TED2003
Jeff Hawkins: How brain science will change computing
傑夫•霍金斯談大腦科學將如何改變電腦
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Readability: 3.2
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Treo 的發明者傑夫•霍金斯鼓勵我們重新檢視大腦 — 不再只把大腦視為一個快速的處理器，而是將之視為一個儲存與播放經驗的記憶系統，而我們平常就是用這個系統來預測、認知即將會發生的事物。
Jeff Hawkins - Computer designer, brain researcher
Jeff Hawkins pioneered the development of PDAs such as the Palm and Treo. Now he's trying to understand how the human brain really works, and adapt its method -- which he describes as a deep system for storing memory -- to create new kinds of computers and tools. Full bio
Jeff Hawkins pioneered the development of PDAs such as the Palm and Treo. Now he's trying to understand how the human brain really works, and adapt its method -- which he describes as a deep system for storing memory -- to create new kinds of computers and tools. Full bio
Double-click the English transcript below to play the video.
00:25
I do two things: I design mobile computers and I study brains.
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我有兩個職業。我設計行動電腦，而且我研究大腦。
00:29
And today's talk is about brains and,
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今天的演講與大腦有關，
00:31
yay, somewhere I have a brain fan out there.
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耶，看來今天聽眾中有人是大腦迷。
00:33
(Laughter)
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（笑聲）
00:35
I'm going to, if I can have my first slide up here,
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如果我的投影片已經準備好了，
00:37
and you'll see the title of my talk and my two affiliations.
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你將會看到今天的演講主題及我的兩個所屬機構，
00:41
So what I'm going to talk about is why we don't have a good brain theory,
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今天我將要談的是 — 為什麼我們沒有一個好的大腦理論，
00:45
why it is important that we should develop one and what we can do about it.
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為什麼發展大腦理論如此重要，還有，我們能利用這個理論做什麼？
00:48
And I'll try to do all that in 20 minutes. I have two affiliations.
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我將會嘗試在廿分鐘內完成全部的主題。我參與兩家公司。
00:51
Most of you know me from my Palm and Handspring days,
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你們大多數是因為我在 Palm 及 Handspring 的工作而認識我的，
00:54
but I also run a nonprofit scientific research institute
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但是我同時也經營一個非營利性的科學研究機構
00:57
called the Redwood Neuroscience Institute in Menlo Park,
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它位於加州門洛帕克，叫做「紅木神經科學研究所」，
00:59
and we study theoretical neuroscience,
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我們專攻理論神經科學相關的研究，
01:01
and we study how the neocortex works.
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我們對研究大腦新皮層如何運作有興趣。
01:03
I'm going to talk all about that.
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我將談談這一方面。
01:05
I have one slide on my other life, the computer life, and that's the slide here.
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我將我的另一個生活面（電腦生活）做成了一張投影片，你現在可以看到。
01:08
These are some of the products I've worked on over the last 20 years,
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我在過去的廿年間參與了一些產品的開發，
01:11
starting back from the very original laptop to some of the first tablet computers
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從第一台筆記型電腦到首批平板電腦等等，
01:15
and so on, and ending up most recently with the Treo,
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最新的一個產品是 Treo，
01:17
and we're continuing to do this.
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我們將會繼續電子產品的開發。
01:19
And I've done this because I really believe that mobile computing
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我之所以會參與這一行主要是因為我相信行動運算
01:21
is the future of personal computing, and I'm trying to make the world
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是個人運算產品的未來，而我試著藉由開發這些產品
01:24
a little bit better by working on these things.
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來讓世界更美好。
01:27
But this was, I have to admit, all an accident.
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但是我必須承認，這一切都是個意外。
01:29
I really didn't want to do any of these products
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我其實本來一點都沒有打算要開發這些產品
01:31
and very early in my career I decided
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而且在我事業剛剛開始的時候我還決定
01:33
I was not going to be in the computer industry.
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我不要從事電腦相關產業。
01:36
And before I tell you about that, I just have to tell you
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但在我告訴你這個故事之前，我必須告訴你
01:38
this one little picture of graffiti there I picked off the web the other day.
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我某天從網路上看到的一張關於 graffiti 輸入法照片的故事。
01:40
I was looking for a picture of graffiti, little text input language,
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當時我在網上尋找 graffiti 的照片，那是一種輸入法程式語言，
01:43
and I found the website dedicated to teachers who want to make these,
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然後我發現一個網站，它是為一群老師們所架設的，你知道的，
01:46
you know, the script writing things across the top of their blackboard,
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利用 script 來控制黑板上的跑馬燈，
01:49
and they had added graffiti to it, and I'm sorry about that.
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他們網站內容竟然包含 graffiti，我對此感到很抱歉。
01:52
(Laughter)
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（笑聲）
01:54
So what happened was, when I was young and got out of engineering school
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當我還年輕，剛剛從工學院畢業的時候，
01:59
at Cornell in '79, I decided -- I went to work for Intel and
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我是康乃爾 79 年畢業班，我決定去 Intel 工作。
02:03
I was in the computer industry -- and three months into that,
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我在電腦業奮鬥了三個月之後，
02:06
I fell in love with something else, and I said, "I made the wrong career choice here,"
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我愛上了另一個東西，我說：「我入錯行了」，
02:10
and I fell in love with brains.
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因為我愛上了大腦。
02:13
This is not a real brain. This is a picture of one, a line drawing.
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這不是真的大腦。這是大腦的描繪圖。
02:16
But I don't remember exactly how it happened,
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我已經記不清當初是如何開始的了，
02:19
but I have one recollection, which was pretty strong in my mind.
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在我腦海中只有一個鮮明的回憶。
02:22
In September 1979, Scientific American came out
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1979 年九月，新一期的科學美國人出刊
02:25
with a single topic issue about the brain. And it was quite good.
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那是一期談論大腦的特刊。非常的棒。
02:28
It was one of the best issues ever. And they talked about the neuron
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那是有史以來最棒的一期雜誌之一。那期刊物中談論神經、
02:31
and development and disease and vision and all the things
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發育、疾病以及視力等等所有的
02:33
you might want to know about brains. It was really quite impressive.
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跟大腦相關且你會感興趣的主題。真的非常令人印象深刻。
02:36
And one might have the impression that we really knew a lot about brains.
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而人會得到一種錯誤的印象，那就是我們已經非常了解我們的大腦了。
02:39
But the last article in that issue was written by Francis Crick of DNA fame.
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但是那一期的最後一篇文章是由發現 DNA 結構而成名的法蘭西斯•克里克所撰寫。
02:43
Today is, I think, the 50th anniversary of the discovery of DNA.
