and, well, frankly, it's kind of exhilarating.
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确实，这样的科学实验令人心情振奋。
01:59
But at the same time, it's my responsibility
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但在同时，我担负着
02:02
to teach a large course to undergraduates on the brain,
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给本科生讲授神经科学的教职。
02:05
and that's a big subject,
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这是个很重要的任务。
02:06
and it takes quite a while to organize that,
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我需要花费很长时间来组织讲课内容。
02:08
and it's quite challenging and it's quite interesting,
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授课是非常有挑战性和非常有趣的工作。
02:11
but I have to say, it's not so exhilarating.
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但我不得不说，对我来说，它不是那么令人振奋。
02:14
So what was the difference?
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那么这其中的区别是什么呢？
02:16
Well, the course I was and am teaching
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我的授课程内容
02:18
is called Cellular and Molecular Neuroscience - I. (Laughs)
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叫“细胞和分子神经科学（第一部分）”（笑声）
02:24
It's 25 lectures full of all sorts of facts,
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它分为二十五讲，充满了各种各样的事实。
02:29
it uses this giant book called "Principles of Neural Science"
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使用的教科书是《神经科学原理》这本
02:33
by three famous neuroscientists.
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由三个著名的神经学家写就的鸿篇巨制。
02:36
This book comes in at 1,414 pages,
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一共有 1,414 页。
02:39
it weighs a hefty seven and a half pounds.
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重达7.5磅。
02:42
Just to put that in some perspective,
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要是换个角度来衡量，
02:44
that's the weight of two normal human brains.
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这本书相当于两个正常人类大脑的重量。
02:47
(Laughter)
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（笑声）
02:51
So I began to realize, by the end of this course,
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所以我开始意识到，这门课结束后，
02:54
that the students maybe were getting the idea
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学生们也许会觉得
02:56
that we must know everything there is to know about the brain.
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我们要想认识大脑，就必须要掌握这本书提供的所有知识
02:59
That's clearly not true.
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这显然是不对的。
03:01
And they must also have this idea, I suppose,
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我想，他们一定也有这个想法，
03:04
that what scientists do is collect data and collect facts
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科学家们做的就是收集数据，整理出事实，
03:07
and stick them in these big books.
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然后把他们订在这样的厚重教科书里。
03:09
And that's not really the case either.
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这同样也不是事实。
03:11
When I go to a meeting, after the meeting day is over
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我去参加学术会议的时候，在会议结束后
03:14
and we collect in the bar over a couple of beers with my colleagues,
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我和我的同事们聚在酒吧里喝上几瓶啤酒。
03:17
we never talk about what we know.
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我们从来不谈论我们已经知道的东西。
03:19
We talk about what we don't know.
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我们谈论的是我们不知道的东西。
03:21
We talk about what still has to get done,
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我们谈论还有什么是需要被研究的。
03:24
what's so critical to get done in the lab.
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什么是实验室下一步的重点工作。
03:26
Indeed, this was, I think, best said by Marie Curie
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事实上，我认为，玛丽 · 居里给出了最好的诠释：
03:29
who said that one never notices what has been done
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她说，一个科学家不在意已经落实的东西，
03:31
but only what remains to be done.
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而是在意那些还没有落实的东西。
03:33
This was in a letter to her brother after obtaining
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这是她在取得第二个研究生学位后，
03:35
her second graduate degree, I should say.
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在给她弟弟的信里这样说的。
03:39
I have to point out this has always been one of my favorite pictures of Marie Curie,
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告诉大家，这一直是我最喜爱的玛丽 · 居里照片之一。
03:42
because I am convinced that that glow behind her
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我相信，在她身后的光芒
03:44
is not a photographic effect. (Laughter)
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绝对不是摄影棚的特效。（笑声）
03:47
That's the real thing.
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那一定是真的光芒。
03:48
It is true that her papers are, to this day,
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她所撰写的论文现在
03:53
stored in a basement room in the Bibliothèque Française
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珍藏在法国国家图书馆的
03:56
in a concrete room that's lead-lined,
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一个有含铅内墙的混凝土地下室里。
03:58
and if you're a scholar and you want access to these notebooks,
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如果你是一个学者，想要看看这些笔记本，
04:01
you have to put on a full radiation hazmat suit,
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你必须穿上一整套辐射防护服，
04:03
so it's pretty scary business.
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那是相当可怕的事儿。
04:06
Nonetheless, this is what I think we were leaving out
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尽管如此，我认为我们的课程遗漏了（居里夫人所说的）
04:08
of our courses
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没有教给学生。
04:10
and leaving out of the interaction that we have
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作为科学家，在与公众的互动中，
04:13
with the public as scientists, the what-remains-to-be-done.
