So, I have a strange career. I know it because people come up to me, like colleagues, and say, "Chris, you have a strange career."
我的職業很奇怪。 這麼講是因為常有人這樣告訴我,例如我的同事 都會說:「克里斯,你的職業很奇怪耶。」
(Laughter)
(觀眾笑聲)
And I can see their point, because I started my career as a theoretical nuclear physicist. And I was thinking about quarks and gluons and heavy ion collisions, and I was only 14 years old -- No, no, I wasn't 14 years old. But after that, I actually had my own lab in the Computational Neuroscience department, and I wasn't doing any neuroscience. Later, I would work on evolutionary genetics, and I would work on systems biology.
其實我理解他們的意思, 因為我一開始當過 理論核子物理學家。 那時我成天想的都是夸克和膠子, 還有重離子的撞擊, 那時我才14歲而已。 不,不對,不是14歲那年的事。 不過在那之後 我有了一間專屬的實驗室, 就在計算神經科學系那邊, 但是我完全都沒有做神經科學的研究。 後來我開始研究演化基因, 接著便是系統生物學。
But I'm going to tell you about something else today. I'm going to tell you about how I learned something about life. And I was actually a rocket scientist. I wasn't really a rocket scientist, but I was working at the Jet Propulsion Laboratory in sunny California, where it's warm; whereas now I am in the mid-West, and it's cold. But it was an exciting experience. One day, a NASA manager comes into my office, sits down and says, "Can you please tell us, how do we look for life outside Earth?" And that came as a surprise to me, because I was actually hired to work on quantum computation. Yet, I had a very good answer. I said, "I have no idea."
不過以上這些跟我今天要講的主題一點關係也沒有。 我要講的是 我如何探悉到生命的一些東西。 我以前當過火箭專家。 但嚴格上來說我不算是真正的火箭專家, 只不過我曾經在 位於陽光普照的加州的太空總署的 噴射推進實驗室工作過; 而我現在在中西部, 氣候很寒冷。 不過這是一個很有趣的經驗。 有一天NASA主管走進我的辦公室, 坐下後說: 「請你告訴我們, 如何能尋找到外太空的生命?」 我當時很驚訝, 因為我當初是被請來 進行量子計算研究的。 然而我回答得很漂亮。 我答:「我一點也不知道。」
(Laughter)
接著他對我說:「生命跡象,
And he told me, "Biosignatures, we need to look for a biosignature." And I said, "What is that?" And he said, "It's any measurable phenomenon that allows us to indicate the presence of life." And I said, "Really? Because isn't that easy? I mean, we have life. Can't you apply a definition, for example, a Supreme Court-like definition of life?"
我們必須找出生命跡象。」 我問他:「那是什麼?」 他說:「生命跡象就是能讓我們能 辨識出任何可量化 生命的存在的現象。」 我說:「真的嗎? 真的有這麼簡單嗎? 我是說,我們有生命。 但你能為生命下一個 類似最高法院般的終極定義嗎?」
And then I thought about it a little bit, and I said, "Well, is it really that easy? Because, yes, if you see something like this, then all right, fine, I'm going to call it life -- no doubt about it. But here's something." And he goes, "Right, that's life too. I know that." Except, if you think that life is also defined by things that die, you're not in luck with this thing, because that's actually a very strange organism. It grows up into the adult stage like that and then goes through a Benjamin Button phase, and actually goes backwards and backwards until it's like a little embryo again, and then actually grows back up, and back down and back up -- sort of yo-yo -- and it never dies. So it's actually life, but it's actually not as we thought life would be. And then you see something like that. And he was like, "My God, what kind of a life form is that?" Anyone know? It's actually not life, it's a crystal.
