ABOUT THE SPEAKER
Aaron Koblin - Data artist
Aaron Koblin is an artist specializing in data and digital technologies. His work takes real world and community-generated data and uses it to reflect on cultural trends and the changing relationship between humans and technology.

Why you should listen

Aaron Koblin finds art through the unlikely confluence of massive data sets and personal intimacy. His work ranges from animating the paths of every North American airline flight, to using Amazon’s Mechanical Turk crowdsourcing platform to pay workers to “draw a sheep facing left,” which were then placed in "The Sheep Market."

Koblin was creative director for Johnny Cash's final music video, "Ain't No Grave," and worked on Radiohead’s video “House of Cards,” both of which received a Grammy nomination. He is now the Creative Director of the Data Arts team in Google's Creative Lab. His team collaborated with Arcade Fire to produce an online music video that allows viewers to incorporate images of their home neighborhood into the experience using Google Street View.

More profile about the speaker
Aaron Koblin | Speaker | TED.com
TED2011

Aaron Koblin: Visualizing ourselves ... with crowd-sourced data

Filmed:
1,731,467 views

Artist Aaron Koblin takes vast amounts of data -- and at times vast numbers of people -- and weaves them into stunning visualizations. From elegant lines tracing airline flights to landscapes of cell phone data, from a Johnny Cash video assembled from crowd-sourced drawings to the "Wilderness Downtown" video that customizes for the user, his works brilliantly explore how modern technology can make us more human.
- Data artist
Aaron Koblin is an artist specializing in data and digital technologies. His work takes real world and community-generated data and uses it to reflect on cultural trends and the changing relationship between humans and technology. Full bio

Double-click the English transcript below to play the video.

