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Because I usually take the role
譯者: yinxi zhang 審譯者: Zoe Chen
of trying to explain to people
由於我經常
how wonderful the new technologies
向人們解釋
that are coming along are going to be,
即將到來的新科技
and I thought that, since I was among friends here,
將會多麼的美妙
I would tell you what I really think
我想既然我跟各位朋友們一起在這
and try to look back and try to understand
就讓我來說說我真正的想法
what is really going on here
並試著回顧和理解
with these amazing jumps in technology
這到底是如何發生的
that seem so fast that we can barely keep on top of it.
有了這些科技上的驚人進步。
So I'm going to start out
科技的進步似乎快到我們根本無法趕上它的腳步。
by showing just one very boring technology slide.
讓我先從這開始
And then, so if you can just turn on the slide that's on.
一頁很無趣的科技幻燈片。
This is just a random slide
然後現在可以放幻燈片了。(對工作人員說)
that I picked out of my file.
這只是我從我的文件中
What I want to show you is not so much the details of the slide,
隨機挑選出的一張。
but the general form of it.
我想要你們看的並不是它的細節,
This happens to be a slide of some analysis that we were doing
而是它的總體形式。
about the power of RISC microprocessors
這個是我們做的
versus the power of local area networks.
關於RISC微處理器功率
And the interesting thing about it
與本地網路功率分析的幻燈片。
is that this slide,
有趣的是
like so many technology slides that we're used to,
這頁幻燈片
is a sort of a straight line
就像很多我們所熟悉的幻燈片一樣,
on a semi-log curve.
是半對數曲線圖
In other words, every step here
上的一條直線。
represents an order of magnitude
也就是這裡的每一層,
in performance scale.
代表了性能程度
And this is a new thing
大小的一級。
that we talk about technology
在半對數曲線圖上
on semi-log curves.
討論科技,
Something really weird is going on here.
這很新鮮。
And that's basically what I'm going to be talking about.
這其中有點奇特。
So, if you could bring up the lights.
這基本上是我接下來要說的。
If you could bring up the lights higher,
(對工作人員)麻煩開一下燈。
because I'm just going to use a piece of paper here.
請把燈開亮點,
Now why do we draw technology curves
因為我要用張紙。
in semi-log curves?
為什麼我們要用對數曲線
Well the answer is, if I drew it on a normal curve
描繪科技曲線呢?
where, let's say, this is years,
嗯,答案是,如果我用普通曲線畫,
this is time of some sort,
我們說,這是年份,
and this is whatever measure of the technology
這是某個時間,
that I'm trying to graph,
這是我準備畫的
the graphs look sort of silly.
科技的某種測量值,
They sort of go like this.
這圖看起來有點傻。
And they don't tell us much.
就有點像是這樣。
Now if I graph, for instance,
而且並沒有提供什麼資訊。
some other technology, say transportation technology,
現在,如果我畫,比如說,
on a semi-log curve,
另一種技術,像是交通運輸,
it would look very stupid, it would look like a flat line.
在半對數曲線上,
But when something like this happens,
它看起來很蠢,會像條很平的線。
things are qualitatively changing.
但是如果出現像這種
So if transportation technology
質變的情況。
was moving along as fast as microprocessor technology,
如果交通運輸技術
then the day after tomorrow,
進步地像微處理器業一樣快的話,
I would be able to get in a taxi cab
那,後天
and be in Tokyo in 30 seconds.
我就能搭量計程車
It's not moving like that.
然後在30秒內到東京。
And there's nothing precedented
但它並沒有進步得那麼快。
in the history of technology development
在科技發展歷史中
of this kind of self-feeding growth
也沒有任何
where you go by orders of magnitude every few years.
這種自給自足,
Now the question that I'd like to ask is,
每幾年程度翻倍增長的先例。
if you look at these exponential curves,
現在我想要問的是,
they don't go on forever.
如果你觀察這些指數曲線,
Things just can't possibly keep changing
他們並非永遠的持續下去。
as fast as they are.
事物不可能一直
One of two things is going to happen.
