ABOUT THE SPEAKER
Tim Berners-Lee - Inventor
Tim Berners-Lee invented the World Wide Web. He leads the World Wide Web Consortium (W3C), overseeing the Web's standards and development.

Why you should listen

In the 1980s, scientists at CERN were asking themselves how massive, complex, collaborative projects -- like the fledgling LHC -- could be orchestrated and tracked. Tim Berners-Lee, then a contractor, answered by inventing the World Wide Web. This global system of hypertext documents, linked through the Internet, brought about a massive cultural shift ushered in by the new tech and content it made possible: AOL, eBay, Wikipedia, TED.com...

Berners-Lee is now director of the World Wide Web Consortium (W3C), which maintains standards for the Web and continues to refine its design. Recently he has envisioned a "Semantic Web" -- an evolved version of the same system that recognizes the meaning of the information it carries. He's the 3Com Founders Professor of Engineering in the School of Engineering with a joint appointment in the Department of Electrical Engineering and Computer Science at the Laboratory for Computer Science and Artificial Intelligence (CSAIL) at the MIT, where he also heads the Decentralized Information Group (DIG). He is also a Professor in the Electronics and Computer Science Department at the University of Southampton, UK.

More profile about the speaker
Tim Berners-Lee | Speaker | TED.com
TED2009

Tim Berners-Lee: The next web

蒂牧泊纳思-李 谈下一代网络

Filmed:
1,638,798 views

20年前,蒂牧泊纳思-李 发明了万维网。在他的下一个项目中,他正在建立一个开放的、将大量文档、图片、视频建成关联数据:释放我们的数据,重新构建我们使用数据的方式。
- Inventor
Tim Berners-Lee invented the World Wide Web. He leads the World Wide Web Consortium (W3C), overseeing the Web's standards and development. Full bio

Double-click the English transcript below to play the video.

