TEDxVancouver
Jer Thorp: Make data more human
Filmed:
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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.
我想告诉你们两件非常振奋人心的事，
00:10
I want to talk to you about two
of the most exciting possible things.
of the most exciting possible things.
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你们可能已经猜到是什么了，
00:16
You've probably guessed what they are --
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数据和历史。
00:18
data and history.
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不是吗？
00:21
Right?
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00:24
So, I'm not a historian.
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我不是个历史学家。
00:26
I'm not going to give you
a definition of history.
a definition of history.
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我不是来跟你们讲历史定义的。
00:29
But let's think instead
of history within a framework.
of history within a framework.
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而是想让你们通过一个框架看待历史。
当我们创造历史
00:32
So, when we're making history,
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或是撰写历史文献时，
00:33
or when we're creating
historical documents,
historical documents,
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00:36
we're taking things
that have happened in the past,
that have happened in the past,
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我们是在把过去发生的事
衔接在一起变成一个故事。
00:39
and we're stitching them
together into a story.
together into a story.
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让我先来讲一个我自己的故事。
00:41
So let me start with a little bit
of my own story.
of my own story.
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和大多数年龄相仿的
计算机工作者一样，
计算机工作者一样，
00:44
Like anybody my age
who works creatively with computers,
who works creatively with computers,
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00:48
I was a popular, socially
well-adjusted young man --
well-adjusted young man --
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我曾是个善于社交、受欢迎的年轻人，
00:52
(Laughter)
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（笑声）
00:53
And sporty!
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而且擅长运动！
擅长运动的年轻人。
00:56
Sporty young man.
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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,
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和大多数年龄差不多的同行一样，
01:03
I was influenced tremendously by Apple.
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我深受苹果公司的影响，
01:07
But notice my choice of logo here, right?
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但是注意看我选的这个商标，
01:10
The Apple on the left,
not the Apple on the right.
not the Apple on the right.
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左边那个苹果，不是右边那个。
01:15
I'm influenced as much
by the Apple on the right
by the Apple on the right
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我深受右边那个苹果的影响，
01:17
as the next person,
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就像每个人一样，
01:19
but the Apple on the left --
I mean, look at that logo!
I mean, look at that logo!
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但是左边那个苹果，看看这标志，
是个彩虹，但是顺序是错的！
01:22
It's a rainbow.
It's not even in the right order!
It's not even in the right order!
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01:24
(Laughter)
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（笑声）
01:25
That's how crazy Apple was.
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真不知道苹果公司在搞什么鬼。
01:28
(Laughter)
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（笑声）
01:29
But I don't want to talk too much
about the company.
about the company.
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但对苹果公司我不想说太多。
01:32
I'll start talking about
a machine, though.
a machine, though.
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我想跟你们说一个机器的事儿。
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.
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我现在回过头来想，真是不可思议啊。
01:38
Wednesday -- one Wednesday,
when I was about 12 years old,
when I was about 12 years old,
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那是个周三，是我大概12岁的时候，
我还没有电脑。
01:41
I didn't have a computer.
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01:44
On Thursday, I had a computer.
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到了周四，我就有了一台电脑。
你能想象这变化吗？
01:48
Can you imagine that change?
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01:50
It's so drastic.
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翻天覆地的变化。
01:52
I can't even think about anything
that could change our lives that way.
that could change our lives that way.
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没有事物可以像电脑
那样改变我们的生活。
那样改变我们的生活。
但我其实也不想聊电脑的事儿。
01:56
But I'm actually not even going
to talk about the computer.
to talk about the computer.
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我想聊聊电脑上的一个程序。
01:58
I'm going to talk about a program
that came loaded on that computer.
that came loaded on that computer.
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程序的创始人是……不是左边那个，
02:02
And it was build by,
not the guy on the left,
not the guy on the left,
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右边那个才是。
02:04
but the guy on the right.
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大家知道右边那人是谁吗？
02:05
Does anybody know
who the guy on the right is?
who the guy on the right is?
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从来都没人知道。
02:09
Nobody ever knows the answer
to this question.
to this question.
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这是比尔·阿特肯森。
02:12
This is Bill Atkinson.
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02:13
And Bill Atkinson was responsible
for tons of things
for tons of things
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多亏比尔·阿特肯森做的很多事，
才有了我们现在
才有了我们现在
02:16
that you see on your computer every day.
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每天在电脑上看到的东西。
但是我想重点说说比尔写的一个程序，
02:19
But I want to talk about one program
that Bill Atkinson wrote,
that Bill Atkinson wrote,
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02:22
called HyperCard.
