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  • 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,

    究竟他們是如何相連

  • and what does that global conversation look like?

    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)

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

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B1 中級 中文 美國腔 TED 演講 構想 肖恩 想法 基因

TED】Eric Berlow and Sean Gourley:繪製值得傳播的理念(Eric Berlow和Sean Gourley:繪製值得傳播的理念)。 (【TED】Eric Berlow and Sean Gourley: Mapping ideas worth spreading (Eric Berlow and Sean Gourley: Mapping ideas worth spreading))

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    CUChou 發佈於 2021 年 01 月 14 日
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