TEDxMidAtlantic
Albert-László Barabási: The real relationship between your age and your chance of success
阿尔伯特·拉兹洛·巴拉巴斯: 年纪和成功几率的真正联系
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网络理论家阿尔伯特·拉兹洛·巴拉巴斯（Albert-Laszlo Barabasi）在数据分析的支撑下，探索了各个行业驱动成功的隐藏机制——并揭示了年龄与成功几率之间的有趣联系。
Albert-László Barabási - Network scientist
A pioneer in network science, Albert-László Barabási uncovers the hidden order behind complex systems. Full bio
A pioneer in network science, Albert-László Barabási uncovers the hidden order behind complex systems. Full bio
Double-click the English transcript below to play the video.
00:12
Today, actually, is
a very special day for me,
a very special day for me,
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今天对我来说很特别，
00:14
because it is my birthday.
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因为是我的生日。
00:16
(Applause)
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（鼓掌）
00:20
And so, thanks to all of you
for joining the party.
for joining the party.
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谢谢大家参与这个聚会。
00:24
(Laughter)
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（笑声）
00:25
But every time you throw a party,
there's someone there to spoil it. Right?
there's someone there to spoil it. Right?
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可是，每次你举办聚会的时候，
总是有人捣蛋，对吧？
总是有人捣蛋，对吧？
00:30
(Laughter)
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（笑声）
00:31
And I'm a physicist,
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我是个物理学家，
00:32
and this time I brought
another physicist along to do so.
another physicist along to do so.
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这次我带来了另一个物理学家。
00:36
His name is Albert Einstein --
also Albert -- and he's the one who said
also Albert -- and he's the one who said
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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
也叫阿尔伯特——他是那个说过
00:41
that the person who has not made
his great contributions to science
his great contributions to science
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如果一个人到30岁时对科学
00:46
by the age of 30
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都没啥大贡献，
00:47
will never do so.
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也就永远不会有贡献了。
00:49
(Laughter)
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（笑声）
00:50
Now, you don't need to check Wikipedia
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你不需要查维基百科
00:52
that I'm beyond 30.
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去了解我是不是超过30岁。
00:54
(Laughter)
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（笑声）
00:55
So, effectively, what
he is telling me, and us,
he is telling me, and us,
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实际上他是想告诉我们，
00:59
is that when it comes to my science,
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当涉及到我在科学领域的作为时，
01:01
I'm deadwood.
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我是朽木难雕了。
01:04
Well, luckily, I had my share
of luck within my career.
of luck within my career.
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幸运的是，我的事业运还算不错。
01:10
Around age 28, I became
very interested in networks,
very interested in networks,
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在28岁时，我对网络产生了兴趣，
01:13
and a few years later, we managed
to publish a few key papers
to publish a few key papers
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几年后，我成功发表了几篇
01:18
that reported the discovery
of scale-free networks
of scale-free networks
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关于发现无标度网络的核心论文，
01:22
and really gave birth to a new discipline
that we call network science today.
that we call network science today.
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并催生了一门我们今天称为
网络科学的新学科。
网络科学的新学科。
01:26
And if you really care about it,
you can get a PhD now in network science
you can get a PhD now in network science
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如果你对这个学科也很感兴趣，
可以在布达佩斯，在波士顿
可以在布达佩斯，在波士顿
01:30
in Budapest, in Boston,
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读取网络科学的博士学位，
01:32
and you can study it all over the world.
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也可以在全球各地学习这门课程。
01:35
A few years later,
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几年后，
01:37
when I moved to Harvard
first as a sabbatical,
first as a sabbatical,
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当我第一次在哈佛进行学术休假时，
01:40
I became interested
in another type of network:
in another type of network:
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我对另一种形态的网络产生了兴趣：
01:43
that time, the networks within ourselves,
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在我们自身的网络中，
01:46
how the genes and the proteins
and the metabolites link to each other
and the metabolites link to each other
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基因、蛋白质和代谢物如何相互联系
01:50
and how they connect to disease.
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以及它们与疾病的关系。
01:53
And that interest led
to a major explosion within medicine,
to a major explosion within medicine,
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这个兴趣引发了
医学领域的一阵轰动，
医学领域的一阵轰动，
01:57
including the Network Medicine
Division at Harvard,
Division at Harvard,
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包括哈佛大学的网络医学部，
02:01
that has more than 300 researchers
who are using this perspective
who are using this perspective
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有300多名研究人员
基于这个想法来治疗病人，
基于这个想法来治疗病人，
02:05
to treat patients and develop new cures.
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开发新的治疗方法。
02:09
And a few years ago,
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几年以前，
02:11
I thought that I would take
this idea of networks
this idea of networks
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我觉得我应该把网络的概念
02:13
and the expertise we had in networks
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和关于网络的专业知识
02:15
in a different area,
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应用于一个新的领域，
02:17
that is, to understand success.
