TEDxVancouver
Jer Thorp: Make data more human
Filmed:
Readability: 4.1
300,699 views
Jer Thorp creates beautiful data visualizations to put abstract data into a human context. At TEDxVancouver, he shares his moving projects, from graphing an entire year’s news cycle, to mapping the way people share articles across the internet.
(Filmed at TEDxVancouver.)
Jer Thorp - Data artist
Jer Thorp’s work focuses on adding meaning and narrative to huge amounts of data as a way to help people take control of the information that surrounds them. Full bio
Jer Thorp’s work focuses on adding meaning and narrative to huge amounts of data as a way to help people take control of the information that surrounds them. Full bio
Double-click the English transcript below to play the video.
00:10
I want to talk to you about two
of the most exciting possible things.
of the most exciting possible things.
0
1674
6250
我想要向各位講所有可能的事物中
兩個最讓人興奮的主題。
兩個最讓人興奮的主題。
00:16
You've probably guessed what they are --
1
7948
1949
你們可能已經猜到是什麼了——
00:18
data and history.
2
9921
2319
數據和歷史。
00:21
Right?
3
13211
1171
對吧？
00:24
So, I'm not a historian.
4
15871
1982
嗯，我不是歷史學家。
00:26
I'm not going to give you
a definition of history.
a definition of history.
5
17877
2728
我不會向各位闡述「歷史」的定義。
00:29
But let's think instead
of history within a framework.
of history within a framework.
6
20629
3113
但是，讓我們換種方式，
在一個框架中來看待歷史。
在一個框架中來看待歷史。
00:32
So, when we're making history,
7
23766
1611
所以，當我們在創造歷史時，
00:33
or when we're creating
historical documents,
historical documents,
8
25401
2892
或當我們在創造歷史文獻時，
00:36
we're taking things
that have happened in the past,
that have happened in the past,
9
28317
2428
我們是把過去已經
發生過的事情列出來，
發生過的事情列出來，
00:39
and we're stitching them
together into a story.
together into a story.
10
30769
2552
並把它們拼湊起來，組成一個故事。
所以，讓我從我自己的故事開始吧。
00:41
So let me start with a little bit
of my own story.
of my own story.
11
33345
2530
00:44
Like anybody my age
who works creatively with computers,
who works creatively with computers,
12
35899
3678
和那些藉助電腦來進行
創意性的工作的同齡人一樣，
創意性的工作的同齡人一樣，
00:48
I was a popular, socially
well-adjusted young man --
well-adjusted young man --
13
39601
4456
（反話）我當然也是個很受歡迎，
且社交適應良好的年輕人——
且社交適應良好的年輕人——
00:52
(Laughter)
14
44081
1122
（笑聲）
00:53
And sporty!
15
45227
2541
而且愛運動！是個愛運動的年輕人。
00:56
Sporty young man.
16
47792
1733
00:58
And like a lot of people my age
in the type of business that I'm in,
in the type of business that I'm in,
17
50075
5353
而且也和我工作的這個
產業中許多同齡人一樣，
產業中許多同齡人一樣，
01:03
I was influenced tremendously by Apple.
18
55452
2645
蘋果公司對我的影響非常巨大。
01:07
But notice my choice of logo here, right?
19
58635
3722
但是，注意一下我所選擇的商標。
01:10
The Apple on the left,
not the Apple on the right.
not the Apple on the right.
20
62381
3585
是左邊的蘋果商標，而不是右邊的。
01:15
I'm influenced as much
by the Apple on the right
by the Apple on the right
21
66621
2293
右邊的蘋果對我的影響與普通人無異，
01:17
as the next person,
22
68938
2083
01:19
but the Apple on the left --
I mean, look at that logo!
I mean, look at that logo!
23
71045
2633
但左邊的蘋果——
我的意思是，看看那商標！
我的意思是，看看那商標！
01:22
It's a rainbow.
It's not even in the right order!
It's not even in the right order!
24
73702
2397
它是彩虹。顏色順序還不對呢！
01:24
(Laughter)
25
76123
1134
（笑聲）
01:25
That's how crazy Apple was.
26
77281
2273
蘋果曾經就是那麼瘋狂。
01:28
(Laughter)
27
79578
1037
（笑聲）
01:29
But I don't want to talk too much
about the company.
about the company.
28
80639
2945
但我想談的不是這間公司。
01:32
I'll start talking about
a machine, though.
a machine, though.
29
83608
2177
不過，我會先從一台機器談起。
01:34
How amazing it is to think about this.
I go back and I think about this.
I go back and I think about this.
30
85809
4127
想想這有多麼不可思議。
我回想起這件事。
我回想起這件事。
01:38
Wednesday -- one Wednesday,
when I was about 12 years old,
when I was about 12 years old,
31
89960
3314
星期三，某個星期三，
我大約十二歲的時候，
我大約十二歲的時候，
01:41
I didn't have a computer.
32
93298
2111
我沒有電腦。
01:44
On Thursday, I had a computer.
33
96034
2779
星期四，我有了一台電腦。
01:48
Can you imagine that change?
34
99965
1999
你能想像那種改變嗎？
01:50
It's so drastic.
35
102417
1681
太巨大了。
01:52
I can't even think about anything
that could change our lives that way.
that could change our lives that way.
36
104122
3451
我甚至無法想出其他東西也
能夠像這樣改變你的人生。
能夠像這樣改變你的人生。
01:56
But I'm actually not even going
to talk about the computer.
to talk about the computer.
37
107597
2767
但其實我要談的並不是那台電腦。
我要談的是裝載在
那台電腦中的一個程式。
那台電腦中的一個程式。
01:58
I'm going to talk about a program
that came loaded on that computer.
that came loaded on that computer.
38
110388
3230
創造它的人，不是左邊的那位，
02:02
And it was build by,
not the guy on the left,
not the guy on the left,
39
113642
2272
02:04
but the guy on the right.
40
115938
1435
而是右邊的那位。
02:05
Does anybody know
who the guy on the right is?
who the guy on the right is?
41
117397
2144
有人知道右邊的那位是誰嗎？
02:09
Nobody ever knows the answer
to this question.
to this question.