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也省略了告诉他们，什么是未被发现的。
04:16
This is the stuff that's exhilarating and interesting.
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这是令人振奋和有趣的东西。
04:18
It is, if you will, the ignorance.
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如果你愿意，你可以叫它无知。
04:21
That's what was missing.
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这正是我们没注意到的方面。
04:22
So I thought, well, maybe I should teach a course
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所以我想，嗯，也许我应该开一门课
04:25
on ignorance,
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这门课讲的是“无知”
04:27
something I can finally excel at, perhaps, for example.
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这也许就是我真正擅长的。
04:31
So I did start teaching this course on ignorance,
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后来我真的就开设了这门关于无知的课，
04:33
and it's been quite interesting
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它真的是个相当有趣的课，
04:34
and I'd like to tell you to go to the website.
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我会告诉你们这门课程的网址。
04:36
You can find all sorts of information there. It's wide open.
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你可以在那里找到各种各样的信息。它完全是对外开放的。
04:39
And it's been really quite an interesting time for me
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对我来说，教这门课的那段时间是段美妙时光。
04:43
to meet up with other scientists who come in and talk
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我得以见到其他来参与的科学家，
04:45
about what it is they don't know.
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和他们讨论他们所不知道的事情。
04:46
Now I use this word "ignorance," of course,
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当然，我现在使用"无知"这个词，
04:48
to be at least in part intentionally provocative,
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听起来好像有些恶意挑衅的意味，
04:51
because ignorance has a lot of bad connotations
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因为无知有很多贬义的内涵
04:54
and I clearly don't mean any of those.
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我说的当然不是那样的意思。
04:56
So I don't mean stupidity, I don't mean a callow indifference
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我不是说愚蠢，我也不是说对事实、原因和数据的
04:59
to fact or reason or data.
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那种没心没肺的幼稚无知。
05:02
The ignorant are clearly unenlightened, unaware,
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我说的无知是，未被启蒙的，没意识到的，
05:05
uninformed, and present company today excepted,
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不了解的，除了占用自己的公司，
05:08
often occupy elected offices, it seems to me.
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还经常占用选举办公室，在我看来。
05:11
That's another story, perhaps.
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这也许是另一个故事。
05:13
I mean a different kind of ignorance.
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我这里指得的是另一种无知。
05:15
I mean a kind of ignorance that's less pejorative,
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我所说的无知可没那么多贬义。
05:17
a kind of ignorance that comes from a communal gap in our knowledge,
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我所说的无知是说我们在知识上共同的差距。
05:20
something that's just not there to be known
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一些我们还没有了解的东西
05:22
or isn't known well enough yet or we can't make predictions from,
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或者了解得还不够得东西，或者我们无法预知的东西。
05:25
the kind of ignorance that's maybe best summed up
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这种无知在
05:27
in a statement by James Clerk Maxwell,
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詹姆斯·克拉克·麦克斯韦发表的声明中有最好的诠释：
05:29
perhaps the greatest physicist between Newton and Einstein,
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也许是最伟大的物理学家牛顿或者爱因斯坦
05:33
who said, "Thoroughly conscious ignorance
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说过，"完全自觉自醒的无知
05:35
is the prelude to every real advance in science."
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是每一次科学进步的前奏。“
05:38
I think it's a wonderful idea:
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我认为这句话说得非常好，
05:39
thoroughly conscious ignorance.
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完全自觉自醒的无知。
05:42
So that's the kind of ignorance that I want to talk about today,
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这正是我今天所要讲的无知。
05:44
but of course the first thing we have to clear up
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但当然，首先我们要搞清楚
05:46
is what are we going to do with all those facts?
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我们该如何利用我们已经掌握的事实？
05:48
So it is true that science piles up at an alarming rate.
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科学发现正以惊人的速度积累着，
05:52
We all have this sense that science is this mountain of facts,
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我们知道科学发现就是这些堆积如山的事实。
05:55
this accumulation model of science, as many have called it,
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科学的这种积累模式，就象很多人说的，
05:59
and it seems impregnable, it seems impossible.
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它看似坚不可摧，它看似毫无可能。
06:01
How can you ever know all of this?
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你怎么可能掌握这一切呢？
06:02
And indeed, the scientific literature grows at an alarming rate.
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事实上，科学文献在以惊人的速度增长。
06:06
In 2006, there were 1.3 million papers published.