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用来理解成功。
02:19
And why did we do that?
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我们为什么要这么做？
02:20
Well, we thought that, to some degree,
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我们认为，在某种程度上，
02:23
our success is determined
by the networks we're part of --
by the networks we're part of --
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我们的成功取决于我们所处的网络——
02:26
that our networks can push us forward,
they can pull us back.
they can pull us back.
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我们的网络可以推动我们前进，
也能拖我们后腿。
也能拖我们后腿。
02:30
And I was curious if we could use
the knowledge and big data and expertise
the knowledge and big data and expertise
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我好奇我们能否使用
在网络中获得的这些知识，
在网络中获得的这些知识，
02:35
where we develop the networks
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结合大数据和专长
02:36
to really quantify
how these things happen.
how these things happen.
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来量化事情是如何发生的。
02:40
This is a result from that.
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这是一个结果。
02:41
What you see here is a network
of galleries in museums
of galleries in museums
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你在这里看到的是博物馆里
02:44
that connect to each other.
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相互连接的画廊网络。
02:46
And through this map
that we mapped out last year,
that we mapped out last year,
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通过这张我们去年绘制的图，
02:50
we are able to predict very accurately
the success of an artist
the success of an artist
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如果给我他或她在他们的
职业生涯举办的前五个展览，
职业生涯举办的前五个展览，
02:55
if you give me the first five exhibits
that he or she had in their career.
that he or she had in their career.
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我们就能够非常准确地预测
一个艺术家是否成功。
一个艺术家是否成功。
03:01
Well, as we thought about success,
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当我们思考成功时，
03:04
we realized that success
is not only about networks;
is not only about networks;
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我们意识到成功不仅跟网络有关；
03:07
there are so many
other dimensions to that.
other dimensions to that.
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还有很多其他的维度。
03:10
And one of the things
we need for success, obviously,
we need for success, obviously,
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其中一个成功的必要因素，
03:13
is performance.
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很明显就是业绩。
03:14
So let's define what's the difference
between performance and success.
between performance and success.
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让我们定义一下业绩和成功的差别。
03:18
Well, performance is what you do:
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业绩是你做的事情：
03:20
how fast you run,
what kind of paintings you paint,
what kind of paintings you paint,
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你跑得有多快，你画的是什么画，
03:23
what kind of papers you publish.
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你发表的是什么论文。
03:25
However, in our working definition,
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然而，在我们的工作定义中，
03:28
success is about what the community
notices from what you did,
notices from what you did,
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成功是社群从你的业绩中
03:32
from your performance:
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注意到你做的哪些事情，
03:34
How does it acknowledge it,
and how does it reward you for it?
and how does it reward you for it?
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如何承认你的成就，如何奖励你？
03:38
In other terms,
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换句话说，
03:39
your performance is about you,
but your success is about all of us.
but your success is about all of us.
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你的业绩跟你有关，
但你的成功跟大家都有关。
但你的成功跟大家都有关。
03:45
And this was a very
important shift for us,
important shift for us,
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这对我们来说是个非常重要的转变，
03:48
because the moment we defined success
as being a collective measure
as being a collective measure
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因为我们把成功定义为社群
03:52
that the community provides to us,
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给予我们的集体评价。
03:54
it became measurable,
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这样一来成功就变得可衡量，
03:56
because if it's in the community,
there are multiple data points about that.
there are multiple data points about that.
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因为在一个社群中，
关于成功包含着很多数据点。
关于成功包含着很多数据点。
04:00
So we go to school,
we exercise, we practice,
we exercise, we practice,
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我们上学，我们练习，我们实践，
04:06
because we believe
that performance leads to success.
that performance leads to success.
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因为我们相信业绩会让我们成功。
04:09
But the way we actually
started to explore,
started to explore,
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但当我们开始探索，
04:11
we realized that performance and success
are very, very different animals
are very, very different animals
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我们开始意识到
以数学的方式看待这个问题时，
以数学的方式看待这个问题时，
04:15
when it comes to
the mathematics of the problem.
the mathematics of the problem.
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业绩和成功是非常，
非常不同的概念，
非常不同的概念，
04:18
And let me illustrate that.
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让我来解释一下。
04:20
So what you see here is
the fastest man on earth, Usain Bolt.
the fastest man on earth, Usain Bolt.
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你在这里看到的是世界上
最快的人，尤塞恩·博尔特。
最快的人，尤塞恩·博尔特。
04:25
And of course, he wins most of
the competitions that he enters.
the competitions that he enters.
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当然，他赢得了大多数参与的比赛。
04:30
And we know he's the fastest on earth
because we have a chronometer
because we have a chronometer
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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
因为我们有精密的计时器
04:33
to measure his speed.
