TEDSalon Berlin 2014
Kenneth Cukier: Big data is better data
肯尼斯．庫基耶: 「大」數據是「好」數據
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自動駕駛汽車只是開端。大數據驅動的科技與設計，將帶給我們什麼樣的未來？在這個令人振奮的科學演講中，肯尼斯．庫基耶探討機器學習以及人類知識的明天。
Kenneth Cukier - Data Editor of The Economist
Kenneth Cukier is the Data Editor of The Economist. From 2007 to 2012 he was the Tokyo correspondent, and before that, the paper’s technology correspondent in London, where his work focused on innovation, intellectual property and Internet governance. Kenneth is also the co-author of Big Data: A Revolution That Will Transform How We Live, Work, and Think with Viktor Mayer-Schönberger in 2013, which was a New York Times Bestseller and translated into 16 languages. Full bio
Kenneth Cukier is the Data Editor of The Economist. From 2007 to 2012 he was the Tokyo correspondent, and before that, the paper’s technology correspondent in London, where his work focused on innovation, intellectual property and Internet governance. Kenneth is also the co-author of Big Data: A Revolution That Will Transform How We Live, Work, and Think with Viktor Mayer-Schönberger in 2013, which was a New York Times Bestseller and translated into 16 languages. Full bio
Double-click the English transcript below to play the video.
00:12
America's favorite pie is?
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美國人最喜歡哪一種派？
00:16
Audience: Apple.
Kenneth Cukier: Apple. Of course it is.
Kenneth Cukier: Apple. Of course it is.
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觀眾：蘋果派。
講者：蘋果派，當然啦！
我們怎麼知道？
00:20
How do we know it?
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00:21
Because of data.
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因為有數據。
00:24
You look at supermarket sales.
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我們分析超市銷售數據，
00:26
You look at supermarket
sales of 30-centimeter pies
sales of 30-centimeter pies
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分析直徑 30 公分冷凍蘋果派的
超市銷售數據，
超市銷售數據，
00:29
that are frozen, and apple wins, no contest.
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蘋果派最夯，銷量一面倒。
顧客幾乎都是買蘋果派。
00:33
The majority of the sales are apple.
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00:38
But then supermarkets started selling
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但是後來，超市開始賣小派，
00:41
smaller, 11-centimeter pies,
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直徑 11 公分的派，
00:43
and suddenly, apple fell to fourth or fifth place.
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突然，蘋果派銷量掉到第四、五名，
00:48
Why? What happened?
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為什麼？發生了什麼事？
00:50
Okay, think about it.
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好，你想想：
00:53
When you buy a 30-centimeter pie,
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如果是買 30 公分的大派，
00:57
the whole family has to agree,
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全家人都得同意，
00:59
and apple is everyone's second favorite.
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而蘋果是全家每個人的第二選擇，
01:03
(Laughter)
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（觀眾笑聲）
01:05
But when you buy an individual 11-centimeter pie,
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但是當你分開買 11 公分的小派，
01:09
you can buy the one that you want.
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就可以買你自己想吃的，
01:12
You can get your first choice.
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每個人都可以選自己最愛的口味。
01:16
You have more data.
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這就會產生更多的數據。
01:18
You can see something
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你會有新發現，
01:20
that you couldn't see
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看出數據少的時候，
無法發現的現象。
無法發現的現象。
01:21
when you only had smaller amounts of it.
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01:25
Now, the point here is that more data
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現在，這個例子的重點是，
數據增加，不只是讓我們看見更「多」，
01:27
doesn't just let us see more,
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01:29
more of the same thing we were looking at.
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更多我們本來就已經知道的；
01:31
More data allows us to see new.
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數據增加，讓我們看見「新」資訊，
01:35
It allows us to see better.
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看得更「準確」，
01:38
It allows us to see different.
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看見「不同」。
01:42
In this case, it allows us to see
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在這個例子，它使我們看到
01:45
what America's favorite pie is:
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美國人真正最喜歡的派是什麼：
01:48
not apple.
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不是蘋果派。
01:50
Now, you probably all have heard the term big data.
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你們可能都聽過「大數據」這個詞，
01:54
In fact, you're probably sick of hearing the term
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其實，你們可能已經聽膩了。
01:56
big data.
