How we can find ourselves in data | Giorgia Lupi

112,053 views ・ 2017-05-04

TED


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譯者: Aaron Shoo 審譯者: 易帆 余
00:12
This is what my last week looked like.
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我上週的生活長這樣。
00:16
What I did,
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我做了什麼、
00:18
who I was with,
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和誰在一起、
00:20
the main sensations I had for every waking hour ...
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清醒時,每個小時的感受......
00:23
If the feeling came as I thought of my dad
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是否我思念起了
00:26
who recently passed away,
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剛過世的父親。
00:28
or if I could have just definitely avoided the worries and anxieties.
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或有些無法避免的煩惱和焦慮。
00:32
And if you think I'm a little obsessive,
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若你覺得我有點走火入魔,
00:34
you're probably right.
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你可能是對的。
00:36
But clearly, from this visualization,
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但顯然這些視覺化的圖表,
00:38
you can learn much more about me than from this other one,
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比起其它方式,讓你更了解我,
00:41
which are images you're probably more familiar with
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像是一些大家都很熟悉的圖表,
00:44
and which you possibly even have on your phone right now.
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可能你手機裡現在就有了。
00:47
Bar charts for the steps you walked,
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比如記錄走路步數的長條圖、
00:49
pie charts for the quality of your sleep --
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表示睡眠品質的圓餅圖、
00:52
the path of your morning runs.
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晨跑的路徑圖......
00:55
In my day job, I work with data.
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我的工作就是與數據打交道。
00:57
I run a data visualization design company,
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我有一間數據視覺化設計公司,
00:59
and we design and develop ways to make information accessible
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負責設計和開發
01:03
through visual representations.
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視覺化呈現資訊的方式。
01:05
What my job has taught me over the years
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過去幾年的工作經驗告訴我,
01:08
is that to really understand data and their true potential,
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想要真正了解數據和它的潛力,
01:12
sometimes we actually have to forget about them
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有時不能只看表象,
01:15
and see through them instead.
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而是要深入核心。
01:18
Because data are always just a tool we use to represent reality.
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因為數據只是表達現實的工具。
01:21
They're always used as a placeholder for something else,
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它們只是一些代碼,
01:24
but they are never the real thing.
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不是實際的狀況。
01:26
But let me step back for a moment
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讓我退一步說明,
01:28
to when I first understood this personally.
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回到我第一次有所體會的那年。
01:32
In 1994, I was 13 years old.
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1994 年,我 13 歲,
01:35
I was a teenager in Italy.
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一名生活在義大利的年輕人。
01:37
I was too young to be interested in politics,
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當時還小,對政治沒興趣,
01:40
but I knew that a businessman, Silvio Berlusconi,
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但我知道有個商人, 叫做貝魯斯柯尼,
01:42
was running for president for the moderate right.
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當時正代表右翼溫和派競選總統。
01:45
We lived in a very liberal town,
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我住的地方是左派重鎮,
01:47
and my father was a politician for the Democratic Party.
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我爸還是民主黨的政治人物。
01:51
And I remember that no one thought that Berlusconi could get elected --
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我還記得,大家都說 貝魯斯柯尼選不上,
01:55
that was totally not an option.
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沒人覺得他會選上。
01:58
But it happened.
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結果他當選了。
01:59
And I remember the feeling very vividly.
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當時的感受我仍記憶猶新。
02:02
It was a complete surprise,
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完全出乎我們的意料,
02:04
as my dad promised that in my town he knew nobody who voted for him.
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我爸信誓旦旦地說, 鎮上不會有人投給他。
02:10
This was the first time
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這是第一次,
02:12
when the data I had gave me a completely distorted image of reality.
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我收集的數據與現實有落差。
02:17
My data sample was actually pretty limited and skewed,
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我的數據樣本既狹隘又偏頗,
02:20
so probably it was because of that, I thought, I lived in a bubble,
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也因此我覺得我只活在同溫層,
02:24
and I didn't have enough chances to see outside of it.
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沒機會看到外面的真實情況。
02:27
Now, fast-forward to November 8, 2016
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接著快轉到 2016 年 11 月 8 日。
02:31
in the United States.
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美國的總統大選。
02:33
The internet polls,
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網路民調、
02:35
statistical models,
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統計模型、
02:36
all the pundits agreeing on a possible outcome for the presidential election.
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專家學者都說希拉蕊會贏。
02:41
It looked like we had enough information this time,
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好像這一次我們的資訊很充足,
02:43
and many more chances to see outside the closed circle we lived in --
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而且有更多機會看到, 同溫層以外的世界。
02:48
but we clearly didn't.
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但我們根本沒有。
02:49
The feeling felt very familiar.