00:15
So I think data can actually make us more human.
0
0
4000
00:19
We're collecting and creating all kinds of data about how we're living our lives,
1
4000
3000
00:22
and it's enabling us to tell some amazing stories.
2
7000
2000
00:24
Recently, a wise media theorist Tweeted,
3
9000
3000
00:27
"The 19th century culture was defined by the novel,
4
12000
2000
00:29
the 20th century culture was defined by the cinema,
5
14000
2000
00:31
and the culture of the 21st century
6
16000
2000
00:33
will be defined by the interface."
7
18000
2000
00:35
And I believe this is going to prove true.
8
20000
2000
00:37
Our lives are being driven by data,
9
22000
2000
00:39
and the presentation of that data is an opportunity
10
24000
2000
00:41
for us to make some amazing interfaces that tell great stories.
11
26000
2000
00:43
So I'm going to show you a few of the projects
12
28000
2000
00:45
that I've been working on over the last couple years
13
30000
2000
00:47
that reflect on our lives and our systems.
14
32000
2000
00:49
This is a project called Flight Patterns.
15
34000
2000
00:51
What you're looking at is airplane traffic
16
36000
2000
00:53
over North America for a 24-hour period.
17
38000
3000
00:56
As you see, everything starts to fade to black,
18
41000
2000
00:58
and you see people going to sleep.
19
43000
2000
01:00
Followed by that, you see on the West coast
20
45000
2000
01:02
planes moving across, the red-eye flights to the East coast.
21
47000
3000
01:05
And you'll see everybody waking up on the East coast,
22
50000
3000
01:08
followed by European flights coming in the upper right-hand corner.
23
53000
3000
01:11
Everybody's moving from the East coast to the West coast.
24
56000
3000
01:14
You see San Francisco and Los Angeles
25
59000
2000
01:16
start to make their journeys down to Hawaii in the lower left-hand corner.
26
61000
3000
01:19
I think it's one thing to say there's 140,000 planes
27
64000
2000
01:21
being monitored by the federal government at any one time,
28
66000
3000
01:24
and it's another thing to see that system as it ebbs and flows.
29
69000
3000
01:29
This is a time-lapse image of that exact same data,
30
74000
2000
01:31
but I've color-coded it by type,
31
76000
2000
01:33
so you can see the diversity of aircraft that are in the skies above us.
32
78000
3000
01:36
And I started making these, and I put them into Google Maps
33
81000
3000
01:39
and allow you to zoom in and see individual airports
34
84000
2000
01:41
and the patterns that are occurring there.
35
86000
2000
01:43
So here we can see the white represents low altitudes,
36
88000
3000
01:46
and the blue are higher altitudes.
37
91000
2000
01:48
And you can zoom in. This is taking a look at Atlanta.
38
93000
2000
01:50
You can see this is a major shipping airport,
39
95000
2000
01:52
and there's all kinds of activity there.
40
97000
2000
01:54
You can also toggle between altitude
41
99000
3000
01:57
for model and manufacturer.
42
102000
2000
01:59
See again, the diversity.
43
104000
2000
02:01
And you can scroll around and see
44
106000
2000
02:03
some of the different airports and the different patterns that they have.
45
108000
3000
02:06
This is scrolling up the East coast.
46
111000
2000
02:08
You can see some of the chaos that's happening in New York
47
113000
2000
02:10
with the air traffic controllers
48
115000
2000
02:12
having to deal with all those major airports next to each other.
49
117000
4000
02:17
So zooming back out real quick,
50
122000
2000
02:19
we see, again, the U.S. -- you get Florida down in the right-hand corner.
51
124000
3000
02:22
Moving across to the West coast,
52
127000
2000
02:24
you see San Francisco and Los Angeles --
53
129000
2000
02:26
big low-traffic zones
54
131000
2000
02:28
across Nevada and Arizona.
55
133000
2000
02:30
And that's us down there in L.A. and Long Beach on the bottom.
56
135000
4000
02:36
I started taking a look as well at different perimeters,
57
141000
2000
02:38
because you can choose what you want to pull out from the data.
58
143000
2000
02:40
This is looking at ascending versus descending flights.
59
145000
3000
02:43
And you can see, over time, the ways the airports change.
60
148000
2000
