TED2013
Eric Berlow and Sean Gourley: Mapping ideas worth spreading
艾瑞克．伯勞與肖恩．古爾利: 繪出值得宣揚的想法地圖
Filmed:
Readability: 4.3
1,131,373 views
二萬四千個想法究竟看似甚麼? 生態學家艾瑞克．伯勞及肖恩．古爾利把演算法套用到所有的 TEDx 演講中，帶我們往一個充滿創發性的旅程，為我們展示全球的想法是怎樣連接起來的。
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. Full bioSean 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
TED Senior Fellow Eric Berlow studies ecology and networks, exposing the interconnectedness of our ecosystems with climate change, government, corporations and more. Full bioSean 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
Double-click the English transcript below to play the video.
00:12
Eric Berlow: I'm an ecologist, and Sean's a physicist,
0
562
3061
艾瑞克．伯勞: 我是生態學家 肖恩是物理學家
00:15
and we both study complex networks.
1
3623
2108
我們都研究複雜的網絡
00:17
And we met a couple years ago when we discovered
2
5731
1835
幾年前認識對方是因為
00:19
that we had both given a short TED Talk
3
7566
2000
我們都在 TED 這個平台上
00:21
about the ecology of war,
4
9566
2303
發表過有關生態大戰的演講
00:23
and we realized that we were connected
5
11869
1447
這才發現我們還沒見面之前
00:25
by the ideas we shared before we ever met.
6
13316
2818
就已經因我們分享的構想而有關係
00:28
And then we thought, you know, there are thousands
7
16134
1556
然後我們就想: 世界上有
00:29
of other talks out there, especially TEDx Talks,
8
17690
2114
這麼多的演講，尤其是 TEDx 的演講
00:31
that are popping up all over the world.
9
19804
2211
在全球各地如雨後春筍般湧現
00:34
How are they connected,
10
22015
923
究竟他們是如何相連
00:34
and what does that global conversation look like?
11
22938
2010
這個全球性對話像似什麼呢?
00:36
So Sean's going to tell you a little bit about how we did that.
12
24948
2810
現在肖恩將會為你們講解我們的做法
00:39
Sean Gourley: Exactly. So we took 24,000 TEDx Talks
13
27758
3767
肖恩．古爾利: 沒錯。我們從全球一百四十七個國家
00:43
from around the world, 147 different countries,
14
31525
3046
選取了二萬四千場 TEDx 演講
00:46
and we took these talks and we wanted to find
15
34571
2123
我們想要找出
00:48
the mathematical structures that underly
16
36694
2040
這些蘊藏在演講背後
00:50
the ideas behind them.
17
38734
1722
藏在構想背後的數學模型結構
00:52
And we wanted to do that so we could see how
18
40456
1370
這樣一來我們可以看出
00:53
they connected with each other.
19
41826
2053
構想與構想之間是如何相連的
00:55
And so, of course, if you're going to do this kind of stuff,
20
43879
1676
當然，如果你要做這樣的分析
00:57
you need a lot of data.
21
45555
956
你需要大量的數據
00:58
So the data that you've got is a great thing called YouTube,
22
46511
3686
而這些數據蘊藏在一個偉大的發明中 -- 叫做 YouTube
01:02
and we can go down and basically pull
23
50197
1768
我們就是上 Youtube
01:03
all the open information from YouTube,
24
51965
2267
下載所有公開的信息
01:06
all the comments, all the views, who's watching it,
25
54232
2349
全部的評論、點擊率、誰看過這個影片
01:08
where are they watching it, what are they saying in the comments.
26
56581
2779
他們在哪裏看這個影片，他們在評論中說了甚麼
01:11
But we can also pull up, using speech-to-text translation,
27
59360
3292
我們還可以用語音翻譯
01:14
we can pull the entire transcript,
28
62652
2128
把整篇講稿呈現出來
01:16
and that works even for people with kind of funny accents like myself.
29
64780
2680
這招對於我這些有奇異口音的人也管用
01:19
So we can take their transcript
30
67460
2106
得到了他們的講稿以後
01:21
and actually do some pretty cool things.
31
69566
2098
我們就能做出各樣有趣的事
01:23
We can take natural language processing algorithms
32
71664
2160
我們以自然語言運算法
01:25
to kind of read through with a computer, line by line,
33
73824
2629
用電腦，逐行逐行的去讀取講稿
01:28
extracting key concepts from this.