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這感覺似曾相識。
02:51
I had been there before.
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我以前就經歷過。
02:54
I think it's fair to say the data failed us this time --
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這次真的可以說數據騙了我們,
02:57
and pretty spectacularly.
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而且騙慘了。
02:58
We believed in data,
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我們太相信數據了,
03:00
but what happened,
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結果呢?
03:02
even with the most respected newspaper,
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連最權威的報紙,
03:04
is that the obsession to reduce everything to two simple percentage numbers
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都只想將所有事情
03:09
to make a powerful headline
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簡化成兩位數的支持率,
03:11
made us focus on these two digits
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製作出最聳動的標題,
03:13
and them alone.
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讓大眾只看到數字。
03:15
In an effort to simplify the message
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他們費盡心思簡化資料,
03:17
and draw a beautiful, inevitable red and blue map,
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畫出精美的紅藍分布圖,
03:20
we lost the point completely.
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我們完全失去焦點。
03:23
We somehow forgot that there were stories --
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我們忘記數據背後的故事,
03:25
stories of human beings behind these numbers.
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數字背後關於人的故事。
03:29
In a different context,
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這邊要岔個題,
03:30
but to a very similar point,
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但要說的道理是一樣的,
03:32
a peculiar challenge was presented to my team by this woman.
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這名女子向我的團隊 提出了一個特殊的挑戰。
03:36
She came to us with a lot of data,
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她帶著一堆數據找上我們,
03:38
but ultimately she wanted to tell one of the most humane stories possible.
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但最終她想要說出的, 就是一個最有人情味的故事。
03:43
She's Samantha Cristoforetti.
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這個人就是 薩曼莎‧克里斯托福雷蒂。
03:44
She has been the first Italian woman astronaut,
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她是義大利第一位女太空人,
03:47
and she contacted us before being launched
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她在出任務前找上我們,
03:49
on a six-month-long expedition to the International Space Station.
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她要到國際太空站待六個月。
03:53
She told us, "I'm going to space,
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她告訴我們:「我要上太空了,
03:56
and I want to do something meaningful with the data of my mission
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我想用任務中的數據,
03:59
to reach out to people."
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和社會大眾交流。」
04:01
A mission to the International Space Station
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一趟國際太空站的任務,
04:03
comes with terabytes of data
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會有好幾兆位元組的數據,
04:06
about anything you can possibly imagine --
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你能想到的資料都有:
04:08
the orbits around Earth,
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環繞地球的軌道數據、
04:10
the speed and position of the ISS
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國際太空站的速率和位置、
04:12
and all of the other thousands of live streams from its sensors.
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還有感應器上一大堆的即時資訊。
04:16
We had all of the hard data we could think of --
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我們握有太空任務的所有數據,
04:19
just like the pundits before the election --
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專家學者在大選前也都有數據,
04:22
but what is the point of all these numbers?
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但這些數字到底可以做什麼?
04:25
People are not interested in data for the sake of it,
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大家對數據本身根本沒興趣,
04:27
because numbers are never the point.
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因為數字不是重點。
04:29
They're always the means to an end.
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數據只是了解現實的手段。
04:32
The story we needed to tell
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我們要說的故事是,
04:34
is that there is a human being in a teeny box
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在這個小箱子裡有個人,
04:36
flying in space above your head,
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正在你頭上的外太空飛行,
04:39
and that you can actually see her with your naked eye on a clear night.
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而且你能在清朗的夜空 用肉眼看見她。
04:43
So we decided to use data to create a connection
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所以我們要用數據創造連結,
04:46
between Samantha and all of the people looking at her from below.
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連結薩曼莎和地上的我們。
04:50
We designed and developed what we called "Friends in Space,"
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我們設計並開發了 「太空中的朋友」,
04:53
a web application that simply lets you say "hello" to Samantha
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它是一個網路應用程式
可以讓你從所在地透過網頁,
04:58
from where you are,
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跟薩曼莎說「哈囉」,
04:59
and "hello" to all the people who are online at the same time
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同時也可以跟線上的 全球網友們說「哈囉」。
05:03
from all over the world.
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05:05
And all of these "hellos" left visible marks on the map
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如果薩曼莎經過這些「哈囉」,
05:08
as Samantha was flying by
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地圖上就會有記號,
05:10
and as she was actually waving back every day at us
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她每天也都從國際太空站,
05:14
using Twitter from the ISS.
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透過推特跟大家互動。
05:16
This made people see the mission's data from a very different perspective.
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這讓大家用非常不同的角度, 去看任務的數據。
05:21
It all suddenly became much more about our human nature and our curiosity,
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讓一切更貼近人性並 引發我們的好奇心,
05:26
rather than technology.