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去测量他的速度。
04:34
Well, what is interesting about him
is that when he wins,
is that when he wins,
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有趣之处在于当他获胜时，
04:38
he doesn't do so by really significantly
outrunning his competition.
outrunning his competition.
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他并没有明显地超过竞争对手。
04:44
He's running at most a percent faster
than the one who loses the race.
than the one who loses the race.
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他跑得比输掉比赛的人
最多快百分之一。
最多快百分之一。
04:49
And not only does he run only
one percent faster than the second one,
one percent faster than the second one,
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他不仅只比第二名快百分之一，
04:53
but he doesn't run
10 times faster than I do --
10 times faster than I do --
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他的速度也不超过我的10倍——
04:56
and I'm not a good runner,
trust me on that.
trust me on that.
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并且我还不是个擅长跑步的人，
这点请相信我。
这点请相信我。
04:58
(Laughter)
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（笑声）
04:59
And every time we are able
to measure performance,
to measure performance,
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每次我们能够评估业绩时，
05:03
we notice something very interesting;
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我们都会注意到一些有趣的事情：
05:05
that is, performance is bounded.
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业绩是有界限的。
05:07
What it means is that there are
no huge variations in human performance.
no huge variations in human performance.
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这意味着人类的业绩
并没有巨大的差异。
并没有巨大的差异。
05:11
It varies only in a narrow range,
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它变化的范围非常小，
05:14
and we do need the chronometer
to measure the differences.
to measure the differences.
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我们确实需要精密的计时器
来测量这个差异。
来测量这个差异。
05:18
This is not to say that we cannot
see the good from the best ones,
see the good from the best ones,
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不是说我们不能从
最好的人身上看到好的一面，
最好的人身上看到好的一面，
05:21
but the best ones
are very hard to distinguish.
are very hard to distinguish.
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但最好的人非常难以识别。
05:24
And the problem with that
is that most of us work in areas
is that most of us work in areas
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并且问题在于我们很多人的工作领域
05:27
where we do not have a chronometer
to gauge our performance.
to gauge our performance.
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并没有精密的计时器
来衡量我们的业绩。
来衡量我们的业绩。
05:31
Alright, performance is bounded,
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好了，业绩是有界限的，
05:32
there are no huge differences between us
when it comes to our performance.
when it comes to our performance.
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当涉及我们的业绩时，
我们之间并没有显著的差异。
我们之间并没有显著的差异。
05:36
How about success?
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那么成功呢？
05:37
Well, let's switch to
a different topic, like books.
a different topic, like books.
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让我们转到另一个话题，比如书籍。
05:40
One measure of success for writers is
how many people read your work.
how many people read your work.
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评估作家成功的一个方法是
有多少人阅读了你的作品。
有多少人阅读了你的作品。
05:46
And so when my previous book
came out in 2009,
came out in 2009,
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当我早先那本书
在2009年出版时，
在2009年出版时，
05:51
I was in Europe talking with my editor,
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我在欧洲和编辑谈话，
05:53
and I was interested:
Who is the competition?
Who is the competition?
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我感兴趣的是：谁是我的竞争对手？
05:56
And I had some fabulous ones.
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我有一些炙手可热的对手。
05:59
That week --
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那周——
06:00
(Laughter)
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（笑声）
丹·布朗出版了《失落的秘符》，
06:01
Dan Brown came out with "The Lost Symbol,"
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并且尼古拉斯·斯帕克斯
06:04
and "The Last Song" also came out,
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的《最后一首歌》也问世了。
06:07
Nicholas Sparks.
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06:09
And when you just look at the list,
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当你看这个书单时，
06:12
you realize, you know, performance-wise,
there's hardly any difference
there's hardly any difference
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你意识到，就业绩而言，这些书
06:15
between these books or mine.
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和我的之间并无多大差别。
06:17
Right?
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是吧？
06:18
So maybe if Nicholas Sparks's team
works a little harder,
works a little harder,
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如果尼古拉斯·斯帕克斯
的团队再努力一点，
的团队再努力一点，
06:23
he could easily be number one,
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他就可以轻松进入榜首，
06:25
because it's almost by accident
who ended up at the top.
who ended up at the top.
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因为最终谁在畅销榜顶端
几乎是随机的。
几乎是随机的。
06:28
So I said, let's look at the numbers --
I'm a data person, right?
I'm a data person, right?
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所以我说，让我们看看数字吧——
我就是干这行的，对吧？
我就是干这行的，对吧？
06:31
So let's see what were
the sales for Nicholas Sparks.
the sales for Nicholas Sparks.
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让我们看看尼古拉斯·斯帕克斯
的作品销量。
的作品销量。
06:36
And it turns out that
that opening weekend,
that opening weekend,
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结果在新书发售的那个周末，
06:38
Nicholas Sparks sold more than
a hundred thousand copies,
a hundred thousand copies,
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尼古拉斯·斯帕克斯
卖出了10万多本书，
卖出了10万多本书，
06:41
which is an amazing number.
