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大脑神经科学如何改变计算机

Jeff Hawkins: How brain science will change computing

一年只有一次
Once a year
1000名来自全世界的卓越人才聚集在加利福尼亚的蒙特利
1000 remarkable people gather in Monterey, California
他们在这里交流一些价值不可估量的
to exchange something of incalculable value
想法或者说是思想
Their ideas
这些想法从来没有被分享过
What happens there has never been shared
直到现在
…untile now
您正在观看的是由BMW赞助的 灵感之思
This inspired thinking shared with you by:BMW Where great ideas live on.
我有两个专业 设计微型电脑和研究大脑
I do two things: I design mobile computers and I study brains.
今天的演说是关于大脑的
And today’s talk is about brain and,
嘿 我们听众里面好像有大脑研究的粉丝
Yay, somewhere I have a brain fan out there.
(笑声)
(Laughter)
请把我演说的首页播放
I’m going to, if I could have my first slide up here,
你们可以看到我演说的标题和我的两个专业资格
and you’ll see the title of my talk and my two affiliations.
我会先说一下为什么我们没有一个好的大脑理论
So what I’m going to talk about is why we don’t have a good brain theory,
为什么研究出一个大脑理论是如此的重要
why it is important that we should develop one
以及我们该怎样应用它
and what we can do about it.
我会试着在20分钟之内完成
I’ll try to do all that in 20 minutes.
我有两个职业
I have two affiliations.
你们中的大多数人可能认识我其中的职业和我的发明 Palm和Handspring掌上电脑
Most of you know me from my Palm and Handspring days,
但我还有一个非盈利的研究院 :
but I also run a nonprofit scientific research institute
位于美国门洛帕克市的瑞德伍德神经科学研究院
called the Redwood Neuroscience Institute in Menlo Park.
在那里我们研究神经系统科学理论以及新(大脑)皮质是如何运作的
We study theoretical neuroscience and how the neocortex works.
我将会讲解相关的研究
I’m going to talk all about that.
我有一页演说是关于我电脑方面的工作
I have one slide on my other life, the computer life,
就是这张
and that’s this slide here.
这些都是我在近20年来设计过的电子产品
These are some of the products I’ve worked on over the last 20 years,
从最原始的笔记本电脑
starting back from the very original laptop
到第一台手写笔记本电脑
to some of the first tablet computers
到最近的微型笔记本Treo
and so on, ending up most recently with the Treo,
我们还会继续这方面的工作
and we’re continuing to do this.
我做这个工作是因为我深信移动计算技术
And I’ve done this because I believe that mobile computing
是个人计算系统的未来
is the future of personal computing,
而我在尝试通过这些研究性的工作
and I’m trying to make the world a little bit better
让世界变得更加美好
by working on these things.
但是 我不得不承认这一切都是巧合
But this was, I have to admit, all an accident.
我真的不想做这些产品中的任何一样
I really didn’t want to do any of these products
在我事业早期的时候
and very early in my career
我决定我不会从事计算机行业
I decided I was not going to be in the computer industry.
在那之前 让我先告诉你们
And before I tell you about that, I just have to tell you this one little
我在网上找到的这个涂鸦图片
about this picture of Graffiti there I picked off the web the other day.
我在网上找有关涂鸦的图片 或者说是文字很少的图片
I was looking for a picture of Graffiti, little text input language,
然后我发现了这个专为教师们而设的网站
and I found the website dedicated to teachers who want to make these,
他们教学中在黑板上写的板书
you know, the script writing thing across the top of their blackboard,
而他们却把它涂鸦上了 真是很可惜
and they had added Graffiti to it, and I’m sorry about that.
(笑声)
(Laughter)
所以事情的经过是这样的
So what happened was,
1979年 我还年轻的时候 刚刚从康奈尔大学工程学院毕业
when I was young and got out of engineering school at Cornell in ’79,
我决定去到英特尔工作 从事计算机行业
I decided — I went to work for Intel and I was in the computer industry
三个月后 我爱上了另一个东西
–and three months into that, I fell in love with something else,
我发现我选错了行业
And I said, “I made the wrong career choice here,”
而我喜欢上了大脑
and I fell in love with brains.
这个不是真的大脑
This is not a real brain.
这个一张大脑的图画 用线条构成的涂鸦
This is a picture of one, a line drawing.
我不太记得是怎么发生的
But, and I don’t remember exactly how it happened,
但是到今天为止 那对于我来说仍然是一段很强烈的记忆
but I have one recollection, which was pretty strong in my mind.
在1979的九月
In September of 1979,
美国科学杂志发表了一本关于大脑研究的特刊
Scientific American came out with a single topic issue about the brain.
真的很不错 那是该系列杂志中最好的一期
And It was quite good. It was one of the best issues ever.
那特刊讨论脑细胞的发展 疾病 视觉
And they talked about the neuron, development, disease, vision
和其他一切关于大脑你可能想知道的事情
and all the things you might want to know about brains.
这真的是很棒的
It was really quite impressive.
你可能认为我们很了解大脑 事实并非如此
And one might have the impression that we really knew a lot about brains.
在特刊的最后 有一篇弗朗西斯·克里克写的关于DNA的文章
But the last article in that issue was written by Francis Crick of DNA fame.
今天应该是发现DNA的50周年
Today is, I think, the 50th anniversary of the discovery of DNA.
