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成功,真的和年龄没多大关系

发布者: 千缘 | 发布时间: 2020-10-19 23:17| 查看数: 159| 评论数: 0|

伟大的物理学家爱因斯坦曾经说过,如果一个人到30岁时对科学都没啥大贡献,也就永远不会有贡献了。但另一位物理学家对这一说法予以否定,他就是本期TED演讲者Albert,作为网络科学的先驱者,他揭示了很多复杂系统背后隐藏的秩序。他认为,创意并无年龄限制,生产力才是关键。只有不断尝试,你才可以成功。



Today, actually, is a very special day for me, because it is my birthday.

今天对我来说很特别,因为是我的生日。

And so, thanks to all of you for joining the party.

谢谢大家参与这个聚会。

But every time you throw a party, there's someone there to spoil it. Right?

可是,每次你举办聚会的时候,总是有人捣蛋,对吧?

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.

我是个物理学家,这次我带来了另一个物理学家。他的名字是阿尔伯特·爱因斯坦——也叫阿尔伯特——他是那个说过如果一个人到30岁时对科学都没啥大贡献,也就永远不会有贡献了。

Now, you don't need to check Wikipedia that I'm beyond 30.

你不需要查维基百科去了解我是不是超过30岁。

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.

实际上他是想告诉我们,当涉及到我在科学领域的作为时,我是朽木难雕了。幸运的是,我的事业运还算不错。在28岁时,我对网络产生了兴趣,几年后,我成功发表了几篇关于发现无标度网络的核心论文,并催生了一门我们今天称为网络科学的新学科。如果你对这个学科也很感兴趣,可以在布达佩斯,在波士顿读取网络科学的博士学位,也可以在全球各地学习这门课程。

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.

几年后,当我第一次在哈佛进行学术休假时,我对另一种形态的网络产生了兴趣:在我们自身的网络中,基因、蛋白质和代谢物如何相互联系以及它们与疾病的关系。这个兴趣引发了医学领域的一阵轰动,包括哈佛大学的网络医学部,有300多名研究人员基于这个想法来治疗病人,开发新的治疗方法。

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.

几年以前,我觉得我应该把网络的概念和关于网络的专业知识应用于一个新的领域,用来理解成功。我们为什么要这么做?我们认为,在某种程度上,我们的成功取决于我们所处的网络——我们的网络可以推动我们前进,也能拖我们后腿。我好奇我们能否使用在网络中获得的这些知识,结合大数据和专长来量化事情是如何发生的。

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.

这是一个结果。你在这里看到的是博物馆里相互连接的画廊网络。通过这张我们去年绘制的图,如果给我他或她在他们的职业生涯举办的前五个展览,我们就能够非常准确地预测一个艺术家是否成功。

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.

当我们思考成功时,我们意识到成功不仅跟网络有关;还有很多其他的维度。其中一个成功的必要因素,很明显就是业绩。让我们定义一下业绩和成功的差别。业绩是你做的事情:你跑得有多快,你画的是什么画,你发表的是什么论文。然而,在我们的工作定义中,成功是社群从你的业绩中注意到你做的哪些事情,如何承认你的成就,如何奖励你?换句话说,你的业绩跟你有关,但你的成功跟大家都有关。这对我们来说是个非常重要的转变,因为我们把成功定义为社群给予我们的集体评价。这样一来成功就变得可衡量,因为在一个社群中,关于成功包含着很多数据点。我们上学,我们练习,我们实践,因为我们相信业绩会让我们成功。但当我们开始探索,我们开始意识到以数学的方式看待这个问题时,业绩和成功是非常,非常不同的概念,让我来解释一下。

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.

你在这里看到的是世界上最快的人,尤塞恩·博尔特。当然,他赢得了大多数参与的比赛。我们知道是他是世界上最快的人,因为我们有精密的计时器去测量他的速度。有趣之处在于当他获胜时,他并没有明显地超过竞争对手。他跑得比输掉比赛的人最多快百分之一。他不仅只比第二名快百分之一,他的速度也不超过我的10倍——并且我还不是个擅长跑步的人,这点请相信我。

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.