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叫做 HyperCard。
02:25
Someone's cheering over there.
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我听到那边有观众在欢呼。
02:27
(Laughter)
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（笑声）
02:28
HyperCard was a program
that shipped with the Mac,
that shipped with the Mac,
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HyperCard 曾是苹果电脑的附赠品。
02:31
and it was designed
for users of the computer
for users of the computer
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为苹果电脑使用者设计的，
在电脑上编程时用。
02:34
to make programs on their computers.
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如今听起来很疯狂。
02:38
Crazy idea today.
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02:39
And these programs were not the apps
that we think about today,
that we think about today,
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这些程序不是我们如今使用的app，
02:42
with their large budgets
and their big distribution.
and their big distribution.
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app是有很大的预算和传播度的。
而这些程序只是很小的程序，
02:45
These were small things,
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有人用它来记录当地篮球赛的比分，
02:46
people making applications to keep track
of their local basketball team scores
of their local basketball team scores
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02:50
or to organize their research
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有人用来整理论文，
有人用来做古典音乐的教学
02:53
or to teach people about classical music
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02:56
or to calculate weird astronomical dates.
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或者计算奇怪的天文日期。
03:00
And then, of course,
there were some art projects.
there were some art projects.
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当然还有一些是艺术项目。
这是我最喜欢的一个。
03:02
This is my favorite one.
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叫做“If Monks Had Macs，”
03:03
It's called "If Monks Had Macs,"
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03:05
and it's a nonlinear
kind of exploratory environment.
kind of exploratory environment.
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是个非线性探索环境。
因为HyperCard，我感谢上苍。
03:10
I thank the stars for HyperCard
all of the time.
all of the time.
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03:16
And I thank the stars
for putting me in this era
for putting me in this era
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感谢上苍让我生在这个时代，
03:18
where I got to use HyperCard.
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让我有机会使用HyperCard。
03:20
HyperCard was the last program to ship
on a public computer
on a public computer
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Hypercard是最后一个
和公共电脑一起寄出
和公共电脑一起寄出
03:25
that was designed for the users
of the computer to make programs with it.
of the computer to make programs with it.
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设计给用户编程的附赠品。
03:30
If you talked to the people
who invented the computer
who invented the computer
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如果你告诉电脑的发明者们，
03:33
and you told them there would be
a day, a magical day,
a day, a magical day,
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有那么一日，
03:36
when everybody had a computer
but none of them knew how to program,
but none of them knew how to program,
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所有人都有了电脑，
却没人知道如何编程，
却没人知道如何编程，
03:41
they would think you were crazy.
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他们一定会觉得你疯了。
03:43
So let's skip forward a few years.
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让我们快进几年。
03:45
I'm starting my career as an artist,
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我最初的职业是艺术家，
03:48
and I'm building things
with my computer, small-scale things,
with my computer, small-scale things,
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我用电脑创作一些小玩意儿，
03:52
investigating things like
the growth systems of plants.
the growth systems of plants.
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比如研究植物的生长系统。
还有，在这个例子中，
03:55
Or, in this example, I'm building
a simulated economy
a simulated economy
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03:58
in which pixels are trading color
with one another,
with one another,
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我用像素间的颜色互换
来模拟经济模式，
来模拟经济模式，
调查这些系统是如何运作的，
04:02
trying to investigate how
these types of systems work,
these types of systems work,
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04:05
and just kind of having fun.
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我乐在其中。
04:06
And then this project led me
to start working with data.
to start working with data.
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这个项目使我开始从事
数据相关的工作。
数据相关的工作。
04:09
So I'm building graphics like this,
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我建立这样的图表，
04:12
which compare "communism" --
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把纽约时代周刊里
“共产主义”和“恐怖主义”
这两个词的使用频率
这两个词的使用频率
04:15
the frequency of usage of the word
"communism" in the New York Times --
"communism" in the New York Times --
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进行对比。
04:18
to "terrorism," at the top.
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04:20
You see "terrorism" kind of appears
as "communism" is going away.
as "communism" is going away.
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我们可以发现“恐怖主义“
逐渐出现，“共产主义“渐渐消失。
逐渐出现，“共产主义“渐渐消失。
04:25
And with these graphics, I was really
interested in the aesthetic of the graphs.
interested in the aesthetic of the graphs.
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我对这些图像的美观性也很感兴趣。
这是伊朗和伊拉克。
04:29
This is Iran and Iraq.
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04:30
It reads like a clock. It's called
a "timepiece graph."
a "timepiece graph."
