TED2013
Eric Berlow and Sean Gourley: Mapping ideas worth spreading
Kortlægning af ideer der er værd at sprede
Filmed:
Readability: 4.3
1,131,373 views
Hvordan ser 24.000 ideer ud? Miljøforsker Eric Berlow og fysiker Sean Gourley bruger algoritmer på hele TEDx Talks arkivet, og tager os med på en stimulerende visuel tur for at vise hvordan ideer er forbundet globalt.
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
Eric Berlow: Jeg er miljøforsker, og Sean er fysiker,
00:15
and we both study complex networks.
1
3623
2108
og vi forsker begge i komplekse netværk.
00:17
And we met a couple years ago when we discovered
2
5731
1835
Og vi mødte hinanden for et par år siden da vi opdagede
00:19
that we had both given a short TED Talk
3
7566
2000
at vi begge havde givet et kort TED foredrag
00:21
about the ecology of war,
4
9566
2303
om økologien ved krig,
00:23
and we realized that we were connected
5
11869
1447
og vi blev klar over at vi var forbundet gennem
00:25
by the ideas we shared before we ever met.
6
13316
2818
de ideer vi delte inden vi overhovedet mødtes.
00:28
And then we thought, you know, there are thousands
7
16134
1556
Og så tænkte vi, I ved, der er tusindevis
00:29
of other talks out there, especially TEDx Talks,
8
17690
2114
af andre foredrag derude, specielt TEDx Talks,
00:31
that are popping up all over the world.
9
19804
2211
der dukker frem fra hele verden.
00:34
How are they connected,
10
22015
923
Hvordan er de forbundet,
00:34
and what does that global conversation look like?
11
22938
2010
og hvordan ser den globale samtale ud?
00:36
So Sean's going to tell you a little bit about how we did that.
12
24948
2810
Sean vil fortælle jer en lille smule om hvordan vi gjorde det.
00:39
Sean Gourley: Exactly. So we took 24,000 TEDx Talks
13
27758
3767
Sean Gourley: Præcis. Vi tog 24.000 TEDx Talks
00:43
from around the world, 147 different countries,
14
31525
3046
fra hele verden, 147 forskellige lande,
00:46
and we took these talks and we wanted to find
15
34571
2123
og vi tog de foredrag og vi ville finde
00:48
the mathematical structures that underly
16
36694
2040
de matematiske strukturer der ligger under
00:50
the ideas behind them.
17
38734
1722
ideerne bag dem.
00:52
And we wanted to do that so we could see how
18
40456
1370
Og det ville vi gøre så vi kunne se hvordan
00:53
they connected with each other.
19
41826
2053
de var forbundet med hinanden.
00:55
And so, of course, if you're going to do this kind of stuff,
20
43879
1676
Så, selvfølgelig, hvis man vil gøre disse slags ting,
00:57
you need a lot of data.
21
45555
956
har man brug for meget data.
00:58
So the data that you've got is a great thing called YouTube,
22
46511
3686
Den data man har er en ting der hedder YouTube,
01:02
and we can go down and basically pull
23
50197
1768
og vi kan gå ned og dybest set hente
01:03
all the open information from YouTube,
24
51965
2267
al den åbne information fra YouTube,
01:06
all the comments, all the views, who's watching it,
25
54232
2349
alle kommentarerne, det antal gange det er blevet set, hvem der ser det,
01:08
where are they watching it, what are they saying in the comments.
26
56581
2779
hvor de ser det fra, hvad de siger i kommentarerne.
01:11
But we can also pull up, using speech-to-text translation,
27
59360
3292
Men vi kan også hente, ved hjælp af tale-til-tekst oversættelse,
01:14
we can pull the entire transcript,
28
62652
2128
vi kan hente hele udskriften,
01:16
and that works even for people with kind of funny accents like myself.
29
64780
2680
og det fungerer endda for mennesker med den slags sjove accenter
01:19
So we can take their transcript
30
67460
2106
Så vi kan tage deres udskrift
01:21
and actually do some pretty cool things.