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在 2006 年，共有一百三十万篇论文得以发表。
06:10
There's about a two-and-a-half-percent yearly growth rate,
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那是大约 2.5%的年增长率。
06:12
and so last year we saw over one and a half million papers being published.
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去年一年呢，有一百五十万篇论文被发表。
06:17
Divide that by the number of minutes in a year,
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这个数值除以一年的总分钟数，
06:19
and you wind up with three new papers per minute.
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就意味着每一分钟就有三篇论文发表出来。
06:22
So I've been up here a little over 10 minutes,
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我在这儿已经讲了十多分钟了，
06:23
I've already lost three papers.
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就是说我已经失去了三篇论文。
06:25
I have to get out of here actually. I have to go read.
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我需要离开这儿，赶紧去读那些论文呢。
06:28
So what do we do about this? Well, the fact is
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我们怎么利用这些已经发表的论文呢？嗯，事实是
06:32
that what scientists do about it is a kind of a controlled neglect, if you will.
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科学家在做的就是所谓的可控的忽略。
06:36
We just don't worry about it, in a way.
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就是说，我们不需要对我们操心那些发表的论文。
06:39
The facts are important. You have to know a lot of stuff
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事实当然是重要的。为了成为科学家，
06:41
to be a scientist. That's true.
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你要知道很多东西。这是事实。
06:43
But knowing a lot of stuff doesn't make you a scientist.
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但知识多并不会让你成为科学家。
06:46
You need to know a lot of stuff to be a lawyer
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要成为一名律师，你要掌握很多知识。
06:48
or an accountant or an electrician or a carpenter.
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要成为会计师、电工或者木工，你要掌握很多知识。
06:52
But in science, knowing a lot of stuff is not the point.
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但在科学方面，掌握很多知识并不是重点。
06:56
Knowing a lot of stuff is there to help you get
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掌握得知识多可以帮助你更好地
06:59
to more ignorance.
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了解你的无知。
07:01
So knowledge is a big subject, but I would say
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所以掌握知识是很重要的，但我会说，
07:03
ignorance is a bigger one.
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知道自己的无知更重要。
07:06
So this leads us to maybe think about, a little bit
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这就让我们去思考，
07:08
about, some of the models of science that we tend to use,
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我们通常想采用的一些科学模式。
07:11
and I'd like to disabuse you of some of them.
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在这里我要纠正你们对科学研究模式的一些偏见。
07:13
So one of them, a popular one, is that scientists
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其中一个很常见的科学研究模式，就是
07:15
are patiently putting the pieces of a puzzle together
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科学家们在耐心地把拼图上的小图块一张一张地拼在一起，
07:18
to reveal some grand scheme or another.
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去揭示一个又一个重大的发现。
07:20
This is clearly not true. For one, with puzzles,
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这显然不是那么回事。因为，说到拼图，
07:23
the manufacturer has guaranteed that there's a solution.
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厂家能保证你一定能做出完整的拼图。
07:27
We don't have any such guarantee.
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但我们对科学研究却没法打保票。
07:28
Indeed, there are many of us who aren't so sure about the manufacturer.
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实际上，我们中的很多人对制造商也不是那么有信心。
07:31
(Laughter)
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（笑声）
07:34
So I think the puzzle model doesn't work.
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所以我觉得科学研究并不遵循拼图模式。
07:36
Another popular model is that science is busy unraveling things
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另一种普遍的模式是科学研究是需要一层层解开的难题。
07:40
the way you unravel the peels of an onion.
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就象你拨开一层层的洋葱皮。
07:42
So peel by peel, you take away the layers of the onion
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一层一层地，你一点点剥开洋葱的外皮，
07:45
to get at some fundamental kernel of truth.
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去了解其中的核心真相。
07:47
I don't think that's the way it works either.
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我同样不觉得这个模式是正确的。
07:49
Another one, a kind of popular one, is the iceberg idea,
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另一种，也是很普遍的一个，就是冰山模式。
07:52
that we only see the tip of the iceberg but underneath
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我们只能看到水面上的冰山尖，但是水面下
07:55
is where most of the iceberg is hidden.
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才是隐藏着的大部分的冰山。
07:57
But all of these models are based on the idea of a large body of facts
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所有这些上述模式都是基于我们掌握的事实基础上的。
08:01
that we can somehow or another get completed.
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那些我们已经部分或者全部了解的事实。
08:03
We can chip away at this iceberg and figure out what it is,
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我们可以铲开冰山去了解它是怎么回事，
08:06
or we could just wait for it to melt, I suppose, these days,
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或者象现在的气候，我们等着它融化了就行了。
08:09
but one way or another we could get to the whole iceberg. Right?