我再想了想, 然後說, 「就只有這樣而已嗎? 沒錯,如果你看到這個, 毫無疑問,我會稱它為生命-- 這是無庸置疑的。 但如果換成這個。」 他說:「沒錯,這個也是生命。我很確定。」 可是倘若你認為得生命 是由會死亡的物體來定義, 那你就無法解釋這個東西, 因為這是一個相當奇怪的有機體。 當它進入成年期的時候就像這樣, 然後就像班傑明的奇幻旅程一樣 不斷退化, 直到胚胎為止, 接著又長回來,再長大 -- 像溜溜球一樣過程循環 -- 而永生不死。 這也算是生命的一種, 只不過它不是 我們一般所認知的型態。 再來你如果看到這個。 他問:「天啊,這到底是什麼樣的生命形態呢?」 有人知道嗎? 其實這不算是生命,這是一種結晶體。
So once you start looking and looking at smaller and smaller things -- so this particular person wrote a whole article and said, "Hey, these are bacteria." Except, if you look a little bit closer, you see, in fact, that this thing is way too small to be anything like that. So he was convinced, but, in fact, most people aren't. And then, of course, NASA also had a big announcement, and President Clinton gave a press conference, about this amazing discovery of life in a Martian meteorite. Except that nowadays, it's heavily disputed. If you take the lesson of all these pictures, then you realize, well, actually, maybe it's not that easy. Maybe I do need a definition of life in order to make that kind of distinction.
所以當你觀察的東西 越來越小時-- 有位老兄 花了整篇文章的篇幅 只為傳達一件事:「嗨, 這是細菌。」 但如果你靠近一點觀察 你會發現,事實上這個物體 已經比細菌還要小。 於是他被說服了, 可是大部分的人還是不相信。 當然, NASA做了一個重大的宣布, 此外前總統柯林頓也召開記者會, 宣布在火星的隕石裡 發現有生命的存在。 但是現今這個說法受到嚴重的質疑。 如果你仔細地研究這些照片, 就會發覺區別生命並沒有那麼簡單。 也許我需要 一個生命的定義 才能夠來做區別。
So can life be defined? Well how would you go about it? Well of course, you'd go to Encyclopedia Britannica and open at L. No, of course you don't do that; you put it somewhere in Google. And then you might get something.
生命能被定義嗎? 你會如何著手? 當然 你會從大英百科的L開始查起。 不,你當然不會那樣做; 你會用Google搜尋。 然後你或會得到一些資料。
(Laughter)
接著把你搜尋到的 --
And what you might get -- and anything that actually refers to things that we are used to, you throw away. And then you might come up with something like this. And it says something complicated with lots and lots of concepts. Who on Earth would write something as convoluted and complex and inane? Oh, it's actually a really, really, important set of concepts. So I'm highlighting just a few words and saying definitions like that rely on things that are not based on amino acids or leaves or anything that we are used to, but in fact on processes only. And if you take a look at that, this was actually in a book that I wrote that deals with artificial life. And that explains why that NASA manager was actually in my office to begin with. Because the idea was that, with concepts like that, maybe we can actually manufacture a form of life.
所有我們習以為常的觀念 拋諸腦後。 然後你可能會得到這段 複雜的解釋, 裡頭包括許許多多的概念。 到底有誰會寫出 這麼人費解,複雜 又空洞的東西? 但是這段定義確實涵蓋了 一堆非常重要的概念。 我標出了幾個關鍵字眼, 這類的定義 不是基於 胺基酸或葉子 或者我們熟悉的東西, 而是只基於過程。 如果你仔細看這段話的出處, 就知道是從我寫的一本 有關人造生命的書而來。 這說明了 那位NASA主管來辦公室找我的原因。 因為用這樣的想法與概念, 我們也許能創造 一個生命的形式。
And so if you go and ask yourself, "What on Earth is artificial life?", let me give you a whirlwind tour of how all this stuff came about. And it started out quite a while ago, when someone wrote one of the first successful computer viruses. And for those of you who aren't old enough, you have no idea how this infection was working -- namely, through these floppy disks. But the interesting thing about these computer virus infections was that, if you look at the rate at which the infection worked, they show this spiky behavior that you're used to from a flu virus. And it is in fact due to this arms race between hackers and operating system designers that things go back and forth. And the result is kind of a tree of life of these viruses, a phylogeny that looks very much like the type of life that we're used to, at least on the viral level.
