TEDSalon Berlin 2014
Kenneth Cukier: Big data is better data
大数据好处多
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无人驾驶汽车只是大数据时代的开始。由大数据所带来的未来技术和设计将会是什么样子的？Kenneth Cukier的精彩演讲给我们展现了由大数据给机器学习和人类知识带来的翻天覆地的变化。
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.
美国人最爱的馅饼是什么？
00:12
America's favorite pie is?
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00:16
Audience: Apple.
Kenneth Cukier: Apple. Of course it is.
Kenneth Cukier: Apple. Of course it is.
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观众：苹果派
Kenneth Cukier：苹果派 毋庸置疑
Kenneth Cukier：苹果派 毋庸置疑
00:20
How do we know it?
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我们是怎么知道的？
00:21
Because of data.
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因为数据
00:24
You look at supermarket sales.
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当你观察超市的销售数据
00:26
You look at supermarket
sales of 30-centimeter pies
sales of 30-centimeter pies
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会发现超市销售的30厘米冷冻馅饼中
00:29
that are frozen, and apple wins, no contest.
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苹果派胜出, 毫无悬念
00:33
The majority of the sales are apple.
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绝大多数的销售份额就是来自苹果派
00:38
But then supermarkets started selling
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但是之后超市开始销售
00:41
smaller, 11-centimeter pies,
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比较小的11厘米的馅饼
00:43
and suddenly, apple fell to fourth or fifth place.
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突然间苹果派的销量下降到了第4或第5名
00:48
Why? What happened?
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为什么？怎么了？
00:50
Okay, think about it.
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好, 想象一下
00:53
When you buy a 30-centimeter pie,
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当你准备买一个30厘米的馅饼时
00:57
the whole family has to agree,
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全家都不得不同意(选择苹果派馅饼)
00:59
and apple is everyone's second favorite.
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虽然苹果派只是每个人的次选项
01:03
(Laughter)
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（笑声）
01:05
But when you buy an individual 11-centimeter pie,
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但当你给自己选一个11厘米馅饼时
01:09
you can buy the one that you want.
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你可以买你最爱吃的口味
01:12
You can get your first choice.
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你会选你的首选项
01:16
You have more data.
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你有了更多数据
01:18
You can see something
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你可以知道些事情
01:20
that you couldn't see
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这些事情在你只有少量数据时
01:21
when you only had smaller amounts of it.
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你是无法知道的
01:25
Now, the point here is that more data
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这里, 关键的是更多的数据
01:27
doesn't just let us see more,
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不单单让我们知道更多
01:29
more of the same thing we were looking at.
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知道更多我们正在关注的同样事物
01:31
More data allows us to see new.
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更多的数据使我们能了解新的事情
01:35
It allows us to see better.
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让我们更好地了解
01:38
It allows us to see different.
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让我们有不同的视角
01:42
In this case, it allows us to see
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在这个例子里 更多的数据让我们知道
01:45
what America's favorite pie is:
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美国人最喜欢的馅饼
01:48
not apple.
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不是苹果派
01:50
Now, you probably all have heard the term big data.
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你或许听说过大数据这个词
01:54
In fact, you're probably sick of hearing the term
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事实上, 你可能对这个词
01:56
big data.
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已经心生厌恶
01:58
It is true that there is a lot of hype around the term,
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确实, 大数据受到了空前的宣传炒作
02:01
and that is very unfortunate,
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这很不应该
02:03
because big data is an extremely important tool
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因为大数据是一个非常重要的工具
02:06
by which society is going to advance.
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社会将由此而不断进步
02:10
In the past, we used to look at small data
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过去我们习惯于处理小数据
02:14
and think about what it would mean
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思考这些小数据的意义
02:15
to try to understand the world,
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并以此来了解世界
02:17
and now we have a lot more of it,
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现在我们有很多很多的数据
02:19
more than we ever could before.