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01:58
It is true that there is a lot of hype around the term,
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的確有很多大肆宣傳，
02:01
and that is very unfortunate,
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非常遺憾。
02:03
because big data is an extremely important tool
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因為大數據是極為重要的工具，
02:06
by which society is going to advance.
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將會推動社會進步。
02:10
In the past, we used to look at small data
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過去，我們依賴少量數據，
02:14
and think about what it would mean
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研究其含義，
02:15
to try to understand the world,
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試圖了解我們的世界。
02:17
and now we have a lot more of it,
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現在我們有了更多數據，
02:19
more than we ever could before.
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遠超過以往能力所及。
02:22
What we find is that when we have
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我們發現，
當我們擁有龐大的數據，
02:23
a large body of data, we can fundamentally do things
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就可以做過去數據較少時做不到的事。
02:26
that we couldn't do when we
only had smaller amounts.
only had smaller amounts.
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02:29
Big data is important, and big data is new,
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大數據很重要，
大數據也很新。
02:32
and when you think about it,
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你想一想，
02:34
the only way this planet is going to deal
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唯一能幫助地球因應全球的挑戰：
02:36
with its global challenges —
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02:38
to feed people, supply them with medical care,
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解決饑荒、
提供醫療、
提供能源和電力、
02:41
supply them with energy, electricity,
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02:44
and to make sure they're not burnt to a crisp
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確保我們不被全球暖化烤焦，
02:46
because of global warming —
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02:47
is because of the effective use of data.
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唯一的方法，就是靠善用數據。
02:51
So what is new about big
data? What is the big deal?
data? What is the big deal?
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所以大數據有什麼稀奇？
有什麼好「大」驚小怪？
02:55
Well, to answer that question, let's think about
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要回答這個問題，
讓我們先來看資訊以前長什麼樣子。
02:58
what information looked like,
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03:00
physically looked like in the past.
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03:03
In 1908, on the island of Crete,
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好，
1908 年，在克里特島，
03:06
archaeologists discovered a clay disc.
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考古學家發現一個泥土圓盤，
03:11
They dated it from 2000 B.C., so it's 4,000 years old.
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鑑定大約是公元前 2 千年製成的，
所以已經有 4 千年之久。
03:15
Now, there's inscriptions on this disc,
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圓盤上刻有古文字，
03:17
but we actually don't know what it means.
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但無法解讀，
03:18
It's a complete mystery, but the point is that
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是個謎團。
但重點是，4 千年前資訊是這個樣貌，
03:21
this is what information used to look like
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03:22
4,000 years ago.
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古人是用這種方式儲存、傳遞資訊。
03:25
This is how society stored
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03:27
and transmitted information.
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03:31
Now, society hasn't advanced all that much.
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到現在，社會並沒有進步那麼多，
03:35
We still store information on discs,
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我們還是把資訊存在碟片上，
03:38
but now we can store a lot more information,
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只是現在可以儲存更多資訊，
03:41
more than ever before.
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空前的多。
03:43
Searching it is easier. Copying it easier.
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搜尋更容易，複製更容易，
03:46
Sharing it is easier. Processing it is easier.
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分享更容易，處理更容易。
03:49
And what we can do is we can reuse this information
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我們可以重複使用這些資訊，
03:52
for uses that we never even imagined
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用途之廣，超乎想像，
03:54
when we first collected the data.
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超乎我們蒐集資訊時的預期。
03:57
In this respect, the data has gone
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這樣看來，資訊已經
03:59
from a stock to a flow,
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從「存料」 變成「流動」；
04:03
from something that is stationary and static
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從靜止、靜態的，
04:07
to something that is fluid and dynamic.
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變成流體、動態的。
04:10
There is, if you will, a liquidity to information.
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資訊可說是，有流動性。
04:14
The disc that was discovered off of Crete
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那個 4 千年之久的克里特圓盤，
04:18
that's 4,000 years old, is heavy,
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它很重，
04:22
it doesn't store a lot of information,
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儲存的資訊量不多，
04:24
and that information is unchangeable.
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內容也不能更改。
04:27
By contrast, all of the files
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相較之下，
愛德華．史諾登盜走的所有檔案，
04:31
that Edward Snowden took
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04:33
from the National Security
Agency in the United States
Agency in the United States
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就是他從美國國安局竊走的資料，
04:35
fits on a memory stick
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可以全部存在一個記憶卡，
04:38
the size of a fingernail,
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體積只有指甲般的大小。
04:41
and it can be shared at the speed of light.