改變得那麼快。
Either it's going to turn into a sort of classical S-curve like this,
兩件事會發生,
until something totally different comes along,
要麼它會變成像這樣典型的S曲線
or maybe it's going to do this.
直到完全不同的情況出現。
That's about all it can do.
或是會變成這樣。
Now I'm an optimist,
這就是所有可能。
so I sort of think it's probably going to do something like that.
現在我是個樂觀主義者,
If so, that means that what we're in the middle of right now
所以我覺得它很有可能就會變成這樣。
is a transition.
如果是這樣,意味著我們目前所在的
We're sort of on this line
是過渡階段。
in a transition from the way the world used to be
我們似乎在這條線上,
to some new way that the world is.
在世界從過去
And so what I'm trying to ask, what I've been asking myself,
到將來的轉變中。
is what's this new way that the world is?
所有我要問的,我一直在問自己的,
What's that new state that the world is heading toward?
就是這世界未來道路在哪?
Because the transition seems very, very confusing
它趨向的新時代是什麼樣的?
when we're right in the middle of it.
由於這個變化似乎非常,非常迷惑人,
Now when I was a kid growing up,
當我們正處在其中時。
the future was kind of the year 2000,
我小時候,長大過程中
and people used to talk about what would happen in the year 2000.
未來就像是2000年,
Now here's a conference
人們都在討論2000年將會發生什麼。
in which people talk about the future,
現在這個會議上,
and you notice that the future is still at about the year 2000.
大家在談論未來,
It's about as far as we go out.
而且你能發現這未來指的還是那個"2000年"。
So in other words, the future has kind of been shrinking
這就是我們能達到的程度。
one year per year
換句話說,未來正在縮水,
for my whole lifetime.
一生中
Now I think that the reason
每年縮短一年。
is because we all feel
我想原因是
that something's happening there.
我們都感覺到
That transition is happening. We can all sense it.
正在發生些什麼。
And we know that it just doesn't make too much sense
變化正在發生。我們都能查覺到。
to think out 30, 50 years
我們知道去考慮那未來的三,五十年
because everything's going to be so different
已經沒什麼意義了,
that a simple extrapolation of what we're doing
因為每件事都將如此不同
just doesn't make any sense at all.
以至於推測將來
So what I would like to talk about
不再有意義。
is what that could be,
所以我要聊聊
what that transition could be that we're going through.
那會是怎樣,
Now in order to do that
我們正在經歷的轉變會是怎樣。
I'm going to have to talk about a bunch of stuff
為達到這個目的,
that really has nothing to do
我得介紹一堆東西
with technology and computers.
它們與
Because I think the only way to understand this
科技和電腦完全無關。
is to really step back
因為我決定理解這個的唯一方法
and take a long time scale look at things.
就是回顧過去
So the time scale that I would like to look at this on
拉長時間軸去看。
is the time scale of life on Earth.
而我所要看的時間軸
So I think this picture makes sense
是以地球上生命的時間尺來看。
if you look at it a few billion years at a time.
我想這幅圖合理了
So if you go back
如果你一次從幾十億年來看。
about two and a half billion years,
如果回溯/所以如果你回溯個
the Earth was this big, sterile hunk of rock
大概25億年,
with a lot of chemicals floating around on it.
地球這麼大,貧瘠的大塊石頭
And if you look at the way
上面浮著些化學物質。
that the chemicals got organized,
要是觀察
we begin to get a pretty good idea of how they do it.
這些化學物質怎樣組合的,
And I think that there's theories that are beginning to understand
我們開始弄明白它們怎麼形成的。
about how it started with RNA,
我想有些理論是從理解
but I'm going to tell a sort of simple story of it,
生命怎樣從核糖核酸演變開始,
which is that, at that time,
但是我想講一個生命簡單的故事,
there were little drops of oil floating around
就是,在那個時候,
with all kinds of different recipes of chemicals in them.