31
69566
2098
og faktisk gøre nogle temmelig fede ting.
01:23
We can take natural language processing algorithms
32
71664
2160
Vi kan tage naturlige sprogbehandlings algoritmer
01:25
to kind of read through with a computer, line by line,
33
73824
2629
til på en måde at læse dem igennem med en computer, linje for linje,
01:28
extracting key concepts from this.
34
76453
2359
og udtrækker nøgle koncepter fra dette.
01:30
And we take those key concepts and they sort of form
35
78812
2525
Og vi tager de nøgle koncepter og de former på en måde
01:33
this mathematical structure of an idea.
36
81337
3565
denne matematisk struktur om en ide.
01:36
And we call that the meme-ome.
37
84902
1757
Og vi kalder det meme-ome.
01:38
And the meme-ome, you know, quite simply,
38
86659
2151
Og meme-omet, I ved, simpelt sagt,
01:40
is the mathematics that underlies an idea,
39
88810
2426
er matematikken der ligger under en ide,
01:43
and we can do some pretty interesting analysis with it,
40
91236
1932
og vi kan gøre nogle temmelig interessante analyser med det,
01:45
which I want to share with you now.
41
93168
1981
som jeg vil dele med jer nu.
01:47
So each idea has its own meme-ome,
42
95149
2190
Hver ide har sin egen meme-ome,
01:49
and each idea is unique with that,
43
97339
1951
og hver ide er unik med det,
01:51
but of course, ideas, they borrow from each other,
44
99290
2488
men selvfølgelig, ideer, de låner fra hinanden,
01:53
they kind of steal sometimes,
45
101778
1184
de stjæler på en måde nogen gange,
01:54
and they certainly build on each other,
46
102962
1827
og de kan bestemt bygge på hinanden,
01:56
and we can go through mathematically
47
104789
1616
og vi kan gå gennem det matematisk
01:58
and take the meme-ome from one talk
48
106405
1840
og tage det meme-ome fra et foredrag
02:00
and compare it to the meme-ome from every other talk,
49
108245
2454
og sammenligne det med et meme-ome fra et hvert andet foredrag,
02:02
and if there's a similarity between the two of them,
50
110699
1973
og hvis der er en lighed mellem de to,
02:04
we can create a link and represent that as a graph,
51
112672
3250
kan vi skabe et link og repræsentere det som en graf,
02:07
just like Eric and I are connected.
52
115922
2394
ligesom Eric og jeg er forbundet.
02:10
So that's theory, that's great.
53
118316
1394
Det er teori, det er fantastisk.
02:11
Let's see how it works in actual practice.
54
119710
2526
Lad os se hvordan det fungerer i praksis.
02:14
So what we've got here now is the global footprint
55
122236
2788
Det vi har her nu er et globalt fodaftryk
02:17
of all the TEDx Talks over the last four years
56
125024
2293
af alle TEDx foredrag i løbet af de sidste fire år
02:19
exploding out around the world
57
127317
1550
der eksploderer ud over hele verden
02:20
from New York all the way down to little old New Zealand in the corner.
58
128867
3329
fra New York hele vejen ned til lille bitte New Zealand nede i hjørnet.
02:24
And what we did on this is we analyzed the top 25 percent of these,
59
132196
3835
Og det vi gjorde ved dette var at vi analyserede top 25 procent af disse,
02:28
and we started to see where the connections occurred,
60
136031
2534
og vi begyndte med at se hvor forbindelserne opstod,
02:30
where they connected with each other.
61
138565
1537
hvor de forbandt sig med hinanden.
02:32
Cameron Russell talking about image and beauty
62
140102
1874
Cameron Russel taler om billede og skønhed
02:33
connected over into Europe.
63
141976
1575
der blev forbundet ind i Europa.
02:35
We've got a bigger conversation about Israel and Palestine
64
143551
2412
Vi har en større samtale om Israel og Palestina
02:37
radiating outwards from the Middle East.
