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仅做 整合 / 美化 处理
Today. actually. is a very special day for me.
[AI] 今天事实上对我来说是非常特别的一天。
because it is my birthday.
[AI] 因为今天是我的生日。
(Applause)
[AI] （掌声）
And so. thanks to all of you for joining the party.
[AI] 等等。谢谢你们所有人参加聚会。
(Laughter)
[AI] （众笑）
But every time you throw a party. there's someone there to spoil it. Right?
[AI] 但每次你举办派对。有人会破坏它。正当
(Laughter)
[AI] （众笑）
And I'm a physicist.
[AI] 我是个物理学家。
and this time I brought another physicist along to do so.
[AI] 这次我带了另一位物理学家来做这件事。
His name is Albert Einstein -- also Albert -- and he's the one who said
[AI] 他的名字叫阿尔伯特·爱因斯坦，也是阿尔伯特，他说
that the person who has not made his great contributions to science
[AI] 没有对科学做出重大贡献的人
by the age of 30
[AI] 30岁时
will never do so.
[AI] 我永远不会这样做。
(Laughter)
[AI] （众笑）
Now. you don't need to check Wikipedia
[AI] 现在你不需要查看维基百科
that I'm beyond 30.
[AI] 我已经超过30岁了。
(Laughter)
[AI] （众笑）
So. effectively. what he is telling me. and us.
[AI] 所以有效地他在告诉我什么。还有我们。
is that when it comes to my science.
[AI] 这就是我的科学。
I'm deadwood.
[AI] 我是戴德伍德。
Well. luckily. I had my share of luck within my career.
[AI] 好幸运的是在我的职业生涯中，我有我那份幸运。
Around age 28. I became very interested in networks.
[AI] 28岁左右。我对网络非常感兴趣。
and a few years later. we managed to publish a few key papers
[AI] 几年后。我们设法发表了几篇重要论文
that reported the discovery of scale-free networks
[AI] 报告了无标度网络的发现
and really gave birth to a new discipline that we call network science today.
[AI] 并且真正产生了一门新学科，我们今天称之为网络科学。
And if you really care about it. you can get a PhD now in network science
[AI] 如果你真的在乎它。你现在可以获得网络科学博士学位了
in Budapest. in Boston.
[AI] 在布达佩斯。在波士顿。
and you can study it all over the world.
[AI] 你可以在世界各地学习。
A few years later.
[AI] 几年后。
when I moved to Harvard first as a sabbatical.
[AI] 当我第一次去哈佛大学休假时。
I became interested in another type of network:
[AI] 我开始对另一种类型的网络感兴趣：
that time. the networks within ourselves.
[AI] 那次。我们内部的网络。
how the genes and the proteins and the metabolites link to each other
[AI] 基因、蛋白质和代谢物是如何相互联系的
and how they connect to disease.
[AI] 以及它们与疾病的联系。
And that interest led to a major explosion within medicine.
[AI] 这种兴趣导致了医学界的一次大爆炸。
including the Network Medicine Division at Harvard.
[AI] 包括哈佛大学的网络医学部。
that has more than 300 researchers who are using this perspective
[AI] 有300多名研究人员正在使用这一观点
to treat patients and develop new cures.
[AI] 治疗患者并开发新疗法。
And a few years ago.
[AI] 几年前。
I thought that I would take this idea of networks
[AI] 我想我会接受这个网络的想法
and the expertise we had in networks
[AI] 以及我们在网络方面的专业知识
in a different area.
[AI] 在另一个地区。
that is. to understand success.
[AI] 就是。理解成功。
And why did we do that?
[AI] 我们为什么要这样做？
Well. we thought that. to some degree.
[AI] 好我们认为。在某种程度上。
our success is determined by the networks we're part of --
[AI] 我们的成功取决于我们所属的网络--
that our networks can push us forward. they can pull us back.
[AI] 我们的网络可以推动我们前进。他们可以把我们拉回来。
And I was curious if we could use the knowledge and big data and expertise
[AI] 我很好奇我们是否能利用这些知识、大数据和专业知识
where we develop the networks
[AI] 我们在哪里开发网络
to really quantify how these things happen.