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数据量前所未有的巨大
02:22
What we find is that when we have
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当我们掌握海量数据时
02:23
a large body of data, we can fundamentally do things
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我们可以做一些事
02:26
that we couldn't do when we
only had smaller amounts.
only had smaller amounts.
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一些在只有较少数据时不可能办到的事
02:29
Big data is important, and big data is new,
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大数据很重要, 它也是一个新兴事物
02:32
and when you think about it,
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想象一下
02:34
the only way this planet is going to deal
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能够帮助我们应对
02:36
with its global challenges —
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世界性难题
02:38
to feed people, supply them with medical care,
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像食物短缺 医疗短缺
02:41
supply them with energy, electricity,
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能源短缺 电力短缺
02:44
and to make sure they're not burnt to a crisp
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还有确保人类家园
02:46
because of global warming —
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不会因为全球变暖而生灵涂炭
02:47
is because of the effective use of data.
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的唯一办法是有效利用大数据
02:51
So what is new about big
data? What is the big deal?
data? What is the big deal?
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那么大数据新在何处, 重在何处呢？
02:55
Well, to answer that question, let's think about
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为了回答这个问题, 让我们看一下
02:58
what information looked like,
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信息看上去是什么样的
03:00
physically looked like in the past.
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信息在以前是什么样的
03:03
In 1908, on the island of Crete,
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1908年在克里特岛上
(注：位于地中海 为希腊第一大岛)
(注：位于地中海 为希腊第一大岛)
03:06
archaeologists discovered a clay disc.
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考古学家发现了一个粘土做的盘子
03:11
They dated it from 2000 B.C., so it's 4,000 years old.
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这是个公元前2000年的盘子
距今约有4000年的历史
距今约有4000年的历史
03:15
Now, there's inscriptions on this disc,
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盘子上有铭文
03:17
but we actually don't know what it means.
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但是我们不知道它们是什么意思
03:18
It's a complete mystery, but the point is that
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这完全是个谜团
03:21
this is what information used to look like
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但这就是4000年前
03:22
4,000 years ago.
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信息的样子
03:25
This is how society stored
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这就是当时社会
03:27
and transmitted information.
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存储和传递信息的方式
03:31
Now, society hasn't advanced all that much.
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现代社会也没有什么很大的进步
03:35
We still store information on discs,
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我们还是把数据存储在盘中
（注：指磁盘)
（注：指磁盘)
03:38
but now we can store a lot more information,
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但我们可以存储更多的信息
03:41
more than ever before.
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远远超过以前的信息容量
03:43
Searching it is easier. Copying it easier.
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这些信息搜索和复制起来更简单
03:46
Sharing it is easier. Processing it is easier.
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分享和处理起来也更便捷
03:49
And what we can do is we can reuse this information
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我们也可以重新利用这些数据
03:52
for uses that we never even imagined
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一些我们当初收集的时候
03:54
when we first collected the data.
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从来没有料想过的用途
03:57
In this respect, the data has gone
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从这个方面来说
数据已经从储存状态到了流动状态
03:59
from a stock to a flow,
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04:03
from something that is stationary and static
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从静态的统计性的数据
04:07
to something that is fluid and dynamic.
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变成动态的数据流
04:10
There is, if you will, a liquidity to information.
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这就是信息的流动性
04:14
The disc that was discovered off of Crete
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克里特岛发现的粘土盘
04:18
that's 4,000 years old, is heavy,
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有4000年的历史, 非常笨重
04:22
it doesn't store a lot of information,
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但它不能记录太多的信息
04:24
and that information is unchangeable.
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并且它所记录的信息是不能更改的
04:27
By contrast, all of the files
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与此相反
爱德华·斯诺登从美国国家安全局
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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所获得的文件
04:35
fits on a memory stick
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可以放在一个
04:38
the size of a fingernail,
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仅有指甲大小的存储盘里
04:41
and it can be shared at the speed of light.
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并且可以以光速进行数据共享
04:45
More data. More.