02:45
You see the holding patterns that start to develop
61
150000
2000
02:47
in the bottom of the screen.
62
152000
2000
02:49
And you can see, eventually the airport actually flips directions.
63
154000
3000
02:53
So this is another project that I worked on with the Sensible Cities Lab at MIT.
64
158000
3000
02:56
This is visualizing international communications.
65
161000
2000
02:58
So it's how New York communicates
66
163000
2000
03:00
with other international cities.
67
165000
2000
03:02
And we set this up as a live globe in the Museum of Modern Art in New York
68
167000
3000
03:05
for the Design the Elastic Mind exhibition.
69
170000
2000
03:07
And it had a live feed with a 24-hour offset,
70
172000
2000
03:09
so you could see the changing relationship
71
174000
2000
03:11
and some demographic info
72
176000
2000
03:13
coming through AT&T's data and revealing itself.
73
178000
3000
03:16
This is another project I worked on with Sensible Cities Lab
74
181000
2000
03:18
and CurrentCity.org.
75
183000
2000
03:20
And it's visualizing SMS messages being sent in the city of Amsterdam.
76
185000
3000
03:23
So you're seeing the daily ebb and flow
77
188000
2000
03:25
of people sending SMS messages from different parts of the city,
78
190000
2000
03:27
until we approach New Year's Eve, where everybody says, "Happy New Year!"
79
192000
3000
03:30
(Laughter)
80
195000
2000
03:32
So this is an interactive tool
81
197000
2000
03:34
that you can move around and see different parts of the city.
82
199000
3000
03:37
This is looking at another event. This is called Queen's Day.
83
202000
3000
03:40
So again, you get this daily ebb and flow
84
205000
2000
03:42
of people sending SMS messages from different parts of the city.
85
207000
3000
03:45
And then you're going to see people start to gather in the center of the city
86
210000
2000
03:47
to celebrate the night before,
87
212000
2000
03:49
which happens right here.
88
214000
2000
03:51
And then you can see people celebrating the next day.
89
216000
2000
03:53
And you can pause it and step back and forth and see different phases.
90
218000
3000
03:56
So now on to something completely different.
91
221000
2000
03:58
Some of you may recognize this.
92
223000
2000
04:00
This is Baron Wolfgang von Kempelen's mechanical chess playing machine.
93
225000
3000
04:03
And it's this amazing robot that plays chess extremely well,
94
228000
2000
04:05
except for one thing: it's not a robot at all.
95
230000
3000
04:08
There's actually a legless man that sits in that box
96
233000
2000
04:10
and controls this chess player.
97
235000
2000
04:12
This was the inspiration for a web service by Amazon
98
237000
2000
04:14
called the Mechanical Turk -- named after this guy.
99
239000
3000
04:17
And it's based on the premise that there are certain things
100
242000
2000
04:19
that are easy for people, but really difficult for computers.
101
244000
2000
04:21
So they made this web service and said,
102
246000
2000
04:23
"Any programmer can write a piece of software
103
248000
2000
04:25
and tap into the minds of thousands of people."
104
250000
2000
04:27
The nerdy side of me thought, "Wow, this is amazing.
105
252000
2000
04:29
I can tap into thousands of people's minds."
106
254000
2000
04:31
And the other nerdy side of me thought,
107
256000
2000
04:33
"This is horrible. This is completely bizarre.
108
258000
3000
04:36
What does this mean for the future of mankind,
109
261000
2000
04:38
where we're all plugged into this borg?"
110
263000
2000
04:40
I was probably being a little extreme.
111
265000
2000
04:42
But what does this mean when we have no context for what it is that we're working on,
112
267000
2000
04:44
and we're just doing these little labors?
113
269000
2000
04:46
So I created this drawing tool.
114
271000
2000
04:48
I asked people to draw a sheep facing to the left.
115
273000
2000
04:50
And I said, "I'll pay you two cents for your contribution."
116
275000
2000
04:52
And I started collecting sheep.
117
277000
3000
04:55
And I collected a lot, a lot of different sheep.
118
280000
3000
04:59
Lots of sheep.
119
284000
2000
05:01
I took the first 10,000 sheep that I collected,
120
286000
2000
05:03