65
145963
2255
der stråler ud fra Mellemøsten.
02:40
And we've got something a little broader
66
148218
1298
Og vi har noget lidt bredere
02:41
like big data with a truly global footprint
67
149516
2156
som store data med et virkelig globalt fodaftryk
02:43
reminiscent of a conversation
68
151672
2179
der minder om en samtale
02:45
that is happening everywhere.
69
153851
2016
der sker over det hele.
02:47
So from this, we kind of run up against the limits
70
155867
2173
Fra dette, løber vi på en måde op imod grænserne
02:50
of what we can actually do with a geographic projection,
71
158040
2530
for hvad vi faktisk kan gøre med en geografisk projicering,
02:52
but luckily, computer technology allows us to go out
72
160570
2052
men heldigvis, tillader computer teknologien os at tage ud
02:54
into multidimensional space.
73
162622
1546
i det multidimensionelle rum.
02:56
So we can take in our network projection
74
164168
1875
Vi kan kigge på vores netværks projektion
02:58
and apply a physics engine to this,
75
166043
1750
og bruge en fysisk motor til dette,
02:59
and the similar talks kind of smash together,
76
167793
1885
og de lignende foredrag brager sammen på en måde,
03:01
and the different ones fly apart,
77
169678
2004
og de anderledes flyver væk,
03:03
and what we're left with is something quite beautiful.
78
171682
2072
og det vi bliver efterladt med noget der er ganske smukt.
03:05
EB: So I want to just point out here that every node is a talk,
79
173754
2957
EB: Jeg vil bare pointere, at hver knudepunkt her er et foredrag,
03:08
they're linked if they share similar ideas,
80
176711
2589
de er kædet sammen hvis de deler ens ideer,
03:11
and that comes from a machine reading
81
179300
2084
og det kommer fra en maskine der læser
03:13
of entire talk transcripts,
82
181384
2067
udskriften fra hele foredraget,
03:15
and then all these topics that pop out,
83
183451
2231
og alle de emner der så stikker frem,
03:17
they're not from tags and keywords.
84
185682
1790
de ikke kommer fra tags og nøgleord.
03:19
They come from the network structure
85
187472
1725
De kommer fra netværkets struktur
03:21
of interconnected ideas. Keep going.
86
189197
2168
af forbundne ideer. Fortsæt.
03:23
SG: Absolutely. So I got a little quick on that,
87
191365
2022
SG: Bestemt. Det gik lige lidt hurtigt for mig med det,
03:25
but he's going to slow me down.
88
193387
1475
men han vil få mig til at sætte farten ned.
03:26
We've got education connected to storytelling
89
194862
2034
Vi har uddannelse forbundet med fortællekunst
03:28
triangulated next to social media.
90
196896
1643
trianguleret ved siden af sociale medier.
03:30
You've got, of course, the human brain right next to healthcare,
91
198539
2475
Man har, selvfølgelig, den menneskelige hjerne ved siden af sundhedspleje,
03:33
which you might expect,
92
201014
1386
som man måske ville forvente,
03:34
but also you've got video games, which is sort of adjacent,
93
202400
2395
men der er også computerspil, hvilket er nærliggende,
03:36
as those two spaces interface with each other.
94
204795
2740
som de to rum har berøringsflade med hinanden.
03:39
But I want to take you into one cluster
95
207535
1535
Men jeg vil tage jer med ned i en gruppe
03:41
that's particularly important to me, and that's the environment.
96
209070
2868
der er specielt vigtig for mig, og det er miljøet.
03:43
And I want to kind of zoom in on that
97
211938
1493
Og jeg vil zoome ind på den
03:45
and see if we can get a little more resolution.
98
213431
2363
og se om vi kan få lidt mere opløsning.
