Time flies. It's actually almost 20 years ago when I wanted to reframe the way we use information, the way we work together: I invented the World Wide Web. Now, 20 years on, at TED, I want to ask your help in a new reframing.
光阴似箭 差不多是20年前 当我想重新构造我们使用信息 协同工作方式的时候 - 我发明了万维网 20年过去了,现在,在TED 我请求你们帮助创建新的架构
So going back to 1989, I wrote a memo suggesting the global hypertext system. Nobody really did anything with it, pretty much. But 18 months later -- this is how innovation happens -- 18 months later, my boss said I could do it on the side, as a sort of a play project, kick the tires of a new computer we'd got. And so he gave me the time to code it up. So I basically roughed out what HTML should look like: hypertext protocol, HTTP; the idea of URLs, these names for things which started with HTTP. I wrote the code and put it out there.
回到1989年 我在备忘录中建议使用一种全球的超链接系统 几乎没有什么人在真正用它 但是,18个月后 - 革新就是这么开始的 18个月后,老板说,我可以兼职做这件事 做一种游戏性质的项目 就拿我们新买来的电脑 他给了我些时间写代码实现 我草拟了下HTML应该是什么样子 超文本协议 - HTTP - 关于URLs 的想法 - 事物的名称 这些事物都是以HTTP开头命名的 我完成了代码并发布出来。
Why did I do it? Well, it was basically frustration. I was frustrated -- I was working as a software engineer in this huge, very exciting lab, lots of people coming from all over the world. They brought all sorts of different computers with them. They had all sorts of different data formats, all sorts, all kinds of documentation systems. So that, in all that diversity, if I wanted to figure out how to build something out of a bit of this and a bit of this, everything I looked into, I had to connect to some new machine, I had to learn to run some new program, I would find the information I wanted in some new data format. And these were all incompatible. It was just very frustrating. The frustration was all this unlocked potential.
我为什么要这么做? 这是一个充满挫败感的过程 我感到很挫败 - 因为我作为名软件工程师 工作在这个令人兴奋的超大的实验室中 很多人从世界各地来到这里 他们的电脑各不相同 数据格式各不相同 文件系统各不相同 所以,这其中有很大的差异性 如果我想建立一点点东西 在这些差异性很大的电脑上 我要找一些数据,我不得不连接到一些新的机器 运行一些新的程序 以便我能在新的数据格式中找到一些信息 这些都是不兼容的 这非常令人沮丧 这种挫败感却正显示出这个项目的潜力所在
In fact, on all these discs there were documents. So if you just imagined them all being part of some big, virtual documentation system in the sky, say on the Internet, then life would be so much easier. Well, once you've had an idea like that it kind of gets under your skin and even if people don't read your memo -- actually he did, it was found after he died, his copy. He had written, "Vague, but exciting," in pencil, in the corner.
事实上,这些磁盘里全是文件 所以如果你仅仅把他们 想象成天空中某些大型虚拟文件系统的一部分 比如Internet 生活就会简单得多 这样,一旦你有了这样的想法 即使人们并没有读到你的备忘录 事实上他读到了,因为在他死后,在他的草稿拷贝中 他用铅笔在角落写到“模糊,但是令人兴奋”。
(Laughter)
(笑声)
But in general it was difficult -- it was really difficult to explain what the web was like. It's difficult to explain to people now that it was difficult then. But then -- OK, when TED started, there was no web so things like "click" didn't have the same meaning. I can show somebody a piece of hypertext, a page which has got links, and we click on the link and bing -- there'll be another hypertext page. Not impressive. You know, we've seen that -- we've got things on hypertext on CD-ROMs. What was difficult was to get them to imagine: so, imagine that that link could have gone to virtually any document you could imagine. Alright, that is the leap that was very difficult for people to make. Well, some people did. So yeah, it was difficult to explain, but there was a grassroots movement. And that is what has made it most fun. That has been the most exciting thing, not the technology, not the things people have done with it, but actually the community, the spirit of all these people getting together, sending the emails. That's what it was like then.