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今天，如果我沒記錯的話，剛好是發現 DNA 結構五十週年紀念日。
02:46
And he wrote a story basically saying,
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他寫了一個故事，主要是告訴我們：
02:48
well, this is all well and good, but you know what,
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這個嘛～這些研究都很棒，可是你知道嗎？
02:51
we don't know diddley squat about brains
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我們對大腦一點都不了解
02:53
and no one has a clue how these things work,
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沒有人知道大腦是如何運作的，
02:55
so don't believe what anyone tells you.
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所以別相信其他人告訴你的事情。
02:57
This is a quote from that article. He said, "What is conspicuously lacking,"
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這是從文章中摘錄下來的一句話。他說：「這裡顯著缺乏的是，」
03:00
he's a very proper British gentleman so, "What is conspicuously lacking
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他是一個非常有禮的英國紳士，「我們會注意到可以用來解釋這些研究
03:04
is a broad framework of ideas in which to interpret these different approaches."
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的廣泛概念架構是明顯地不足的。」
03:07
I thought the word framework was great.
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我認為他用「架構」一詞用得非常洽當。
03:09
He didn't say we didn't even have a theory. He says,
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他並沒有說我們連一個理論都沒有。他所說得是，
03:11
we don't even know how to begin to think about it --
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我們連如何開始建立理論都不知道該如何下手 —
03:13
we don't even have a framework.
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我們連個架構都沒有。
03:15
We are in the pre-paradigm days, if you want to use Thomas Kuhn.
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如果你想要引用湯瑪斯•孔恩的說法，我們處在一個前典範的時代。
03:18
And so I fell in love with this, and said look,
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因此我愛上這個領域了，然後說：看看，
03:21
we have all this knowledge about brains. How hard can it be?
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我們已經知道這麼多關於腦的知識。這會有多難？
03:24
And this is something we can work on my lifetime. I felt I could make a difference,
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而且這是個可以一輩子鑽研的題目。我認為我能對世界做出一點貢獻，
03:27
and so I tried to get out of the computer business, into the brain business.
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因此我嘗試著離開電腦業，轉行到腦科學研究領域。
03:31
First, I went to MIT, the AI lab was there,
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首先，我跑去麻省理工裡的一間人工智慧實驗室，
03:33
and I said, well, I want to build intelligent machines, too,
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我說，嘿，我也想要建造智能機器，
03:35
but the way I want to do it is to study how brains work first.
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但是我覺得達到這個目標前必須要先能了解大腦是如何運作的。
03:38
And they said, oh, you don't need to do that.
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然而他們說，喔，你並不需要知道那個。
03:41
We're just going to program computers; that's all we need to do.
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我們只需要設計電腦程式，不需要做其他不相干的事。
03:43
And I said, no, you really ought to study brains. They said, oh, you know,
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我再說，不，你們真的應該研究大腦。他們說，喔，你知道嗎？
03:46
you're wrong. And I said, no, you're wrong, and I didn't get in.
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你錯了。然後我說，不，你才錯了，所以當然我沒被錄取。
03:48
(Laughter)
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（笑聲）
03:50
But I was a little disappointed -- pretty young -- but I went back again
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但我有點失望 — 因為我還年輕，但幾年以後我又嘗試了一次
03:52
a few years later and this time was in California, and I went to Berkeley.
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這次是在加州，我跑去柏克萊。
03:55
And I said, I'll go in from the biological side.
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然後我說，我要從生物方面開始著手。
03:59
So I got in -- in the Ph.D. program in biophysics, and I was, all right,
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所以我被錄取了，進入了生物物理博士班。然後我心想，太棒了，
04:02
I'm studying brains now, and I said, well, I want to study theory.
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我現在開始研究大腦了，然後我說，好的，我想要鑽研理論。
04:05
And they said, oh no, you can't study theory about brains.
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但他們告訴我，喔，不，你不能研究關於腦的理論。
04:07
That's not something you do. You can't get funded for that.
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你不想做那個的。沒有人會給你經費支持你做這種研究。
04:09
And as a graduate student, you can't do that. So I said, oh my gosh.
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身為一個研究生，你不能這麼做。所以我又說了，我的老天，
04:13
I was very depressed. I said, but I can make a difference in this field.
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我非常沮喪。我說，但我能在這方面有所成就。
04:15
So what I did is I went back in the computer industry
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所以我唯一能做的是，我回到了電腦業
04:18
and said, well, I'll have to work here for a while, do something.
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然後說，好吧，我將留下來工作一段時間，做出一番成就。
04:20
That's when I designed all those computer products.
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然後我就開始設計出所有這些電子產品。
04:23
(Laughter)
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（笑聲）
04:24
And I said, I want to do this for four years, make some money,
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我告訴自己，我在這邊待四年，賺些錢，
04:27
like I was having a family, and I would mature a bit,
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我會成家，變得更成熟些，
04:31
and maybe the business of neuroscience would mature a bit.
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同時也許神經科學領域也會發展得成熟一點。
04:34
Well, it took longer than four years. It's been about 16 years.
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好吧，我花了超過四年的時間。時光飛逝，已經 16 年了。
04:37
But I'm doing it now, and I'm going to tell you about it.
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但是我終於在研究大腦了，而我將會跟你們談談我的研究。
04:39
So why should we have a good brain theory?
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為什麼我們應該要有一個好的大腦理論？
04:42
Well, there's lots of reasons people do science.
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人們為了千百種不同的理由研究科學。
04:45
One is -- the most basic one is -- people like to know things.
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其中一個理由 — 最基本的理由 — 是我們想要了解事物。
04:48
We're curious, and we just go out and get knowledge, you know?
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人類是好奇的，我們只是想要獲取新知而已，你了解嗎？
04:50
Why do we study ants? Well, it's interesting.
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為什麼我們要研究螞蟻？不為什麼，只因為它很有趣。
04:52
Maybe we'll learn something really useful about it, but it's interesting and fascinating.
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也許我們能從中學到新知，但是研究本身既有趣又吸引人。
04:55
But sometimes, a science has some other attributes
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但有時，科學有一些其他的屬性
04:57
which makes it really, really interesting.
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而這些屬性會讓它額外的吸引人。
04:59
Sometimes a science will tell something about ourselves,
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有時候科學能夠讓我們更加認識自己，
05:02
it'll tell us who we are.
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它會讓我們知道我們是誰。
05:03
Rarely, you know: evolution did this and Copernicus did this,
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雖然這極少發生，如你所知演化學說是一例，哥白尼也做到了，
05:06
where we have a new understanding of who we are.
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它們徹底地改變了我們對自己身份地位上的認知。
05:08
And after all, we are our brains. My brain is talking to your brain.
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但是最基本的，我們代表著我們的大腦。我的大腦正在和你的交談著。
05:12
Our bodies are hanging along for the ride, but my brain is talking to your brain.
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雖然我們的身體隨時陪伴著我們，但是是我的腦在和你的腦交談。
05:15
And if we want to understand who we are and how we feel and perceive,
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所以如果我們想要了解我們到底是誰，我們是如何感覺、理解事物，
05:18
we really understand what brains are.