03:47
So as we go in here, what we start to see,
99
215794
2347
I takt med at vi går derind, begynder vi at se,
03:50
apply the physics engine again,
100
218141
1504
bruger fysik motoren igen,
03:51
we see what's one conversation
101
219645
1676
ser vi at det der faktisk er en samtale
03:53
is actually composed of many smaller ones.
102
221321
2560
faktisk udgøres af mange mindre.
03:55
The structure starts to emerge
103
223881
1929
Strukturen begynder at opstå
03:57
where we see a kind of fractal behavior
104
225810
2070
hvor vi ser en form for fraktal adfærd
03:59
of the words and the language that we use
105
227880
1619
i ordene og sproget som vi bruger
04:01
to describe the things that are important to us
106
229499
1702
til at beskrive de ting der er vigtige for os
04:03
all around this world.
107
231201
1433
rundt om i hele verden.
04:04
So you've got food economy and local food at the top,
108
232634
2332
I toppen er mad økonomi og lokal mad,
04:06
you've got greenhouse gases, solar and nuclear waste.
109
234966
2719
der er drivhusgasser, solar og nukleart affald.
04:09
What you're getting is a range of smaller conversations,
110
237685
2631
Det man får er en række mindre samtaler,
04:12
each connected to each other through the ideas
111
240316
2301
hver forbundet med hinanden gennem ideerne
04:14
and the language they share,
112
242617
1301
og sproget de deler,
04:15
creating a broader concept of the environment.
113
243918
2450
og skaber et bredere koncept om miljøet.
04:18
And of course, from here, we can go
114
246368
1532
Og selvfølgelig, herfra, kan vi gå
04:19
and zoom in and see, well, what are young people looking at?
115
247900
3534
ind og zoome ind og se, jamen, hvad ser unge mennesker på?
04:23
And they're looking at energy technology and nuclear fusion.
116
251434
2345
Og de ser på energi teknologi og nuklear fusion.
04:25
This is their kind of resonance
117
253779
1674
Dette er deres form for resonans
04:27
for the conversation around the environment.
118
255453
2406
til samtalen om miljøet.
04:29
If we split along gender lines,
119
257859
1899
Hvis vi deler det op i køn,
04:31
we can see females resonating heavily
120
259758
1987
kan vi se at kvinder giver kraftig genlyd
04:33
with food economy, but also out there in hope and optimism.
121
261745
3645
med mad økonomi, men også der ude i håb og optimism.
04:37
And so there's a lot of exciting stuff we can do here,
122
265390
2482
Så der er mange spændende ting vi kan gøre her,
04:39
and I'll throw to Eric for the next part.
123
267872
1762
og jeg vil kaste den næste del videre til Eric.
04:41
EB: Yeah, I mean, just to point out here,
124
269634
1602
EB: Ja, jeg mener, bare for at pointere her,
04:43
you cannot get this kind of perspective
125
271236
1538
man kan ikke få denne slags perspektiv
04:44
from a simple tag search on YouTube.
126
272774
3360
fra en simple tag søgning på YouTube.
04:48
Let's now zoom back out to the entire global conversation
127
276134
4188
Lad os zoome tilbage til hele den globale samtale
04:52
out of environment, and look at all the talks together.
128
280322
2534
ud af miljøet, og se på alle foredragene sammen.
04:54
Now often, when we're faced with this amount of content,
129
282856
2927
Tit, når vi står overfor denne mængde indhold,
04:57
we do a couple of things to simplify it.
130
285783
2431
gør vi et par ting til at simplificere det.
05:00
We might just say, well,
131
288214
1314
Vi siger måske, jamen,
05:01
what are the most popular talks out there?
132
289528
2829
hvad er de mest populære foredrag derude?
05:04
And a few rise to the surface.
133
292357
1397
Og der kommer et par til overfladen.
05:05
There's a talk about gratitude.
134
293754
1828
Der er et foredrag om taknemmelighed.
05:07
There's another one about personal health and nutrition.
135
295582
3344
Der er et andet om personlig helbred og ernæring.