00:18
Time flies苍蝇.
0
0
2000
光阴似箭
00:20
It's actually其实 almost几乎 20 years年份 ago
1
2000
2000
差不多是20年前
00:22
when I wanted to reframe重构 the way we use information信息,
2
4000
4000
当我想重新构造我们使用信息
00:26
the way we work together一起: I invented发明 the World世界 Wide Web卷筒纸.
3
8000
3000
协同工作方式的时候 - 我发明了万维网
00:29
Now, 20 years年份 on, at TEDTED,
4
11000
3000
20年过去了,现在,在TED
00:32
I want to ask your help in a new reframing重新定义.
5
14000
4000
我请求你们帮助创建新的架构
00:37
So going back to 1989,
6
19000
4000
回到1989年
00:41
I wrote a memo备忘录 suggesting提示 the global全球 hypertext超文本 system系统.
7
23000
3000
我在备忘录中建议使用一种全球的超链接系统
00:44
Nobody没有人 really did anything with it, pretty漂亮 much.
8
26000
3000
几乎没有什么人在真正用它
00:47
But 18 months个月 later后来 -- this is how innovation革新 happens发生 --
9
29000
4000
但是,18个月后 - 革新就是这么开始的
00:51
18 months个月 later后来, my boss老板 said I could do it on the side,
10
33000
4000
18个月后,老板说,我可以兼职做这件事
00:55
as a sort分类 of a play project项目,
11
37000
2000
做一种游戏性质的项目
00:57
kick the tires轮胎 of a new computer电脑 we'd星期三 got.
12
39000
2000
就拿我们新买来的电脑
00:59
And so he gave me the time to code it up.
13
41000
3000
他给了我些时间写代码实现
01:02
So I basically基本上 roughed粗暴对待 out what HTMLHTML should look like:
14
44000
5000
我草拟了下HTML应该是什么样子
01:07
hypertext超文本 protocol协议, HTTPHTTP;
15
49000
3000
超文本协议 - HTTP -
01:10
the idea理念 of URLs网址, these names for things
16
52000
3000
关于URLs 的想法 - 事物的名称
01:13
which哪一个 started开始 with HTTPHTTP.
17
55000
2000
这些事物都是以HTTP开头命名的
01:15
I wrote the code and put it out there.
18
57000
2000
我完成了代码并发布出来。
01:17
Why did I do it?
19
59000
2000
我为什么要这么做?
01:19
Well, it was basically基本上 frustration挫折.
20
61000
2000
这是一个充满挫败感的过程
01:21
I was frustrated受挫 -- I was working加工 as a software软件 engineer工程师
21
63000
4000
我感到很挫败 - 因为我作为名软件工程师
01:25
in this huge巨大, very exciting扣人心弦 lab实验室,
22
67000
2000
工作在这个令人兴奋的超大的实验室中
01:27
lots of people coming未来 from all over the world世界.
23
69000
2000
很多人从世界各地来到这里
01:29
They brought all sorts排序 of different不同 computers电脑 with them.
24
71000
3000
他们的电脑各不相同
01:32
They had all sorts排序 of different不同 data数据 formats格式,
25
74000
3000
数据格式各不相同
01:35
all sorts排序, all kinds of documentation文件 systems系统.
26
77000
2000
文件系统各不相同
01:37
So that, in all that diversity多样,
27
79000
3000
所以,这其中有很大的差异性
01:40
if I wanted to figure数字 out how to build建立 something
28
82000
2000
如果我想建立一点点东西
01:42
out of a bit of this and a bit of this,
29
84000
2000
在这些差异性很大的电脑上
01:44
everything I looked看着 into, I had to connect to some new machine,
30
86000
4000
我要找一些数据,我不得不连接到一些新的机器
01:48
I had to learn学习 to run some new program程序,
31
90000
2000
运行一些新的程序
01:50
I would find the information信息 I wanted in some new data数据 format格式.
32
92000
5000
以便我能在新的数据格式中找到一些信息
01:55
And these were all incompatible不相容.
33
97000
2000
这些都是不兼容的
01:57
It was just very frustrating泄气.
34
99000
2000
这非常令人沮丧
01:59
The frustration挫折 was all this unlocked解锁 potential潜在.
35
101000
2000
这种挫败感却正显示出这个项目的潜力所在
02:01
In fact事实, on all these discs光盘 there were documents文件.
36
103000
3000
事实上,这些磁盘里全是文件
02:04
So if you just imagined想象 them all
37
106000
3000
所以如果你仅仅把他们
02:07
being存在 part部分 of some big, virtual虚拟 documentation文件 system系统 in the sky天空,
38
109000
5000
想象成天空中某些大型虚拟文件系统的一部分
02:12
say on the Internet互联网,
39
114000
2000
比如Internet
02:14
then life would be so much easier更轻松.
40
116000
2000
生活就会简单得多
02:16
Well, once一旦 you've had an idea理念 like that it kind of gets得到 under your skin皮肤
41
118000
4000
这样,一旦你有了这样的想法
02:20
and even if people don't read your memo备忘录 --
42
122000
2000
即使人们并没有读到你的备忘录
02:22
actually其实 he did, it was found发现 after he died死亡, his copy复制.
43
124000
3000
事实上他读到了,因为在他死后,在他的草稿拷贝中
02:25
He had written书面, "Vague模糊, but exciting扣人心弦," in pencil铅笔, in the corner.
44
127000
3000
他用铅笔在角落写到“模糊,但是令人兴奋”。
02:28
(Laughter笑声)
45
130000
2000
(笑声)
02:30
But in general一般 it was difficult -- it was really difficult to explain说明
46
132000
4000
但一般情况下,很难有这样的想法 – 的确很难解释
02:34
what the web卷筒纸 was like.
47
136000
2000
网络是什么样的
02:36
It's difficult to explain说明 to people now that it was difficult then.
48
138000
2000
现在都很难向人们解释,更别提当初了
02:38
But then -- OK, when TEDTED started开始, there was no web卷筒纸
49
140000
3000
但是 - 对,当TED开始时,那时没有网络
02:41
so things like "click点击" didn't have the same相同 meaning含义.
50
143000
3000
所以像点击这样的事情含义是不同的
02:44
I can show显示 somebody a piece of hypertext超文本,
51
146000
2000
我现在可以向某人展示一大堆超链接
02:46
a page which哪一个 has got links链接,
52
148000
2000
某个包含链接的网页
02:48
and we click点击 on the link链接 and bing -- there'll有会 be another另一个 hypertext超文本 page.
53
150000
4000
我们点击一个链接,然后bing -- 就会转到另一个超链接的页面
02:52
Not impressive有声有色.
54
154000
2000
没什么令人印象深刻的
02:54
You know, we've我们已经 seen看到 that -- we've我们已经 got things on hypertext超文本 on CD-ROMs光盘.
55
156000
3000
我们已经见到,通过超链接找到CD-ROMs中的内容
02:57
What was difficult was to get them to imagine想像:
56
159000
3000
困难的是把它们想象出来
03:00
so, imagine想像 that that link链接 could have gone走了
57
162000
4000
所以,想象那个链接可以到
03:04
to virtually实质上 any document文件 you could imagine想像.
58
166000
2000
任何实际的你能想象得到的文件
03:07
Alright好的, that is the leap飞跃 that was very difficult for people to make.
59
169000
4000
好的,这个跳跃对于人们是很难做到的
03:11
Well, some people did.
60
173000
2000
然而,一些人做到了
03:13
So yeah, it was difficult to explain说明, but there was a grassroots基层 movement运动.