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看起来像个钟表，叫做“钟表图。”
04:34
This is another timepiece graph,
overlaying "despair" over "hope."
overlaying "despair" over "hope."
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这是另一个钟表图的例子：
在“希望”上叠加“绝望。”
在“希望”上叠加“绝望。”
04:39
And there's only three times -- actually,
it's "crisis" over "hope" --
it's "crisis" over "hope" --
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实际上，是在“希望”上叠加”危机“——
“希望”只有三次被”危机“覆盖，
04:43
there's only three times
when "crisis" eclipses "hope."
when "crisis" eclipses "hope."
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我们目前正身处其中一次。
04:45
We're in the middle
of one of them right now.
of one of them right now.
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但这事你们还是别多想了。
04:48
But don't think about that too much.
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（笑声）
04:49
(Laughter)
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04:51
And finally, the culmination of this work
with the New York Times data
with the New York Times data
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这一系列纽约时报作品的巅峰是
几年前，
04:55
a few years ago
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04:56
was the attempt to combine
an entire year's news cycle
an entire year's news cycle
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我尝试把一整年的新闻
整合到一张图中。
05:00
into a single graphic.
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05:01
So these graphics actually show us
a full year of news, all the people,
a full year of news, all the people,
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于是这一整年的新闻、人物，
以及他们之间的关系，
以及他们之间的关系，
05:05
and how they're connected
into a single graphic.
into a single graphic.
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都在这一张图里了。
05:08
And from there, I started to be
interested again in more active systems.
interested again in more active systems.
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由此，我对更活跃的系统产生了兴趣。
05:12
Here's a project called "Just Landed,"
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这个项目叫“Just Landed，”
05:14
where I'm looking at people
tweeting on Twitter.
tweeting on Twitter.
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我看人们发推特。
05:17
"Hey! I just landed
in Hawaii!" -- you know,
in Hawaii!" -- you know,
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“我刚飞到夏威夷！”
——你们懂的，
——你们懂的，
05:19
how people just casually try to sneak
that into their Twitter conversation.
that into their Twitter conversation.
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人们总是不经意地在推特上谈到这些。
“我真的不是在炫耀，但我刚到夏威夷。“
05:23
"I'm not showing off. Really.
But I did just land in Hawaii."
But I did just land in Hawaii."
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05:26
And then I'm plotting
those people's trips,
those people's trips,
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然后我开始描绘人们的旅程，
05:29
in the hopes that maybe
we can use social network
we can use social network
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希望可以利用社交网络
和背后的数据
05:32
and the data that it leaves behind
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建立一个模型来跟踪人们的动向，
05:34
to provide a model of how people move,
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对流行病学家来说，
这将是十分宝贵的信息。
这将是十分宝贵的信息。
05:36
which would be valuable
to epidemiologists, among other people.
to epidemiologists, among other people.
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05:39
And, more fun -- this
is a similar project,
is a similar project,
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这是个类似的项目——它更有趣，
05:42
looking at people
saying "Good morning" to each other
saying "Good morning" to each other
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在推特上看世界各地的人们
互道早安。
05:44
all around the world.
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顺便说一句，我才知道，
05:45
Which taught me, by the way,
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在温哥华西岸的人真的比东岸的人
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
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起床晚，
05:51
and say "Good morning" much later
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互相道早安也晚，
05:53
than the people on the East Coast,
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05:55
who are more adventurous.
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东岸的人也更有冒险精神。
05:57
Here's a more useful -- maybe -- project,
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再给你们看一个项目
——这个可能更实用，
——这个可能更实用，
05:59
where I took all the information
from the Kepler Project
from the Kepler Project
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我试图把开普勒项目的数据
06:02
and tried to put it into some visual form
that made sense to me.
that made sense to me.
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做成更易懂的图像。
我刚才给你们看的所有作品
06:05
And I should say that everything
I've shown you up to now --
I've shown you up to now --
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都是做着玩的。
06:08
these are all things
that I just did for fun.
that I just did for fun.
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听起来有点奇怪，
但这就像HyperCard。
但这就像HyperCard。
06:10
It may seem weird,
but this comes back from HyperCard.
but this comes back from HyperCard.
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06:13
I'm building tools for myself.
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我自己创造一些工具，
然后我可以和一些人分享，
06:15
I may share them with a few other people,
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但都是为了自己开心，做着玩的。
06:17
but they're for fun, they're for me.
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所以其实很难给这些工具明确的定位。
06:21
So, all these tools I show you
kind of occupy this weird space
kind of occupy this weird space
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06:25
somewhere between science, art and design.