05:10
And of course, there's got to be one about porn, right?
136
298926
2929
Og selvfølgelig, der er nød til at være et om porno, ikke?
05:13
And so then we might say, well, gratitude, that was last year.
137
301855
3234
Så vi kan måske sige, jamen, taknemmelighed, det var sidste år.
05:17
What's trending now? What's the popular talk now?
138
305089
2522
Hvad er tendensen nu? Hvad er det populære foredrag nu?
05:19
And we can see that the new, emerging, top trending topic
139
307611
3321
Og vi kan se at den nye, fremspirende, tendens
05:22
is about digital privacy.
140
310932
2666
handler om digital privatliv.
05:25
So this is great. It simplifies things.
141
313598
1693
Dette er fantastisk. Det simplificerer ting.
05:27
But there's so much creative content
142
315291
1827
Men der er så meget kreativt indhold
05:29
that's just buried at the bottom.
143
317118
1921
der bare er begravet i bunden.
05:31
And I hate that. How do we bubble stuff up to the surface
144
319039
3318
Og det hader jeg. Hvordan får vi gravet ting op til overfladen
05:34
that's maybe really creative and interesting?
145
322357
2458
der måske er virkelig kreative og interessante?
05:36
Well, we can go back to the network structure of ideas
146
324815
2931
Jamen, vi kan gå tilbage til netværksstrukturen om ideer
05:39
to do that.
147
327746
1430
for at gøre det.
05:41
Remember, it's that network structure
148
329176
2114
Husk på, at det er netværksstrukturen
05:43
that is creating these emergent topics,
149
331290
2268
der skaber disse fremspirende emner,
05:45
and let's say we could take two of them,
150
333558
1515
og lad os sige at vi kunne tage to af dem,
05:47
like cities and genetics, and say, well, are there any talks
151
335073
3047
som byer og genetik, og lad os sige, jamen, er der nogen foredrag
05:50
that creatively bridge these two really different disciplines.
152
338120
2569
der kreativt bygger bro mellem disse to virkelig forskellige discipliner.
05:52
And that's -- Essentially, this kind of creative remix
153
340689
2275
Og det er -- dybest set, denne slags kreative remix
05:54
is one of the hallmarks of innovation.
154
342964
1840
er en af kendetegnene ved innovation.
05:56
Well here's one by Jessica Green
155
344804
1606
Jamen her er et af Jessica Green
05:58
about the microbial ecology of buildings.
156
346410
2379
om bygningers mikrobiologiske økologi.
06:00
It's literally defining a new field.
157
348789
2010
Det definerer bogstavelig talt et nyt område.
06:02
And we could go back to those topics and say, well,
158
350799
2103
Og vi kunne gå tilbage til de emner og sige, jamen,
06:04
what talks are central to those conversations?
159
352902
2768
hvilke foredrag er centrale til de samtaler?
06:07
In the cities cluster, one of the most central
160
355670
1690
I by grupperne, var et af de mest centrale
06:09
was one by Mitch Joachim about ecological cities,
161
357360
3952
af Mitch Joachim om økologiske byer,
06:13
and in the genetics cluster,
162
361312
1720
og i de genetiske grupper,
06:15
we have a talk about synthetic biology by Craig Venter.
163
363032
3193
har vi et foredrag om syntetisk biologi af Craig Venter.
06:18
These are talks that are linking many talks within their discipline.
164
366225
3353
Dette er foredrag der samler mange foredrag indenfor deres disciplin.
06:21
We could go the other direction and say, well,
165
369578
1843
Vi kan gå i den anden retning og sige, jamen,
06:23
what are talks that are broadly synthesizing
166
371421
2272
hvad er foredrag der bredt syntetiserer
06:25
a lot of different kinds of fields.
167
373693
1448
mange forskellige felter.
06:27
We used a measure of ecological diversity to get this.
168
375141
2533
Vi brugte en målestok fra økologisk diversitet for at få dette.