[AI] 真正量化这些事情是如何发生的。
This is a result from that.
[AI] 这是一个结果。
What you see here is a network of galleries in museums
[AI] 你在这里看到的是博物馆中的画廊网络
that connect to each other.
[AI] 相互连接。
And through this map that we mapped out last year.
[AI] 通过我们去年绘制的这张地图。
we are able to predict very accurately the success of an artist
[AI] 我们能够非常准确地预测艺术家的成功
if you give me the first five exhibits that he or she had in their career.
[AI] 如果你给我他或她在职业生涯中的前五件展品。
Well. as we thought about success.
[AI] 好当我们想到成功的时候。
we realized that success is not only about networks;
[AI] 我们意识到，成功不仅仅在于网络；
there are so many other dimensions to that.
[AI] 这还有很多其他方面。
And one of the things we need for success. obviously.
[AI] 这也是我们成功所需要的。明显地
is performance.
[AI] 这就是表演。
So let's define what's the difference between performance and success.
[AI] 那么，让我们来定义绩效和成功之间的区别。
Well. performance is what you do:
[AI] 好表现就是你所做的：
how fast you run. what kind of paintings you paint.
[AI] 你跑得多快啊。你画的是什么样的画。
what kind of papers you publish.
[AI] 你发表什么样的论文。
However. in our working definition.
[AI] 然而在我们的工作定义中。
success is about what the community notices from what you did.
[AI] 成功在于社区从你的所作所为中注意到了什么。
from your performance:
[AI] 从你的表现来看：
How does it acknowledge it. and how does it reward you for it?
[AI] 它如何承认这一点。它是如何回报你的呢？
In other terms.
[AI] 换句话说。
your performance is about you. but your success is about all of us.
[AI] 你的表现与你有关。但你的成功关乎我们所有人。
And this was a very important shift for us.
[AI] 这对我们来说是一个非常重要的转变。
because the moment we defined success as being a collective measure
[AI] 因为当我们把成功定义为一种集体衡量的时候
that the community provides to us.
[AI] 社区提供给我们的。
it became measurable.
[AI] 它变得可以测量。
because if it's in the community. there are multiple data points about that.
[AI] 因为如果是在社区里。关于这一点有多个数据点。
So we go to school. we exercise. we practice.
[AI] 所以我们去上学。我们锻炼身体。我们练习。
because we believe that performance leads to success.
[AI] 因为我们相信表现会带来成功。
But the way we actually started to explore.
[AI] 但我们实际上开始探索的方式。
we realized that performance and success are very. very different animals
[AI] 我们意识到绩效和成功是非常重要的。非常不同的动物
when it comes to the mathematics of the problem.
[AI] 当涉及到这个问题的数学时。
And let me illustrate that.
[AI] 让我来说明一下。
So what you see here is the fastest man on earth. Usain Bolt.
[AI] 所以你在这里看到的是地球上跑得最快的人。乌塞恩·博尔特。
And of course. he wins most of the competitions that he enters.
[AI] 当然了。他参加的大部分比赛都赢了。
And we know he's the fastest on earth because we have a chronometer
[AI] 我们知道他是地球上跑得最快的，因为我们有一个计时器
to measure his speed.
[AI] 测量他的速度。
Well. what is interesting about him is that when he wins.
[AI] 好他有趣的是当他赢了。
he doesn't do so by really significantly outrunning his competition.
[AI] 他并不是通过真正显著地超越竞争对手来做到这一点的。
He's running at most a percent faster than the one who loses the race.
[AI] 他比比赛失利的人跑得最多快百分之一。
And not only does he run only one percent faster than the second one.
[AI] 他不仅比第二个跑得快百分之一。
but he doesn't run 10 times faster than I do --
[AI] 但他跑得并不比我快10倍--
and I'm not a good runner. trust me on that.
[AI] 而且我跑得不好。相信我。
(Laughter)
[AI] （众笑）
And every time we are able to measure performance.