有一滴滴的油四處浮動,
And some of those drops of oil
裡面有各種不同化學成分組合。
had a particular combination of chemicals in them
有些油滴
which caused them to incorporate chemicals from the outside
裡面含有特殊的化學構成
and grow the drops of oil.
這導致它們可以從外界聚集化學物質
And those that were like that
並慢慢變大。
started to split and divide.
像這樣的油滴
And those were the most primitive forms of cells in a sense,
又開始分化,分離。
those little drops of oil.
最原始的那些在某種程度上形成了細胞,
But now those drops of oil weren't really alive, as we say it now,
這些小小的油滴。
because every one of them
但目前為止這些油滴不是真的活的,在我們現在看來,
was a little random recipe of chemicals.
因為每一個
And every time it divided,
都是化學物質的隨機合成。
they got sort of unequal division
每分裂一次,
of the chemicals within them.
都不是平均分佈
And so every drop was a little bit different.
內部的化學物。
In fact, the drops that were different in a way
所以每個油滴都有點不同。
that caused them to be better
實際上,油滴不同的方式
at incorporating chemicals around them,
是讓它們能更好地
grew more and incorporated more chemicals and divided more.
集成周圍的化合物,
So those tended to live longer,
長的更大,吸收更多,分裂更多。
get expressed more.
所以它們會活的更長,
Now that's sort of just a very simple
表現的更多。
chemical form of life,
這就有點像個很簡單的
but when things got interesting
生命的化學形式,
was when these drops
但過程變得有趣
learned a trick about abstraction.
是當這些油滴
Somehow by ways that we don't quite understand,
學會了一個提取資訊的技巧時。
these little drops learned to write down information.
不知怎麼用我們不能完全理解的方式,
They learned to record the information
這些小油滴學會了記錄資訊。
that was the recipe of the cell
它們學會把
onto a particular kind of chemical
細胞形成的秘訣
called DNA.
記錄到一種特殊物質上,
So in other words, they worked out,
叫做去氧核糖核酸。
in this mindless sort of evolutionary way,
也就是說,它們想出了,
a form of writing that let them write down what they were,
以這種隨性的進化方式,
so that that way of writing it down could get copied.
可以寫下它們是什麼的記錄方式,
The amazing thing is that that way of writing
以便這種記錄方式能被複製。
seems to have stayed steady
驚奇的是這種記錄方式
since it evolved two and a half billion years ago.
似乎可以保持穩定
In fact the recipe for us, our genes,
由於它25億年前演化出來的。
is exactly that same code and that same way of writing.
實際上我們,我們的基因的組成
In fact, every living creature is written
就是完全一樣的代碼,一樣的記錄方式。
in exactly the same set of letters and the same code.
事實上,任何生物都是
In fact, one of the things that I did
用完全一樣的字母和代碼記錄下來的。
just for amusement purposes
實際上,我所做的
is we can now write things in this code.
僅是為了娛樂效果的一件事
And I've got here a little 100 micrograms of white powder,
就是我們能用這個代碼記錄事件。
which I try not to let the security people see at airports.
我這有100微克的白粉,
(Laughter)
我盡力不讓機場安檢人員發現它們。
But this has in it --
(笑聲)
what I did is I took this code --
不過這裡面有代碼
the code has standard letters that we use for symbolizing it --
我所做的是我拿著這代碼
and I wrote my business card onto a piece of DNA
它裡面有我們用來標記它的標準字母,
and amplified it 10 to the 22 times.
然後我把我的名片寫到一條去氧核糖核酸上
So if anyone would like a hundred million copies of my business card,
再放大10到22倍。
I have plenty for everyone in the room,
所以如果有人需要數百萬我的名片,
and, in fact, everyone in the world,
我有足夠多分給在座每個人,
and it's right here.
甚至是全世界每個人,
(Laughter)
就在這。
If I had really been a egotist,
(笑聲)
I would have put it into a virus and released it in the room.
要是我是個自大的人,
(Laughter)
我就會把它放大病毒裡散步到屋子中。
So what was the next step?
(笑聲)
Writing down the DNA was an interesting step.
所以下一步是什麼?