每次我们能够评估业绩时,我们都会注意到一些有趣的事情:业绩是有界限的。这意味着人类的业绩并没有巨大的差异。它变化的范围非常小,我们确实需要精密的计时器来测量这个差异。不是说我们不能从最好的人身上看到好的一面,但最好的人非常难以识别。并且问题在于我们很多人的工作领域并没有精密的计时器来衡量我们的业绩。

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 --

好了,业绩是有界限的,当涉及我们的业绩时,我们之间并没有显著的差异。那么成功呢?让我们转到另一个话题,比如书籍。评估作家成功的一个方法是有多少人阅读了你的作品。当我早先那本书在2009年出版时,我在欧洲和编辑谈话,我感兴趣的是:谁是我的竞争对手?我有一些炙手可热的对手。那周——

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·布朗出版了《失落的秘符》,并且尼古拉斯·斯帕克斯的《最后一首歌》也问世了。当你看这个书单时,你意识到,就业绩而言,这些书和我的之间并无多大差别。是吧?如果尼古拉斯·斯帕克斯的团队再努力一点,他就可以轻松进入榜首,因为最终谁在畅销榜顶端几乎是随机的。所以我说,让我们看看数字吧——我就是干这行的,对吧?让我们看看尼古拉斯·斯帕克斯的作品销量。结果在新书发售的那个周末,尼古拉斯·斯帕克斯卖出了10万多本书,这是个惊人的数字。你可以看看纽约时报每周销量在1万册以上的畅销书榜单,所以他只凭借新书销量的十分之一就能轻松登上榜首。然而他不是第一名。为什么?因为有丹·布朗,他在那个周末卖出了120万册。

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.

我喜欢这个数字的原因是因为它真正显示了,当涉及到成功时,它是没有界限的,最好的不止比第二名好一点点,而超越了好几个数量级,因为成功是集体的衡量标准。我们给予他们成功,而不是通过我们的业绩获得它。

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?

我们意识到业绩是有界限的,但成功,属于集体衡量的,是无界的,这一定让你心生疑惑:当人们的业绩表现差异很小的时候,为何成功的差异如此之大?最近,我出版了一本关于这个问题的书。我没有太多时间详细介绍这本书,所以我打算回到这个问题,成功通常会在什么时候出现呢?

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?

那么让我们回到派对捣乱者的话题,问问我们自己:为什么爱因斯坦要发表这样荒谬的言论,人的创造力止步于30岁?因为他发现周围所有这些创造量子力学和现代物理学的伟大物理学家,他们的伟大成就都是诞生在20多岁和30岁出头。并不是只有他这样想。这不仅是观察偏差,因为事实上有一整个领域的天才研究都证明了这一点,如果回顾一下我们崇拜的先人,然后再看他们做出最大贡献的年纪,不管在音乐,在科学,还是在工程领域,大部分人都是在他们20岁,30岁,最多40岁出头时做出了这些成绩。但这个天才研究有个问题。首先,它为大众制造了一种印象,即创造力等于年轻,真让人伤心,不是吗?

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.

并且它也存在观察偏差,因为它只观察了天才,并没研究普通科学家,并没有看着我们这些人问,随着年龄的增长,创造力真的会消失吗?所以这正是我们尝试做的,并且有参照对象很重要。

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.

那么让我们看看像我这样平凡科学家的职业生涯。这里是我发表的全部论文,从1989年发表的最早一篇论文;当时我还在罗马尼亚,直到今年这个时候。纵坐标,你可以看到论文的影响,也就是被引用的次数,有多少其他人发表的论文引用了我的工作。当你看这个数据时,可以看到我的职业生涯有三个阶段。我第一个10年,工作很多,但却并没有多少成就。似乎没人关注我做的事情,对吧?没有一点影响力。

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.

当时,我在做材料科学,然后我自己发现了网络,然后开始发表网络的文章,从那以后,高影响力的文章我发表了一篇又一篇。那时感觉真是很好,那是我职业生涯的高光时刻。

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.

那么问题是,现在发生了什么?我们不知道,现在就去计算出这些论文会产生怎样的影响还为时尚早,需要时间来获取这些信息。当你看这个数据时,会觉得爱因斯坦和天才研究的结论是对的,我在我职业生涯的高光阶段。

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.