[AI] 每次我们都能衡量绩效。
we notice something very interesting;
[AI] 我们注意到一些非常有趣的事情；
that is. performance is bounded.
[AI] 就是。性能是有限的。
What it means is that there are no huge variations in human performance.
[AI] 这意味着人的表现没有巨大的变化。
It varies only in a narrow range.
[AI] 它的变化范围很窄。
and we do need the chronometer to measure the differences.
[AI] 我们确实需要天文钟来测量差异。
This is not to say that we cannot see the good from the best ones.
[AI] 这并不是说我们不能从最好的事物中看到好处。
but the best ones are very hard to distinguish.
[AI] 但是最好的很难区分。
And the problem with that is that most of us work in areas
[AI] 问题是，我们大多数人都在一些领域工作
where we do not have a chronometer to gauge our performance.
[AI] 我们没有一个计时器来衡量我们的表现。
Alright. performance is bounded.
[AI] 好吧性能是有限的。
there are no huge differences between us when it comes to our performance.
[AI] 说到我们的表现，我们之间没有太大的差异。
How about success?
[AI] 成功呢？
Well. let's switch to a different topic. like books.
[AI] 好我们换个话题吧。我喜欢书。
One measure of success for writers is how many people read your work.
[AI] 衡量作家成功的一个标准是有多少人读了你的作品。
And so when my previous book came out in 2009.
[AI] 所以当我上一本书在2009年出版时。
I was in Europe talking with my editor.
[AI] 我在欧洲和我的编辑谈话。
and I was interested: Who is the competition?
[AI] 我很感兴趣：谁是竞争对手？
And I had some fabulous ones.
[AI] 我有一些很棒的。
That week --
[AI] 那个星期--
(Laughter)
[AI] （众笑）
Dan Brown came out with "The Lost Symbol."
[AI] 丹·布朗推出了《失落的象征》
and "The Last Song" also came out.
[AI] 《最后一首歌》也出来了。
Nicholas Sparks.
[AI] 尼古拉斯·斯帕克斯。
And when you just look at the list.
[AI] 当你只看清单的时候。
you realize. you know. performance-wise. there's hardly any difference
[AI] 你知道。你知道的。性能方面。几乎没有什么区别
between these books or mine.
[AI] 在这些书和我的书之间。
Right?
[AI] 正当
So maybe if Nicholas Sparks's team works a little harder.
[AI] 所以如果尼古拉斯·斯帕克斯的团队更加努力的话。
he could easily be number one.
[AI] 他很可能是头号人物。
because it's almost by accident who ended up at the top.
[AI] 因为几乎是偶然的，谁最终登上了榜首。
So I said. let's look at the numbers -- I'm a data person. right?
[AI] 所以我说。让我们看看数字——我是一个数据员。正当
So let's see what were the sales for Nicholas Sparks.
[AI] 让我们看看尼古拉斯·斯帕克斯的销售情况。
And it turns out that that opening weekend.
[AI] 结果证明，开幕式的那个周末。
Nicholas Sparks sold more than a hundred thousand copies.
[AI] 尼古拉斯·斯帕克斯卖出了超过十万本。
which is an amazing number.
[AI] 这是一个惊人的数字。
You can actually get to the top of the "New York Times" best-seller list
[AI] 实际上，你可以登上《纽约时报》畅销书排行榜的榜首
by selling 10.000 copies a week.
[AI] 一周卖出10000本。
so he tenfold overcame what he needed to be number one.
[AI] 因此，他十倍地克服了成为第一名所需要的困难。
Yet he wasn't number one.
[AI] 但他并不是头号人物。
Why?
[AI] 为什么？
Because there was Dan Brown. who sold 1.2 million copies that weekend.
[AI] 因为有丹·布朗。他在那个周末卖出了120万册。
(Laughter)
[AI] （众笑）
And the reason I like this number is because it shows that. really.
[AI] 我喜欢这个数字的原因是因为它显示了这一点。真正地
when it comes to success. it's unbounded.