如果你反問自己 「到底什麼是人工生命?」 就讓我快速地帶你認識 人工生命的由來。 這是好幾年前發生的, 有人寫了早期史上 上最具破壞力的電腦病毒。 對年紀較輕的觀眾來說, 你們可能不清楚這種病毒是從哪裡散播開來的... 就是從這種磁碟片傳染的。 不過這種電腦中毒有趣的地方 可以從電腦的 感染速率來看, 這張圖表反映出的上下波動 跟一般的流感病毒沒有兩樣。 事實上因為駭客 和作業系統開發人員之間發生的爭奪戰, 而使結果反反復複。 這張電腦病毒的關係圖 便成樹狀展開, 一個看似我們熟悉的生命發展史, 至少從病毒的層面來看是如此。
So is that life? Not as far as I'm concerned. Why? Because these things don't evolve by themselves. In fact, they have hackers writing them. But the idea was taken very quickly a little bit further, when a scientist working at the Santa Fe Institute decided, "Why don't we try to package these little viruses in artificial worlds inside of the computer and let them evolve?" And this was Steen Rasmussen. And he designed this system, but it really didn't work, because his viruses were constantly destroying each other. But there was another scientist who had been watching this, an ecologist. And he went home and says, "I know how to fix this." And he wrote the Tierra system, and, in my book, is in fact one of the first truly artificial living systems -- except for the fact that these programs didn't really grow in complexity.
病毒能算是生命嗎? 我可不這麼認為。 怎麼說呢? 因為它們無法自行演化。 事實上,電腦病毒是駭客寫出來的。 但是這個想法不久就有了一點進展, 有一個在新墨西哥州的科學家決定, 「我們為何不把這些電腦病毒 放進電腦的虛擬世界, 讓它們自行演化?」 這位科學家就是斯蒂恩•拉斯穆森。 他設計了這套系統,不過沒效, 因為他的病毒會不斷自相殘殺。 但當時還有一位科學家對這件事情很關心,是一名生態學者。 他回了家說:「我知道怎麼解決。」 他寫出Tierra系統, 根據我書裡寫的,Tierra正是最早出現的 人造生命系統之一-- 只不過這些程式沒有真正複雜性的成長。
So having seen this work, worked a little bit on this, this is where I came in. And I decided to create a system that has all the properties that are necessary to see, in fact, the evolution of complexity, more and more complex problems constantly evolving. And of course, since I really don't know how to write code, I had help in this. I had two undergraduate students at California Institute of Technology that worked with me. That's Charles Ofria on the left, Titus Brown on the right. They are now, actually, respectable professors at Michigan State University, but I can assure you, back in the day, we were not a respectable team. And I'm really happy that no photo survives of the three of us anywhere close together.
看過這個成果之後,我自己也做了一點研究, 而我的研究就從此展開。 我決定創造一個系統, 該系統必須滿足 複雜演化的所有必要條件, 有越來越多複雜的問題持續在演變。 當然,由於我不會編碼,所以我找了槍手。 我請到了兩位 在加州理工學院與我共事的大學生。 左邊的是查爾斯•奧佛瑞亞,右邊這位是提多•布朗。 他們如今都是在密西根州立大學 備受尊崇的教授了, 但我可以向你保證, 在當時 我們並不是可受尊敬的團隊。 我很慶幸我們三人形影不離的合照, 一張都沒有留下。
But what is this system like? Well I can't really go into the details, but what you see here is some of the entrails. But what I wanted to focus on is this type of population structure. There's about 10,000 programs sitting here. And all different strains are colored in different colors. And as you see here, there are groups that are growing on top of each other, because they are spreading. Any time there is a program that's better at surviving in this world, due to whatever mutation it has acquired, it is going to spread over the others and drive the others to extinction.
