ABOUT THE SPEAKER
Kwabena Boahen - Bioengineer
Kwabena Boahen wants to understand how brains work -- and to build a computer that works like the brain by reverse-engineering the nervous system. His group at Stanford is developing Neurogrid, a hardware platform that will emulate the cortex’s inner workings.

Why you should listen

Kwabena Boahen is the principal investigator at the Brains in Silicon lab at Stanford. He writes of himself:

Being a scientist at heart, I want to understand how cognition arises from neuronal properties. Being an engineer by training, I am using silicon integrated circuits to emulate the way neurons compute, linking the seemingly disparate fields of electronics and computer science with neurobiology and medicine.

My group's contributions to the field of neuromorphic engineering include a silicon retina that could be used to give the blind sight and a self-organizing chip that emulates the way the developing brain wires itself up. Our work is widely recognized, with over sixty publications, including a cover story in the May 2005 issue of Scientific American.

My current research interest is building a simulation platform that will enable the cortex's inner workings to be modeled in detail. While progress has been made linking neuronal properties to brain rhythms, the task of scaling up these models to link neuronal properties to cognition still remains. Making the supercomputer-performance required affordable is the goal of our Neurogrid project. It is at the vanguard of a profound shift in computing, away from the sequential, step-by-step Von Neumann machine towards a parallel, interconnected architecture more like the brain.

More profile about the speaker
Kwabena Boahen | Speaker | TED.com
TEDGlobal 2007

Kwabena Boahen: A computer that works like the brain

Kwabena Boahen : 像人脑那样工作的电脑

Filmed:
718,375 views

Kwabena Boahen 正在研究如何用硅片来模拟人脑超强的运算能力,因为人脑的这种复杂而重复的运作过程可以在一台小巧轻便而且运行超快的电脑上实现。
- Bioengineer
Kwabena Boahen wants to understand how brains work -- and to build a computer that works like the brain by reverse-engineering the nervous system. His group at Stanford is developing Neurogrid, a hardware platform that will emulate the cortex’s inner workings. Full bio

Double-click the English transcript below to play the video.