[AI] 说到成功。它是无限的。
that the best doesn't only get slightly more than the second best
[AI] 最好的不只是比第二好的多一点
but gets orders of magnitude more.
[AI] 但得到的数量级更多。
because success is a collective measure.
[AI] 因为成功是一个集体的尺度。
We give it to them. rather than we earn it through our performance.
[AI] 我们把它给他们。而不是通过我们的表现来赢得。
So one of things we realized is that performance. what we do. is bounded.
[AI] 所以我们意识到的一点是性能。我们所做的。是有界的。
but success. which is collective. is unbounded.
[AI] 但是成功。这是集体的。是无限的。
which makes you wonder:
[AI] 这让你想知道：
How do you get these huge differences in success
[AI] 你是如何获得这些巨大的成功差异的
when you have such tiny differences in performance?
[AI] 当你在表现上有如此微小的差异时？
And recently. I published a book that I devoted to that very question.
[AI] 最近。我出版了一本书，专门讨论这个问题。
And they didn't give me enough time to go over all of that.
[AI] 他们没有给我足够的时间来检查这些。
so I'm going to go back to the question of.
[AI] 所以我要回到这个问题上。
alright. you have success; when should that appear?
[AI] 好吧你成功了；什么时候会出现？
So let's go back to the party spoiler and ask ourselves:
[AI] 那么，让我们回到派对的扰流者，问问自己：
Why did Einstein make this ridiculous statement.
[AI] 爱因斯坦为什么做出这种荒谬的声明。
that only before 30 you could actually be creative?
[AI] 只有在30岁之前你才有创造力？
Well. because he looked around himself and he saw all these fabulous physicists
[AI] 好因为他环顾四周，看到了所有这些神奇的物理学家
that created quantum mechanics and modern physics.
[AI] 这创造了量子力学和现代物理学。
and they were all in their 20s and early 30s when they did so.
[AI] 他们这么做的时候都是20多岁和30出头。
And it's not only him.
[AI] 而且不仅仅是他。
It's not only observational bias.
[AI] 这不仅仅是观察偏差。
because there's actually a whole field of genius research
[AI] 因为实际上有一整个天才研究领域
that has documented the fact that.
[AI] 这证明了这样一个事实。
if we look at the people we admire from the past
[AI] 如果我们看看过去崇拜的人
and then look at what age they made their biggest contribution.
[AI] 然后看看他们做出最大贡献的年龄。
whether that's music. whether that's science.
[AI] 不管那是音乐。这是否是科学。
whether that's engineering.
[AI] 不管那是工程。
most of them tend to do so in their 20s. 30s. early 40s at most.
[AI] 他们中的大多数人往往在20多岁时这样做。30多岁。最多40出头。
But there's a problem with this genius research.
[AI] 但这项天才研究有一个问题。
Well. first of all. it created the impression to us
[AI] 好首先它给我们留下了深刻的印象
that creativity equals youth.
[AI] 创造力等于青春。
which is painful. right?
[AI] 这很痛苦。正当
(Laughter)
[AI] （众笑）
And it also has an observational bias.
[AI] 而且它也有观测偏差。
because it only looks at geniuses and doesn't look at ordinary scientists
[AI] 因为它只关注天才，不关注普通科学家
and doesn't look at all of us and ask.
[AI] 而不是看着我们所有人问。
is it really true that creativity vanishes as we age?
[AI] 创造力真的会随着年龄的增长而消失吗？
So that's exactly what we tried to do.
[AI] 所以这正是我们试图做的。
and this is important for that to actually have references.
[AI] 这是很重要的，因为它有参考文献。
So let's look at an ordinary scientist like myself.
[AI] 让我们看看像我这样的普通科学家。
and let's look at my career.
[AI] 让我们看看我的职业生涯。
So what you see here is all the papers that I've published
[AI] 你在这里看到的是我发表的所有论文
from my very first paper. in 1989; I was still in Romania when I did so.
[AI] 从我的第一篇论文开始。1989年；当时我还在罗马尼亚。
till kind of this year.