61
175000
3000
尽管很难解释,但是这是一场草根运动
03:17
And that is what has made制作 it most fun开玩笑.
62
179000
4000
这正是使它好玩的地方
03:21
That has been the most exciting扣人心弦 thing,
63
183000
2000
也是最令人激动人心的事情
03:23
not the technology技术, not the things people have doneDONE with it,
64
185000
2000
不是技术,不是人们用它所做的东西
03:25
but actually其实 the community社区, the spirit精神 of all these people
65
187000
2000
而是实际的交流,所有这些人的思想汇聚
03:27
getting得到 together一起, sending发出 the emails电子邮件.
66
189000
2000
在一起,发送电子邮件
03:29
That's what it was like then.
67
191000
2000
这是那时的情况
03:31
Do you know what? It's funny滑稽, but right now it's kind of like that again.
68
193000
3000
你知道吗?有趣的是,现在跟那时候又有点像了
03:34
I asked everybody每个人, more or less, to put their documents文件 --
69
196000
2000
我问每一个人,他们或多或少都发布过文档
03:36
I said, "Could you put your documents文件 on this web卷筒纸 thing?"
70
198000
3000
我说“你能把你的文档放到网络上吗?”
03:39
And you did.
71
201000
3000
然后,你做了
03:42
Thanks谢谢.
72
204000
1000
谢谢
03:43
It's been a blast爆破, hasn't有没有 it?
73
205000
2000
这已经是一场疾风,不是吗?
03:45
I mean, it has been quite相当 interesting有趣
74
207000
2000
我的意思是,它已经非常有趣
03:47
because we've我们已经 found发现 out that the things that happen发生 with the web卷筒纸
75
209000
2000
因为我们发现,网络上发生的事情似乎
03:49
really sort分类 of blow打击 us away.
76
211000
2000
已经把我们吹到了一边
03:51
They're much more than we'd星期三 originally本来 imagined想象
77
213000
2000
现在它的功能得比我们想象的还多
03:53
when we put together一起 the little, initial初始 website网站
78
215000
2000
最初的设计只是想把文档放在一起
03:55
that we started开始 off with.
79
217000
2000
在我们最初开始使用网络时
03:57
Now, I want you to put your data数据 on the web卷筒纸.
80
219000
3000
现在我想让你把你的数据放在网上
04:00
Turns out that there is still huge巨大 unlocked解锁 potential潜在.
81
222000
4000
还是有巨大的可释放潜力
04:04
There is still a huge巨大 frustration挫折
82
226000
2000
也有很大的挫败感
04:06
that people have because we haven't没有 got data数据 on the web卷筒纸 as data数据.
83
228000
4000
因为我们从网上得到的数据不是我们想要的数据
04:10
What do you mean, "data数据"? What's the difference区别 -- documents文件, data数据?
84
232000
2000
你说的数据是什么?文档和数据之间有什么区别?
04:12
Well, documents文件 you read, OK?
85
234000
3000
文档是你阅读的东西
04:15
More or less, you read them, you can follow跟随 links链接 from them, and that's it.
86
237000
3000
或多或少,你都读过,你可以追踪他们的链接,就是这样
04:18
Data数据 -- you can do all kinds of stuff东东 with a computer电脑.
87
240000
2000
数据—你可以通过一台电脑使用各种数据
04:20
Who was here or has otherwise除此以外 seen看到 Hans汉斯 Rosling's罗斯林的 talk?
88
242000
6000
谁在这里或者其他地方听过汉斯罗素玲的演讲?
04:26
One of the great -- yes a lot of people have seen看到 it --
89
248000
4000
一个伟大的 – 很多人已经看过了 –
04:30
one of the great TEDTED Talks会谈.
90
252000
2000
一个伟大的TED演讲
04:32
Hans汉斯 put up this presentation介绍
91
254000
2000
汉斯在他的演示文档中
04:34
in which哪一个 he showed显示, for various各个 different不同 countries国家, in various各个 different不同 colors颜色 --
92
256000
5000
使用不同的颜色表示不同的国家
04:39
he showed显示 income收入 levels水平 on one axis
93
261000
3000
他在一个轴上显示收入水平
04:42
and he showed显示 infant婴儿 mortality死亡, and he shot射击 this thing animated动画 through通过 time.
94
264000
3000
同时他用动画按年份显示婴儿死亡率
04:45
So, he'd他会 taken采取 this data数据 and made制作 a presentation介绍
95
267000
4000
他使用这些数据完成了一场演讲,
04:49
which哪一个 just shattered破灭 a lot of myths神话 that people had
96
271000
3000
这个演讲打破了很多人
04:52
about the economics经济学 in the developing发展 world世界.
97
274000
4000
对发展中国家经济的神话
04:56
He put up a slide滑动 a little bit like this.
98
278000
2000
他展示了一个类似的幻灯片
04:58
It had underground地下 all the data数据
99
280000
2000
数据都被埋在地下
05:00
OK, data数据 is brown棕色 and boxy四四方方 and boring无聊,
100
282000
3000
对,数据是这些棕色的、无趣的四方盒子
05:03
and that's how we think of it, isn't it?
101
285000
2000
我们就是这样看待数据的,不是吗?
05:05
Because data数据 you can't naturally自然 use by itself本身
102
287000
3000
因为,你不能漫无目的地使用数据
05:08
But in fact事实, data数据 drives驱动器 a huge巨大 amount of what happens发生 in our lives生活
103
290000
4000
但事实上,数据驱动了我们的生活
05:12
and it happens发生 because somebody takes that data数据 and does something with it.
104
294000
3000
因为某些人使用了数据并且做了些事情
05:15
In this case案件, Hans汉斯 had put the data数据 together一起
105
297000
2000
在这个例子中,汉斯将数据放到了一起
05:17
he had found发现 from all kinds of United联合的 Nations国家 websites网站 and things.
106
299000
5000
汉斯在美国网站找到各种数据和事物
05:22
He had put it together一起,
107
304000
2000
他把数据放到了一起
05:24
combined结合 it into something more interesting有趣 than the original原版的 pieces
108
306000
3000
将它们组合起来使之比原始数据有趣得多
05:27
and then he'd他会 put it into this software软件,
109
309000
5000
然后把数据放到这个软件中
05:32
which哪一个 I think his son儿子 developed发达, originally本来,
110
314000
2000
这个软件我觉得是他儿子开发的
05:34
and produces产生 this wonderful精彩 presentation介绍.
111
316000
3000
最终他做出了这个美妙的演示
05:37
And Hans汉斯 made制作 a point
112
319000
2000
最后汉斯说道
05:39
of saying, "Look, it's really important重要 to have a lot of data数据."
113
321000
4000
“瞧,有大量的数据是非常重要的”
05:43
And I was happy快乐 to see that at the party派对 last night
114
325000
3000
我高兴地看到在昨天的晚会上
05:46
that he was still saying, very forcibly强制, "It's really important重要 to have a lot of data数据."
115
328000
4000
他仍然强烈地表示“有大量数据是非常重要的”
05:50
So I want us now to think about
116
332000
2000
现在我想让大家想的是
05:52
not just two pieces of data数据 being存在 connected连接的, or six like he did,
117
334000
4000
不仅仅是两条数据间的连接,或者像他所说的那样六条数据
05:56
but I want to think about a world世界 where everybody每个人 has put data数据 on the web卷筒纸
118
338000
