Today, actually, is a very special day for me, because it is my birthday.
今天對我來說是個很特別的一天, 因為今天是我的生日。
(Applause)
(掌聲)
And so, thanks to all of you for joining the party.
謝謝大家來參加這場派對。
(Laughter)
(笑聲)
But every time you throw a party, there's someone there to spoil it. Right?
但,每次你辦派對時, 總會掃興的人,對吧?
(Laughter)
(笑聲)
And I'm a physicist, and this time I brought another physicist along to do so. His name is Albert Einstein -- also Albert -- and he's the one who said that the person who has not made his great contributions to science by the age of 30 will never do so.
我是物理學家, 這次,我帶來了 另一位來掃興的物理學家。 他叫做阿爾伯特愛因斯坦—— 也叫阿爾伯特——他說過 如果一個人到了三十歲 都還沒有對科學 做出偉大的貢獻, 就永遠不會有貢獻了。
(Laughter)
(笑聲)
Now, you don't need to check Wikipedia that I'm beyond 30.
各位不需要去維基百科查證, 我已經超過三十歲了。
(Laughter)
(笑聲)
So, effectively, what he is telling me, and us, is that when it comes to my science, I'm deadwood. Well, luckily, I had my share of luck within my career. Around age 28, I became very interested in networks, and a few years later, we managed to publish a few key papers that reported the discovery of scale-free networks and really gave birth to a new discipline that we call network science today. And if you really care about it, you can get a PhD now in network science in Budapest, in Boston, and you can study it all over the world.
所以,實際上,他要 告訴我以及我們的是, 在我的科學領域中, 我已經是枯枝。 嗯,幸運的是,我在 我的職涯中有好運氣。 大約二十八歲時, 我對於網路非常感興趣, 幾年後,我們出版了 幾篇重要論文, 闡述我們發現了無尺度網路, 創造出了一門新的學科, 就是現今所稱的網路科學。 如果各位想知道,現在可以 取得網路科學博士學位的地方 包括布達佩斯、波士頓, 且在全世界各地都可以研讀它。
A few years later, when I moved to Harvard first as a sabbatical, I became interested in another type of network: that time, the networks within ourselves, how the genes and the proteins and the metabolites link to each other and how they connect to disease. And that interest led to a major explosion within medicine, including the Network Medicine Division at Harvard, that has more than 300 researchers who are using this perspective to treat patients and develop new cures.
幾年後, 我搬到哈佛,一開始是學術休假, 我開始對另一種網路產生了興趣: 我們體內的網路, 基因、蛋白質、代謝物 彼此之間如何連結, 以及它們和疾病的關係。 那項興趣導致了醫學上的大爆炸, 包括哈佛的網路醫學部門, 有超過三百名研究者使用這種觀點 來治療病人和開發新解藥。
And a few years ago, I thought that I would take this idea of networks and the expertise we had in networks in a different area, that is, to understand success. And why did we do that? Well, we thought that, to some degree, our success is determined by the networks we're part of -- that our networks can push us forward, they can pull us back. And I was curious if we could use the knowledge and big data and expertise where we develop the networks to really quantify how these things happen.
幾年前, 我認為我可以把網路的這個點子 以及我們對網路的專長 帶到不同的領域去, 也就是,用來了解成功。 為什麼要那樣做? 嗯,我們認為,在某種程度上, 我們的成功是由我們所屬的網路決定, 我們的網路將我們向前推進, 也可以讓我們遲滯不前。 我很好奇,我們是否 能用這知識和大數據 及我們開發網路的專門技術 來將成功的發生給量化。
This is a result from that. What you see here is a network of galleries in museums that connect to each other. And through this map that we mapped out last year, we are able to predict very accurately the success of an artist if you give me the first five exhibits that he or she had in their career.