42
121161
2410
從來沒有人知道這個問題的答案。
那是比爾 · 阿特金森。
02:12
This is Bill Atkinson.
43
123595
1686
02:13
And Bill Atkinson was responsible
for tons of things
for tons of things
44
125305
3107
現今你每天在電腦上
看到的很多東西，
看到的很多東西，
02:16
that you see on your computer every day.
45
128436
2482
都出自比爾 · 阿特金森之手。
02:19
But I want to talk about one program
that Bill Atkinson wrote,
that Bill Atkinson wrote,
46
130942
3107
但我想談其中一個他所寫的程式，
02:22
called HyperCard.
47
134073
1500
叫做 HyperCard。
02:25
Someone's cheering over there.
48
137025
2160
那邊有人在歡呼。
02:27
(Laughter)
49
139209
1205
（笑聲）
02:28
HyperCard was a program
that shipped with the Mac,
that shipped with the Mac,
50
140438
2602
HyperCard 這個程式
是內建在麥金塔電腦上的，
是內建在麥金塔電腦上的，
02:31
and it was designed
for users of the computer
for users of the computer
51
143064
2569
它設計的目的，
是要讓電腦的使用者
是要讓電腦的使用者
02:34
to make programs on their computers.
52
145657
3197
能在自己的電腦上寫程式。
02:38
Crazy idea today.
53
149505
1597
現今是個瘋狂的點子。
02:39
And these programs were not the apps
that we think about today,
that we think about today,
54
151126
2973
這些程式並不是我們
現今認為的應用程式，
現今認為的應用程式，
02:42
with their large budgets
and their big distribution.
and their big distribution.
55
154123
2449
並沒有高額預算和大量銷售。
這些是小東西，
02:45
These were small things,
56
156596
1189
大家會寫些應用程式來追蹤
他們當地籃球隊的戰績，
他們當地籃球隊的戰績，
02:46
people making applications to keep track
of their local basketball team scores
of their local basketball team scores
57
157809
3882
02:50
or to organize their research
58
161715
2825
或是整理他們的研究，
02:53
or to teach people about classical music
59
164564
3016
或是教別人古典音樂，
02:56
or to calculate weird astronomical dates.
60
167604
4095
或是計算很奇怪的天文學日期。
03:00
And then, of course,
there were some art projects.
there were some art projects.
61
171723
2381
當然，還有一些藝術專案計畫。
03:02
This is my favorite one.
62
174128
1220
這個是我的最愛。它叫做
「如果僧侶有麥金塔」，
「如果僧侶有麥金塔」，
03:03
It's called "If Monks Had Macs,"
63
175372
2089
03:05
and it's a nonlinear
kind of exploratory environment.
kind of exploratory environment.
64
177485
4534
這是一個非線性的探索式環境。
03:10
I thank the stars for HyperCard
all of the time.
all of the time.
65
182043
5573
我總是感謝上天，讓我
幸運地擁有 HyperCard。
幸運地擁有 HyperCard。
03:16
And I thank the stars
for putting me in this era
for putting me in this era
66
187640
2447
我也感謝上天讓我生在這個時代，
03:18
where I got to use HyperCard.
67
190111
2300
讓我可以用到 HyperCard。
03:20
HyperCard was the last program to ship
on a public computer
on a public computer
68
192435
4640
HyperCard 是公共電腦上
內建的最後一個
內建的最後一個
03:25
that was designed for the users
of the computer to make programs with it.
of the computer to make programs with it.
69
197099
5129
能讓使用者在電腦上寫程式的程式。
03:30
If you talked to the people
who invented the computer
who invented the computer
70
202252
2705
如果你和發明電腦的人談談，
03:33
and you told them there would be
a day, a magical day,
a day, a magical day,
71
204981
2749
你告訴他們，將來有一天，
很神奇的一天，
很神奇的一天，
03:36
when everybody had a computer
but none of them knew how to program,
but none of them knew how to program,
72
207754
5062
每個人都有一台電腦，
但沒有人會寫程式，
03:41
they would think you were crazy.
73
212840
1811
他們會認為你瘋了。
03:43
So let's skip forward a few years.
74
215486
1664
所以，咱們來看看幾年之後。
03:45
I'm starting my career as an artist,
75
217174
2588
我的職涯一開始是藝術家，
03:48
and I'm building things
with my computer, small-scale things,
with my computer, small-scale things,
76
219786
3962
我用我的電腦來創造
圖像，小規模的圖像，
圖像，小規模的圖像，
03:52
investigating things like
the growth systems of plants.
the growth systems of plants.
77
223772
3603
類似于研究植物的生長系統之類的。
03:55
Or, in this example, I'm building
a simulated economy
a simulated economy
78
227399
2999
或者，在這個例子中，
我在打造的是一種模擬的經濟，
我在打造的是一種模擬的經濟，
03:58
in which pixels are trading color
with one another,
with one another,
79
230422
3961
像素在彼此之間交換顏色，
04:02
trying to investigate how
these types of systems work,
these types of systems work,
80
234407
2575
試圖了解這類系統如何
運作，這些也挺好玩的。
運作，這些也挺好玩的。
04:05
and just kind of having fun.
81
237006
1402
04:06
And then this project led me
to start working with data.
to start working with data.
82
238432
2628
這個專案計畫讓我開始用到數據。
04:09
So I'm building graphics like this,
83
241084
2989
所以我會創造像這樣的圖像，
04:12
which compare "communism" --
84
244097
2594
它把「共產主義」這個詞在
《紐約時報》中出現的次數
《紐約時報》中出現的次數
04:15
the frequency of usage of the word
"communism" in the New York Times --
"communism" in the New York Times --
85
246715
3395
和上半部分「恐怖主義」的出現次數做比較。
04:18
to "terrorism," at the top.
86
250134
1937
04:20
You see "terrorism" kind of appears
as "communism" is going away.
as "communism" is going away.
87
252095
4625
你們可以看到，在「共產主義」
減弱時「恐怖主義」就會出現。
減弱時「恐怖主義」就會出現。
04:25
And with these graphics, I was really
interested in the aesthetic of the graphs.
interested in the aesthetic of the graphs.