00:18
I got my first computer电脑 when I was a teenager青少年 growing生长 up in Accra阿克拉,
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我成长在阿克拉(加纳首都),在我还是个少年的时候,我有了第一台电脑。
00:23
and it was a really cool device设备.
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它真的是个很酷的玩意。
00:26
You could play games游戏 with it. You could program程序 it in BASICBASIC.
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你可以用它来玩游戏,你可以用BASIC语言在上面编程。
00:31
And I was fascinated入迷.
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我被它迷住了。
00:33
So I went into the library图书馆 to figure数字 out how did this thing work.
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于是我跑去图书馆想要弄明白这东西究竟是怎么工作的。
00:39
I read about how the CPU中央处理器 is constantly经常 shuffling洗牌 data数据 back and forth向前
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我了解到CPU(中央处理器)是如何不断地让数据来回穿梭于
00:44
between之间 the memory记忆, the RAM内存 and the ALUALU,
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存储器--RAM (随机存取存储器)和 ALU --
00:48
the arithmetic算术 and logic逻辑 unit单元.
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算术逻辑运算器。
00:50
And I thought to myself, this CPU中央处理器 really has to work like crazy
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我心想,CPU只有这样玩命地工作
00:54
just to keep all this data数据 moving移动 through通过 the system系统.
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才能让所有的数据在电脑系统中不停地运转呀。
00:58
But nobody没有人 was really worried担心 about this.
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但并没有人真正担心过这些。
01:01
When computers电脑 were first introduced介绍,
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当电脑首次问世时,
01:03
they were said to be a million百万 times faster更快 than neurons神经元.
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曾号称比人脑神经细胞快上一百万倍,
01:06
People were really excited兴奋. They thought they would soon不久 outstrip超过
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人们相当激动,他们以为电脑将很快就能超越
01:11
the capacity容量 of the brain.
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人脑。
01:14
This is a quote引用, actually其实, from Alan艾伦 Turing图灵:
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Alan Turing 是这样说的:
01:17
"In 30 years年份, it will be as easy简单 to ask a computer电脑 a question
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“不出30年,向电脑提问就会变得和向人提问一样的
01:21
as to ask a person."
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简单。”
01:23
This was in 1946. And now, in 2007, it's still not true真正.
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这句话是在1946年说的。现在都2007年了,还是没能兑现。
01:30
And so, the question is, why aren't we really seeing眼看
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问题就在于,为什么我们不能真正地
01:34
this kind of power功率 in computers电脑 that we see in the brain?
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让电脑具备人脑的功能呢?
01:38
What people didn't realize实现, and I'm just beginning开始 to realize实现 right now,
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过去大家都没意识到,而我也刚刚开始意识到的是
01:42
is that we pay工资 a huge巨大 price价钱 for the speed速度
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我们为了提升电脑的速度而付出了巨大的代价
01:44
that we claim要求 is a big advantage优点 of these computers电脑.
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这是因为速度被认为是电脑的一大优势。
01:48
Let's take a look at some numbers数字.
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让我们看一些数字。
01:50
This is Blue蓝色 Gene基因, the fastest最快的 computer电脑 in the world世界.
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这是Blue Gene,世上最快的电脑。
01:54
It's got 120,000 processors处理器; they can basically基本上 process处理
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它拥有120,000个处理器;基本上它们每秒可以处理
01:59
10 quadrillion万亿 bits of information信息 per second第二.
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一万兆位元的信息。
02:02
That's 10 to the sixteenth第十六. And they consume消耗 one and a half megawatts兆瓦 of power功率.
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相当于10的16次方。并且它们还要消耗掉1.5兆瓦特的电力。
02:09
So that would be really great, if you could add that
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如果你能把这些能量用到
02:12
to the production生产 capacity容量 in Tanzania坦桑尼亚.
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坦桑尼亚的生产力上的话,那就简直棒极了。
02:14
It would really boost促进 the economy经济.
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它肯定能振兴经济。
02:16
Just to go back to the States状态,
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再回来看看美国,
02:20
if you translate翻译 the amount of power功率 or electricity电力
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如果你把这个电脑消耗的电能换算成
02:22
this computer电脑 uses使用 to the amount of households in the States状态,
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美国家庭的用电量,
02:25
you get 1,200 households in the U.S.
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那你会发现这相当于1200户美国家庭的用电量。
02:29
That's how much power功率 this computer电脑 uses使用.
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如此多的能量都被这个电脑消耗了。
02:31
Now, let's compare比较 this with the brain.
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现在,我们把这个电脑跟人脑做个比较,
02:34
This is a picture图片 of, actually其实 Rory罗里 Sayres'Sayres' girlfriend's女友 brain.
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这是Rory Sayres 女友的大脑图片,
02:39
Rory罗里 is a graduate毕业 student学生 at Stanford斯坦福.
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Rory 是斯坦佛大学的研究生,
02:41
He studies学习 the brain using运用 MRIMRI, and he claims索赔 that
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他用MRI(核磁共振成像)研究大脑,他宣称
02:45
this is the most beautiful美丽 brain that he has ever scanned扫描.
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这是他扫描过的最美丽的人脑。
02:48
(Laughter笑声)
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(笑声)
02:50
So that's true真正 love, right there.
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这就是真爱吧。
02:53
Now, how much computation计算 does the brain do?
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那么人脑究竟能计算多少呢?
02:56
I estimate估计 10 to the 16 bits per second第二,
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我估计是每秒10到16位元
02:58
which哪一个 is actually其实 about very similar类似 to what Blue蓝色 Gene基因 does.
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这其实很接近Blue Gene (世界上最快的电脑)的运算能力了。
03:02
So that's the question. The question is, how much --
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那么问题就在这儿。那就是——
03:04
they are doing a similar类似 amount of processing处理, similar类似 amount of data数据 --
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他们的计算量相似,处理的数据量相似——
03:07
the question is how much energy能源 or electricity电力 does the brain use?
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可是人脑耗用了多少电能呢?
03:12
And it's actually其实 as much as your laptop笔记本电脑 computer电脑:
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实际上就相当于你的笔记本电脑的用电量:
03:15
it's just 10 watts.
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只有10瓦。
03:17
So what we are doing right now with computers电脑
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我们目前使用电脑做的事情
03:20
with the energy能源 consumed消费 by 1,200 houses房屋,
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消耗着相当于1200户家庭的总用电量,
03:23
the brain is doing with the energy能源 consumed消费 by your laptop笔记本电脑.
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而人脑做下来却只需要相当于笔记本电脑的用电量。
03:28
So the question is, how is the brain able能够 to achieve实现 this kind of efficiency效率?
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那么问题是,大脑是怎么达到如此高效的?
03:31
And let me just summarize总结. So the bottom底部 line线:
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让我总结一下,这个结论是:
03:33
the brain processes流程 information信息 using运用 100,000 times less energy能源
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人脑用十万倍分之一的能量就处理了
03:37
than we do right now with this computer电脑 technology技术 that we have.
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我们目前的用电脑所处理的信息量。
03:41
How is the brain able能够 to do this?
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人脑是怎么做到这点的呢?
03:43
Let's just take a look about how the brain works作品,
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我们先看看人脑是如何工作的,
03:46
and then I'll compare比较 that with how computers电脑 work.
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然后我同电脑是怎么工作的相比较。
03:50
So, this clip is from the PBSPBS series系列, "The Secret秘密 Life of the Brain."
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这个视频片段,剪辑自PBS(公共电视网)的系列片:“神秘的大脑”。
03:54
It shows节目 you these cells细胞 that process处理 information信息.
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它向你展示了这些处理信息的细胞。
03:57
They are called neurons神经元.
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这些细胞叫做神经元。
03:58
They send发送 little pulses脉冲 of electricity电力 down their processes流程 to each other,
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神经元之间在传送信息的过程中会发出微小的电脉冲,
04:04
and where they contact联系 each other, those little pulses脉冲
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神经元互相接触时,这些微小的电脉冲
04:06
of electricity电力 can jump from one neuron神经元 to the other.
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能够从一个神经元跳到另一个神经元。
04:08
That process处理 is called a synapse突触.
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这个过程被称为神经突触。
04:11
You've got this huge巨大 network网络 of cells细胞 interacting互动 with each other --
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人脑所拥有的由神经元相互交织而成的网络相当庞大,
04:13
about 100 million百万 of them,
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其中有大概一亿个神经元,
04:15
sending发出 about 10 quadrillion万亿 of these pulses脉冲 around every一切 second第二.
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每秒发送约十万亿个这样的脉冲。
04:19
And that's basically基本上 what's going on in your brain right now as you're watching观看 this.
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你正观看这个片子的时候,你的大脑基本上就这样运转着。
04:25
How does that compare比较 with the way computers电脑 work?
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怎么拿这个跟电脑的工作方式比较呢?
04:27
In the computer电脑, you have all the data数据
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电脑把所有数据
04:29
going through通过 the central中央 processing处理 unit单元,
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都通过中央处理器来处理,
04:31
and any piece of data数据 basically基本上 has to go through通过 that bottleneck瓶颈,
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任何数据都必须通过那个瓶颈。
04:34
whereas in the brain, what you have is these neurons神经元,
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然而在大脑中,你所拥有的是这些神经元,
04:38
and the data数据 just really flows流动 through通过 a network网络 of connections连接
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数据是顺着连接神经元的网络流动
04:42
among其中 the neurons神经元. There's no bottleneck瓶颈 here.
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这里不存在瓶颈,
04:44
It's really a network网络 in the literal文字 sense of the word.
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这是一个名副其实的网络,
04:48
The net is doing the work in the brain.
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就是这个网络担负着大脑的运转。
04:52
If you just look at these two pictures图片,
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看看这两张图片,
04:54
these kind of words pop流行的 into your mind心神.
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你的脑海中会跳出这样的词,
04:56
This is serial串行 and it's rigid死板 -- it's like cars汽车 on a freeway高速公路,
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这一幅连续又呆板:就像在高速路上的汽车——
05:00
everything has to happen发生 in lockstep锁步 --
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一切必须按部就班;
05:03
whereas this is parallel平行 and it's fluid流体.
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而这幅图平行而且有流动感,
05:05
Information信息 processing处理 is very dynamic动态 and adaptive自适应.
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其信息处理既非常活跃又很具适应性。
05:08
So I'm not the first to figure数字 this out. This is a quote引用 from Brian布赖恩 Eno伊诺:
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我并不是第一个有这样想法的人。Brian Eno如是说:
05:12
"the problem问题 with computers电脑 is that there is not enough足够 Africa非洲 in them."
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“电脑的问题就在于它还不能大到足以装下整个非洲。”
05:16
(Laughter笑声)
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(笑声)
05:22
Brian布赖恩 actually其实 said this in 1995.
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Brian 说这话时是1995年
05:25
And nobody没有人 was listening then,
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当时没有任何人听进去,
05:28
but now people are beginning开始 to listen
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但现在人们开始当真了
05:30
because there's a pressing紧迫, technological技术性 problem问题 that we face面对.
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因为我们正面临着一个急迫的技术问题
05:35
And I'll just take you through通过 that a little bit in the next下一个 few少数 slides幻灯片.
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在下面几张幻灯片中我会带你们简略地了解一下这个问题:
05:40
This is -- it's actually其实 really this remarkable卓越 convergence收敛
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这个——实际上它是个真正非凡的聚合体
05:44
between之间 the devices设备 that we use to compute计算 in computers电脑,
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算是介于用电脑来计算的装置
05:49
and the devices设备 that our brains大脑 use to compute计算.
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和用人脑来计算的装置之间。
05:53
The devices设备 that computers电脑 use are what's called a transistor晶体管.
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电脑用的装置我们称之为晶体管。
05:57
This electrode电极 here, called the gate, controls控制 the flow of current当前