這就是研究的結果。 各位現在看到的是 博物館的畫廊的網路, 它們彼此連結。 透過我們去年畫的這張地圖, 我們就可以很精確地預測 一位藝術家是否會成功, 只要給我這位藝術家 在職涯中的最早五件展示品。
Well, as we thought about success, we realized that success is not only about networks; there are so many other dimensions to that. And one of the things we need for success, obviously, is performance. So let's define what's the difference between performance and success. Well, performance is what you do: how fast you run, what kind of paintings you paint, what kind of papers you publish. However, in our working definition, success is about what the community notices from what you did, from your performance: How does it acknowledge it, and how does it reward you for it? In other terms, your performance is about you, but your success is about all of us. And this was a very important shift for us, because the moment we defined success as being a collective measure that the community provides to us, it became measurable, because if it's in the community, there are multiple data points about that. So we go to school, we exercise, we practice, because we believe that performance leads to success. But the way we actually started to explore, we realized that performance and success are very, very different animals when it comes to the mathematics of the problem. And let me illustrate that.
當我們在思考成功時, 我們發現,成功不只和網路有關; 還有好多其他的維度。 很顯然,我們想要成功 就一定需要的一樣東西 就是表現。 所以,咱們來定義一下 表現和成功之間的差別。 表現是你所做的事: 你能跑多快、你畫出什麼樣的畫、 你出版什麼樣的論文。 然而,根據我們的工作定義, 成功的重點在於大家 能注意到你做了什麼、 你的表現如何: 怎麼認可你的表現, 你的表現帶給你什麼報償? 換言之, 你的表現是你的事, 但你的成功是我們所有人的事。 這對我們來說是很重要的轉變, 因為當我們把成功定義為 團體提供我們的一個集體測量值, 它就變成可測量的, 因為如果它是在團體中, 就有相關的許多資料點。 所以我們去學校, 我們做作業,我們練習, 因為我們相信表現會導致成功。 但我們這樣開始探究之後, 便了解到在數學問題上, 表現和成功非常不同。 讓我說明一下。
So what you see here is the fastest man on earth, Usain Bolt. And of course, he wins most of the competitions that he enters. And we know he's the fastest on earth because we have a chronometer to measure his speed. Well, what is interesting about him is that when he wins, he doesn't do so by really significantly outrunning his competition. He's running at most a percent faster than the one who loses the race. And not only does he run only one percent faster than the second one, but he doesn't run 10 times faster than I do -- and I'm not a good runner, trust me on that.
各位在這裡看到的是世界上 最快的人,尤塞恩博爾特。 當然,他參加的比賽, 他大部分都有贏。 我們知道他跑得最快,因為我們 有精密計時器來測量速度。 關於他,有一點很有趣, 那就是當他贏的時候, 他並不是明顯超越他的對手許多。 他最多是比輸家快 1% 而已。 他不僅只比第二名快 1%, 他也沒有跑得比我快十倍—— 我不是個好跑者,相信我。
(Laughter)
(笑聲)
And every time we are able to measure performance, we notice something very interesting; that is, performance is bounded. What it means is that there are no huge variations in human performance. It varies only in a narrow range, and we do need the chronometer to measure the differences. This is not to say that we cannot see the good from the best ones, but the best ones are very hard to distinguish. And the problem with that is that most of us work in areas where we do not have a chronometer to gauge our performance.