那么让我们看看这究竟是如何发生的,首先看看科学领域。为了不产生选择偏差,只看天才,我们最终重建了1900年至今每一位科学家的职业生涯,并找到了所有科学家的个人最高成就,不管他获得了诺贝尔奖还是没有,或是没人问津,即便是他最好的成就。这就是你们在这张幻灯片上看到的。每条线是个职业生涯,在职业生涯的顶端有一个浅蓝色的点,代表着他们个人的最好成就。问题是,他们最重大的发现发生在什么时候?要量化这点,我们看的是你获得最大发现的概率是多少,比如你职业生涯的的第1,2,3或者10年。我们真正要看的并不是年纪。我们看的是所谓的“学术年龄。”你的学术年龄始于你发表第一篇论文的时候。我知道你们有些人还是婴儿。

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.

那么让我们来看看你发表最高影响力论文的概率。你看到的是,的确,天才研究的结论是正确的。很多科学家发表的影响力最高的论文倾向于发表在他们职业生涯的前10到15年,在那之后就会直线下降。它下降得如此之快——我如今正处在我职业的第30个年头,我发表一篇比过往有更高影响力的论文的概率不到1%。根据这个数据,我正处在职业生涯的这个阶段。但这里有个问题。我们的对照数据有问题。对照数据就是,对科学做出随机贡献的科学家会是什么样子?或者科学家的生产力怎样?他们什么时候写的论文?所以我们评估了生产力,令人惊讶的是,生产力,你在职业生涯的第1年、第10年或第20年写论文的概率,与论文产生影响的概率几乎无法区分。

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.

长话短说,在很多的数据检验后,只有一个解释,真相是,我们科学家的工作,我们写的每篇论文,做的每个项目都有同样的概率成为我们个人的最佳成果。那就是,发现就像中彩票。我们买了越多的彩票,我们中奖的几率就越高。碰巧的是,很多科学家在他们职业生涯的头10年,15年买了大部分的彩票,在那之后,他们的生产力就下降了。他们不再买更多的彩票。所以看起来他们没有创造力了。现实中,他们停止了尝试。所以当我们把数据放在一起时,结论非常简单:成功可能随时会来。它可能是你职业生涯中最早或最后的论文。它在项目的空间中完全是随机的。改变的是你的生产力。

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.

让我解释一下。这是获得诺贝尔物理学奖的弗兰克·威尔切克,他得奖要归功于研究生时写的第一篇论文。

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.

更有趣的是约翰·芬,他在70岁时,被耶鲁大学强制退休,他们关闭了他的实验室,那时,他搬到了弗吉尼亚联邦大学,开了另一个实验室,就在那里,在年纪72岁时,他发表了一篇论文,这篇论文在15年后获得了诺贝尔化学奖。

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.

你会想,科学领域比较特殊,但其他需要我们有创造力的领域呢?那么让我们再看看另一个典型的例子:创业。硅谷。年轻人的领地,对吧?确实,当你看这个领域时,你发现最大的奖励,TechCrunchAwards或其他奖励,全都给了平均年纪在30岁左右的人。再看看VC的钱都给了谁,一些最大的VC企业——几乎所有的人都在30岁出头。当然,我们知道;硅谷有这样一种风气:年轻等于成功。不过,当你看数据的时候就不会这样认为了。看看这些人当中有谁真正成立了一家成功的公司——成立一个公司就像生产力,尝试,尝试,再尝试。因为这不仅关于成立一个公司。最近,我们的几位同事正好研究了这个问题。果不期然,这些年纪在20多岁和30多岁的人创立了大量的公司,很多公司,但大部分都破产了。再看看那些成功的退出,你在这个图中可以看到,你年纪越大,就越有可能轰动股票市场或者成功出售公司。数据很显著,事实上,如果你50多岁,你成功退出的机会是你30岁时的两倍。

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 --

所以最后,我们看到了什么?我们看到的是创意并无年龄限制。生产力才是关键,对吧?这就告诉我们,如果你不断尝试——

you could still succeed and succeed over and over. So my conclusion is very simple: I am off the stage, back in my lab.

你仍然可以不断取得成功。所以我的结论很简单:演讲结束后,我得回到实验干活儿了。

Thank you.

谢谢。


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