但一般情况下,很难有这样的想法 – 的确很难解释 网络是什么样的 现在都很难向人们解释,更别提当初了 但是 - 对,当TED开始时,那时没有网络 所以像点击这样的事情含义是不同的 我现在可以向某人展示一大堆超链接 某个包含链接的网页 我们点击一个链接,然后bing -- 就会转到另一个超链接的页面 没什么令人印象深刻的 我们已经见到,通过超链接找到CD-ROMs中的内容 困难的是把它们想象出来 所以,想象那个链接可以到 任何实际的你能想象得到的文件 好的,这个跳跃对于人们是很难做到的 然而,一些人做到了 尽管很难解释,但是这是一场草根运动 这正是使它好玩的地方 也是最令人激动人心的事情 不是技术,不是人们用它所做的东西 而是实际的交流,所有这些人的思想汇聚 在一起,发送电子邮件 这是那时的情况
Do you know what? It's funny, but right now it's kind of like that again. I asked everybody, more or less, to put their documents -- I said, "Could you put your documents on this web thing?" And you did. Thanks. It's been a blast, hasn't it? I mean, it has been quite interesting because we've found out that the things that happen with the web really sort of blow us away. They're much more than we'd originally imagined when we put together the little, initial website that we started off with. Now, I want you to put your data on the web. Turns out that there is still huge unlocked potential. There is still a huge frustration that people have because we haven't got data on the web as data.
你知道吗?有趣的是,现在跟那时候又有点像了 我问每一个人,他们或多或少都发布过文档 我说“你能把你的文档放到网络上吗?” 然后,你做了 谢谢 这已经是一场疾风,不是吗? 我的意思是,它已经非常有趣 因为我们发现,网络上发生的事情似乎 已经把我们吹到了一边 现在它的功能得比我们想象的还多 最初的设计只是想把文档放在一起 在我们最初开始使用网络时 现在我想让你把你的数据放在网上 还是有巨大的可释放潜力 也有很大的挫败感 因为我们从网上得到的数据不是我们想要的数据
What do you mean, "data"? What's the difference -- documents, data? Well, documents you read, OK? More or less, you read them, you can follow links from them, and that's it. Data -- you can do all kinds of stuff with a computer. Who was here or has otherwise seen Hans Rosling's talk? One of the great -- yes a lot of people have seen it -- one of the great TED Talks. Hans put up this presentation in which he showed, for various different countries, in various different colors -- he showed income levels on one axis and he showed infant mortality, and he shot this thing animated through time. So, he'd taken this data and made a presentation which just shattered a lot of myths that people had about the economics in the developing world.
你说的数据是什么?文档和数据之间有什么区别? 文档是你阅读的东西 或多或少,你都读过,你可以追踪他们的链接,就是这样 数据—你可以通过一台电脑使用各种数据 谁在这里或者其他地方听过汉斯罗素玲的演讲? 一个伟大的 – 很多人已经看过了 – 一个伟大的TED演讲 汉斯在他的演示文档中 使用不同的颜色表示不同的国家 他在一个轴上显示收入水平 同时他用动画按年份显示婴儿死亡率 他使用这些数据完成了一场演讲, 这个演讲打破了很多人 对发展中国家经济的神话
He put up a slide a little bit like this. It had underground all the data OK, data is brown and boxy and boring, and that's how we think of it, isn't it? Because data you can't naturally use by itself But in fact, data drives a huge amount of what happens in our lives and it happens because somebody takes that data and does something with it. In this case, Hans had put the data together he had found from all kinds of United Nations websites and things. He had put it together, combined it into something more interesting than the original pieces and then he'd put it into this software, which I think his son developed, originally, and produces this wonderful presentation. And Hans made a point of saying, "Look, it's really important to have a lot of data." And I was happy to see that at the party last night that he was still saying, very forcibly, "It's really important to have a lot of data."