這個系統是什麼樣子? 我不方便探討細節, 不過我可以給你們看一點內部的構造。 我著重的是 這種族群結構圖。 這裡大約有一萬個程式。 每個種類都用不同顏色來分類。 你會發現族群間會相互掩蓋, 因為它們散播開來了。 不論何時都有一個程式 較能夠在虛擬世界中存活下來, 因為經過突變的過程, 這個程式將會蓋過其它群體甚至把它們趕盡殺絕。
So I'm going to show you a movie where you're going to see that kind of dynamic. And these kinds of experiments are started with programs that we wrote ourselves. We write our own stuff, replicate it, and are very proud of ourselves. And we put them in, and what you see immediately is that there are waves and waves of innovation. By the way, this is highly accelerated, so it's like a 1000 generations a second. But immediately, the system goes like, "What kind of dumb piece of code was this? This can be improved upon in so many ways, so quickly." So you see waves of new types taking over the other types. And this type of activity goes on for quite a while, until the main easy things have been acquired by these programs. And then, you see sort of like a stasis coming on where the system essentially waits for a new type of innovation, like this one, which is going to spread over all the other innovations that were before and is erasing the genes that it had before, until a new type of higher level of complexity has been achieved. And this process goes on and on and on.
在我接下來要放的影片裡你們可以觀察到這樣的變化。 這個實驗是從我們 自行開發的程式進行的。 我們寫了程式, 然後進行複製, 我們對此感到非常驕傲。 我們把程式放到系統裡, 就成了你現在看到不斷變動的波形。 順便提一下,這段影片是加快播放, 所以變化的速度大約是一秒衍生一千次。 很快系統就有了改變, 「這究竟是什麼蠢代碼呢? 這可以藉由很多種方法 快速獲得改善。」 你可以看到新種類 取代其它種類的過程。 這樣子的過程會持續一段時間, 直到這些程式把大多數單純的結構納入為止。 接下來系統會面臨停滯期, 系統在等待一個 全新的轉變,就像這樣。 它將會覆蓋 先前所有的變化 並且消滅之前所有的基因, 直到系統演化到更具複雜性的層面。 這個過程會不斷重複上演。
So what we see here is a system that lives in very much the way we're used to how life goes. But what the NASA people had asked me really was, "Do these guys have a biosignature? Can we measure this type of life? Because if we can, maybe we have a chance of actually discovering life somewhere else without being biased by things like amino acids." So I said, "Well, perhaps we should construct a biosignature based on life as a universal process. In fact, it should perhaps make use of the concepts that I developed just in order to sort of capture what a simple living system might be."
所以我們在這看到的 就是一個與我們熟悉的 生命形式雷同的系統。 但NASA官員問我的是 「這些玩意兒 有生命跡象嗎?」 我們可不可以衡量這樣的生命形式? 因為如果我們可以, 也許我們就能以客觀角度 證實其它星球有生命存在, 而不需靠胺基酸來判別。」 我說:「我們必須建立一個 生命跡象, 並假設所有生命都會經歷一個共通的過程。 實際上,這必須應用我 開發的概念來達成, 得以了解 一個簡單的生命體系如何運作。」
And the thing I came up with -- I have to first give you an introduction about the idea, and maybe that would be a meaning detector, rather than a life detector. And the way we would do that -- I would like to find out how I can distinguish text that was written by a million monkeys, as opposed to text that is in our books. And I would like to do it in such a way that I don't actually have to be able to read the language, because I'm sure I won't be able to. As long as I know that there's some sort of alphabet. So here would be a frequency plot of how often you find each of the 26 letters of the alphabet in a text written by random monkeys. And obviously, each of these letters comes off about roughly equally frequent.
為了解釋我想到的方法-- 首先我得介紹一個概念, 或許這概念是個存在探測器, 而不是生命探測器。 我們的做法就是-- 先辨認出一段文字, 是由一百萬隻猴子聯合寫出來的, 還是從我們平常看的書籍中節錄出來的。 我會這樣處理, 我不需要看懂這段文字的語言, 因為我知道我根本辦不到。 但只要我可以認出其中有的是字母。 這是一張次數分配圖, 告訴你在這段 由猴群隨機寫出來的文字裡 其中26個字母出現的次數。 顯然這些字母 出現的頻率大約相等。
But if you now look at the same distribution in English texts, it looks like that. And I'm telling you, this is very robust across English texts. And if I look at French texts, it looks a little bit different, or Italian or German. They all have their own type of frequency distribution, but it's robust. It doesn't matter whether it writes about politics or about science. It doesn't matter whether it's a poem or whether it's a mathematical text. It's a robust signature, and it's very stable. As long as our books are written in English -- because people are rewriting them and recopying them -- it's going to be there.
