Chris Anderson: You were something of a mathematical phenom. You had already taught at Harvard and MIT at a young age. And then the NSA came calling. What was that about?
(安德森) 你算是數學界的奇葩 很早便在哈佛和麻省理工教書 之後NSA找上門 這段故事是?
Jim Simons: Well the NSA -- that's the National Security Agency -- they didn't exactly come calling. They had an operation at Princeton, where they hired mathematicians to attack secret codes and stuff like that. And I knew that existed. And they had a very good policy, because you could do half your time at your own mathematics, and at least half your time working on their stuff. And they paid a lot. So that was an irresistible pull. So, I went there.
(西蒙斯) 喔,NSA是美國國家安全局 並沒有實際找我 它在普林斯頓有個機構 請了許多數學家 來破解密碼之類的 我知道這事 它們的規定挺不錯 你可以一半研究數學 只要一半做它們的事 給薪很優渥 這點很難抗拒 所以我就去了
CA: You were a code-cracker.
(安) 你曾是密碼破解員
JS: I was.
(西) 對,是的
CA: Until you got fired.
(安) 直到被解雇
JS: Well, I did get fired. Yes.
(西) 對,我被炒魷魚
CA: How come?
(安) 怎會這樣?
JS: Well, how come? I got fired because, well, the Vietnam War was on, and the boss of bosses in my organization was a big fan of the war and wrote a New York Times article, a magazine section cover story, about how we would win in Vietnam. And I didn't like that war, I thought it was stupid. And I wrote a letter to the Times, which they published, saying not everyone who works for Maxwell Taylor, if anyone remembers that name, agrees with his views. And I gave my own views ...
(西) 嗯,原因嘛 被解雇因為...那時越戰爆發 我單位的上司迷上越戰 他替紐約時報寫了文章 成為封面故事 談如何打贏越戰 我不喜歡越戰,覺得很愚蠢 便寫信給《時代》雜誌,後來被登出來 說並非麥克斯維爾·泰勒所有屬下 都贊同他,如果還有人記得這個名字 我提出我的看法
CA: Oh, OK. I can see that would --
(安) OK,我瞭解這會...
JS: ... which were different from General Taylor's. But in the end, nobody said anything. But then, I was 29 years old at this time, and some kid came around and said he was a stringer from Newsweek magazine and he wanted to interview me and ask what I was doing about my views. And I told him, "I'm doing mostly mathematics now, and when the war is over, then I'll do mostly their stuff." Then I did the only intelligent thing I'd done that day -- I told my local boss that I gave that interview. And he said, "What'd you say?" And I told him what I said. And then he said, "I've got to call Taylor." He called Taylor; that took 10 minutes. I was fired five minutes after that.
(西) 與泰勒將軍不同 但最後也沒人說什麼 那年我29歲,有個小子來找我 自稱是《新聞週刊》特約記者 想問我怎麼實踐自己的看法 我說:「現在我幾乎都弄數學 等戰爭結束,才會做他們的事」 於是我便做了那天最明智的事 我把訪談一事告訴主管 他問我:「你說了什麼?」 我就照實說 接著他說:「我要打電話給泰勒」 他打給泰勒,講了10分鐘 再5分鐘我就被解雇了
CA: OK.
(安) 這樣啊
JS: But it wasn't bad.
(西) 不過這並非壞事
CA: It wasn't bad, because you went on to Stony Brook and stepped up your mathematical career. You started working with this man here. Who is this?
(安) 這不糟,因為你去了 紐約州立大學石溪分校 數學生涯更上層樓 也開始跟這人合作 他是誰?
JS: Oh, [Shiing-Shen] Chern. Chern was one of the great mathematicians of the century. I had known him when I was a graduate student at Berkeley. And I had some ideas, and I brought them to him and he liked them. Together, we did this work which you can easily see up there. There it is.
(西) 喔,陳省身 陳是那世紀最厲害的數學家 我在柏克萊念碩士時,就知道他 我有些想法 告訴了他,他很喜歡 我們便一起努力,就上面你看到的 就是這個
CA: It led to you publishing a famous paper together. Can you explain at all what that work was?
(安) 你們共同發表了著名論文 可以談研究內容嗎?
JS: No.