[AI] 直到今年。
And vertically. you see the impact of the paper.
[AI] 垂直的。你可以看到报纸的影响。
that is. how many citations.
[AI] 就是。引用了多少次。
how many other papers have been written that cited that work.
[AI] 有多少其他论文引用了这项工作。
And when you look at that.
[AI] 当你看到这个的时候。
you see that my career has roughly three different stages.
[AI] 你看，我的职业生涯大致有三个不同的阶段。
I had the first 10 years where I had to work a lot
[AI] 在最初的10年里，我不得不大量工作
and I don't achieve much.
[AI] 我也没有取得多大成就。
No one seems to care about what I do. right?
[AI] 似乎没有人关心我做什么。正当
There's hardly any impact.
[AI] 几乎没有任何影响。
(Laughter)
[AI] （众笑）
That time. I was doing material science.
[AI] 那次。我在做材料科学。
and then I kind of discovered for myself networks
[AI] 然后我发现了自己的网络
and then started publishing in networks.
[AI] 然后开始在网络上发布。
And that led from one high-impact paper to the other one.
[AI] 这导致了从一份高影响力的论文到另一份。
And it really felt good. That was that stage of my career.
[AI] 感觉真的很好。那是我职业生涯的那个阶段。
(Laughter)
[AI] （众笑）
So the question is. what happens right now?
[AI] 所以问题是。现在发生了什么？
And we don't know. because there hasn't been enough time passed yet
[AI] 我们不知道。因为时间还不够
to actually figure out how much impact those papers will get;
[AI] 真正弄清楚这些论文会产生多大的影响；
it takes time to acquire.
[AI] 这需要时间来获得。
Well. when you look at the data.
[AI] 好当你看数据的时候。
it seems to be that Einstein. the genius research. is right.
[AI] 似乎是爱因斯坦。天才研究。这是对的。
and I'm at that stage of my career.
[AI] 我正处于职业生涯的那个阶段。
(Laughter)
[AI] （众笑）
So we said. OK. let's figure out how does this really happen.
[AI] 所以我们说。好啊让我们看看这到底是怎么发生的。
first in science.
[AI] 科学第一。
And in order not to have the selection bias.
[AI] 为了不产生选择偏差。
to look only at geniuses.
[AI] 只看天才。
we ended up reconstructing the career of every single scientist
[AI] 我们最终重建了每一位科学家的职业生涯
from 1900 till today
[AI] 从1900年到今天
and finding for all scientists what was their personal best.
[AI] 并为所有科学家找到了他们个人最好的东西。
whether they got the Nobel Prize or they never did.
[AI] 无论他们是否获得诺贝尔奖。
or no one knows what they did. even their personal best.
[AI] 或者没人知道他们做了什么。甚至是他们个人最好的。
And that's what you see in this slide.
[AI] 这就是你在这张幻灯片中看到的。
Each line is a career.
[AI] 每一行都是一种职业。
and when you have a light blue dot on the top of that career.
[AI] 当你在事业的顶端有一个浅蓝色的圆点。
it says that was their personal best.
[AI] 上面说那是他们个人的最好成绩。
And the question is.
[AI] 问题是。
when did they actually make their biggest discovery?
[AI] 他们什么时候真正有了最大的发现？
To quantify that.
[AI] 要量化这一点。
we look at what's the probability that you make your biggest discovery.
[AI] 我们来看看你最大的发现的概率是多少。
let's say. one. two. three or 10 years into your career?
[AI] 比如说。一二职业生涯开始三年还是十年？
We're not looking at real age.
[AI] 我们不是在看真实年龄。
We're looking at what we call "academic age."
[AI] 我们正在研究我们所谓的“学术年龄”
Your academic age starts when you publish your first papers.
[AI] 你的学术年龄从你发表第一篇论文开始。
I know some of you are still babies.
[AI] 我知道你们有些人还是婴儿。
(Laughter)
[AI] （众笑）
So let's look at the probability
[AI] 让我们看看概率
that you publish your highest-impact paper.