每當我們能夠測量表現時, 我們就會注意到一件很有趣的事; 那就是,表現是受限的。 意思就是說,人類的表現 並沒有太大的變動。 人類表現只在一個小範圍中變動, 我們的確需要很精密的 計時器才能測出差別。 這並不是說我們分不出 好和最好的差別, 而是很難分辨出最好的人。 那所造成的問題就是, 我們大部分人工作的領域中 並沒有精密的計時器 來測量我們的表現。
Alright, performance is bounded, there are no huge differences between us when it comes to our performance. How about success? Well, let's switch to a different topic, like books. One measure of success for writers is how many people read your work. And so when my previous book came out in 2009, I was in Europe talking with my editor, and I was interested: Who is the competition? And I had some fabulous ones. That week --
好,表現是受限的, 我們之間在表現上 沒有很大的差異。 那成功呢? 咱們切換到一個不同的 主題,以書籍為例。 對作家來說,成功的測量值之一 就是有多少人讀你的作品。 我的上一本書在 2009 年推出時, 我在歐洲跟我的編輯談, 我很感興趣:競爭對手是誰? 我有一些很棒的對手。 那週——
(Laughter)
(笑聲)
Dan Brown came out with "The Lost Symbol," and "The Last Song" also came out, Nicholas Sparks. And when you just look at the list, you realize, you know, performance-wise, there's hardly any difference between these books or mine. Right? So maybe if Nicholas Sparks's team works a little harder, he could easily be number one, because it's almost by accident who ended up at the top. So I said, let's look at the numbers -- I'm a data person, right? So let's see what were the sales for Nicholas Sparks. And it turns out that that opening weekend, Nicholas Sparks sold more than a hundred thousand copies, which is an amazing number. You can actually get to the top of the "New York Times" best-seller list by selling 10,000 copies a week, so he tenfold overcame what he needed to be number one. Yet he wasn't number one. Why? Because there was Dan Brown, who sold 1.2 million copies that weekend.
丹布朗推出《失落的符號》, 《最後一首歌》也推出了, 尼可拉斯史派克的作品。 當你只是看列表, 你會知道,就表現來說, 這些書和我的書之間 幾乎沒有什麼差別。 對吧? 所以,也許尼可拉斯史派克的 團隊更努力一點, 他很容易成為第一名, 因為誰會在頂端幾乎都是意外。 所以,我說,咱們來看看數字, 我是研究資料的人,對吧? 咱們來看看尼可拉斯 史派克的銷售額如何。 結果發現,在第一個週末, 尼可拉斯史派克 賣出了超過十萬本書, 這個數字很驚人。 只要一週銷售一萬本, 就可以登上《紐約時報》 暢銷書排行榜了, 所以他超越了成為第一名 需要的數字足足十倍。 但,他並非第一名。為什麼? 因為還有丹布朗,那個週末, 他的書賣了一百二十萬本。
(Laughter)
(笑聲)
And the reason I like this number is because it shows that, really, when it comes to success, it's unbounded, that the best doesn't only get slightly more than the second best but gets orders of magnitude more, because success is a collective measure. We give it to them, rather than we earn it through our performance.
我喜歡這些數字是因為, 它呈現出成功是沒有限制的, 第一名並不會只比第二名多一點, 而是用指數倍數來算, 因為成功是集體的測量值。 我們把成功給他們,而不是 透過自己的表現贏來成功的。
So one of things we realized is that performance, what we do, is bounded, but success, which is collective, is unbounded, which makes you wonder: How do you get these huge differences in success when you have such tiny differences in performance? And recently, I published a book that I devoted to that very question. And they didn't give me enough time to go over all of that, so I'm going to go back to the question of, alright, you have success; when should that appear?
我們了解到,表現, 也就是我們所做的,會受限, 但成功,是集體的,沒有限制, 這就會讓人納悶: 如果在表現上只能有小小的差別, 在成功上如何造成 這麼巨大的差別? 最近,我出版了一本書, 就是針對這個問題而寫的。 他們沒有給我足夠的時間 去談所有這些, 所以我要回到這個問題, 好,你有成功;它會何時出現?
So let's go back to the party spoiler and ask ourselves: Why did Einstein make this ridiculous statement, that only before 30 you could actually be creative? Well, because he looked around himself and he saw all these fabulous physicists that created quantum mechanics and modern physics, and they were all in their 20s and early 30s when they did so. And it's not only him. It's not only observational bias, because there's actually a whole field of genius research that has documented the fact that, if we look at the people we admire from the past and then look at what age they made their biggest contribution, whether that's music, whether that's science, whether that's engineering, most of them tend to do so in their 20s, 30s, early 40s at most. But there's a problem with this genius research. Well, first of all, it created the impression to us that creativity equals youth, which is painful, right?