5000
而是这个世界上任何人
06:01
and so virtually实质上 everything you can imagine想像 is on the web卷筒纸
119
343000
2000
都把数据和可以虚拟化的一切内容放到网络上
06:03
and then calling调用 that linked关联 data数据.
120
345000
2000
然后把它们称为关联数据
06:05
The technology技术 is linked关联 data数据, and it's extremely非常 simple简单.
121
347000
2000
这个技术就是关联数据,它是极其简单的
06:07
If you want to put something on the web卷筒纸 there are three rules规则:
122
349000
4000
如果你想把什么东西放在网络,有三条规则
06:11
first thing is that those HTTPHTTP names --
123
353000
3000
第一条规则是,需要有HTTP的名字
06:14
those things that start开始 with "httpHTTP:" --
124
356000
2000
那些东西要以http:开头
06:16
we're using运用 them not just for documents文件 now,
125
358000
4000
我们现在不仅对文档这样用
06:20
we're using运用 them for things that the documents文件 are about.
126
362000
2000
对文档描述的事物也这样用
06:22
We're using运用 them for people, we're using运用 them for places地方,
127
364000
2000
我们对人物、地点
06:24
we're using运用 them for your products制品, we're using运用 them for events事件.
128
366000
4000
产品,事件等都这样用
06:28
All kinds of conceptual概念上的 things, they have names now that start开始 with HTTPHTTP.
129
370000
4000
所有概念化的东西现在都以HTTP开头命名
06:32
Second第二 rule规则, if I take one of these HTTPHTTP names and I look it up
130
374000
5000
第二条规则,如果我有一个HTTP名称,然后我根据它在网络上进行查找
06:37
and I do the web卷筒纸 thing with it and I fetch the data数据
131
379000
2000
我可以从网上获取数据
06:39
using运用 the HTTPHTTP protocol协议 from the web卷筒纸,
132
381000
2000
通过HTTP协议
06:41
I will get back some data数据 in a standard标准 format格式
133
383000
3000
我将得到一些标准的格式化数据
06:44
which哪一个 is kind of useful有用 data数据 that somebody might威力 like to know
134
386000
5000
这些有用数据或许是关于人们希望了解
06:49
about that thing, about that event事件.
135
391000
2000
某个事物或者事件的
06:51
Who's谁是 at the event事件? Whatever随你 it is about that person,
136
393000
2000
事件的主人公是谁?关于这个人的所有信息
06:53
where they were born天生, things like that.
137
395000
2000
他们什么时候生的,等等
06:55
So the second第二 rule规则 is I get important重要 information信息 back.
138
397000
2000
所以,第二条规则就是我通过HTTP获得了重要的数据
06:57
Third第三 rule规则 is that when I get back that information信息
139
399000
4000
第三条规则是,我得到的信息
07:01
it's not just got somebody's某人的 height高度 and weight重量 and when they were born天生,
140
403000
3000
不仅仅是某人的身高、体重和出生日期
07:04
it's got relationships关系.
141
406000
2000
还有数据间的关系
07:06
Data数据 is relationships关系.
142
408000
2000
数据是有联系的
07:08
Interestingly有趣的是, data数据 is relationships关系.
143
410000
2000
很有趣,数据是有联系的
07:10
This person was born天生 in Berlin柏林; Berlin柏林 is in Germany德国.
144
412000
4000
这个人出生在柏林,柏林在德国
07:14
And when it has relationships关系, whenever每当 it expresses表达 a relationship关系
145
416000
3000
当数据有联系时,无论何时它表现出这种联系
07:17
then the other thing that it's related有关 to
146
419000
3000
另一件与之有联系的事物
07:20
is given特定 one of those names that starts启动 HTTPHTTP.
147
422000
4000
就以HTTP开头命名
07:24
So, I can go ahead and look that thing up.
148
426000
2000
所以,我可以直接去找那件事
07:26
So I look up a person -- I can look up then the city where they were born天生; then
149
428000
3000
比如,我查一个人 -- 我查他出生的城市
07:29
I can look up the region地区 it's in, and the town it's in,
150
431000
3000
这个城市的所在区域,城市的城镇
07:32
and the population人口 of it, and so on.
151
434000
3000
人口等等
07:35
So I can browse浏览 this stuff东东.
152
437000
2000
这样我就能浏览这些信息
07:37
So that's it, really.
153
439000
2000
真的,就是这样
07:39
That is linked关联 data数据.
154
441000
2000
这就是关联数据
07:41
I wrote an article文章 entitled标题 "Linked关联 Data数据" a couple一对 of years年份 ago
155
443000
3000
我多年前在一篇文章中给它命名为“关联数据”
07:44
and soon不久 after that, things started开始 to happen发生.
156
446000
4000
之后不久,有些事开始发生了
07:48
The idea理念 of linked关联 data数据 is that we get lots and lots and lots
157
450000
4000
关联数据的想法就像我们得到了很多很多
07:52
of these boxes盒子 that Hans汉斯 had,
158
454000
2000
类似汉斯拥有的盒子
07:54
and we get lots and lots and lots of things sprouting发芽.
159
456000
2000
很多很多的事物开始发芽生长
07:56
It's not just a whole整个 lot of other plants植物.
160
458000
3000
它带给我们相当多的植物
07:59
It's not just a root supplying供应 a plant,
161
461000
2000
不仅仅是一个根供给一个植物
08:01
but for each of those plants植物, whatever随你 it is --
162
463000
3000
对于这的每一个植物,无论它是什么
08:04
a presentation介绍, an analysis分析, somebody's某人的 looking for patterns模式 in the data数据 --
163
466000
3000
一个演示,一个分析,某些人查看数据的样式
08:07
they get to look at all the data数据
164
469000
3000
它们都着眼于所有的数据
08:10
and they get it connected连接的 together一起,
165
472000
2000
并且它们把数据联系起来
08:12
and the really important重要 thing about data数据
166
474000
2000
关于数据真正重要的是
08:14
is the more things you have to connect together一起, the more powerful强大 it is.
167
476000
2000
你把很多东西联系起来,数据就更加有价值
08:16
So, linked关联 data数据.
168
478000
2000
所以,关联数据
08:18
The meme米姆 went out there.
169
480000
2000
由此而来
08:20
And, pretty漂亮 soon不久 Chris克里斯 BizerBizer at the Freie柏林自由 UniversitatUniversität大学 in Berlin柏林
170
482000
4000
很快,来自柏林自由大学的克里斯拜泽
08:24
who was one of the first people to put interesting有趣 things up,
171
486000
2000
做为第一人把有趣的东西放在一起
08:26
he noticed注意到 that Wikipedia维基百科 --
172
488000
2000
他注意到维基百科
08:28
you know Wikipedia维基百科, the online线上 encyclopedia百科全书
173
490000
3000
一部在线百科全书
08:31
with lots and lots of interesting有趣 documents文件 in it.
174
493000
2000
有很多有趣的文档
08:33
Well, in those documents文件, there are little squares广场, little boxes盒子.
175
495000
4000
在这些文档中,有些小方格子和小盒子
08:37
And in most information信息 boxes盒子, there's data数据.