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不管怎么说我们最终会知道冰山到底什么样，对吧？
08:12
Or make it manageable. But I don't think that's the case.
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这都是可控的。但我认为科学研究与此不同。
08:15
I think what really happens in science
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我觉得真正的科学研究模式
08:17
is a model more like the magic well,
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更像是个魔力井。
08:19
where no matter how many buckets you take out,
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不管你捞出多少桶水，
08:21
there's always another bucket of water to be had,
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井里总有另桶水着你去捞。
08:23
or my particularly favorite one,
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还有一个我特别喜欢的说法，
08:25
with the effect and everything, the ripples on a pond.
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科学研究就象池塘上的一圈圈涟漪。
08:28
So if you think of knowledge being this ever-expanding ripple on a pond,
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科学研究就象是池塘里那不断扩展的一圈圈涟漪一样。
08:31
the important thing to realize is that our ignorance,
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重要的是我们要意识到我们的无知和
08:34
the circumference of this knowledge, also grows with knowledge.
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知识的边界，是和我们所掌握的知识同时增长的。
08:38
So the knowledge generates ignorance.
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所以说知识造就无知。
08:41
This is really well said, I thought, by George Bernard Shaw.
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我觉得这句话真是说得太好。这是乔治 · 萧伯纳
08:43
This is actually part of a toast that he delivered
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在庆祝爱因斯坦工作成绩的晚宴上
08:46
to celebrate Einstein at a dinner celebrating Einstein's work,
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所说的祝酒词中的一句话。
08:50
in which he claims that science
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他认为：科学
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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只会创造出比答案更多的问题。["科学总是错误的。不创造出十个问题科学就没法解决任何一个问题。"]
08:53
I find that kind of glorious, and I think he's precisely right,
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我觉得这真是至理名言了。他说的一点没错。
08:57
plus it's a kind of job security.
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不过这也算是给科学界职业安全感。
09:00
As it turns out, he kind of cribbed that
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结果呢，他可能是抄袭了
09:02
from the philosopher Immanuel Kant
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哲学家康德。
09:04
who a hundred years earlier had come up with this idea
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早在一百年多年前，康德就有了同样的
09:07
of question propagation, that every answer begets more questions.
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问题繁殖的想法。就是说，每一个答案都会产生更多的问题。
09:11
I love that term, "question propagation,"
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我喜欢"问题繁殖"这个词的意思，
09:13
this idea of questions propagating out there.
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把问题不断繁殖下去。
09:16
So I'd say the model we want to take is not
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所以我说搞科研的模式不是说
09:17
that we start out kind of ignorant and we get some facts together
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我们从无知开始找到事实答案，
09:21
and then we gain knowledge.
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然后我们获得知识这样的步骤。
09:23
It's rather kind of the other way around, really.
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其实真的正好相反。
09:25
What do we use this knowledge for?
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我们会自问，我们用我们掌握的知识做什么好？
09:27
What are we using this collection of facts for?
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我们用我们收集到的事实来证明什么？
09:30
We're using it to make better ignorance,
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我们正是用知识和事实来推出更好的无知，
09:33
to come up with, if you will, higher-quality ignorance.
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如果可能，推出更高质的无知。
09:36
Because, you know, there's low-quality ignorance
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因为，你知道有低质量的无知
09:38
and there's high-quality ignorance. It's not all the same.
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也有高质量的无知。这两个可不是一样的。
09:40
Scientists argue about this all the time.
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科学家总是为这个话题辩论。
09:42
Sometimes we call them bull sessions.
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有时我们叫它牛市会议。
09:44
Sometimes we call them grant proposals.
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有时我们叫它研究提案。
09:46
But nonetheless, it's what the argument is about.
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不管怎么说，它就是关于无知的讨论。
09:50
It's the ignorance. It's the what we don't know.
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它是我们的无知。它是我们所不知道的。
09:52
It's what makes a good question.
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它也是一个好的提问。
09:54
So how do we think about these questions?
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那么我们怎么考虑这些问题呢？
09:56
I'm going to show you a graph that shows up
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这里我给你们看一个图表
09:58
quite a bit on happy hour posters in various science departments.
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这个图表经常被各个科学部门用来做聚会的海报。
10:02
This graph asks the relationship between what you know
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这张图表关于你的科学兴趣
10:06
and how much you know about it.
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和你掌握知识多少的关系。
10:08
So what you know, you can know anywhere from nothing to everything, of course,
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你的科学兴趣可能是从什么都不想知道到想掌握一切，当然，
10:12
and how much you know about it can be anywhere
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你掌握知识的多少可以在
10:13
from a little to a lot.
