Eric Berlow: I'm an ecologist, and Sean's a physicist, and we both study complex networks. And we met a couple years ago when we discovered that we had both given a short TED Talk about the ecology of war, and we realized that we were connected by the ideas we shared before we ever met. And then we thought, you know, there are thousands of other talks out there, especially TEDx Talks, that are popping up all over the world. How are they connected, and what does that global conversation look like? So Sean's going to tell you a little bit about how we did that.
艾瑞克.伯勞: 我是生態學家 肖恩是物理學家 我們都研究複雜的網絡 幾年前認識對方是因為 我們都在 TED 這個平台上 發表過有關生態大戰的演講 這才發現我們還沒見面之前 就已經因我們分享的構想而有關係 然後我們就想: 世界上有 這麼多的演講,尤其是 TEDx 的演講 在全球各地如雨後春筍般湧現 究竟他們是如何相連 這個全球性對話像似什麼呢? 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 from around the world, 147 different countries, and we took these talks and we wanted to find the mathematical structures that underly the ideas behind them. And we wanted to do that so we could see how they connected with each other.
肖恩.古爾利: 沒錯。我們從全球一百四十七個國家 選取了二萬四千場 TEDx 演講 我們想要找出 這些蘊藏在演講背後 藏在構想背後的數學模型結構 這樣一來我們可以看出 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, you need a lot of data. So the data that you've got is a great thing called YouTube, and we can go down and basically pull all the open information from YouTube, all the comments, all the views, who's watching it, where are they watching it, what are they saying in the comments. But we can also pull up, using speech-to-text translation, we can pull the entire transcript, and that works even for people with kind of funny accents like myself. So we can take their transcript and actually do some pretty cool things. We can take natural language processing algorithms to kind of read through with a computer, line by line, extracting key concepts from this. And we take those key concepts and they sort of form this mathematical structure of an idea. And we call that the meme-ome. And the meme-ome, you know, quite simply, is the mathematics that underlies an idea, and we can do some pretty interesting analysis with it, which I want to share with you now.
當然,如果你要做這樣的分析 你需要大量的數據 而這些數據蘊藏在一個偉大的發明中 -- 叫做 YouTube 我們就是上 Youtube 下載所有公開的信息 全部的評論、點擊率、誰看過這個影片 他們在哪裏看這個影片,他們在評論中說了甚麼 我們還可以用語音翻譯 把整篇講稿呈現出來 這招對於我這些有奇異口音的人也管用 得到了他們的講稿以後 我們就能做出各樣有趣的事 我們以自然語言運算法 用電腦,逐行逐行的去讀取講稿 再從中抽取講稿中的要旨 我們以這些要旨構成 這個包含不同構想的數學模型 我們稱之為 meme-ome (想法基因) 簡單來說,想法基因 就是藏在構想背後的數學 我們可以做一些相當有趣的分析 現在我想跟你們分享一下
So each idea has its own meme-ome, and each idea is unique with that, but of course, ideas, they borrow from each other, they kind of steal sometimes, and they certainly build on each other, and we can go through mathematically and take the meme-ome from one talk and compare it to the meme-ome from every other talk, and if there's a similarity between the two of them, we can create a link and represent that as a graph, just like Eric and I are connected.
每一個想法都有它的「想法基因」 而每一個想法都是獨一無二的 不過當然,有些想法是從別的地方借用過來的 有些時候是偷來的 所以它們會建立在其他的想法之上 我們可以以數學方法 從一個演講選取它的「想法基因」 再用它來跟其他演講的想法基因做比對 看看兩者之間是否有相似的地方 我們可以建立一個連繫,並以圖象顯示出來 這就像艾瑞克跟我一樣連接起來
So that's theory, that's great. Let's see how it works in actual practice. So what we've got here now is the global footprint of all the TEDx Talks over the last four years exploding out around the world from New York all the way down to little old New Zealand in the corner. And what we did on this is we analyzed the top 25 percent of these, and we started to see where the connections occurred, where they connected with each other. Cameron Russell talking about image and beauty connected over into Europe. We've got a bigger conversation about Israel and Palestine radiating outwards from the Middle East. And we've got something a little broader like big data with a truly global footprint reminiscent of a conversation that is happening everywhere.
這就是我們的理論,看似不錯吧 現在我們看看它實際運作吧 我們這裏有過去四年間 TEDx 演講在全球的足跡 它遍佈全世界 從紐約一直到在另一角落中小小的紐西蘭 我們所做的是分析當中的四分之一 之後我們就開始發現它們當中的連繫 以及它們從哪一個地方連接起來 卡梅倫.羅素講述影像與美學 把我們帶到歐洲 有關以色列及巴勒斯坦的演講其範圍更廣了些 從中東一直延伸開去 我們還有一個比較廣議題 像是世界各地都在討論的巨量資料(大數據) 讓人想起 到處都在發生的對話
So from this, we kind of run up against the limits of what we can actually do with a geographic projection, but luckily, computer technology allows us to go out into multidimensional space. So we can take in our network projection and apply a physics engine to this, and the similar talks kind of smash together, and the different ones fly apart, and what we're left with is something quite beautiful.