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我的创作介于
科学，艺术和设计之间。
06:28
That's where my practice lies.
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06:30
And still today,
from my experience with HyperCard,
from my experience with HyperCard,
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从HyperCard开始直到今天，
我都在建立可视化工具
来帮助我理解各种系统。
来帮助我理解各种系统。
06:33
what I'm doing is building visual tools
to help me understand systems.
to help me understand systems.
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我今天在纽约时报工作，
06:38
So today, I work at the New York Times.
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06:40
I'm the data artist in residence
at the New York Times.
at the New York Times.
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我是个数据艺术家。
06:43
And I've had an opportunity at the Times
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工作期间，
我接触到很多有趣的项目，
06:45
to work on a variety
of really interesting projects,
of really interesting projects,
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今天会给你们看其中两个。
06:48
two of which I'm going
to share with you today.
to share with you today.
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第一个是和马克·汉森一起做的。
06:50
The first one, I've been working on
in conjunction with Mark Hansen.
in conjunction with Mark Hansen.
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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.
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马克是加州洛杉矶的
统计学教授和传媒艺术家。
统计学教授和传媒艺术家。
马克来时报时提过一个有趣
06:58
And Mark came to the Times
with a very interesting question
with a very interesting question
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07:01
to what may seem like an obvious problem:
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而又似乎显而易见的问题：
当人们在网上传播信息时，
07:04
When people share content on the internet,
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07:07
how does that content get
from person A to person B?
from person A to person B?
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信息是如何从甲传到乙，
或从甲传到乙、丙、丁的？
07:11
Or maybe, person A to person B
to person C to person D?
to person C to person D?
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07:16
We know that people share content
in the internet,
in the internet,
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我们都知道人们在网络上分享信息，
却不知道传播过程中
07:18
but what we don't know
is what happens in that gap
is what happens in that gap
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发生了什么。
07:21
between one person to the other.
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所以我们决定创造工具来探索这个问题，
07:23
So we decided to build
the tool to explore that,
the tool to explore that,
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这个工具叫做Cascade。
07:25
and this tool is called Cascade.
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我们看这些系统时，
07:27
If we look at these systems
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07:30
that start with one event
that leads to other events,
that leads to other events,
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一件事导致另一些事，
我们称之为建立cascade。
07:35
we call that structure a cascade.
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07:37
And these cascades
actually happen over time.
actually happen over time.
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这些cascade是逐渐发生的，
07:39
So we can model these things over time.
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所以我们的跟踪建模也需要一段时间。
07:41
Now, the New York Times has
a lot of people who share our content,
a lot of people who share our content,
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很多人都在传播纽约时报上的信息，
07:45
so the cascades do not look like that one,
they look more like this.
they look more like this.
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所以Cascade看起来其实是这样的。
这是个常见的Cascade。
07:49
Here's a typical cascade.
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07:50
At the bottom left, the very first event.
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最左下方是第一个事件。
07:54
And then as people are sharing
the content from one person to another,
the content from one person to another,
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当信息从一个人传播到另一个人时，
这个点向上沿y轴延伸，
y轴是分离程度,
y轴是分离程度,
07:59
we go up in the Y axis,
degrees of separation,
degrees of separation,
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08:02
and over on the X axis, for time.
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同时向x轴延伸，x轴是时间。
08:05
So we're able to look at that conversation
in a couple of different views:
in a couple of different views:
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现在我们可以从很多角度
看这个问题：
看这个问题：
这是线型角度，
08:09
this one, which shows us
the threads of conversation,
the threads of conversation,
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这个是把线型堆叠，
08:11
and this one, which combines
that stacked view
that stacked view
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08:15
with a view that lets us see the threads.
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成为这样的立体角度。
08:18
Now, the Times publishes
about 7,000 pieces of content
about 7,000 pieces of content
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今天，时报每个月发表
约7000篇文章。
08:21
every month.
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所以建立这个工具时很重要的一点是，
08:23
So it was important for us,
when we were building this tool,
when we were building this tool,
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把它建成一个可探索的模型，
08:25
to make it an exploratory one,
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这样人们可以在大量数据中
挖掘他们需要的信息。
挖掘他们需要的信息。
08:27
so that people could dig through
this vast terrain of data.
this vast terrain of data.
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08:31
I think of it as a vehicle
that we're giving people
that we're giving people
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就像是给人们提供了一辆车，
08:34
to traverse this really big
terrain of data.
terrain of data.