and I put them on a website called TheSheepMarket.com
121
288000
3000
05:06
where you can actually buy collections of 20 sheep.
122
291000
3000
05:09
You can't pick individual sheep,
123
294000
2000
05:11
but you can buy a single plate block of stamps as a commodity.
124
296000
4000
05:15
And juxtaposed against this grid,
125
300000
2000
05:17
you see actually, by rolling over each individual one,
126
302000
2000
05:19
the humanity behind this hugely mechanical process.
127
304000
3000
05:22
I think there's something really interesting
128
307000
2000
05:24
to watching people as they go through this creative toil --
129
309000
3000
05:27
something we can all relate to,
130
312000
2000
05:29
this creative process of trying to come up with something from nothing.
131
314000
3000
05:32
I think it was really interesting to juxtapose this humanity
132
317000
2000
05:34
versus this massive distributed grid.
133
319000
2000
05:36
Kind of amazing what some people did.
134
321000
3000
05:39
So here's a few statistics from the project.
135
324000
2000
05:41
Approximate collection rate of 11 sheep per hour,
136
326000
2000
05:43
which would make a working wage of 69 cents per hour.
137
328000
3000
05:46
There were 662 rejected sheep
138
331000
2000
05:48
that didn't meet "sheep-like" criteria and were thrown out of the flock.
139
333000
3000
05:51
(Laughter)
140
336000
2000
05:53
The amount of time spent drawing ranged from four seconds to 46 minutes.
141
338000
3000
05:56
That gives you an idea of the different types of motivations and dedication.
142
341000
3000
05:59
And there were 7,599 people that contributed to the project,
143
344000
3000
06:02
or were unique IP addresses --
144
347000
2000
06:04
so about how many people contributed.
145
349000
2000
06:06
But only one of them out of the 7,599 said this.
146
351000
4000
06:10
(Laughter)
147
355000
4000
06:14
Which I was pretty surprised by.
148
359000
2000
06:16
I expected people to be wondering, "Why did I draw a sheep?"
149
361000
3000
06:19
And I think it's a pretty valid question.
150
364000
2000
06:21
And there's a lot of reasons why I chose sheep.
151
366000
2000
06:23
Sheep were the first animal
152
368000
2000
06:25
to be raised from mechanically processed byproducts,
153
370000
2000
06:27
the first to be selectively bred for production traits,
154
372000
2000
06:29
the first animal to be cloned.
155
374000
2000
06:31
Obviously, we think of sheep as followers.
156
376000
2000
06:33
And there's this reference to "Le Petit Prince"
157
378000
2000
06:35
where the narrator asks the prince to draw a sheep.
158
380000
2000
06:37
He draws sheep after sheep.
159
382000
2000
06:39
The narrator's only appeased when he draws a box.
160
384000
2000
06:41
And he says, "It's not about a scientific rendering of a sheep.
161
386000
2000
06:43
It's about your own interpretation and doing something different."
162
388000
3000
06:46
And I like that.
163
391000
2000
06:48
So this is a clip from Charlie Chaplin's "Modern Times."
164
393000
2000
06:50
It's showing Charlie Chaplin dealing with some of the major changes
165
395000
3000
06:53
during the Industrial Revolution.
166
398000
2000
06:55
So there were no longer shoe makers,
167
400000
2000
06:57
but now there are people slapping soles on people's shoes.
168
402000
2000
06:59
And the whole idea of one's relationship to their work changed a lot.
169
404000
3000
07:02
So I thought this was an interesting clip
170
407000
2000
07:04
to divide into 16 pieces
171
409000
2000
07:06
and feed into the Mechanical Turk with a drawing tool.
172
411000
3000
07:09
This basically allowed -- what you see on the left side is the original frame,
173
414000
3000
07:12
and on the right side you see that frame
174
417000
2000
07:14
as interpreted by 16 people
175
419000
2000
07:16
who have no idea what it is they're doing.
176
421000
2000
07:18
And this was the inspiration for a project
177
423000
2000
07:20
that I worked on with my friend Takashi Kawashima.
178
425000
2000
07:22
We decided to use the Mechanical Turk for exactly what it was meant for,
179
427000
2000
07:24
which is making money.
180
429000
2000
07:26
So we took a hundred dollar bill and divided it into 10,000 teeny pieces,
