Eric Berlow and Sean Gourley: Mapping ideas worth spreading

翻译人员: Li Li 校对人员: Lorraine Teng

Eric Berlow: I'm an ecologist, and Sean's a physicist,

Eric Berlow: 我是一个生态学家，而Sean是个物理学家，

and we both study complex networks.

我们都在研究一些复杂的网络系统。

And we met a couple years ago when we discovered

几年前我们见了次面

that we had both given a short TED Talk

发现我们都曾在TED

about the ecology of war,

做过关于战争生态学的演讲，

and we realized that we were connected

然后发现，即便没见过彼此，

by the ideas we shared before we ever met.

我们也因那些共同的想法联系在一起了。

And then we thought, you know, there are thousands

我们觉得，你们也知道，TED有成千上万个演讲，

of other talks out there, especially TEDx Talks,

其中TEDx的演讲特别多，

that are popping up all over the world.

已经遍布世界各地了。

How are they connected,

and what does that global conversation look like?

他们是如何联系在一起的，

这种跨越国界的对话会是怎样的？

So Sean's going to tell you a little bit about how we did that.

下面就由Sean为大家讲一下我们所做的事。

Sean Gourley: Exactly. So we took 24,000 TEDx Talks

Sean Gourley: 没错，我们从世界各地147个国家

from around the world, 147 different countries,

挑选了24,000个TEDx演讲。

and we took these talks and we wanted to find

在这些演讲中，我们想要找到

the mathematical structures that underly

一种数学结构

the ideas behind them.

来揭示视频背后的思想。

And we wanted to do that so we could see how

我们想通过这样做来找出

they connected with each other.

这些演讲是如何联系在一起的。

And so, of course, if you're going to do this kind of stuff,

当然，如果想要完成这个目标

you need a lot of data.

得需要很多数据。

So the data that you've got is a great thing called YouTube,

这些数据就是来自伟大的YouTube，

and we can go down and basically pull

我们能深入到YouTube，把它

all the open information from YouTube,

所有公开的信息都找出来，

all the comments, all the views, who's watching it,

包括评论、点击率、浏览者信息

where are they watching it, what are they saying in the comments.

浏览的地点、评论的具体内容。

But we can also pull up, using speech-to-text translation,

但我们也能直接地，通过演讲内容翻译成文本的方式，

we can pull the entire transcript,

能获取整个字幕文本。

and that works even for people with kind of funny accents like myself.

这方法对像我一样有搞笑口音的人也是行得通的。

So we can take their transcript

然后我们拿着这些文本

and actually do some pretty cool things.

做一些很酷的事情。

We can take natural language processing algorithms

我们让电脑用自然语言处理算法

to kind of read through with a computer, line by line,

去逐行地阅读文本，

extracting key concepts from this.

从中提取关键思想。

And we take those key concepts and they sort of form

这些关键思想后会形成

this mathematical structure of an idea.

该样的思想数学结构。

And we call that the meme-ome.

我们称之为“文化基因集合”。

And the meme-ome, you know, quite simply,

所谓文化基因集合，其实很简单，

is the mathematics that underlies an idea,

就是一种解释思想的数学，

and we can do some pretty interesting analysis with it,

在此之上我们能做一些很有趣的分析，

which I want to share with you now.

现在就和大家分享一下这些分析。

So each idea has its own meme-ome,

每种思想都有它独自的文化基因集合，

and each idea is unique with that,

每个文化基因集合也不尽相同，

but of course, ideas, they borrow from each other,

当然，思想嘛，总是大同小异的，

they kind of steal sometimes,

有时也会相互借鉴，

and they certainly build on each other,

当然也会有所发展，

and we can go through mathematically

我们能从数学层面去检查

and take the meme-ome from one talk

然后从演讲里提取文化基因集合，

and compare it to the meme-ome from every other talk,

然后和其他视频的文化基因集合做比较，

and if there's a similarity between the two of them,

如果这两者中有相似之处，

we can create a link and represent that as a graph,

我们就能建立一种联系，用图表来表示，

just like Eric and I are connected.

就像Eric和我的联系。

So that's theory, that's great.

理论上就这样，挺好的。

Let's see how it works in actual practice.

那让我们看看在实际中它是如何运作的。

So what we've got here now is the global footprint

我们现在看到的就是一个全球分布图

of all the TEDx Talks over the last four years

代表过去四年

exploding out around the world

全球所有的TEDx演讲出现的轨迹，

from New York all the way down to little old New Zealand in the corner.

从纽约一直到在地球板块角落小小的古老的新西兰。

And what we did on this is we analyzed the top 25 percent of these,

然后我们分析了前25%的演讲，

and we started to see where the connections occurred,

开始意识到联系是在哪里产生的了以及

where they connected with each other.

他们是在哪里相互联系的。

Cameron Russell talking about image and beauty

Cameron Russell讲了欧洲各地相互联系的图像和美。

connected over into Europe.

Cameron Russell讲了欧洲各地相互联系的图像和美。

We've got a bigger conversation about Israel and Palestine

关于以色列和巴勒斯坦联系的交流更多，

radiating outwards from the Middle East.

