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TED2013

Eric Berlow and Sean Gourley: Mapping ideas worth spreading

エリック・バーロウとショーン・ゴーリー: 広げる価値のあるアイデアの地図作り

February 28, 2013

2万4千ものアイデアとはどんなものでしょうか。生態学者のエリック・バーローと物理学者のショーン・ゴーリーは、世界中のTEDxトークの記録にアルゴリズムを適用し、アイデアの地図を私たちに示しながら、アイデアがどのようにグローバルに繋がっているのかを示します。

Sean Gourley - Physicist and military theorist
Sean Gourley, trained as a physicist, has turned his scientific mind to analyzing data about a messier topic: modern war and conflict. He is a TED Fellow. Full bio

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Double-click the English subtitles below to play the video.
Eric Berlow: I'm an ecologist, and Sean's a physicist,
エリック・バーロウ:私は生態学者で
ショーンは物理学者です
00:12
and we both study complex networks.
私達は複雑なネットワークを
研究しています
00:15
And we met a couple years ago when we discovered
私たちが数年前出会った時
2人共 戦争の生態について
00:17
that we had both given a short TED Talk
私たちが数年前出会った時
2人共 戦争の生態について
00:19
about the ecology of war,
TEDで話した事を知り
00:21
and we realized that we were connected
会う前から同じ考えで
00:23
by the ideas we shared before we ever met.
通じ合っている事が解りました
00:25
And then we thought, you know, there are thousands
TEDxもそうですが
00:27
of other talks out there, especially TEDx Talks,
世界中に何千もの
00:29
that are popping up all over the world.
トークメディアが出現していますが
00:31
How are they connected,
それ等の繋がりや
00:33
and what does that global conversation look like?
グローバルな会話とは
どんなものでしょう
00:34
So Sean's going to tell you a little bit about how we did that.
ショーンが私たちの研究を
簡単にお話しします
00:36
Sean Gourley: Exactly. So we took 24,000 TEDx Talks
ショーン・ゴーリー:
2万4千ものTEDxトークを
00:39
from around the world, 147 different countries,
147カ国から集めました
00:43
and we took these talks and we wanted to find
そして これらのトークの背景にある
00:46
the mathematical structures that underly
潜在的なアイデアの
数学的構造を見つけ
00:48
the ideas behind them.
潜在的なアイデアの
数学的構造を見つけ
00:50
And we wanted to do that so we could see how
それ等のトークがどうお互い
00:52
they connected with each other.
繋がり合っているのか
知りたかったのです
00:53
And so, of course, if you're going to do this kind of stuff,
勿論 それには
00:55
you need a lot of data.
多くのデータが必要です
00:57
So the data that you've got is a great thing called YouTube,
その素晴らしいデーターとは
YouTubeです
00:58
and we can go down and basically pull
基本的にYouTubeから
公開情報を取り出す事が出来ます
01:02
all the open information from YouTube,
基本的にYouTubeから
公開情報を取り出す事が出来ます
01:03
all the comments, all the views, who's watching it,
コメントや再生回数
どこで誰が見ているか
01:06
where are they watching it, what are they saying in the comments.
コメントの内容も解ります
01:08
But we can also pull up, using speech-to-text translation,
その上 音声テキスト変換を使い
01:11
we can pull the entire transcript,
トークの原稿全体を
取り出す事が出来ます
01:14
and that works even for people with kind of funny accents like myself.
私の様な訛のあっても
大丈夫です
01:16
So we can take their transcript
そんな原稿を取り出し
01:19
and actually do some pretty cool things.
すごい事が出来るのです
01:21
We can take natural language processing algorithms
自然言語処理アルゴリズムをつかって
01:23
to kind of read through with a computer, line by line,
鍵となる考えを一行ごとに
01:25
extracting key concepts from this.
コンピュータで読み込みます
01:28
And we take those key concepts and they sort of form
そして鍵となるコンセプトを取り出し
01:30
this mathematical structure of an idea.
アイデアの数学的構造の様な形にします
01:33
And we call that the meme-ome.
それを私達は「ミーモム」と呼びます
01:36
And the meme-ome, you know, quite simply,
「ミーモム」は簡単に言うと
01:38
is the mathematics that underlies an idea,
あるアイデアが元になった数学なのです
01:40
and we can do some pretty interesting analysis with it,
これを使って
とても面白い分析ができます
01:43
which I want to share with you now.
それをここでお見せしたいのです
01:44
So each idea has its own meme-ome,
1つ1つのアイデアに
「ミーモム」があり
01:46