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或少或多的任何地方。
10:16
So let's put a point on the graph. There's an undergraduate.
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所以我来举个例子。有一个本科生。
10:20
Doesn't know much but they have a lot of interest.
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他知道的不多，但他有很多兴趣。
10:22
They're interested in almost everything.
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他们几乎对所有的东西感兴趣。
10:24
Now you look at a master's student, a little further along in their education,
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现在来看一个硕士生，因为他受教育的时间更长，
10:28
and you see they know a bit more,
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你看他知道得更多
10:29
but it's been narrowed somewhat.
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但是他的兴趣却有点儿减少了。
10:31
And finally you get your Ph.D., where it turns out
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最后你来看看博士生，你会发现
10:34
you know a tremendous amount about almost nothing. (Laughter)
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他们感兴趣的东西几乎都没了。（笑声）
10:39
What's really disturbing is the trend line that goes through that
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在你沿着图表的线条在看下去的话，真正让人不安的是
10:42
because, of course, when it dips below the zero axis, there,
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在零点的下方的一个点，
10:46
it gets into a negative area.
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它是在负值区域的。
10:48
That's where you find people like me, I'm afraid.
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恐怕你们可以找到的就是像我这样的人了。
10:51
So the important thing here is that this can all be changed.
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但重要的是这些是可以改变的。
10:55
This whole view can be changed
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仅仅是改变X轴上的某个标记，
10:57
by just changing the label on the x-axis.
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就可以改变整个视角。
11:00
So instead of how much you know about it,
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因此，与其问你知道多少，
11:02
we could say, "What can you ask about it?"
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我们不如问，"你想问的问题是什么?"
11:05
So yes, you do need to know a lot of stuff as a scientist,
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所以可以肯定，你确实需要掌握很多科学知识，
11:08
but the purpose of knowing a lot of stuff
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但是掌握这些知识的目的
11:11
is not just to know a lot of stuff. That just makes you a geek, right?
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不仅仅是为了了解这些知识。那只能让你变成一个科学怪人，对吧？
11:13
Knowing a lot of stuff, the purpose is
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掌握很多知识的目的是
11:15
to be able to ask lots of questions,
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为了能够 提出更多的问题，
11:17
to be able to frame thoughtful, interesting questions,
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是为了能够提出细致、有趣的问题，
11:20
because that's where the real work is.
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那才是科学工作的重点。
11:22
Let me give you a quick idea of a couple of these sorts of questions.
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让我给你讲讲两个诸如此类的问题。
11:24
I'm a neuroscientist, so how would we come up
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我是一个神经科学家，那么我们如何提出
11:27
with a question in neuroscience?
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神经科学领域里的问题呢？
11:28
Because it's not always quite so straightforward.
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因为这些问题并不总是那么直接。
11:31
So, for example, we could say, well what is it that the brain does?
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那么比如说，我们可以问，大脑是干嘛用的？
11:33
Well, one thing the brain does, it moves us around.
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嗯，大脑可以做到的一件事就是它能让我们移动。
11:35
We walk around on two legs.
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它能让我们用双腿行走。
11:37
That seems kind of simple, somehow or another.
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这似乎有点太简单，是不是？
11:39
I mean, virtually everybody over 10 months of age
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我的意思是，几乎每个都超过 10 个月龄的人
11:42
walks around on two legs, right?
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都可以用双腿走路，对吧？
11:44
So that maybe is not that interesting.
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所以说这个问题没什么意思。
11:45
So instead maybe we want to choose something a little more complicated to look at.
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所以我们可能会选择提出一些更复杂些的问题去研究。
11:48
How about the visual system?
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视觉系统怎么样？
11:51
There it is, the visual system.
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对，就是视觉系统了。
11:53
I mean, we love our visual systems. We do all kinds of cool stuff.
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我是说，我们喜欢研究视觉系统，可以搞很酷的研究。
11:56
Indeed, there are over 12,000 neuroscientists
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事实上，超过 一万两千位神经科学家
11:59
who work on the visual system,
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是研究视觉系统的。
12:01
from the retina to the visual cortex,
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从视网膜到视觉皮层的
12:03
in an attempt to understand not just the visual system
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这些科研不仅仅是局限在视觉系统，
12:06
but to also understand how general principles
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还包括如何通过视觉系统研究去了解
12:09
of how the brain might work.