(西) 不行
(Laughter)
(笑聲)
JS: I mean, I could explain it to somebody.
(西) 我是說,可以講給別人聽
(Laughter)
(笑聲)
CA: How about explaining this?
(安) 如果說明這個呢?
JS: But not many. Not many people.
(西) 可是, 不會向太多人
CA: I think you told me it had something to do with spheres, so let's start here.
(安) 你曾告訴我,這跟球體有關 從這說起吧
JS: Well, it did, but I'll say about that work -- it did have something to do with that, but before we get to that -- that work was good mathematics. I was very happy with it; so was Chern. It even started a little sub-field that's now flourishing. But, more interestingly, it happened to apply to physics, something we knew nothing about -- at least I knew nothing about physics, and I don't think Chern knew a heck of a lot. And about 10 years after the paper came out, a guy named Ed Witten in Princeton started applying it to string theory and people in Russia started applying it to what's called "condensed matter." Today, those things in there called Chern-Simons invariants have spread through a lot of physics. And it was amazing. We didn't know any physics. It never occurred to me that it would be applied to physics. But that's the thing about mathematics -- you never know where it's going to go.
(西) 我要講那研究- 是有關球形, 但我想先說 那是一流的數學研究 我非常高興,陳也是 它甚至促成一個次領域,現在很興盛 更棒的是它被用於物理 一個未知領域,至少我不懂物理 我想陳也只略知皮毛 論文發表10年後 普林斯頓的愛德華·維騰 把它用在弦理論 俄國人則用於所謂"凝聚體"研究 如今這些被稱為"陳-西蒙不變式" 廣泛應用在物理界 這太不可思議 我們完全是物理門外漢 從沒想過會被用於物理 然而,這就是數學 你總猜不到它的去向
CA: This is so incredible. So, we've been talking about how evolution shapes human minds that may or may not perceive the truth. Somehow, you come up with a mathematical theory, not knowing any physics, discover two decades later that it's being applied to profoundly describe the actual physical world. How can that happen?
(安) 真難以置信 我們談到演化如何形塑人類思想 無論思想是否關於真理 你就這樣得出一個數學理論 完全不懂物理 這理論20年後被用來 深入描述實際物理世界 怎麼辦到的?
JS: God knows.
(西) 天曉得
(Laughter)
(笑聲)
But there's a famous physicist named [Eugene] Wigner, and he wrote an essay on the unreasonable effectiveness of mathematics. Somehow, this mathematics, which is rooted in the real world in some sense -- we learn to count, measure, everyone would do that -- and then it flourishes on its own. But so often it comes back to save the day. General relativity is an example. [Hermann] Minkowski had this geometry, and Einstein realized, "Hey! It's the very thing in which I can cast general relativity." So, you never know. It is a mystery. It is a mystery.
知名物理學家尤金·維格納 曾撰文談到數學不合理的有效性 不管怎樣, 數學本就源自真實世界 例如學計算、測量,大家都這麼做 這學門自己繁盛起來 常常一回到數學,困難就迎刃而解 廣義相對論就是一例 愛因斯坦學了閔可夫斯基的幾何學後 驚呼「就是它了! 幫我釐清廣義相對論」 所以, 你搞不懂的,這太奧秘了 超乎常理
CA: So, here's a mathematical piece of ingenuity. Tell us about this.
(安) 關於數學的獨創性 講講這個
JS: Well, that's a ball -- it's a sphere, and it has a lattice around it -- you know, those squares. What I'm going to show here was originally observed by [Leonhard] Euler, the great mathematician, in the 1700s. And it gradually grew to be a very important field in mathematics: algebraic topology, geometry. That paper up there had its roots in this. So, here's this thing: it has eight vertices, 12 edges, six faces. And if you look at the difference -- vertices minus edges plus faces -- you get two. OK, well, two. That's a good number. Here's a different way of doing it -- these are triangles covering -- this has 12 vertices and 30 edges and 20 faces, 20 tiles. And vertices minus edges plus faces still equals two. And in fact, you could do this any which way -- cover this thing with all kinds of polygons and triangles and mix them up. And you take vertices minus edges plus faces -- you'll get two. Here's a different shape. This is a torus, or the surface of a doughnut: 16 vertices covered by these rectangles, 32 edges, 16 faces. Vertices minus edges comes out to be zero. It'll always come out to zero. Every time you cover a torus with squares or triangles or anything like that, you're going to get zero. So, this is called the Euler characteristic. And it's what's called a topological invariant. It's pretty amazing. No matter how you do it, you're always get the same answer. So that was the first sort of thrust, from the mid-1700s, into a subject which is now called algebraic topology.