And that caused these cells --
記錄去氧核糖核酸是有趣的一步。
that kept them happy for another billion years.
它導致了細胞的形成——
But then there was another really interesting step
讓它們又高興了幾十億年。
where things became completely different,
不過還有個很有趣的環節
which is these cells started exchanging and communicating information,
事情開始變得完全不同,
so that they began to get communities of cells.
那就是這些細胞開始交換和交流資訊,
I don't know if you know this,
從而形成細胞團體。
but bacteria can actually exchange DNA.
我不知道你們是否知道這個,
Now that's why, for instance,
細菌實際上就可以交換去氧核糖核酸。
antibiotic resistance has evolved.
這就是為什麼,比如,
Some bacteria figured out how to stay away from penicillin,
演變出抗菌免疫。
and it went around sort of creating its little DNA information
有些細菌知道怎麼遠離青黴素,
with other bacteria,
然後它創造它這點去氧核糖核酸資訊,
and now we have a lot of bacteria that are resistant to penicillin,
並在別的細菌中到處遊走,
because bacteria communicate.
現在我們有很多對青黴素免疫的細菌了,
Now what this communication allowed
因為細菌會交流資訊。
was communities to form
這樣,這些交流致使
that, in some sense, were in the same boat together;
群落的形成,
they were synergistic.
在某種意義上,它們在同一條船上了;
So they survived
它們是協作的。
or they failed together,
因此它們一起倖存下來
which means that if a community was very successful,
或者一起死去,
all the individuals in that community
也就是說如果一個群落成功了,
were repeated more
所有群落裡的個體
and they were favored by evolution.
都能複製更多,
Now the transition point happened
在進化更有利。
when these communities got so close
於是,轉捩點到了,
that, in fact, they got together
當這些族群很親近時,
and decided to write down the whole recipe for the community
事實上,它們聚集到一起
together on one string of DNA.
並決定一起在一條去氧核糖核酸上
And so the next stage that's interesting in life
寫下整個族群的成分譜。
took about another billion years.
生命中下一個有趣的階段
And at that stage,
又要幾十億年。
we have multi-cellular communities,
在這個時期,
communities of lots of different types of cells,
有多細胞族群,
working together as a single organism.
就是有很多種不同細胞的群落,
And in fact, we're such a multi-cellular community.
作為有機體一起合作。
We have lots of cells
實際上,我們就是這樣的多細胞族群。
that are not out for themselves anymore.
我們有很多細胞,
Your skin cell is really useless
它們不再是是只為自己存活。
without a heart cell, muscle cell,
皮膚細胞根本沒用,
a brain cell and so on.
要是沒有心臟細胞,肌肉細胞,
So these communities began to evolve
腦細胞等等。
so that the interesting level on which evolution was taking place
所以這些族群開始進化
was no longer a cell,
這樣發生有趣的進化的
but a community which we call an organism.
不再僅僅是單一細胞。
Now the next step that happened
而是我們稱為機體的族群。
is within these communities.
接下來發生
These communities of cells,
就是在這些族群中。
again, began to abstract information.
這些細胞群落,
And they began building very special structures
再次,開始提取資訊。
that did nothing but process information within the community.
它們開始構建非常特別的
And those are the neural structures.
專門處理群落內資訊的結構。
So neurons are the information processing apparatus
這些就是神經結構。
that those communities of cells built up.
所以神經元是
And in fact, they began to get specialists in the community
這些細胞群建立的資訊處理儀器。
and special structures
實際上,群落裡開始出現專家
that were responsible for recording,
以及特殊結構
understanding, learning information.
負責記錄,
And that was the brains and the nervous system
理解,學習資訊。
of those communities.
這就是這些細胞群的
And that gave them an evolutionary advantage.
大腦和神經系統。
Because at that point,
這給了它們進化的有力條件。
an individual --
因為這樣的話,
learning could happen
對每個個體——
within the time span of a single organism,
學習可以發生
instead of over this evolutionary time span.