and each idea is unique with that,
それはそれぞれユニークですが
01:49
but of course, ideas, they borrow from each other,
勿論お互いアイデアを借り合い
01:51
they kind of steal sometimes,
時にはアイデアを盗んだり
01:53
and they certainly build on each other,
確かに相互関係にあります
01:54
and we can go through mathematically
そこで数学的に
01:56
and take the meme-ome from one talk
一つのトークから「ミーモム」をとり
01:58
and compare it to the meme-ome from every other talk,
他の個々のトークから
取ったものと比べます
02:00
and if there's a similarity between the two of them,
もし類似点があれば
02:02
we can create a link and represent that as a graph,
リンクで繋ぎ
グラフに表します
02:04
just like Eric and I are connected.
私とエリックが
繋がった様にです
02:07
So that's theory, that's great.
それが理論です それでは
02:10
Let's see how it works in actual practice.
実際にどんな働きをするのか
見てみましょう
02:11
So what we've got here now is the global footprint
ここにあるのは過去4年間の
02:14
of all the TEDx Talks over the last four years
TEDxトークの足跡です
02:16
exploding out around the world
世界中に爆発的に広がってます
02:19
from New York all the way down to little old New Zealand in the corner.
ニューヨークから
ずっとニュージーランドまで
02:20
And what we did on this is we analyzed the top 25 percent of these,
これらのトップ25%を分析し
02:24
and we started to see where the connections occurred,
その繋がりの起点から
02:27
where they connected with each other.
見ていきました
02:30
Cameron Russell talking about image and beauty
イメージと美について話している
キャメロンとラッセルは
02:31
connected over into Europe.
ヨーロッパで繋がりました
02:33
We've got a bigger conversation about Israel and Palestine
会話は中東の話から発し
イスラエルとパレスチナの
02:35
radiating outwards from the Middle East.
にぎやかな会話に広がりました
02:37
And we've got something a little broader
そしてもう少し一般的な
雑談とも思えるような
02:40
like big data with a truly global footprint
そしてもう少し一般的な
雑談とも思えるような
02:41
reminiscent of a conversation
ビッグデータ的な
ものも得られました
02:43
that is happening everywhere.
真にグローバルな軌跡です
02:45
So from this, we kind of run up against the limits
ここで私達がぶつかったのは
02:47
of what we can actually do with a geographic projection,
地図的表現の限界です
02:49
but luckily, computer technology allows us to go out
でも幸運にもコンピュータ技術で
02:52
into multidimensional space.
多次元の空間を扱えます
02:54
So we can take in our network projection
ネットワーク表現を使い
02:55
and apply a physics engine to this,
これに物理演算エンジンを適用します
02:57
and the similar talks kind of smash together,
同じ様なトークはお互いぶつかり合い
02:59
and the different ones fly apart,
異なるものは飛び離れ
03:01
and what we're left with is something quite beautiful.
本当に美しいイメージが残ります
03:03
EB: So I want to just point out here that every node is a talk,
ここで大切なのは
個々のノードはトークを表していて
03:05
they're linked if they share similar ideas,
同じ様なアイデアは結ばれます
03:08
and that comes from a machine reading
全てのトークスクリプトを
03:11
of entire talk transcripts,
機械が読んで作っています
03:13
and then all these topics that pop out,
現れて来るトピックは
タグやキーワードから
03:15
they're not from tags and keywords.
作ったものではありません
03:17
They come from the network structure
関連し合うアイデアの
03:19
of interconnected ideas. Keep going.
ネットワーク構成から
生まれたものです 続けて下さい
03:21
SG: Absolutely. So I got a little quick on that,
その通り
先を急ぎ過ぎたので
03:23
but he's going to slow me down.
彼が補足してくれました
03:25
We've got education connected to storytelling
「教育」と「語り聞かせ」が
「ソーシャルメディア」と
03:26
triangulated next to social media.
三画に繋がっています
03:28
You've got, of course, the human brain right next to healthcare,
「医療」のすぐ側は
勿論 「頭脳」です
03:30
which you might expect,
これは予想できますが
03:32
but also you've got video games, which is sort of adjacent,
この2つのスペースが繋ぎ合う
03:34
as those two spaces interface with each other.
割と近くに「ビデオゲーム」があるのです
03:36
But I want to take you into one cluster
私が特に大切に思う
03:39
that's particularly important to me, and that's the environment.
「環境」の塊をお見せしましょう
03:40
And I want to kind of zoom in on that
