Eric Berlow and Sean Gourley: Mapping ideas worth spreading

譯者: Jonas Lau 審譯者: Kuan Hsien Lee

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

艾瑞克．伯勞: 我是生態學家 肖恩是物理學家

and we both study complex networks.

我們都研究複雜的網絡

And we met a couple years ago when we discovered

幾年前認識對方是因為

that we had both given a short TED Talk

我們都在 TED 這個平台上

about the ecology of war,

發表過有關生態大戰的演講

and we realized that we were connected

這才發現我們還沒見面之前

by the ideas we shared before we ever met.

就已經因我們分享的構想而有關係

And then we thought, you know, there are thousands

然後我們就想: 世界上有

of other talks out there, especially TEDx Talks,

這麼多的演講，尤其是 TEDx 的演講

that are popping up all over the world.

在全球各地如雨後春筍般湧現

How are they connected,

and what does that global conversation look like?

究竟他們是如何相連

這個全球性對話像似什麼呢?

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

現在肖恩將會為你們講解我們的做法

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

肖恩．古爾利: 沒錯。我們從全球一百四十七個國家

from around the world, 147 different countries,

選取了二萬四千場 TEDx 演講

and we took these talks and we wanted to find

我們想要找出

the mathematical structures that underly

這些蘊藏在演講背後

the ideas behind them.

藏在構想背後的數學模型結構

And we wanted to do that so we could see how

這樣一來我們可以看出

they connected with each other.

構想與構想之間是如何相連的

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

當然，如果你要做這樣的分析

you need a lot of data.

你需要大量的數據

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

而這些數據蘊藏在一個偉大的發明中 -- 叫做 YouTube

and we can go down and basically pull

我們就是上 Youtube

all the open information from YouTube,

下載所有公開的信息

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

全部的評論、點擊率、誰看過這個影片

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

他們在哪裏看這個影片，他們在評論中說了甚麼

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

我們還可以用語音翻譯

we can pull the entire transcript,

把整篇講稿呈現出來

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

這招對於我這些有奇異口音的人也管用

So we can take their transcript

得到了他們的講稿以後

and actually do some pretty cool things.

我們就能做出各樣有趣的事

We can take natural language processing algorithms

我們以自然語言運算法

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

用電腦，逐行逐行的去讀取講稿

extracting key concepts from this.

再從中抽取講稿中的要旨

And we take those key concepts and they sort of form

我們以這些要旨構成

this mathematical structure of an idea.

這個包含不同構想的數學模型

And we call that the meme-ome.

我們稱之為 meme-ome (想法基因)

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

簡單來說，想法基因

is the mathematics that underlies an idea,

就是藏在構想背後的數學

and we can do some pretty interesting analysis with it,

我們可以做一些相當有趣的分析

which I want to share with you now.

現在我想跟你們分享一下

So each idea has its own meme-ome,

每一個想法都有它的「想法基因」

and each idea is unique with that,

而每一個想法都是獨一無二的

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

不過當然，有些想法是從別的地方借用過來的

they kind of steal sometimes,

有些時候是偷來的

and they certainly build on each other,

所以它們會建立在其他的想法之上

and we can go through mathematically

我們可以以數學方法

and take the meme-ome from one talk

從一個演講選取它的「想法基因」

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

再用它來跟其他演講的想法基因做比對

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

看看兩者之間是否有相似的地方

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

我們可以建立一個連繫，並以圖象顯示出來

just like Eric and I are connected.

這就像艾瑞克跟我一樣連接起來

So that's theory, that's great.

這就是我們的理論，看似不錯吧

Let's see how it works in actual practice.

現在我們看看它實際運作吧

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

我們這裏有過去四年間

of all the TEDx Talks over the last four years

TEDx 演講在全球的足跡

exploding out around the world

它遍佈全世界

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

從紐約一直到在另一角落中小小的紐西蘭

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

我們所做的是分析當中的四分之一

and we started to see where the connections occurred,

之後我們就開始發現它們當中的連繫

where they connected with each other.

以及它們從哪一個地方連接起來

Cameron Russell talking about image and beauty

卡梅倫．羅素講述影像與美學

connected over into Europe.

把我們帶到歐洲

We've got a bigger conversation about Israel and Palestine

有關以色列及巴勒斯坦的演講其範圍更廣了些

radiating outwards from the Middle East.