88
256744
3816
因為有這些圖，讓我對
圖的美感產生很大的興趣。
圖的美感產生很大的興趣。
這描繪的是伊朗和伊拉克。
04:29
This is Iran and Iraq.
89
260584
1150
04:30
It reads like a clock. It's called
a "timepiece graph."
a "timepiece graph."
90
261758
3910
它看起來就像時鐘一樣，
它叫做「鐘表圖」。
它叫做「鐘表圖」。
04:34
This is another timepiece graph,
overlaying "despair" over "hope."
overlaying "despair" over "hope."
91
265692
5711
這是另一張鐘表圖，
把「絕望」疊在「希望」之上。
04:39
And there's only three times -- actually,
it's "crisis" over "hope" --
it's "crisis" over "hope" --
92
271427
3310
僅有三次——其實，這是把
「危機」疊在「希望」之上——
「危機」疊在「希望」之上——
04:43
there's only three times
when "crisis" eclipses "hope."
when "crisis" eclipses "hope."
93
274761
2609
僅有三次是「危機」
完全遮蔽了「希望」。
完全遮蔽了「希望」。
我們現在正處於其中一次。
但對這點別想太多。
但對這點別想太多。
04:45
We're in the middle
of one of them right now.
of one of them right now.
94
277394
2155
04:48
But don't think about that too much.
95
279573
1772
（笑聲）
04:49
(Laughter)
96
281369
1888
04:51
And finally, the culmination of this work
with the New York Times data
with the New York Times data
97
283281
3780
最後，幾年前，結合
《紐約時報》的資料
《紐約時報》的資料
讓這項工作達到了高潮，
04:55
a few years ago
98
287085
1202
04:56
was the attempt to combine
an entire year's news cycle
an entire year's news cycle
99
288311
3176
它試圖將整年的新聞週期
整合、體現在一張圖像上。
整合、體現在一張圖像上。
05:00
into a single graphic.
100
291511
1313
05:01
So these graphics actually show us
a full year of news, all the people,
a full year of news, all the people,
101
292848
4227
這些圖像呈現給我們的是
全年的新聞以及所有的人物，
全年的新聞以及所有的人物，
05:05
and how they're connected
into a single graphic.
into a single graphic.
102
297099
2630
以及他們如何相互連結
而成為一張圖像。
而成為一張圖像。
05:08
And from there, I started to be
interested again in more active systems.
interested again in more active systems.
103
299753
3938
從這時，我再次開始對
更加活躍的系統感興趣。
更加活躍的系統感興趣。
05:12
Here's a project called "Just Landed,"
104
303715
2264
這是一個叫做
「剛降落」的專案計畫，
「剛降落」的專案計畫，
05:14
where I'm looking at people
tweeting on Twitter.
tweeting on Twitter.
105
306003
3151
在這之中，我觀察人們
在推特上發表推文：
在推特上發表推文：
05:17
"Hey! I just landed
in Hawaii!" -- you know,
in Hawaii!" -- you know,
106
309178
2060
「嘿！我剛在夏威夷降落了！」
——你們知道的，
——你們知道的，
05:19
how people just casually try to sneak
that into their Twitter conversation.
that into their Twitter conversation.
107
311262
3702
大家就是這樣隨意地在推特
對話中泄露那些資訊。
對話中泄露那些資訊。
05:23
"I'm not showing off. Really.
But I did just land in Hawaii."
But I did just land in Hawaii."
108
314988
3117
「我真的不是在炫耀。
但我剛在夏威夷降落了。」
但我剛在夏威夷降落了。」
05:26
And then I'm plotting
those people's trips,
those people's trips,
109
318129
2743
接著，我把這些人的行程畫出來，
05:29
in the hopes that maybe
we can use social network
we can use social network
110
320896
3212
希望我們能夠利用社交網路
05:32
and the data that it leaves behind
111
324132
1681
以及它所留下的資料來建立
一個描述大家如何移動的模型，
一個描述大家如何移動的模型，
05:34
to provide a model of how people move,
112
325837
2199
05:36
which would be valuable
to epidemiologists, among other people.
to epidemiologists, among other people.
113
328060
2975
這對於流行病學家
及其他人會很有價值。
及其他人會很有價值。
05:39
And, more fun -- this
is a similar project,
is a similar project,
114
331059
2579
還有更好玩的——這是個
類似的專案計畫，
類似的專案計畫，
05:42
looking at people
saying "Good morning" to each other
saying "Good morning" to each other
115
333662
2491
觀察全世界的人
對彼此說「早安」。
對彼此說「早安」。
05:44
all around the world.
116
336177
1183
05:45
Which taught me, by the way,
117
337384
1434
順道一提，這還讓我學到，
05:47
that it is true that people in Vancouver
on the West Coast wake up much later
on the West Coast wake up much later
118
338842
4350
溫哥華西岸的人起床、
說「早安」的時間
說「早安」的時間
05:51
and say "Good morning" much later
119
343216
1583
比起東岸的人要晚得多，
05:53
than the people on the East Coast,
120
344823
1861
05:55
who are more adventurous.
121
346708
1799
而東岸的人比較愛冒險。
05:57
Here's a more useful -- maybe -- project,
122
348531
1974
這也許又是一個
更有用的專案計畫，
更有用的專案計畫，
05:59
where I took all the information
from the Kepler Project
from the Kepler Project
123
350529
3351
我利用了克普勒計畫的所有資料，
06:02
and tried to put it into some visual form
that made sense to me.
that made sense to me.
124
353904
3043
試著將它們轉換成一種
讓我覺得合理的視覺形式。
讓我覺得合理的視覺形式。
06:05
And I should say that everything
I've shown you up to now --
I've shown you up to now --
125
356971
2884
我得說，到目前為止，
我給各位看的所有東西——
我給各位看的所有東西——
都是因為好玩而做的。
06:08
these are all things
that I just did for fun.
that I just did for fun.
126
359879
2152
聽起來可能很奇怪，
但這些都源自 HyperCard。
但這些都源自 HyperCard。
06:10
It may seem weird,
but this comes back from HyperCard.
but this comes back from HyperCard.
127
362055
2735
06:13
I'm building tools for myself.