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並且可以用光速來傳輸分享。
04:45
More data. More.
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更多的數據！
更多。
今天之所以有這麼多的數據，
04:51
Now, one reason why we have
so much data in the world today
so much data in the world today
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04:53
is we are collecting things
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原因之一是
我們正在蒐集過去
我們正在蒐集過去
04:54
that we've always collected information on,
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儲存資訊的物體；
04:57
but another reason why is we're taking things
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原因之二是，
我們把一些經常很資訊性的東西——
05:00
that have always been informational
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05:03
but have never been rendered into a data format
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從未數據化的資訊，
05:05
and we are putting it into data.
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把它們變成數據，
05:08
Think, for example, the question of location.
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例如，地理位置。
05:11
Take, for example, Martin Luther.
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舉馬丁．路德為例，
05:13
If we wanted to know in the 1500s
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如果我們想知道十六世紀時，
05:15
where Martin Luther was,
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馬丁．路德去過哪些地方，
05:18
we would have to follow him at all times,
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我們必須隨時跟著他到處跑，
05:20
maybe with a feathery quill and an inkwell,
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可能還要帶著羽毛筆和墨水瓶，
05:22
and record it,
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隨時記錄。
05:23
but now think about what it looks like today.
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但是看看現在的做法，
05:26
You know that somewhere,
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你知道世界上某處，
05:28
probably in a telecommunications carrier's database,
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可能是電信商的資料庫裡面，
05:30
there is a spreadsheet or at least a database entry
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有一個試算表
或至少有一筆記錄，
或至少有一筆記錄，
05:33
that records your information
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存著關於你的資訊，
05:35
of where you've been at all times.
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記錄你去過的所有地方。
05:37
If you have a cell phone,
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如果你有一支手機，
05:39
and that cell phone has GPS,
but even if it doesn't have GPS,
but even if it doesn't have GPS,
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手機有 GPS，但就算沒有 GPS，
05:42
it can record your information.
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還是可以記錄你的資訊。
05:44
In this respect, location has been datafied.
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就這個角度來說，位置已經被數據化。
05:48
Now think, for example, of the issue of posture,
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現在再想想這個例子：姿勢，
05:53
the way that you are all sitting right now,
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就是你們現在的坐姿，
05:54
the way that you sit,
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你的坐姿、
05:56
the way that you sit, the way that you sit.
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你的坐姿，和你的坐姿，
05:59
It's all different, and it's a function of your leg length
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都不一樣，取決於你的腿長、
06:01
and your back and the contours of your back,
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你的背和背部輪廓。
06:03
and if I were to put sensors,
maybe 100 sensors
maybe 100 sensors
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要是我現在裝 1 百個感應器，
06:05
into all of your chairs right now,
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到你們每個人的椅子上，
06:07
I could create an index that's fairly unique to you,
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我可以建出你個人獨特的索引資料，
06:11
sort of like a fingerprint, but it's not your finger.
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有點像指紋，但不是你的手指。
06:15
So what could we do with this?
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這有什麼用？
06:18
Researchers in Tokyo are using it
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東京的研究員用這種數據
06:21
as a potential anti-theft device in cars.
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來研發汽車防盜裝置。
06:25
The idea is that the carjacker sits behind the wheel,
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概念是，偷車賊坐在駕駛座，
06:28
tries to stream off, but the car recognizes
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急著開車逃逸，
但是車子辨識出開車的人未經授權，
06:30
that a non-approved driver is behind the wheel,
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06:32
and maybe the engine just stops, unless you
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引擎就自動熄火，
除非你輸入密碼到儀表板，
06:35
type in a password into the dashboard
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告訴系統：「嘿，我可是有經授權喔！」
06:38
to say, "Hey, I have authorization to drive." Great.
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很好。
06:42
What if every single car in Europe
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若歐洲每輛汽車都有這個裝置呢？
06:45
had this technology in it?
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那又能做什麼？
06:46
What could we do then?
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06:50
Maybe, if we aggregated the data,
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或許，我們可以聚集所有的數據，
06:52
maybe we could identify telltale signs
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或許能提早偵測到警訊，
06:56
that best predict that a car accident
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預測車禍
06:58
is going to take place in the next five seconds.