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我們真的需要了解大腦是什麼。
05:20
Another thing is sometimes science
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另一方面，有時科學
05:22
leads to really big societal benefits and technologies,
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能對社會利益、科技、
05:24
or businesses, or whatever, that come out of it. And this is one, too,
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商業，各式各樣領域做出極大的貢獻。這也是其中之一，
05:26
because when we understand how brains work, we're going to be able
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因為當我們了解大腦是如何運作之後，我們將能夠
05:29
to build intelligent machines, and I think that's actually a good thing on the whole,
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建造智慧機器，我相信整體來說，這會是件好事，
05:32
and it's going to have tremendous benefits to society,
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這將會對社會有極大助益
05:34
just like a fundamental technology.
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就如同基礎科技一般。
05:36
So why don't we have a good theory of brains?
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所以，為什麼我們沒有一個好的大腦理論？
05:38
And people have been working on it for 100 years.
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而且人們研究大腦的歷史已經有百來年了。
05:41
Well, let's first take a look at what normal science looks like.
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那麼，讓我們先來看看普通科學領域的狀況。
05:43
This is normal science.
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這是普通科學領域。
05:45
Normal science is a nice balance between theory and experimentalists.
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普通科學領域中的理論與實作家呈現一個良好的平衡。
05:49
And so the theorist guys say, well, I think this is what's going on,
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因此當理論學者說，嗯，我認為事情是這般這般，
05:51
and the experimentalist says, no, you're wrong.
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然後實驗科學家說，不，你錯了。
05:53
And it goes back and forth, you know?
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然後就像這樣一直反覆來回，對吧？
05:55
This works in physics. This works in geology. But if this is normal science,
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這方法對物理適用。對地理適用。但這些是普通科學領域，
05:57
what does neuroscience look like? This is what neuroscience looks like.
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神經科學看起來是什麼樣子？這就是神經科學的狀況。
06:00
We have this mountain of data, which is anatomy, physiology and behavior.
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我們的數據累積得比山還高，解剖學、生理學和行為學的數據。
06:05
You can't imagine how much detail we know about brains.
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你無法想像我們對大腦的枝微末節了解得如何透徹。
06:08
There were 28,000 people who went to the neuroscience conference this year,
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今年 (2003) 的神經科學研討會共有 28,000 人參加，
06:12
and every one of them is doing research in brains.
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每一個都在研究大腦。
06:14
A lot of data. But there's no theory. There's a little, wimpy box on top there.
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太多資訊。但沒有理論。在上層的這一塊是如此的微小，搖搖欲墜。
06:18
And theory has not played a role in any sort of grand way in the neurosciences.
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而且理論在神經科學中尚未扮演任何重要的角色。
06:23
And it's a real shame. Now why has this come about?
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這真可恥。為什麼會這樣？
06:26
If you ask neuroscientists, why is this the state of affair,
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如果你問神經科學家，為什麼會是這種狀況？
06:28
they'll first of all admit it. But if you ask them, they'll say,
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一開始他們都會承認此事。但如果你接著問，他們會說，
06:31
well, there's various reasons we don't have a good brain theory.
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這個嘛，有很多的原因使我們沒有一個好的大腦理論。
06:34
Some people say, well, we don't still have enough data,
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有些人會說，呃，我們還沒有足夠的數據，
06:36
we need to get more information, there's all these things we don't know.
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我們還需要更多資訊，還有很多我們不知道的事。
06:39
Well, I just told you there's so much data coming out your ears.
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我才剛剛告訴你們，我們有的數據多到你們的腦袋都裝不下。
06:42
We have so much information, we don't even know how to begin to organize it.
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我們擁有如此多的資訊；我們不知道如何開始整理這些資訊。
06:45
What good is more going to do?
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再有更多資訊又能怎樣？
06:47
Maybe we'll be lucky and discover some magic thing, but I don't think so.
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也許我們會幸運的發現某些寶藏，但我不這麼認為。
06:50
This is actually a symptom of the fact that we just don't have a theory.
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這其實只是因為我們沒有理論這個事實所導致的症狀罷了。
06:53
We don't need more data -- we need a good theory about it.
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我們不需要更多數據 — 我們需要一個好理論。
06:56
Another one is sometimes people say, well, brains are so complex,
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有時候某些人會回答另一個說法，因為大腦是如此複雜，
06:59
it'll take another 50 years.
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我們還需要 50 年的研究。
07:01
I even think Chris said something like this yesterday.
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我甚至好像聽到 Chris 昨天才說了類似的話。
07:03
I'm not sure what you said, Chris, but something like,
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我不確定你說了什麼，Chris，但好像是類似
07:05
well, it's one of the most complicated things in the universe. That's not true.
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— 大腦是宇宙中最複雜的事物之一。這不是真的。
07:08
You're more complicated than your brain. You've got a brain.
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你比你的大腦還要複雜。腦只是你身體的一部分。
07:10
And it's also, although the brain looks very complicated,
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並且，雖然大腦看起來非常複雜，
07:12
things look complicated until you understand them.
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但是我們常認為我們所不了解的事物是複雜的。
07:15
That's always been the case. And so all we can say, well,
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總是這樣子的。我們能夠說的只是，這個嘛，
07:18
my neocortex, which is the part of the brain I'm interested in, has 30 billion cells.
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我的新皮層，大腦中我感興趣的部份，有三百億個細胞。
07:22
But, you know what? It's very, very regular.
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但你知道嗎？它非常、非常的規則。
07:24
In fact, it looks like it's the same thing repeated over and over and over again.
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事實上，它看起來像是同一個東西不斷的重複、重複再重複。
07:27
It's not as complex as it looks. That's not the issue.
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它不像看起來般如此複雜。所以這不是問題。
07:30
Some people say, brains can't understand brains.
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某些人說，大腦無法了解大腦。
07:32
Very Zen-like. Whoo. (Laughter)
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非常具有禪意。呼，是吧 —
07:35
You know,
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（笑聲）
07:36
it sounds good, but why? I mean, what's the point?
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聽起來很有道理，但為什麼？我是說，真的有道理嗎？
07:39
It's just a bunch of cells. You understand your liver.
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大腦只不過是一堆細胞。你能了解你的肝臟呀。
07:42
It's got a lot of cells in it too, right?
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肝臟中也有很多細胞，對吧？
07:44
So, you know, I don't think there's anything to that.
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所以，你知道，我不覺得這有什麼問題。
07:46
And finally, some people say, well, you know,
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最後，某些人會說，那麼，你知道，
07:48
I don't feel like a bunch of cells, you know. I'm conscious.
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我不覺得我是一堆細胞，你能理解嗎？我有意識。
07:52
I've got this experience, I'm in the world, you know.