他在特刊里写过这样一段 真的 每一处都很好
And he wrote a story basically saying, well, this is all well and good,
大概意思是 事实上我们对大脑几乎一无所知
but you know what, we don’t know diddley squat about brains,
也没有人知道大脑究竟是怎么工作的
and no one has a clue how these things work,
所以别随便相信其他人说的话
so don’t believe what anyone tells you.
他在去那篇文章中说到:我们现在显著地缺少的是
This is a quote from that article. He said, “What is conspicuously lacking,”
他是一个很传统的英国绅士
he’s a very proper British gentleman so,
现在显著的问题是缺少一个大的框架
“What is conspicuously lacking is a broad framework of ideas
一个可以理解大脑多种处理方式的框架
in which to interpret these different approaches.”
我觉得框架这个词用得很好
I thought the word framework was great.
他没有说我们没有大脑理论
He didn’t say we didn’t have a theory.
他只是说 我们连框架都没有 我们甚至都不知道怎样去开始思考大脑
He says, we don’t even know how to begin to think about it –we don’t even have a framework.
我们正处于Thomas Kuhn所说的规范前时期
We are in the pre-paradigm days, if you want to use Thomas Kuhn.
后来我就爱上大脑研究了 我想
And so I fell in love with this, and said look,
我们有这么多关于大脑的知识 那么想要完全了解大脑能又能有多难呢
We have all this knowledge about brains. How hard can it be?
后来这成为我毕生的工作 我觉得我可以有所贡献
And It’s something we can work on my lifetime. I felt I could make a difference.
所以我尝试离开计算机行业而专注于大脑研究
and so I tried to get out of the computer business, into the brain business.
首先我去了麻省理工的人工智能研究院
First, I went to MIT, the AI lab was there.
我说 我也想制造一个智能机器
And I said, well, I want to build intelligent machines, too,
但我的想法是先研究大脑怎么运作
but I want to do it is to study how brains work first.
他们说 呃 你不需要这样做
And they said, oh, you don’t need to do that.
我们只需要做好计算机编程就可以了
We’re just going to program computers.
那才是我们应该做的
That’s all we need to do.
而我说 你们真的应该先研究大脑
And I said, no, you really ought to study brains.
他们说 呃 你知道吗 你错了
They said, oh you know, you’re wrong.
而我说 不 是你们错了 最后我没有被录取
And I said, no, you’re wrong,and I didn’t get in.
(笑声)
(Laughter)
我当时真的有点失望 那时候还很年轻
But I was a little disappointed — pretty young
但是几年后我再次尝试
but I went back again a few years later and this time was
这次我去了加州大学伯克利分校
in California, and I went to Berkeley.
这次我尝试去学习生物研究方面
And I said, I’ll go in from the biological side.
我开始攻读生物物理博士课程
So I got in –in the Ph.D. program in biophysics.
现在我已经在学习大脑 但是我想要去学习理论
and that’s all right.I’m studying brains now, and I said well, I want to study theory.
而他们说 不 你不可以学大脑的理论
And they said, oh no, You can’t study theory about brains.
这是不可以的 因为你拿不到研究经费
That’s not something you do. You can’t get funded for that.
此外 你作为一个研究生 你也没有足够的能力
And as a graduate student, you can’t do that.
我的天啊
So I said, oh my gosh.
我很沮丧但是我仍然坚信我可以在这个领域有所建树
I was depressed. I said, but I can make a difference in this field.
所以最后我回到了计算机行业
So what I did is I went back in the computer industry
我安慰自己说 我先做这份工作 做一些有意义的
and said, I’ll have to work here for a while.
于是我就在那段时间设计了你们认识的一系列微型电子产品
That’s when I designed all those computer products.
(笑声)
(Laughter)
我计划干四年 挣点钱
I said, I want to do this for four years, make some money,
组织自己的家庭 我可能会成熟点
like I was having a family, and I would mature a bit,
也可能那时候神经系统科学也会成熟一点了
and maybe the business of neuroscience would mature a bit.
但这个实际时间竟比原计划的四年长多了 大概是16年
Well, it took longer than four years. It’s been about 16 years.
但我终于做到了 并且我现在也可以给你们解说这门科学了
But I’m doing it now, and I’m going to tell you about it.
那么为什么我们需要一个好的大脑理论呢
So why should we have a good brain theory?
嗯 科学研究有很多目的
Well, there’s lots of reasons people do science.
其中比较简单的是 我们喜欢了解各种的事物
One is, the most basic one is, people like to know things.
我们好奇 而我们渴求知识
We’re curious, and we go out and get knowledge, you know?
比如 为什么我们研究蚂蚁 因为这很有趣
Why do we study ants? Well, It’s interesting.
我们可能会学到一些有用的东西 但本质上这研究很有趣
Maybe we’ll learn something really useful about it, but it’s interesting and fascinating.
当然了 有时候科学会有其他本质
But sometimes, a science has some other attributes
使得研究变得有趣
which makes it really, really interesting.
有时候科学会告诉我们一些关于我们自身的奥秘
Sometimes a science will tell something about ourselves,
它将告诉我们我们是谁
it’ll tell us who we are.
这很罕见的 例如 进化论和哥白尼他们做的科学研究
Rarely, you know evolution did this and Copernicus did this,
这都能让我们对自身有新一层的理解
where we have a new understanding of who we are.
毕竟 我们就是我们的大脑 我的大脑正在跟你们的大脑沟通
And after all, we are our brains. My brain is talking to your brain.
