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