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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。

Because I usually take the role

譯者: yinxi zhang 審譯者: Zoe Chen

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B1 US TED 進化 細胞 程式 群落 資訊

【TED】丹尼-希里斯:回到未來(1994年的)(丹尼-希里斯:回到未來(1994年的))。 (【TED】Danny Hillis: Back to the future (of 1994) (Danny Hillis: Back to the future (of 1994)))

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    Zenn posted on 2021/01/14
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