もっと解像度を上げられないか
03:43
and see if we can get a little more resolution.
ズームインしてみます
03:45
So as we go in here, what we start to see,
ここに入って物理演算エンジンを使い
03:47
apply the physics engine again,
現れて来たのは・・・
03:49
we see what's one conversation
この1つの会話は
03:51
is actually composed of many smaller ones.
いくつかの小さなものの
集まりだとわかります
03:53
The structure starts to emerge
この構造からわかるのは
03:55
where we see a kind of fractal behavior
私たちが大切なトピックを
03:57
of the words and the language that we use
表すのに使う単語や言葉の
03:59
to describe the things that are important to us
フラクタル的な挙動です
04:01
all around this world.
フラクタル的な挙動です
04:03
So you've got food economy and local food at the top,
ここでは「食料経済学」と
「地元の食材」が上部にあり
04:04
you've got greenhouse gases, solar and nuclear waste.
「温室効果ガス」や
「太陽光発電」に「核廃棄物」もあります
04:06
What you're getting is a range of smaller conversations,
小規模な会話が
04:09
each connected to each other through the ideas
共通の言葉やアイデアで
04:12
and the language they share,
互いに結びつき
04:14
creating a broader concept of the environment.
環境に関する
より大きな考えを築いています
04:15
And of course, from here, we can go
勿論ここからズームインすれば
04:18
and zoom in and see, well, what are young people looking at?
若者が何を見ているか解ります
04:19
And they're looking at energy technology and nuclear fusion.
彼らは「核融合」や
「エネルギー技術」を見ています
04:23
This is their kind of resonance
言わばこれらが彼らの
04:25
for the conversation around the environment.
環境についての会話と
共鳴するトピックなのです
04:27
If we split along gender lines,
性別に分けてみると
04:29
we can see females resonating heavily
女性は「食糧経済学」に
とても同調しており
04:31
with food economy, but also out there in hope and optimism.
また そこに「希望と楽観」も
見てとれます
04:33
And so there's a lot of exciting stuff we can do here,
いろんな面白いことができるんです
04:37
and I'll throw to Eric for the next part.
次はエリックにお願いしましょう
04:39
EB: Yeah, I mean, just to point out here,
ええ ここで言いたいのは
04:41
you cannot get this kind of perspective
こうした観点はYouTubeの
04:43
from a simple tag search on YouTube.
単なるタグ検索だけでは
得られないと言う事です
04:44
Let's now zoom back out to the entire global conversation
「環境」からグローバルな
話題全体に
04:47
out of environment, and look at all the talks together.
ズームアウトし
トークを一望してみます
04:52
Now often, when we're faced with this amount of content,
通常 これ程の量の
情報に遭遇すると
04:54
we do a couple of things to simplify it.
単純化する為に
いくつかの方法を取ります
04:57
We might just say, well,
こう検索するかもしれません
05:00
what are the most popular talks out there?
今一番人気のあるトークは?
05:01
And a few rise to the surface.
すると数個が現れてきます
05:04
There's a talk about gratitude.
感謝に関するトーク
05:05
There's another one about personal health and nutrition.
健康や栄養に関するトーク
05:07
And of course, there's got to be one about porn, right?
そして勿論ポルノについてですね
05:10
And so then we might say, well, gratitude, that was last year.
去年は感謝に
関するものでしたが
05:13
What's trending now? What's the popular talk now?
今年はどんなトークが
人気があるか?と見てみると
05:16
And we can see that the new, emerging, top trending topic
新しい人気トップの
候補が現れます
05:19
is about digital privacy.
インターネット上の
プライバシーについてです
05:22
So this is great. It simplifies things.
いいですね 
解りやすいです
05:25
But there's so much creative content
でも このような検索に
ひっかからない
05:27
that's just buried at the bottom.
もっと創造的な
内容のものもあるんです
05:28
And I hate that. How do we bubble stuff up to the surface
この様なものを
どうやって表面に持ってくるか?
05:30
that's maybe really creative and interesting?
この様なものを
どうやって表面に持ってくるか?
05:34
Well, we can go back to the network structure of ideas
アイデアのネットワーク構造に
戻れば
05:36
to do that.
これが可能です
05:39
Remember, it's that network structure
ここに現れるトピックを
作っているのは
05:40
that is creating these emergent topics,
ネットワーク構造だと
お話ししましたが
05:43
and let's say we could take two of them,
ここから2つを選んで --
05:45