從這裏,我們就好像遇見了一個 平面的地域投影給我們設的限制 慶幸地,電腦科技容許我們 走進多維空間 所以我們可以理解我們的網路投射 透過物理引擎的運用 而相似的演講相似碰撞在一起 不同的演講則會遠離 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, they're linked if they share similar ideas, and that comes from a machine reading of entire talk transcripts, and then all these topics that pop out, they're not from tags and keywords. They come from the network structure of interconnected ideas. Keep going.
艾瑞克: 我想指出這裏每一點都代表一場演講 如果它個有相似的構想,它們就會連起來 這是一個機器讀取 所有演講稿 然後抽取當中的主旨所得出的結果 它們並非來自標籤及關鍵詞 它們實際上是來自互相關連的構想 所組成的網絡結構。你繼續吧
SG: Absolutely. So I got a little quick on that, but he's going to slow me down. We've got education connected to storytelling triangulated next to social media. You've got, of course, the human brain right next to healthcare, which you might expect, but also you've got video games, which is sort of adjacent, as those two spaces interface with each other.
肖恩: 絕對是。我比說的有點太快了 但他會降低我的節奏 我們可以將教育、故事敍述 與社交媒體連成一個三角形 你可以得出: 人腦就在醫療的旁邊 這或許也是你預期之內的 但你也會得出電玩遊戲... 很接近地 它們兩者之間有所互動
But I want to take you into one cluster that's particularly important to me, and that's the environment. And I want to kind of zoom in on that and see if we can get a little more resolution. So as we go in here, what we start to see, apply the physics engine again, we see what's one conversation is actually composed of many smaller ones. The structure starts to emerge where we see a kind of fractal behavior of the words and the language that we use to describe the things that are important to us all around this world. So you've got food economy and local food at the top, you've got greenhouse gases, solar and nuclear waste. What you're getting is a range of smaller conversations, each connected to each other through the ideas and the language they share, creating a broader concept of the environment. And of course, from here, we can go and zoom in and see, well, what are young people looking at? And they're looking at energy technology and nuclear fusion. This is their kind of resonance for the conversation around the environment. If we split along gender lines, we can see females resonating heavily with food economy, but also out there in hope and optimism.
不過我希望帶你們到一組主題 這對我來說是一個特別的群組,這是「環境」 而我又想再放大這個部分 看看我們可否再多提高一點它的解像度 當我們進入這個群組時,我們可以看到 再一次運用我們的物理引擎 我們可以看到一場演講 實際上是由很多較小規模的對話交幟而成 這個組織開始顯露出來了 我們可以看到一些 一些我們用來形容在我們周圍、 對我們很重要的詞語及語言 有不規則的行為 你可以看到食物經濟學及本土食物在最頂層 你也可以看到溫室氣體、太陽能、核廢料 你可以得到的是一系列較小規模的對話 每一個都以它的構思 和它們的共通語言與其他對話連在一起 最後構成一個有關於環境,但更寛更廣的想法 當然,從這裏,我們可以 繼續放大及看看,究竟年輕人在看甚麼呢? 原來他們在看有關能源科技及核聚變的資訊 這是他們對有關環境的對話 所產生出的共鳴 如果我們以性別劃分 我們可以看到女性對於食物經濟學、以及 「希望與樂觀」有較大的共鳴
And so there's a lot of exciting stuff we can do here, and I'll throw to Eric for the next part.
這裏有很多令人興奮的東西可以做 而我會將以下的部分交給艾瑞克
EB: Yeah, I mean, just to point out here, you cannot get this kind of perspective from a simple tag search on YouTube. Let's now zoom back out to the entire global conversation out of environment, and look at all the talks together. Now often, when we're faced with this amount of content, we do a couple of things to simplify it. We might just say, well, what are the most popular talks out there? And a few rise to the surface. There's a talk about gratitude. There's another one about personal health and nutrition. And of course, there's got to be one about porn, right? And so then we might say, well, gratitude, that was last year. What's trending now? What's the popular talk now? And we can see that the new, emerging, top trending topic is about digital privacy.