但是如果你看到的是一段英文段落的字母次數分配, 就會長成這樣。 而且這種現象在英文裡非常明顯。 如果是法語就會不太一樣, 甚至是義大利文或德文。 各種語言都有獨特的次數分配模式, 但是結果都很一致。 不管內容是有關政治或科學。 還是一首詩, 甚至是一段數學式子。 都有一個明顯的特徵, 而且非常穩定。 只要我們的書籍是用英文寫的-- 因為人們會不斷重寫並抄寫書籍-- 就會產生這個特徵。
So that inspired me to think about, well, what if I try to use this idea in order, not to detect random texts from texts with meaning, but rather detect the fact that there is meaning in the biomolecules that make up life. But first I have to ask: what are these building blocks, like the alphabet, elements that I showed you? Well it turns out, we have many different alternatives for such a set of building blocks. We could use amino acids, we could use nucleic acids, carboxylic acids, fatty acids. In fact, chemistry's extremely rich, and our body uses a lot of them.
這讓我想到 如果我用這個概念, 不是為了要從有意義的文章中 挑出雜亂無章的文字, 而是探測 形成生命體的生物分子特徵。 但首先我有個問題: 這些組成的基本單位是什麼? 就像我剛給你們看的字母。 事實證明,我們有很多種選擇 可用來做為構成的基礎。 我們能利用胺基酸, 核酸、羧酸或不飽和脂肪酸。 事實上化學物質存在相當廣泛,我們人體就充滿許多化學物質。 所以,為了試驗這個想法,
So that we actually, to test this idea, first took a look at amino acids and some other carboxylic acids. And here's the result. Here is, in fact, what you get if you, for example, look at the distribution of amino acids on a comet or in interstellar space or, in fact, in a laboratory, where you made very sure that in your primordial soup, there is no living stuff in there. What you find is mostly glycine and then alanine and there's some trace elements of the other ones. That is also very robust -- what you find in systems like Earth where there are amino acids, but there is no life.
我們研究了胺基酸和其他的羧酸。 這就是結果。 事實上, 譬如, 如果你觀察一個彗星或星際空間, 或者一個實驗室裡 所充斥的胺基酸, 但必須保證在原生湯裡 沒有任何生命存在。 你能找到的大部分是甘氨酸和丙氨酸, 還有一些其它的元素。 這個結果也相當明顯-- 你可以在地球的生態系統裡 找到胺基酸
But suppose you take some dirt and dig through it
但是沒有生命。
and then put it into these spectrometers, because there's bacteria all over the place; or you take water anywhere on Earth, because it's teaming with life, and you make the same analysis; the spectrum looks completely different. Of course, there is still glycine and alanine, but in fact, there are these heavy elements, these heavy amino acids, that are being produced because they are valuable to the organism. And some other ones that are not used in the set of 20, they will not appear at all in any type of concentration. So this also turns out to be extremely robust. It doesn't matter what kind of sediment you're using to grind up, whether it's bacteria or any other plants or animals. Anywhere there's life, you're going to have this distribution, as opposed to that distribution. And it is detectable not just in amino acids.
但假設你採集一些土壤 在裡面找尋一番 放到光譜儀下, 因為土壤佈滿了細菌; 或者是你取地球上任何一處的水, 因為水裡富含生命, 然後你做一樣的分析; 光譜結果會截然不同。 當然結果仍然含有甘氨酸和丙氨酸, 但是更重要的因素是大量的胺基酸, 因而大量產生, 因為胺基酸對有機體而言非常重要。 而那些二十個以外 的沒被用的, 在任何情況下, 則毫無出現的可能。 這個結果極為顯著。 不管你是要研磨哪種沙土, 不管是細菌或是動植物。 到處都有生命存在, 你會得到這個分配圖, 而不是無生物的分配圖。 不光是胺基酸可被探测。
Now you could ask: Well, what about these Avidians? The Avidians being the denizens of this computer world where they are perfectly happy replicating and growing in complexity. So this is the distribution that you get if, in fact, there is no life. They have about 28 of these instructions. And if you have a system where they're being replaced one by the other, it's like the monkeys writing on a typewriter. Each of these instructions appears with roughly the equal frequency. But if you now take a set of replicating guys like in the video that you saw, it looks like this. So there are some instructions that are extremely valuable to these organisms, and their frequency is going to be high. And there's actually some instructions that you only use once, if ever. So they are either poisonous or really should be used at less of a level than random. In this case, the frequency is lower. And so now we can see, is that really a robust signature? I can tell you indeed it is, because this type of spectrum, just like what you've seen in books, and just like what you've seen in amino acids, it doesn't really matter how you change the environment, it's very robust, it's going to reflect the environment.