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我能累積經驗，我生活在世界中，類似這些話。
07:54
I can't be just a bunch of cells. Well, you know,
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我不可能只是一堆細胞。是的，你知道，
07:56
people used to believe there was a life force to be living,
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人們總是相信生物體內存在某種「生命力」，
07:59
and we now know that's really not true at all.
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我們現在知道這一點都不是事實。
08:01
And there's really no evidence that says -- well, other than people
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這一點都沒有事實根據，好吧，除了人們不想相信
08:04
just have disbelief that cells can do what they do.
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細胞可以做到人們平日在做的事情。
08:06
And so, if some people have fallen into the pit of metaphysical dualism,
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因此，如果某些人們落入形而上學二元論的泥淖中，
08:09
some really smart people, too, but we can reject all that.
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一些很聰明的人也不例外，但是我們可以駁斥他們的所有說法。
08:12
(Laughter)
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（笑聲）
08:14
No, I'm going to tell you there's something else,
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不，我將要告訴你們還有別的，
08:17
and it's really fundamental, and this is what it is:
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而且非常基本，就是我下面要說的這句話：
08:19
there's another reason why we don't have a good brain theory,
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我們沒有一個好的大腦理論的另一個理由是，
08:21
and it's because we have an intuitive, strongly-held,
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我們被一種直觀的、根深蒂固的
08:24
but incorrect assumption that has prevented us from seeing the answer.
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但是錯誤的假設所蒙蔽，因此一直無法找到問題的答案。
08:29
There's something we believe that just, it's obvious, but it's wrong.
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我們所相信的某些事情，雖然表面上很顯而易見，但是它是錯的。
08:32
Now, there's a history of this in science and before I tell you what it is,
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事實上，科學界的歷史中已經發生過同樣的事情，而在我告訴你以前，
08:36
I'm going to tell you a bit about the history of it in science.
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我要先跟你談談科學界的歷史。
08:38
You look at some other scientific revolutions,
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你們看看其他的科學革命，
08:40
and this case, I'm talking about the solar system, that's Copernicus,
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這邊，我們來談談太陽系，那是哥白尼的貢獻，
08:42
Darwin's evolution, and tectonic plates, that's Wegener.
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達爾文的演化還有魏格納的板塊構造論。
08:45
They all have a lot in common with brain science.
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他們都與大腦科學有很多共通之處。
08:48
First of all, they had a lot of unexplained data. A lot of it.
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首先，他們有很多無法解釋的數據，一堆數據。
08:51
But it got more manageable once they had a theory.
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但是當他們有了理論之後，這些數據變得容易處理的多。
08:54
The best minds were stumped -- really, really smart people.
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偉大的心靈總是會遭遇許多困難，那些極端、極端聰明的人們。
08:57
We're not smarter now than they were then.
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我們現在並不比他們當時聰明。
08:59
It just turns out it's really hard to think of things,
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思考問題是極端困難的，
09:01
but once you've thought of them, it's kind of easy to understand it.
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但一旦你想通了，事情就會得容易理解得多。
09:03
My daughters understood these three theories
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我女兒能夠了解這三個理論
09:05
in their basic framework by the time they were in kindergarten.
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至少了解他們的基本架構，而那時她只是個幼稚園學童而已。
09:08
And now it's not that hard, you know, here's the apple, here's the orange,
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因此，這並沒有這麼難，就像這樣，這是蘋果，這是柳丁，
09:11
you know, the Earth goes around, that kind of stuff.
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你知道的，地球在公轉，類似的這種東西。
09:14
Finally, another thing is the answer was there all along,
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最後，另一件事是答案始終在那邊，
09:16
but we kind of ignored it because of this obvious thing, and that's the thing.
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但是我們卻因為錯誤而明顯的假設而忽略了它，這就是問題所在。
09:19
It was an intuitive, strong-held belief that was wrong.
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問題就是這個直觀且根深蒂固的認知是錯的。
09:22
In the case of the solar system, the idea that the Earth is spinning
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拿太陽系的例子來說，地球自轉的概念
09:25
and the surface of the Earth is going like a thousand miles an hour,
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還有地球表面以每小時幾千英哩的速度在轉動著，
09:28
and the Earth is going through the solar system about a million miles an hour.
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不用說還有地球本身以幾百萬英哩的時速在太陽系中移動著。
09:31
This is lunacy. We all know the Earth isn't moving.
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這真是瘋了。我們都知道地球並沒有在動。
09:33
Do you feel like you're moving a thousand miles an hour?
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你覺得你有在以千哩的時速移動嗎？
09:35
Of course not. You know, and someone who said,
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當然沒有。你知道，當有人說，
09:37
well, it was spinning around in space and it's so huge,
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地球在太空中自轉，而太空是如此之大，
09:39
they would lock you up, and that's what they did back then.
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然後他們會把你關起來，這就是當時他們所做的事。
09:41
(Laughter)
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（笑聲）
09:42
So it was intuitive and obvious. Now what about evolution?
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所以這是直觀且顯而易見的。現在，我們談談演化…
09:45
Evolution's the same thing. We taught our kids, well, the Bible says,
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發生在演化上的情形是一樣的。我們教導孩子，嗯，聖經上說，
09:48
you know, God created all these species, cats are cats, dogs are dogs,
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你知道的，上帝創造了所有生命，貓是貓，狗是狗，
09:50
people are people, plants are plants, they don't change.
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人是人，樹木是樹木，他們是不變的。
09:53
Noah put them on the Ark in that order, blah, blah, blah. And, you know,
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諾亞奉命將他們放到方舟內，如此這般。而且，你知道，
09:57
the fact is, if you believe in evolution, we all have a common ancestor,
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事實上，如果你相信演化，我們都來自同一個祖先，
10:01
and we all have a common ancestry with the plant in the lobby.
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則我們和大廳裡那些植物有共同的祖先。
10:04
This is what evolution tells us. And, it's true. It's kind of unbelievable.
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這是演化告訴我們的。並且它是真的。儘管有點難令人相信。
10:07
And the same thing about tectonic plates, you know?
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板塊構造論也遭遇類似情形，不是嗎？
10:10
All the mountains and the continents are kind of floating around
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所有的山嶽與大陸都飄浮在地球的表面，
10:12
on top of the Earth, you know? It's like, it doesn't make any sense.
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你相信嗎？這真的一點都不合邏輯。
10:16
So what is the intuitive, but incorrect assumption,
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所以什麼是我說的關於大腦直觀但是不正確的假設，
10:20
that's kept us from understanding brains?
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並使我們不能真正的了解大腦？
10:22
Now I'm going to tell it to you, and it's going to seem obvious that that is correct,
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現在我將要告訴你們，而且它將會看起來正確無誤不容懷疑，
10:24
and that's the point, right? Then I'm going to have to make an argument
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但這就是我想要說明的，不是嗎？然後我將會作一番論述
10:26
why you're incorrect about the other assumption.