176
499000
3000
在许多信息盒子中,就是数据
08:40
So he wrote a program程序 to take the data数据, extract提取 it from Wikipedia维基百科,
177
502000
4000
他写了 一个程序将数据从维基百科中提取出来
08:44
and put it into a blobBLOB of linked关联 data数据
178
506000
2000
然后将它放到关联数据的blob(二进制大对象)中
08:46
on the web卷筒纸, which哪一个 he called dbpediaDBpedia中.
179
508000
3000
在网络上,被他称之为dbpedia(数据库百科)
08:49
DbpediaDBpedia中 is represented代表 by the blue蓝色 blobBLOB in the middle中间 of this slide滑动
180
511000
4000
这张幻灯片中部蓝色的blob表示Dbpedia
08:53
and if you actually其实 go and look up Berlin柏林,
181
515000
2000
如果你去找柏林
08:55
you'll你会 find that there are other blobs斑点 of data数据
182
517000
2000
你会发现还有其他的数据
08:57
which哪一个 also have stuff东东 about Berlin柏林, and they're linked关联 together一起.
183
519000
3000
也有柏林的信息,它们被联系到了一起
09:00
So if you pull the data数据 from dbpediaDBpedia中 about Berlin柏林,
184
522000
3000
所以,如果你要从dbpedia中摘出关于柏林的数据
09:03
you'll你会 end结束 up pulling up these other things as well.
185
525000
2000
你也最终会摘出其他内容
09:05
And the exciting扣人心弦 thing is it's starting开始 to grow增长.
186
527000
3000
令人兴奋的事情是它正在成长
09:08
This is just the grassroots基层 stuff东东 again, OK?
187
530000
2000
这又是一个草根做的事情,对吗?
09:10
Let's think about data数据 for a bit.
188
532000
3000
让我们多想想数据
09:13
Data数据 comes in fact事实 in lots and lots of different不同 forms形式.
189
535000
3000
数据实际上来源于很多很多不同的形式
09:16
Think of the diversity多样 of the web卷筒纸. It's a really important重要 thing
190
538000
3000
想想网络的多样性,很重要的一点
09:19
that the web卷筒纸 allows允许 you to put all kinds of data数据 up there.
191
541000
3000
网络允许你将各式各样的数据放在一起
09:22
So it is with data数据. I could talk about all kinds of data数据.
192
544000
2000
说到数据,我能说出各种各样的数据
09:25
We could talk about government政府 data数据, enterprise企业 data数据 is really important重要,
193
547000
4000
我们可以说政府数据,企业数据真的很重要
09:29
there's scientific科学 data数据, there's personal个人 data数据,
194
551000
3000
还有科学数据,个人数据
09:32
there's weather天气 data数据, there's data数据 about events事件,
195
554000
2000
天气数据,关于事件的数据
09:34
there's data数据 about talks会谈, and there's news新闻 and there's all kinds of stuff东东.
196
556000
4000
关于谈话的数据,还有新闻和各种类似的东西
09:38
I'm just going to mention提到 a few少数 of them
197
560000
3000
我只提到了一小部分数据
09:41
so that you get the idea理念 of the diversity多样 of it,
198
563000
2000
你们就可以看出其多样性
09:43
so that you also see how much unlocked解锁 potential潜在.
199
565000
4000
所以你可以看到其中的潜力
09:47
Let's start开始 with government政府 data数据.
200
569000
2000
让我们从政府数据说起
09:49
Barack巴拉克 Obama奥巴马 said in a speech言语,
201
571000
2000
让我们从政府数据说起
09:51
that he -- American美国 government政府 data数据 would be available可得到 on the Internet互联网
202
573000
5000
美国的政府数据将在互联网上被应用
09:56
in accessible无障碍 formats格式.
203
578000
2000
以一种可访问的形式
09:58
And I hope希望 that they will put it up as linked关联 data数据.
204
580000
2000
美国的政府数据将在互联网上以一种可访问的形式被应用
10:00
That's important重要. Why is it important重要?
205
582000
2000
这非常重要,难道不是吗?
10:02
Not just for transparency透明度, yeah transparency透明度 in government政府 is important重要,
206
584000
3000
不仅仅是为了透明性,透明性对政府很重要
10:05
but that data数据 -- this is the data数据 from all the government政府 departments部门
207
587000
3000
尤其是从政府部门出来的数据更重要
10:08
Think about how much of that data数据 is about how life is lived生活 in America美国.
208
590000
5000
想想有多少关系到在美国如何生活的数据
10:13
It's actual实际 useful有用. It's got value.
209
595000
2000
它的确很有用,很有价值
10:15
I can use it in my company公司.
210
597000
2000
我可以把它用在我的公司
10:17
I could use it as a kid孩子 to do my homework家庭作业.
211
599000
2000
我可以像个小孩子般把它用在我的家庭作业中
10:19
So we're talking about making制造 the place地点, making制造 the world世界 run better
212
601000
3000
所以,我们谈论的是让世界变得更好
10:22
by making制造 this data数据 available可得到.
213
604000
2000
通过将这些数据变得更有用
10:24
In fact事实 if you're responsible主管 -- if you know about some data数据
214
606000
4000
事实上,如果你们在负责 - 如果你知道一些数据
10:28
in a government政府 department, often经常 you find that
215
610000
2000
关于政府的, 你经常会发现
10:30
these people, they're very tempted动心 to keep it --
216
612000
3000
有些人,他们会被这些数据所吸引
10:33
Hans汉斯 calls电话 it database数据库 hugging拥抱.
217
615000
3000
Hans称之为数据库拥抱
10:36
You hug拥抱 your database数据库, you don't want to let it go
218
618000
2000
你拥抱你的数据库,你不会放它走
10:38
until直到 you've made制作 a beautiful美丽 website网站 for it.
219
620000
2000
直到你为它建立了一个漂亮的网站
10:40
Well, I'd like to suggest建议 that rather --
220
622000
2000
嗯,我想建议的是,除了建一个漂亮的网站
10:42
yes, make a beautiful美丽 website网站,
221
624000
2000
是的,建一个漂亮的网站
10:44
who am I to say don't make a beautiful美丽 website网站?
222
626000
2000
我没说不要建一个漂亮的网站
10:46
Make a beautiful美丽 website网站, but first
223
628000
3000
建一个漂亮的网站,首先
10:49
give us the unadulterated纯正 data数据,
224
631000
3000
要给我们纯粹的数据
10:52
we want the data数据.
225
634000
2000
我们要的是数据
10:54
We want unadulterated纯正 data数据.
226
636000
2000
我们要纯粹的数据
10:56
OK, we have to ask for raw生的 data数据 now.
227
638000
3000
好,现在我们不得不要求原始数据了
10:59
And I'm going to ask you to practice实践 that, OK?
228
641000
2000
我要请你们练习一下,好吗?
11:01
Can you say "raw生的"?
229
643000
1000
请说“原始”
11:02
Audience听众: Raw生的.
230
644000
1000
原始
11:03
Tim蒂姆 Berners-Lee伯纳斯 - 李: Can you say "data数据"?
231
645000
1000
请说“数据”
11:04
Audience听众: Data数据.
232
646000
1000
数据
11:05
TBLTBL: Can you say "now"?
233
647000
1000
请说‘现在“
11:06
Audience听众: Now!
234
648000
1000
现在
11:07
TBLTBL: Alright好的, "raw生的 data数据 now"!
235
649000