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这儿是电极,称为闸极,控制着从源极流向汲极的电流
06:01
from the source资源 to the drain排水 -- these two electrodes电极.
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就是这两个电极。
06:04
And that current当前, electrical电动 current当前,
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而那电流,电流是由电子传送的,
06:06
is carried携带的 by electrons电子, just like in your house and so on.
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就如同你房子里的电流一样,诸如此类。
06:12
And what you have here is, when you actually其实 turn on the gate,
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这里你要明白的是,当你打开闸极时,
06:17
you get an increase增加 in the amount of current当前, and you get a steady稳定 flow of current当前.
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电流量会增加,并且是股稳定电流。
06:21
And when you turn off the gate, there's no current当前 flowing流动 through通过 the device设备.
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当你关掉闸极时,就没有任何电流流过这个装置了。
06:25
Your computer电脑 uses使用 this presence存在 of current当前 to represent代表 a one,
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我们用“一”来代表你的电脑有电流,
06:30
and the absence缺席 of current当前 to represent代表 a zero.
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而用“零”来代表电脑无电流。
06:34
Now, what's happening事件 is that as transistors晶体管 are getting得到 smaller and smaller and smaller,
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现在的情况是当晶体管变得越来越小越来越小的话,
06:40
they no longer behave表现 like this.
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它们就不会再像这样运转了,
06:42
In fact事实, they are starting开始 to behave表现 like the device设备 that neurons神经元 use to compute计算,
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事实上,它们会开始像神经元用来计算的装置那样来运作,
06:47
which哪一个 is called an ion离子 channel渠道.
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这种装置被称作离子通道
06:49
And this is a little protein蛋白 molecule分子.
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这是个小小的蛋白质分子。
06:51
I mean, neurons神经元 have thousands数千 of these.
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我的意思是,神经元有成千上万个这样的分子。
06:55
And it sits坐镇 in the membrane of the cell细胞 and it's got a pore in it.
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而它就分布在细胞膜中并且自身还带了个小孔,
06:59
And these are individual个人 potassium ions离子
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而这些是单独的钾离子,
07:02
that are flowing流动 through通过 that pore.
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在小孔中穿来过去。
07:04
Now, this pore can open打开 and close.
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现在,这个孔是能开能闭的,
07:06
But, when it's open打开, because these ions离子 have to line线 up
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但是,当它打开时,由于这些离子必须排成一行
07:11
and flow through通过, one at a time, you get a kind of sporadic零星的, not steady稳定 --
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一个一个地穿过小孔,因此产生零星的,而非稳定的——
07:16
it's a sporadic零星的 flow of current当前.
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一股断断续续的电流。
07:19
And even when you close the pore -- which哪一个 neurons神经元 can do,
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甚至当小孔闭合的时候——神经元做得到这点的,
07:22
they can open打开 and close these pores毛孔 to generate生成 electrical电动 activity活动 --
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它们可以通过开关这些小孔来产生电活动——
07:27
even when it's closed关闭, because these ions离子 are so small,
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甚至当孔闭合时,由于这些离子如此之小,
07:30
they can actually其实 sneak潜行 through通过, a few少数 can sneak潜行 through通过 at a time.
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它们实际上可以偷偷地穿过,其中一些还可以同时偷偷地穿过。
07:33
So, what you have is that when the pore is open打开,
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所以你得出的结论就是当小孔张开时,
07:36
you get some current当前 sometimes有时.
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有时候会产生一些电流
07:38
These are your ones那些, but you've got a few少数 zeros thrown抛出 in.
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这些就是“一”,但你也会额外得到些“零”;
07:41
And when it's closed关闭, you have a zero,
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而当小孔闭合时,你则得到个“零”,
07:45
but you have a few少数 ones那些 thrown抛出 in.
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但你也能得到些额外的“一”,是的。
07:48
Now, this is starting开始 to happen发生 in transistors晶体管.
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现在,这一原理正开始运用于晶体管,
07:51
And the reason原因 why that's happening事件 is that, right now, in 2007 --
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而发生的原因就在于,在2007年,
07:56
the technology技术 that we are using运用 -- a transistor晶体管 is big enough足够
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我们目前使用的技术,晶体管的大小足以使
08:00
that several一些 electrons电子 can flow through通过 the channel渠道 simultaneously同时, side by side.
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好几个电子能同时穿过通道,而且是并排地。
08:05
In fact事实, there's about 12 electrons电子 can all be flowing流动 this way.
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事实上,大概有12个电子都可以这样穿过去。
08:09
And that means手段 that a transistor晶体管 corresponds对应
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这意味着一个晶体管就相当于
08:11
to about 12 ion离子 channels渠道 in parallel平行.
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12个并列的离子通道。
08:14
Now, in a few少数 years年份 time, by 2015, we will shrink收缩 transistors晶体管 so much.
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在几年时间内,到2015,我们会把晶体管体积缩得非常小,
08:19
This is what Intel英特尔 does to keep adding加入 more cores核心 onto the chip芯片.
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这正是英特尔所致力于的事业:不断地往芯片上
08:24
Or your memory记忆 sticks that you have now can carry携带 one gigabyte技嘉
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或是你的记忆棒上添加更多的核,这样就使它们能有1G的内存
08:27
of stuff东东 on them -- before, it was 256.
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——而以前才256MB。
08:29
Transistors晶体管 are getting得到 smaller to allow允许 this to happen发生,
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晶体管变得越来越小才使这一切得以实现
08:32
and technology技术 has really benefitted受益 from that.
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而技术更是得益于此
08:35
But what's happening事件 now is that in 2015, the transistor晶体管 is going to become成为 so small,
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但现在的情况是到2015年,晶体管将变得如此之小,
08:40
that it corresponds对应 to only one electron电子 at a time
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以至于它一次只能让一个电子
08:43
can flow through通过 that channel渠道,
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通过通道,
08:45
and that corresponds对应 to a single ion离子 channel渠道.
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相当于一个单离子通道,
08:47
And you start开始 having the same相同 kind of traffic交通 jams果酱 that you have in the ion离子 channel渠道.