這時你可能會問: 那Avidians呢? Avidians是存在電腦世界裡的產物, 它們在那快樂地繁殖成長。 這就是Avida的分配圖, 假設Avida裡沒有生命存在。 圖裡有28種指令。 而且你如果可以創造一個供這些指令相互取代的系統, 彷彿是猴群在打字機上亂打字。 則每一種指令 所出現的頻率會大約相等。 但是如果是剛在影片裡出現的 會複製的玩意兒, 看起來會像這樣。 有部分的指令 對於有機體相當重要, 所以這些指令的出現頻率相對會很高。 不過也有一些指令 只出現過一次。 它們不是有毒 不然就是使用上必須低於隨機的水平。 這種情況下出現頻率會比較低。 那麼我們現在所看到的算是一個明顯的指標嗎? 我可以告訴你的確是, 因為這種分配型態,如同你剛看到的書, 還有胺基酸的例子, 不管你怎麼改變環境,特徵就是這麼明顯; 並且會反映出環境的特色。
So I'm going to show you now a little experiment that we did. And I have to explain to you, the top of this graph shows you that frequency distribution that I talked about. Here, that's the lifeless environment where each instruction occurs at an equal frequency. And below there, I show, in fact, the mutation rate in the environment. And I'm starting this at a mutation rate that is so high that even if you would drop a replicating program that would otherwise happily grow up to fill the entire world, if you drop it in, it gets mutated to death immediately. So there is no life possible at that type of mutation rate. But then I'm going to slowly turn down the heat, so to speak, and then there's this viability threshold where now it would be possible for a replicator to actually live. And indeed, we're going to be dropping these guys into that soup all the time.
我現在要給你們看一個我們做的實驗。 我得解釋一下, 這張圖表的上方 指的是我剛講到的頻率分配。 事實上,這是個無生命的環境, 每種指令出現的頻率 都相等。 下面的圖表 代表環境的突變率。 我把一開始的突變率調得很高, 高到就算你 放入一個會 快速成長的複製程式, 然後佈滿整個空間, 當你把程式放進去時,它會立刻突變至死。 在這種突變率之下 沒有任何生命能夠存活。 但是接下來我要把突變率降低, 到一個適當的程度, 如此一來就有一個複製體 能夠存活。 然後我們要把這些玩意兒 放到原生湯裡。
So let's see what that looks like. So first, nothing, nothing, nothing. Too hot, too hot. Now the viability threshold is reached, and the frequency distribution has dramatically changed and, in fact, stabilizes. And now what I did there is, I was being nasty, I just turned up the heat again and again. And of course, it reaches the viability threshold. And I'm just showing this to you again because it's so nice. You hit the viability threshold. The distribution changes to "alive!" And then, once you hit the threshold where the mutation rate is so high that you cannot self-reproduce, you cannot copy the information forward to your offspring without making so many mistakes that your ability to replicate vanishes. And then, that signature is lost.