在單個機體的時間範圍內,
So an organism could, for instance,
而不是整個進化時間跨度。
learn not to eat a certain kind of fruit
所以一個機體能夠,比如說,
because it tasted bad and it got sick last time it ate it.
學會不吃某種水果
That could happen within the lifetime of a single organism,
因為它不好吃而且上次吃的覺得噁心。
whereas before they'd built these special information processing structures,
這可以發生在一個機體的一生中,
that would have had to be learned evolutionarily
然後在這種特殊信心處理結構建成前,
over hundreds of thousands of years
這得要進化學習
by the individuals dying off that ate that kind of fruit.
千萬年,
So that nervous system,
通過吃了這種水果前赴後繼死去的個體。
the fact that they built these special information structures,
所以神經系統,
tremendously sped up the whole process of evolution.
生物組建這種特殊結構的事實,
Because evolution could now happen within an individual.
極大地加速了進化的進程。
It could happen in learning time scales.
因為至此進化可以在個體中發生了。
But then what happened
它能發生在學習的時間刻度內。
was the individuals worked out,
但是接下來發生的
of course, tricks of communicating.
是每個個體發現了,
And for example,
當然,交流的秘訣。
the most sophisticated version that we're aware of is human language.
比如說,
It's really a pretty amazing invention if you think about it.
我們所知道的最精密的版本就是人類語言。
Here I have a very complicated, messy,
想想看,這真是個奇妙的發明。
confused idea in my head.
我腦子裡有個很複雜,混亂,
I'm sitting here making grunting sounds basically,
疑惑的的想法。
and hopefully constructing a similar messy, confused idea in your head
我坐在這,基本上就是吐字發聲,
that bears some analogy to it.
希望在你們頭腦裡建立一個類似的混亂
But we're taking something very complicated,
跟它有點類似的想法。
turning it into sound, sequences of sounds,
但是我們正在把很複雜的東西
and producing something very complicated in your brain.
轉化成聲音,一連串的聲音,
So this allows us now
並在你們大腦產生很複雜的東西。
to begin to start functioning
所以現在這推動我們
as a single organism.
開始運作,
And so, in fact, what we've done
作為單個機體。
is we, humanity,
所以,實際上,我們已經完成的
have started abstracting out.
就是我們,人類,
We're going through the same levels
開始抽離出來。
that multi-cellular organisms have gone through --
我們正在經歷多細胞機體經歷的
abstracting out our methods of recording,
相同的階段——
presenting, processing information.
提取我們記錄,
So for example, the invention of language
展示,處理資訊的方式。
was a tiny step in that direction.
比如說,語言的發明
Telephony, computers,
就是這個方向上很小一步。
videotapes, CD-ROMs and so on
電話,電腦,
are all our specialized mechanisms
影碟,光碟等等
that we've now built within our society
都是我們的特殊機制,
for handling that information.
我們正在社會裡構建
And it all connects us together
用來處理資訊的機制。
into something
這些都是把我們聯繫在一起,
that is much bigger
變的
and much faster
比我們之前
and able to evolve
更大,
than what we were before.
更快,
So now, evolution can take place
更有能力進化。
on a scale of microseconds.
所以,現在進化可以發生在
And you saw Ty's little evolutionary example
微秒的數量級上。
where he sort of did a little bit of evolution
你們看過泰伊的那個的進化的小例子
on the Convolution program right before your eyes.
他好像就在你們眼前在卷積程式上
So now we've speeded up the time scales once again.
展現了一點進化了。
So the first steps of the story that I told you about
所以現在我們再次加快時間跨度。
took a billion years a piece.
我講的故事的第一步
And the next steps,
每一塊花費了幾十億年。
like nervous systems and brains,
下一步,
took a few hundred million years.
像神經系統和大腦,
Then the next steps, like language and so on,
消耗幾百萬年。
took less than a million years.
再接下來,像語言等等,
And these next steps, like electronics,
需要不到一百萬年。
seem to be taking only a few decades.
再下一步,像電子器件,
The process is feeding on itself
仿佛只要幾十年。
and becoming, I guess, autocatalytic is the word for it --
這個過程是自給自足,
when something reinforces its rate of change.