181
431000
3000
07:29
and we fed those into the Mechanical Turk.
182
434000
2000
07:31
We asked people to draw what it was that they saw.
183
436000
2000
07:33
But here there was no sheep-like criteria.
184
438000
2000
07:35
People, if they drew a stick figure or a smiley face,
185
440000
3000
07:38
it actually made it into the bill.
186
443000
2000
07:40
So what you see is actually a representation of how well people did
187
445000
2000
07:42
what it was they were asked to do.
188
447000
2000
07:44
So we took these hundred dollar bills,
189
449000
2000
07:46
and we put them on a website called TenThousandsCents.com,
190
451000
2000
07:48
where you can browse through and see all the individual contributions.
191
453000
3000
07:51
And you can also trade real hundred-dollar bills for fake hundred-dollar bills
192
456000
3000
07:54
and make a donation to the Hundred Dollar Laptop Project,
193
459000
3000
07:57
which is now known as One Laptop Per Child.
194
462000
3000
08:00
This is again showing all the different contributions.
195
465000
2000
08:02
You see some people did beautiful stipple renderings,
196
467000
2000
08:04
like this one on top --
197
469000
2000
08:06
spent a long time making realistic versions.
198
471000
3000
08:09
And other people would draw stick figures or smiley faces.
199
474000
3000
08:12
Here on the right-hand side in the middle
200
477000
2000
08:14
you see this one guy writing, "$0.01!!! Really?"
201
479000
3000
08:17
That's all I'm getting paid for this?
202
482000
4000
08:21
(Laughter)
203
486000
2000
08:23
So the last Mechanical Turk project I'm going to talk to you about
204
488000
2000
08:25
is called Bicycle Built for 2000.
205
490000
2000
08:27
This is a collaboration with my friend Daniel Massey.
206
492000
2000
08:29
You may recognize these two guys.
207
494000
2000
08:31
This is Max Mathews and John Kelly from Bell Labs in the '60s,
208
496000
3000
08:34
where they created the song "Daisy Bell,"
209
499000
2000
08:36
which was the world's first singing computer.
210
501000
2000
08:38
You may recognize it from "2001: A Space Odyssey."
211
503000
2000
08:40
When HAL's dying at the end of the film he starts singing this song,
212
505000
3000
08:43
as a reference to when computers became human.
213
508000
3000
08:46
So we resynthesized this song.
214
511000
2000
08:48
This is what that sounded like.
215
513000
2000
08:50
We broke down all the individual notes
216
515000
2000
08:52
in the singing as well as the phonemes in the singing.
217
517000
3000
08:55
Daisy Bell: ♫ Daisy, Daisy ... ♫
218
520000
4000
08:59
Aaron Koblin: And we took all of those individual pieces,
219
524000
2000
09:01
and we fed them into another Turk request.
220
526000
2000
09:03
This is what it would look like if you went to the site.
221
528000
2000
09:05
You type in your code,
222
530000
2000
09:07
but you first test your mic.
223
532000
2000
09:09
You'd be fed a simple audio clip.
224
534000
2000
09:11
(Honk)
225
536000
2000
09:13
And then you'd do your best to recreate that with your own voice.
226
538000
3000
09:22
After previewing it and confirming it's what you submitted,
227
547000
3000
09:25
you could submit it into the Mechanical Turk with no other context.
228
550000
3000
09:28
And this is what we first got back from the very first set of submissions.
229
553000
3000
09:31
Recording: ♫ Daisy, Daisy ♫
230
556000
5000
09:36
♫ give me your answer do ♫
231
561000
5000
09:41
♫ I'm half crazy ♫
232
566000
4000
09:45
♫ all for the love of you ♫
233
570000
5000
09:50
♫ It can't be a stylish marriage ♫
234
575000
5000
09:55
♫ I can't afford a carriage ♫
235
580000
4000
09:59
♫ But you'll look sweet upon the seat ♫
236
584000
5000
10:04
♫ of a bicycle built for two ♫
237
589000
5000
10:09
AK: So James Surowieki has this idea of the wisdom of crowds,
238
594000
3000
10:12
that says that a whole bunch of people are smarter than any individual.
239
597000
3000
10:15
We wanted to see how this applies to collaborative, distributed music making,
240
600000
3000
10:18
where nobody has any idea what it is they're working on.
241
603000
3000
10:21
So if you go to the BicycleBuiltforTwoThousand.com
242
606000