06:29
Like, a talk by Steven Pinker on the history of violence,
169
377674
2736
Som, et foredrag af Steven Pinker om voldens historie,
06:32
very synthetic.
170
380410
1180
meget syntetisk.
06:33
And then, of course, there are talks that are so unique
171
381590
2078
Og så, selvfølgelig, er der foredrag der er så unikke
06:35
they're kind of out in the stratosphere, in their own special place,
172
383668
3090
at de på en måde er derude i stratosfæren, på deres eget specielle sted,
06:38
and we call that the Colleen Flanagan index.
173
386758
2514
og vi kalder det Colleen Flanagan indekset.
06:41
And if you don't know Colleen, she's an artist,
174
389272
3034
Og hvis man ikke kender Colleen, hun er en kunstner,
06:44
and I asked her, "Well, what's it like out there
175
392306
1543
og jeg spurgte hende, "Jamen, hvordan er det derude
06:45
in the stratosphere of our idea space?"
176
393849
1672
i vores ide rums stratosfære?"
06:47
And apparently it smells like bacon.
177
395521
3255
Og det lugter åbenbart som bacon.
06:50
I wouldn't know.
178
398776
1791
Jeg ved det ikke.
06:52
So we're using these network motifs
179
400567
2248
Vi bruger altså disse netværks temaer
06:54
to find talks that are unique,
180
402815
1186
til at finde foredrag der er så unikke,
06:56
ones that are creatively synthesizing a lot of different fields,
181
404001
2710
foredrag der er så kreativt syntetiserer mange forskellige områder,
06:58
ones that are central to their topic,
182
406711
1659
foredrag der er centrale for deres emne,
07:00
and ones that are really creatively bridging disparate fields.
183
408370
3374
og foredrag der virkelig kreativt slår bro mellem helt forskellige områder.
07:03
Okay? We never would have found those with our obsession
184
411744
2102
Okay? Vi ville aldrig have fundet dem med vores besættelse
07:05
with what's trending now.
185
413846
2313
af det der er populært nu.
07:08
And all of this comes from the architecture of complexity,
186
416159
2886
Og dette kommer alt sammen fra kompleksitetens arkitektur,
07:11
or the patterns of how things are connected.
187
419045
2960
eller mønstrene om hvordan tingene er forbundet.
07:14
SG: So that's exactly right.
188
422005
1625
SG: Det er helt rigtigt.
07:15
We've got ourselves in a world
189
423630
2479
Vi er i en verden
07:18
that's massively complex,
190
426109
2044
der er utrolig kompleks,
07:20
and we've been using algorithms to kind of filter it down
191
428153
2867
og vi har brugt algoritmer til på en måde at filtrere det
07:23
so we can navigate through it.
192
431020
1786
så vi kan navigere igennem det.
07:24
And those algorithms, whilst being kind of useful,
193
432806
2338
Og de algoritmer, mens de på en måde er nyttige,
07:27
are also very, very narrow, and we can do better than that,
194
435144
3476
er de også meget, meget snævre og vi kan gøre det bedre end det,
07:30
because we can realize that their complexity is not random.
195
438620
2566
fordi vi kan blive klar over at deres kompleksitet ikke er tilfældig.
07:33
It has mathematical structure,
196
441186
1954
Den har matematisk struktur,
07:35
and we can use that mathematical structure
197
443140
1803
og vi kan bruge den matematiske struktur
07:36
to go and explore things like the world of ideas
198
444943
2214
til at tage ud og udforske ting som en verden med ideer
07:39
to see what's being said, to see what's not being said,
199
447157
3000
for at se hvad der bliver sagt, for at se hvad der ikke bliver sagt,
07:42
and to be a little bit more human
200
450157
1407
og for at være en smule mere menneskelig
07:43
and, hopefully, a little smarter.
201
451564
1867
og, forhåbentlig, en smule klogere.
07:45
Thank you.
202
453431
966
Tak.
07:46
(Applause)
203
454397
4220
(Bifald)
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