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為什麼你們另一個假設也是錯的。
10:28
The intuitive but obvious thing is that somehow intelligence
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這個直觀且明顯的事情就是：智能可以藉由
10:31
is defined by behavior,
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行為來界定，
10:33
that we are intelligent because of the way that we do things
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我們擁有智能乃是因為我們行事的方法
10:35
and the way we behave intelligently, and I'm going to tell you that's wrong.
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還有我們展現智慧的行為，但是我要告訴你們這是錯的。
10:38
What it is is intelligence is defined by prediction.
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智能其實應該是由預測能力來界定的。
10:40
And I'm going to work you through this in a few slides here,
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接下來的幾張投影片，我將解釋我的論點，
10:43
give you an example of what this means. Here's a system.
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給你們一個可以了解它的意義的例子。這裡有一個系統。
10:47
Engineers like to look at systems like this. Scientists like to look at systems like this.
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工程師喜歡這樣看待系統。科學家也喜歡這樣看待系統。
10:50
They say, well, we have a thing in a box, and we have its inputs and its outputs.
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他們說，嗯，這個箱子裡面有某種東西，然後我們有輸入跟輸出。
10:53
The AI people said, well, the thing in the box is a programmable computer
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研究人工智慧的人說，我知道，箱子裡的東西是可編程的電腦
10:56
because that's equivalent to a brain, and we'll feed it some inputs
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因為它和腦是對等的，我們將會給它一些輸入訊號
10:58
and we'll get it to do something, have some behavior.
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然後我們可以讓它做些事情，產生行為。
11:00
And Alan Turing defined the Turing test, which is essentially saying,
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然後艾倫•涂林訂定了涂林測驗，這個測驗基本上是說，
11:03
we'll know if something's intelligent if it behaves identical to a human.
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如果某物的行為可以表現得跟人一模一樣，我們知道它有智能。
11:06
A behavioral metric of what intelligence is,
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對於智能本質上的一個行為標準，
11:09
and this has stuck in our minds for a long period of time.
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這個假設佔據了我們的想法很長的一段時間。
11:12
Reality though, I call it real intelligence.
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但是事實上，我稱之為真實智慧。
11:14
Real intelligence is built on something else.
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真實智慧是建築在其它東西上。
11:16
We experience the world through a sequence of patterns, and we store them,
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我們藉由一序列的模式來體驗這個世界，我們儲存這些模式，
11:20
and we recall them. And when we recall them, we match them up
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我們也會回憶這些模式。當我們回憶時，我們會將現實與記憶中的
11:23
against reality, and we're making predictions all the time.
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模式對照，並且我們無時無刻不在預測下一刻。
11:27
It's an eternal metric. There's an eternal metric about us sort of saying,
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這是永恆的標準。有一個關於我們的外在標準大概是這樣的，
11:30
do we understand the world? Am I making predictions? And so on.
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我們了解這個世界嗎？我正在做預測嗎？等等這些。
11:33
You're all being intelligent right now, but you're not doing anything.
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你們現在都顯示出智慧，但是你們並沒有在做任何事。
11:35
Maybe you're scratching yourself, or picking your nose,
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也許你剛剛正在搔癢，或者挖鼻孔，
11:37
I don't know, but you're not doing anything right now,
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我不知道，但是你現在並沒有在做任何事，
11:39
but you're being intelligent; you're understanding what I'm saying.
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但是你是有智慧的，你了解我在說什麼。
11:42
Because you're intelligent and you speak English,
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因為你有智慧而且你聽得懂英文，
11:44
you know what word is at the end of this -- (Silence)
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你知道這句話最後一個 — （沉默）
11:45
sentence.
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字是什麼。
11:47
The word came into you, and you're making these predictions all the time.
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這個字會自己顯現，你無時無刻不在做類似這種的預測。
11:50
And then, what I'm saying is,
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所以，我要說的是，
11:52
is that the eternal prediction is the output in the neocortex.
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這個永恆的預測是我們大腦新皮層的訊號輸出。
11:54
And that somehow, prediction leads to intelligent behavior.
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不知怎麼的，預測最終導致智能行為。
11:57
And here's how that happens. Let's start with a non-intelligent brain.
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這裡我來解釋它是如何發生的。讓我們先從非智能大腦開始看起。
12:00
Well I'll argue a non-intelligent brain, we got hold of an old brain,
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其實我不贊成稱之為非智能大腦，這種原始的大腦也是我們的一部分，
12:04
and we're going to say it's like a non-mammal, like a reptile,
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所以下面我們稱之為非哺乳動物的腦，例如爬蟲類，
12:07
so I'll say, an alligator; we have an alligator.
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所以我說，就鱷魚吧，我們拿鱷魚來當例子。
12:09
And the alligator has some very sophisticated senses.
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鱷魚擁有一些非常複雜的感知能力。
12:12
It's got good eyes and ears and touch senses and so on,
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牠有非常好的視覺、聽覺、觸覺等等。
12:15
a mouth and a nose. It has very complex behavior.
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一張嘴一隻鼻子。牠擁有非常複雜的行為。
12:19
It can run and hide. It has fears and emotions. It can eat you, you know.
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牠可以奔跑、躲藏。牠擁有恐懼與情緒。牠能將你吃了，你知道吧。
12:23
It can attack. It can do all kinds of stuff.
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牠可以攻擊。牠可以做各種事。
12:27
But we don't consider the alligator very intelligent, not like in a human sort of way.
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但是我們不認為鱷魚智力很高，跟人類一點都不能相比。
12:32
But it has all this complex behavior already.
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但是牠已經擁有如此複雜的行為了。
12:34
Now, in evolution, what happened?
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在演化過程中，到底發生了什麼事？
12:36
First thing that happened in evolution with mammals,
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在哺乳類的演化過成中首先，
12:39
we started to develop a thing called the neocortex.
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我們開始發展出所謂的新皮層。
12:41
And I'm going to represent the neocortex here,
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我將在這邊用此來表示新皮層，
12:43
by this box that's sticking on top of the old brain.
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用這個建基於原始大腦上方的方塊來表示。
12:45
Neocortex means new layer. It is a new layer on top of your brain.
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新皮層就是一層新的組織。一層覆蓋在你大腦上方的新組織。
12:48
If you don't know it, it's the wrinkly thing on the top of your head that,
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如果你不知道，它就是你頭裡面最外層那個充滿皺摺的東西，
12:51
it's got wrinkly because it got shoved in there and doesn't fit.
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因為它不合身且被胡亂地塞在你的腦袋裡，所以它充滿了皺摺。
12:54
(Laughter)
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（笑聲）
12:55
No, really, that's what it is. It's about the size of a table napkin.
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不，我說真的，真的是這樣。它大約跟張桌巾一般大小。
12:57
And it doesn't fit, so it gets all wrinkly. Now look at how I've drawn this here.
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它並不合身，所以它充滿皺摺。看看在這邊我是怎麼畫它的。
13:00
The old brain is still there. You still have that alligator brain.