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即將在 5 秒鐘內發生。
07:04
And then what we will have datafied
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然後我們還可以數據化
07:07
is driver fatigue,
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駕駛員的疲勞狀態，
07:09
and the service would be when the car senses
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汽車系統可以偵測到
07:11
that the person slumps into that position,
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駕駛癱坐成某個姿勢，
07:14
automatically knows, hey, set an internal alarm
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自動感知，發出指令啟動響鈴，
07:18
that would vibrate the steering wheel, honk inside
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導致方向盤震動，
07:20
to say, "Hey, wake up,
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車內喇叭作響，大喊：「嘿，快醒來！
07:22
pay more attention to the road."
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注意路況！」
07:24
These are the sorts of things we can do
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這一類的事都可以做到，
07:26
when we datafy more aspects of our lives.
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當我們把更多的生活層面數據化。
07:29
So what is the value of big data?
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那麼，大數據究竟有什麼價值？
07:32
Well, think about it.
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想想看，
07:35
You have more information.
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現在有更多資訊，
07:37
You can do things that you couldn't do before.
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可以做過去不能做的事。
07:40
One of the most impressive areas
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這概念的應用當中，最驚人的領域之一，
07:42
where this concept is taking place
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07:44
is in the area of machine learning.
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就是「機器學習」。
07:47
Machine learning is a branch of artificial intelligence,
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機器學習是人工智慧的一個分支，
07:50
which itself is a branch of computer science.
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人工智慧又是電腦科學的分支。
07:53
The general idea is that instead of
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基本概念是：
07:55
instructing a computer what do do,
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不必告訴電腦要做什麼，
07:57
we are going to simply throw data at the problem
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只要把數據輸入到問題裡，
08:00
and tell the computer to figure it out for itself.
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然後叫電腦自己想辦法。
08:03
And it will help you understand it
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我們回顧一下源頭，
就會比較容易了解。
就會比較容易了解。
08:05
by seeing its origins.
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08:08
In the 1950s, a computer scientist
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1950 年代，IBM 有位電腦科學家
08:11
at IBM named Arthur Samuel liked to play checkers,
172
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名叫亞瑟．山姆爾，很愛下跳棋，
08:14
so he wrote a computer program
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1402
所以他寫了一個電腦程式，
08:16
so he could play against the computer.
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叫電腦跟他對打。
08:18
He played. He won.
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他開始下棋，結果他贏了。
08:21
He played. He won.
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他再開始下棋，結果他又贏了。
08:23
He played. He won,
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他再下，還是他贏。
08:26
because the computer only knew
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因為電腦只會
08:28
what a legal move was.
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棋步的規則。
08:30
Arthur Samuel knew something else.
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而亞瑟．山姆爾會得更多，
08:32
Arthur Samuel knew strategy.
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他懂得策略。
08:37
So he wrote a small sub-program alongside it
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所以他又寫了一個副程式，
08:39
operating in the background, and all it did
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在背景執行，只做一件事：
08:41
was score the probability
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就是計算機率，
08:43
that a given board configuration would likely lead
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2563
評估目前的棋局，
08:46
to a winning board versus a losing board
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比較贏棋和輸棋的機率，
08:49
after every move.
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每下一步棋，就重算一次。
08:51
He plays the computer. He wins.
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然後他又跟電腦對打，結果他贏。
08:54
He plays the computer. He wins.
189
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再對打，還是他贏。
08:57
He plays the computer. He wins.
190
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再對打，還是他贏。
09:01
And then Arthur Samuel leaves the computer
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然後亞瑟．山姆爾讓電腦自己對打。
09:03
to play itself.
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09:05
It plays itself. It collects more data.
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它就自己下棋，一邊收集數據。
09:09
It collects more data. It increases
the accuracy of its prediction.
the accuracy of its prediction.
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越收集越多，它的預測準確度就提高。
09:13
And then Arthur Samuel goes back to the computer
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2104
然後亞瑟．山姆爾再回來跟電腦對打。
09:15
and he plays it, and he loses,
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2318
他開始下棋，結果他輸了。
09:17
and he plays it, and he loses,
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他又下，又輸了。
09:19
and he plays it, and he loses,
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再下，還是輸。
09:21
and Arthur Samuel has created a machine
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亞瑟．山姆爾創造了一台機器，
09:24
that surpasses his ability in a task that he taught it.