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而不只是冷冰冰的科技。
05:28
So data powered the experience,
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數據能強化體驗,
05:30
but stories of human beings were the drive.
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但人的故事才是關鍵。
05:34
The very positive response of its thousands of users
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數千位使用者的正面回饋,
05:38
taught me a very important lesson --
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給我上了非常重要的一課:
05:39
that working with data means designing ways
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與數據為伍就是要設計出
05:42
to transform the abstract and the uncountable
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可以把抽象、不可數的概念,
05:45
into something that can be seen, felt and directly reconnected
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轉化成看得見、感受得到、
05:49
to our lives and to our behaviors,
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並直接與生活和行為 重新連結的方法,
05:51
something that is hard to achieve
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有時候很難做到,
05:53
if we let the obsession for the numbers and the technology around them
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如果我們只著迷於數字及科技,
05:57
lead us in the process.
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就會走偏掉。
06:00
But we can do even more to connect data to the stories they represent.
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但我們能進一步 連結數據與背後的故事。
06:05
We can remove technology completely.
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不需要科技也辦得到。
06:08
A few years ago, I met this other woman,
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幾年前,我遇見一名女子,
06:10
Stefanie Posavec --
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史黛芬妮‧波薩維克。
06:11
a London-based designer who shares with me the passion and obsession about data.
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她是住倫敦的設計師, 跟我一樣對數據癡迷。
06:17
We didn't know each other,
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我們之前不認識,
06:18
but we decided to run a very radical experiment,
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但我們做了一個大膽的實驗,
06:22
starting a communication using only data,
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就是只用數據交談,
06:24
no other language,
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而不是語言。
06:26
and we opted for using no technology whatsoever to share our data.
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而且不用任何科技當媒介。
06:30
In fact, our only means of communication
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事實上,我們聯絡的唯一管道,
06:33
would be through the old-fashioned post office.
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就是最老派的郵政系統。
06:36
For "Dear Data," every week for one year,
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《親愛的數據》計畫長達一年,
06:39
we used our personal data to get to know each other --
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我們每週透過數據了解對方。
06:42
personal data around weekly shared mundane topics,
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每週都是很普通的一些主題:
06:46
from our feelings
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從各自的情緒、
06:47
to the interactions with our partners,
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到跟另一半的互動、
06:49
from the compliments we received to the sounds of our surroundings.
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收到的讚美或周圍的聲音。
06:53
Personal information that we would then manually hand draw
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這些資訊我們都手繪在
06:56
on a postcard-size sheet of paper
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明信片大小的表格上,
06:59
that we would every week send from London to New York,
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每週她會從倫敦寄明信片到
07:02
where I live,
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我住的紐約,
07:03
and from New York to London, where she lives.
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我也從紐約寄到她住的倫敦。
07:06
The front of the postcard is the data drawing,
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明信片的正面是手繪的圖表,
07:10
and the back of the card
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卡片的背面,
07:11
contains the address of the other person, of course,
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除了對方的地址,
07:13
and the legend for how to interpret our drawing.
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還有前面圖表的註解。
07:17
The very first week into the project,
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計畫開始的第一週,
07:19
we actually chose a pretty cold and impersonal topic.
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我們選了個很生冷、客套主題。
07:22
How many times do we check the time in a week?
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「我們一週內會看幾次錶?」
07:26
So here is the front of my card,
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這是我畫的紀錄,
07:28
and you can see that every little symbol
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上面的那些小記號,
07:30
represents all of the times that I checked the time,
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就是我每次看時間的記錄,
07:33
positioned for days and different hours chronologically --
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按照每天、每小時依序紀錄,
07:37
nothing really complicated here.
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其實不會很複雜。
07:40
But then you see in the legend
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但在註解這邊,
07:41
how I added anecdotal details about these moments.
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我說明了記號的涵義。
07:45
In fact, the different types of symbols indicate why I was checking the time --
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不同的記號代表不同的理由,
07:49
what was I doing?
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當時在幹嘛?
07:50
Was I bored? Was I hungry?
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無聊嗎?餓了嗎?
07:52
Was I late?
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遲到了嗎?
07:53
Did I check it on purpose or just casually glance at the clock?
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我是認真看時間, 還是隨意瞄一下?
07:57
And this is the key part --
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這些才是關鍵,
07:59
representing the details of my days and my personality
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我每天、個性上的細節,
08:03
through my data collection.
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透過數據表現出來。
08:05
Using data as a lens or a filter to discover and reveal, for example,
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把數據當鏡頭或濾鏡,
去發現和揭露,比如說,
08:09
my never-ending anxiety for being late,
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就算我一定會準時到, 我仍對遲到這件事非常焦慮,
08:12
even though I'm absolutely always on time.