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原始大腦仍然在那邊。你還擁有著與鱷魚相似的腦。
13:04
You do. It's your emotional brain.
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是真的。那是你原始情緒的腦。
13:06
It's all those things, and all those gut reactions you have.
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就是那些東西，所有你會有的直覺反應。
13:09
And on top of it, we have this memory system called the neocortex.
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而在那個上方。我們有一個稱為新皮層的記憶系統。
13:12
And the memory system is sitting over the sensory part of the brain.
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而這個記憶系統座落在大腦感知區的上方。
13:16
And so as the sensory input comes in and feeds from the old brain,
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所以當感官訊號輸入進來並刺激了原始大腦，
13:19
it also goes up into the neocortex. And the neocortex is just memorizing.
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它開始往更上層的新皮層傳遞。而新皮層只是將之記憶下來。
13:23
It's sitting there saying, ah, I'm going to memorize all the things that are going on:
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它待在那邊說，呃，我將要把正在發生的事情全部記下來，
13:27
where I've been, people I've seen, things I've heard, and so on.
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我去了哪裡，我見了哪些人，我聽到了什麼東西，如此這般。
13:29
And in the future, when it sees something similar to that again,
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到了未來，當它再次見到類似的東西，
13:33
so in a similar environment, or the exact same environment,
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處於類似或者同樣的環境下，
13:36
it'll play it back. It'll start playing it back.
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它就會重播。它會開始重播。
13:38
Oh, I've been here before. And when you've been here before,
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喔，我到過這裡。當你上次在這裡的時候，
13:40
this happened next. It allows you to predict the future.
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接下來發生了這件事。它能讓你對未來產生預測。
13:43
It allows you to, literally it feeds back the signals into your brain;
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它能讓你，就是它提供你腦部信號回饋，
13:47
they'll let you see what's going to happen next,
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他們能讓你了解即將會發生的事，
13:49
will let you hear the word "sentence" before I said it.
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能讓你聽到一句話的最後一個「字」，即使我還沒說出口。
13:52
And it's this feeding back into the old brain
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就是這種給原始大腦的回饋
13:55
that'll allow you to make very more intelligent decisions.
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能夠讓你做出更多有智慧的決定。
13:58
This is the most important slide of my talk, so I'll dwell on it a little bit.
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這是我這次演講中最重要的一張投影片，因此我會再花點時間來解釋。
14:01
And so, all the time you say, oh, I can predict the things.
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所以，每次當你說，喔，我能預測到這些事情。
14:05
And if you're a rat and you go through a maze, and then you learn the maze,
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就像如果你是一隻迷宮中的老鼠，然後你認識了這個迷宮，
14:08
the next time you're in a maze, you have the same behavior,
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下一次當你在迷宮中的時候，你會做一樣的事情，
14:10
but all of a sudden, you're smarter
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但是突然間，你變聰明了
14:12
because you say, oh, I recognize this maze, I know which way to go,
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因為你會說，喔，我認得這個迷宮，我知道該往哪邊走，
14:15
I've been here before, I can envision the future. And that's what it's doing.
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我曾經到過這裡，我能夠預見未來。這就是智慧在做的事。
14:18
In humans -- by the way, this is true for all mammals;
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在人身上，換句話說，這適用於所有哺乳動物，
14:21
it's true for other mammals -- and in humans, it got a lot worse.
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同樣適用於其他哺乳動物，但在人類身上，這個額外重要。
14:23
In humans, we actually developed the front part of the neocortex
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在人身上，我們事實上發展出了新皮層的前段部份
14:26
called the anterior part of the neocortex. And nature did a little trick.
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稱為新皮層前緣。自然界在這邊耍了一個小手段。
14:30
It copied the posterior part, the back part, which is sensory,
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它複製了後緣部份，後段的感知部份，
14:32
and put it in the front part.
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然後把它放來前面。
14:34
And humans uniquely have the same mechanism on the front,
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因此人類很特殊的在腦前段也有此相同的構造，
14:36
but we use it for motor control.
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但是我們使用它來控制運動功能。
14:38
So we are now able to make very sophisticated motor planning, things like that.
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所以現在我們能夠策劃非常複雜的運動計畫，和類似的事情。
14:41
I don't have time to get into all this, but if you want to understand how a brain works,
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我沒有時間詳細解說所有的這些東西，但是如果你們想要了解大腦是如何運作的，
14:44
you have to understand how the first part of the mammalian neocortex works,
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你們必須了解上一段我所解釋的哺乳動物新皮層運作的原理，
14:47
how it is we store patterns and make predictions.
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它是如何的使我們具有儲存模式和進行預測的能力。
14:49
So let me give you a few examples of predictions.
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現在讓我給你們一些關於預測的實例。
14:52
I already said the word "sentence." In music,
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我已經說過那個關於「字」的例子了。在音樂中，
14:54
if you've heard a song before, if you heard Jill sing those songs before,
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如果你曾經聽過一首歌，如果你之前聽過 Jill 唱這些歌，
14:57
when she sings them, the next note pops into your head already --
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當她唱歌時，下一個音符就已經躍進你的耳朵了 —
15:00
you anticipate it as you're going. If it was an album of music,
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當你一邊在聽歌的時候，你一邊在預期著。如果是一張音樂專輯，
15:02
the end of one album, the next song pops into your head.
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當一首歌結束，下一首歌會自動在你腦海中浮現。
15:05
And these things happen all the time. You're making these predictions.
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而且這種事情一直不斷的在發生。你一直在做這些預測。
15:07
I have this thing called the altered door thought experiment.
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我聽過一個稱作「變更的門」的思想實驗。
15:10
And the altered door thought experiment says, you have a door at home,
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這個思想實驗指出，如果你在家裏有一個門，
15:13
and when you're here, I'm changing it, I've got a guy
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當你在這裡聽演講的時候，我去更動它，我找了一個人
15:16
back at your house right now, moving the door around,
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在這時候回到你家，任意對那扇門做變更，
15:18
and they're going to take your doorknob and move it over two inches.
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他們將把你們的門把移動約兩寸的距離。
15:20
And when you go home tonight, you're going to put your hand out there,
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然後當你今晚回到家的時候，你將會把你的手伸出，
15:22
and you're going to reach for the doorknob and you're going to notice
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然後你將會碰到門把，就在這時，你會注意到
15:24
it's in the wrong spot, and you'll go, whoa, something happened.
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門把的位置不對了，然後你會驚覺，哇，有事情發生了。
15:27
It may take a second to figure out what it was, but something happened.
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你仍然需要一兩秒來思考到底發生了什麼事，但是一定有什麼不一樣。
15:29
Now I could change your doorknob in other ways.