我们的身体只是随行的部分
Our bodies are hanging along for the ride,
但我的大脑正在跟你们的大脑沟通
but my brain is talking to your brain.
如果我们想了解我们是什么和我们怎么去感受和察觉 我们就先要明白大脑是什么
And if we want to understand who we are and how we feel and perceive, we need to understand what brains are.
科学也会让我们有新的科技以及科学也能为社会带来很大好处
Another thing is sometimes science leads to really big societal
甚至商业 和其他 而大脑科学研究也会有这些好处
benefits and technologies, or businesses or whatever, that come out of it.
因为如果我们明白了大脑怎么运作
This is one, too, because when we understand how brains work,
我们就可以制作有智能的机器
we’re going to be able to build intelligent machines, and
而这总体来说是好的
I think that’s actually a good thing on the whole,
而且能给社会带来好处
and it’s going to have tremendous benefits to society,
就跟很基本的科技一样
just like a fundamental technology.
那么为什么我们没有一个好的大脑理论呢?
So why don’t we have a good theory of brains?
即使人们已经研究了大概100多年了
People have been working on it for 100 years.
我们先看一看一般的科学研究是怎么进行的
Well, let’s first take a look at what normal science looks like.
这是一般的科学
This is normal science.
一般的科学是平衡于理论和实验的
Normal science is a nice balance between theory and experimentalists.
比方说 理论家说 我认为是这样的
And so the theorist guy says, well, I think this is what’s going on,
而实验家说 不 你错了
the experimentalist says, no, you’re wrong.
反复的验证 你们明白吗?物理学和地理学也是这样研究的
And It goes back and forth, you know? This works in physics. This works in geology.
但这是一般的科学 那神经系统科学研究又怎样进行呢 我们看一看
But if this is normal science, what does neuroscience look like?
神经系统科学研究是这样进行的
This is what neuroscience looks like.
我们有巨多的数据
We have this mountain of data,
包括 解剖学 生理学和行为学
which is anatomy, physiology and behavior.
你们很难想象我们已经有多少数据
You can’t imagine how much detail we know about brains.
今年的神经系统科学研讨会我们有28000个专家参与
There were 28,000 people who went to the neuroscience conference this year,
而每一个都在研究大脑
and every one of them is doing research in brains.
成吨的数据 但是没有理论
A lot of data. But no theory.
也可能有着一点点理论 就像最上边的那小的可怜的箱子
There’s a little, wimpy box on top there.
在神经系统科学研究领域中
And theory has not played a role in any sort of grand way
理论从没有像它在其他一般科学里那样占据过主导地位
in the neurosciences.
这是很可惜的
And it’s a real shame.
那么 为什么会这样呢
Now, why has this come about?
如果你问神经系统科学专家 为什么会出现这种情况呢
If you ask neuroscientists why is this the state of affairs,
他们会同意情况是这样
they’ll first of all admit it.
但是如果你问为什么 他们会说
But if you ask them, they’ll say,
有很多原因导致我们没有一个好的大脑理论
there’s various reasons we don’t have a good brain theory.
有些专家会说 我们还没有足够的数据
Some people say, well, we don’t still have enough data,
我们需要更多的数据 我们还有很多不明白的
we need to get more information, there’s all these things we don’t know.
我刚刚告诉过你们了
Well, I just told you there’s so much data coming out of your ears.
我们有太多的数据不知道如何去组织
We have so much information, we don’t even know how to organize it.
那么就算有再多的数据又有何用
What good is more going to do?
可能我们会幸运的突然发现谜底 但是我认为这几乎不会发生
Maybe we’ll be lucky and discover some magic thing, but I don’t think so.
种种证据都在说明我们根本没有一个好的理论
This is actually a symptom of the fact that we just don’t have a theory.
我们不需要更多的数据 我们只需要一个好的理论
We don’t need more data, we need a good theory.
另一些专家或许会说
Another one is sometimes people say, well,
大脑太复杂了 这研究会再花费50年
brains are so complex, it’ll take another 50 years.
我想克里斯在昨天也说过类似的话
I even think Chris said something like this yesterday, something like,
我不能肯定克里斯你所说的内容 但是大体应该如此
I’m not sure what you said, Chris, but something like,
大脑研究是宇宙中最复杂的
well, it’s one of the most complicated things in the universe.
我并不认同 你们都比大脑复杂 你们都有大脑
That’s not true. You’re more complicated than your brain. You’ve got a brain.
而且 大脑只是看似复杂
And it’s also, although the brain looks very complicated,
所有事物在弄明白前都是复杂的
things look complicated until you understand them.
一直都是这样的
That’s always been the case.
我们可以说 大脑里面我们最感兴趣的部分是新大脑皮层
And so all we can say, well, my neocortex, which is the part of the brain I’m interested in,
它有300亿细胞
has 30 billion cells.
但你们知道吗 它非常有规律
But, you know what? It’s very, very regular.
实际上 它就像是同样的组织不停的重复
In fact, it looks like it’s the same thing repeated over and over and over again.
它不像我们想象中的那么复杂 那不是问题
It’s not as complex as it looks. That’s not the issue.
有些人说 大脑不能明白大脑
Some people say, brains can’t understand brains.
很玄 喔
Very Zen-like. Whoo.
(笑声)
(Laughter)
听起来挺好 但是有什么用呢
You know, it sounds good, but why? I mean, what’s the point?
它只是一堆细胞 就好像你了解你的肝脏
It’s just a bunch of cells. You understand your liver.