128
364814
1830
我在為我自己建造工具，
我可能會和少數人分享這些工具，
我可能會和少數人分享這些工具，
06:15
I may share them with a few other people,
129
366668
1983
06:17
but they're for fun, they're for me.
130
368675
2107
但我是樂在其中，為我自己而做。
06:21
So, all these tools I show you
kind of occupy this weird space
kind of occupy this weird space
131
373341
3970
所以，我給各位看的這些工具，
算是隸屬於科學、藝術
和設計之間的奇怪領域。
和設計之間的奇怪領域。
06:25
somewhere between science, art and design.
132
377335
2544
06:28
That's where my practice lies.
133
379903
1805
我的工作領域就在這塊空間上。
06:30
And still today,
from my experience with HyperCard,
from my experience with HyperCard,
134
381732
3156
現今也一樣，根據我用
HyperCard 的經驗，
HyperCard 的經驗，
06:33
what I'm doing is building visual tools
to help me understand systems.
to help me understand systems.
135
384912
4230
我在做的事情，就是建立視覺
工具來協助我理解多種系統。
工具來協助我理解多種系統。
06:38
So today, I work at the New York Times.
136
390083
2221
所以，現今，
我在《紐約時報》工作，
我在《紐約時報》工作，
06:40
I'm the data artist in residence
at the New York Times.
at the New York Times.
137
392328
2873
我是《紐約時報》的
常駐數據藝術家。
常駐數據藝術家。
06:43
And I've had an opportunity at the Times
138
395225
1933
在《紐約時報》，我有機會
06:45
to work on a variety
of really interesting projects,
of really interesting projects,
139
397182
2464
可以參與各種非常
有趣的專案計畫，
有趣的專案計畫，
今天我會和各位分享其中兩個。
06:48
two of which I'm going
to share with you today.
to share with you today.
140
399670
2222
第一個，我一直在和馬克 · 漢森合作。
06:50
The first one, I've been working on
in conjunction with Mark Hansen.
in conjunction with Mark Hansen.
141
401916
3202
06:53
Mark Hansen is a professor of statistics
at UCLA. He's also a media artist.
at UCLA. He's also a media artist.
142
405142
5142
馬克 · 漢森是加州大學
洛杉磯分校的統計學教授。
洛杉磯分校的統計學教授。
他也是位媒體藝術家。
06:58
And Mark came to the Times
with a very interesting question
with a very interesting question
143
410308
2786
馬克帶著一個很有趣的
問題來到《紐約時報》，
問題來到《紐約時報》，
07:01
to what may seem like an obvious problem:
144
413118
2660
這似乎是個很明顯的問題：
07:04
When people share content on the internet,
145
415802
3151
當大家在網路上分享內容，
07:07
how does that content get
from person A to person B?
from person A to person B?
146
418977
3615
這些內容如何從用戶甲
傳播到用戶乙那邊？
傳播到用戶乙那邊？
07:11
Or maybe, person A to person B
to person C to person D?
to person C to person D?
147
423358
4724
或者也許是從甲傳到乙，
再傳到丙，再傳到丁？
再傳到丙，再傳到丁？
07:16
We know that people share content
in the internet,
in the internet,
148
428106
2354
我們都知道大家會
在網路上分享內容，
在網路上分享內容，
但我們不知道在兩位用戶
之間，到底發生了什麽。
之間，到底發生了什麽。
07:18
but what we don't know
is what happens in that gap
is what happens in that gap
149
430484
2358
07:21
between one person to the other.
150
432866
1791
07:23
So we decided to build
the tool to explore that,
the tool to explore that,
151
434681
2356
所以我們決定建造
一個工具來探索這件事，
一個工具來探索這件事，
07:25
and this tool is called Cascade.
152
437061
1823
這個工具叫做 Cascade。
07:27
If we look at these systems
153
439471
2595
如果我們來觀察這些系統，
07:30
that start with one event
that leads to other events,
that leads to other events,
154
442090
4430
以某個事件作為起點，
接著導致其他事件，
接著導致其他事件，
07:35
we call that structure a cascade.
155
446544
2238
我們就把這個結構
叫做一個「事件串」。
叫做一個「事件串」。
07:37
And these cascades
actually happen over time.
actually happen over time.
156
448806
2409
實際上，這些「事件串」隨
時間的增長而不斷發生。
時間的增長而不斷發生。
07:39
So we can model these things over time.
157
451239
2020
所以我們可以根據
時間來對它們建立模型。
時間來對它們建立模型。
07:41
Now, the New York Times has
a lot of people who share our content,
a lot of people who share our content,
158
453283
4031
《紐約時報》有很多人
在分享我們的內容，
在分享我們的內容，
07:45
so the cascades do not look like that one,
they look more like this.
they look more like this.
159
457338
3491
所以這個「事件串」看起來不像是
之前那樣，而是比較像是這樣子。
之前那樣，而是比較像是這樣子。
這是一個典型的事件串。
最初的事件在左下方。
最初的事件在左下方。
07:49
Here's a typical cascade.
160
460853
1540
07:50
At the bottom left, the very first event.
161
462417
2714
07:54
And then as people are sharing
the content from one person to another,
the content from one person to another,
162
466237
4272
接著，當大家分享內容，
從一個人傳給另一個人，
從一個人傳給另一個人，
07:59
we go up in the Y axis,
degrees of separation,
degrees of separation,
163
470533
3794
Y 軸就會向上延伸，
此軸衡量逐級傳播的程度，
此軸衡量逐級傳播的程度，
08:02
and over on the X axis, for time.
164
474351
2768
X 軸則是表示時間。
08:05
So we're able to look at that conversation
in a couple of different views:
in a couple of different views:
165
477143
3501
所以我們可以從幾個不同的
視角來觀察這整個交流過程：
視角來觀察這整個交流過程：
08:09
this one, which shows us
the threads of conversation,
the threads of conversation,
166
480668
2615
這個視角，讓我們看到
交流過程中的一條條脈絡，
交流過程中的一條條脈絡，
08:11
and this one, which combines
that stacked view
that stacked view
167
483307
3194
這個，則是結合了堆疊的視角，
08:15
with a view that lets us see the threads.