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我可以任意更動你的門把。
15:31
I can make it larger or smaller, I can change its brass to silver,
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我可以使它變大或變小，我可以由黃銅改成鍍銀，
15:33
I could make it a lever. I can change your door, put colors on;
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我可以將門把改為門桿。我可以改變你的門本身，為它上色，
15:35
I can put windows in. I can change a thousand things about your door,
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或者加上窗戶。我有一千種以上的方法來變更你的門，
15:38
and in the two seconds you take to open your door,
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然後在你開門的兩秒內，
15:40
you're going to notice that something has changed.
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你將會注意到某些變更的存在。
15:43
Now, the engineering approach to this, the AI approach to this,
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你沒辦法藉由工程學來完成這件事，人工智慧的解決途徑是，
15:45
is to build a door database. It has all the door attributes.
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建立一個門的資料庫。它擁有所有這些與門相關的特性表。
15:48
And as you go up to the door, you know, let's check them off one at time.
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然後當你走到門前時，你知道，讓我們按照表來一個個檢查這些項目。
15:51
Door, door, door, you know, color, you know what I'm saying.
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門、門、門、你知道的、顏色，你知道我想說什麼嗎？
15:53
We don't do that. Your brain doesn't do that.
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我們不是這麼做的。你的大腦不是這樣運作的。
15:55
What your brain is doing is making constant predictions all the time
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你的大腦事實上是一直在做預測
15:57
about what is going to happen in your environment.
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預測在你的環境中將會發生什麼事。
15:59
As I put my hand on this table, I expect to feel it stop.
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當我把我的手放上這張桌子，我會預期感覺到我的手停止。
16:02
When I walk, every step, if I missed it by an eighth of an inch,
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當我走路時，每一步，即使只差了 1/8 英吋，
16:05
I'll know something has changed.
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我也會察覺某些事情不一樣了。
16:07
You're constantly making predictions about your environment.
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你持續的在對周遭的環境做預測。
16:09
I'll talk about vision here briefly. This is a picture of a woman.
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我將簡短的談談視覺。這是一張女人的照片。
16:12
And when you look at people, your eyes are caught
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當你看著人時，你的眼睛大約會以
16:14
over at two to three times a second.
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每秒兩至三次的頻率移動。
16:15
You're not aware of this, but your eyes are always moving.
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你不自覺，可是你的眼睛是不停的在移動著。
16:17
And so when you look at someone's face,
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因此當你在看某人的臉時，
16:19
you'd typically go from eye to eye to eye to nose to mouth.
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一般來說你會從一隻眼睛看到另一隻眼睛，再從眼睛到鼻子到嘴巴。
16:21
Now, when your eye moves from eye to eye,
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現在，當你的眼睛在對方眼睛間移動的時候，
16:23
if there was something else there like, a nose,
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如果一個鼻子出現在那邊，
16:25
you'd see a nose where an eye is supposed to be,
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你會在本來應該出現眼睛的地方看到鼻子，
16:27
and you'd go, oh shit, you know --
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然後你會像，喔，天呀，你知道 —
16:30
(Laughter)
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（笑聲）
16:31
There's something wrong about this person.
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這個人不太對勁。
16:33
And that's because you're making a prediction.
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而這是因為你一直在做預測。
16:35
It's not like you just look over there and say, what am I seeing now?
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你不是只是往那邊看，然後說：我現在看到什麼東西？
16:37
A nose, that's okay. No, you have an expectation of what you're going to see.
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一個鼻子，那沒什麼。不，你會預期你將看到的東西。
16:40
(Laughter)
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（笑聲）
16:41
Every single moment. And finally, let's think about how we test intelligence.
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無時無刻。最後，讓我們來想想我們是如何做智力測驗的。
16:45
We test it by prediction. What is the next word in this, you know?
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我們用預測能力來測驗它。下一個字是什麼，對吧？
16:48
This is to this as this is to this. What is the next number in this sentence?
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這個之於這個等於那個之於那個。這個序列的下一個數字是什麼？
16:51
Here's three visions of an object.
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這是一個物體的三視圖。
16:53
What's the fourth one? That's how we test it. It's all about prediction.
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第四面可能是什麼？這就是我們測驗智力的方法。全部都跟預測能力有關。
16:57
So what is the recipe for brain theory?
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那麼大腦理論的配方到底是什麼？
17:00
First of all, we have to have the right framework.
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首先，我們必須要有正確的架構。
17:03
And the framework is a memory framework,
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而這個架構是記憶架構，
17:05
not a computation or behavior framework. It's a memory framework.
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而不是計算或是行為架構。是一個記憶架構。
17:07
How do you store and recall these sequences or patterns? It's spatio-temporal patterns.
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你如何儲存並回憶這些序列與模式？一個時間與空間的模式。
17:11
Then, if in that framework, you take a bunch of theoreticians.
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然後，如果在那個架構中，你有一群好的理論學者。
17:14
Now biologists generally are not good theoreticians.
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現在的生物學家通常不是好的理論學者。
17:16
It's not always true, but in general, there's not a good history of theory in biology.
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並不是總是這樣，但是通常是，生物學沒有建夠好理論的歷史習慣。
17:20
So I found the best people to work with are physicists,
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我能找到最好的工作夥伴是物理學家，
17:23
engineers and mathematicians, who tend to think algorithmically.
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工程師和數學家，他們習於演算思維模式。
17:26
Then they have to learn the anatomy, and they've got to learn the physiology.
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然後他們必須學習解剖學和生理學。
17:29
You have to make these theories very realistic in anatomical terms.
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你必須使這些理論在解剖層面上也是非常真實的。
17:33
Anyone who gets up and tells you their theory about how the brain works
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任何人當他跳出來告訴你他們關於大腦運行的理論
17:37
and doesn't tell you exactly how it's working in the brain
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但是不能解釋這些事情如何在腦內發生
17:39
and how the wiring works in the brain, it is not a theory.
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還有腦內的連結關係是什麼，這就不是一個理論。
17:41
And that's what we're doing at the Redwood Neuroscience Institute.
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這就是我們在紅木神經科學研究所進行的研究。
17:44
I would love to have more time to tell you we're making fantastic progress in this thing,
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我希望我能有更多時間來告訴你們，我們已經在這方面有了驚人的進步，
17:48
and I expect to be back up on this stage,
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而我預期未來還能再回到這裡演講，
17:50
maybe this will be some other time in the not too distant future and tell you about it.
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因此也許在不久的將來我將能有機會再次跟你們談談。
17:52
I'm really, really excited. This is not going to take 50 years at all.
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我真的非常、非常興奮。這絕對不需要再五十年。
17:55
So what will brain theory look like?
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因此大腦理論究竟看起來會是什麼樣子？
17:57
First of all, it's going to be a theory about memory.
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首先，它會是一個關於記憶的理論。
17:59
Not like computer memory. It's not at all like computer memory.