like cities and genetics, and say, well, are there any talks
例えば「都市」と「遺伝学」を選び
05:46
that creatively bridge these two really different disciplines.
この全く異なる分野を
うまく繋ぐトークはあるか探します
05:49
And that's -- Essentially, this kind of creative remix
この創造的リミックスの様なものが
05:52
is one of the hallmarks of innovation.
イノベーションの特徴とも言えます
05:54
Well here's one by Jessica Green
これはジェシカ・グリーンのもので
05:56
about the microbial ecology of buildings.
建物の微生物生態学についです
05:58
It's literally defining a new field.
全く新しい分野を築いています
06:00
And we could go back to those topics and say, well,
これら2つのトピックに戻り
06:02
what talks are central to those conversations?
どのトークが 各々のトピックの
中心にあるかもわかります
06:04
In the cities cluster, one of the most central
都市の塊で最も中心にあるのは
06:07
was one by Mitch Joachim about ecological cities,
エコロジー都市についての
ミッチ・ジョアキムのもので
06:09
and in the genetics cluster,
「遺伝学」の塊の中心には
06:13
we have a talk about synthetic biology by Craig Venter.
クレイグ・ベンターの
合成生物学のトークがあります
06:14
These are talks that are linking many talks within their discipline.
これらはそれぞれの分野の中で
多くのトークを繋げています
06:18
We could go the other direction and say, well,
反対の方に行ってみましょう
06:21
what are talks that are broadly synthesizing
あらゆる分野を広く
06:23
a lot of different kinds of fields.
総合したトークはどうでしょう
06:25
We used a measure of ecological diversity to get this.
これには生態的多様性から見ました
06:26
Like, a talk by Steven Pinker on the history of violence,
例えば 暴力の歴史についての
スティーブ・ピンカーのトークは
06:29
very synthetic.
とても総合的です
06:32
And then, of course, there are talks that are so unique
もちろん 大変ユニークで
06:33
they're kind of out in the stratosphere, in their own special place,
はるか彼方の独自の場所に
属するトークもあります
06:35
and we call that the Colleen Flanagan index.
コリーン・フラナガン指数と
私達は呼びます
06:38
And if you don't know Colleen, she's an artist,
ご存知でしょうか
彼女はアーティストですが
06:41
and I asked her, "Well, what's it like out there
「アイデアの世界の果て」は
06:44
in the stratosphere of our idea space?"
どんな所か彼女に
尋ねてみました
06:45
And apparently it smells like bacon.
ベーコンの様な匂いが
する場所だそうです
06:47
I wouldn't know.
私には解りませんが
06:50
So we're using these network motifs
このようなネットワークの
パターンを使って
06:52
to find talks that are unique,
ユニークなトークや
06:54
ones that are creatively synthesizing a lot of different fields,
様々な分野を
うまく統合したもの
06:55
ones that are central to their topic,
トピックの中心になっているもの
06:58
and ones that are really creatively bridging disparate fields.
完全に異なる分野を
うまく繋げているものが探せます
07:00
Okay? We never would have found those with our obsession
人気のあるものだけに
注目していたら
07:03
with what's trending now.
このようなものは
見つからなかったでしょう
07:05
And all of this comes from the architecture of complexity,
これ等全ては複雑な構造や
07:07
or the patterns of how things are connected.
繋がり方のパターンから
探し出されたものです
07:10
SG: So that's exactly right.
全くその通りです
07:13
We've got ourselves in a world
私たちは非常に複雑な世界に
07:15
that's massively complex,
生きるようになり
07:17
and we've been using algorithms to kind of filter it down
様々なアルゴリズムを使って
世界を簡素化して
07:19
so we can navigate through it.
対応しています
07:22
And those algorithms, whilst being kind of useful,
これらのアルゴリズムは
便利ですが
07:24
are also very, very narrow, and we can do better than that,
限られたものなので
もっと良い方法があるはずです
07:26
because we can realize that their complexity is not random.
複雑さは無秩序ではなく
07:30
It has mathematical structure,
数学的構造があると解れば
07:33
and we can use that mathematical structure
その考えを使い
07:34
to go and explore things like the world of ideas
アイデアの世界を探り
07:36
to see what's being said, to see what's not being said,
何が語られ
何が語られていないかを知り
07:38
and to be a little bit more human
もう少し人間らしく生き
07:41
and, hopefully, a little smarter.
願わくば少し賢くもなるのです
07:43
Thank you.
有り難うございました
07:45
(Applause)
(拍手)
07:46
Translator:Reiko O Bovee
Reviewer:Wataru Terada