咱們回到讓派對掃興的 那個人,問問我們自己: 為什麼愛因斯坦 會說出那句荒謬的話, 說只有在三十歲之前 你才可能真的有創意? 因為他看看自己身邊, 這些很出色的物理學家, 發明了量子力學和近代物理的人, 他們提出發明時都是 二十多歲或三十初頭。 不只是他而已。 這並不是觀察偏見, 因為有一整個領域的天才研究 記錄這個事實, 如果我們去看我們 所欣賞的過去人物, 看看他們做出最大貢獻的年齡, 不論是音樂、不論是科學、 不論是工程, 大部分都是在二、三十歲時達成, 最多四十初頭。 但這種天才研究有一個問題。 首先,它讓我們有一種印象, 認為創意等同年輕, 這很痛,對吧?
(Laughter)
(笑聲)
And it also has an observational bias, because it only looks at geniuses and doesn't look at ordinary scientists and doesn't look at all of us and ask, is it really true that creativity vanishes as we age? So that's exactly what we tried to do, and this is important for that to actually have references.
它也有存在觀察偏見, 因為它只研究天才, 沒有研究一般科學家, 且沒有研究我們所有人並問: 真的在我們年長之後 創意就消失嗎? 那就是我們試圖要做的, 能真正有參考是很重要的。
So let's look at an ordinary scientist like myself, and let's look at my career. So what you see here is all the papers that I've published from my very first paper, in 1989; I was still in Romania when I did so, till kind of this year. And vertically, you see the impact of the paper, that is, how many citations, how many other papers have been written that cited that work. And when you look at that, you see that my career has roughly three different stages. I had the first 10 years where I had to work a lot and I don't achieve much. No one seems to care about what I do, right? There's hardly any impact.
咱們來看看一般的 科學家,像我自己, 來看看我的職涯。 這裡是我出版過的所有論文, 我的第一篇論文在 1989 年出版, 當時我還在羅馬尼亞, 直到今年。 垂直來看,可以看到論文的影響, 也就是引用數, 有多少篇其他論文 曾經引用過那篇文章。 如果去看那些,就會發現 我的職涯大致可以分為三個階段。 前十年,我很努力工作, 沒有很高的成就。 似乎沒有人在乎我做什麼,對吧? 幾乎沒有任何影響力。
(Laughter)
(笑聲)
That time, I was doing material science, and then I kind of discovered for myself networks and then started publishing in networks. And that led from one high-impact paper to the other one. And it really felt good. That was that stage of my career.
那段時間,我在做材料科學, 接著,我發現了網路, 接著開始出版網路的文章。 導致了一篇又一篇的 高影響力論文出現。 感覺真的很好,我職涯的那個階段。
(Laughter)
(笑聲)
So the question is, what happens right now? And we don't know, because there hasn't been enough time passed yet to actually figure out how much impact those papers will get; it takes time to acquire. Well, when you look at the data, it seems to be that Einstein, the genius research, is right, and I'm at that stage of my career.
問題是,現在會發生什麼事? 我們不知道,因為 還沒有經過那麼多時間, 無法得知那些論文的影響會有 多大;那需要時間才能知道。 如果去看資料,似乎,愛因斯坦, 那些天才研究,是對的, 我正在職涯的那個階段。
(Laughter)
(笑聲)
So we said, OK, let's figure out how does this really happen, first in science. And in order not to have the selection bias, to look only at geniuses, we ended up reconstructing the career of every single scientist from 1900 till today and finding for all scientists what was their personal best, whether they got the Nobel Prize or they never did, or no one knows what they did, even their personal best. And that's what you see in this slide. Each line is a career, and when you have a light blue dot on the top of that career, it says that was their personal best. And the question is, when did they actually make their biggest discovery? To quantify that, we look at what's the probability that you make your biggest discovery, let's say, one, two, three or 10 years into your career? We're not looking at real age. We're looking at what we call "academic age." Your academic age starts when you publish your first papers. I know some of you are still babies.