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大脑是如何运作的普遍原理。
12:11
But now here's the thing:
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目前的情况是：
12:12
Our technology has actually been pretty good
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我们现在拥有很好的
12:15
at replicating what the visual system does.
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复制视觉系统的技术。
12:17
We have TV, we have movies,
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我们有电视，我们有电影，
12:20
we have animation, we have photography,
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我们有动画，我们有摄影、
12:23
we have pattern recognition, all of these sorts of things.
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我们有模型识别技术，很多其他的这一类技术。
12:26
They work differently than our visual systems in some cases,
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有些视觉技术的工作原理和视觉系统不大一样。
12:29
but nonetheless we've been pretty good at
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尽管如此，我们现有的视觉技术
12:30
making a technology work like our visual system.
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已经与视觉系统非常近似了。
12:34
Somehow or another, a hundred years of robotics,
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但是，机器人技术的发展已经有一百年了，
12:37
you never saw a robot walk on two legs,
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你还没见过一个用两条腿走路的机器人。
12:39
because robots don't walk on two legs
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因为机器人不是用两条腿走路的，
12:41
because it's not such an easy thing to do.
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这可不是一件很容易做到的事。
12:43
A hundred years of robotics,
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一百年的机器人技术，
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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我们甚至不能让机器人走上一步或者两步。
12:48
You ask them to go up an inclined plane, and they fall over.
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你让机器人走个斜面试试，它们肯定会摔倒。
12:51
Turn around, and they fall over. It's a serious problem.
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让它们转身，它们也会摔倒了。这是个科技上的难题。
12:53
So what is it that's the most difficult thing for a brain to do?
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那么，对大脑来说，什么是最难完成的任务呢？
12:57
What ought we to be studying?
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我们应该学着研究什么呢？
12:58
Perhaps it ought to be walking on two legs, or the motor system.
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也许应该研究用两腿行走是怎么完成的，研究运动系统。
13:02
I'll give you an example from my own lab,
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我给你们举个我自己实验室的例子，
13:04
my own particularly smelly question,
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我们对嗅觉问题特别感兴趣。
13:06
since we work on the sense of smell.
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我们研究嗅觉。
13:08
But here's a diagram of five molecules
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这里是五个分子构图
13:11
and sort of a chemical notation.
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和他们的化学排序。
13:13
These are just plain old molecules, but if you sniff those molecules
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这都是些最普通的分子了，但如果你用你脸上那两个鼻梁下的小洞洞
13:16
up these two little holes in the front of your face,
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来闻闻那些分子的话，
13:18
you will have in your mind the distinct impression of a rose.
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你会在脑海记住那个印象非常深刻的“玫瑰”。
13:22
If there's a real rose there, those molecules will be the ones,
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如果说真的有玫瑰的话，那些分子就是“玫瑰”。
13:24
but even if there's no rose there,
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但即使没有玫瑰，
13:26
you'll have the memory of a molecule.
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你也会有关于这些分子的记忆。
13:27
How do we turn molecules into perceptions?
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我们是如何把一些分子转变成脑海里的印象的？
13:30
What's the process by which that could happen?
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这个怎样的一个过程呢？
13:32
Here's another example: two very simple molecules, again in this kind of chemical notation.
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还有另一个例子： 两个非常简单的分子结构，带着这些化学符号。
13:36
It might be easier to visualize them this way,
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也许这样看着它们的分子式更容易，
13:38
so the gray circles are carbon atoms, the white ones
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灰色的圈圈是碳原子，白色的
13:41
are hydrogen atoms and the red ones are oxygen atoms.
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是氢原子，红色的是氧原子。
13:44
Now these two molecules differ by only one carbon atom
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那么这两个分子式的差别就在于一个碳原子
13:48
and two little hydrogen atoms that ride along with it,
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和两个与碳原子相连的氢原子，
13:51
and yet one of them, heptyl acetate,
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其中的一个分子叫乙酸庚酯
13:53
has the distinct odor of a pear,
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带着特殊的梨味儿。
13:55
and hexyl acetate is unmistakably banana.
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但是醋酸己酯却带来一种很明显的香蕉味儿。
13:59
So there are two really interesting questions here, it seems to me.
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我就想问两个非常有趣的问题。
14:02
One is, how can a simple little molecule like that
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其中一个是，一个如此简单的小分子
14:05
create a perception in your brain that's so clear
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是如何在你的脑海里建立起如此清晰的认识
14:07
as a pear or a banana?
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让你轻松辨别出一只梨或是一个香蕉的呢？
14:09
And secondly, how the hell can we tell the difference
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第二，我们到底是怎样辨别出这个差异的呢？
14:12
between two molecules that differ by a single carbon atom?