他展示了一个类似的幻灯片 数据都被埋在地下 对,数据是这些棕色的、无趣的四方盒子 我们就是这样看待数据的,不是吗? 因为,你不能漫无目的地使用数据 但事实上,数据驱动了我们的生活 因为某些人使用了数据并且做了些事情 在这个例子中,汉斯将数据放到了一起 汉斯在美国网站找到各种数据和事物 他把数据放到了一起 将它们组合起来使之比原始数据有趣得多 然后把数据放到这个软件中 这个软件我觉得是他儿子开发的 最终他做出了这个美妙的演示 最后汉斯说道 “瞧,有大量的数据是非常重要的” 我高兴地看到在昨天的晚会上 他仍然强烈地表示“有大量数据是非常重要的”
So I want us now to think about not just two pieces of data being connected, or six like he did, but I want to think about a world where everybody has put data on the web and so virtually everything you can imagine is on the web and then calling that linked data. The technology is linked data, and it's extremely simple. If you want to put something on the web there are three rules: first thing is that those HTTP names -- those things that start with "http:" -- we're using them not just for documents now, we're using them for things that the documents are about. We're using them for people, we're using them for places, we're using them for your products, we're using them for events. All kinds of conceptual things, they have names now that start with HTTP.
现在我想让大家想的是 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 而是这个世界上任何人 都把数据和可以虚拟化的一切内容放到网络上 然后把它们称为关联数据 这个技术就是关联数据,它是极其简单的 如果你想把什么东西放在网络,有三条规则 第一条规则是,需要有HTTP的名字 那些东西要以http:开头 我们现在不仅对文档这样用 对文档描述的事物也这样用 我们对人物、地点 产品,事件等都这样用 所有概念化的东西现在都以HTTP开头命名
Second rule, if I take one of these HTTP names and I look it up and I do the web thing with it and I fetch the data using the HTTP protocol from the web, I will get back some data in a standard format which is kind of useful data that somebody might like to know about that thing, about that event. Who's at the event? Whatever it is about that person, where they were born, things like that. So the second rule is I get important information back.
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 我可以从网上获取数据 通过HTTP协议 我将得到一些标准的格式化数据 这些有用数据或许是关于人们希望了解 某个事物或者事件的 事件的主人公是谁?关于这个人的所有信息 他们什么时候生的,等等 所以,第二条规则就是我通过HTTP获得了重要的数据
Third rule is that when I get back that information it's not just got somebody's height and weight and when they were born, it's got relationships. Data is relationships. Interestingly, data is relationships. This person was born in Berlin; Berlin is in Germany. And when it has relationships, whenever it expresses a relationship then the other thing that it's related to is given one of those names that starts HTTP. So, I can go ahead and look that thing up. So I look up a person -- I can look up then the city where they were born; then I can look up the region it's in, and the town it's in, and the population of it, and so on. So I can browse this stuff.
第三条规则是,我得到的信息 不仅仅是某人的身高、体重和出生日期 还有数据间的关系 数据是有联系的 很有趣,数据是有联系的 这个人出生在柏林,柏林在德国 当数据有联系时,无论何时它表现出这种联系 另一件与之有联系的事物 就以HTTP开头命名 所以,我可以直接去找那件事 比如,我查一个人 -- 我查他出生的城市 这个城市的所在区域,城市的城镇 人口等等 这样我就能浏览这些信息
So that's it, really. That is linked data. I wrote an article entitled "Linked Data" a couple of years ago and soon after that, things started to happen. The idea of linked data is that we get lots and lots and lots of these boxes that Hans had, and we get lots and lots and lots of things sprouting. It's not just a whole lot of other plants. It's not just a root supplying a plant, but for each of those plants, whatever it is -- a presentation, an analysis, somebody's looking for patterns in the data -- they get to look at all the data and they get it connected together, and the really important thing about data is the more things you have to connect together, the more powerful it is.
真的,就是这样 这就是关联数据 我多年前在一篇文章中给它命名为“关联数据” 之后不久,有些事开始发生了 关联数据的想法就像我们得到了很多很多 类似汉斯拥有的盒子 很多很多的事物开始发芽生长 它带给我们相当多的植物 不仅仅是一个根供给一个植物 对于这的每一个植物,无论它是什么 一个演示,一个分析,某些人查看数据的样式 它们都着眼于所有的数据 并且它们把数据联系起来 关于数据真正重要的是 你把很多东西联系起来,数据就更加有价值
So, linked data. The meme went out there. And, pretty soon Chris Bizer at the Freie Universitat in Berlin who was one of the first people to put interesting things up, he noticed that Wikipedia -- you know Wikipedia, the online encyclopedia with lots and lots of interesting documents in it. Well, in those documents, there are little squares, little boxes. And in most information boxes, there's data. So he wrote a program to take the data, extract it from Wikipedia, and put it into a blob of linked data on the web, which he called dbpedia. Dbpedia is represented by the blue blob in the middle of this slide and if you actually go and look up Berlin, you'll find that there are other blobs of data which also have stuff about Berlin, and they're linked together. So if you pull the data from dbpedia about Berlin, you'll end up pulling up these other things as well. And the exciting thing is it's starting to grow. This is just the grassroots stuff again, OK?