肝脏也是一堆细胞 对吗
It’s got a lot of cells in it too, right?
所以 我并不觉得大脑和其它器官有什么区别
So, you know, I don’t think there’s anything to that.
还有一些人说 你可能听说过
And finally, some people say, well, you know,
我不认为自己是一堆细胞 我是神智清醒的
And I don’t feel like a bunch of cells, you know. I’m conscious.
我有很多经历 我处在一个世界之中 你明白吗
I’ve got this experience, I’m in the world, you know.
所以我不可能只是一堆细胞
I can’t be just a bunch of cells.
人们曾经相信有生命力
Well, people used to believe there was a life force to be living,
我们现在已经知道那根本不正确
and we now know that’s really not true at all.
而且根本就没有证据可以证明
And there’s really no evidence that says well,
所以说人类相信一堆细胞能做人能做的事
other than people just have disbelieve that cells can do what they do.
有些人沉迷于形而上学唯物论
And so some people have fallen into the pit of metaphysical dualism,
包括一些很聪明的人 但我们可以完全否定
some really smart people, too, but we can reject all that.
(笑声)
(Laughter)
不 我会告诉你们一些别的事情
No, I’m going to tell you there’s something else,
很基础很根本的
and it’s really fundamental, and this is what it is:
那就是导致我们无法拥有一个好的大脑理论的原因
there’s another reason why we don’t have a good brain theory,
因为我们有很根深蒂固但错误的假设
and it’s because we have an intuitive, strongly-held, but incorrect assumption
这阻止了我们去寻找答案
that has prevented us from seeing the answer.
我们相信这个明显的假设 但它是错的
There’s something we believe that just, it’s obvious, but it’s wrong.
这在科学研究中是有先例的 但在说那个之前
Now, there’s a history of this in science and before I tell you what it is,
我先告诉你一些科学的历史
I’m going to tell you about the history of it in science.
看看其它的科学革命 比方说
You look at other scientific revolutions, and this case,I’m talking about
哥白尼的天体运行学说
the solar system, that’s Copernicus,
达尔文的进化论 以及魏格纳的大陆漂移学说
Darwin’s evolution, and tectonic plates, that’s Wegener.
它们跟大脑理论有很多共同点
They all have a lot in common with brain science.
第一点 它们都有很多无法解析的数据
Firstly, they had a lot of unexplained data. A lot of it.
但有理论后就变的容易处理了
But it got more manageable once they had a theory.
那时候众多很聪明的学者都被困惑
The best minds were stumped — really, really smart people.
我们并不比他们聪明
We’re not smarter now than they were then.
只是想出理论是很困难的
it just turns out it’s really hard to think of things,
但是一旦想到了 就很容易明白
but once you’ve thought of them, it’s kind of easy to understand it.
我的女儿都明白那三个理论的大概
My daughters understood these three theories
在幼儿园的时候就明白
in their basic framework by the time they were in kindergarten.
所以并不是那么困难 就像这是苹果橘子
And now it’s not that hard, you know, here’s the apple, here’s the orange,
地球围着太阳走等等
the Earth goes around, that kind of stuff.
还有 答案早就存在
Finally, another thing is the answerwas there all along,
只不过这件显而易见的事情被我们忽略了
but we kind of ignored it because of this obvious thing,and that’s the thing.
第二点 我们有很根深蒂固但是错误的想法
It was an intuitive, strongly-held belief that was wrong.
比如说在天体运行学中
In the case of the solar system,
关于地球自转的想法
the idea that the Earth is spinning,
地球表面在以千多英里在移动
and the surface of the Earth is going like a thousand miles an hour,
同时地球在太阳系里的轨道以百万多英里运行
and the earth it’s going through the solar system about a million miles an hour.
疯了吧 我们都知道地球并没有动
This is lunacy. We all know the Earth isn’t moving.
其实此时此刻你正在以千多英里的速度移动 你能想象得到吗
Do you feel like you’re moving a thousand miles an hour?
肯定不能 如果有人说
Of course not. You know, and someone who said,
地球在太空里自转 而且它很大
well, it was spining around in space and it’s so huge,
他们一定会把那个人关起来 他们当时的确就是这样做的
they would lock you up, and that’s what they did back then.
这是最直接感觉并且显而易见的例子 我们再看看进化论
So it was intuitive and obvious. Now, what about evolution?
进化论其实是同样的
Evolution’s the same thing.
我们教孩子圣经 里面说 上帝创造万物
We taught our kids, well, the Bible says, you know, God created all these species,
猫是猫 狗是狗 人是人 植物是植物 他们都不会变的
cats are cats, dogs are dogs,people are people,plants are plants,they don’t change.
诺亚把他们都放进方舟 等等 这些你都知道
Noah put them on the Ark in that order, blah, blah, blah. And you know,
事实上 如果你相信进化论 我们都有共同的祖先
The fact is, if you believe in evolution, we all have a common ancestor.
你们和我们 以及植物都有共同的祖先
And you all, we all have a common ancestor with the plant in the lobby.
进化论是这样说的 而这是真的 虽然有点难以置信
This is what evolution tells us. And,it’s true. It’s kind of unbelievable.
你知道吗 其实大陆漂移说也是一样的
And the same thing about tectonic plates, you know?
所有高山和大洲
All the mountains and the continents are kind of floating around
都浮动于地球之上
on top of the Earth. It’s like,
这听起来好像不合情理
It doesn’t make any sense.
