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.
光陰似箭 差不多是20年前 當我想重新構造我們使用資訊 共同工作方式的時候 - 我發明了網際網路 20年過去了,現在,在TED 我請求你們幫助建立一個新的架構
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 我在備忘錄中建議,使用一種全球的超連結系統 幾乎沒有什麼人在真正用它 但是,18個月後 - 革新就是這麼開始的 18個月後,老闆說,我可以兼職做這件事 做一種遊戲性的計劃 就當試用我們新買來的電腦 他給了我些時間寫代碼 我草擬了下HTML應該是什麼樣子 超文件傳輸協定 - HTTP - 關於URLs 的想法 - 這些事物的名稱 都是以HTTP開頭命名的 我完成了代碼並發佈出來。
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.
我為什麼要這麼做? 這是一個充滿挫敗感的過程 我感到很挫敗 - 因為我作為一個軟體工程師 在這個令人興奮的超大實驗室中工作 很多人從世界各地來到這裡 他們的電腦各不相同 資料格式各不相同 檔案系統各不相同 所以,這其中有很大的差異性 如果我想建立一點點東西 在這些差異性很大的電腦上 每一項我找到的資料,我不得不連接到一些新的機器 運行一些新的程式 以便我能在新的資料格式中找到我需要的資訊 而這些都是不相容的 這非常令人沮喪 這種挫敗感卻正顯示出這個專案的潛力所在
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.
事實上,過去這些磁片裡全都是檔案 所以如果你僅僅把他們 想像成天空中某些大型虛擬檔案系統的一部分 比如在網際網路上 生活就會簡單得多 這樣,一旦你有了這樣的想法 即使人們並沒有讀到你的備忘錄 事實上他讀到了,因為在他死後,在他的備份草稿中 他用鉛筆在角落寫到“模糊,但是令人興奮”。
(Laughter)
(笑聲)
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.
但一般情況下,很難有這樣的想法 – 的確很難解釋 網路是什麼樣的 現在都很難向人們解釋,更別提當初了 但是,當 TED 開始時,那時沒有網路 所以像“點選”這樣的事情含義是不同的 我現在可以向某人展示一大堆超連結 某個包含連結的網頁 我們點選一個連結,然後叮 -- 就會轉到另一個超連結的頁面 沒什麼令人印象深刻的 我們已經見到,通過超連結找到CD-ROMs中的內容 困難的是把它們想像出來 所以,想像那個連結可以到 任何實際的你能想像得到的文件 好的,這個跳躍對於人們是很難做到的 然而,一些人做到了 儘管很難解釋,但是這是一場草根運動 這正是使它好玩的地方 也是最令人激動人心的事情 不是技術,不是人們用它所做的東西 而是實際的交流,所有這些人的思想彙聚 在一起,發送電子郵件 這是那時的情況
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.
你知道嗎?有趣的是,現在跟那時候又有點像了 我問每一個人,他們或多或少都發佈過文檔 我說“你能把你的文檔放到網路上嗎?” 然後,你做了 謝謝 這已經是一種風潮,不是嗎? 我的意思是,它已經非常有趣 因為我們發現,網路上發生的事情似乎 已經把我們吹到了一邊 現在它的功能得比我們想像的還多 最初的設計只是想把檔案湊在一起 在我們最初開始使用網路時 現在我想讓你把你的資料放在網上 原來這還是有許多未釋放的潛力 也有很大的挫敗感 因為我們從網上得到的資料不是我們想要的資料
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.
你說的數據是什麼?數據和文件之間有什麼區別? 文件檔是你閱讀的東西 或多或少,你都讀過,你可以追蹤他們的連結,就是這樣 數據—你可以通過一台電腦使用各種資料 誰在這裡或者其他地方聽過漢斯羅素令的演講? 一個偉大的 – 很多人已經看過了 – 一個偉大的TED演講 漢斯在他的演說中 使用不同的顏色表示不同的國家 他在一個軸上顯示收入水準 同時他用動畫按年份顯示嬰兒死亡率 他使用這些資料完成了一場演講, 這個演講打破了很多人 對發展中國家經濟的神話
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."
他展示了一個類似的幻燈片 數據都被埋在地下 對,資料是這些棕色的、無趣的四方盒子 我們就是這樣看待資料的,不是嗎? 因為,你不能漫無目的地使用資料 但事實上,資料驅動了我們的生活 因為某些人使用了資料並且做了些事情 在這個例子中,漢斯將資料放到了一起 漢斯在聯合國網站找到各種資料和事物 他把資料放到了一起 將它們組合起來使之比原始資料有趣得多 然後把資料放到這個軟體中 這個軟體好像原本是他兒子開發的 最終他做出了這個美妙的簡報 最後漢斯說道 “瞧,有大量的資料是非常重要的” 我高興地看到在昨天的晚會上 他仍然強烈地表示“有大量資料是非常重要的”
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.
現在我想讓大家想的是 不僅僅是兩條資料間的連接,或者像他所說的那樣六條資料 而是這個世界上任何人 都把資料和可以虛擬化的一切內容放到網路上 然後把它們稱為關聯資料 這個技術就是關聯資料,它是極其簡單的 如果你想把什麼東西放在網路,有三條規則 第一條規則是,需要有HTTP的名字 那些東西要以http:開頭 我們現在不僅對文件檔這樣用 對文件檔描述的事物也這樣用 我們對人物、地點 產品,事件等都這樣用 所有概念化的東西現在都以HTTP開頭命名
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.