所以,关联数据 由此而来 很快,来自柏林自由大学的克里斯拜泽 做为第一人把有趣的东西放在一起 他注意到维基百科 一部在线百科全书 有很多有趣的文档 在这些文档中,有些小方格子和小盒子 在许多信息盒子中,就是数据 他写了 一个程序将数据从维基百科中提取出来 然后将它放到关联数据的blob(二进制大对象)中 在网络上,被他称之为dbpedia(数据库百科) 这张幻灯片中部蓝色的blob表示Dbpedia 如果你去找柏林 你会发现还有其他的数据 也有柏林的信息,它们被联系到了一起 所以,如果你要从dbpedia中摘出关于柏林的数据 你也最终会摘出其他内容 令人兴奋的事情是它正在成长 这又是一个草根做的事情,对吗?
Let's think about data for a bit. Data comes in fact in lots and lots of different forms. Think of the diversity of the web. It's a really important thing that the web allows you to put all kinds of data up there. So it is with data. I could talk about all kinds of data. We could talk about government data, enterprise data is really important, there's scientific data, there's personal data, there's weather data, there's data about events, there's data about talks, and there's news and there's all kinds of stuff. I'm just going to mention a few of them so that you get the idea of the diversity of it, so that you also see how much unlocked potential.
让我们多想想数据 数据实际上来源于很多很多不同的形式 想想网络的多样性,很重要的一点 网络允许你将各式各样的数据放在一起 说到数据,我能说出各种各样的数据 我们可以说政府数据,企业数据真的很重要 还有科学数据,个人数据 天气数据,关于事件的数据 关于谈话的数据,还有新闻和各种类似的东西 我只提到了一小部分数据 你们就可以看出其多样性 所以你可以看到其中的潜力
Let's start with government data. Barack Obama said in a speech, that he -- American government data would be available on the Internet in accessible formats. And I hope that they will put it up as linked data. That's important. Why is it important? Not just for transparency, yeah transparency in government is important, but that data -- this is the data from all the government departments Think about how much of that data is about how life is lived in America. It's actual useful. It's got value. I can use it in my company. I could use it as a kid to do my homework. So we're talking about making the place, making the world run better by making this data available.
让我们从政府数据说起 让我们从政府数据说起 美国的政府数据将在互联网上被应用 以一种可访问的形式 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 这非常重要,难道不是吗? 不仅仅是为了透明性,透明性对政府很重要 尤其是从政府部门出来的数据更重要 想想有多少关系到在美国如何生活的数据 它的确很有用,很有价值 我可以把它用在我的公司 我可以像个小孩子般把它用在我的家庭作业中 所以,我们谈论的是让世界变得更好 通过将这些数据变得更有用
In fact if you're responsible -- if you know about some data in a government department, often you find that these people, they're very tempted to keep it -- Hans calls it database hugging. You hug your database, you don't want to let it go until you've made a beautiful website for it. Well, I'd like to suggest that rather -- yes, make a beautiful website, who am I to say don't make a beautiful website? Make a beautiful website, but first give us the unadulterated data, we want the data. We want unadulterated data. OK, we have to ask for raw data now. And I'm going to ask you to practice that, OK? Can you say "raw"?
事实上,如果你们在负责 - 如果你知道一些数据 关于政府的, 你经常会发现 有些人,他们会被这些数据所吸引 Hans称之为数据库拥抱 你拥抱你的数据库,你不会放它走 直到你为它建立了一个漂亮的网站 嗯,我想建议的是,除了建一个漂亮的网站 是的,建一个漂亮的网站 我没说不要建一个漂亮的网站 建一个漂亮的网站,首先 要给我们纯粹的数据 我们要的是数据 我们要纯粹的数据 好,现在我们不得不要求原始数据了 我要请你们练习一下,好吗? 请说“原始”
Audience: Raw.