所以,我們說,好, 咱們來研究看看這是如何發生的, 先看科學。 為了避免選樣偏誤, 只去研究天才, 我們最後為每一位 科學家都重建了職涯, 從 1900 年至今的所有科學家, 並針對所有科學家, 找出他們個人的顛峰時期, 不論他們是否有得到諾貝爾獎, 或者即使他們在顛峰時 也沒有人知道他們做了什麼。 那就是這張投影片呈現的。 每一條線就是一段職涯, 淡藍色的點就是那職涯的顛峰, 那是他們個人的最佳狀態。 問題是,他們何時 有最重大的發現? 為了量化它,我們去研究 做出最重大發現的機率, 比如,你的職涯開始之後的 一、二、三,或十年? 我們研究的不是真實年齡, 而是所謂的「學術年齡」。 你的學術年齡開始於 你的第一篇論文被刊出時。 我知道在座還有一些嬰兒。
(Laughter)
(笑聲)
So let's look at the probability that you publish your highest-impact paper. And what you see is, indeed, the genius research is right. Most scientists tend to publish their highest-impact paper in the first 10, 15 years in their career, and it tanks after that. It tanks so fast that I'm about -- I'm exactly 30 years into my career, and the chance that I will publish a paper that would have a higher impact than anything that I did before is less than one percent. I am in that stage of my career, according to this data. But there's a problem with that. We're not doing controls properly. So the control would be, what would a scientist look like who makes random contribution to science? Or what is the productivity of the scientist? When do they write papers? So we measured the productivity, and amazingly, the productivity, your likelihood of writing a paper in year one, 10 or 20 in your career, is indistinguishable from the likelihood of having the impact in that part of your career.
咱們來看看你出版 最有影響力的論文的機率。 各位可以看見,的確, 天才研究是對的。 大部分的科學家傾向會在 職涯的前十、十五年 出版他們最有影響力的論文, 之後就開始下滑。 下滑的速度很快,我大約—— 我現在正在我職涯的三十年, 我有可能出版一篇 比我以前所有論文 都更有影響力的論文的機率, 低於 1%。 根據這些資料,我現在 就處在職涯的那個階段。 但有個問題。 我們沒有把控制做好。 控制指的是, 對科學做出隨機貢獻的科學家 看起來會是什麼樣子的? 或,那位科學家的產能會是什麼? 他們何時撰寫論文? 所以我們測量了產能, 很驚人的是,產能, 你在職涯第一、十、二十年 寫一篇論文的可能性, 很接近在你職涯的那個部分 有所影響的可能性。
And to make a long story short, after lots of statistical tests, there's only one explanation for that, that really, the way we scientists work is that every single paper we write, every project we do, has exactly the same chance of being our personal best. That is, discovery is like a lottery ticket. And the more lottery tickets we buy, the higher our chances. And it happens to be so that most scientists buy most of their lottery tickets in the first 10, 15 years of their career, and after that, their productivity decreases. They're not buying any more lottery tickets. So it looks as if they would not be creative. In reality, they stopped trying. So when we actually put the data together, the conclusion is very simple: success can come at any time. It could be your very first or very last paper of your career. It's totally random in the space of the projects. It is the productivity that changes.
長話短說, 經過許多統計檢定, 只找出了一個解釋, 那就是,我們科學家工作的方式, 我們所寫的每一篇論文, 我們所做的每一個研究計畫, 都有同等的機會成為 我們個人的最佳作。 也就是說,探究 就像是買彩券一樣。 我們買越多彩券, 機會就越高。 只是剛好 大部分的科學家是在 職涯的前十、十五年 買了他們大部分的彩券而已, 那之後,他們的產能就下降了。 他們不再買更多的彩券。 所以看起來就好像是 他們沒有創意了。 現實上,他們只是沒再嘗試。 所以當我們把資料拼在一起, 結論就非常簡單: 成功隨時都可能到來。 可能是你職涯中的第一篇 或最後一篇論文。 在研究計畫的空間中, 這完全是隨機的。 改變的是產能。
Let me illustrate that. Here is Frank Wilczek, who got the Nobel Prize in Physics for the very first paper he ever wrote in his career as a graduate student.