(西) 這是顆球-球體,球面被格狀劃分 就那些四方形 我要講的是(萊昂納多)歐拉發現的 18世紀偉大的數學家 這現象逐漸成為重要的數學領域 代數拓樸學、幾何學 我的研究即從這來 是這樣的 這裡有8頂點、12邊和6面 如果加以運算:頂點數-邊數+面數 得到2 嗯,好一個2 換方法做,佈滿三角形 有12個頂點,30個邊 和20個面 此時點-邊+面仍是2 事實上,你可用任何方法 球上蓋滿各種多邊形和三角形 混合在一起 再把點-邊+面,得到2 這是另一種形狀 這是環面,甜甜圈形表面16頂點 覆蓋長方形,32邊,16面 點-邊+面得出0 答案永遠是0 只要用長方或三角形 覆蓋環面 答案總是0 這稱為歐拉示性數 也叫做拓樸不變量 這很神奇 不論你怎麼劃,答案總是一樣 這是18世紀中以來第一個刺激 後來變成代數拓樸學
CA: And your own work took an idea like this and moved it into higher-dimensional theory, higher-dimensional objects, and found new invariances?
(安) 你對此更深入研究 到更高維度理論 更高維度的物體,找新的不變量?
JS: Yes. Well, there were already higher-dimensional invariants: Pontryagin classes -- actually, there were Chern classes. There were a bunch of these types of invariants. I was struggling to work on one of them and model it sort of combinatorially, instead of the way it was typically done, and that led to this work and we uncovered some new things. But if it wasn't for Mr. Euler -- who wrote almost 70 volumes of mathematics and had 13 children, who he apparently would dandle on his knee while he was writing -- if it wasn't for Mr. Euler, there wouldn't perhaps be these invariants.
是, 高維不變量已找到了 龐特里亞金示性類,還有陳示性類 一大堆這類不變量 那時我努力研究其中一個 發展成某種組合模型 不用既有標準方法 這變成我們的研究,也發現新東西 但如果沒有歐拉 寫下70卷數學書 養育13個子女 想必是邊寫邊逗弄幼兒 若非歐拉, 就沒有這些不變量
CA: OK, so that's at least given us a flavor of that amazing mind in there. Let's talk about Renaissance. Because you took that amazing mind and having been a code-cracker at the NSA, you started to become a code-cracker in the financial industry. I think you probably didn't buy efficient market theory. Somehow you found a way of creating astonishing returns over two decades. The way it's been explained to me, what's remarkable about what you did wasn't just the size of the returns, it's that you took them with surprisingly low volatility and risk, compared with other hedge funds. So how on earth did you do this, Jim?
(安) 恩, 我們瞭解了,奇特的心路歷程 現在讓我們談談文藝復興科技公司 由於你曾任NSA解碼員的研究經歷 你開始當金融界的解碼員 我想你不相信效率市場理論 20年來,你有辦法獲利驚人 對我來說你的方法 驚人之處不在於獲利金額多寡 而是大幅降低變動性與風險 相較其他對沖基金 你到底怎麼辦到的?
JS: I did it by assembling a wonderful group of people. When I started doing trading, I had gotten a little tired of mathematics. I was in my late 30s, I had a little money. I started trading and it went very well. I made quite a lot of money with pure luck. I mean, I think it was pure luck. It certainly wasn't mathematical modeling. But in looking at the data, after a while I realized: it looks like there's some structure here. And I hired a few mathematicians, and we started making some models -- just the kind of thing we did back at IDA [Institute for Defense Analyses]. You design an algorithm, you test it out on a computer. Does it work? Doesn't it work? And so on.