那么阻止我们理解大脑的假想 即
So what is the intuitive, but incorrect assumption,
依靠直觉获取的错误设想究竟是什么呢
that’s kept us from understanding brains?
我现在就告诉你们 而且很明显是正确的 那才是重点对吗
Now I’m going to tell you, and it’s going to seem obvious that that is correct, and that’s the point,right?
然后我会提出论据 为什么这个假设是错误的
Then I am going to have to make an argument why you’re incorrect on the other assumption.
直觉告诉我们智慧
The intuitive but obvious thing is that somehow intelligence
界定于行为
is defined by behavior,
我们之所以被界定为聪明 是因为我们做事的方法看起来有智慧
that we’re intelligent because of the way that we do things
以及我们行为上表现聪明 我会告诉你们这想法是错的
and the way we behave intelligently, and I’m going to tell you that’s wrong.
智慧应该界定于推测能力
What it is, is intelligence is defined by prediction.
我会用这几张笔记
And I’m going to work you through this in a few slides here,
给你们看一看例子
give you an example of what this means.
这是一个系统
Here’s a system.
工程师喜欢看系统 科学家也喜欢
Engineers like to look at systems like this. Scientists like to look at systems like this.
我们有一箱子 我们有输入和输出
They say, well, we have a thing in a box, and we have its inputs and outputs.
人工智能专家会说 那箱子里面是可编程计算机
The AI people said, well, the thing in the box is a programmable computer
因为它等同大脑 而我们输入数据
because that’s equivalent to a brain, and we’ll feed it some inputs
我们会得到输出的行为
and we’ll get it to do something, have some behavior.
阿兰·图灵的图灵测试说
And Alan Turing defined the Turing test, which is essentially saying,
我们把与人类行为接近的行为界定为有智慧的行为
we’ll know if something’s intelligent if it behaves identical to a human.
这是测度智慧的行为指标
A behavioral metric of what intelligence is,
而我们被这想法困住了很长时间
and this has stuck in our minds for a long period of time.
实际上 我称这为真正智慧
Reality though, I call it real intelligence,
事实上 真正的智慧是建立于其它层面之上的
Real intelligence is built on something else.
我们通过一系列不同的方式来感受所处的世界环境
We experience the world through a sequence of patterns,
然后贮存 再回想
and we store them, and we recall them.
当我们回想时 我们会比较和对应实际情况
And when we recall them, we match them up against reality,
就这样我们不断的推测
and we’re making predictions all the time.
这是永恒的指标 一个测度我们对世界环境了解的指标
It’s an internal metric. There’s an external metric about us sort of saying,
以及我是否在推测 等等
do we understand the world? Am I making predictions? And so on.
你们都是有智慧的 但是实际上你们什么都没做
You’re all being intelligent right now, but you’re not doing anything.
可能你在搔痒 可能在挖鼻孔 但没有在做什么特别的能体现出有真正智慧的事
Maybe you’re scratching yourself, or picking your nose, I don’t know, but you’re not doing anything right now,
但你们还是有理性有智慧的 你们明白我在说什么
But you’re being intelligent; you’re understanding what I’m saying.
因为你们都有智慧 而且你们都会说英语
Because you’re intelligent and you speak English,
你们都知道我所说的
you know the word at the end of this
这个句子
sentence
(笑声)
(Laughter)
你们都猜到那字 因为你们不断的推测
The word came into you, you make these predictions all the time.
而我想说
And then, what I’m saying is,
新大脑皮层的输出就是不断的推测
is thay the internal prediction is the output in the neocortex,
推测导致有理性有智慧的行为
and that somehow, prediction leads to intelligent behavior.
而过程是这样的
Here’s how that happens:
我们从大脑里没有智慧的部分开始
Let’s start with a non-intelligent brain.
我认为我们脑里面有部分是没有智慧的 是古老的
Well I’ll argue a non-intelligent brain, we got hold of an old brain.
它甚至不属于哺乳类的 是属于爬行类年代的
And we are going to say it’s a non-mammal, like a reptile,
比方说 鳄鱼
so I’ll say, an alligator; we have an alligator.
鳄鱼有很复杂强大的感官系统
And the alligator has some very sophisticated senses.
有很好的眼睛 耳朵 触觉 等等
It’s got good eyes and ears and touch senses and so on,
还有口和鼻
a mouth and a nose.
也有很复杂的行为
It has very complex behavior.
会走会躲 会害怕会有情绪 会吃人
It can run and hide. It has fears and emotions. It can eat you, you know.
会攻击 等等
It can attack. It can do all kinds of stuff.
但我们不会认为鳄鱼很有智慧
But we don’t consider the alligator very intelligent,
不像人类的智慧
not in a human sort of way.
即使它已经拥有很复杂的行为
But it has all this complex behavior already.
进化论里是怎么说的?
Now in evolution, what happened?
第一件事就是哺乳类的进化
First thing that happened in evolution with mammals,
从开发新大脑皮层开始
is we started to develop a thing called the neocortex.
我们用这个来代表新大脑皮层 这个在小脑上面的箱子
And I’m going to represent the neocortex here, by this box on the top of the old brain.
新大脑皮层的解释是大脑上面的新一层
Neocortex means new layer. It’s a new layer on top of your brain.
如果你还是不能够明白 那么就把它看成你头顶的褶皱物
If you don’t know it, it’s the wrinkly thing on the top of your head that,
它之所以呈褶皱形状是因为它被挤进去而没有空间了
it’s got wrinkly because it got shoved in there and doesn’t fit.