是从中东开始的。

And we've got something a little broader

我们也有一些全球范围内的比较广泛的对话，

like big data with a truly global footprint

就像一种遍布全球的大数据，反映了某特定话题。

reminiscent of a conversation

就像一种遍布全球的大数据，反映了某特定话题。

that is happening everywhere.

就像一种遍布全球的大数据，反映了某特定话题。

So from this, we kind of run up against the limits

所以从这里，我们似乎碰上了瓶颈，

of what we can actually do with a geographic projection,

这些地理投影到底能做什么，

but luckily, computer technology allows us to go out

幸运的是，电脑技术能让我们跳出常规框架

into multidimensional space.

进入多维空间。

So we can take in our network projection

因此，我们能利用我们的网状投影

and apply a physics engine to this,

使用物理引擎

and the similar talks kind of smash together,

将类似的演讲一起做离心运动，

and the different ones fly apart,

不同的则会飞离，

and what we're left with is something quite beautiful.

剩下的就是很美的东西。

EB: So I want to just point out here that every node is a talk,

EB:我只想说明一下，每个节点就是一个演讲，

they're linked if they share similar ideas,

如果他们内容类似，就会连在一起

and that comes from a machine reading

这一些都由一个机器去读取

of entire talk transcripts,

所有演讲的字幕文本，

and then all these topics that pop out,

所有这些弹出来的标题，

they're not from tags and keywords.

他们不是取自标签或者关键字。

They come from the network structure

他们是取自相互联系思想的网络结构。你继续

of interconnected ideas. Keep going.

他们是取自相互联系思想的网络结构。你继续

SG: Absolutely. So I got a little quick on that,

SG：好的。我其实说得有点快了，

but he's going to slow me down.

他只是想我讲慢一点。

We've got education connected to storytelling

现在我们看到“教育”是和“'讲故事”

triangulated next to social media.

还有“社交媒体”形成的三角架构。

You've got, of course, the human brain right next to healthcare,

还有，当然，“人类大脑”紧挨着“医疗保健”，

which you might expect,

这估计在预料中，

but also you've got video games, which is sort of adjacent,

但同时还有“电子游戏”似乎在和

as those two spaces interface with each other.

这两个联系在一起的又有一定的重叠。

But I want to take you into one cluster

但我想让你们看一个群

that's particularly important to me, and that's the environment.

这对我尤其重要，那就是“环境”。

And I want to kind of zoom in on that

我们放大一点

and see if we can get a little more resolution.

看看能不能清晰一点。

So as we go in here, what we start to see,

放大之后，我们将看到的

apply the physics engine again,

再次代入物理引擎，

we see what's one conversation

就可以看到一个话题

is actually composed of many smaller ones.

其实由很多小话题组成的。

The structure starts to emerge

这个结构开始显现出

where we see a kind of fractal behavior

一些我们所用遣词造句

of the words and the language that we use

的分形行为的地方以及在全球人们用来形容重要事物的语言。

to describe the things that are important to us

的分形行为的地方以及在全球人们用来形容重要事物的语言。

all around this world.

的分形行为的地方以及在全球人们用来形容重要事物的语言。

So you've got food economy and local food at the top,

所以“食品经济”和“当地食品”在顶端，

you've got greenhouse gases, solar and nuclear waste.

“温室气体”，“太阳能和核能浪费”也在前列。

What you're getting is a range of smaller conversations,

你能看到一系列小的话题，

each connected to each other through the ideas

每个都因他们的思想和语言

and the language they share,

而联系在一起，

creating a broader concept of the environment.

从而创造了一个更大的关于环境的概念。

And of course, from here, we can go

当然，从这里

and zoom in and see, well, what are young people looking at?

我们通过放大能看到年轻人在看什么。

And they're looking at energy technology and nuclear fusion.

他们聚焦在能源技术和核聚变。

This is their kind of resonance

这是他们对环境话题

for the conversation around the environment.

产生的一种共鸣。

If we split along gender lines,

如果我们按性别来分类的话，

we can see females resonating heavily

能看到女性的共鸣

with food economy, but also out there in hope and optimism.

更与食品经济有关，是充满希望和乐观的。

And so there's a lot of exciting stuff we can do here,

当然还有很多有趣的发现，

and I'll throw to Eric for the next part.

我就交给Eric来讲下一部分。

EB: Yeah, I mean, just to point out here,

EB：恩，我就想指出，

you cannot get this kind of perspective

你是没法从YouTube那个简单的搜索栏里

from a simple tag search on YouTube.

得到这种回馈的。

Let's now zoom back out to the entire global conversation

现在我们跳出环境话题，重新回到全球的对话，

out of environment, and look at all the talks together.

来看下所有的这些演讲。

Now often, when we're faced with this amount of content,

当面对如此数量的内容时，我们经常

we do a couple of things to simplify it.

通过其他手段去简化它。

We might just say, well,

我们可能会说，

what are the most popular talks out there?

那里面最受欢迎的演讲有哪些，

And a few rise to the surface.

然后就有一些浮现出来。

There's a talk about gratitude.

其中一个是关于感恩的。

There's another one about personal health and nutrition.