看看會發生什麼事。 一開始沒有事情發生。一點都沒有。 然後發生劇烈的變化。 現在已經達到可行數值 以及頻率分配。 一開始發生劇烈變化, 然後, 事實上, 緩和下來。 接著我就很邪惡的 把溫度一再再調高。 當然它就能達到可行數值。 這實在是太讚了,所以我要再放一次給你們看。 一旦降到可行數值 分配就變成「有生命的, 萬歲!」。 當點突變率 回升到可行數值 就不能自我複製, 也不能將信息 毫無錯誤地 傳給後代, 因此複製能力就消失了。 代表生命指標也消失了。
What do we learn from that? Well, I think we learn a number of things from that. One of them is, if we are able to think about life in abstract terms -- and we're not talking about things like plants, and we're not talking about amino acids, and we're not talking about bacteria, but we think in terms of processes -- then we could start to think about life not as something that is so special to Earth, but that, in fact, could exist anywhere. Because it really only has to do with these concepts of information, of storing information within physical substrates -- anything: bits, nucleic acids, anything that's an alphabet -- and make sure that there's some process so that this information can be stored for much longer than you would expect -- the time scales for the deterioration of information. And if you can do that, then you have life.
我們從中學到了什麼? 我認為我們學到了幾個重點。 第一, 如果我們能夠 以抽象的定義來思考生命-- 我們不提植物, 不提胺基酸, 也不提細菌, 而我們思考的是過程-- 如此一來就能探討生命 不限於地球特有的生命, 而是, 任何地方都可能有生物的存在。 因為它只跟這些 儲存訊息的 概念有關, 在物質基底之內-- 可以是任何東西: 位元組、核酸, 任何可當成跟字母一樣的單位-- 並且確保能有一個 遠比你預期的時間還要長, 不被時間比例影響信息衰退, 可讓信息儲存起來的程序。 如果條件都滿足, 就算是生命。
So the first thing that we learn is that it is possible to define life in terms of processes alone, without referring at all to the type of things that we hold dear, as far as the type of life on Earth is. And that, in a sense, removes us again, like all of our scientific discoveries, or many of them -- it's this continuous dethroning of man -- of how we think we're special because we're alive. Well, we can make life; we can make life in the computer. Granted, it's limited, but we have learned what it takes in order to actually construct it. And once we have that, then it is not such a difficult task anymore to say, if we understand the fundamental processes that do not refer to any particular substrate, then we can go out and try other worlds, figure out what kind of chemical alphabets might there be, figure enough about the normal chemistry, the geochemistry of the planet, so that we know what this distribution would look like in the absence of life, and then look for large deviations from this -- this thing sticking out, which says, "This chemical really shouldn't be there." Now we don't know that there's life then, but we could say, "Well at least I'm going to have to take a look very precisely at this chemical and see where it comes from." And that might be our chance of actually discovering life when we cannot visibly see it.
所以我們學到的第一件事就是 生命可以單獨 依照程序來定義, 無須借助 其它我們重視的東西, 例如地球的生命型式。 這個結論又一次 就像所有的科學發現一樣-- 再度證明了 我們的存在並沒有那麼獨特。 我們能夠製造生命,在電腦裡頭製造生命。 當然, 這的確是有限的, 不過我們藉此了解到 架構生命的要素。 一旦我們有了這些要素, 創造生命便不再是難事, 如果我們能掌握基本的過程 而不透過任何特殊基底, 我們就能走出現有的框架 探索地球以外的世界, 了解那裏會有什麼樣的化學元素符號, 認識普遍的化學物質, 還有該星球的地球科學, 如此一來我們便能了解 沒有生命存在的分配型態會如何呈現, 然後利用分配型態找到一些特例-- 因為偏離值能凸顯出: 「這個化學物質不應該在那裏出現」。 我們現在不確定那裏是否有生命存在, 但至少, 「我會仔細研究這個化學物質 辨識出它是從哪而來」。 這也許會成為 我們發現新生命的機會, 即使我們不能看見生命的形體。
And so that's really the only take-home message that I have for you. Life can be less mysterious than we make it out to be when we try to think about how it would be on other planets. And if we remove the mystery of life, then I think it is a little bit easier for us to think about how we live, and how perhaps we're not as special as we always think we are. And I'm going to leave you with that.
這便是我要給你們唯一的 重要結論。 當我們知道其他星球也存在生命, 生命就沒有 我們想像中的那般神祕了。 如果我們能夠揭開生命神祕的面紗, 那對我來說, 要思考我們如何生存, 以及我們不是那麼獨特這類的議題,就不再是難事。 我要把這部分留給你們去想。
And thank you very much.
謝謝大家。
(Applause)
(鼓掌聲)