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两个分子仅仅只有一个碳原子键的不同而已。
14:16
I mean, that's remarkable to me,
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我的意思是，这样的问题对我来说简直是太有趣了。
14:18
clearly the best chemical detector on the face of the planet.
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我们每个人都拥有这个星球上最优质的化学探测器呀！
14:21
And you don't even think about it, do you?
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你甚至从来都没想过这些，对吧？
14:24
So this is a favorite quote of mine that takes us
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让我们再回来谈谈无知和设问。
14:27
back to the ignorance and the idea of questions.
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这是我最喜欢讲给大家的一句名言。
14:28
I like to quote because I think dead people
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我喜欢引用名言是因为我想既使是死人
14:30
shouldn't be excluded from the conversation.
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也应该参与这样的讨论。
14:33
And I also think it's important to realize that
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我认为（关于无知的）
14:35
the conversation's been going on for a while, by the way.
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谈论其实已经进行很久了。
14:37
So Erwin Schrodinger, a great quantum physicist
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埃尔温 · 薛定谔是伟大的量子物理学家。
14:40
and, I think, philosopher, points out how you have to
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我认为他也是哲学家。他指出你需要不懈地
14:43
"abide by ignorance for an indefinite period" of time.
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“保持永久无限期的无知”
14:46
And it's this abiding by ignorance
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正是这种如何持久得保持无知
14:48
that I think we have to learn how to do.
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我认为，正是我们需要学会的。
14:50
This is a tricky thing. This is not such an easy business.
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这是件很不容易的事情。可没有那么简单。
14:53
I guess it comes down to our education system,
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现在我该讲讲我们的教育系统了。
14:55
so I'm going to talk a little bit about ignorance and education,
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我要讲讲无知和教育，
14:57
because I think that's where it really has to play out.
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因为我觉得教育系统需要重视“无知”。
14:59
So for one, let's face it,
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那么现在让我们正视现实吧。
15:02
in the age of Google and Wikipedia,
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这个时代有谷歌，维基百科这样的网站，
15:05
the business model of the university
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大学的运营模式，
15:07
and probably secondary schools is simply going to have to change.
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甚至是我们的中学，真的都需要一些实质的改变。
15:10
We just can't sell facts for a living anymore.
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我们真的不能光靠贩卖“事实”为生了。
15:12
They're available with a click of the mouse,
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你只要轻松的点击鼠标就可以找到事实的。
15:14
or if you want to, you could probably just ask the wall
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如果你想，在现在这样的年代，你甚至可能仅仅对着墙提问，
15:17
one of these days, wherever they're going to hide the things
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墙里面藏着的机器就可能
15:18
that tell us all this stuff.
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回答你所有的问题。
15:20
So what do we have to do? We have to give our students
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我们要做的是什么？我们必须让我们的学生
15:23
a taste for the boundaries, for what's outside that circumference,
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尝到探索的滋味，去了解事实以外的世界。
15:27
for what's outside the facts, what's just beyond the facts.
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事实之外还有什么，除了事实还有什么。
15:31
How do we do that?
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我们应该怎么做？
15:33
Well, one of the problems, of course,
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是呀，现在有个问题，
15:35
turns out to be testing.
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很难解决，那就是--考试。
15:37
We currently have an educational system
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目前，我们的教育制度
15:39
which is very efficient but is very efficient at a rather bad thing.
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是非常高效的，但是非常高效地干了件坏事。
15:43
So in second grade, all the kids are interested in science,
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在二年级时，几乎所有的孩子都对科学感兴趣，
15:46
the girls and the boys.
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无论女孩还是男孩。
15:47
They like to take stuff apart. They have great curiosity.
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他们喜欢把东西拆开。他们有很强的好奇心。
15:51
They like to investigate things. They go to science museums.
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他们喜欢做调查。他们喜欢去科学博物馆。
15:54
They like to play around. They're in second grade.
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他们喜欢四处玩耍。他们是小学二年级的学生。
16:00
They're interested.
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他们对什么都感兴趣。
16:01
But by 11th or 12th grade, fewer than 10 percent
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等到了第 11 或 12 年级（高中），只有 不到10%
16:04
of them have any interest in science whatsoever,
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的学生对科学感兴趣，
16:07
let alone a desire to go into science as a career.
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更别说想把科学探索作为自己的职业了。
16:10
So we have this remarkably efficient system
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所以我说，我们有这个极其高效的系统
16:13
for beating any interest in science out of everybody's head.
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很善于打击这些孩子们心里的科学兴趣。
16:17
Is this what we want?