艾瑞克: 是的,我認為,在指說明 你無法得到這些觀點 從 YouTube 中簡單的標籤搜尋中 現在回到全球性的對話 將全部的演講一同觀察 很多時,當我們面對這樣龐大的內容 我們會用一系列的方法去簡化它 我們或許會說,譬如 哪一個是最受歡迎的演講呢? 有數個演講浮到表面來 這裏有一個演講關於感恩 這裏有另一個演講關於個人健康與營養 當然,有另一個演講關於色情行業,對嗎? 接着,我們會說,好,感恩,那是去年的演講 那現在的趨勢是甚麼呢? 哪一個是現在最流行的演講呢? 我們可以看到這個新的、正冒起來的、最流行的題目 是有關於數位隱私
So this is great. It simplifies things. But there's so much creative content that's just buried at the bottom. And I hate that. How do we bubble stuff up to the surface that's maybe really creative and interesting? Well, we can go back to the network structure of ideas to do that. Remember, it's that network structure that is creating these emergent topics, and let's say we could take two of them, like cities and genetics, and say, well, are there any talks that creatively bridge these two really different disciplines. And that's -- Essentially, this kind of creative remix is one of the hallmarks of innovation. Well here's one by Jessica Green about the microbial ecology of buildings. It's literally defining a new field. And we could go back to those topics and say, well, what talks are central to those conversations? In the cities cluster, one of the most central was one by Mitch Joachim about ecological cities, and in the genetics cluster, we have a talk about synthetic biology by Craig Venter. These are talks that are linking many talks within their discipline. We could go the other direction and say, well, what are talks that are broadly synthesizing a lot of different kinds of fields. We used a measure of ecological diversity to get this. Like, a talk by Steven Pinker on the history of violence, very synthetic.
這是極好的。這簡化了不少事情 但這裏有很多具創意的內容 被埋在最底層 我討厭這種感覺。我們怎樣可以令這些可能是具創意 及有趣的東西浮到表面呢? 我們可以回到那個包含不同構思的網絡 去尋找它們 記住,這就是那個製造出不同的、 處於萌芽階段的題目的網絡 不如我們拿當中的兩個題目 像是城市和基因,再看看有哪些演講 很有想像力的把這兩個截然不同的科目連在一起 這個 -- 實際上,這種具創新性的重組 就是創新的特徵之一 這裏有一個謝西嘉.格林主講 有關建築物裏的微生物生態學的演講 她的確是在界定一個新的領域 我們可以回到這些主題,並問問 這些談話間核心的演講是什麼? 在城市這個群組裏,一個最中心的演講 是由米茨.祖詹主講,主題是主張生態保護的城市 在基因研究這個群組 我們有一個克萊格·凡特主講、關於人工生物學的演講 這些演講都連繫着很多在相同範疇的其他演講 我們可以向另一個方向出發 問問哪些演講是廣泛綜合 許多不同的領域 我們用一個生態學多樣性的量度單位去看看 一個史迪芬.平克的演講、關於暴力的歷史 就很有綜合性
And then, of course, there are talks that are so unique they're kind of out in the stratosphere, in their own special place, and we call that the Colleen Flanagan index. And if you don't know Colleen, she's an artist, and I asked her, "Well, what's it like out there in the stratosphere of our idea space?" And apparently it smells like bacon. I wouldn't know. So we're using these network motifs to find talks that are unique, ones that are creatively synthesizing a lot of different fields, ones that are central to their topic, and ones that are really creatively bridging disparate fields. Okay? We never would have found those with our obsession with what's trending now. And all of this comes from the architecture of complexity, or the patterns of how things are connected.
當然,也有些演講是很獨特的 它們就是遠離平流層,在它們自己的一個特別位置 我們叫它做「歌蓮.費拿根系數」 如果你不認識歌蓮,她是一個藝術家 當我問她: 「唔,在平流層裏 我們的想法看似甚麼呢?」 顯然地,它的嗅味像一塊煙肉 我不會知道 所以我們就用這些網絡中心思想 去尋找獨特的演講 有些是創意地結合不同範疇 有些是在它們的領域中具有代表性 以及有些是相當創意去連繫截然不同範疇的演講 可以嗎? 即使我們着了魔一樣去找尋現時最流行的演講 也未必會找到它們 它們隱藏在複雜的結構裏 或是事物間如何連結的模式
SG: So that's exactly right. We've got ourselves in a world that's massively complex, and we've been using algorithms to kind of filter it down so we can navigate through it. And those algorithms, whilst being kind of useful, are also very, very narrow, and we can do better than that, because we can realize that their complexity is not random. It has mathematical structure, and we can use that mathematical structure to go and explore things like the world of ideas to see what's being said, to see what's not being said, and to be a little bit more human and, hopefully, a little smarter.
肖恩: 這完全是對的 我們就在一個 無比複雜的世界中 我們用一系列的運算法去拆解它 以致我們可以在中間游走 這些運算法,雖然是很有用 但它們仍然是不夠全面的,我們定當能夠做得更好 因為我們發現這些複雜性並不是偶然性的 它有一個數學結構 我們可以用這個數學結構 去探索世界上不同的構思 去看看別人說過甚麼,甚麼沒有被提出過 再去做些更人性化的事 亦希望變得聰明一些
Thank you.
謝謝
(Applause)
(掌聲)