34
76453
2359
再從中抽取講稿中的要旨
01:30
And we take those key concepts and they sort of form
35
78812
2525
我們以這些要旨構成
01:33
this mathematical structure of an idea.
36
81337
3565
這個包含不同構想的數學模型
01:36
And we call that the meme-ome.
37
84902
1757
我們稱之為 meme-ome (想法基因)
01:38
And the meme-ome, you know, quite simply,
38
86659
2151
簡單來說，想法基因
01:40
is the mathematics that underlies an idea,
39
88810
2426
就是藏在構想背後的數學
01:43
and we can do some pretty interesting analysis with it,
40
91236
1932
我們可以做一些相當有趣的分析
01:45
which I want to share with you now.
41
93168
1981
現在我想跟你們分享一下
01:47
So each idea has its own meme-ome,
42
95149
2190
每一個想法都有它的「想法基因」
01:49
and each idea is unique with that,
43
97339
1951
而每一個想法都是獨一無二的
01:51
but of course, ideas, they borrow from each other,
44
99290
2488
不過當然，有些想法是從別的地方借用過來的
01:53
they kind of steal sometimes,
45
101778
1184
有些時候是偷來的
01:54
and they certainly build on each other,
46
102962
1827
所以它們會建立在其他的想法之上
01:56
and we can go through mathematically
47
104789
1616
我們可以以數學方法
01:58
and take the meme-ome from one talk
48
106405
1840
從一個演講選取它的「想法基因」
02:00
and compare it to the meme-ome from every other talk,
49
108245
2454
再用它來跟其他演講的想法基因做比對
02:02
and if there's a similarity between the two of them,
50
110699
1973
看看兩者之間是否有相似的地方
02:04
we can create a link and represent that as a graph,
51
112672
3250
我們可以建立一個連繫，並以圖象顯示出來
02:07
just like Eric and I are connected.
52
115922
2394
這就像艾瑞克跟我一樣連接起來
02:10
So that's theory, that's great.
53
118316
1394
這就是我們的理論，看似不錯吧
02:11
Let's see how it works in actual practice.
54
119710
2526
現在我們看看它實際運作吧
02:14
So what we've got here now is the global footprint
55
122236
2788
我們這裏有過去四年間
02:17
of all the TEDx Talks over the last four years
56
125024
2293
TEDx 演講在全球的足跡
02:19
exploding out around the world
57
127317
1550
它遍佈全世界
02:20
from New York all the way down to little old New Zealand in the corner.
58
128867
3329
從紐約一直到在另一角落中小小的紐西蘭
02:24
And what we did on this is we analyzed the top 25 percent of these,
59
132196
3835
我們所做的是分析當中的四分之一
02:28
and we started to see where the connections occurred,
60
136031
2534
之後我們就開始發現它們當中的連繫
02:30
where they connected with each other.
61
138565
1537
以及它們從哪一個地方連接起來
02:32
Cameron Russell talking about image and beauty
62
140102
1874
卡梅倫．羅素講述影像與美學
02:33
connected over into Europe.
63
141976
1575
把我們帶到歐洲
02:35
We've got a bigger conversation about Israel and Palestine
64
143551
2412
有關以色列及巴勒斯坦的演講其範圍更廣了些
02:37
radiating outwards from the Middle East.
65
145963
2255
從中東一直延伸開去
02:40
And we've got something a little broader
66
148218
1298
我們還有一個比較廣議題
02:41
like big data with a truly global footprint
67
149516
2156
像是世界各地都在討論的巨量資料(大數據)
02:43
reminiscent of a conversation
68
151672
2179
讓人想起
02:45
that is happening everywhere.
69
153851
2016
到處都在發生的對話
02:47
So from this, we kind of run up against the limits
70
155867
2173
從這裏，我們就好像遇見了一個
02:50
of what we can actually do with a geographic projection,
71
158040
2530
平面的地域投影給我們設的限制
02:52
but luckily, computer technology allows us to go out
72
160570
2052
慶幸地，電腦科技容許我們
02:54
into multidimensional space.
73
162622
1546
走進多維空間
02:56
So we can take in our network projection
74
164168
1875
所以我們可以理解我們的網路投射
02:58
and apply a physics engine to this,
75
166043
1750
透過物理引擎的運用
02:59
and the similar talks kind of smash together,
76
167793
1885
而相似的演講相似碰撞在一起
03:01
and the different ones fly apart,
77
169678
2004
不同的演講則會遠離
03:03
and what we're left with is something quite beautiful.