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在这大量的数据中畅通无阻。
实况中的cascade，
08:37
So here's what it really looks like,
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看起来是这样的。
08:39
and here's the cascade
playing in real time.
playing in real time.
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08:42
I have to say, this was
a tremendous moment.
a tremendous moment.
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不得不说，这是一个重要的时刻。
那么久以来，我们应付了太多假新闻，
08:44
We had been working with canned
data, fake data, for so long,
data, fake data, for so long,
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08:48
that when we saw this
for the first moment,
for the first moment,
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所以当我们第一次看到这一幕时，
08:51
it was like an archaeologist who had
just dusted off these dinosaur bones.
just dusted off these dinosaur bones.
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就好像考古学家把灰尘
从恐龙骨架上抖落一样。
从恐龙骨架上抖落一样。
我们发现了并第一次看到，
08:56
We discovered this thing,
and we were seeing it for the first time,
and we were seeing it for the first time,
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这些网络共享信息的结构。
09:00
these sharing structures
that underlie the internet.
that underlie the internet.
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拿恐龙来打比方好像挺合适的，
09:04
And maybe the dinosaur
analogy is a good one,
analogy is a good one,
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因为我们是在对这些事之间的关联
09:07
because we're actually making
some probabilistic guesses
some probabilistic guesses
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09:10
about how these things link.
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做概率性的推测。
09:11
We're looking at some of these
pieces and making some guesses,
pieces and making some guesses,
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当我们看着这些碎片信息做出假设时，
09:14
but we try to make sure that those
are as statistically rigorous as possible.
are as statistically rigorous as possible.
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我们尽力确保它们的严谨性。
推特是故事的一部分，
09:19
Now tweets, in this case,
they become parts of stories.
they become parts of stories.
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09:23
They become parts of narratives.
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叙事的一部分。
09:25
So we are building histories here,
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我们在创建历史，
09:28
but they're very short-term histories.
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但它们不过是短暂的历史。
09:30
And sometimes these very large cascades
are the most interesting ones,
are the most interesting ones,
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这些大型的cascades
往往是最有趣的，
往往是最有趣的，
09:34
but sometimes the small ones
are also interesting.
are also interesting.
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当然有些小型的cascades
也是很有意思的。
也是很有意思的。
这是我很喜欢的一个，
叫“rabbi cascade”，
叫“rabbi cascade”，
09:37
This is one of my favorites.
We call this the "Rabbi Cascade."
We call this the "Rabbi Cascade."
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是拉比们（犹太教学者）围绕
纽约时报中的一篇文章的对话，
纽约时报中的一篇文章的对话，
09:41
It's a conversation amongst rabbis
about this article in the New York Times,
about this article in the New York Times,
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09:46
about the fact that religious workers
don't get a lot of time off.
don't get a lot of time off.
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实际上，宗教工作者
休息时间非常有限。
休息时间非常有限。
09:49
I guess Saturdays and Sundays are bad days
for them to take off.
for them to take off.
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周六和周日他们好像不太能放假。
于是在这个cascade里，
有一群拉比在谈论
有一群拉比在谈论
09:54
So, in this cascade, there's a group
of rabbis having a conversation
of rabbis having a conversation
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一个纽约时报发表的故事。
09:57
about a New York Times story.
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其中一个拉比给自己取的
推特用户名很厉害——
推特用户名很厉害——
09:59
One of them has the best
Twitter name ever --
Twitter name ever --
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叫“ The Velveteen Rabbi”
（注：Velve teen Rabbit/绒布小兔子
是一本英国儿童读物，此处取名去掉了t）
（注：Velve teen Rabbit/绒布小兔子
是一本英国儿童读物，此处取名去掉了t）
10:01
he's called "The Velveteen Rabbi."
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10:03
(Laughter)
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(笑声）
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.
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如果没有这个初步工具，
我们永远不会找到这些信息。
我们永远不会找到这些信息。
10:10
This would just be sitting somewhere,
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这些信息只会停留在某些角落，
永不得见天日。
10:11
and we would have never
been able to see that.
been able to see that.
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把信息整合，
10:14
But this exercise of taking
single pieces of information
single pieces of information
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然后建立叙事性结构，创作历史，
10:18
and building narrative structures,
building histories out of them,
building histories out of them,
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10:22
I find tremendously interesting.
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我发现了无穷的乐趣。
我两年前搬到纽约，
10:24
You know, I moved to New York
about two years ago.
about two years ago.