[AI] 你发表了影响最大的论文。
And what you see is. indeed. the genius research is right.
[AI] 你看到的是。的确天才研究是正确的。
Most scientists tend to publish their highest-impact paper
[AI] 大多数科学家倾向于发表影响最大的论文
in the first 10. 15 years in their career.
[AI] 在前10年。15年的职业生涯。
and it tanks after that.
[AI] 在那之后它就变成坦克了。
It tanks so fast that I'm about -- I'm exactly 30 years into my career.
[AI] 它发展得如此之快，以至于我差不多——我的职业生涯已经整整30年了。
and the chance that I will publish a paper that would have a higher impact
[AI] 我有机会发表一篇影响更大的论文
than anything that I did before
[AI] 比我以前做的任何事都要多
is less than one percent.
[AI] 不到百分之一。
I am in that stage of my career. according to this data.
[AI] 我正处于职业生涯的那个阶段。根据这些数据。
But there's a problem with that.
[AI] 但这有个问题。
We're not doing controls properly.
[AI] 我们控制得不好。
So the control would be.
[AI] 所以控制应该是。
what would a scientist look like who makes random contribution to science?
[AI] 对科学做出随机贡献的科学家长什么样？
Or what is the productivity of the scientist?
[AI] 或者科学家的生产力是什么？
When do they write papers?
[AI] 他们什么时候写论文？
So we measured the productivity.
[AI] 所以我们测量了生产率。
and amazingly. the productivity.
[AI] 令人惊讶的是。生产力。
your likelihood of writing a paper in year one. 10 or 20 in your career.
[AI] 你第一年写论文的可能性。在你的职业生涯中10或20岁。
is indistinguishable from the likelihood of having the impact
[AI] 与产生影响的可能性无法区分
in that part of your career.
[AI] 在你职业生涯的那一部分。
And to make a long story short.
[AI] 长话短说。
after lots of statistical tests. there's only one explanation for that.
[AI] 经过大量的统计测试。对此只有一种解释。
that really. the way we scientists work
[AI] 真的。我们科学家的工作方式
is that every single paper we write. every project we do.
[AI] 这就是我们写的每一篇论文。我们做的每一个项目。
has exactly the same chance of being our personal best.
[AI] 完全有可能成为我们个人最好的。
That is. discovery is like a lottery ticket.
[AI] 就是。发现就像一张彩票。
And the more lottery tickets we buy.
[AI] 我们买的彩票越多。
the higher our chances.
[AI] 我们的机会越大。
And it happens to be so
[AI] 而事实恰恰如此
that most scientists buy most of their lottery tickets
[AI] 大多数科学家购买了他们大部分的彩票
in the first 10. 15 years of their career.
[AI] 在前10年。15年的职业生涯。
and after that. their productivity decreases.
[AI] 在那之后。他们的生产力下降了。
They're not buying any more lottery tickets.
[AI] 他们不再买彩票了。
So it looks as if they would not be creative.
[AI] 所以看起来他们不会有创意。
In reality. they stopped trying.
[AI] 实际上。他们停止了尝试。
So when we actually put the data together. the conclusion is very simple:
[AI] 所以当我们把数据放在一起的时候。结论很简单：
success can come at any time.
[AI] 成功随时可能到来。
It could be your very first or very last paper of your career.
[AI] 这可能是你职业生涯中的第一篇或最后一篇论文。
It's totally random in the space of the projects.
[AI] 在项目的空间里完全是随机的。
It is the productivity that changes.
[AI] 改变的是生产力。
Let me illustrate that.
[AI] 让我举例说明。
Here is Frank Wilczek. who got the Nobel Prize in Physics
[AI] 这是弗兰克·威尔切克。谁获得了诺贝尔物理学奖
for the very first paper he ever wrote in his career as a graduate student.
[AI] 这是他在研究生生涯中写的第一篇论文。
(Laughter)
[AI] （众笑）
More interesting is John Fenn.
[AI] 更有趣的是约翰·芬恩。
who. at age 70. was forcefully retired by Yale University.