從中東一直延伸開去

And we've got something a little broader

我們還有一個比較廣議題

like big data with a truly global footprint

像是世界各地都在討論的巨量資料(大數據)

reminiscent of a conversation

讓人想起

that is happening everywhere.

到處都在發生的對話

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

從這裏，我們就好像遇見了一個

of what we can actually do with a geographic projection,

平面的地域投影給我們設的限制

but luckily, computer technology allows us to go out

慶幸地，電腦科技容許我們

into multidimensional space.

走進多維空間

So we can take in our network projection

所以我們可以理解我們的網路投射

and apply a physics engine to this,

透過物理引擎的運用

and the similar talks kind of smash together,

而相似的演講相似碰撞在一起

and the different ones fly apart,

不同的演講則會遠離

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

我們最後得出這樣漂亮的結果

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

艾瑞克: 我想指出這裏每一點都代表一場演講

they're linked if they share similar ideas,

如果它個有相似的構想，它們就會連起來

and that comes from a machine reading

這是一個機器讀取

of entire talk transcripts,

所有演講稿

and then all these topics that pop out,

然後抽取當中的主旨所得出的結果

they're not from tags and keywords.

它們並非來自標籤及關鍵詞

They come from the network structure

它們實際上是來自互相關連的構想

of interconnected ideas. Keep going.

所組成的網絡結構。你繼續吧

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

肖恩: 絕對是。我比說的有點太快了

but he's going to slow me down.

但他會降低我的節奏

We've got education connected to storytelling

我們可以將教育、故事敍述

triangulated next to social media.

與社交媒體連成一個三角形

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

你可以得出: 人腦就在醫療的旁邊

which you might expect,

這或許也是你預期之內的

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

但你也會得出電玩遊戲... 很接近地

as those two spaces interface with each other.

它們兩者之間有所互動

But I want to take you into one cluster

不過我希望帶你們到一組主題

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

這對我來說是一個特別的群組，這是「環境」

And I want to kind of zoom in on that

而我又想再放大這個部分

and see if we can get a little more resolution.

看看我們可否再多提高一點它的解像度

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

當我們進入這個群組時，我們可以看到

apply the physics engine again,

再一次運用我們的物理引擎

we see what's one conversation

我們可以看到一場演講

is actually composed of many smaller ones.

實際上是由很多較小規模的對話交幟而成

The structure starts to emerge

這個組織開始顯露出來了

where we see a kind of fractal behavior

我們可以看到一些

of the words and the language that we use

一些我們用來形容在我們周圍、

to describe the things that are important to us

對我們很重要的詞語及語言

all around this world.

有不規則的行為

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

你可以看到食物經濟學及本土食物在最頂層

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

你也可以看到溫室氣體、太陽能、核廢料

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

你可以得到的是一系列較小規模的對話

each connected to each other through the ideas

每一個都以它的構思

and the language they share,

和它們的共通語言與其他對話連在一起

creating a broader concept of the environment.

最後構成一個有關於環境，但更寛更廣的想法

And of course, from here, we can go

當然，從這裏，我們可以

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

繼續放大及看看，究竟年輕人在看甚麼呢?

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

原來他們在看有關能源科技及核聚變的資訊

This is their kind of resonance

這是他們對有關環境的對話

for the conversation around the environment.

所產生出的共鳴

If we split along gender lines,

如果我們以性別劃分

we can see females resonating heavily

我們可以看到女性對於食物經濟學、以及

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

「希望與樂觀」有較大的共鳴

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

這裏有很多令人興奮的東西可以做

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

而我會將以下的部分交給艾瑞克

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

艾瑞克: 是的，我認為，在指說明

you cannot get this kind of perspective

你無法得到這些觀點

from a simple tag search on YouTube.

從 YouTube 中簡單的標籤搜尋中

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

現在回到全球性的對話

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

將全部的演講一同觀察

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

很多時，當我們面對這樣龐大的內容

we do a couple of things to simplify it.

我們會用一系列的方法去簡化它

We might just say, well,

我們或許會說，譬如

what are the most popular talks out there?

哪一個是最受歡迎的演講呢?

And a few rise to the surface.

有數個演講浮到表面來

There's a talk about gratitude.

這裏有一個演講關於感恩

There's another one about personal health and nutrition.

這裏有另一個演講關於個人健康與營養

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

當然，有另一個演講關於色情行業，對嗎?