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在纽约，人人都有一个故事
10:27
And in New York, everybody has a story
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是关于
10:29
that surrounds this
tremendously impactful event
tremendously impactful event
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发生在2001年9月11日
的那个重大事件。
的那个重大事件。
10:32
that happened on September 11 of 2001.
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我自己的那个故事有些复杂，
10:35
And my own story with September 11
has really become a more intricate one,
has really become a more intricate one,
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10:42
because I spent a great deal of time
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因为我花了很多时间
10:44
working on a piece
of the 9/11 Memorial in Manhattan.
of the 9/11 Memorial in Manhattan.
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在曼哈顿的9/11事件纪念碑。
10:49
The central idea about the 9/11 Memorial
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9/11事件纪念碑的核心理念
10:51
is that the names in the memorial
are not laid out in alphabetical order
are not laid out in alphabetical order
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在于那些纪念碑上的名字
不是按字母顺序排列，
不是按字母顺序排列，
也不是按年份排列，
10:56
or chronological order,
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而是通过
10:57
but instead, they're laid out in a way
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10:59
in which the relationships
between the people who were killed
between the people who were killed
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可以体现遇难者之间的关系
的方式排列的。
11:03
are embodied in the memorial.
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弟兄和弟兄一起，
11:05
Brothers are placed next to brothers,
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同事和同事一起，
11:08
coworkers are placed together.
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11:10
So this memorial actually considers
all of these myriad connections
all of these myriad connections
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所以这个纪念碑考虑了种种连接，
11:15
that were part of these people's lives.
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这些人曾经在生活中的连接。
我和一个叫做Local Projects
的公司合作
的公司合作
11:18
I worked with a company
called Local Projects
called Local Projects
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11:22
to work on an algorithm
and a software tool
and a software tool
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做了一个算法软件
来帮助建筑师们决定这个
纪念碑的排列方式：
纪念碑的排列方式：
11:24
to help the architects build
the layout for the memorial:
the layout for the memorial:
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11:28
almost 3,000 names
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一共有将近3000个名字，
11:30
and almost 1,500 of these
adjacency requests,
adjacency requests,
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将近1500个邻接的请求，
11:34
these requests for connection --
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这些连接的请求——
11:35
so a very dense story,
a very dense narrative,
a very dense narrative,
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所以这是一个很密集的故事和叙事，
11:39
that becomes an embodied part
of this memorial.
of this memorial.
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需要在一个纪念碑上呈现。
11:42
Working with Jake Barton,
we produce the software tool,
we produce the software tool,
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我和Jake Barton一起制作了这个软件
让建筑师可以首先制作一个
11:46
which allows the architects to,
first of all, generate a layout
first of all, generate a layout
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可以满足所有请求的布局。
11:50
that satisfied all of those
adjacency requests,
adjacency requests,
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11:53
but then second, make little adjustments
where they needed to
where they needed to
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然后在某些地方做改动，
11:56
to tell the stories
that they wanted to tell.
that they wanted to tell.
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从而可以表达他们想要的故事。
我想在我们这个社交网络统领的时代，
11:59
So this memorial, I think,
has an incredibly timely concept
has an incredibly timely concept
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这个纪念碑是个与时俱进的概念，
12:03
in our era of social networks,
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12:06
because these networks -- these real-life
networks that make up people's lives --
networks that make up people's lives --
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因为这些现实中的社交网络
12:10
are actually embodied
inside of the memorial.
inside of the memorial.
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在纪念碑中能够得以呈现。
12:13
And one of the most tremendously
moving experiences
moving experiences
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最令人感动的
12:17
is to go to the memorial
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就是前去纪念碑
看到这些人的名字是如何彼此相邻
12:18
and see how these people
are placed next to each other,
are placed next to each other,
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来呈现他们在世时的生活的。
12:23
so that this memorial
is representing their own lives.
is representing their own lives.
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那么，这些对于我们的
生活有什么影响呢？
生活有什么影响呢？
12:27
How does this affect our lives?
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12:29
Well, I don't know if you remember,
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我不知道你们还记不记得，
12:31
but in the spring,
there was a controversy,
there was a controversy,
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今年春天出了
这么一件事，饱受争议，
这么一件事，饱受争议，
12:34
because it was discovered
that on the iPhone
that on the iPhone
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人们发现在iphone上，
12:36
and, actually, on your computer,
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还有在电脑上，
12:37
we were storing a tremendous amount
of the location data.
of the location data.
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有大量定位信息被储存。
苹果公司回应说，
这些定位信息跟你们无关，
这些定位信息跟你们无关，
12:41
So Apple responded, saying,
this was not location data about you,
this was not location data about you,
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12:45
it was location data
about wireless networks
about wireless networks
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而跟你们居所的
12:48
that were in the area where you are.