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跟電腦記憶體不一樣。它一點都不會像是電腦記憶體。
18:02
It's very, very different. And it's a memory of these very
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會非常、非常的不同。它會是這些非常高維模式
18:04
high-dimensional patterns, like the things that come from your eyes.
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的記憶，就跟你從眼睛看到的東西一般。
18:07
It's also memory of sequences.
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它會是序列的記憶。
18:09
You cannot learn or recall anything outside of a sequence.
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你不能學習或是回憶序列外的任何事物。
18:11
A song must be heard in sequence over time,
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一首歌必須按照時間的順序來聽，
18:14
and you must play it back in sequence over time.
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你也必須按照時間順序來播放。
18:17
And these sequences are auto-associatively recalled, so if I see something,
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然後這些順序就會自動被相關連在一起重播，因此如果我看到某些東西，
18:20
I hear something, it reminds me of it, and then it plays back automatically.
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聽到某些東西，它讓我回一起相關的事物，然後就會自動重播。
18:23
It's an automatic playback. And prediction of future inputs is the desired output.
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它是自動重播。然後對於未來所將輸入訊息的預測是我們所希望的輸出。
18:27
And as I said, the theory must be biologically accurate,
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像我提過的，這個理論必須是生物學正確的。
18:30
it must be testable, and you must be able to build it.
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它必須能被測試，然且你必須能夠建造它。
18:32
If you don't build it, you don't understand it. So, one more slide here.
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如果你不能建造它，你就是不了解它。因此，最後一張投影片。
18:36
What is this going to result in? Are we going to really build intelligent machines?
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這最終會產生什麼結果？我們能夠真的建造出智能機器嗎？
18:40
Absolutely. And it's going to be different than people think.
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絕對可以。而且它會和一般人們所想的不同。
18:44
No doubt that it's going to happen, in my mind.
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我認為這無疑的會發生。
18:47
First of all, it's going to be built up, we're going to build the stuff out of silicon.
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首先，它會被建造，我們將會用矽建出這個東西。
18:51
The same techniques we use for building silicon computer memories,
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跟我們用來建造以矽為原料的電腦記憶體同樣的技術，
18:54
we can use for here.
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我們在這邊也同樣可以使用。
18:55
But they're very different types of memories.
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但是它們會是非常不同種類的記憶體。
18:57
And we're going to attach these memories to sensors,
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然後我們將會將這些記憶體連結上感應器，
18:59
and the sensors will experience real-live, real-world data,
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這些感應器將會經歷真實世界的即時數據，
19:02
and these things are going to learn about their environment.
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然後這些東西將會認識它們的環境。
19:04
Now it's very unlikely the first things you're going to see are like robots.
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而且你將會看到的第一批成品應該非常不可能會長得像個機器人。
19:07
Not that robots aren't useful and people can build robots.
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不是因為機器人沒有用而且人們可以建造機器人。
19:10
But the robotics part is the hardest part. That's the old brain. That's really hard.
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但是機器人的部份是最難的部份。那是原始的大腦。非常的難。
19:14
The new brain is actually kind of easier than the old brain.
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這個新的腦袋要比原始腦袋簡單一些。
19:16
So the first thing we're going to do are the things that don't require a lot of robotics.
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所以我們將建造的第一個東西將會是不需要太多機器人特徵的東西。
19:19
So you're not going to see C-3PO.
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所以你將不會看到 C-3PO。
19:21
You're going to more see things like, you know, intelligent cars
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你可能會比較常看到類似，例如，智慧車
19:23
that really understand what traffic is and what driving is
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真的能了解交通狀況和駕駛
19:26
and have learned that certain types of cars with the blinkers on for half a minute
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而且能夠解讀某些方向燈在閃的車輛過半分鐘後
19:29
probably aren't going to turn, things like that.
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也許即將轉彎，如此這般的事情。
19:31
(Laughter)
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（笑聲）
19:32
We can also do intelligent security systems.
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我們也可以設計智慧型保全系統。
19:34
Anywhere where we're basically using our brain, but not doing a lot of mechanics.
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任何我們需要動用到腦力，但是不會執行太多機械動作的場合。
19:38
Those are the things that are going to happen first.
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這些將會是首先發生的情況。
19:40
But ultimately, the world's the limit here.
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但是最終，沒什麼是不可能的。
19:42
I don't know how this is going to turn out.
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我不知道這將會發展的如何。
19:44
I know a lot of people who invented the microprocessor
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我知道許多發明微處理器的人
19:46
and if you talk to them, they knew what they were doing was really significant,
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如果你問他們，他們知道他們是在從事一些非常重要的事情，
19:51
but they didn't really know what was going to happen.
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但是他們不知道將會發生什麼事。
19:54
They couldn't anticipate cell phones and the Internet and all this kind of stuff.
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他們不能預測到手機、網路等等這些事情的發生。
19:59
They just knew like, hey, they were going to build calculators
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他們只知道像，嘿，他們將要建造計算機
20:01
and traffic light controllers. But it's going to be big.
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和交通號誌燈。但是這將會很重要。
20:03
In the same way, this is like brain science and these memories
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同樣的道理，大腦理論和這些記憶體
20:06
are going to be a very fundamental technology, and it's going to lead
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將會是非常基礎的科技，而且會
20:09
to very unbelievable changes in the next 100 years.
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在未來的一百年內帶來非常不可思議的改變。
20:12
And I'm most excited about how we're going to use them in science.
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我最興奮的是我們將會如何將它們應用到科學研究上。
20:16
So I think that's all my time, I'm over it, and I'm going to end my talk
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我想我的時間已經到了，我超時了，所以我將要結束這次演講
20:19
right there.
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就在這裡結束。
ABOUT THE SPEAKER
Jeff Hawkins - Computer designer, brain researcherJeff Hawkins pioneered the development of PDAs such as the Palm and Treo. Now he's trying to understand how the human brain really works, and adapt its method -- which he describes as a deep system for storing memory -- to create new kinds of computers and tools.
Why you should listen
Jeff Hawkins' Palm PDA became such a widely used productivity tool during the 1990s that some fanatical users claimed it replaced their brains. But Hawkins' deepest interest was in the brain itself. So after the success of the Palm and Treo, which he brought to market at Handspring, Hawkins delved into brain research at the Redwood Center for Theoretical Neuroscience in Berkeley, Calif., and a new company called Numenta.
Hawkins' dual goal is to achieve an understanding of how the human brain actually works -- and then develop software to mimic its functionality, delivering true artificial intelligence. In his book On Intelligence (2004) he lays out his compelling, controversial theory: Contrary to popular AI wisdom, the human neocortex doesn't work like a processor; rather, it relies on a memory system that stores and plays back experiences to help us predict, intelligently, what will happen next. He thinks that "hierarchical temporal memory" computer platforms, which mimic this functionality (and which Numenta might pioneer), could enable groundbreaking new applications that could powerfully extend human intelligence.
Jeff Hawkins | Speaker | TED.com