並且變成,我猜,應該自我催化描述更合適——
The more it changes, the faster it changes.
當事物加快改變的速度。
And I think that that's what we're seeing here in this explosion of curve.
變化越多,變化就越快。
We're seeing this process feeding back on itself.
我想這就是我們在這看到的激增曲線。
Now I design computers for a living,
我們看到這個過程回饋到自己。
and I know that the mechanisms
我現在工作就是自己設計電腦,
that I use to design computers
我知道用來設計電腦的
would be impossible
這些機制
without recent advances in computers.
不可能存在,
So right now, what I do
要是沒有近期電腦的進步。
is I design objects at such complexity
現在,我做的
that it's really impossible for me to design them in the traditional sense.
是設計複雜到
I don't know what every transistor in the connection machine does.
不可能從傳統意義上設計的物體。
There are billions of them.
我不知道連接機器上每個電晶體的作用。
Instead, what I do
有幾十億電晶體。
and what the designers at Thinking Machines do
實際上,我所做的
is we think at some level of abstraction
思考機器的設計師們做的,
and then we hand it to the machine
我們認為是在某種程度的資訊抽取,
and the machine takes it beyond what we could ever do,
然後把它傳給機器
much farther and faster than we could ever do.
而機器把它運用到超出我們所能做的範圍,
And in fact, sometimes it takes it by methods
而且比我們從前所做的更遠更快。
that we don't quite even understand.
實際上,有時候他採用的方法
One method that's particularly interesting
我們並不很懂。
that I've been using a lot lately
有個尤其有趣
is evolution itself.
我最近一直在用的
So what we do
就是進化本身。
is we put inside the machine
我們做的就是
a process of evolution
在機器裡
that takes place on the microsecond time scale.
放入一個進化進程,
So for example,
這個進程在微妙級別上就能發生。
in the most extreme cases,
比如,
we can actually evolve a program
大部分極端情況下,
by starting out with random sequences of instructions.
我們實際上能
Say, "Computer, would you please make
通過從隨機的指令序列開始進化一個程式。
a hundred million random sequences of instructions.
(就像)說“電腦,請你產生
Now would you please run all of those random sequences of instructions,
一億隨機指令序列。
run all of those programs,
現在請你運行所有這些隨機指令列,
and pick out the ones that came closest to doing what I wanted."
運行所有程式,
So in other words, I define what I wanted.
並選出最接近我想要的。”
Let's say I want to sort numbers,
也就是說,我定義我要什麼。
as a simple example I've done it with.
假設我需要分類資料,
So find the programs that come closest to sorting numbers.
這是個我用它試驗過的簡單例子。
So of course, random sequences of instructions
找到最接近資料分類的程式。
are very unlikely to sort numbers,
當然,隨機的指令序列
so none of them will really do it.
很不可能分類資料,
But one of them, by luck,
所有它們中沒有一個能完成。
may put two numbers in the right order.
但是中間有一個,運氣很好,
And I say, "Computer,
可能會把兩個數按順序排列。
would you please now take the 10 percent
我說,“電腦,
of those random sequences that did the best job.
請你現在選出序列中百分之十
Save those. Kill off the rest.
完成得最好的。
And now let's reproduce
保存這些。刪掉其他的。
the ones that sorted numbers the best.
現在來複製
And let's reproduce them by a process of recombination
資料分類得最好的這些。
analogous to sex."
以類似交配的重組過程
Take two programs and they produce children
來複製他們。”
by exchanging their subroutines,
取兩個程式
and the children inherit the traits of the subroutines of the two programs.
交換他們的副程式讓它們產生子女,
So I've got now a new generation of programs
這些子女繼承了兩個程式副程式的特徵。
that are produced by combinations
所以我得到新一代的
of the programs that did a little bit better job.
由組合做的比較好的程式
Say, "Please repeat that process."
而產生的程式。
Score them again.
(指令)說,“請重複這個過程。”
Introduce some mutations perhaps.