(笑声)
(Laughter)
是真的 它大概是一张台布的大小
No, really, it’s what it is. It’s about the size of a table napkin.
因为空间太小放不下 所以它就变得褶皱起来了
And it doesn’t fit, so it gets all wrinkly.
现在让我们看看我画的这个
Now, look at how I’ve drawn this.
(旧)小脑还在这里
The old brain is still there.
那鳄鱼的脑袋还在 你们都有 那是你脑里情绪和感官的部分
You still have that alligator brain. You do. It’s your emotional brain.
它负责所有直觉 本能反应
It’s all those gut reactions you have.
在它上面 是我们所说的新大脑皮层
On top of it, we have this memory system called the neocortex.
它是包围着脑里感官系统的记忆系统
And the memory system is sitting over the sensory part of the brain.
感官输入先进小脑
And so as the sensory input comes in and feeds from the old brain,
再走上新大脑皮层
it also goes up into the neocortex.
而新大脑皮层只是记忆着
And the neocortex is just memorizing.
它记着所有正在发生的事情
It’s sitting there saying, ah, I’m going to memorize all the things that are going on:
像去了哪里 见过的人 听过的事 等等
where I’ve been, people I’ve seen, things I’ve heard, and so on.
在以后见到类似的情况
And in the future, when it sees something similar to that again,
类似的环境 或一样的环境
so in a similar environment, or the exact same environment,
它会把它自己重播 也会把记忆重播 就会发现以前来过这地方
it’ll play it back. It’ll start playing back. Oh, I’ve been here before.
而如果你曾经来过这里 你记得什么会发生
And when you’ve been here before, this happened next.
让你可以猜测未来
It allows you to predict the future.
就好像 外界的信号传入大脑
It allows you to, literally it feeds back the signals into your brain;
让你看到什么将会发生
they’ll let you see what’s going to happen next,
就像刚才你们会知道我准备会说的词
will let you hear the word “sentence” before I said it.
正是这个信号传递回小脑
And it’s this feeding back into the old brain
所以你们才能去作出很理性的决定
that’ll allow you to make very more intelligent decisions.
这是我演说中最重要的一张幻灯片 我会这里多停留一会儿
This is the most important slide of my talk, so I’ll dwell on it a little bit.
所以 你们总是说 噢 天呐 我能预知未来了
And, and so, all the time you say, Oh, I can predict things.
如果我们像白老鼠一样在走迷宫 那就学习那个迷宫
And if you’re a rat and you go through a maze, and then you learn the maze,
下次再走 行为一样
the next time you’re in a maze, you have the same behavior,
但是会突然变聪明了 因为会开始认得那迷宫 知道应该走哪条路
But all of a sudden, you’re smarter because you say, oh, I recognize this maze, I know which way to go,
曾经走过 可以预想
I’ve been here before, I can envision the future.
预想接下来会发生的一切
And that’s what it’s doing.
人类和其他哺乳类动物都会这样
In humans — by the way, this is true for all mammals;
只是人类的情况会更极端
and in humans, it got a lot worse.
我们会发展新大脑皮层前面的部分
In humans, we actually developed the front part of the neocortex
我们称之为大脑皮层的前端
called the anterior part of the neocortex.
然后大自然会弄一些小把戏
And nature did a little trick.
它将新大脑皮层后端 也就是感官的部分
It copied the posterior part, the back part, which is sensory,
拷贝到前端
and put it in the front part.
人类大脑前端有独特的构造 跟后端是一样的
And humans uniquely have the same mechanism on the front,
但我们用来控制运动
but we use it for motor control.
所以我们可以进行很复杂的计划行动
So we are now able to make very sophisticated motor planning, things like that.
这个我们先不说 要理解大脑怎么运作
I don’t have time to explain, but if you want to understand how a brain works,
我们先了解第一代哺乳类动物新大脑皮层的运作
you have to understand how the first part of the mammalian neocortex works,
以及怎么去贮存资料样式和作出猜测
how it is we store patterns and make predictions.
我先列出几个猜测的例子
Let me give you a few examples of predictions.
我已经说过句子了
I already said the word “sentence.”
在音乐中 如果你听过一首歌 如果你听过吉尔唱歌
In music, if you’ve heard a song before, if you heard Jill sing those songs before,
当她唱的时候 下一个音符就会在你脑海中了
when she sings them, the next notepops into your head already —
你会有预感
you anticipate it as you’re going.
如果是一张音乐专辑 听完一首歌 下一首已在你脑海出现
If it was an album of music, at the end of one album, the next song pops into your head.
这情况经常发生 你在不断的猜测
And these things happen all the time. You’re making these predictions.
我有一个用门的实验 叫做 门之改变
I have this thing called the altered door thought experiment.
是这样的 你家有一扇门
And the altered door thought expriment says, you have a door at home,
当你在这里的时候 我去把它改了 我们有一个人
and when you’re here, I’m changing it, I’ve got a guy
他现在在你家 把门改过来
back at your house right now, moving the door around,
他会把你的门把手移动2英寸
and they’re going to take your doorknob and move it over two inches.
当你今晚回家 找把手开门
And when you go home tonight, you’re going to put your hand out there,
当你找到门把手的时候
and you’re going to reach for the doorknob and you’re going to notice
你会发现把手在错的位置
it’s in the wrong spot,
你会感觉 嗯 有点问题
and you’ll go, Whoa, something happened.