有个是关于个人健康与营养的。

And of course, there's got to be one about porn, right?

当然，肯定会有一个是关于色情的，对吧？

And so then we might say, well, gratitude, that was last year.

所以我们就能说，感恩，是去年的主题。

What's trending now? What's the popular talk now?

现在的趋势是什么？现在流行的演讲是什么？

And we can see that the new, emerging, top trending topic

这样我们就能看到数字隐私成为新生的热门话题。

is about digital privacy.

这样我们就能看到数字隐私成为新生的热门话题。

So this is great. It simplifies things.

这很棒，它简化了一切。

But there's so much creative content

但这样就有很多新鲜的内容

that's just buried at the bottom.

被淹没在底部了。

And I hate that. How do we bubble stuff up to the surface

我不喜欢这样。我们要怎样才能让这些

that's maybe really creative and interesting?

可能真的有新意有趣的话题回到顶层呢？

Well, we can go back to the network structure of ideas

其实我们可以回到思想的网络结构上

to do that.

来实现这一点。

Remember, it's that network structure

记住，是这个网络结构

that is creating these emergent topics,

让这些话题显现出来，

and let's say we could take two of them,

我们不妨拿出其中的两个

like cities and genetics, and say, well, are there any talks

像“城市”和“遗传”，然后看看

that creatively bridge these two really different disciplines.

是否有其他演讲能有创意地把这两个不同的科目联系起来。

And that's -- Essentially, this kind of creative remix

而这——基本上，这种创新性的混合

is one of the hallmarks of innovation.

就是革新的标记之一。

Well here's one by Jessica Green

这儿有一个Jessica Green

about the microbial ecology of buildings.

关于建筑的微生物生态学的演讲。

It's literally defining a new field.

它事实上是在定义一个新的领域。

And we could go back to those topics and say, well,

然后我们再回到这两个话题上，

what talks are central to those conversations?

想象在这里，有哪些是比较核心的？

In the cities cluster, one of the most central

在“城市”的那堆里，其中一个最核心的

was one by Mitch Joachim about ecological cities,

就是Mitch Joachim的生态城市，

and in the genetics cluster,

而在“遗传”的那堆里

we have a talk about synthetic biology by Craig Venter.

有Craig Venter的一个关于合成生物学的演讲。

These are talks that are linking many talks within their discipline.

还有很多演讲以他们的内容与其他许多演讲联系在一起的。

We could go the other direction and say, well,

我们还可以从另一个方向入手，比方说

what are talks that are broadly synthesizing

有哪些演讲，是广泛地

a lot of different kinds of fields.

综合了很多不同领域的。

We used a measure of ecological diversity to get this.

我们利用一种生态多样性的方法得到这个答案。

Like, a talk by Steven Pinker on the history of violence,

例如，Steven Pinker的一个演讲是关于暴力的历史，

very synthetic.

非常综合。

And then, of course, there are talks that are so unique

当然，还有一些演讲是非常独特的，

they're kind of out in the stratosphere, in their own special place,

已经到了一定境界，不是常人能理解的，

and we call that the Colleen Flanagan index.

我们称之为Colleen Flanagan指数。

And if you don't know Colleen, she's an artist,

如果你不知道Colleen没关系，她是个艺术家，

and I asked her, "Well, what's it like out there

所以我问她，“我们空间概念的最高层

in the stratosphere of our idea space?"

是什么样的？”

And apparently it smells like bacon.

显然那地方闻着像培根。

I wouldn't know.

我也不知道。

So we're using these network motifs

所以我们用这些网络图形

to find talks that are unique,

来寻找那些独特的演讲，

ones that are creatively synthesizing a lot of different fields,

那些创造性地综合了很多领域的演讲，

ones that are central to their topic,

那些中心明确的演讲，

and ones that are really creatively bridging disparate fields.

还有那些创新地把不相干的领域联系起来的演讲。

Okay? We never would have found those with our obsession

对吧？一味地看趋势的话

with what's trending now.

我们是没法找到这些演讲的。

And all of this comes from the architecture of complexity,

所有的这一切来自复杂性架构

or the patterns of how things are connected.

或是事物联系的模式。

SG: So that's exactly right.

SG：非常有道理。

We've got ourselves in a world

我们处在一个非复杂的世界，

that's massively complex,

我们处在一个非常复杂的世界，

and we've been using algorithms to kind of filter it down

我们一直试图用算法简化它

so we can navigate through it.

以便去驾驭它。

And those algorithms, whilst being kind of useful,

而这些算法就算有时有用，

are also very, very narrow, and we can do better than that,

也是非常有限的，而我们能做得更好，

because we can realize that their complexity is not random.

因为我们能意识到这种复杂性不是偶然。

It has mathematical structure,

它有数学架构，

and we can use that mathematical structure

而我们能用这个数学架构去深入研究

to go and explore things like the world of ideas

例如这个世界上的所有思想，

to see what's being said, to see what's not being said,

去看看人们都讨论些什么，还有什么没讨论过的，

and to be a little bit more human

从而使得这些数据显得更人性化

and, hopefully, a little smarter.

更富有智慧。

Thank you.

谢谢

(Applause)

（掌声）