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Eric Berlow - Ecologist
TED Senior Fellow Eric Berlow studies ecology and networks, exposing the interconnectedness of our ecosystems with climate change, government, corporations and more.

Why you should listen

Eric Berlow is an ecologist and network scientist who specializes in not specializing. A TED Senior Fellow, Berlow is recognized for his research on food webs and ecological networks and for creative approaches to complex problems. He was the founding director of the University of California's first environmental science center inside Yosemite National Park, where he continues to develop data-driven approaches to managing natural ecosystems. 

In 2012 Berlow founded Vibrant Data Labs, which builds tools to use data for social good. Berlow's current projects range from helping spark an egalitarian personal data economy to protecting endangered amphibians in Yosemite to crowd-sourcing novel insights about human creativity. Berlow holds a Ph.D. from Oregon State University in marine ecology.

 

 

Sean Gourley - Physicist and military theorist
Sean Gourley, trained as a physicist, has turned his scientific mind to analyzing data about a messier topic: modern war and conflict. He is a TED Fellow.

Why you should listen

Sean Gourley's twin passions are physics (working on nanoscale blue-light lasers and self-assembled quantum nanowires) and politics (he once ran for a national elected office back home in New Zealand).

A Rhodes scholar, he's spent the past five years working at Oxford on complex adaptive systems and collective intelligent systems -- basically, using data to understand the nature of human conflict. As he puts it, "This research has taken me all over the world from the Pentagon, to the House of Lords, the United Nations and most recently to Iraq". Originally from New Zealand, he now lives in San Francisco, where he is the co-founder and CTO of Quid which is building a global intelligence platform. He's a 2009 TED Fellow.

In December 2009, Gourley and his team's research was published in the scientific journal Nature. He is co-founder and CTO of Quid.

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