(西) 我靠集合一群優秀的人 我開始經商時,我對數學已有些厭煩 年紀快40,手頭有點錢 便開始做買賣,結果非常成功 純靠運氣賺了一大筆錢 我說,我認為是好運 而肯定不是數學模型 但我審視這些數字後 發覺現似有固定模式 我便請幾位數學家,弄了幾個模型 類似我在防衛分析研究所做的 設計一套演算法,用電腦測試 能用?不能用? 之類的
CA: Can we take a look at this? Because here's a typical graph of some commodity. I look at that, and I say, "That's just a random, up-and-down walk -- maybe a slight upward trend over that whole period of time." How on earth could you trade looking at that, and see something that wasn't just random?
(安) 可否看看這個? 這是常見的商品銷售圖 我想:「不過是隨機走高走低- 整體趨勢緩升」 你到底怎麼看這隨機圖 就能做生意、發現東西?
JS: In the old days -- this is kind of a graph from the old days, commodities or currencies had a tendency to trend. Not necessarily the very light trend you see here, but trending in periods. And if you decided, OK, I'm going to predict today, by the average move in the past 20 days -- maybe that would be a good prediction, and I'd make some money. And in fact, years ago, such a system would work -- not beautifully, but it would work. You'd make money, you'd lose money, you'd make money. But this is a year's worth of days, and you'd make a little money during that period. It's a very vestigial system.
(西) 這圖很老套了 商品或貨幣有其趨勢 不必然像這樣,但一段時間有其走向 如果你決定,好,我要預測今天 靠前20日的平均變化 也許可猜得準,也賺到錢 事實上幾年前,這系統還可行 不漂亮,但過得去 賺了,賠了,又賺 但這是一年內表現最好的幾天 這期間賺得不多 這系統老掉牙了
CA: So you would test a bunch of lengths of trends in time and see whether, for example, a 10-day trend or a 15-day trend was predictive of what happened next.
(安) 所以你用不同期間長短 的趨勢來檢視,例如 是10天還是15天的走勢預測較準
JS: Sure, you would try all those things and see what worked best. Trend-following would have been great in the '60s, and it was sort of OK in the '70s. By the '80s, it wasn't.
(西) 沒錯,都得試過才知道 順勢投資法 在60年代或許非常好用 70年代還可以 到80年代就玩不通了
CA: Because everyone could see that. So, how did you stay ahead of the pack?
(安) 因為任何人都看得出來 你是怎麼持續領先的?
JS: We stayed ahead of the pack by finding other approaches -- shorter-term approaches to some extent. The real thing was to gather a tremendous amount of data -- and we had to get it by hand in the early days. We went down to the Federal Reserve and copied interest rate histories and stuff like that, because it didn't exist on computers. We got a lot of data. And very smart people -- that was the key. I didn't really know how to hire people to do fundamental trading. I had hired a few -- some made money, some didn't make money. I couldn't make a business out of that. But I did know how to hire scientists, because I have some taste in that department. So, that's what we did. And gradually these models got better and better, and better and better.
(西) 我們靠開發其他方法保持領先- 像是期間更短的方法 實際上是蒐集無數資料 早期都一筆筆抄回來 我們到聯準會影印歷史利率 之類的,那時還沒有電腦 我們取得大批資料 和絕頂聰明的人——這是關鍵 我不太會找人做實際買賣 我請過幾個——有人能賺,有的不行 我不能這樣做生意 但我知道怎麼請科學家 這方面我比較有品味 所以就這麼做了 模型表現越來越好 越來越順
CA: You're credited with doing something remarkable at Renaissance, which is building this culture, this group of people, who weren't just hired guns who could be lured away by money. Their motivation was doing exciting mathematics and science.
(安) 你帶領文藝復興公司的成果驚艷 塑造了一種文化、一群人 他們不是老想錢的傭兵 而一心想玩數學和科學
JS: Well, I'd hoped that might be true. But some of it was money.
(西) 我希望這是真的 但有些動機真的是錢
CA: They made a lot of money.
(安) 他們賺了好多
JS: I can't say that no one came because of the money. I think a lot of them came because of the money. But they also came because it would be fun.
(西) 我不信沒人在乎錢 我想許多人來都想賺錢 但他們也想樂在其中
CA: What role did machine learning play in all this?
(安) 當中機器學習的角色是?
JS: In a certain sense, what we did was machine learning. You look at a lot of data, and you try to simulate different predictive schemes, until you get better and better at it. It doesn't necessarily feed back on itself the way we did things. But it worked.