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因此就会开始出现像在单离子通道里发生的那种交通堵塞,
08:51
The current当前 will turn on and off at random随机,
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电流会随机地时断时续,
08:54
even when it's supposed应该 to be on.
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甚至在它理应通电的时候
08:56
And that means手段 your computer电脑 is going to get
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而那也就意味着你的电脑将会把
08:58
its ones那些 and zeros mixed up, and that's going to crash紧急 your machine.
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它的“一”和“零”们搞混淆,那么你这台机器就完蛋了。
09:02
So, we are at the stage阶段 where we
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所以,我们现在还处于
09:06
don't really know how to compute计算 with these kinds of devices设备.
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尚不真正清楚如何使用这类装置来运算的阶段
09:09
And the only kind of thing -- the only thing we know right now
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而我们目前知道的唯一一件事,唯一
09:12
that can compute计算 with these kinds of devices设备 are the brain.
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能用这类装置来进行运算的是我们人类的大脑
09:15
OK, so a computer电脑 picks精选 a specific具体 item项目 of data数据 from memory记忆,
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好吧,所以说电脑是从内存中中挑取特定的一项数据
09:19
it sends发送 it into the processor处理器 or the ALUALU,
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把它传送给处理器或是ALU,
09:22
and then it puts看跌期权 the result结果 back into memory记忆.
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然后再将运算结果送还给内存。
09:24
That's the red path路径 that's highlighted突出.
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就是这着重标出的红色路线。
09:26
The way brains大脑 work, I told you all, you have got all these neurons神经元.
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人脑工作的方式,用上了你能有的所有神经元。
09:30
And the way they represent代表 information信息 is
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它们呈现信息的方式是
09:32
they break打破 up that data数据 into little pieces
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把数据粉碎成很小的碎片
09:34
that are represented代表 by pulses脉冲 and different不同 neurons神经元.
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并用脉冲和不同的神经元来表达。
09:37
So you have all these pieces of data数据
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而所有的数据碎片
09:39
distributed分散式 throughout始终 the network网络.
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都分散在这网络中。
09:41
And then the way that you process处理 that data数据 to get a result结果
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而你处理数据得到结果的方式则是
09:44
is that you translate翻译 this pattern模式 of activity活动 into a new pattern模式 of activity活动,
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将这一种活动模式转化成一种新的活动模式,
09:48
just by it flowing流动 through通过 the network网络.
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仅仅就是让它在网络中流过而已。
09:51
So you set up these connections连接
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这样你就建立起这些连接,
09:53
such这样 that the input输入 pattern模式 just flows流动
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仅仅让输入模式流动
09:56
and generates生成 the output产量 pattern模式.
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就能产生输出模式
09:58
What you see here is that there's these redundant connections连接.
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现在你看到的是一堆多余的连接
10:02
So if this piece of data数据 or this piece of the data数据 gets得到 clobbered重挫,
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所以如果这块信息碎片,或这块信息碎片被损毁的话
10:06
it doesn't show显示 up over here, these two pieces can activate启用 the missing失踪 part部分
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它就不会在这儿显示出来了,这两份信息可以
10:11
with these redundant connections连接.
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通过这些多余的连接来激活缺失的部分信息
10:13
So even when you go to these crappy蹩脚的 devices设备
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所以即使你用的是这么些蹩脚的装置
10:15
where sometimes有时 you want a one and you get a zero, and it doesn't show显示 up,
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有时会在你想要个一的时候给你个零
10:18
there's redundancy冗余 in the network网络
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网络中的重复性
10:20
that can actually其实 recover恢复 the missing失踪 information信息.
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实际上却能恢复那些缺失的信息。
10:23
It makes品牌 the brain inherently本质 robust强大的.
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它令大脑自然而然地强大。
10:26
What you have here is a system系统 where you store商店 data数据 locally本地.
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你这里所拥有的是一个只能存储本地数据的系统
10:29
And it's brittle, because each of these steps脚步 has to be flawless完美无瑕,
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而且很脆弱,因为它的每一个步骤都必须是准确无误的
10:33
otherwise除此以外 you lose失去 that data数据, whereas in the brain, you have a system系统
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否则你就会丢失数据。然而大脑系统
10:36
that stores商店 data数据 in a distributed分散式 way, and it's robust强大的.
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以分散的方式存储数据,且强大无比。
10:40
What I want to basically基本上 talk about is my dream梦想,
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我想讨论的基本问题是我的梦想,
10:44
which哪一个 is to build建立 a computer电脑 that works作品 like the brain.
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那就是建造一个像大脑那样工作的电脑。
10:47
This is something that we've我们已经 been working加工 on for the last couple一对 of years年份.
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过去数年来我们一直在为此而努力。
10:51
And I'm going to show显示 you a system系统 that we designed设计
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而现在我将向你们展示一个我们设计的
10:54
to model模型 the retina视网膜,
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模拟视网膜的系统
10:57
which哪一个 is a piece of brain that lines线 the inside of your eyeball眼球.
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这一模拟系统就是覆盖在你眼球内部的一层大脑
11:02
We didn't do this by actually其实 writing写作 code, like you do in a computer电脑.
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实际上我们做的这一模拟系统并不像做电脑系统那样编程,
11:08
In fact事实, the processing处理 that happens发生
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事实上,这一小片大脑的
11:11
in that little piece of brain is very similar类似
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运作过程非常类似于
11:13
to the kind of processing处理 that computers电脑
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电脑从因特网上获取视频流的
11:14
do when they stream video视频 over the Internet互联网.
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过程。
11:18
They want to compress压缩 the information信息 --
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人们想要压缩信息——
11:19
they just want to send发送 the changes变化, what's new in the image图片, and so on --
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人们只想发送新发生改变的图像,诸如此类——