2000
好,原始数据现在!
11:09
Audience听众: Raw生的 data数据 now!
236
651000
2000
原始数据现在!
11:11
Practice实践 that. It's important重要 because you have no idea理念 the number of excuses借口
237
653000
4000
这样练习是非常重要的
11:15
people come up with to hang onto their data数据
238
657000
2000
因为你不知道那些拥有数据的人
11:17
and not give it to you, even though虽然 you've paid支付 for it as a taxpayer纳税人.
239
659000
4000
有多少理由拒绝将数据给你,甚至你作为一个纳税人是为此付了钱的
11:21
And it's not just America美国. It's all over the world世界.
240
663000
2000
这不仅仅存在于美国,全世界都一样
11:23
And it's not just governments政府, of course课程 -- it's enterprises企业 as well.
241
665000
3000
也不仅仅在政府,当然也存在于企业。
11:26
So I'm just going to mention提到 a few少数 other thoughts思念 on data数据.
242
668000
3000
我还想再谈谈关于数据的其他想法
11:29
Here we are at TEDTED, and all the time we are very conscious意识
243
671000
5000
在TED,我们一直关注于
11:34
of the huge巨大 challenges挑战 that human人的 society社会 has right now --
244
676000
5000
人类社会目前所面临的巨大问题
11:39
curing养护 cancer癌症, understanding理解 the brain for Alzheimer's老年痴呆症,
245
681000
3000
癌症治疗,了解阿尔茨海默病
11:42
understanding理解 the economy经济 to make it a little bit more stable稳定,
246
684000
3000
了解经济好让它稳定点
11:45
understanding理解 how the world世界 works作品.
247
687000
2000
了解世界是如何运转的
11:47
The people who are going to solve解决 those -- the scientists科学家们 --
248
689000
2000
那些致力于解决这些问题的科学家
11:49
they have half-formed半形成 ideas思路 in their head,
249
691000
2000
他们脑海中有些还不成熟的想法
11:51
they try to communicate通信 those over the web卷筒纸.
250
693000
3000
他们试图在网络上与他人交流
11:54
But a lot of the state of knowledge知识 of the human人的 race种族 at the moment时刻
251
696000
3000
但是现状是很多人类的知识
11:57
is on databases数据库, often经常 sitting坐在 in their computers电脑,
252
699000
3000
现在都在数据库中,放在他们的电脑里
12:00
and actually其实, currently目前 not shared共享.
253
702000
3000
现在实际上也没被共享
12:03
In fact事实, I'll just go into one area --
254
705000
3000
事实上,我就从一个方面来说明 -
12:06
if you're looking at Alzheimer's老年痴呆症, for example,
255
708000
2000
如果你在研究阿尔茨海默病,以此为例,
12:08
drug药物 discovery发现 -- there is a whole整个 lot of linked关联 data数据 which哪一个 is just coming未来 out
256
710000
3000
以药物发现为例 -- 这个领域具有相当多的刚刚出现的关联数据
12:11
because scientists科学家们 in that field领域 realize实现
257
713000
2000
因为这个领域的科学家们意识到
12:13
this is a great way of getting得到 out of those silos筒仓,
258
715000
3000
关联数据是一种很好的方法,可以帮助他们摆脱数据孤岛
12:16
because they had their genomics基因组学 data数据 in one database数据库
259
718000
4000
因为他们在一个数据库中建立了基因图组
12:20
in one building建造, and they had their protein蛋白 data数据 in another另一个.
260
722000
3000
他们在另一个数据库中建立蛋白质数据
12:23
Now, they are sticking症结 it onto -- linked关联 data数据 --
261
725000
3000
现在,他们将基因图组和蛋白质数据形成了关联数据
12:26
and now they can ask the sort分类 of question, that you probably大概 wouldn't不会 ask,
262
728000
3000
他们可以问排序的问题,也许你不会问
12:29
I wouldn't不会 ask -- they would.
263
731000
2000
我不会问,但是他们会
12:31
What proteins蛋白质 are involved参与 in signal信号 transduction转导
264
733000
2000
哪些蛋白质参与信号转导
12:33
and also related有关 to pyramidal金字塔 neurons神经元?
265
735000
2000
并且也和锥体神经元相关?
12:35
Well, you take that mouthful一口 and you put it into Google谷歌.
266
737000
3000
当你将这个问题放到Google上搜索
12:38
Of course课程, there's no page on the web卷筒纸 which哪一个 has answered回答 that question
267
740000
3000
自然没有回答结果的页面
12:41
because nobody没有人 has asked that question before.
268
743000
2000
因为之前没有人问过这样的问题
12:43
You get 223,000 hits点击 --
269
745000
2000
虽然你得到了223,000个结果
12:45
no results结果 you can use.
270
747000
2000
但是没有一个你用得上
12:47
You ask the linked关联 data数据 -- which哪一个 they've他们已经 now put together一起 --
271
749000
3000
但是没有一个你用得上 -- 现在他们已经被放到了一起
12:50
32 hits点击, each of which哪一个 is a protein蛋白 which哪一个 has those properties性能
272
752000
4000
命中32个结果,每一个结果都是与特征相关的蛋白质
12:54
and you can look at.
273
756000
2000
并且你可以看到
12:56
The power功率 of being存在 able能够 to ask those questions问题, as a scientist科学家 --
274
758000
3000
做为一个科学家, 询问那些问题的能力
12:59
questions问题 which哪一个 actually其实 bridge across横过 different不同 disciplines学科 --
275
761000
2000
那些问题基本上都是跨学科的问题
13:01
is really a complete完成 sea change更改.
276
763000
3000
是真正的C-change
13:04
It's very very important重要.
277
766000
2000
这是非常非常重要的
13:06
Scientists科学家们 are totally完全 stymied陷入困境 at the moment时刻 --
278
768000
2000
科学家们那时完全陷入了困境
13:08
the power功率 of the data数据 that other scientists科学家们 have collected is locked锁定 up
279
770000
5000
因为其他科学家搜集的数据,其价值被锁起来了
13:13
and we need to get it unlocked解锁 so we can tackle滑车 those huge巨大 problems问题.
280
775000
3000
我们需要将之解锁,以便处理那些大问题
13:16
Now if I go on like this, you'll你会 think that all the data数据 comes from huge巨大 institutions机构
281
778000
4000
现在,如果我继续像这样讲
13:20
and has nothing to do with you.
282
782000
3000
和你没有一点关系
13:23
But, that's not true真正.
283
785000
2000
但是,这种想法并不对
13:25
In fact事实, data数据 is about our lives生活.
284
787000
2000
事实上,数据关乎我们的生活
13:27
You just -- you log日志 on to your social社会 networking联网 site现场,
285
789000
3000
你刚刚登陆了你的社会化网络站点
13:30
your favorite喜爱 one, you say, "This is my friend朋友."
286
792000
2000
你最喜欢的一个,你说“这是我朋友”
13:32
Bing! Relationship关系. Data数据.
287
794000
3000
叮!联系,数据
13:35
You say, "This photograph照片, it's about -- it depicts描绘 this person. "
288
797000
3000
你说“这副照片,是这个人的”
13:38
Bing! That's data数据. Data数据, data数据, data数据.
289
800000
3000
叮!那是数据。数据,数据,数据
13:41
Every一切 time you do things on the social社会 networking联网 site现场,