168
486525
2932
加上另一個讓我們能看見脈絡的視角。
08:18
Now, the Times publishes
about 7,000 pieces of content
about 7,000 pieces of content
169
489924
3345
《紐約時報》每個月
會刊出約七千則內容。
會刊出約七千則內容。
08:21
every month.
170
493293
1210
08:23
So it was important for us,
when we were building this tool,
when we were building this tool,
171
494527
2842
所以當我們創造這個工具時，讓它
具有探索性，對我們來說是很重要的，
具有探索性，對我們來說是很重要的，
08:25
to make it an exploratory one,
172
497393
1633
08:27
so that people could dig through
this vast terrain of data.
this vast terrain of data.
173
499050
4207
這樣大家就能用它來挖掘
這廣闊的數據地帶。
這廣闊的數據地帶。
08:31
I think of it as a vehicle
that we're giving people
that we're giving people
174
503281
2436
我認為我們是給了
大家一種交通工具，
大家一種交通工具，
讓他們能在這廣闊的數據地帶中穿梭。
08:34
to traverse this really big
terrain of data.
terrain of data.
175
505741
3473
08:37
So here's what it really looks like,
176
509238
1718
這是它真正的樣子，
08:39
and here's the cascade
playing in real time.
playing in real time.
177
510980
2740
這是即時進行中的「事件串」。
08:42
I have to say, this was
a tremendous moment.
a tremendous moment.
178
513744
2079
我必須要說，這是很棒的一刻。
08:44
We had been working with canned
data, fake data, for so long,
data, fake data, for so long,
179
515847
4017
過去一直以來，我們用的資料
很多都是二手資料、假資料，
很多都是二手資料、假資料，
08:48
that when we saw this
for the first moment,
for the first moment,
180
519888
2805
所以當我們第一次看到這個成果時，
08:51
it was like an archaeologist who had
just dusted off these dinosaur bones.
just dusted off these dinosaur bones.
181
522717
4878
就如同考古學家拂去恐龍
骨頭上的灰塵時的反應。
骨頭上的灰塵時的反應。
08:56
We discovered this thing,
and we were seeing it for the first time,
and we were seeing it for the first time,
182
527619
3878
我們發現了它，第一次看到了它，
09:00
these sharing structures
that underlie the internet.
that underlie the internet.
183
531521
3712
在網際網路的背後，
內容分享的傳播結構。
內容分享的傳播結構。
09:04
And maybe the dinosaur
analogy is a good one,
analogy is a good one,
184
536475
2105
也許上述的恐龍的比喻十分貼切，
09:07
because we're actually making
some probabilistic guesses
some probabilistic guesses
185
538604
3047
因為我們實際上是對這些
事物如何連結做可能性的猜測。
事物如何連結做可能性的猜測。
09:10
about how these things link.
186
541675
1359
09:11
We're looking at some of these
pieces and making some guesses,
pieces and making some guesses,
187
543058
2926
我們觀察其中一些
片段，做出猜測，
片段，做出猜測，
09:14
but we try to make sure that those
are as statistically rigorous as possible.
are as statistically rigorous as possible.
188
546008
3937
但我們也盡可能確保這些
猜測在統計學上的嚴謹性。
猜測在統計學上的嚴謹性。
09:19
Now tweets, in this case,
they become parts of stories.
they become parts of stories.
189
550720
4662
在這個案例中，推特的推文
變成了故事的一部分。
變成了故事的一部分。
09:23
They become parts of narratives.
190
555406
1925
它們變成了敘事的一部分。
09:25
So we are building histories here,
191
557355
2420
在這裡，我們是在建造歷史，
09:28
but they're very short-term histories.
192
559799
2175
但是是非常短期的歷史。
09:30
And sometimes these very large cascades
are the most interesting ones,
are the most interesting ones,
193
561998
3838
有時，這些非常大的
「事件串」是最有趣的，
「事件串」是最有趣的，
09:34
but sometimes the small ones
are also interesting.
are also interesting.
194
565860
3135
但有時，小的「事件串」也很有趣。
09:37
This is one of my favorites.
We call this the "Rabbi Cascade."
We call this the "Rabbi Cascade."
195
569019
3525
這是我的最愛之一。
我們稱它為「拉比事件串」。
我們稱它為「拉比事件串」。
09:41
It's a conversation amongst rabbis
about this article in the New York Times,
about this article in the New York Times,
196
572568
5089
這是拉比之間的對談，
談論《紐約時報》上的一篇文章，
09:46
about the fact that religious workers
don't get a lot of time off.
don't get a lot of time off.
197
577681
3772
文章寫的是宗教工作者
沒有很多休假的議題。
沒有很多休假的議題。
09:49
I guess Saturdays and Sundays are bad days
for them to take off.
for them to take off.
198
581477
4035
我想他們大概不適合
在星期六和星期日休假吧。
在星期六和星期日休假吧。
09:54
So, in this cascade, there's a group
of rabbis having a conversation
of rabbis having a conversation
199
585536
3692
在這個事件串中，有一群拉比
在談《紐約時報》的報導。
09:57
about a New York Times story.
200
589252
1402
09:59
One of them has the best
Twitter name ever --
Twitter name ever --
201
590678
2124
其中一個人有著史上
最棒的推特名稱——
最棒的推特名稱——
10:01
he's called "The Velveteen Rabbi."
202
592826
1855
他自稱「棉製天鵝絨拉比」。
10:03
(Laughter)
203
594705
2323
（笑聲）
10:05
But we would have never found this
if it weren't for this exploratory tool.
if it weren't for this exploratory tool.
204
597052
4507
但若沒有這個探索工具，
我們永遠不會發現這些。
我們永遠不會發現這些。
10:10
This would just be sitting somewhere,
205
601583
1802
這些事可能就只會存在於某處，
但我們永遠不會發現。
但我們永遠不會發現。
10:11
and we would have never
been able to see that.
been able to see that.
206
603409
2186
10:14
But this exercise of taking
single pieces of information
single pieces of information
207
605619
4141
但這項做法，使用單獨的片段資訊
10:18
and building narrative structures,
building histories out of them,
building histories out of them,
208
609784
4221
來建立敘事結構，
從中建立出歷史來，
從中建立出歷史來，
10:22
I find tremendously interesting.