(西) 某些情況下,我們就是做機器學習 你面對成堆資料,試著模擬各種預測系統 直到越發熟練 它不一定會跟人一樣主動回饋資料 但仍滿好用的
CA: So these different predictive schemes can be really quite wild and unexpected. I mean, you looked at everything, right? You looked at the weather, length of dresses, political opinion.
(安) 所以不同預測系統很難駕馭與掌握 意思是,你什麼都算,是嗎? 天氣、裙長、政治評論
JS: Yes, length of dresses we didn't try.
(西) 是的,裙長倒沒試過
CA: What sort of things?
(安) 哪類東西?
JS: Well, everything. Everything is grist for the mill -- except hem lengths. Weather, annual reports, quarterly reports, historic data itself, volumes, you name it. Whatever there is. We take in terabytes of data a day. And store it away and massage it and get it ready for analysis. You're looking for anomalies. You're looking for -- like you said, the efficient market hypothesis is not correct.
(西) 所有東西 什麼都可用-除了衣擺長度 天氣、年報 季報、歷史資料、冊數,只要你叫得出來 管他是什麼 我們每天取得1T的資料 接著儲存、處理、準備分析 尋找突出的現象 在找——就像你說的 效率市場假說並不正確
CA: But any one anomaly might be just a random thing. So, is the secret here to just look at multiple strange anomalies, and see when they align?
(安) 但任何奇特現象都可能只是隨機現象 所以秘訣是在與注意多次出現的異狀, 並觀察何時接連出現嗎?
JS: Any one anomaly might be a random thing; however, if you have enough data you can tell that it's not. You can see an anomaly that's persistent for a sufficiently long time -- the probability of it being random is not high. But these things fade after a while; anomalies can get washed out. So you have to keep on top of the business.
(西) 任何異常狀可能只是恰巧 不過看夠多資料後 就知並非如此 會發現異常持續很久 隨機出現的機率反而不高 一陣子它會不見,異常會消失 所以我們得保持領先
CA: A lot of people look at the hedge fund industry now and are sort of ... shocked by it, by how much wealth is created there, and how much talent is going into it. Do you have any worries about that industry, and perhaps the financial industry in general? Kind of being on a runaway train that's -- I don't know -- helping increase inequality? How would you champion what's happening in the hedge fund industry?
(安) 目前人們看對沖基金產業 都感到震驚 竟創造這麼多財富 又得投入大量腦力 你擔心這產業嗎? 或對整個金融業? 好似脫韁野馬 我不曉得——助長社會不平等? 你為何支持對沖基金的近來發展?
JS: I think in the last three or four years, hedge funds have not done especially well. We've done dandy, but the hedge fund industry as a whole has not done so wonderfully. The stock market has been on a roll, going up as everybody knows, and price-earnings ratios have grown. So an awful lot of the wealth that's been created in the last -- let's say, five or six years -- has not been created by hedge funds. People would ask me, "What's a hedge fund?" And I'd say, "One and 20." Which means -- now it's two and 20 -- it's two percent fixed fee and 20 percent of profits. Hedge funds are all different kinds of creatures.
(西) 我想近3、4年 對沖基金表現平平 我們曾風光一時 但這產業走得不太順 眾所周知,股市向來平步青雲 本益比增加了 過去5、6年錢賺到嚇死人 但對沖基金就較差 人們問我:「什麼是對沖基金?」 我說:「1和20」 意思是——現在是2和20 2%的固定手續費,20%的獲利抽成 各家對沖基金差異很大
CA: Rumor has it you charge slightly higher fees than that.
(安) 有流言說,你收的高些
JS: We charged the highest fees in the world at one time. Five and 44, that's what we charge.
(西) 我們的手續費一度是世界最高 5和44,就這個價格
CA: Five and 44. So five percent flat, 44 percent of upside. You still made your investors spectacular amounts of money.
(安) 5和44 5%固定費用,44%獲利抽成 你仍幫客戶賺進大把鈔票
JS: We made good returns, yes. People got very mad: "How can you charge such high fees?" I said, "OK, you can withdraw." But "How can I get more?" was what people were --
(西) 是的,收益很不錯 人們氣我:「這太貴了」 我說:「OK,你可退出」 但「如何賺更多」就是人們...