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无线网络有关。
所以这跟你们无关。
12:50
So it's not about you,
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而是跟你们在哪有关。
12:52
but it's about where you are.
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12:53
(Laughter)
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（笑声）
12:55
This is very valuable data.
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这是很宝贵的数据。
12:58
It's like gold to researchers,
this human-mobility data.
this human-mobility data.
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对研究者来说，这些移动数据
像金子一样宝贵。
像金子一样宝贵。
13:02
So we thought, "Man!
How many people have iPhones?"
How many people have iPhones?"
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于是我们想到：有多少人
都在用iPhone啊？
都在用iPhone啊？
13:06
How many of you have iPhones?
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在座的有多少人用iPhone？
所以在这个房间里，就有研究者们
13:09
So in this room, we have this tremendous
database of location data
database of location data
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13:14
that researchers
would really, really like.
would really, really like.
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很喜欢的大量的定位信息。
13:18
So we built this system called Open Paths,
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于是我们创造了一个叫做
Open Paths的系统，
Open Paths的系统，
13:20
which lets people upload their iPhone data
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它可以让人们上传iPhone的数据
13:23
and broker relationships
with researchers to share that data,
with researchers to share that data,
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并与研究人员建立代理关系
来共享这些数据，
来共享这些数据，
把这些信息贡献给有需要的人。
13:26
to donate that data to people
that can actually put it to use.
that can actually put it to use.
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13:30
Open Paths was a great
success as a prototype.
success as a prototype.
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Open Paths的初步模型很成功。
13:33
We received thousands of data sets,
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我们收到了成千套的数据，
我们制作了一个界面
13:36
and we built this interface
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让人们可以看到自己的
生活是如何展开的，
生活是如何展开的，
13:37
which allows people to actually
see their lives unfolding
see their lives unfolding
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13:41
from these traces
that are left behind on your devices.
that are left behind on your devices.
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从这些被你忽视在手机里
的蛛丝马迹中。
的蛛丝马迹中。
13:45
Now, what we didn't expect
was how moving this experience would be.
was how moving this experience would be.
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我们没有想到这个体验
会是这样感人。
会是这样感人。
我上传数据的时候心想：
“没什么大不了的，
“没什么大不了的，
13:50
When I uploaded my data,
I thought, "Big deal.
I thought, "Big deal.
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我知道我住在哪，我知道我在哪上班，
通过这个我能看到什么？”
通过这个我能看到什么？”
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?"
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3416
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
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结果我看到了我来到纽约
走下飞机的那一刻；
13:59
to start my new life in New York;
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14:02
the restaurant where I had Thai food
that first night,
that first night,
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那一晚去吃泰餐的餐馆，
想象着纽约新生活的开始；
14:04
thinking about this new experience
of being in New York;
of being in New York;
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14:07
the day that I met my girlfriend.
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我遇到女友的那一天。
14:11
This is LaGuardia airport.
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这是拉瓜迪亚机场。
14:13
(Laughter)
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1487
（笑声）
14:14
This is this Thai restaurant
on Amsterdam Avenue.
on Amsterdam Avenue.
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这是在阿姆斯特丹大道上的泰国餐厅。
这是我遇到我女友的时候。
14:19
This is the moment I met my girlfriend.
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14:22
See how that changes the first time
I told you about those stories
I told you about those stories
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你们看到了吗，我第一次讲这些故事
和我第二次讲的时候，有什么区别？
14:26
and the second time I told
you about those stories?
you about those stories?
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14:28
Because what we do
in the tool, inadvertently,
in the tool, inadvertently,
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我们不经意间
把这些信息放在了人类语境中。
14:31
is we put these pieces of data
into a human context.
into a human context.
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通过把信息放在生活语境中，
14:35
And by placing data into a human context,
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14:37
it gains meaning.
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信息就产生了意义。
14:39
And I think this is tremendously,
tremendously important,
tremendously important,
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这非常非常重要，
14:42
because these are our histories
that are being stored on these devices.
that are being stored on these devices.
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因为我们的历史被保存
在这些手机里。
在这些手机里。
14:49
And by thinking about them that way,
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从这个角度来看，
14:52
putting them in a human context --
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这个人类语境的角度——
14:53
first of all, what we do with our own data
is get a better understanding
is get a better understanding
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首先，我们可以更好理解我们
14:57
of the type of information
that we're sharing.
that we're sharing.