209
614029
1925
我覺得非常有意思。
10:24
You know, I moved to New York
about two years ago.
about two years ago.
210
616319
2344
我大約兩年前搬到紐約。
10:27
And in New York, everybody has a story
211
618687
2720
在紐約，每個人都有一個故事：
10:29
that surrounds this
tremendously impactful event
tremendously impactful event
212
621431
2960
和 2001 年 9 月 11 日那天
發生的震撼事件有關的故事。
發生的震撼事件有關的故事。
10:32
that happened on September 11 of 2001.
213
624415
2299
10:35
And my own story with September 11
has really become a more intricate one,
has really become a more intricate one,
214
627373
6367
我自己的與 911 相關的故事
變得很錯綜複雜，
10:42
because I spent a great deal of time
215
633764
2064
因為我花了相當多時間
10:44
working on a piece
of the 9/11 Memorial in Manhattan.
of the 9/11 Memorial in Manhattan.
216
635852
4149
在做一件關於曼哈頓
911 紀念碑的案子。
911 紀念碑的案子。
10:49
The central idea about the 9/11 Memorial
217
640530
2564
911 紀念碑的中心想法在於，
10:51
is that the names in the memorial
are not laid out in alphabetical order
are not laid out in alphabetical order
218
643118
4459
紀念碑上的名字並不是
照字母或時間順序來排列，
10:56
or chronological order,
219
647601
1685
10:57
but instead, they're laid out in a way
220
649310
1824
而要改用另一種方式呈現，
10:59
in which the relationships
between the people who were killed
between the people who were killed
221
651158
3424
讓死者之間的關係
能夠被具體呈現在紀念碑上。
11:03
are embodied in the memorial.
222
654606
1960
11:05
Brothers are placed next to brothers,
223
657153
2538
兄弟要放在彼此旁邊，
11:08
coworkers are placed together.
224
659715
2185
同事要擺在一起。
11:10
So this memorial actually considers
all of these myriad connections
all of these myriad connections
225
661924
4665
所以，這個紀念碑其實
考量了極大量的連結，
考量了極大量的連結，
11:15
that were part of these people's lives.
226
666613
2421
這些連結是這些人生命的一部分。
11:18
I worked with a company
called Local Projects
called Local Projects
227
670310
3433
我和 Local Projects
這間公司合作，
這間公司合作，
11:22
to work on an algorithm
and a software tool
and a software tool
228
673767
2674
撰寫演算法和軟體工具
11:24
to help the architects build
the layout for the memorial:
the layout for the memorial:
229
676465
3004
來協助建築師建立
紀念碑的版面編排：
紀念碑的版面編排：
11:28
almost 3,000 names
230
680331
1722
近三千個名字，
11:30
and almost 1,500 of these
adjacency requests,
adjacency requests,
231
682077
3627
且有近一千五百個
使名字相鄰擺放的需求，
使名字相鄰擺放的需求，
11:34
these requests for connection --
232
685728
1610
這些連結的需求——
11:35
so a very dense story,
a very dense narrative,
a very dense narrative,
233
687362
3386
非常緊密的故事，
非常緊密的敘事，
非常緊密的敘事，
11:39
that becomes an embodied part
of this memorial.
of this memorial.
234
690772
2816
變成了紀念碑體現出來的一部分。
11:42
Working with Jake Barton,
we produce the software tool,
we produce the software tool,
235
694195
3331
我和傑克 · 巴頓合作，
製造出了這個軟體工具，
製造出了這個軟體工具，
11:46
which allows the architects to,
first of all, generate a layout
first of all, generate a layout
236
697550
4119
讓建築師首先就可以產生出一種編排，
11:50
that satisfied all of those
adjacency requests,
adjacency requests,
237
701693
3129
滿足所有那些毗鄰的需求，
11:53
but then second, make little adjustments
where they needed to
where they needed to
238
704846
3033
接著，第二，做一些必要的調整，
11:56
to tell the stories
that they wanted to tell.
that they wanted to tell.
239
707903
2348
來將他們想要說的故事說出來。
11:59
So this memorial, I think,
has an incredibly timely concept
has an incredibly timely concept
240
711219
4135
所以，我認為，在我們
這個社交網路的時代，
這個社交網路的時代，
12:03
in our era of social networks,
241
715378
2990
這個紀念碑有著非常適時的概念，
12:06
because these networks -- these real-life
networks that make up people's lives --
networks that make up people's lives --
242
718392
3975
因為這些網路——這些構成
人們生命的現實生活網路——
人們生命的現實生活網路——
12:10
are actually embodied
inside of the memorial.
inside of the memorial.
243
722391
2432
實際上正呈現在這個紀念碑當中。
12:13
And one of the most tremendously
moving experiences
moving experiences
244
725286
3471
最感人的體驗之一，
12:17
is to go to the memorial
245
728781
1661
就是去看這個紀念碑，
12:18
and see how these people
are placed next to each other,
are placed next to each other,
246
730466
4200
看看這些人如何
被安排在彼此旁邊，
被安排在彼此旁邊，
12:23
so that this memorial
is representing their own lives.
is representing their own lives.
247
734690
2862
這個紀念碑重新展現出
他們自己的生活。
他們自己的生活。
12:27
How does this affect our lives?
248
738859
1687
這對我們的人生有什麼影響？
12:29
Well, I don't know if you remember,
249
741133
1676
嗯，我不知道你們是否記得，
12:31
but in the spring,
there was a controversy,
there was a controversy,
250
742833
2713
在今天春天發生了一件充滿爭議的事，
12:34
because it was discovered
that on the iPhone
that on the iPhone
251
745570
2198
有人發現，在 iPhone 上，
而且實際上，在你的電腦上也是，
而且實際上，在你的電腦上也是，
12:36
and, actually, on your computer,
252
747792
1606
12:37
we were storing a tremendous amount
of the location data.
of the location data.