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更多数据 更多
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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今天我们有这么多数据的一个原因是
04:53
is we are collecting things
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我们一直在收集信息
04:54
that we've always collected information on,
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就像我们一直在做的一样
04:57
but another reason why is we're taking things
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另一个原因是我们记录了
05:00
that have always been informational
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许多蕴含丰富信息的事物
05:03
but have never been rendered into a data format
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但是从没把信息转换成数据形式
05:05
and we are putting it into data.
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现在我们正在把信息转变成数据
05:08
Think, for example, the question of location.
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举个例子, 定位问题
05:11
Take, for example, Martin Luther.
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比如说马丁·路德
05:13
If we wanted to know in the 1500s
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在16世纪 如果我们想知道
05:15
where Martin Luther was,
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马丁·路德在哪里
05:18
we would have to follow him at all times,
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我们必须一直跟着他
05:20
maybe with a feathery quill and an inkwell,
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或许用羽毛笔和墨水
05:22
and record it,
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把这些情况记录下来
05:23
but now think about what it looks like today.
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那现今是什么样的情形呢？
05:26
You know that somewhere,
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在某些地方
05:28
probably in a telecommunications carrier's database,
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可能在电信运营商的数据库里
05:30
there is a spreadsheet or at least a database entry
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有个电子数据表或者至少一个数据目录
05:33
that records your information
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记录着所有关于你
05:35
of where you've been at all times.
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任何时候在什么地点的信息
05:37
If you have a cell phone,
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如果你有个手机
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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这个手机有GPS, 或者即使没有GPS
05:42
it can record your information.
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它还是可以记录你的信息
05:44
In this respect, location has been datafied.
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从这方面来说, 位置信息被数据化了
05:48
Now think, for example, of the issue of posture,
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再举个例子, 关于姿势
05:53
the way that you are all sitting right now,
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你们现在坐着的姿势
05:54
the way that you sit,
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你坐着的姿势
05:56
the way that you sit, the way that you sit.
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你坐着的姿势 你坐着的姿势
05:59
It's all different, and it's a function of your leg length
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这些都不一样 这是一个关于腿长
06:01
and your back and the contours of your back,
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你的背部和背部轮廓的函数
06:03
and if I were to put sensors,
maybe 100 sensors
maybe 100 sensors
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如果我现在放一些传感器 或许100个
06:05
into all of your chairs right now,
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在你的椅子里
06:07
I could create an index that's fairly unique to you,
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我可以算出你的独一无二的参数
06:11
sort of like a fingerprint, but it's not your finger.
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就像你的指纹 但不是针对你的手指
06:15
So what could we do with this?
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那我们能用它来干什么呢？
06:18
Researchers in Tokyo are using it
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东京的研究者把它
06:21
as a potential anti-theft device in cars.
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运用在一个汽车防盗设施的雏形上
06:25
The idea is that the carjacker sits behind the wheel,
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它的设想是盗贼坐在驾驶座上
06:28
tries to stream off, but the car recognizes
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企图把车开走 但是汽车识别出
06:30
that a non-approved driver is behind the wheel,
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驾驶座上的是个未授权驾驶人
06:32
and maybe the engine just stops, unless you
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那汽车可能就会熄火
06:35
type in a password into the dashboard
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除非你在仪表盘上输入密码
06:38
to say, "Hey, I have authorization to drive." Great.
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来表明“我已获得授权”
06:42
What if every single car in Europe
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如果欧洲的每辆汽车
06:45
had this technology in it?
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都装备了这项技术会是怎样的情形？
06:46
What could we do then?
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我们还能做些什么呢？
06:50
Maybe, if we aggregated the data,
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或许如果我们整合数据
06:52
maybe we could identify telltale signs
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我们可以识别示警信号
06:56
that best predict that a car accident
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对于在下一个五秒钟内
06:58
is going to take place in the next five seconds.