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分享的是哪一类的信息。
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,
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但如果我们可以把其他信息
也放在人类语境中，
也放在人类语境中，
15:04
I think we can change a lot of things,
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我想很多事情都会被改变，
因为它能自动让在这些系统
的人们身临其境。
的人们身临其境。
15:07
because it builds, automatically, empathy
for the people involved in these systems.
for the people involved in these systems.
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15:14
And that, in turn, results
in a fundamental respect,
in a fundamental respect,
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这会导致最基本的尊重，
在我看来这一点在
技术行业中往往是缺失的，
技术行业中往往是缺失的，
15:17
which, I believe, is missing
in a large part of technology,
in a large part of technology,
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15:20
when we start to deal
with issues like privacy,
with issues like privacy,
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当我们在处理一些事情，比如隐私时，
如果我们明白数字不仅仅是数字，
15:25
by understanding that these numbers
are not just numbers,
are not just numbers,
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而是与现实连接在一起的。
15:28
but instead they're attached, tethered to,
pieces of the real world.
pieces of the real world.
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它们就变得举足轻重。
15:31
They carry weight.
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15:33
By understanding that,
the dialog becomes a lot different.
the dialog becomes a lot different.
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有了这一层理解，
对话就可以变得不同。
对话就可以变得不同。
15:38
How many of you have ever clicked a button
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你们中多少人曾点过按钮
许可第三方公司获取
你的定位信息的？
你的定位信息的？
15:40
that enables a third party to access
your location data on your phone?
your location data on your phone?
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15:46
Lots of you.
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很多人吧。
15:47
So the third party is the developer,
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第三方公司是开发商，
第二方公司是苹果。
15:49
the second party is Apple.
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可是第一方却从没有获得这些信息！
15:52
The only party that never gets access
to this information is the first party!
to this information is the first party!
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15:58
And I think that's because we think
about these pieces of data
about these pieces of data
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我想这是因为我们把这些信息
看作是抽象的，可以被搁置不顾的。
16:01
in this stranded, abstract way.
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16:03
We don't put them into a context
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我们没有把它们放入人类语境中
16:05
which, I think, makes them
a lot more important.
a lot more important.
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使它们的价值变得更重要。
16:08
So what I'm asking you
to do is really simple:
to do is really simple:
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我请求你们做的事很简单：
从更人类语境的角度看待数据。
16:10
start to think about data
in a human context.
in a human context.
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这真的不难。
16:13
It doesn't really take anything.
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16:15
When you read stock prices,
think about them in a human context.
think about them in a human context.
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当你看到股价时，
想一下背后的人类语境。
想一下背后的人类语境。
当你看到贷款报告时，
想一下背后的人类语境。
想一下背后的人类语境。
16:18
When you think about mortgage reports,
think about them in a human context.
think about them in a human context.
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很显然，大数据是巨大的商业。
16:22
There's no doubt that big data
is big business.
is big business.
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一个产业巨头在崛起。
16:26
There's an industry being developed here.
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16:30
Think about how well we've done
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想一想我们在之前的资源产业中
16:31
in previous industries
that we've developed involving resources.
that we've developed involving resources.
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做得如何。
我们做得不好。
16:34
Not very well at all.
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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
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我想一部分问题在于，
我们没有积极参与到
我们没有积极参与到
16:40
from multiple pieces of human society.
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有关人文语境的各方面对话中。
16:45
So the other thing that I'm asking for
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我要请求你们做的另一件事是
16:48
is an inclusion in this dialogue
from artists, from poets, from writers --
from artists, from poets, from writers --
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让更多人参与到这个对话中，
艺术家，诗人，作家，
艺术家，诗人，作家，
让有人文学科背景的人们
加入到讨论中。
加入到讨论中。
16:52
from people who can bring a human element
into this discussion.
into this discussion.
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因为我相信数据世界
16:57
Because I believe that this world of data
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16:59
is going to be transformative for us.
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可以革新我们的生活。
17:03
And unlike our attempts
with the resource industry
with the resource industry
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这和我们在资源产业，
财政产业的尝试不同，
财政产业的尝试不同，
17:06
and our attempts
with the financial industry,
with the financial industry,
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让我们把人文元素带到故事中，
17:08
by bringing the human
element into this story,
element into this story,
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我相信我们一定能带着它
走向无限潜能的地方。
走向无限潜能的地方。
17:11
I think we can take it
to tremendous places.
to tremendous places.
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17:14
Thank you.
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谢谢。
（掌声）
17:15
(Applause)
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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