[AI] 谁70岁的时候。被耶鲁大学强制退休。
They shut his lab down.
[AI] 他们关闭了他的实验室。
and at that moment. he moved to Virginia Commonwealth University.
[AI] 就在那一刻。他搬到弗吉尼亚联邦大学。
opened another lab.
[AI] 又开了一个实验室。
and it is there. at age 72. that he published a paper
[AI] 它就在那里。72岁的时候。他发表了一篇论文
for which. 15 years later. he got the Nobel Prize for Chemistry.
[AI] 为此。15年后。他获得了诺贝尔化学奖。
And you think. OK. well. science is special.
[AI] 你会想。好啊好科学是特殊的。
but what about other areas where we need to be creative?
[AI] 但是我们需要创新的其他领域呢？
So let me take another typical example: entrepreneurship.
[AI] 让我举另一个典型的例子：企业家精神。
Silicon Valley.
[AI] 硅谷。
the land of the youth. right?
[AI] 年轻人的土地。正当
And indeed. when you look at it.
[AI] 的确如此。当你看着它的时候。
you realize that the biggest awards. the TechCrunch Awards and other awards.
[AI] 你知道最大的奖项。TechCrunch奖和其他奖项。
are all going to people
[AI] 都是给人的
whose average age is late 20s. very early 30s.
[AI] 他们的平均年龄是20多岁。30出头。
You look at who the VCs give the money to. some of the biggest VC firms --
[AI] 你看看风投公司把钱给了谁。一些最大的风险投资公司--
all people in their early 30s.
[AI] 所有30出头的人。
Which. of course. we know;
[AI] 哪一个当然我们知道,；
there is this ethos in Silicon Valley that youth equals success.
[AI] 硅谷有这样一种风气：年轻就等于成功。
Not when you look at the data.
[AI] 当你看数据的时候就不会了。
because it's not only about forming a company --
[AI] 因为这不仅仅是组建一家公司--
forming a company is like productivity. trying. trying. trying --
[AI] 组建一家公司就像生产力。尝试尝试尝试--
when you look at which of these individuals actually put out
[AI] 当你看到这些人中的哪一个真的
a successful company. a successful exit.
[AI] 一家成功的公司。成功的退出。
And recently. some of our colleagues looked at exactly that question.
[AI] 最近。我们的一些同事正好看到了这个问题。
And it turns out that yes. those in the 20s and 30s
[AI] 结果证明是的。二三十岁的人
put out a huge number of companies. form lots of companies.
[AI] 推出了大量的公司。成立许多公司。
but most of them go bust.
[AI] 但大多数都破产了。
And when you look at the successful exits. what you see in this particular plot.
[AI] 当你看到成功的退出。你在这个特殊的情节中看到了什么。
the older you are. the more likely that you will actually hit the stock market
[AI] 你年纪越大。你真正进入股市的可能性越大
or the sell the company successfully.
[AI] 或者成功地出售公司。
This is so strong. actually. that if you are in the 50s.
[AI] 这是如此强烈。事实上如果你是50多岁。
you are twice as likely to actually have a successful exit
[AI] 您实际成功退出的可能性是以前的两倍
than if you are in your 30s.
[AI] 如果你30多岁的话。
(Applause)
[AI] （掌声）
So in the end. what is it that we see. actually?
[AI] 所以最后。我们看到的是什么。事实上
What we see is that creativity has no age.
[AI] 我们看到的是创造力没有年龄。
Productivity does. right?
[AI] 生产力确实如此。正当
Which is telling me that at the end of the day.
[AI] 这告诉我，在一天结束的时候。
if you keep trying --
[AI] 如果你继续努力--
(Laughter)
[AI] （众笑）
you could still succeed and succeed over and over.
[AI] 你仍然可以一次又一次地成功。
So my conclusion is very simple:
[AI] 所以我的结论很简单：
I am off the stage. back in my lab.
[AI] 我不在舞台上了。回到我的实验室。
Thank you.
[AI] 非常感谢。
(Applause)
[AI] （掌声）