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它的能力青出於藍，更甚於藍。
09:30
And this idea of machine learning
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而這種機器學習的概念，
09:33
is going everywhere.
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現在到處可見。
09:37
How do you think we have self-driving cars?
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你想我們怎麼會有自動駕駛汽車？
09:40
Are we any better off as a society
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把全部交通規則都輸入到軟體，
可以改善社會嗎？
可以改善社會嗎？
09:42
enshrining all the rules of the road into software?
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09:45
No. Memory is cheaper. No.
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2598
不是。
因為記憶體更便宜嗎？不是。
09:48
Algorithms are faster. No. Processors are better. No.
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576408
3994
演算法變快了？不。
有更好的處理器？不。
09:52
All of those things matter, but that's not why.
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這些都很重要，但不是真正的原因。
09:55
It's because we changed the nature of the problem.
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3141
真正的原因是
我們改變了問題的本質。
我們改變了問題的本質。
09:58
We changed the nature of the problem from one
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我們把問題從
09:59
in which we tried to overtly and explicitly
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明確指示電腦如何開車，
10:02
explain to the computer how to drive
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10:04
to one in which we say,
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改成對電腦說：
10:05
"Here's a lot of data around the vehicle.
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「我給你大量的開車數據，
10:07
You figure it out.
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你自個兒看著辦吧！」
10:09
You figure it out that that is a traffic light,
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你自己判斷出那是紅綠燈，
10:11
that that traffic light is red and not green,
217
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2081
而且現在亮紅燈，不是綠燈，
10:13
that that means that you need to stop
218
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2014
表示你要停車，
10:15
and not go forward."
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3083
不能繼續開。」
10:18
Machine learning is at the basis
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機器學習也是
10:19
of many of the things that we do online:
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我們許多網路活動的基礎：
10:21
search engines,
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搜尋引擎、
10:23
Amazon's personalization algorithm,
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亞馬遜的個人化演算法、
10:27
computer translation,
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電腦翻譯、
10:29
voice recognition systems.
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語音辨識系統。
10:34
Researchers recently have looked at
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研究專家近來研究
10:36
the question of biopsies,
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活組織切片檢查，
10:40
cancerous biopsies,
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癌組織切片，
10:42
and they've asked the computer to identify
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他們叫電腦自己判別，
10:45
by looking at the data and survival rates
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電腦分析數據和存活率，
10:47
to determine whether cells are actually
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判斷是否為癌症細胞。
10:52
cancerous or not,
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10:54
and sure enough, when you throw the data at it,
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果然，當你把數據丟給電腦，
10:56
through a machine-learning algorithm,
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透過一個機器學習的演算法，
10:58
the machine was able to identify
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電腦真的能找出
11:00
the 12 telltale signs that best predict
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2262
12 大危險徵兆，
預測這個乳房癌細胞的切片
11:02
that this biopsy of the breast cancer cells
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3299
11:06
are indeed cancerous.
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真的就是癌腫瘤。
11:09
The problem: The medical literature
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問題來了：醫學文獻只知道
11:11
only knew nine of them.
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其中 9 項。
11:14
Three of the traits were ones
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1800
另外 3 項特性
11:16
that people didn't need to look for,
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是我們以前不需檢查的，
卻被電腦找出來了。
11:19
but that the machine spotted.
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5531
好。
11:24
Now, there are dark sides to big data as well.
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不過，大數據也有不好的一面。
11:30
It will improve our lives, but there are problems
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它會改善我們的生活，
但是也有我們必須注意的問題。
11:32
that we need to be conscious of,
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11:35
and the first one is the idea
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第一，
我們可能因為預測而受罰，
11:38
that we may be punished for predictions,
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11:40
that the police may use big data for their purposes,
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警察可能會利用大數據來辦案，
11:44
a little bit like "Minority Report."
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有點像電影《關鍵報告》。
11:47
Now, it's a term called predictive policing,
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這叫做「預測性警務」，
11:49
or algorithmic criminology,
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或「演算犯罪學」。
11:51
and the idea is that if we take a lot of data,
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原理是，我們蒐集大量數據，
11:53
for example where past crimes have been,
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2159
例如，分析過去犯罪發生地點的大數據，
11:56
we know where to send the patrols.