78
171682
2072
我們最後得出這樣漂亮的結果
03:05
EB: So I want to just point out here that every node is a talk,
79
173754
2957
艾瑞克: 我想指出這裏每一點都代表一場演講
03:08
they're linked if they share similar ideas,
80
176711
2589
如果它個有相似的構想，它們就會連起來
03:11
and that comes from a machine reading
81
179300
2084
這是一個機器讀取
03:13
of entire talk transcripts,
82
181384
2067
所有演講稿
03:15
and then all these topics that pop out,
83
183451
2231
然後抽取當中的主旨所得出的結果
03:17
they're not from tags and keywords.
84
185682
1790
它們並非來自標籤及關鍵詞
03:19
They come from the network structure
85
187472
1725
它們實際上是來自互相關連的構想
03:21
of interconnected ideas. Keep going.
86
189197
2168
所組成的網絡結構。你繼續吧
03:23
SG: Absolutely. So I got a little quick on that,
87
191365
2022
肖恩: 絕對是。我比說的有點太快了
03:25
but he's going to slow me down.
88
193387
1475
但他會降低我的節奏
03:26
We've got education connected to storytelling
89
194862
2034
我們可以將教育、故事敍述
03:28
triangulated next to social media.
90
196896
1643
與社交媒體連成一個三角形
03:30
You've got, of course, the human brain right next to healthcare,
91
198539
2475
你可以得出: 人腦就在醫療的旁邊
03:33
which you might expect,
92
201014
1386
這或許也是你預期之內的
03:34
but also you've got video games, which is sort of adjacent,
93
202400
2395
但你也會得出電玩遊戲... 很接近地
03:36
as those two spaces interface with each other.
94
204795
2740
它們兩者之間有所互動
03:39
But I want to take you into one cluster
95
207535
1535
不過我希望帶你們到一組主題
03:41
that's particularly important to me, and that's the environment.
96
209070
2868
這對我來說是一個特別的群組，這是「環境」
03:43
And I want to kind of zoom in on that
97
211938
1493
而我又想再放大這個部分
03:45
and see if we can get a little more resolution.
98
213431
2363
看看我們可否再多提高一點它的解像度
03:47
So as we go in here, what we start to see,
99
215794
2347
當我們進入這個群組時，我們可以看到
03:50
apply the physics engine again,
100
218141
1504
再一次運用我們的物理引擎
03:51
we see what's one conversation
101
219645
1676
我們可以看到一場演講
03:53
is actually composed of many smaller ones.
102
221321
2560
實際上是由很多較小規模的對話交幟而成
03:55
The structure starts to emerge
103
223881
1929
這個組織開始顯露出來了
03:57
where we see a kind of fractal behavior
104
225810
2070
我們可以看到一些
03:59
of the words and the language that we use
105
227880
1619
一些我們用來形容在我們周圍、
04:01
to describe the things that are important to us
106
229499
1702
對我們很重要的詞語及語言
04:03
all around this world.
107
231201
1433
有不規則的行為
04:04
So you've got food economy and local food at the top,
108
232634
2332
你可以看到食物經濟學及本土食物在最頂層
04:06
you've got greenhouse gases, solar and nuclear waste.
109
234966
2719
你也可以看到溫室氣體、太陽能、核廢料
04:09
What you're getting is a range of smaller conversations,
110
237685
2631
你可以得到的是一系列較小規模的對話
04:12
each connected to each other through the ideas
111
240316
2301
每一個都以它的構思
04:14
and the language they share,
112
242617
1301
和它們的共通語言與其他對話連在一起
04:15
creating a broader concept of the environment.
113
243918
2450
最後構成一個有關於環境，但更寛更廣的想法
04:18
And of course, from here, we can go
114
246368
1532
當然，從這裏，我們可以
04:19
and zoom in and see, well, what are young people looking at?
115
247900
3534
繼續放大及看看，究竟年輕人在看甚麼呢?
04:23
And they're looking at energy technology and nuclear fusion.
116
251434
2345
原來他們在看有關能源科技及核聚變的資訊
04:25
This is their kind of resonance
117
253779
1674
這是他們對有關環境的對話
04:27
for the conversation around the environment.
118
255453
2406
所產生出的共鳴
04:29
If we split along gender lines,
119
257859
1899
如果我們以性別劃分
04:31
we can see females resonating heavily
120
259758
1987
我們可以看到女性對於食物經濟學、以及
04:33
with food economy, but also out there in hope and optimism.