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这是个惊人的数字。
06:42
You can actually get to the top
of the "New York Times" best-seller list
of the "New York Times" best-seller list
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你可以看看纽约时报
每周销量在1万册以上的
每周销量在1万册以上的
06:46
by selling 10,000 copies a week,
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畅销书榜单，
06:48
so he tenfold overcame
what he needed to be number one.
what he needed to be number one.
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所以他只凭借新书销量的
十分之一就能轻松登上榜首。
十分之一就能轻松登上榜首。
06:52
Yet he wasn't number one.
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然而他不是第一名。
06:53
Why?
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为什么？
06:54
Because there was Dan Brown,
who sold 1.2 million copies that weekend.
who sold 1.2 million copies that weekend.
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因为有丹·布朗，他在
那个周末卖出了120万册。
那个周末卖出了120万册。
06:59
(Laughter)
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（笑声）
07:01
And the reason I like this number
is because it shows that, really,
is because it shows that, really,
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我喜欢这个数字的原因
是因为它真正显示了，
是因为它真正显示了，
07:05
when it comes to success, it's unbounded,
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当涉及到成功时，它是没有界限的，
07:08
that the best doesn't only get
slightly more than the second best
slightly more than the second best
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最好的不止比第二名好一点点，
07:14
but gets orders of magnitude more,
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而超越了好几个数量级，
07:17
because success is a collective measure.
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因为成功是集体的衡量标准。
07:20
We give it to them, rather than
we earn it through our performance.
we earn it through our performance.
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我们给予他们成功，而不是
通过我们的业绩获得它。
通过我们的业绩获得它。
07:24
So one of things we realized is that
performance, what we do, is bounded,
performance, what we do, is bounded,
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我们意识到业绩是有界限的，
07:30
but success, which is
collective, is unbounded,
collective, is unbounded,
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但成功，属于集体衡量的，是无界的，
07:32
which makes you wonder:
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这一定让你心生疑惑：
07:34
How do you get these
huge differences in success
huge differences in success
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当人们的业绩表现差异很小的时候，
07:37
when you have such tiny
differences in performance?
differences in performance?
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为何成功的差异如此之大？
07:40
And recently, I published a book
that I devoted to that very question.
that I devoted to that very question.
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最近，我出版了一本
关于这个问题的书。
关于这个问题的书。
07:44
And they didn't give me enough time
to go over all of that,
to go over all of that,
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我没有太多时间详细介绍这本书，
07:47
so I'm going to go back
to the question of,
to the question of,
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所以我打算回到这个问题，
07:49
alright, you have success;
when should that appear?
when should that appear?
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成功通常会在什么时候出现呢？
07:52
So let's go back to the party spoiler
and ask ourselves:
and ask ourselves:
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那么让我们回到派对捣乱者
的话题，问问我们自己：
的话题，问问我们自己：
07:57
Why did Einstein make
this ridiculous statement,
this ridiculous statement,
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为什么爱因斯坦要发表
这样荒谬的言论，
这样荒谬的言论，
08:00
that only before 30
you could actually be creative?
you could actually be creative?
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人的创造力止步于30岁？
08:03
Well, because he looked around himself
and he saw all these fabulous physicists
and he saw all these fabulous physicists
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因为他发现周围
所有这些创造量子力学
所有这些创造量子力学
08:08
that created quantum mechanics
and modern physics,
and modern physics,
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和现代物理学的伟大物理学家，
08:11
and they were all in their 20s
and early 30s when they did so.
and early 30s when they did so.
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他们的伟大成就都是诞生在
20多岁和30岁出头。
20多岁和30岁出头。
08:15
And it's not only him.
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并不是只有他这样想。
08:16
It's not only observational bias,
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这不仅是观察偏差，
08:18
because there's actually
a whole field of genius research
a whole field of genius research
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3997
因为事实上有一整个
领域的天才研究
领域的天才研究
08:22
that has documented the fact that,
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2256
都证明了这一点，
08:24
if we look at the people
we admire from the past
we admire from the past
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3160
如果回顾一下我们崇拜的先人，
08:28
and then look at what age
they made their biggest contribution,
they made their biggest contribution,
162
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3358
然后再看他们做出
最大贡献的年纪，
最大贡献的年纪，
08:31
whether that's music,
whether that's science,
whether that's science,
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2096
不管在音乐，在科学，
08:33
whether that's engineering,
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1619
还是在工程领域，
08:35
most of them tend to do so
in their 20s, 30s, early 40s at most.
in their 20s, 30s, early 40s at most.
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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。
最多40岁出头时做出了这些成绩。
08:41
But there's a problem
with this genius research.
with this genius research.
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2791
但这个天才研究有个问题。
08:45
Well, first of all, it created
the impression to us
the impression to us
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3280
首先，它为大众制造了一种印象，
08:48
that creativity equals youth,
168
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3479
即创造力等于年轻，
08:52
which is painful, right?