255
704146
2543
我們就知道要往哪裡派送警力。
11:58
That makes sense, but the problem, of course,
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2115
這很合邏輯。但問題是，當然，
12:00
is that it's not simply going to stop on location data,
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4544
這種策略不會
只限犯罪地點的數據，
只限犯罪地點的數據，
12:05
it's going to go down to the level of the individual.
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而會一直延伸到個人資料。
12:08
Why don't we use data about the person's
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何不利用人們的
12:10
high school transcript?
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高中成績單？
12:12
Maybe we should use the fact that
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1561
或許我們可以看看
12:14
they're unemployed or not, their credit score,
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2028
他們是否失業、信用評等、
12:16
their web-surfing behavior,
263
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1552
上網瀏覽行為、
12:17
whether they're up late at night.
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1878
是否熬夜、
12:19
Their Fitbit, when it's able
to identify biochemistries,
to identify biochemistries,
265
727804
3161
Fitbit 智慧健康手環，
當它能識別個人生化數據，
當它能識別個人生化數據，
12:22
will show that they have aggressive thoughts.
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4236
可看出主人是否有攻擊性的想法。
12:27
We may have algorithms that are likely to predict
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2221
可能有演算法
會預測我們將要做什麼事，
會預測我們將要做什麼事，
12:29
what we are about to do,
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1633
12:31
and we may be held accountable
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1244
可能還沒有付諸行動，就得負責。
12:32
before we've actually acted.
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2590
12:34
Privacy was the central challenge
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1732
在小數據時代，
最重要的挑戰是隱私。
最重要的挑戰是隱私。
12:36
in a small data era.
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2880
12:39
In the big data age,
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2149
在大數據時代，
12:41
the challenge will be safeguarding free will,
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4523
挑戰則變成保衛自由意志、
12:46
moral choice, human volition,
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道德選擇、人的意志、
12:49
human agency.
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人的「能動性」(human agency)。
12:54
There is another problem:
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還有一個問題：
12:56
Big data is going to steal our jobs.
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3556
大數據會搶走我們的工作。
13:00
Big data and algorithms are going to challenge
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3512
大數據和演算法將會挑戰
13:03
white collar, professional knowledge work
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3061
21 世紀的白領、專業知識工作，
13:06
in the 21st century
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1653
13:08
in the same way that factory automation
282
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2434
就像工廠自動化和生產線
13:10
and the assembly line
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2189
13:13
challenged blue collar labor in the 20th century.
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3026
在 20 世紀挑戰藍領工作者一樣。
13:16
Think about a lab technician
285
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2092
試想一位實驗室技術員，
13:18
who is looking through a microscope
286
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1409
他正在用顯微鏡看腫瘤切片，
13:19
at a cancer biopsy
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1624
13:21
and determining whether it's cancerous or not.
288
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2637
要判斷是否為癌細胞。
13:23
The person went to university.
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791958
1972
他唸過大學，
13:25
The person buys property.
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1430
買了房子，
13:27
He or she votes.
291
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1741
會投票，
13:29
He or she is a stakeholder in society.
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3666
他與社會利害相關。
13:32
And that person's job,
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他的工作，及許多像他一樣的專業人士，
13:34
as well as an entire fleet
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1609
13:35
of professionals like that person,
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1969
將發現他們的工作起了劇變，
13:37
is going to find that their jobs are radically changed
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3150
13:40
or actually completely eliminated.
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2357
甚至完全被淘汰。
13:43
Now, we like to think
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1284
我們喜歡相信
13:44
that technology creates jobs over a period of time
299
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3187
長遠來說，科技創造工作機會，
13:47
after a short, temporary period of dislocation,
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3465
即使剛開始會先經歷
短暫的錯亂與重組，
短暫的錯亂與重組，
13:51
and that is true for the frame of reference
301
819182
1941
這對我們所處的工業革命時代來說，
並沒有錯，
並沒有錯，
13:53
with which we all live, the Industrial Revolution,
302
821123
2142
13:55
because that's precisely what happened.
303
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2328
因為事實的確如此。
13:57
But we forget something in that analysis:
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2333
但是這個分析遺漏了一點：
13:59
There are some categories of jobs
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1830
有些工作類別其實已經消失，
14:01
that simply get eliminated and never come back.