121
261745
3645
「希望與樂觀」有較大的共鳴
04:37
And so there's a lot of exciting stuff we can do here,
122
265390
2482
這裏有很多令人興奮的東西可以做
04:39
and I'll throw to Eric for the next part.
123
267872
1762
而我會將以下的部分交給艾瑞克
04:41
EB: Yeah, I mean, just to point out here,
124
269634
1602
艾瑞克: 是的，我認為，在指說明
04:43
you cannot get this kind of perspective
125
271236
1538
你無法得到這些觀點
04:44
from a simple tag search on YouTube.
126
272774
3360
從 YouTube 中簡單的標籤搜尋中
04:48
Let's now zoom back out to the entire global conversation
127
276134
4188
現在回到全球性的對話
04:52
out of environment, and look at all the talks together.
128
280322
2534
將全部的演講一同觀察
04:54
Now often, when we're faced with this amount of content,
129
282856
2927
很多時，當我們面對這樣龐大的內容
04:57
we do a couple of things to simplify it.
130
285783
2431
我們會用一系列的方法去簡化它
05:00
We might just say, well,
131
288214
1314
我們或許會說，譬如
05:01
what are the most popular talks out there?
132
289528
2829
哪一個是最受歡迎的演講呢?
05:04
And a few rise to the surface.
133
292357
1397
有數個演講浮到表面來
05:05
There's a talk about gratitude.
134
293754
1828
這裏有一個演講關於感恩
05:07
There's another one about personal health and nutrition.
135
295582
3344
這裏有另一個演講關於個人健康與營養
05:10
And of course, there's got to be one about porn, right?
136
298926
2929
當然，有另一個演講關於色情行業，對嗎?
05:13
And so then we might say, well, gratitude, that was last year.
137
301855
3234
接着，我們會說，好，感恩，那是去年的演講
05:17
What's trending now? What's the popular talk now?
138
305089
2522
那現在的趨勢是甚麼呢?
哪一個是現在最流行的演講呢?
哪一個是現在最流行的演講呢?
05:19
And we can see that the new, emerging, top trending topic
139
307611
3321
我們可以看到這個新的、正冒起來的、最流行的題目
05:22
is about digital privacy.
140
310932
2666
是有關於數位隱私
05:25
So this is great. It simplifies things.
141
313598
1693
這是極好的。這簡化了不少事情
05:27
But there's so much creative content
142
315291
1827
但這裏有很多具創意的內容
05:29
that's just buried at the bottom.
143
317118
1921
被埋在最底層
05:31
And I hate that. How do we bubble stuff up to the surface
144
319039
3318
我討厭這種感覺。我們怎樣可以令這些可能是具創意
05:34
that's maybe really creative and interesting?
145
322357
2458
及有趣的東西浮到表面呢?
05:36
Well, we can go back to the network structure of ideas
146
324815
2931
我們可以回到那個包含不同構思的網絡
05:39
to do that.
147
327746
1430
去尋找它們
05:41
Remember, it's that network structure
148
329176
2114
記住，這就是那個製造出不同的、
05:43
that is creating these emergent topics,
149
331290
2268
處於萌芽階段的題目的網絡
05:45
and let's say we could take two of them,
150
333558
1515
不如我們拿當中的兩個題目
05:47
like cities and genetics, and say, well, are there any talks
151
335073
3047
像是城市和基因，再看看有哪些演講
05:50
that creatively bridge these two really different disciplines.
152
338120
2569
很有想像力的把這兩個截然不同的科目連在一起
05:52
And that's -- Essentially, this kind of creative remix
153
340689
2275
這個 -- 實際上，這種具創新性的重組
05:54
is one of the hallmarks of innovation.
154
342964
1840
就是創新的特徵之一
05:56
Well here's one by Jessica Green
155
344804
1606
這裏有一個謝西嘉．格林主講
05:58
about the microbial ecology of buildings.
156
346410
2379
有關建築物裏的微生物生態學的演講
06:00
It's literally defining a new field.
157
348789
2010
她的確是在界定一個新的領域
06:02
And we could go back to those topics and say, well,
158
350799
2103
我們可以回到這些主題，並問問
06:04
what talks are central to those conversations?
159
352902
2768
這些談話間核心的演講是什麼?
06:07
In the cities cluster, one of the most central
160
355670
1690
在城市這個群組裏，一個最中心的演講
06:09
was one by Mitch Joachim about ecological cities,
161
357360
3952
是由米茨．祖詹主講，主題是主張生態保護的城市
06:13
and in the genetics cluster,
162
361312
1720
在基因研究這個群組
06:15
we have a talk about synthetic biology by Craig Venter.