原始
Tim Berners-Lee: Can you say "data"?
请说“数据”
Audience: Data.
数据
TBL: Can you say "now"?
请说‘现在“
Audience: Now!
现在
TBL: Alright, "raw data now"!
好,原始数据现在!
Audience: Raw data now!
原始数据现在!
Practice that. It's important because you have no idea the number of excuses people come up with to hang onto their data and not give it to you, even though you've paid for it as a taxpayer. And it's not just America. It's all over the world. And it's not just governments, of course -- it's enterprises as well.
这样练习是非常重要的 因为你不知道那些拥有数据的人 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 这不仅仅存在于美国,全世界都一样 也不仅仅在政府,当然也存在于企业。
So I'm just going to mention a few other thoughts on data. Here we are at TED, and all the time we are very conscious of the huge challenges that human society has right now -- curing cancer, understanding the brain for Alzheimer's, understanding the economy to make it a little bit more stable, understanding how the world works. The people who are going to solve those -- the scientists -- they have half-formed ideas in their head, they try to communicate those over the web. But a lot of the state of knowledge of the human race at the moment is on databases, often sitting in their computers, and actually, currently not shared.
我还想再谈谈关于数据的其他想法 在TED,我们一直关注于 人类社会目前所面临的巨大问题 癌症治疗,了解阿尔茨海默病 了解经济好让它稳定点 了解世界是如何运转的 那些致力于解决这些问题的科学家 他们脑海中有些还不成熟的想法 他们试图在网络上与他人交流 但是现状是很多人类的知识 现在都在数据库中,放在他们的电脑里 现在实际上也没被共享
In fact, I'll just go into one area -- if you're looking at Alzheimer's, for example, drug discovery -- there is a whole lot of linked data which is just coming out because scientists in that field realize this is a great way of getting out of those silos, because they had their genomics data in one database in one building, and they had their protein data in another. Now, they are sticking it onto -- linked data -- and now they can ask the sort of question, that you probably wouldn't ask, I wouldn't ask -- they would. What proteins are involved in signal transduction and also related to pyramidal neurons? Well, you take that mouthful and you put it into Google. Of course, there's no page on the web which has answered that question because nobody has asked that question before. You get 223,000 hits -- no results you can use. You ask the linked data -- which they've now put together -- 32 hits, each of which is a protein which has those properties and you can look at. The power of being able to ask those questions, as a scientist -- questions which actually bridge across different disciplines -- is really a complete sea change. It's very very important. Scientists are totally stymied at the moment -- the power of the data that other scientists have collected is locked up and we need to get it unlocked so we can tackle those huge problems.
事实上,我就从一个方面来说明 - 如果你在研究阿尔茨海默病,以此为例, 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 因为这个领域的科学家们意识到 关联数据是一种很好的方法,可以帮助他们摆脱数据孤岛 因为他们在一个数据库中建立了基因图组 他们在另一个数据库中建立蛋白质数据 现在,他们将基因图组和蛋白质数据形成了关联数据 他们可以问排序的问题,也许你不会问 我不会问,但是他们会 哪些蛋白质参与信号转导 并且也和锥体神经元相关? 当你将这个问题放到Google上搜索 自然没有回答结果的页面 因为之前没有人问过这样的问题 虽然你得到了223,000个结果 但是没有一个你用得上 但是没有一个你用得上 -- 现在他们已经被放到了一起 命中32个结果,每一个结果都是与特征相关的蛋白质 并且你可以看到 做为一个科学家, 询问那些问题的能力 那些问题基本上都是跨学科的问题 是真正的C-change 这是非常非常重要的 科学家们那时完全陷入了困境 因为其他科学家搜集的数据,其价值被锁起来了 我们需要将之解锁,以便处理那些大问题
Now if I go on like this, you'll think that all the data comes from huge institutions and has nothing to do with you. But, that's not true. In fact, data is about our lives. You just -- you log on to your social networking site, your favorite one, you say, "This is my friend." Bing! Relationship. Data. You say, "This photograph, it's about -- it depicts this person. " Bing! That's data. Data, data, data. Every time you do things on the social networking site, the social networking site is taking data and using it -- re-purposing it -- and using it to make other people's lives more interesting on the site. But, when you go to another linked data site -- and let's say this is one about travel, and you say, "I want to send this photo to all the people in that group," you can't get over the walls. The Economist wrote an article about it, and lots of people have blogged about it -- tremendous frustration. The way to break down the silos is to get inter-operability between social networking sites. We need to do that with linked data.