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3420
且從未起死回生。
14:05
The Industrial Revolution wasn't very good
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833176
2004
如果你是一匹馬，
那麼工業革命對你並不利。
那麼工業革命對你並不利。
14:07
if you were a horse.
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835180
4002
14:11
So we're going to need to be careful
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2055
所以我們必須非常謹慎，
14:13
and take big data and adjust it for our needs,
310
841237
3514
正確駕馭大數據，
調整它以適應我們所需，
調整它以適應我們所需，
14:16
our very human needs.
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844751
3185
滿足我們的人性需求。
14:19
We have to be the master of this technology,
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1954
我們必須成為這項科技的主人，
14:21
not its servant.
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1656
而不是淪為它的奴隸。
14:23
We are just at the outset of the big data era,
314
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2958
大數據時代才正開始，
14:26
and honestly, we are not very good
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3150
老實說，我們並不是很擅長
14:29
at handling all the data that we can now collect.
316
857654
4207
處理我們能蒐集的龐大數據資料。
14:33
It's not just a problem for
the National Security Agency.
the National Security Agency.
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3330
這不只是國安局的問題，
14:37
Businesses collect lots of
data, and they misuse it too,
data, and they misuse it too,
318
865191
3038
企業也蒐集大量資料，
同樣也誤用、濫用。
同樣也誤用、濫用。
14:40
and we need to get better at
this, and this will take time.
this, and this will take time.
319
868229
3667
我們都必須學習怎麼正確運用，
而這需要時間。
而這需要時間。
14:43
It's a little bit like the challenge that was faced
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871896
1822
有點像原始人用火
所面臨的挑戰。
所面臨的挑戰。
14:45
by primitive man and fire.
321
873718
2407
14:48
This is a tool, but this is a tool that,
322
876125
1885
大數據是個工具，
14:50
unless we're careful, will burn us.
323
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3559
如果運用失當，就會燒傷我們。
14:56
Big data is going to transform how we live,
324
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3120
大數據將改變我們如何生活、
14:59
how we work and how we think.
325
887128
2801
工作，和思考。
15:01
It is going to help us manage our careers
326
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1889
它可以幫助我們管理職涯，
15:03
and lead lives of satisfaction and hope
327
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3634
讓我們過滿意、夢想的生活，
15:07
and happiness and health,
328
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2992
帶來快樂與健康。
15:10
but in the past, we've often
looked at information technology
looked at information technology
329
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3306
以往，我們常在看待「資訊科技」時，
15:13
and our eyes have only seen the T,
330
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2208
只專注在「科技」，
15:15
the technology, the hardware,
331
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1686
只重視硬體，
15:17
because that's what was physical.
332
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2262
因為它具體可見。
15:19
We now need to recast our gaze at the I,
333
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2924
現在我們必須重新對焦，
15:22
the information,
334
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1380
轉向「資訊」，
15:24
which is less apparent,
335
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1373
它比較不明顯，
15:25
but in some ways a lot more important.
336
913583
4109
但是就某些方面來說，卻重要得多。
15:29
Humanity can finally learn from the information
337
917692
3465
人性總算可以向我們蒐集來的資訊學習，
15:33
that it can collect,
338
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2418
15:35
as part of our timeless quest
339
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2115
成為我們永恆追尋的一部份，
15:37
to understand the world and our place in it,
340
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3159
藉此了解我們的世界，和人類的角色，
15:40
and that's why big data is a big deal.
341
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5631
這是為什麼大數據將「大」有可為。
15:46
(Applause)
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3568
（觀眾掌聲）
ABOUT THE SPEAKER
Kenneth Cukier - Data Editor of The EconomistKenneth Cukier is the Data Editor of The Economist. From 2007 to 2012 he was the Tokyo correspondent, and before that, the paper’s technology correspondent in London, where his work focused on innovation, intellectual property and Internet governance. Kenneth is also the co-author of Big Data: A Revolution That Will Transform How We Live, Work, and Think with Viktor Mayer-Schönberger in 2013, which was a New York Times Bestseller and translated into 16 languages.
Why you should listen
As Data Editor of The Economist and co-author of Big Data: A Revolution That Will Transform How We Live, Work, and Think, Kenneth Cukier has spent years immersed in big data, machine learning -- and the impact of both. What's the future of big data-driven technology and design? To find out, watch this talk.
Kenneth Cukier | Speaker | TED.com