再做一次。
And try that again and do that for another generation.
可能引入一些突變。
Well every one of those generations just takes a few milliseconds.
再試一次並用在新的一代上。
So I can do the equivalent
這一代上每個程式只需要幾毫秒。
of millions of years of evolution on that
所以我在電腦上用幾分鐘
within the computer in a few minutes,
能做等同於
or in the complicated cases, in a few hours.
幾百萬年的進化過程,
At the end of that, I end up with programs
或者,情況複雜時,在幾小時內完成。
that are absolutely perfect at sorting numbers.
結束時,我得到
In fact, they are programs that are much more efficient
絕對完美地分類資料的程式。
than programs I could have ever written by hand.
實際上,這些程式比我手寫的
Now if I look at those programs,
任何程式都要有效率。
I can't tell you how they work.
現在,如果我讀這些程式,
I've tried looking at them and telling you how they work.
我說不出他們怎麼工作的。
They're obscure, weird programs.
我嘗試過閱讀並且解釋他們如何工作的。
But they do the job.
他們很抽象,奇怪。
And in fact, I know, I'm very confident that they do the job
但是他們能完成任務。
because they come from a line
實際上,我知道,我很有信心他們能完成任務
of hundreds of thousands of programs that did the job.
因為他們來自于一行
In fact, their life depended on doing the job.
上千萬能完成認為的程式。
(Laughter)
事實上,他們的生命就是靠著這工作。
I was riding in a 747
(笑聲)
with Marvin Minsky once,
我曾經有一次
and he pulls out this card and says, "Oh look. Look at this.
和馬文明斯基一起坐747,
It says, 'This plane has hundreds of thousands of tiny parts
他拿出一張卡,說,“看,看這。
working together to make you a safe flight.'
這上面說“本飛機有很多精密部件
Doesn't that make you feel confident?"
協作,保障您飛行安全。”
(Laughter)
這是不是讓你很有信心?”
In fact, we know that the engineering process doesn't work very well
(笑聲)
when it gets complicated.
事實上,我們知道工程過程複雜化
So we're beginning to depend on computers
並不能很好工作。
to do a process that's very different than engineering.
所以我們開始依賴電腦
And it lets us produce things of much more complexity
來做與工程有很大不同的一個過程。
than normal engineering lets us produce.
它能讓我們生產出
And yet, we don't quite understand the options of it.
比普通工程能生產的更複雜的東西。
So in a sense, it's getting ahead of us.
然而,我們還不明白他的選擇。
We're now using those programs
從某種意義上說,它比我們超前。
to make much faster computers
我們現在正用這些程式
so that we'll be able to run this process much faster.
創造更快的電腦
So it's feeding back on itself.
以便能更快的運行這個進程。
The thing is becoming faster
所以它是自我回饋的。
and that's why I think it seems so confusing.
這正變得更快,
Because all of these technologies are feeding back on themselves.
這也是為什麼我覺得它似乎很讓人摸不清。
We're taking off.
由於所有這些技術都回饋到自己。
And what we are is we're at a point in time
我們正在起飛。
which is analogous to when single-celled organisms
我們正是在時間的某一點,
were turning into multi-celled organisms.
這一點類似於單細胞機體
So we're the amoebas
正轉變成多細胞機體的時刻。
and we can't quite figure out what the hell this thing is we're creating.
我們就像變形蟲,
We're right at that point of transition.
搞不清自己正在創造的是什麼東西。
But I think that there really is something coming along after us.
我們正在轉捩點上。
I think it's very haughty of us
不過我認為一定有跟隨著我們的東西。
to think that we're the end product of evolution.
我想它是很崇拜我們的,
And I think all of us here
認為我們是進化的終級產物。
are a part of producing
我認為我們這所有人
whatever that next thing is.
都是繁衍的一部分,
So lunch is coming along,
無論下一步是什麼。
and I think I will stop at that point,
午飯時間快到了,
before I get selected out.
趁我還沒被選走,
(Applause)
我就在這停下。/我想我就在這裡結束。