253
749422
3315
我們都儲存了非常
大量的位置資料。
大量的位置資料。
12:41
So Apple responded, saying,
this was not location data about you,
this was not location data about you,
254
753173
3861
所以，蘋果公司的回應是，
這些位置資料都與你無關，
這些位置資料都與你無關，
12:45
it was location data
about wireless networks
about wireless networks
255
757058
2805
這些位置資料記錄的是
你所在地區的無線網路。
12:48
that were in the area where you are.
256
759887
2287
不是在記錄你本身，
而是在記錄你所在的地方。
而是在記錄你所在的地方。
12:50
So it's not about you,
257
762198
1428
12:52
but it's about where you are.
258
763650
1584
12:53
(Laughter)
259
765258
1648
（笑聲）
12:55
This is very valuable data.
260
766930
2808
這些是非常珍貴的資料。
12:58
It's like gold to researchers,
this human-mobility data.
this human-mobility data.
261
769762
4625
這些人群流動資料對研究者
來說就像是金礦一樣。
來說就像是金礦一樣。
13:02
So we thought, "Man!
How many people have iPhones?"
How many people have iPhones?"
262
774411
3664
所以，我們心想：「天哪！
有多少人有 iPhone？」
有多少人有 iPhone？」
13:06
How many of you have iPhones?
263
778099
1448
在座有誰有用 iPhone？
13:09
So in this room, we have this tremendous
database of location data
database of location data
264
780608
5478
所以，在這間房間中，
我們有巨大的
我們有巨大的
位置資料庫，
13:14
that researchers
would really, really like.
would really, really like.
265
786110
3775
研究者們會非常非常喜歡。
13:18
So we built this system called Open Paths,
266
789909
2031
所以，我們打造了一個
叫 Open Paths 的系統，
叫 Open Paths 的系統，
13:20
which lets people upload their iPhone data
267
791964
2656
讓大家上傳他們的 iPhone 數據，
13:23
and broker relationships
with researchers to share that data,
with researchers to share that data,
268
794644
3796
與研究人員建立關係來分享這些數據，
13:26
to donate that data to people
that can actually put it to use.
that can actually put it to use.
269
798464
3387
將這些數據捐給可以實際使用的人。
13:30
Open Paths was a great
success as a prototype.
success as a prototype.
270
802256
2350
Open Paths 是個很成功的原型。
13:33
We received thousands of data sets,
271
804630
3433
我們收到了數千個數據集，
13:36
and we built this interface
272
808087
1349
我們建立了這個介面，讓大家
可以實實在在地看見他們的人生
可以實實在在地看見他們的人生
13:37
which allows people to actually
see their lives unfolding
see their lives unfolding
273
809460
3318
從遺留在裝置中的這些痕跡展開。
13:41
from these traces
that are left behind on your devices.
that are left behind on your devices.
274
812802
3156
13:45
Now, what we didn't expect
was how moving this experience would be.
was how moving this experience would be.
275
816593
5267
我們沒有預期到的是
這樣的經驗會有多感人。
13:50
When I uploaded my data,
I thought, "Big deal.
I thought, "Big deal.
276
821884
2227
當我上傳我的資料時，
我心想：「沒甚麼了不起的。
我心想：「沒甚麼了不起的。
13:52
I know where I live. I know where I work.
What am I going to see here?"
What am I going to see here?"
277
824135
3416
我知道我住在哪裡、在哪裡工作。
我還能看到什麼？」
我還能看到什麼？」
13:56
Well, it turns out, what I saw
was that moment I got off the plane
was that moment I got off the plane
278
827575
3501
結果發現，我看到我剛下飛機、
開始紐約新生活的那一刻；
13:59
to start my new life in New York;
279
831100
1623
14:02
the restaurant where I had Thai food
that first night,
that first night,
280
833588
2606
第一晚我吃泰國菜的餐廳，
14:04
thinking about this new experience
of being in New York;
of being in New York;
281
836218
2953
想著在紐約的這個新體驗；
14:07
the day that I met my girlfriend.
282
839195
1623
我遇見我女友的那一天。
14:11
This is LaGuardia airport.
283
842587
2275
這個座標是拉瓜地亞機場。
14:13
(Laughter)
284
844886
1487
（笑聲）
14:14
This is this Thai restaurant
on Amsterdam Avenue.
on Amsterdam Avenue.
285
846397
3641
這是阿姆斯特丹大道上的泰國餐廳。
14:19
This is the moment I met my girlfriend.
286
850559
2050
這是我遇見我女友的時刻。
14:22
See how that changes the first time
I told you about those stories
I told you about those stories
287
854146
3451
注意到了嗎？這些故事前後兩次的
敘述使得這些數據變得不一樣了。
敘述使得這些數據變得不一樣了。
14:26
and the second time I told
you about those stories?
you about those stories?
288
857621
2468
14:28
Because what we do
in the tool, inadvertently,
in the tool, inadvertently,
289
860113
3207
因為我們不經意地
14:31
is we put these pieces of data
into a human context.
into a human context.
290
863344
3115
將這些片段數據放到了
人文情境當中。
人文情境當中。
14:35
And by placing data into a human context,
291
866935
2498
透過將數據置於人文情境中，
14:37
it gains meaning.
292
869457
1474
它就產生了意義。
14:39
And I think this is tremendously,
tremendously important,
tremendously important,
293
870955
3328
我認為這是非常非常重要的，
14:42
because these are our histories
that are being stored on these devices.
that are being stored on these devices.
294
874307
4918
因為儲存在這些裝置當中的
是我們的歷史。
是我們的歷史。
14:49
And by thinking about them that way,
295
880809
1994
用這樣的方式來看待它們，
14:52
putting them in a human context --
296
883543
1902
將它們放到人文情境當中——
14:53
first of all, what we do with our own data
is get a better understanding
is get a better understanding
297
885469
3662
首先，我們用自己的數據，
來更進一步了解
來更進一步了解
14:57
of the type of information
that we're sharing.
that we're sharing.
298
889155
2479
我們所分享的那些數據是
屬於什麼類型的。
屬於什麼類型的。
15:00
But if we can do this with other data,
if we can put data into a human context,
if we can put data into a human context,
299
891658
4053
但是，若我們能配合使用其他數據，
若我們能把數據放到人文情境中，
若我們能把數據放到人文情境中，
15:04
I think we can change a lot of things,
300
895735
2918
我認為我們能改變許多事物，
15:07
because it builds, automatically, empathy
for the people involved in these systems.
for the people involved in these systems.