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08:16
Stefanie and I spent one year collecting our data manually
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我們花了一年收集對方的數據,
08:20
to force us to focus on the nuances that computers cannot gather --
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專注在電腦抓不到的細節——
08:24
or at least not yet --
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至少目前還無法收集,
08:26
using data also to explore our minds and the words we use,
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用數據去了解想法、用字遣詞,
08:29
and not only our activities.
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而不只是行為。
08:31
Like at week number three,
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像在第三週,
08:33
where we tracked the "thank yous" we said and were received,
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我們記錄了道謝和被道謝情況,
08:36
and when I realized that I thank mostly people that I don't know.
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才發現我常和不認識的人道謝。
08:41
Apparently I'm a compulsive thanker to waitresses and waiters,
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顯然我會制式地向服務生道謝,
08:46
but I definitely don't thank enough the people who are close to me.
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對身邊的人卻沒那麼客氣。
08:50
Over one year,
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一年以後,
08:52
the process of actively noticing and counting these types of actions
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有意識地關注、記錄這些事,
08:56
became a ritual.
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變成了一個習慣。
08:57
It actually changed ourselves.
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我們開始有些改變。
09:00
We became much more in tune with ourselves,
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我們更清楚自己的步調,
09:02
much more aware of our behaviors and our surroundings.
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更了解自己的行為和周遭環境。
09:06
Over one year, Stefanie and I connected at a very deep level
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一年後,因為這個計畫,
09:09
through our shared data diary,
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我們兩個有了很深的牽絆。
09:11
but we could do this only because we put ourselves in these numbers,
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這都是因為我們在數字之外,
09:15
adding the contexts of our very personal stories to them.
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加上了自己的故事。
09:19
It was the only way to make them truly meaningful
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數據因此有了意義,
09:22
and representative of ourselves.
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因此能代表我們。
09:26
I am not asking you to start drawing your personal data,
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我不是要大家開始手繪數據,
09:29
or to find a pen pal across the ocean.
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或是去找個海外的筆友。
09:32
But I'm asking you to consider data --
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是希望今後大家面對數據,
09:34
all kind of data --
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各式各樣的數據,
09:36
as the beginning of the conversation
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都當成對話的開始,
09:38
and not the end.
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而不是終結。
09:39
Because data alone will never give us a solution.
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因為數據本身不會提供解答。
09:43
And this is why data failed us so badly --
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所以我們才會一直被數據所騙,
09:45
because we failed to include the right amount of context
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因為我們忘記數據背後
09:49
to represent reality --
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所呈現的現實,
09:50
a nuanced, complicated and intricate reality.
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是細微、複雜、盤根錯節的。
09:54
We kept looking at these two numbers,
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我們看到候選人的支持率,
09:57
obsessing with them
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就只看到數字,
09:58
and pretending that our world could be reduced
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假裝我們的世界可以被簡化成
10:01
to a couple digits and a horse race,
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兩個數字和一場競賽,
10:03
while the real stories,
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然而真實的故事、
10:04
the ones that really mattered,
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真正重要的事,
10:06
were somewhere else.
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卻被拋在一旁。
10:07
What we missed looking at these stories only through models and algorithms
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不要只專注在模型和演算法,
10:12
is what I call "data humanism."
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也就是所謂的「數據人文主義」。
10:15
In the Renaissance humanism,
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在文藝復興人文主義時代,
10:17
European intellectuals
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歐洲的知識分子,
10:19
placed the human nature instead of God at the center of their view of the world.
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將眼光從「上帝」轉向「人性」。
10:24
I believe something similar needs to happen
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我覺得類似的轉變,
10:26
with the universe of data.
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也該發生在數據的研究。
10:28
Now data are apparently treated like a God --
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現在大家都把數據當上帝來拜,
10:31
keeper of infallible truth for our present and our future.
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覺得數據是貫通古今的真理。
10:35
The experiences that I shared with you today
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我今天跟各位分享的經驗,
10:38
taught me that to make data faithfully representative of our human nature
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就是要讓數據去真實呈現人性,
10:43
and to make sure they will not mislead us anymore,
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而不是再次誤導大眾。
10:47
we need to start designing ways to include empathy, imperfection
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我們要將同理心、不完美
10:50
and human qualities
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以及人性,
10:52
in how we collect, process, analyze and display them.
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投入數據的收集、 處裡、分析、呈現。
10:57
I do see a place where, ultimately,
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我相信未來有一天,
11:00
instead of using data only to become more efficient,
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數據不只讓我們更有效率,
11:03
we will all use data to become more humane.
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也讓我們更有人情味。
11:06
Thank you.
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謝謝。
11:07
(Applause)
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(掌聲)
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