290
803000
2000
每次你在社会化网络上做的事
13:43
the social社会 networking联网 site现场 is taking服用 data数据 and using运用 it -- re-purposing再重新考虑 it --
291
805000
4000
社会化网络站点就获取数据并利用它
13:47
and using运用 it to make other people's人们 lives生活 more interesting有趣 on the site现场.
292
809000
4000
重新设计数据的目的是为了让这个站点的其他人过得更有趣
13:51
But, when you go to another另一个 linked关联 data数据 site现场 --
293
813000
2000
但是,当你上另一个关联数据网站
13:53
and let's say this is one about travel旅行,
294
815000
3000
假设是一个旅游网站
13:56
and you say, "I want to send发送 this photo照片 to all the people in that group,"
295
818000
3000
你说“我想把这张照片发给那个组里的所有人”
13:59
you can't get over the walls墙壁.
296
821000
2000
但你却无法翻过这些墙
14:01
The Economist经济学家 wrote an article文章 about it, and lots of people have blogged博客 about it --
297
823000
2000
经济学家曾经写了一篇关于这个问题的文章,并且许多人也发了相关博文表示出
14:03
tremendous巨大 frustration挫折.
298
825000
1000
巨大的挫败感
14:04
The way to break打破 down the silos筒仓 is to get inter-operability互操作性
299
826000
2000
打破孤岛的方式是实现互操作
14:06
between之间 social社会 networking联网 sites网站.
300
828000
2000
在这些社交网络之间
14:08
We need to do that with linked关联 data数据.
301
830000
2000
我们需要通过关联数据做这件事
14:10
One last type类型 of data数据 I'll talk about, maybe it's the most exciting扣人心弦.
302
832000
3000
最后一种我将要谈到的数据,也许是最令人激动的
14:13
Before I came来了 down here, I looked看着 it up on OpenStreetMapOpenStreetMap的
303
835000
3000
在我来这之前,我通过OpenStreetMap查找了一下
14:16
The OpenStreetMap'sOpenStreetMap的 a map地图, but it's also a Wiki维基.
304
838000
2000
OpenStreetMap是一个地图,但同样也是一个维基
14:18
Zoom放大 in and that square广场 thing is a theater剧院 -- which哪一个 we're in right now --
305
840000
3000
放大这个方块,这是一个剧场 -- 就是我们现在所处的地方 --
14:21
The Terrace阳台 Theater剧院. It didn't have a name名称 on it.
306
843000
2000
特伦斯剧场(位于长滩市,加利福尼亚)。它现在还没有被标上名字
14:23
So I could go into edit编辑 mode模式, I could select选择 the theater剧院,
307
845000
2000
所以我可以到编辑模式,选择剧场
14:25
I could add down at the bottom底部 the name名称, and I could save保存 it back.
308
847000
5000
然后在底下填上名字,然后保存它
14:30
And now if you go back to the OpenStreetMapOpenStreetMap的. org组织,
309
852000
3000
现在你再去访问OpenStreetMap.org
14:33
and you find this place地点, you will find that The Terrace阳台 Theater剧院 has got a name名称.
310
855000
3000
你找到这个地方,你会发现它现在有名字了
14:36
I did that. Me!
311
858000
2000
这都是我做的
14:38
I did that to the map地图. I just did that!
312
860000
2000
我在地图上标的,刚刚做的
14:40
I put that up on there. Hey, you know what?
313
862000
2000
我把它标注在那里。嗨,你知道吗
14:42
If I -- that street map地图 is all about everybody每个人 doing their bit
314
864000
3000
如果除了我,每个人都在这个地图上标注一点
14:45
and it creates创建 an incredible难以置信 resource资源
315
867000
3000
将会产生难以置信的资源
14:48
because everybody每个人 else其他 does theirs他们的.
316
870000
3000
因为其他每个人都做了
14:51
And that is what linked关联 data数据 is all about.
317
873000
3000
这就是关联数据
14:54
It's about people doing their bit
318
876000
3000
每个人都做一点
14:57
to produce生产 a little bit, and it all connecting.
319
879000
3000
生成一点内容,然后把它们连接起来
15:00
That's how linked关联 data数据 works作品.
320
882000
3000
关联数据就是这样工作的
15:03
You do your bit. Everybody每个人 else其他 does theirs他们的.
321
885000
4000
你做一些,每个人都做一些
15:07
You may可能 not have lots of data数据 which哪一个 you have yourself你自己 to put on there
322
889000
4000
也许你的数据在关联数据中只是很小一部分
15:11
but you know to demand需求 it.
323
893000
3000
但你知道你需要它
15:14
And we've我们已经 practiced that.
324
896000
2000
我们已经在实践了
15:16
So, linked关联 data数据 -- it's huge巨大.
325
898000
4000
关联数据 -- 是非常巨大的
15:20
I've only told you a very small number of things
326
902000
3000
我只能告诉你很小一部分
15:23
There are data数据 in every一切 aspect方面 of our lives生活,
327
905000
2000
我们生活的每个方面
15:25
every一切 aspect方面 of work and pleasure乐趣,
328
907000
3000
工作和快乐的每个方面
15:28
and it's not just about the number of places地方 where data数据 comes,
329
910000
3000
不管是数据出处的有多少
15:31
it's about connecting it together一起.
330
913000
3000
关键是把它联系起来
15:34
And when you connect data数据 together一起, you get power功率
331
916000
3000
当你把数据联系起来
15:37
in a way that doesn't happen发生 just with the web卷筒纸, with documents文件.
332
919000
3000
你能从这样的方式中获取在网络或文档中无法获取的能量
15:40
You get this really huge巨大 power功率 out of it.
333
922000
4000
你能从中得到巨大的能量
15:44
So, we're at the stage阶段 now
334
926000
3000
现在我们处在一个阶段
15:47
where we have to do this -- the people who think it's a great idea理念.
335
929000
4000
我们必须要做的阶段 -- 那些认为这是个伟大想法的人们
15:51
And all the people -- and I think there's a lot of people at TEDTED who do things because --
336
933000
3000
而且所有人 -- 我想在TED的大部分人
15:54
even though虽然 there's not an immediate即时 return返回 on the investment投资
337
936000
2000
他们做事情并不是为了要使投资得到立即的回报
15:56
because it will only really pay工资 off when everybody每个人 else其他 has doneDONE it --
338
938000
3000
因为只有当每个人都这么做了才会有所回报
15:59
they'll他们会 do it because they're the sort分类 of person who just does things
339
941000
4000
他们将会这么做,因为他们是那类人
16:03
which哪一个 would be good if everybody每个人 else其他 did them.
340
945000
3000
那类希望每个人都参与进来而让事情变好的人
16:06
OK, so it's called linked关联 data数据.
341
948000
2000
OK,这就是关联数据
16:08
I want you to make it.
342
950000
2000
我希望你参与
16:10
I want you to demand需求 it.
343
952000
2000
我希望你需要它
16:12
And I think it's an idea理念 worth价值 spreading传播.
344
954000
2000
我也认为这个想法值得宣扬
16:14
Thanks谢谢.
345
956000
1000
谢谢
16:15
(Applause掌声)
346
957000
3000
谢谢
Translated by Zheng Xiao
Reviewed by Halei Liu