可能等一秒才发现问题 但感觉到不对劲
It may take a second to figure out what it was, but something happened.
我也可以用别的方法改变门把手
Now I can change your doorknob in other ways.
弄大一点或者小一点 从铜改为银的 当然我也可以给门加装控制杆
I can make it larger or smaller, I can change its brass to silver,I can make it a lever.
我还可以这样改变这个门 给它涂上颜色 甚至说安装上玻璃
I can change the door, put colors on. I can put windows in.
对于这扇门我有一千种修改方案
I can change a thousand things about your door
而在你开门的两秒钟
and in the two seconds you take to open your door
你会发现不对劲
you’re going to notice that something has changed.
那传统的工程或人工智能对这问题的方法是
Now, the engineering approach to this, the AI approach to this,
起一个门的数据库 有所以关于门的参数
is to build a door database. It has all the door attributes.
当你到了门前 便进数据库一个一个比较
And as you go up to the door, you know, let’s check them off one at time.
所以不同样式 不同颜色的
Door, door, door, you know, color, you know what I’m saying.
我们人类肯定不会这样做的 你们的大脑不会这样运作
We don’t do that. Your brain doesn’t do that.
你的大脑会不停作出猜测
Your brain is making constant predictions all the time
对你附近环境有可能会发生的作出猜测
about what will happen in your environment.
当我把手放在桌子上 我预料手会停在上面
As I put my hand on this table, I expect to feel it stop.
当我走路的时候 每一步 如果只是差了八分之一英寸
When I walk, every step, if I missed it by an eighth of an inch,
我都会知道有情况改变
I’ll know something has changed.
你们不停的对身边环境作出猜测
You’re constantly making predictions about your environment.
让我们看看视觉系统
I’ll talk about vision herw briefly.
这是一张女人的图片
This is a picture of a woman.
当你看人的时候 你的眼神会停留 大概两到三秒
And when you look at people, your eyes are caught over at two to three times a second.
你应该意识不到 但是你的眼球不停在动
You’re not aware of it, but our eyes are always moving.
所以当你看一个人的脸
And so when you look at someone’s face,
你通常会从看着眼到鼻 然后到口
you’d typically go from eye to eye to eye to nose to mouth.
如果你在看眼的位置的时候
Now, when your eye moves from eye to eye,
出现像鼻子的东西
if there was something else there like, a nose,
你看见鼻子长在眼睛的位置
you’d see a nose where an eye is supposed to be,
你一定会吓一跳
and you’d go, oh shit, you know
(笑声)
(Laughter)
你一定会这样想 这个人有点问题
There’s something wrong about this person.
这都是因为你在推测
And that’s because you’re making a prediction.
你看着那个人眼睛的位置 不会这样想:我看到了什么?
If it’s not like you just look over there and say, what am I seeing?
一个鼻子 哦好的吧
A nose, that’s okay.
不是这样的 无论在什么时候 你对即将看到的事物都会有一个预期猜想
No, you have an expection of what you’re going to see every single moment.
最后 让我们看看我们是怎样测试智慧的
And finally, let’s think about how we test intelligence.
我们用猜测能力来测试的 下一个词是什么
We test it by prediction. What is the next word in this,you know?
这个搭配这个那个搭配那个 下一个数是什么
This is to this as this is to this. What is the next number in this sentence?
这是一个东西的三视图
Here’s three visions of an object.
那么第四个是什么呢
What’s the fourth one?
这就是测试方法 关于猜测能力的测试
That’s how we test it. It’s all about prediction.
那么什么是大脑理论的秘诀
So what is the recipe for brain theory?
第一 我们需要合适的框架
First of all, we have to have the right framework.
一个记忆的框架
And the framework is a memory framework,
不是计算的或行为的框架
not a computational or behavior framework,
是一个记忆的框架
it’s a memory framework.
你怎么贮存和回忆有关联的样式组合
How do you store and recall these sequences of patterns?
这是时间空间样式
It’s spatio-temporal patterns.
然后 如果在框架里 我们找一群理论研究者
Then, if in that framework, you take a bunch of theoreticians.
生物学家一般不是好的理论学家
Now biologists generally are not good theoreticians.
不一定 但是历史里没有好的生物理论
It’s not always true, but in general, there’s not a good history of theory in biology.
我觉得物理学家
So I found the best people to work with are physicists,
工程师和数学家都非常适合
engineers and mathematicians,
他们的想法都是很规则很系统的
who tend to think algorithmically.
然后他们要学解剖学和生理学
Then they have to learn the anatomy and the physiology,
我们需要让这理论非常的实在 从解剖学角度来看
You have to make these theories very realistic in anatomical terms.
如果有人解释大脑理论时
Anyone who tells you their theory about how the brain works
而不告诉你大脑里面怎么运作
and doesn’t tell you exactly how it’s working in the brain
和大脑各部分的联系
and how the wiring works in the brain,
那就不是真正的理论了
it’s not a theory.
而我们的研究院正是研究这方面的
And that’s what we doing at the Redwood Neuroscience Institute.
我很希望有更多的时间告诉你们最近的研究成果
I would love to tell you we’re making fantastic progress in this thing,
我以后会再回来
and I expect to be back up this stage,
在不久的将来 来告诉大家
maybe this will be some other time in the not too distant future and tell you about it.
我真的很兴奋 这肯定不会花50年
I’m really, really excited. This is not going to take 50 years at all.