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

接着，我們會說，好，感恩，那是去年的演講

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

那現在的趨勢是甚麼呢? 哪一個是現在最流行的演講呢?

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

我們可以看到這個新的、正冒起來的、最流行的題目

is about digital privacy.

是有關於數位隱私

So this is great. It simplifies things.

這是極好的。這簡化了不少事情

But there's so much creative content

但這裏有很多具創意的內容

that's just buried at the bottom.

被埋在最底層

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

我討厭這種感覺。我們怎樣可以令這些可能是具創意

that's maybe really creative and interesting?

及有趣的東西浮到表面呢?

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

我們可以回到那個包含不同構思的網絡

to do that.

去尋找它們

Remember, it's that network structure

記住，這就是那個製造出不同的、

that is creating these emergent topics,

處於萌芽階段的題目的網絡

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

不如我們拿當中的兩個題目

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

像是城市和基因，再看看有哪些演講

that creatively bridge these two really different disciplines.

很有想像力的把這兩個截然不同的科目連在一起

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

這個 -- 實際上，這種具創新性的重組

is one of the hallmarks of innovation.

就是創新的特徵之一

Well here's one by Jessica Green

這裏有一個謝西嘉．格林主講

about the microbial ecology of buildings.

有關建築物裏的微生物生態學的演講

It's literally defining a new field.

她的確是在界定一個新的領域

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

我們可以回到這些主題，並問問

what talks are central to those conversations?

這些談話間核心的演講是什麼?

In the cities cluster, one of the most central

在城市這個群組裏，一個最中心的演講

was one by Mitch Joachim about ecological cities,

是由米茨．祖詹主講，主題是主張生態保護的城市

and in the genetics cluster,

在基因研究這個群組

we have a talk about synthetic biology by Craig Venter.

我們有一個克萊格·凡特主講、關於人工生物學的演講

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

這些演講都連繫着很多在相同範疇的其他演講

We could go the other direction and say, well,

我們可以向另一個方向出發

what are talks that are broadly synthesizing

問問哪些演講是廣泛綜合

a lot of different kinds of fields.

許多不同的領域

We used a measure of ecological diversity to get this.

我們用一個生態學多樣性的量度單位去看看

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

一個史迪芬．平克的演講、關於暴力的歷史

very synthetic.

就很有綜合性

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

當然，也有些演講是很獨特的

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

它們就是遠離平流層，在它們自己的一個特別位置

and we call that the Colleen Flanagan index.

我們叫它做「歌蓮．費拿根系數」

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

如果你不認識歌蓮，她是一個藝術家

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

當我問她: 「唔，在平流層裏

in the stratosphere of our idea space?"

我們的想法看似甚麼呢?」

And apparently it smells like bacon.

顯然地，它的嗅味像一塊煙肉

I wouldn't know.

我不會知道

So we're using these network motifs

所以我們就用這些網絡中心思想

to find talks that are unique,

去尋找獨特的演講

ones that are creatively synthesizing a lot of different fields,

有些是創意地結合不同範疇

ones that are central to their topic,

有些是在它們的領域中具有代表性

and ones that are really creatively bridging disparate fields.

以及有些是相當創意去連繫截然不同範疇的演講

Okay? We never would have found those with our obsession

可以嗎? 即使我們着了魔一樣去找尋現時最流行的演講

with what's trending now.

也未必會找到它們

And all of this comes from the architecture of complexity,

它們隱藏在複雜的結構裏

or the patterns of how things are connected.

或是事物間如何連結的模式

SG: So that's exactly right.

肖恩: 這完全是對的

We've got ourselves in a world

我們就在一個

that's massively complex,

無比複雜的世界中

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

我們用一系列的運算法去拆解它

so we can navigate through it.

以致我們可以在中間游走

And those algorithms, whilst being kind of useful,

這些運算法，雖然是很有用

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

但它們仍然是不夠全面的，我們定當能夠做得更好

because we can realize that their complexity is not random.

因為我們發現這些複雜性並不是偶然性的

It has mathematical structure,

它有一個數學結構

and we can use that mathematical structure

我們可以用這個數學結構

to go and explore things like the world of ideas

去探索世界上不同的構思

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

去看看別人說過甚麼，甚麼沒有被提出過

and to be a little bit more human

再去做些更人性化的事

and, hopefully, a little smarter.

亦希望變得聰明一些

Thank you.

謝謝

(Applause)

(掌聲)