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真让人伤心，不是吗？
08:53
(Laughter)
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521638
1951
（笑声）
08:55
And it also has an observational bias,
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4088
并且它也存在观察偏差，
08:59
because it only looks at geniuses
and doesn't look at ordinary scientists
and doesn't look at ordinary scientists
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527725
4962
因为它只观察了天才，
并没研究普通科学家，
并没研究普通科学家，
09:04
and doesn't look at all of us and ask,
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532711
1965
并没有看着我们这些人问，
09:06
is it really true that creativity
vanishes as we age?
vanishes as we age?
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534700
3185
随着年龄的增长，
创造力真的会消失吗？
创造力真的会消失吗？
09:10
So that's exactly what we tried to do,
175
538382
1877
所以这正是我们尝试做的，
09:12
and this is important for that
to actually have references.
to actually have references.
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3803
并且有参照对象很重要。
09:16
So let's look at an ordinary
scientist like myself,
scientist like myself,
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那么让我们看看像我
这样平凡科学家
这样平凡科学家
09:18
and let's look at my career.
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的职业生涯。
09:20
So what you see here is all the papers
that I've published
that I've published
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3202
这里是我发表的全部论文，
09:23
from my very first paper, in 1989;
I was still in Romania when I did so,
I was still in Romania when I did so,
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551549
5115
从1989年发表的最早一篇论文；
当时我还在罗马尼亚，
当时我还在罗马尼亚，
09:28
till kind of this year.
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556688
1593
直到今年这个时候。
09:30
And vertically, you see
the impact of the paper,
the impact of the paper,
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2518
纵坐标，你可以看到论文的影响，
09:33
that is, how many citations,
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1403
也就是被引用的次数，
09:34
how many other papers
have been written that cited that work.
have been written that cited that work.
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562909
3988
有多少其他人发表的论文
引用了我的工作。
引用了我的工作。
09:39
And when you look at that,
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567397
1300
当你看这个数据时，
可以看到我的职业生涯有三个阶段。
09:40
you see that my career
has roughly three different stages.
has roughly three different stages.
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568721
2813
我第一个10年，工作很多，
09:43
I had the first 10 years
where I had to work a lot
where I had to work a lot
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2435
但却并没有多少成就。
09:46
and I don't achieve much.
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574017
1276
似乎没人关注我做的事情，对吧？
09:47
No one seems to care
about what I do, right?
about what I do, right?
189
575317
2118
没有一点影响力。
09:49
There's hardly any impact.
190
577459
1681
09:51
(Laughter)
191
579164
1404
（笑声）
09:52
That time, I was doing material science,
192
580592
2887
当时，我在做材料科学，
09:55
and then I kind of discovered
for myself networks
for myself networks
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3691
然后我自己发现了网络，
09:59
and then started publishing in networks.
194
587218
1947
然后开始发表网络的文章，
10:01
And that led from one high-impact
paper to the other one.
paper to the other one.
195
589189
3073
从那以后，高影响力的文章
我发表了一篇又一篇。
我发表了一篇又一篇。
10:04
And it really felt good.
That was that stage of my career.
That was that stage of my career.
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592286
3104
那时感觉真是很好，那是
我职业生涯的高光时刻。
我职业生涯的高光时刻。
10:07
(Laughter)
197
595414
1282
（笑声）
10:08
So the question is,
what happens right now?
what happens right now?
198
596720
3208
那么问题是，现在发生了什么？
10:12
And we don't know, because there
hasn't been enough time passed yet
hasn't been enough time passed yet
199
600587
3239
我们不知道，现在就去
计算出这些论文
计算出这些论文
会产生怎样的影响还为时尚早，
10:15
to actually figure out how much impact
those papers will get;
those papers will get;
200
603850
2987
需要时间来获取这些信息。
10:18
it takes time to acquire.
201
606861
1227
当你看这个数据时，
10:20
Well, when you look at the data,
202
608112
1569
会觉得爱因斯坦和
天才研究的结论是对的，
天才研究的结论是对的，
10:21
it seems to be that Einstein,
the genius research, is right,
the genius research, is right,
203
609705
2854
10:24
and I'm at that stage of my career.
204
612583
1811
我在我职业生涯的高光阶段。
10:26
(Laughter)
205
614418
2308
（笑声）
10:28
So we said, OK, let's figure out
how does this really happen,
how does this really happen,
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616750
5974
那么让我们看看
这究竟是如何发生的，
这究竟是如何发生的，
10:34
first in science.