163
363032
3193
我們有一個克萊格·凡特主講、關於人工生物學的演講
06:18
These are talks that are linking many talks within their discipline.
164
366225
3353
這些演講都連繫着很多在相同範疇的其他演講
06:21
We could go the other direction and say, well,
165
369578
1843
我們可以向另一個方向出發
06:23
what are talks that are broadly synthesizing
166
371421
2272
問問哪些演講是廣泛綜合
06:25
a lot of different kinds of fields.
167
373693
1448
許多不同的領域
06:27
We used a measure of ecological diversity to get this.
168
375141
2533
我們用一個生態學多樣性的量度單位去看看
06:29
Like, a talk by Steven Pinker on the history of violence,
169
377674
2736
一個史迪芬．平克的演講、關於暴力的歷史
06:32
very synthetic.
170
380410
1180
就很有綜合性
06:33
And then, of course, there are talks that are so unique
171
381590
2078
當然，也有些演講是很獨特的
06:35
they're kind of out in the stratosphere, in their own special place,
172
383668
3090
它們就是遠離平流層，在它們自己的一個特別位置
06:38
and we call that the Colleen Flanagan index.
173
386758
2514
我們叫它做「歌蓮．費拿根系數」
06:41
And if you don't know Colleen, she's an artist,
174
389272
3034
如果你不認識歌蓮，她是一個藝術家
06:44
and I asked her, "Well, what's it like out there
175
392306
1543
當我問她: 「唔，在平流層裏
06:45
in the stratosphere of our idea space?"
176
393849
1672
我們的想法看似甚麼呢?」
06:47
And apparently it smells like bacon.
177
395521
3255
顯然地，它的嗅味像一塊煙肉
06:50
I wouldn't know.
178
398776
1791
我不會知道
06:52
So we're using these network motifs
179
400567
2248
所以我們就用這些網絡中心思想
06:54
to find talks that are unique,
180
402815
1186
去尋找獨特的演講
06:56
ones that are creatively synthesizing a lot of different fields,
181
404001
2710
有些是創意地結合不同範疇
06:58
ones that are central to their topic,
182
406711
1659
有些是在它們的領域中具有代表性
07:00
and ones that are really creatively bridging disparate fields.
183
408370
3374
以及有些是相當創意去連繫截然不同範疇的演講
07:03
Okay? We never would have found those with our obsession
184
411744
2102
可以嗎? 即使我們着了魔一樣去找尋現時最流行的演講
07:05
with what's trending now.
185
413846
2313
也未必會找到它們
07:08
And all of this comes from the architecture of complexity,
186
416159
2886
它們隱藏在複雜的結構裏
07:11
or the patterns of how things are connected.
187
419045
2960
或是事物間如何連結的模式
07:14
SG: So that's exactly right.
188
422005
1625
肖恩: 這完全是對的
07:15
We've got ourselves in a world
189
423630
2479
我們就在一個
07:18
that's massively complex,
190
426109
2044
無比複雜的世界中
07:20
and we've been using algorithms to kind of filter it down
191
428153
2867
我們用一系列的運算法去拆解它
07:23
so we can navigate through it.
192
431020
1786
以致我們可以在中間游走
07:24
And those algorithms, whilst being kind of useful,
193
432806
2338
這些運算法，雖然是很有用
07:27
are also very, very narrow, and we can do better than that,
194
435144
3476
但它們仍然是不夠全面的，我們定當能夠做得更好
07:30
because we can realize that their complexity is not random.
195
438620
2566
因為我們發現這些複雜性並不是偶然性的
07:33
It has mathematical structure,
196
441186
1954
它有一個數學結構
07:35
and we can use that mathematical structure
197
443140
1803
我們可以用這個數學結構
07:36
to go and explore things like the world of ideas
198
444943
2214
去探索世界上不同的構思
07:39
to see what's being said, to see what's not being said,
199
447157
3000
去看看別人說過甚麼，甚麼沒有被提出過
07:42
and to be a little bit more human
200
450157
1407
再去做些更人性化的事
07:43
and, hopefully, a little smarter.
201
451564
1867
亦希望變得聰明一些
07:45
Thank you.
202
453431
966
謝謝
07:46
(Applause)
203
454397
4220
(掌聲)
ABOUT THE SPEAKERS
Eric Berlow - EcologistTED 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.
Eric Berlow | Speaker | TED.com
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.
Sean Gourley | Speaker | TED.com