11:23
and that is how your eyeball眼球
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而那就是你的眼球如何
11:26
is able能够 to squeeze all that information信息 down to your optic视神经 nerve神经,
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能把所有捕捉到的信息传送到你的视神经
11:29
to send发送 to the rest休息 of the brain.
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再传送给大脑的其余部分
11:31
Instead代替 of doing this in software软件, or doing those kinds of algorithms算法,
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取代了用软件或者做各种各样的算法来做这一系统
11:34
we went and talked to neurobiologists神经生物学家
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我们去问了神经生物学家
11:37
who have actually其实 reverse相反 engineered工程 that piece of brain that's called the retina视网膜.
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他们用反工程法解析了被称作视网膜的那片大脑。
11:41
And they figured想通 out all the different不同 cells细胞,
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而且他们分析出所有不同的细胞
11:43
and they figured想通 out the network网络, and we just took that network网络
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还分析出其网络,我们只是拿着那个网络
11:46
and we used it as the blueprint蓝图 for the design设计 of a silicon chip芯片.
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用它作为设计硅片的蓝图。
11:50
So now the neurons神经元 are represented代表 by little nodes节点 or circuits电路 on the chip芯片,
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现在硅片上的小结点或电路代表神经元,
11:56
and the connections连接 among其中 the neurons神经元 are represented代表, actually其实 modeled仿照 by transistors晶体管.
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神经元之间的连接实际上由晶体管模拟
12:01
And these transistors晶体管 are behaving行为 essentially实质上
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这些晶体管的运作模式基本上
12:03
just like ion离子 channels渠道 behave表现 in the brain.
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就如同大脑中的离子通道的运作模式。
12:06
It will give you the same相同 kind of robust强大的 architecture建筑 that I described描述.
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这是同我描述过的一样的强大结构。
12:11
Here is actually其实 what our artificial人造 eye looks容貌 like.
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这就是我们的人造眼睛的样子。
12:15
The retina视网膜 chip芯片 that we designed设计 sits坐镇 behind背后 this lens镜片 here.
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我们设计的视网膜硅片安置在这里的镜片后。
12:20
And the chip芯片 -- I'm going to show显示 you a video视频
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而这硅片——我将给你们看一段视频
12:22
that the silicon retina视网膜 put out of its output产量
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是这个硅片视网膜的输出
12:25
when it was looking at Kareem卡里姆 Zaghloul扎格卢勒,
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当它看着Kareem Zaghloul 的时候,
12:28
who's谁是 the student学生 who designed设计 this chip芯片.
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Kareem是设计这块硅片的学生。
12:30
Let me explain说明 what you're going to see, OK,
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让我解释一下你将看见什么,好吗?
12:32
because it's putting out different不同 kinds of information信息,
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由于输出各种不同信息
12:35
it's not as straightforward直截了当 as a camera相机.
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它不像相机那么简单。
12:37
The retina视网膜 chip芯片 extracts提取物 four different不同 kinds of information信息.
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视网膜硅片摄取四种不同的信息。
12:40
It extracts提取物 regions地区 with dark黑暗 contrast对比,
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它摄取黑色对比区域,
12:43
which哪一个 will show显示 up on the video视频 as red.
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在视频上表现为红色。
12:46
And it extracts提取物 regions地区 with white白色 or light contrast对比,
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它也摄取白色或亮色对比区域,
12:50
which哪一个 will show显示 up on the video视频 as green绿色.
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在视频上显示为绿色。
12:52
This is Kareem's贾巴尔的 dark黑暗 eyes眼睛
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这是Kareem的黑眼睛
12:54
and that's the white白色 background背景 that you see here.
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而这儿是你所看见的白色背景。
12:57
And then it also extracts提取物 movement运动.
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然后硅片也摄取物体的运动。
12:59
When Kareem卡里姆 moves移动 his head to the right,
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当Kareem把头转向右边,
13:01
you will see this blue蓝色 activity活动 there;
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你能看见那儿的蓝色活动区域,
13:03
it represents代表 regions地区 where the contrast对比 is increasing增加 in the image图片,
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它代表图像中对比加强的区域,
13:06
that's where it's going from dark黑暗 to light.
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这一区域由暗变明。
13:09
And you also see this yellow黄色 activity活动,
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而且你也看见这块黄色活动区域,
13:11
which哪一个 represents代表 regions地区 where contrast对比 is decreasing减少;
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它代表对比逐渐减弱区域,
13:15
it's going from light to dark黑暗.
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这一区域由亮变暗。
13:17
And these four types类型 of information信息 --
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而这四种信息类型——
13:20
your optic视神经 nerve神经 has about a million百万 fibers纤维 in it,
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你的视神经约有一百万根神经纤维,
13:24
and 900,000 of those fibers纤维
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这些神经纤维中的90万根
13:27
send发送 these four types类型 of information信息.
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传送这四种信息。
13:29
So we are really duplicating复制 the kind of signals信号 that you have on the optic视神经 nerve神经.
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所以我们真正地复制了视神经上的那类信号。
13:33
What you notice注意 here is that these snapshots快照
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你这里所注意到的是这些
13:36
taken采取 from the output产量 of the retina视网膜 chip芯片 are very sparse, right?
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从视网膜硅片的输出信息所摄取的快照是非常分散的。
13:40
It doesn't light up green绿色 everywhere到处 in the background背景,
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在背景中并非到处都是亮色调的绿色,
13:42
only on the edges边缘, and then in the hair头发, and so on.
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仅仅在边缘如此,等等。
13:45
And this is the same相同 thing you see
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而这同你所看见的一样
13:46
when people compress压缩 video视频 to send发送: they want to make it very sparse,
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当人们压缩视频后发送:他们想把它做得很分散,
13:50
because that file文件 is smaller. And this is what the retina视网膜 is doing,
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因为文件更小了。而这就是视网膜所做的一切
13:53