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可能发生的意外做出最佳预判
07:04
And then what we will have datafied
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我们也可以进行数据化的是
07:07
is driver fatigue,
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司机的疲劳度
07:09
and the service would be when the car senses
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当汽车侦测到司机的坐姿
07:11
that the person slumps into that position,
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倒成某一特定姿势时
07:14
automatically knows, hey, set an internal alarm
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这个设备感知到并发出车内警告
07:18
that would vibrate the steering wheel, honk inside
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可能是震动方向盘或语音提示
07:20
to say, "Hey, wake up,
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“嗨，醒醒
07:22
pay more attention to the road."
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集中精神在路况上”
07:24
These are the sorts of things we can do
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这就是生活的更多方面数据化后
07:26
when we datafy more aspects of our lives.
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我们能做的事情
07:29
So what is the value of big data?
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那么大数据的价值在哪里？
07:32
Well, think about it.
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好 思考一下
07:35
You have more information.
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你有了更多地信息
07:37
You can do things that you couldn't do before.
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你可以做你以前不能做的事
07:40
One of the most impressive areas
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在运用这个概念的领域里
07:42
where this concept is taking place
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让人印象最为最深刻的
07:44
is in the area of machine learning.
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是机器学习
07:47
Machine learning is a branch of artificial intelligence,
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机器学习是人工智能的一个分支
07:50
which itself is a branch of computer science.
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人工智能又是计算机科学的一个分支
07:53
The general idea is that instead of
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它的基本理念是
07:55
instructing a computer what do do,
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把关于某个问题的一堆数据扔给电脑
07:57
we are going to simply throw data at the problem
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让电脑自己找出解决方案
08:00
and tell the computer to figure it out for itself.
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而不是教电脑应该做什么
08:03
And it will help you understand it
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通过机器学习的原型
08:05
by seeing its origins.
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可以帮助你来理解这个理念
08:08
In the 1950s, a computer scientist
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20世纪50年代IBM的计算机科学家
08:11
at IBM named Arthur Samuel liked to play checkers,
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亚瑟·塞缪尔想玩跳棋
08:14
so he wrote a computer program
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所以他写了个程序
08:16
so he could play against the computer.
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这样他就可以和电脑来玩
08:18
He played. He won.
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开始他下一盘 赢一盘
08:21
He played. He won.
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下一盘 赢一盘
08:23
He played. He won,
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下一盘 赢一盘
08:26
because the computer only knew
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因为电脑只知道
08:28
what a legal move was.
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规则允许怎样走
08:30
Arthur Samuel knew something else.
180
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亚瑟·塞缪尔还知道其他东西
08:32
Arthur Samuel knew strategy.
181
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他知道下棋的策略
08:37
So he wrote a small sub-program alongside it
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所以他又写了一个附加程序
08:39
operating in the background, and all it did
183
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这个程序在后台运行
08:41
was score the probability
184
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它的功能只是计算概率
08:43
that a given board configuration would likely lead
185
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在一个给定的棋局里
08:46
to a winning board versus a losing board
186
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每走一步后
08:49
after every move.
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会获胜或者失败的概率
08:51
He plays the computer. He wins.
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再和电脑下棋 还是下一盘 赢一盘
08:54
He plays the computer. He wins.
189
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下一盘 赢一盘
08:57
He plays the computer. He wins.
190
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下一盘 赢一盘
09:01
And then Arthur Samuel leaves the computer
191
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2277
后来亚瑟让电脑
09:03
to play itself.
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自己和自己下棋
09:05
It plays itself. It collects more data.
193
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3509
电脑自己玩的时候收集了更多的数据
09:09
It collects more data. It increases
the accuracy of its prediction.
the accuracy of its prediction.
194
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4309
收集的数据越多, 预测的准确率就越高
09:13
And then Arthur Samuel goes back to the computer
195
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2104
然后亚瑟又继续和电脑下棋
09:15
and he plays it, and he loses,
196
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2318
这次他下一盘 输一盘
09:17
and he plays it, and he loses,
197
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2069
下一盘 输一盘
09:19
and he plays it, and he loses,
198
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2047
下一盘 输一盘
09:21
and Arthur Samuel has created a machine
199
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亚瑟创造了一个机器
09:24
that surpasses his ability in a task that he taught it.