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这真的是我们想要的吗？
16:19
I think this comes from what a teacher colleague of mine
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我的一位大学老师同事把这样的现象
16:22
calls "the bulimic method of education."
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叫做"填鸭式教育"。
16:24
You know. You can imagine what it is.
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你知道吧？你可以想象出来的。
16:26
We just jam a whole bunch of facts down their throats over here
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我们只是把一大勺的“事实”塞进他们的喉咙里，
16:29
and then they puke it up on an exam over here
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然后他们在考试的时候再把它吐出来。
16:31
and everybody goes home with no added intellectual heft whatsoever.
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每个回家的孩子从学校里没得到什么有益的收获。
16:36
This can't possibly continue to go on.
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我们不能这样继续下去了。
16:38
So what do we do? Well the geneticists, I have to say,
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那么，我们该怎么办？那些遗传学家，我不得不说，
16:40
have an interesting maxim they live by.
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他们中流传着很有趣的格言。
16:42
Geneticists always say, you always get what you screen for.
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遗传学家经常说，你总能得到你想要筛选出的东西。
16:47
And that's meant as a warning.
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我们可以把这句话当成警告。
16:50
So we always will get what we screen for,
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我们总能得到我们想要筛选出的东西，
16:52
and part of what we screen for is in our testing methods.
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测评方法是非常重要的。
16:56
Well, we hear a lot about testing and evaluation,
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我们已经听过太多的测试呀，评价呀，
16:59
and we have to think carefully when we're testing
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我们需要认真考虑我们在进行测试的时候
17:01
whether we're evaluating or whether we're weeding,
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我们在评分还是在除草，
17:04
whether we're weeding people out,
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我们是不是要把一些人排除掉，
17:06
whether we're making some cut.
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我们是不是在裁掉一部分人。
17:09
Evaluation is one thing. You hear a lot about evaluation
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评价是一回事。你在教育学类文献中，
17:12
in the literature these days, in the educational literature,
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可以阅读到很多有关如何做测试的书。
17:14
but evaluation really amounts to feedback and it amounts
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测评其实相当于反馈，
17:17
to an opportunity for trial and error.
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测评为试验和错误提供机会。
17:20
It amounts to a chance to work over a longer period of time
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测评提供的这些反馈
17:24
with this kind of feedback.
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应该是为了以后的长期工作提供帮助
17:26
That's different than weeding, and usually, I have to tell you,
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这就和除草不同，但我要告诉你们，通常情况下，
17:29
when people talk about evaluation, evaluating students,
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当人们谈论到评价，评价学生，
17:32
evaluating teachers, evaluating schools,
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评价教师，评价学校，
17:34
evaluating programs, that they're really talking about weeding.
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评价项目，他们谈论的实际上就是除草
17:39
And that's a bad thing, because then you will get what you select for,
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这就不是什么好事了。因为你会得到你想选择的，
17:43
which is what we've gotten so far.
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结果呢我们真的得到了我们想要的（不到10%的高中生想搞科学）。
17:45
So I'd say what we need is a test that says, "What is x?"
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我觉得我们需要有这样的测验问，“某某是什么?"
17:48
and the answers are "I don't know, because no one does,"
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答案可以是"我不知道，因为没有人知道。”
17:51
or "What's the question?" Even better.
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或者更好的答案是"你的问题是什么?"
17:53
Or, "You know what, I'll look it up, I'll ask someone,
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或者，回答，"好的，我会查一下，我会问别人，
17:55
I'll phone someone. I'll find out."
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我会打电话给别人问。我的找到答案"。
17:58
Because that's what we want people to do,
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这才是我们需要的反应，
18:00
and that's how you evaluate them.
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这才是我们评价的方式。
18:01
And maybe for the advanced placement classes,
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对一些优等生班，
18:03
it could be, "Here's the answer. What's the next question?"
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答案可能是，"这就是答案。接下来的问题是什么?"
18:07
That's the one I like in particular.
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这是我特别喜欢的答案。
18:08
So let me end with a quote from William Butler Yeats,
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让我引述威廉 · 巴特勒 · 叶芝的话来结束我的演讲，
18:10
who said "Education is not about filling buckets;
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他说"教育不是把桶填满 ；
18:14
it is lighting fires."
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而是点亮火苗。“
18:16
So I'd say, let's get out the matches.
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所以我建议，让我们拿出火柴来。
18:20
Thank you.
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谢谢。
18:21
(Applause)
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（掌声）
18:24
Thank you. (Applause)
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谢谢大家。（掌声）
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