and it's doing it just with the circuitry电路, and how this network网络 of neurons神经元
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仅仅用电路就做到了,而且这个神经网络是如何
13:57
that are interacting互动 in there, which哪一个 we've我们已经 captured捕获 on the chip芯片.
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在那儿相互作用的,我们都在硅片上捕捉到了。
14:00
But the point that I want to make -- I'll show显示 you up here.
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但是我要说的是,看这里。
14:03
So this image图片 here is going to look like these ones那些,
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这样的图像将看上去像那些图像,
14:06
but here I'll show显示 you that we can reconstruct重建 the image图片,
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但在这儿我将向你们演示我们能重组图像,
14:08
so, you know, you can almost几乎 recognize认识 Kareem卡里姆 in that top最佳 part部分 there.
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所以,你知道,你们几乎可以在那幅顶部图像分辨出Kareem.
14:13
And so, here you go.
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瞧,就是这个。
14:24
Yes, so that's the idea理念.
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是的,这就是我的想法。
14:27
When you stand still, you just see the light and dark黑暗 contrasts对比.
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当你站着不动时,你只看见明暗对比。
14:29
But when it's moving移动 back and forth向前,
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但是当你前后移动时,
14:31
the retina视网膜 picks精选 up these changes变化.
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视网膜就摄取到这些变化。
14:34
And that's why, you know, when you're sitting坐在 here
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那就是为什么,当你坐在这儿,
14:35
and something happens发生 in your background背景,
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在你的背后发生变化时,你也能知道,
14:37
you merely仅仅 move移动 your eyes眼睛 to it.
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你只要需要看一眼。
14:39
There are these cells细胞 that detect检测 change更改
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这些细胞探测到变化
14:41
and you move移动 your attention注意 to it.
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你就把注意力转向它。
14:43
So those are very important重要 for catching somebody
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这对你发现
14:45
who's谁是 trying to sneak潜行 up on you.
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想对你偷偷摸摸的家伙非常重要。
14:47
Let me just end结束 by saying that this is what happens发生
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让我这么说吧,作为这次演讲的结束,这就是
14:50
when you put Africa非洲 in a piano钢琴, OK.
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当你把非洲放入一架钢琴会发生的一切,好吧。
14:53
This is a steel drum here that has been modified改性,
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这是一架已被改装的钢鼓,
14:56
and that's what happens发生 when you put Africa非洲 in a piano钢琴.
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而那是你把非洲放入钢琴所发生的事情。
14:59
And what I would like us to do is put Africa非洲 in the computer电脑,
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而我想让大家所做的是把非洲放入一台电脑,
15:03
and come up with a new kind of computer电脑
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变出一种新电脑
15:05
that will generate生成 thought, imagination想像力, be creative创作的 and things like that.
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这种电脑将产生思想,具有想象力,充满创造力,以及诸如此类的能力。
15:08
Thank you.
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谢谢诸位。
15:10
(Applause掌声)
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(掌声)
15:12
Chris克里斯 Anderson安德森: Question for you, Kwabena夸贝纳.
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Chris Anderson:Kwabena, 有一个问题问你,
15:14
Do you put together一起 in your mind心神 the work you're doing,
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你是否想过,在你正从事的工作
15:18
the future未来 of Africa非洲, this conference会议 --
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非洲的未来,和这次大会——
15:21
what connections连接 can we make, if any, between之间 them?
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在它们之间我们可以获得什么联系,如果有的话?
15:24
Kwabena夸贝纳 BoahenBoahen: Yes, like I said at the beginning开始,
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Kwabena Boahen:是的,正如我一开始所说的,
15:26
I got my first computer电脑 when I was a teenager青少年, growing生长 up in Accra阿克拉.
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我在阿克拉长大,在青少年时期有了第一台电脑。
15:30
And I had this gut肠道 reaction反应 that this was the wrong错误 way to do it.
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我的本能反应是这种做法是错误的。
15:34
It was very brute畜生 force; it was very inelegant不雅.
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这是一种非常不理性的力量,非常不雅。
15:37
I don't think that I would've会一直 had that reaction反应,
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我不认为我会有如此的反应,
15:39
if I'd grown长大的 up reading all this science科学 fiction小说,
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如果我从小读着所有这些科幻小说,
15:42
hearing听力 about RDRD2D2, whatever随你 it was called, and just -- you know,
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听着有关星球大战中的机器人RD2D2,不管你怎么称呼它,只是--你知道的,
15:46
buying购买 into this hype炒作 about computers电脑.
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认同对电脑的这种炒作。
15:47
I was coming未来 at it from a different不同 perspective透视,
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我是从一个不同的视角接触电脑的,
15:49
where I was bringing使 that different不同 perspective透视
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我正是带这这个不同的视角
15:51
to bear on the problem问题.
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来承受这个问题。
15:53
And I think a lot of people in Africa非洲 have this different不同 perspective透视,
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而且我认为非洲许许多多人有这种不同的观点,
15:56
and I think that's going to impact碰撞 technology技术.
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而且我认为这将影响到技术。
15:58
And that's going to impact碰撞 how it's going to evolve发展.
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影响到技术的发展的方向。
16:00
And I think you're going to be able能够 to see, use that infusion注入,
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而且我认为你们将能目睹,利用那种新灌输的思想,
16:02
to come up with new things,
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来创造新事物,
16:04
because you're coming未来 from a different不同 perspective透视.
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因为你们来自于一个不同的背景。
16:07
I think we can contribute有助于. We can dream梦想 like everybody每个人 else其他.
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我觉得我们可以像其他任何人一样贡献自己的力量,构筑自己的梦想。
16:11
CACA: Thanks谢谢 Kwabena夸贝纳, that was really interesting有趣.
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Chris Anderson:谢谢,Kwabena,这真的很有意思。
16:13
Thank you.
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谢谢。
16:14
(Applause掌声)
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(掌声)
Translated by Mingzi Qu
Reviewed by Xu (Jessica) Jiang