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622748
1778
首先看看科学领域。
10:36
And in order not to have
the selection bias,
the selection bias,
208
624550
3632
为了不产生选择偏差，
10:40
to look only at geniuses,
209
628206
1337
只看天才，
10:41
we ended up reconstructing the career
of every single scientist
of every single scientist
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629567
3716
我们最终重建了1900年至今每一位
10:45
from 1900 till today
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2502
科学家的职业生涯，
10:47
and finding for all scientists
what was their personal best,
what was their personal best,
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3712
并找到了所有科学家
的个人最高成就，
的个人最高成就，
10:51
whether they got the Nobel Prize
or they never did,
or they never did,
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639569
2812
不管他获得了诺贝尔奖还是没有，
10:54
or no one knows what they did,
even their personal best.
even their personal best.
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3407
或是没人问津，即便是他最好的成就。
10:57
And that's what you see in this slide.
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645836
1915
这就是你们在这张幻灯片上看到的。
10:59
Each line is a career,
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647775
1573
每条线是个职业生涯，
11:01
and when you have a light blue dot
on the top of that career,
on the top of that career,
217
649372
3003
在职业生涯的顶端
有一个浅蓝色的点，
有一个浅蓝色的点，
11:04
it says that was their personal best.
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652399
2040
代表着他们个人的最好成就。
11:06
And the question is,
219
654463
1155
问题是，
11:07
when did they actually make
their biggest discovery?
their biggest discovery?
220
655642
3568
他们最重大的发现
发生在什么时候？
发生在什么时候？
11:11
To quantify that,
221
659234
1165
要量化这点，
我们看的是你获得
最大发现的概率是多少，
最大发现的概率是多少，
11:12
we look at what's the probability
that you make your biggest discovery,
that you make your biggest discovery,
222
660423
3376
11:15
let's say, one, two, three
or 10 years into your career?
or 10 years into your career?
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663823
2672
比如你职业生涯的
的第1，2，3或者10年。
的第1，2，3或者10年。
11:18
We're not looking at real age.
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666519
1480
我们真正要看的并不是年纪。
我们看的是所谓的“学术年龄。”
11:20
We're looking at
what we call "academic age."
what we call "academic age."
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668023
2134
你的学术年龄始于
你发表第一篇论文的时候。
你发表第一篇论文的时候。
11:22
Your academic age starts
when you publish your first papers.
when you publish your first papers.
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3250
我知道你们有些人还是婴儿。
11:25
I know some of you are still babies.
227
673455
1779
11:27
(Laughter)
228
675258
1397
（笑声）
11:28
So let's look at the probability
229
676679
2706
那么让我们来看看
11:31
that you publish
your highest-impact paper.
your highest-impact paper.
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679409
2066
你发表最高影响力论文的概率。
11:33
And what you see is, indeed,
the genius research is right.
the genius research is right.
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681499
3071
你看到的是，的确，
天才研究的结论是正确的。
天才研究的结论是正确的。
11:36
Most scientists tend to publish
their highest-impact paper
their highest-impact paper
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684594
3024
很多科学家发表的
影响力最高的论文倾向于
影响力最高的论文倾向于
11:39
in the first 10, 15 years in their career,
233
687642
2899
发表在他们职业生涯的
前10到15年，
前10到15年，
11:42
and it tanks after that.
234
690565
3133
在那之后就会直线下降。
11:45
It tanks so fast that I'm about --
I'm exactly 30 years into my career,
I'm exactly 30 years into my career,
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693722
5107
它下降得如此之快——我如今
正处在我职业的第30个年头，
正处在我职业的第30个年头，
11:50
and the chance that I will publish a paper
that would have a higher impact
that would have a higher impact
236
698853
3540
我发表一篇比过往有
更高影响力的论文
更高影响力的论文
11:54
than anything that I did before
237
702417
1940
的概率
11:56
is less than one percent.
238
704381
1353
不到1%。
11:57
I am in that stage of my career,
according to this data.
according to this data.
239
705758
3049
根据这个数据，我正处在
职业生涯的这个阶段。
职业生涯的这个阶段。
12:01
But there's a problem with that.
240
709648
1843
但这里有个问题。
12:03
We're not doing controls properly.
241
711515
3675
我们的对照数据有问题。
12:07
So the control would be,
242
715214
1417
对照数据就是，
12:08
what would a scientist look like
who makes random contribution to science?
who makes random contribution to science?
243
716655
4607
对科学做出随机贡献的
科学家会是什么样子？
科学家会是什么样子？
12:13
Or what is the productivity
of the scientist?
of the scientist?
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721286
2995
或者科学家的生产力怎样？
12:16
When do they write papers?
245
724305
2006
他们什么时候写的论文？
12:18
So we measured the productivity,
246
726335
2444
所以我们评估了生产力，
12:20
and amazingly, the productivity,
247
728803
2052
令人惊讶的是，生产力，
12:22
your likelihood of writing a paper
in year one, 10 or 20 in your career,
in year one, 10 or 20 in your career,
248
730879
4131
你在职业生涯的第1年、第10年
或第20年写论文的概率，
或第20年写论文的概率，
12:27
is indistinguishable from the likelihood
of having the impact
of having the impact
249
735034
3606
与论文产生影响的概率
12:30
in that part of your career.