那大脑理论像什么呢
So what will brain theory look like?
首先 它会是一个关于记忆的理论
First of all, it’s going to be about memory.
不像计算机的记忆 它不完全像计算机的记忆
Not like computer memory. It’s not at all like computer memory.
很不一样
It’s very very different.
它是多维样式的记忆
And it’s a memory of these very high-dimensional patterns,
就像从你们眼睛输出的
like the things that come from your eyes.
它也会是很多组有关联的记忆
It’s also memory of sequences:
你不会学习或回忆没有关联的东西
you cannot learn or recall anything outside of a sequence.
就像一首歌在时间上是有先后的记忆
A song must be heard in sequence over time,
要回忆起来也是一连串的回忆
and you must play it back in sequence over time.
这些关联记忆组群会在回忆时会自动联系连结
And these sequences are auto-associatively recalled, so if I see something,
所以当我们看到或者听到一些类似的东西 记忆就会重播
I hear something, it reminds me of it, and then it plays back automatically.
这是自动的重播 最后输出是未来的猜测
It’s an automatic playback. And prediction of future inputs is the desired output.
我们提过 这理论在生物学上合理
And as I said, the theory must be biologically accurate,
它是能经得起测试的 可被推理出来的
it must be testable and you must be able to build it.
如果你不推理出来 你不会明白
If you don’t build it, you don’t understand it.
还有一张笔记
So, one more slide here.
这研究结果有什么作用呢
What is this going to result in?
我们真的会制造有智慧的机器吗
Are we going to really build intelligent machines?
答案是肯定的
Absolutely.
而且会跟我们想象的不一样
And it’s going to be different than people think.
这种事情一定会发生 我深信不疑
No doubt that it’s going to happen, in my mind.
首先 我们会用硅来制造
First of all, it’s going to build up, we’re going to build the stuff out of silicon.
也就是制造计算机内存的方法
The same techniques we use to build silicon computer memories,
在这里我们可以用上
we can use here.
但是它将会是很不一样的记忆体
But they’re very different types of memories.
我们会把感应器和这些记忆体连接上
And we’re going to attach these memories to sensors,
感应器会接受真实环境的数据
and the sensors will experience real-live, real-world data,
而这些机器会学习它们所处的环境
and these things are going to learn about their environment.
一开始发展出来就像机器人的可能比较低
Now, it’s very unlikely the first things you’ll see are like robots.
不是说机器人没有用处或者我们制造不出来
Not that robots aren’t useful; people can build robots.
但是机器人硬件是最难制造的 那像旧(小)脑
But the robotics part is the hardest part. That’s the old brain. That’s really hard.
(新)大脑比小脑容易
The new brain is actually kind of easier than the old brain.
所以刚开始我们会造一些不需要太多机器人硬件的
So the first thing we’re going to do are the things that don’t require a lot of robotics.
应该不会见到 C-3PO
So you’re not going to see C-3PO.
你会见到比较多类似 智能车
You’re going to see things like, you know, intelligent cars
会理解交通情况和驾驶
that really understand what traffic is and what driving is
以及一些常规类型的问题 比方说有些车的转向显示灯亮了半分钟
and have learned that certain types of cars with the blinkers on for half a minute
应该不是真的想转向
probably aren’t going to turn, things like that.
(笑声)
(Laughter)
我们也可以制造智能保安系统
We can also do intelligent security systems.
任何需要很多大脑分析但不需要很多的机械的领域
Anywhere where we’re basically using our brain, but not doing a lot of mechanics.
都会是在初期发展的
Those are the things that are going to happen first.
但最终会发展到各个方面
But ultimately, the world’s the limit here.
我也不知道最终会发展成什么样
I don’t know how this is going to turn out.
我认识很多发明微处理器的专家
I know a lot of people who inventedthe microprocessor.
你如果问他们
And if you talk to them,
那时候他们知道正在做很有意义的事
they knew what they were doing was really significant,
但也不知道会发展成什么
but they didn’t really know what was going to happen.
他们也没有预计手机 互联网等等的发展
They couldn’t anticipate cell phones and the Internet and all this kind of stuff.
他们只知道会制造计算机
They just knew like, they were going to build calculators
会制造交通灯的控制器等等
and traffic-light controllers.
但都感觉到它是很重大的
But it’s going to be big!”
同样地 大脑的研究和记忆体
In the same way, this is like brain science and these memories
将会成为很基本的科技
are going to be a very fundamental technology,
它将会在未来100年带领着一些很难想象的发展
and it’s going to lead to unbelievable changes in the next 100 years.
而令我最兴奋的是我们怎样利用这科技
And I’m most excited about how we’re going to use them in science.
我想我已经超时了 那么就到这里吧
So I think that’s all my time, I’m over it.
我的演讲就此结束吧
and I’m going to end my talk right there.
登录TED 每周都有新鲜有趣的事哦
NEW TED TALKS EACH WEEK at www.TED.com
BMW 清洁能源 职责与性能同在
BMW CleanEnergy.Responsibility with Performance
BMW 7系 世界上第一款氢动力具有奢华表现的驾驶汽车
BMW Hydrogen7.The world’s first hydrogen-powered luxury performance automobile.

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视频概述

你有大脑 没错 但是你对你的大脑又了解多少呢

听录译者

收集自网络

翻译译者

[B]Azrael

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赖皮

视频来源

https://www.youtube.com/watch?v=G6CVj5IQkzk

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