现在,如果我继续像这样讲 和你没有一点关系 但是,这种想法并不对 事实上,数据关乎我们的生活 你刚刚登陆了你的社会化网络站点 你最喜欢的一个,你说“这是我朋友” 叮!联系,数据 你说“这副照片,是这个人的” 叮!那是数据。数据,数据,数据 每次你在社会化网络上做的事 社会化网络站点就获取数据并利用它 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 但是,当你上另一个关联数据网站 假设是一个旅游网站 你说“我想把这张照片发给那个组里的所有人” 但你却无法翻过这些墙 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 巨大的挫败感 打破孤岛的方式是实现互操作 在这些社交网络之间 我们需要通过关联数据做这件事
One last type of data I'll talk about, maybe it's the most exciting. Before I came down here, I looked it up on OpenStreetMap The OpenStreetMap's a map, but it's also a Wiki. Zoom in and that square thing is a theater -- which we're in right now -- The Terrace Theater. It didn't have a name on it. So I could go into edit mode, I could select the theater, I could add down at the bottom the name, and I could save it back. And now if you go back to the OpenStreetMap. org, and you find this place, you will find that The Terrace Theater has got a name. I did that. Me! I did that to the map. I just did that! I put that up on there. Hey, you know what? If I -- that street map is all about everybody doing their bit and it creates an incredible resource because everybody else does theirs. And that is what linked data is all about. It's about people doing their bit to produce a little bit, and it all connecting. That's how linked data works. You do your bit. Everybody else does theirs. You may not have lots of data which you have yourself to put on there but you know to demand it. And we've practiced that.
最后一种我将要谈到的数据,也许是最令人激动的 在我来这之前,我通过OpenStreetMap查找了一下 OpenStreetMap是一个地图,但同样也是一个维基 放大这个方块,这是一个剧场 -- 就是我们现在所处的地方 -- 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 所以我可以到编辑模式,选择剧场 然后在底下填上名字,然后保存它 现在你再去访问OpenStreetMap.org 你找到这个地方,你会发现它现在有名字了 这都是我做的 我在地图上标的,刚刚做的 我把它标注在那里。嗨,你知道吗 如果除了我,每个人都在这个地图上标注一点 将会产生难以置信的资源 因为其他每个人都做了 这就是关联数据 每个人都做一点 生成一点内容,然后把它们连接起来 关联数据就是这样工作的 你做一些,每个人都做一些 也许你的数据在关联数据中只是很小一部分 但你知道你需要它 我们已经在实践了
So, linked data -- it's huge. I've only told you a very small number of things There are data in every aspect of our lives, every aspect of work and pleasure, and it's not just about the number of places where data comes, it's about connecting it together. And when you connect data together, you get power in a way that doesn't happen just with the web, with documents. You get this really huge power out of it. So, we're at the stage now where we have to do this -- the people who think it's a great idea. And all the people -- and I think there's a lot of people at TED who do things because -- even though there's not an immediate return on the investment because it will only really pay off when everybody else has done it -- they'll do it because they're the sort of person who just does things which would be good if everybody else did them. OK, so it's called linked data. I want you to make it. I want you to demand it. And I think it's an idea worth spreading.
关联数据 -- 是非常巨大的 我只能告诉你很小一部分 我们生活的每个方面 工作和快乐的每个方面 不管是数据出处的有多少 关键是把它联系起来 当你把数据联系起来 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 你能从中得到巨大的能量 现在我们处在一个阶段 我们必须要做的阶段 -- 那些认为这是个伟大想法的人们 而且所有人 -- 我想在TED的大部分人 他们做事情并不是为了要使投资得到立即的回报 因为只有当每个人都这么做了才会有所回报 他们将会这么做,因为他们是那类人 那类希望每个人都参与进来而让事情变好的人 OK,这就是关联数据 我希望你参与 我希望你需要它 我也认为这个想法值得宣扬
Thanks.
谢谢
(Applause)
谢谢