200
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6288
它的能力超越了亚瑟开始时所教给它的
09:30
And this idea of machine learning
201
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机器学习的理念
09:33
is going everywhere.
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3927
现在已经随处可见
09:37
How do you think we have self-driving cars?
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你们觉得无人驾驶汽车(关键的技术)是什么？
09:40
Are we any better off as a society
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是不是把所有交通规则输入软件
09:42
enshrining all the rules of the road into software?
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3285
就万事大吉了？不是
09:45
No. Memory is cheaper. No.
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2598
内存很便宜？不是
09:48
Algorithms are faster. No. Processors are better. No.
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3994
算法更快了 不是 处理器更强大了 不是
09:52
All of those things matter, but that's not why.
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2772
这些都有影响, 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
真正的原因是我们改变了问题的本质
09:58
We changed the nature of the problem from one
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我们把问题的本质从
09:59
in which we tried to overtly and explicitly
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2245
试图明确无误地
10:02
explain to the computer how to drive
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教会电脑怎样驾驶
10:04
to one in which we say,
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1316
变成我们对电脑说
10:05
"Here's a lot of data around the vehicle.
214
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1876
“这里有许多关于汽车的数据
10:07
You figure it out.
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1533
你自己搞定它
10:09
You figure it out that that is a traffic light,
216
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1867
你知道那是交通信号灯
10:11
that that traffic light is red and not green,
217
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2081
那是红灯不是绿灯
10:13
that that means that you need to stop
218
601344
2014
遇到红灯你必须停下来
10:15
and not go forward."
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3083
不能往前走”
10:18
Machine learning is at the basis
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1518
机器学习是许多
10:19
of many of the things that we do online:
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607959
1991
网上在线应用的基础
10:21
search engines,
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1857
搜索引擎
10:23
Amazon's personalization algorithm,
223
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3801
亚马逊的个性化算法
10:27
computer translation,
224
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2212
电脑智能翻译
10:29
voice recognition systems.
225
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4290
语音识别系统
10:34
Researchers recently have looked at
226
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2835
研究者最近在研究
10:36
the question of biopsies,
227
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3195
关于活组织检查的问题
10:40
cancerous biopsies,
228
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2767
关于肿瘤活组织检查
10:42
and they've asked the computer to identify
229
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2315
他们让电脑
10:45
by looking at the data and survival rates
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2471
通过 (历史) 数据和存活率
10:47
to determine whether cells are actually
231
635693
4667
来判断这些细胞
10:52
cancerous or not,
232
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2544
是否是癌症细胞
10:54
and sure enough, when you throw the data at it,
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1778
果不其然 当你把数据交给电脑
10:56
through a machine-learning algorithm,
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2047
电脑通过自主学习
10:58
the machine was able to identify
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1877
可以寻找出
11:00
the 12 telltale signs that best predict
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2262
12个最佳的鉴别特征用来预测
11:02
that this biopsy of the breast cancer cells
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3299
乳腺癌细胞的活检切片
11:06
are indeed cancerous.
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确实是癌症细胞
11:09
The problem: The medical literature
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2498
问题是医学文献
11:11
only knew nine of them.
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2789
只知道其中的九个鉴别特征
11:14
Three of the traits were ones
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662672
1800
其他三个
11:16
that people didn't need to look for,
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2975
人们不会去寻找
11:19
but that the machine spotted.
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5531
但是电脑把它们找了出来
11:24
Now, there are dark sides to big data as well.
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5925
大数据也有黑暗的一面
11:30
It will improve our lives, but there are problems
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2074
它可以改善我们的生活
11:32
that we need to be conscious of,
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2640
但也会带来一些我们需要注意的问题
11:35
and the first one is the idea
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2623
首先就是
11:38
that we may be punished for predictions,
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我们可能因为预测的结果而受到惩罚
11:40
that the police may use big data for their purposes,
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3870
警察可能会用大数据来实现目标
11:44
a little bit like "Minority Report."