(Laughter)
(笑聲)
But at a certain point, as I think I told you, we bought out all the investors because there's a capacity to the fund.
但重點是,我跟你提過 我們收購了所有投資者,因為這基金能賺
CA: But should we worry about the hedge fund industry attracting too much of the world's great mathematical and other talent to work on that, as opposed to the many other problems in the world?
(安) 但該替對沖基金業擔心嗎? 它吸走太多全球優秀的數學等人才 只做這事,而無視世界其他問題
JS: Well, it's not just mathematical. We hire astronomers and physicists and things like that. I don't think we should worry about it too much. It's still a pretty small industry. And in fact, bringing science into the investing world has improved that world. It's reduced volatility. It's increased liquidity. Spreads are narrower because people are trading that kind of stuff. So I'm not too worried about Einstein going off and starting a hedge fund.
(西) 這個嘛,不單數學家 我們也聘請天文學家和物理學家等 我不覺得我們應當過於擔心 這產業規模仍小 事實上,把科學引入投資界 對世界有益 可降低變動性,提高流動性 因人們交易這東西,擴散範圍變更小 我不擔心愛因斯坦出走搞對沖基金
CA: You're at a phase in your life now where you're actually investing, though, at the other end of the supply chain -- you're actually boosting mathematics across America. This is your wife, Marilyn. You're working on philanthropic issues together. Tell me about that.
(安) 你現在的人生階段是,一方面進出市場 但在供應鏈另一端 也正促進全美數學發展 這是您的夫人,瑪麗蓮 您倆攜手從事慈善工作 說說這個
JS: Well, Marilyn started -- there she is up there, my beautiful wife -- she started the foundation about 20 years ago. I think '94. I claim it was '93, she says it was '94, but it was one of those two years.
(西) 嗯,瑪麗蓮- 這就是她,我美麗的老婆 20年前她創立一基金會 我想是1994年 我說1993, 她說1994 就這兩年間
(Laughter)
(笑聲)
We started the foundation, just as a convenient way to give charity. She kept the books, and so on. We did not have a vision at that time, but gradually a vision emerged -- which was to focus on math and science, to focus on basic research. And that's what we've done. Six years ago or so, I left Renaissance and went to work at the foundation. So that's what we do.
我們創立基金會以便做慈善工作 她負責管帳等事 那時我們沒太多想法,後來逐漸找到方向—— 投入數學、科學和基礎研究 這就是我們在做的 6年前我離開文藝復興公司,改在基金會工作 我們在做這個
CA: And so Math for America is basically investing in math teachers around the country, giving them some extra income, giving them support and coaching. And really trying to make that more effective and make that a calling to which teachers can aspire.
(安) Math for America計畫,基本上是投資 全國數學教師 提供額外收入並給予支持和指導 讓計畫更有效運作 號召有理想的老師
JS: Yeah -- instead of beating up the bad teachers, which has created morale problems all through the educational community, in particular in math and science, we focus on celebrating the good ones and giving them status. Yeah, we give them extra money, 15,000 dollars a year. We have 800 math and science teachers in New York City in public schools today, as part of a core. There's a great morale among them. They're staying in the field. Next year, it'll be 1,000 and that'll be 10 percent of the math and science teachers in New York [City] public schools.
(西) 是的——與其懲罰不適任者 會拖累教育士氣的人 特別在數理科 我們著重鼓勵好老師,給他們地位 是的,我們每年給他們1萬5千美元額外收入 目前紐約市有800名公立學校數理教師 是核心成員 他們士氣高昂 專注在這領域 明年將增至1千人 即紐約公立學校10%的數理教師
(Applause)
(掌聲)
CA: Jim, here's another project that you've supported philanthropically: Research into origins of life, I guess. What are we looking at here? JS: Well, I'll save that for a second. And then I'll tell you what you're looking at. Origins of life is a fascinating question. How did we get here? Well, there are two questions: One is, what is the route from geology to biology -- how did we get here? And the other question is, what did we start with? What material, if any, did we have to work with on this route? Those are two very, very interesting questions. The first question is a tortuous path from geology up to RNA or something like that -- how did that all work? And the other, what do we have to work with? Well, more than we think. So what's pictured there is a star in formation. Now, every year in our Milky Way, which has 100 billion stars, about two new stars are created. Don't ask me how, but they're created. And it takes them about a million years to settle out. So, in steady state, there are about two million stars in formation at any time. That one is somewhere along this settling-down period. And there's all this crap sort of circling around it, dust and stuff. And it'll form probably a solar system, or whatever it forms. But here's the thing -- in this dust that surrounds a forming star have been found, now, significant organic molecules. Molecules not just like methane, but formaldehyde and cyanide -- things that are the building blocks -- the seeds, if you will -- of life. So, that may be typical. And it may be typical that planets around the universe start off with some of these basic building blocks. Now does that mean there's going to be life all around? Maybe. But it's a question of how tortuous this path is from those frail beginnings, those seeds, all the way to life. And most of those seeds will fall on fallow planets.