2000
10:23
you can actually hear what all this sounds like together.
243
608000
2000
10:25
I'm sorry for this.
244
610000
2000
10:27
(Noise)
245
612000
5000
10:32
Chorus: ♫ Daisy, Daisy ♫
246
617000
4000
10:36
♫ Give me your answer do ♫
247
621000
5000
10:41
♫ I'm half crazy ♫
248
626000
5000
10:46
♫ all for the love of you ♫
249
631000
4000
10:50
♫ It can't be a stylish marriage ♫
250
635000
5000
10:55
♫ I can't afford a carriage ♫
251
640000
4000
10:59
♫ But you'd look sweet upon the seat ♫
252
644000
5000
11:04
♫ of a bicycle built for two ♫
253
649000
6000
11:10
AK: So stepping back for a quick second,
254
655000
3000
11:13
when I was at UCLA going to grad school,
255
658000
2000
11:15
I was also working at a place called the Center for Embedded Network Sensing.
256
660000
3000
11:18
And I was writing software to visualize laser scanners.
257
663000
3000
11:21
So basically motion through 3D space.
258
666000
2000
11:23
And this was seen by a director in L.A. named James Frost
259
668000
2000
11:25
who said, "Wait a minute.
260
670000
2000
11:27
You mean we can shoot a music video without actually using any video?"
261
672000
2000
11:29
So we did exactly that.
262
674000
2000
11:31
We made a music video for one of my favorite bands, Radiohead.
263
676000
2000
11:33
And I think one of my favorite parts of this project
264
678000
2000
11:35
was not just shooting a video with lasers,
265
680000
2000
11:37
but we also open sourced it,
266
682000
2000
11:39
and we made it released as a Google Code project,
267
684000
2000
11:41
where people could download a bunch of the data and some source code
268
686000
2000
11:43
to build their own versions of it.
269
688000
2000
11:45
And people were making some amazing things.
270
690000
2000
11:47
This is actually two of my favorites:
271
692000
2000
11:49
the pin-board Thom Yorke and a LEGO Thom Yorke.
272
694000
2000
11:51
A whole YouTube channel of people submitting really interesting content.
273
696000
3000
11:54
More recently, somebody even 3D-printed Thom Yorke's head,
274
699000
3000
11:57
which is a little creepy, but pretty cool.
275
702000
3000
12:00
So with everybody making so much amazing stuff
276
705000
2000
12:02
and actually understanding what it was they were working on,
277
707000
3000
12:05
I was really interested in trying to make a collaborative project
278
710000
2000
12:07
where people were working together to build something.
279
712000
2000
12:09
And I met a music video director named Chris Milk.
280
714000
2000
12:11
And we started bouncing around ideas
281
716000
2000
12:13
to make a collaborative music video project.
282
718000
2000
12:15
But we knew we really needed the right person
283
720000
2000
12:17
to kind of rally behind and build something for.
284
722000
3000
12:20
So we put the idea on the back burner for a few months.
285
725000
2000
12:22
And he ended up talking to Rick Rubin,
286
727000
2000
12:24
who was finishing up Johnny Cash's final album
287
729000
2000
12:26
called "Ain't No Grave."
288
731000
2000
12:28
The lyrics to the leading track are "Ain't no grave can hold my body down."
289
733000
3000
12:31
So we thought this was the perfect
290
736000
2000
12:33
project to build a collaborative memorial
291
738000
2000
12:35
and a virtual resurrection for Johnny Cash.
292
740000
2000
12:37
So I teamed up with my good friend Ricardo Cabello, also known as Mr. doob,
293
742000
3000
12:40
who's a much better programmer than I am,
294
745000
2000
12:42
and he made this amazing Flash drawing tool.
295
747000
2000
12:44
As you know,
296
749000
2000
12:46
an animation is a series of images.
297
751000
2000
12:48
So what we did was cross-cut a bunch of archival footage of Johnny Cash,
298
753000
3000
12:51
and at eight frames a second,
299
756000
2000
12:53
we allowed individuals to draw a single frame
300
758000
2000
12:55
that would get woven into
301
760000
2000
12:57
this dynamically changing music video.
302
762000
2000
12:59
So I don't have time to play the entire thing for you,
303
764000
2000
13:01
but I want to show you two short clips.
304
766000
2000
13:03
One is the beginning of the music video.
305
768000
2000
13:05
And that's going to be followed by a short clip