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2351
有点像“少数派报告”
11:47
Now, it's a term called predictive policing,
251
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2441
现在有个词叫做预见性监管
11:49
or algorithmic criminology,
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2363
或者叫算法犯罪学
11:51
and the idea is that if we take a lot of data,
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2036
这个想法是如果我们掌握了大量数据
11:53
for example where past crimes have been,
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2159
比如以往犯罪发生的地点
11:56
we know where to send the patrols.
255
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2543
我们可以就知道把警力派到哪里
11:58
That makes sense, but the problem, of course,
256
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2115
这很合理 但问题是
12:00
is that it's not simply going to stop on location data,
257
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4544
数据分析不会仅限于地点数据
12:05
it's going to go down to the level of the individual.
258
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2959
它会进一步深入到个人层面
12:08
Why don't we use data about the person's
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2250
为什么我们不去分析
12:10
high school transcript?
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2228
某人的中学成绩单
12:12
Maybe we should use the fact that
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1561
或者我们可以了解
12:14
they're unemployed or not, their credit score,
262
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2028
他们的就职情况、信用记录
12:16
their web-surfing behavior,
263
724374
1552
他们的上网行为
12:17
whether they're up late at night.
264
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1878
他们是否熬夜
12:19
Their Fitbit, when it's able
to identify biochemistries,
to identify biochemistries,
265
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3161
当可以通过健康腕带读取生化数据时
12:22
will show that they have aggressive thoughts.
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4236
就可以知道他们是否有激进的想法
12:27
We may have algorithms that are likely to predict
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2221
我们可以用算法来预测
12:29
what we are about to do,
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1633
我们将要做什么
12:31
and we may be held accountable
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1244
可能有些事情还没做
12:32
before we've actually acted.
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2590
我们就要承担责任
12:34
Privacy was the central challenge
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1732
个人隐私在小数据时代
12:36
in a small data era.
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2880
是主要挑战
12:39
In the big data age,
273
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2149
在大数据时代
12:41
the challenge will be safeguarding free will,
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4523
这个挑战将会成为保卫自由意愿
12:46
moral choice, human volition,
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3779
道德选择 、人类意志
12:49
human agency.
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3068
人类的能动性
12:54
There is another problem:
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2225
还有另一个问题
12:56
Big data is going to steal our jobs.
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3556
大数据会偷走我们的工作
13:00
Big data and algorithms are going to challenge
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3512
在21世纪
13:03
white collar, professional knowledge work
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3061
大数据和算法会威胁到
13:06
in the 21st century
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1653
白领和需要专业知识的工作
13:08
in the same way that factory automation
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2434
就像在20世纪工厂自动化
13:10
and the assembly line
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2189
和装配生产线的应用
13:13
challenged blue collar labor in the 20th century.
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3026
威胁到了蓝领们的工作岗位
13:16
Think about a lab technician
285
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2092
想象一下一个研究室技术员
13:18
who is looking through a microscope
286
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1409
他的工作就是通过一个显微镜
13:19
at a cancer biopsy
287
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1624
观察一个癌症活检组织
13:21
and determining whether it's cancerous or not.
288
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2637
来判定它是不是癌症的
13:23
The person went to university.
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1972
这个人上大学
13:25
The person buys property.
290
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1430
买房子
13:27
He or she votes.
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1741
他/她投票选举
13:29
He or she is a stakeholder in society.
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3666
他/她是这个社会的一份子
13:32
And that person's job,
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1394
然后这个人的工作
13:34
as well as an entire fleet
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1609
还有其他
13:35
of professionals like that person,
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1969
像他一样的专业人员
13:37
is going to find that their jobs are radically changed
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3150
将会发现他们的工作被彻底改变了
13:40
or actually completely eliminated.