250
738664
1775
几乎无法区分。
12:33
And to make a long story short,
251
741026
1783
长话短说，
12:34
after lots of statistical tests,
there's only one explanation for that,
there's only one explanation for that,
252
742833
4228
在很多的数据检验后，
只有一个解释，
只有一个解释，
12:39
that really, the way we scientists work
253
747085
2894
真相是，我们科学家的工作，
12:42
is that every single paper we write,
every project we do,
every project we do,
254
750003
3633
我们写的每篇论文，做的每个项目
12:45
has exactly the same chance
of being our personal best.
of being our personal best.
255
753660
4160
都有同样的概率成为
我们个人的最佳成果。
我们个人的最佳成果。
12:49
That is, discovery is like
a lottery ticket.
a lottery ticket.
256
757844
4953
那就是，发现就像中彩票。
12:54
And the more lottery tickets we buy,
257
762821
2351
我们买了越多的彩票，
12:57
the higher our chances.
258
765196
1507
我们中奖的几率就越高。
12:58
And it happens to be so
259
766727
1559
碰巧的是，
13:00
that most scientists buy
most of their lottery tickets
most of their lottery tickets
260
768310
2719
很多科学家在他们
职业生涯的头10年，
职业生涯的头10年，
13:03
in the first 10, 15 years of their career,
261
771053
2460
15年买了大部分的彩票，
13:05
and after that,
their productivity decreases.
their productivity decreases.
262
773537
3413
在那之后，他们的生产力就下降了。
13:09
They're not buying
any more lottery tickets.
any more lottery tickets.
263
777411
2084
他们不再买更多的彩票。
13:11
So it looks as if
they would not be creative.
they would not be creative.
264
779519
3444
所以看起来他们没有创造力了。
13:14
In reality, they stopped trying.
265
782987
1999
现实中，他们停止了尝试。
13:17
So when we actually put the data together,
the conclusion is very simple:
the conclusion is very simple:
266
785509
3915
所以当我们把数据放在一起时，
结论非常简单：
结论非常简单：
13:21
success can come at any time.
267
789448
2331
成功可能随时会来。
13:23
It could be your very first
or very last paper of your career.
or very last paper of your career.
268
791803
3735
它可能是你职业生涯中
最早或最后的论文。
最早或最后的论文。
13:27
It's totally random
in the space of the projects.
in the space of the projects.
269
795562
4288
它在项目的空间中完全是随机的。
13:31
It is the productivity that changes.
270
799874
1931
改变的是你的生产力。
13:33
Let me illustrate that.
271
801829
1252
让我解释一下。
13:35
Here is Frank Wilczek,
who got the Nobel Prize in Physics
who got the Nobel Prize in Physics
272
803105
3269
这是获得诺贝尔物理学奖
的弗兰克·威尔切克，
的弗兰克·威尔切克，
13:38
for the very first paper he ever wrote
in his career as a graduate student.
in his career as a graduate student.
273
806398
4101
他得奖要归功于研究生时
写的第一篇论文。
写的第一篇论文。
13:42
(Laughter)
274
810523
1007
（笑声）
13:43
More interesting is John Fenn,
275
811554
3218
更有趣的是约翰·芬，
13:46
who, at age 70, was forcefully retired
by Yale University.
by Yale University.
276
814796
4598
他在70岁时，被耶鲁大学强制退休，
13:51
They shut his lab down,
277
819418
2056
他们关闭了他的实验室，
13:53
and at that moment, he moved
to Virginia Commonwealth University,
to Virginia Commonwealth University,
278
821498
3666
那时，他搬到了弗吉尼亚联邦大学，
13:57
opened another lab,
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开了另一个实验室，
13:58
and it is there, at age 72,
that he published a paper
that he published a paper
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就在那里，在年纪72岁时，
他发表了一篇论文，
他发表了一篇论文，
14:02
for which, 15 years later, he got
the Nobel Prize for Chemistry.
the Nobel Prize for Chemistry.
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这篇论文在15年后
获得了诺贝尔化学奖。
获得了诺贝尔化学奖。
14:06
And you think, OK,
well, science is special,
well, science is special,
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你会想，科学领域比较特殊，
14:10
but what about other areas
where we need to be creative?
where we need to be creative?
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但其他需要我们有创造力的领域呢？
14:13
So let me take another
typical example: entrepreneurship.
typical example: entrepreneurship.
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那么让我们再看看
另一个典型的例子：创业。
另一个典型的例子：创业。
14:18
Silicon Valley,
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硅谷。
14:20
the land of the youth, right?