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ABOUT THE SPEAKER
Kwabena Boahen - Bioengineer
Kwabena Boahen wants to understand how brains work -- and to build a computer that works like the brain by reverse-engineering the nervous system. His group at Stanford is developing Neurogrid, a hardware platform that will emulate the cortex’s inner workings.

Why you should listen

Kwabena Boahen is the principal investigator at the Brains in Silicon lab at Stanford. He writes of himself:

Being a scientist at heart, I want to understand how cognition arises from neuronal properties. Being an engineer by training, I am using silicon integrated circuits to emulate the way neurons compute, linking the seemingly disparate fields of electronics and computer science with neurobiology and medicine.

My group's contributions to the field of neuromorphic engineering include a silicon retina that could be used to give the blind sight and a self-organizing chip that emulates the way the developing brain wires itself up. Our work is widely recognized, with over sixty publications, including a cover story in the May 2005 issue of Scientific American.

My current research interest is building a simulation platform that will enable the cortex's inner workings to be modeled in detail. While progress has been made linking neuronal properties to brain rhythms, the task of scaling up these models to link neuronal properties to cognition still remains. Making the supercomputer-performance required affordable is the goal of our Neurogrid project. It is at the vanguard of a profound shift in computing, away from the sequential, step-by-step Von Neumann machine towards a parallel, interconnected architecture more like the brain.

More profile about the speaker
Kwabena Boahen | Speaker | TED.com