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2357
或者彻底废除了
13:43
Now, we like to think
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1284
我们一直以为
13:44
that technology creates jobs over a period of time
299
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3187
在短时或者暂时的就业调整期后
13:47
after a short, temporary period of dislocation,
300
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3465
一段时间内科技会创造就业机会
13:51
and that is true for the frame of reference
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1941
这对于我们所处的参考系
13:53
with which we all live, the Industrial Revolution,
302
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2142
工业革命来说就是这样
13:55
because that's precisely what happened.
303
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2328
因为在工业革命时期事情就是这样的
13:57
But we forget something in that analysis:
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2333
但是我们忘记了一件事情
13:59
There are some categories of jobs
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1830
有些类型的职业
14:01
that simply get eliminated and never come back.
306
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3420
已经彻底消失了并且再也不会回来
14:05
The Industrial Revolution wasn't very good
307
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2004
如果你是一匹马
14:07
if you were a horse.
308
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4002
工业革命不是一件好事
14:11
So we're going to need to be careful
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2055
所以我们必须非常小心
14:13
and take big data and adjust it for our needs,
310
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3514
根据我们的需求和整个人类的需求
14:16
our very human needs.
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3185
来利用和适应大数据
14:19
We have to be the master of this technology,
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1954
我们必须是技术的主人
14:21
not its servant.
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1656
而不是技术的仆人
14:23
We are just at the outset of the big data era,
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2958
我们正在步入大数据时代
14:26
and honestly, we are not very good
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3150
老实说, 我们并不能很好地
14:29
at handling all the data that we can now collect.
316
857654
4207
处理所有我们现在能够收集到的数据
14:33
It's not just a problem for
the National Security Agency.
the National Security Agency.
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3330
这不仅仅是国家安全局的问题
14:37
Businesses collect lots of
data, and they misuse it too,
data, and they misuse it too,
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3038
许多企业也搜集并不恰当地使用数据
14:40
and we need to get better at
this, and this will take time.
this, and this will take time.
319
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3667
我们需要时间来纠正这个问题
14:43
It's a little bit like the challenge that was faced
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1822
这有点像原始人类面对火时
14:45
by primitive man and fire.
321
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2407
所面临的挑战
14:48
This is a tool, but this is a tool that,
322
876125
1885
火是一种工具
14:50
unless we're careful, will burn us.
323
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3559
但是如果使用不当就会引火烧身
14:56
Big data is going to transform how we live,
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3120
大数据即将改变我们的生活方式
14:59
how we work and how we think.
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2801
我们的工作方式和思考方式
15:01
It is going to help us manage our careers
326
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1889
它可以帮助我们管理事业
15:03
and lead lives of satisfaction and hope
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3634
帮助我们过想要的满足、充满希望
15:07
and happiness and health,
328
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2992
幸福和健康的生活
15:10
but in the past, we've often
looked at information technology
looked at information technology
329
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3306
但是在过去, 对于信息技术(IT)
15:13
and our eyes have only seen the T,
330
901750
2208
我们经常只看到了T
15:15
the technology, the hardware,
331
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1686
就是技术、硬件
15:17
because that's what was physical.
332
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2262
因为这是切实可见的东西
15:19
We now need to recast our gaze at the I,
333
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2924
现在我们需要把目光放在 I 上
15:22
the information,
334
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1380
信息
15:24
which is less apparent,
335
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1373
它不是那么切实可见
15:25
but in some ways a lot more important.
336
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4109
但某种程度上却更加重要
15:29
Humanity can finally learn from the information
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3465
在人类永无止境的探索过程中
15:33
that it can collect,
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2418
我们可以从我们能收集的信息中
15:35
as part of our timeless quest
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2115
来了解这个世界
15:37
to understand the world and our place in it,
340
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3159
以及人类在这个世界中所处的地位
15:40
and that's why big data is a big deal.
341
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5631
这就是为什么大数据非常重要
15:46
(Applause)
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（掌声）
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