306
770000
2000
13:07
of people who have already contributed to the project
307
772000
2000
13:09
talking about it briefly.
308
774000
3000
13:12
(Music)
309
777000
6000
13:18
(Video) Johnny Cash: ♫ There ain't no grave ♫
310
783000
2000
13:20
♫ can hold my body down ♫
311
785000
4000
13:24
♫ There ain't no grave ♫
312
789000
2000
13:26
♫ can hold body down ♫
313
791000
3000
13:30
♫ When I hear the trumpet sound ♫
314
795000
4000
13:34
♫ I'm going to ride right out of the ground ♫
315
799000
2000
13:36
♫ Ain't no grave ♫
316
801000
3000
13:39
♫ can hold my body ... ♫
317
804000
3000
13:42
(Applause)
318
807000
2000
13:44
AK: What better way to pay tribute to the man
319
809000
3000
13:47
than to make something for one of his songs.
320
812000
3000
13:50
Collaborator: I felt really sad when he died.
321
815000
2000
13:52
And I just thought it'd be wonderful,
322
817000
2000
13:54
it'd be really nice to contribute something to his memory.
323
819000
3000
13:57
Collaborator Two: It really allows
324
822000
2000
13:59
this last recording of his
325
824000
3000
14:02
to be a living, breathing memorial.
326
827000
5000
14:07
Collaborator Three: For all of the frames to be drawn by fans,
327
832000
3000
14:10
each individual frame,
328
835000
2000
14:12
it's got a very powerful feeling to it.
329
837000
2000
14:14
Collaborator Four: I've seen everybody
330
839000
2000
14:16
from Japan, Venezuela, to the States,
331
841000
2000
14:18
to Knoxville, Tennessee.
332
843000
2000
14:20
Collaborator Five: As much as is different from frame to frame,
333
845000
3000
14:23
it really is personal.
334
848000
2000
14:25
Collaborator Six: Watching the video in my room,
335
850000
2000
14:27
I could see me not understanding at the beginning of it.
336
852000
3000
14:30
And I just worked and worked through problems,
337
855000
3000
14:33
until my little wee battles that I was fighting within the picture
338
858000
4000
14:37
all began to resolve themselves.
339
862000
3000
14:40
You can actually see the point when I know what I'm doing,
340
865000
2000
14:42
and a lot of light and dark comes into it.
341
867000
3000
14:45
And in a weird way,
342
870000
2000
14:47
that's what I actually like about Johnny Cash's music as well.
343
872000
2000
14:49
It's the sum total of his life,
344
874000
2000
14:51
all the things that had happened --
345
876000
2000
14:53
the bad things, the good things.
346
878000
2000
14:55
You're hearing a person's life.
347
880000
3000
15:01
AK: So if you go to the website JohnnyCashProject.com,
348
886000
2000
15:03
what you'll see is the video playing above.
349
888000
2000
15:05
And below it are all the individual frames
350
890000
2000
15:07
that people have been submitting to the project.
351
892000
2000
15:09
So this isn't finished at all,
352
894000
2000
15:11
but it's an ongoing project where people can continue to collaborate.
353
896000
2000
15:13
If you roll over any one of those individual thumbnails,
354
898000
2000
15:15
you can see the person who drew that individual thumbnail
355
900000
2000
15:17
and where they were located.
356
902000
2000
15:19
And if you find one that you're interested in,
357
904000
2000
15:21
you can actually click on it and open up an information panel
358
906000
2000
15:23
where you're able to rate that frame,
359
908000
2000
15:25
which helps it bubble up to the top.
360
910000
2000
15:27
And you can also see the way that it was drawn.
361
912000
2000
15:29
Again, you can get the playback and personal contribution.
362
914000
2000
15:31
In addition to that, it's listed, the artist's name, the location,
363
916000
3000
15:34
how long they spent drawing it.
364
919000
2000
15:36
And you can pick a style. So this one was tagged "Abstract."
365
921000
3000
15:39
But there's a bunch of different styles.
366
924000
2000
15:41
And you can sort the video a number of different ways.
367
926000
2000
15:43
You can say, "I want to see the pointillist version
368
928000
2000
15:45
or the sketchy version or the realistic version.
369
930000
2000
15:47
And then this is, again, the abstract version,
370
932000
2000
15:49
which ends up getting a little bit crazy.
371
934000
3000
15:54