▲Back to top

ABOUT THE SPEAKER
Tim Berners-Lee - Inventor
Tim Berners-Lee invented the World Wide Web. He leads the World Wide Web Consortium (W3C), overseeing the Web's standards and development.

Why you should listen

In the 1980s, scientists at CERN were asking themselves how massive, complex, collaborative projects -- like the fledgling LHC -- could be orchestrated and tracked. Tim Berners-Lee, then a contractor, answered by inventing the World Wide Web. This global system of hypertext documents, linked through the Internet, brought about a massive cultural shift ushered in by the new tech and content it made possible: AOL, eBay, Wikipedia, TED.com...

Berners-Lee is now director of the World Wide Web Consortium (W3C), which maintains standards for the Web and continues to refine its design. Recently he has envisioned a "Semantic Web" -- an evolved version of the same system that recognizes the meaning of the information it carries. He's the 3Com Founders Professor of Engineering in the School of Engineering with a joint appointment in the Department of Electrical Engineering and Computer Science at the Laboratory for Computer Science and Artificial Intelligence (CSAIL) at the MIT, where he also heads the Decentralized Information Group (DIG). He is also a Professor in the Electronics and Computer Science Department at the University of Southampton, UK.

More profile about the speaker
Tim Berners-Lee | Speaker | TED.com

Data provided by TED.

This site was created in May 2015 and the last update was on January 12, 2020. It will no longer be updated.

We are currently creating a new site called "eng.lish.video" and would be grateful if you could access it.

If you have any questions or suggestions, please feel free to write comments in your language on the contact form.

Privacy Policy

Developer's Blog

Buy Me A Coffee