讓我說明一下。 這是弗朗克韋爾切克, 得過諾貝爾物理獎, 得獎的是他研究生 職涯中的第一篇論文。
(Laughter)
(笑聲)
More interesting is John Fenn, who, at age 70, was forcefully retired by Yale University. They shut his lab down, and at that moment, he moved to Virginia Commonwealth University, opened another lab, and it is there, at age 72, that he published a paper for which, 15 years later, he got the Nobel Prize for Chemistry.
更有趣的是約翰芬恩, 他在七十歲時被迫 從耶魯大學退休。 他們關掉了他的實驗室, 那時,他搬到維吉尼亞聯邦大學, 開了另一間實驗室, 在那裡,七十二歲時, 他刊出了一篇論文, 十五年後,那篇論文 讓他得了諾貝爾化學獎。
And you think, OK, well, science is special, but what about other areas where we need to be creative? So let me take another typical example: entrepreneurship. Silicon Valley, the land of the youth, right? And indeed, when you look at it, you realize that the biggest awards, the TechCrunch Awards and other awards, are all going to people whose average age is late 20s, very early 30s. You look at who the VCs give the money to, some of the biggest VC firms -- all people in their early 30s. Which, of course, we know; there is this ethos in Silicon Valley that youth equals success. Not when you look at the data, because it's not only about forming a company -- forming a company is like productivity, trying, trying, trying -- when you look at which of these individuals actually put out a successful company, a successful exit. And recently, some of our colleagues looked at exactly that question. And it turns out that yes, those in the 20s and 30s put out a huge number of companies, form lots of companies, but most of them go bust. And when you look at the successful exits, what you see in this particular plot, the older you are, the more likely that you will actually hit the stock market or the sell the company successfully. This is so strong, actually, that if you are in the 50s, you are twice as likely to actually have a successful exit than if you are in your 30s.
你們會想,好,科學是比較特別, 但其他需要有創意的領域呢? 讓我舉另一個很典型的例子: 企業家精神。 矽谷, 年輕人之地,對吧? 的確,當你去看它時, 你會發現,最大的獎項 TechCrunch 獎及其他獎項 得獎人平均都是 快要三十歲或三十歲初頭的人。 可以去看創投公司把錢給誰, 有些最大的創投公司—— 都是三十初頭的人。 當然,我們知道; 在矽谷有一種風氣, 就是年輕等同成功。 資料可不是這麼說的。 因為重點並不只是成立公司—— 成立公司就像是產能, 嘗試、嘗試、嘗試—— 如果你只是去看 這些人當中有誰設立了 成功的公司、成功的退場。 最近,我們的一些同事 就在探究這個問題。 結果發現,是的,二、三十歲的人 成立了很多公司, 創辦了很多公司, 但大部分都破產收場。 如果去看成功退場的公司, 各位在這張圖上可以看到, 你的年紀越大, 你就越有可能上市, 或者成功把公司賣掉。 這個機率強到, 如果你是五十幾歲, 你有可能成功退場的機會, 是你三十幾歲時的兩倍。
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(掌聲)
So in the end, what is it that we see, actually? What we see is that creativity has no age. Productivity does, right? Which is telling me that at the end of the day, if you keep trying --
所以,最後,這些到底是什麼意思? 我們看到的是,創意不分年齡。 產能倒是會有差,對吧? 這就是告訴我,到頭來, 如果你繼續嘗試——
(Laughter)
(笑聲)
you could still succeed and succeed over and over. So my conclusion is very simple: I am off the stage, back in my lab.
你仍然有可能成功,且一再成功。 所以我的結論非常簡單: 我要下台,回到我的實驗室了。
Thank you.
謝謝。
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(掌聲)