So the last project I want to talk to you about is another collaboration with Chris Milk.
372
939000
3000
15:57
And this is called "The Wilderness Downtown."
373
942000
2000
15:59
It's an online music video for the Arcade Fire.
374
944000
3000
16:02
Chris and I were really amazed
375
947000
2000
16:04
by the potential now with modern web browsers,
376
949000
2000
16:06
where you have HTML5 audio and video
377
951000
2000
16:08
and the power of JavaScript to render amazingly fast.
378
953000
3000
16:11
And we wanted to push the idea of the music video that was meant for the Web
379
956000
3000
16:14
beyond the four-by-three or sixteen-by-nine window
380
959000
3000
16:17
and try to make it play out and choreograph throughout the screen.
381
962000
3000
16:20
But most importantly, I think,
382
965000
2000
16:22
we really wanted to make an experience that was unlike the Johnny Cash Project,
383
967000
3000
16:25
where you had a small group of people spending a lot of time
384
970000
3000
16:28
to contribute something for everyone.
385
973000
2000
16:30
What if we had a very low commitment,
386
975000
3000
16:33
but delivered something individually unique to each person who contributed?
387
978000
3000
16:36
So the project starts off by asking you to enter the address
388
981000
2000
16:38
of the home where you grew up.
389
983000
2000
16:40
And you type in the address --
390
985000
2000
16:42
it actually creates a music video specifically for you,
391
987000
2000
16:44
pulling in Google maps and Streetview images
392
989000
2000
16:46
into the experience itself.
393
991000
2000
16:48
So this should really be seen at home with you typing in your own address,
394
993000
3000
16:51
but I'm going to give you a little preview of what you can expect.
395
996000
3000
16:54
(Video) Win Butler: ♫ Now our lives are changing fast ♫
396
999000
4000
16:58
♫ Now our lives are changing fast ♫
397
1003000
4000
17:02
♫ Hope that something pure can last ♫
398
1007000
3000
17:06
♫ Hope that something pure can last ♫
399
1011000
4000
17:13
♫ Ooh we used to wait ♫
400
1018000
4000
17:17
♫ Ooh we used to wait ♫
401
1022000
4000
17:21
♫ Ooh we used to wait ♫
402
1026000
3000
17:24
♫ Sometimes it never came ♫
403
1029000
3000
17:27
♫ Sometimes it never came ♫
404
1032000
4000
17:31
♫ Still moving through the pain ♫
405
1036000
3000
17:34
♫ We used to wait for it ♫
406
1039000
4000
17:38
♫ We used to wait for it ♫
407
1043000
4000
17:42
♫ We used to wait for it ♫
408
1047000
4000
17:50
AK: So I think, if there's one thing to take away from my talk today,
409
1055000
3000
17:53
it's that an interface can be a powerful narrative device.
410
1058000
2000
17:55
And as we collect more and more personally and socially relevant data,
411
1060000
4000
17:59
we have an opportunity, and maybe even an obligation,
412
1064000
2000
18:01
to maintain the humanity and tell some amazing stories
413
1066000
2000
18:03
as we explore and collaborate together.
414
1068000
2000
18:05
Thanks a lot.
415
1070000
2000
18:07
(Applause)
416
1072000
4000

▲Back to top

ABOUT THE SPEAKER
Aaron Koblin - Data artist
Aaron Koblin is an artist specializing in data and digital technologies. His work takes real world and community-generated data and uses it to reflect on cultural trends and the changing relationship between humans and technology.

Why you should listen

Aaron Koblin finds art through the unlikely confluence of massive data sets and personal intimacy. His work ranges from animating the paths of every North American airline flight, to using Amazon’s Mechanical Turk crowdsourcing platform to pay workers to “draw a sheep facing left,” which were then placed in "The Sheep Market."

Koblin was creative director for Johnny Cash's final music video, "Ain't No Grave," and worked on Radiohead’s video “House of Cards,” both of which received a Grammy nomination. He is now the Creative Director of the Data Arts team in Google's Creative Lab. His team collaborated with Arcade Fire to produce an online music video that allows viewers to incorporate images of their home neighborhood into the experience using Google Street View.

More profile about the speaker
Aaron Koblin | Speaker | TED.com