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年轻人的领地，对吧？
14:22
And indeed, when you look at it,
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确实，当你看这个领域时，
你发现最大的奖励，
TechCrunch Awards或其他奖励，
TechCrunch Awards或其他奖励，
14:24
you realize that the biggest awards,
the TechCrunch Awards and other awards,
the TechCrunch Awards and other awards,
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全都给了平均年纪
14:28
are all going to people
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14:31
whose average age
is late 20s, very early 30s.
is late 20s, very early 30s.
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在30岁左右的人。
14:36
You look at who the VCs give the money to,
some of the biggest VC firms --
some of the biggest VC firms --
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再看看VC的钱都给了谁，
一些最大的VC企业——
一些最大的VC企业——
14:42
all people in their early 30s.
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几乎所有的人都在30岁出头。
14:44
Which, of course, we know;
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当然，我们知道；
14:46
there is this ethos in Silicon Valley
that youth equals success.
that youth equals success.
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硅谷有这样一种风气：
年轻等于成功。
年轻等于成功。
14:51
Not when you look at the data,
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不过，当你看数据的时候
就不会这样认为了。
就不会这样认为了。
14:53
because it's not only
about forming a company --
about forming a company --
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看看这些人当中有谁真正
14:56
forming a company is like productivity,
trying, trying, trying --
trying, trying, trying --
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成立了一家成功的公司——
14:59
when you look at which
of these individuals actually put out
of these individuals actually put out
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成立一个公司就像生产力，
尝试，尝试，再尝试。
尝试，尝试，再尝试。
15:02
a successful company, a successful exit.
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因为这不仅关于成立一个公司。
15:05
And recently, some of our colleagues
looked at exactly that question.
looked at exactly that question.
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最近，我们的几位同事
正好研究了这个问题。
正好研究了这个问题。
15:09
And it turns out that yes,
those in the 20s and 30s
those in the 20s and 30s
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果不期然，这些年纪
在20多岁和30多岁的人
在20多岁和30多岁的人
15:12
put out a huge number of companies,
form lots of companies,
form lots of companies,
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创立了大量的公司，很多公司，
15:15
but most of them go bust.
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但大部分都破产了。
15:18
And when you look at the successful exits,
what you see in this particular plot,
what you see in this particular plot,
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再看看那些成功的退出，
你在这个图中可以看到，
你在这个图中可以看到，
15:22
the older you are, the more likely that
you will actually hit the stock market
you will actually hit the stock market
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你年纪越大，就越有可能
轰动股票市场
轰动股票市场
15:26
or the sell the company successfully.
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或者成功出售公司。
15:28
This is so strong, actually,
that if you are in the 50s,
that if you are in the 50s,
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数据很显著，事实上，
如果你50多岁，
如果你50多岁，
15:31
you are twice as likely
to actually have a successful exit
to actually have a successful exit
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你成功退出的机会是
15:35
than if you are in your 30s.
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你30岁时的两倍。
15:38
(Applause)
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（鼓掌）
15:43
So in the end, what is it
that we see, actually?
that we see, actually?
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所以最后，我们看到了什么？
15:46
What we see is that creativity has no age.
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我们看到的是创意并无年龄限制。
15:50
Productivity does, right?
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生产力才是关键，对吧？
15:53
Which is telling me that
at the end of the day,
at the end of the day,
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这就告诉我们，
15:57
if you keep trying --
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如果你不断尝试——
15:59
(Laughter)
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（笑声）
16:02
you could still succeed
and succeed over and over.
and succeed over and over.
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你仍然可以不断取得成功。
16:05
So my conclusion is very simple:
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所以我的结论很简单：
16:08
I am off the stage, back in my lab.
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演讲结束后，我得回到实验干活儿了。
16:10
Thank you.
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谢谢。
16:11
(Applause)
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（鼓掌）
Translated by psjmz mz
ABOUT THE SPEAKER
Albert-László Barabási - Network scientistA pioneer in network science, Albert-László Barabási uncovers the hidden order behind complex systems.
Why you should listen
Albert-László Barabási is fascinated by a wide range of topics, from the structure of the brain and treating diseases with network medicine to the emergence of success in art and how science really works. His work uses the quantitative tools of network science, a research field that he pioneered, and led to the discovery of scale-free networks, helping explain the emergence of many natural, technological and social networks.
Barabási is the Robert Gray Dodge Professor of Network Science at Northeastern University and holds an appointment in the Department of Medicine at Harvard Medical School. He splits his time with Budapest, where he runs a European Research Council project at Central European University. A Hungarian born native of Transylvania, Romania, he received his masters in theoretical physics at the Eötvös University in Budapest, Hungary and his PhD three years later at Boston University.
Barabási’s latest book is The Formula: The Universal Laws of Success. He is also the author of Network Science, Linked and Bursts. He co-edited Network Medicine and The Structure and Dynamics of Networks. His books have been translated into over twenty languages.
Albert-László Barabási | Speaker | TED.com