(安) 你還資助另一計畫 研究生命的起源, 是吧 這是什麼? (西) 先擱一邊 等會再說這圖 生命源起令人著迷 如何找到答案? 這要處理兩個問題 一是,從地質學往生物學 路在哪裡? 二是,從哪下手? 一路上需哪些材料? 這兩問題非常有趣 問題一是條曲折路,從地質學到RNA 之類的——這如何可能? 問題二是需要什麼東西 這超乎我們想像 所以,這是張星體形成圖 在千億星體組成的銀河系裡 每年都誕生兩顆星星 別問過程,反正就誕生了 接著要百萬年才穩定下來 型態固定了 宇宙形成中的星星隨時都有兩百萬顆 那顆星正逐漸穩定 周遭圍繞著廢棄物 塵埃和其他東西 它可能形成太陽系,或者其他什麼 但關鍵是—— 形成中星體周遭的塵土裡 現在研究發現重要的有機分子 不只有甲烷,還有甲醛、氰化物 這種基礎物質——或者生命的種子 這可能是典型過程 宇宙星體也可能經此典型過程 由基礎組成物建立起來 這代表到處都存在生命? 也許 但問題在於這過程多麼迂迴曲折 從渺小的起頭, 種子演變成生命 這類種子絕大多數落在休眠星體上
CA: So for you, personally, finding an answer to this question of where we came from, of how did this thing happen, that is something you would love to see.
(安) 那麼對你個人來說 尋找答案,關於你我的起源 和源起過程是你想知道的
JS: Would love to see. And like to know -- if that path is tortuous enough, and so improbable, that no matter what you start with, we could be a singularity. But on the other hand, given all this organic dust that's floating around, we could have lots of friends out there. It'd be great to know.
(西) 我很期待 也想知道—— 如果這路如此艱辛、渺茫 那不論源頭是什麼,你我都可能是個奇點 但另方面 由於懸浮在外的有機塵埃 遠方我們也許有很多朋友 知道這個感覺很好
CA: Jim, a couple of years ago, I got the chance to speak with Elon Musk, and I asked him the secret of his success, and he said taking physics seriously was it. Listening to you, what I hear you saying is taking math seriously, that has infused your whole life. It's made you an absolute fortune, and now it's allowing you to invest in the futures of thousands and thousands of kids across America and elsewhere. Could it be that science actually works? That math actually works?
(安) 幾年前,我有機會和伊隆·馬斯克對談 我請教他成功的秘訣 他說好好把物理當回事 而你所說的,我覺得是把數學當回事 它飽滿了你的人生 它給你帶來可觀的收入,可以投資 全美、甚至其他地方數千位孩童的未來 真是這學科的功勞嗎? 數學真起作用了?
JS: Well, math certainly works. Math certainly works. But this has been fun. Working with Marilyn and giving it away has been very enjoyable.
(西) 數學本身一定是確實有效的 但有趣的是 和瑪麗蓮同心捐助也真是人生至樂
CA: I just find it -- it's an inspirational thought to me, that by taking knowledge seriously, so much more can come from it. So thank you for your amazing life, and for coming here to TED.
(安) 我發現——這啟發了我 認真做好一門學問,更多好事由此而來 感謝你來 TED 分享不凡的人生
Thank you.
謝謝你
Jim Simons!
詹姆士‧西蒙斯
(Applause)
(掌聲)