301
898677
6385
因為這樣就能夠自發地
與這些系統內的人們建立同理心，
與這些系統內的人們建立同理心，
15:14
And that, in turn, results
in a fundamental respect,
in a fundamental respect,
302
905602
2953
相應地，我們就會產生
一種基本的尊重，
一種基本的尊重，
15:17
which, I believe, is missing
in a large part of technology,
in a large part of technology,
303
908579
3163
我相信，大部分的科技
都少了這樣東西，
都少了這樣東西，
15:20
when we start to deal
with issues like privacy,
with issues like privacy,
304
912329
2938
當我們開始處理
像是隱私這類議題時，
像是隱私這類議題時，
15:25
by understanding that these numbers
are not just numbers,
are not just numbers,
305
916765
2717
能明白這些數字不僅僅是數字，
15:28
but instead they're attached, tethered to,
pieces of the real world.
pieces of the real world.
306
919506
3619
而是和真實世界的
某些部分有所連結。
某些部分有所連結。
15:31
They carry weight.
307
923149
1506
它們是有份量的。
15:33
By understanding that,
the dialog becomes a lot different.
the dialog becomes a lot different.
308
924679
3332
了解了這一點，
產生的討論就會大不相同。
產生的討論就會大不相同。
15:38
How many of you have ever clicked a button
309
929595
2331
在座有多少人曾經按下一個按鈕，
15:40
that enables a third party to access
your location data on your phone?
your location data on your phone?
310
931950
4987
允許第三方使用
你手機上的位置資料？
15:46
Lots of you.
311
937595
1555
很多人。
15:47
So the third party is the developer,
312
939174
2245
第三方就是軟體開發商，
15:49
the second party is Apple.
313
941443
1801
第二方則是蘋果公司。
15:52
The only party that never gets access
to this information is the first party!
to this information is the first party!
314
943954
4823
而從來沒有機會使用
這些資訊的卻只有第一方！
這些資訊的卻只有第一方！
15:58
And I think that's because we think
about these pieces of data
about these pieces of data
315
950198
3135
我想那是因為我們都是以一種停滯的、
抽象的方式來考慮這些數據片段。
抽象的方式來考慮這些數據片段。
16:01
in this stranded, abstract way.
316
953357
2055
16:03
We don't put them into a context
317
955436
1897
我們沒有把它們放到情境當中，
16:05
which, I think, makes them
a lot more important.
a lot more important.
318
957357
2309
我認為，情境能凸顯
它們的重要性。
它們的重要性。
16:08
So what I'm asking you
to do is really simple:
to do is really simple:
319
959690
2166
我想請各位做一件很簡單的事：
16:10
start to think about data
in a human context.
in a human context.
320
961880
2323
開始在人文情境當中思考數據，
16:13
It doesn't really take anything.
321
964918
1657
你不花費什麼。
16:15
When you read stock prices,
think about them in a human context.
think about them in a human context.
322
966599
3359
當你在看股票價格時，
在人文情境中思考它們。
在人文情境中思考它們。
16:18
When you think about mortgage reports,
think about them in a human context.
think about them in a human context.
323
969982
3542
當你在思考抵押貸款報告時，
在人文情境中思考它們。
在人文情境中思考它們。
16:22
There's no doubt that big data
is big business.
is big business.
324
973548
3930
無疑的，大數據代表大事業。
16:26
There's an industry being developed here.
325
977502
3018
這裡有一個產業正在發展中。
16:30
Think about how well we've done
326
981520
1501
想想看先前我們在開發
和資源相關的產業時做得多好。
和資源相關的產業時做得多好。
16:31
in previous industries
that we've developed involving resources.
that we've developed involving resources.
327
983045
3369
16:34
Not very well at all.
328
986438
1300
其實一點也不好。
16:36
I think part of that problem is, we've had
a lack of participation in these dialogues
a lack of participation in these dialogues
329
987762
4522
我認為有部分的問題在於
這些討論缺乏足夠的參與：
這些討論缺乏足夠的參與：
16:40
from multiple pieces of human society.
330
992308
4428
缺乏人類社會多個部分的參與。
16:45
So the other thing that I'm asking for
331
996760
1992
所以我還有一項請求，
16:48
is an inclusion in this dialogue
from artists, from poets, from writers --
from artists, from poets, from writers --
332
999669
4378
希望藝術家、詩人、作家——
那些能夠將人文元素帶入討論中的
這些人——也能夠參與討論。
這些人——也能夠參與討論。
16:52
from people who can bring a human element
into this discussion.
into this discussion.
333
1004071
4013
16:57
Because I believe that this world of data
334
1008725
2356
因為我相信，這個數據的世界
16:59
is going to be transformative for us.
335
1011105
3025
將能夠轉變我們。
17:03
And unlike our attempts
with the resource industry
with the resource industry
336
1014687
3169
不像我們對於資源產業的嘗試，
17:06
and our attempts
with the financial industry,
with the financial industry,
337
1017880
2153
及我們對於金融產業的嘗試，
17:08
by bringing the human
element into this story,
element into this story,
338
1020057
2931
透過將人文元素帶入故事當中，
17:11
I think we can take it
to tremendous places.
to tremendous places.
339
1023012
2178
我認為我們能將它帶入更棒的境界。
17:14
Thank you.
340
1026203
1155
謝謝。
17:15
(Applause)
341
1027382
4052
（掌聲）
ABOUT THE SPEAKER
Jer Thorp - Data artistJer Thorp’s work focuses on adding meaning and narrative to huge amounts of data as a way to help people take control of the information that surrounds them.
Why you should listen
Currently the data artist in residence at the New York Times, Jer’s software-based art has been featured all over the world. His former career as a data artist explains why his art often brings big data sets to life and is deeply influenced by science. Originally from Vancouver, he lives in New York City, where, along with his work at the New York Times, he teaches in NYU’s ITP program.
Jer Thorp | Speaker | TED.com