第二條規則,如果我有一個HTTP名稱,然後我根據它在網路上進行查找 我可以從網上獲取資料 通過HTTP協議 我將得到一些標準的格式化資料 這些有用資料或許是關於人們希望瞭解 某個事物或者事件的 事件的主人公是誰?關於這個人的所有資訊 他們什麼時候出生的,等等 所以,第二條規則就是我通過HTTP獲得了重要的資料
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.
第三條規則是,我得到的資訊 不僅僅是某人的身高、體重和出生日期 還有資料間的關係 數據是有關聯的 很有趣,數據是有關聯的 這個人出生在柏林,柏林在德國 當數據是有關聯時,無論何時它表現出這種關聯 另一件與之有關聯的事物 就以HTTP開頭命名 所以,我可以直接去找那件事 比如,我查一個人 -- 我查他出生的城市 這個城市的所在區域,城市的城鎮 人口等等 這樣我就能流覽這些資訊
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,就是這樣 這就是關聯資料 我多年前在一篇文章中給它命名為“關聯資料” 之後不久,有些事開始發生了 關聯資料的想法就像我們得到了很多很多 就像漢斯的那些盒子 很多很多的事物開始發芽生長 它帶給我們相當多的植物 不僅僅是一個根供給一個植物 對於這的每一個植物,無論它是什麼 一場演說,一個分析,某些人查看數據資料的樣式 它們都著眼於所有的數據 並且它們把數據聯繫起來 關於數據真正重要的是 你把很多東西聯繫起來,數據就更加有價值
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,關聯資料 由此而來 很快,來自柏林自由大學的克里斯拜澤 做為第一人把有趣的東西放在一起 他注意到維琪百科 一部線上百科全書 有很多有趣的文檔 在這些文檔中,有些小方格子和小盒子 在多數的資訊方格中,就有資料 他寫了 一個程式將資料從維琪百科中提取出來 然後將它放到關聯資料的組別中 在網路上,被他稱之為dbpedia(資料庫百科) 這張幻燈片中部藍色的blob表示Dbpedia 如果你去查詢柏林 你會發現還有其他的資料 也有柏林的資訊,它們被聯繫到了一起 所以,如果你要從dbpedia中摘出關於柏林的資料 你也最終會摘出其他內容 令人興奮的事情是它正在成長 這又是一個草根做的事情,對嗎?
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.
讓我們多想想資料 資料實際上來源於很多很多不同的形式 想想網路的多樣性,很重要的一點 網路允許你將各式各樣的資料放在一起 說到資料,我能說出各種各樣的數據 我們可以說政府資料,企業資料真的很重要 還有科學資料,個人資料 天氣資料,關於事件的資料 關於談話的資料,還有新聞和各種類似的東西 我只提到了一小部分資料 你們就可以看出其多樣性 所以你可以看到其中的潛力
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.
讓我們從政府資料說起 美國總統巴拉克歐巴馬在一場演講上表示 美國政府的資料將在互聯網上被應用 以一種可訪問的形式 而我希望他們會將這些訊息以關聯資料放上去 這非常重要,難道不是嗎? 不僅僅是為了透明性,透明性對政府很重要 尤其是從政府部門出來的資料更重要 想想有多少關係到在美國如何生活的資料 它的確很有用,很有價值 我可以把它用在我的公司 我可以像個小孩子般把它用在我的家庭作業中 所以,我們談論的是讓世界變得更好 通過將這些資料變得更有用
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"?
事實上,如果你們在負責 - 如果你知道一些資料 關於政府的, 你經常會發現 有些人,他們會被這些資料所吸引 漢斯稱之為資料庫擁抱 你擁抱你的資料庫,你不會放它走 直到你為它建立了一個漂亮的網站 嗯,我想建議的是,除了建一個漂亮的網站 是的,建一個漂亮的網站 我沒說不要建一個漂亮的網站 建一個漂亮的網站,但是首先 要給我們純粹的數據 我們要的是數據 我們要純粹的數據 好,現在我們不得不要求原始數據了 我要請你們練習一下,好嗎? 請說“原始”
Audience: Raw.
原始
Tim Berners-Lee: Can you say "data"?
請說“數據”
Audience: Data.
數據
TBL: Can you say "now"?
請說‘現在“
Audience: Now!
現在
TBL: Alright, "raw data now"!
好,原始數據現在!
Audience: Raw data now!
原始數據現在!
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.
這樣練習是非常重要的 因為你不知道那些擁有數據的人 有多少理由拒絕將數據給你,甚至你作為一個納稅人是為此付了錢的 這不僅僅存在於美國,全世界都一樣 也不僅僅在政府,當然也存在於企業。
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.
我還想再談談關於數據的其他想法 在TED,我們一直關注於 人類社會目前所面臨的巨大問題 癌症治療,瞭解阿爾茨海默病 瞭解經濟好讓它穩定點 瞭解世界是如何運轉的 那些致力於解決這些問題的科學家 他們腦海中有些還不成熟的想法 他們試圖在網路上與他人交流 但是現狀是很多人類的知識 現在都在資料庫中,放在他們的電腦裡 現在實際上也沒被共用
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.
事實上,我就從一個方面來說明 - 如果你在研究阿爾茨海默病,以此為例, 以藥物發現為例 -- 這個領域具有相當多的剛剛出現的關聯資料 因為這個領域的科學家們意識到 關聯資料是一種很好的方法,可以説明他們擺脫資料孤島 因為他們在一個資料庫中建立了基因圖組 他們在另一個資料庫中建立蛋白質數據 現在,他們將基因圖組和蛋白質數據形成了關聯資料 然後他們現在可以問一些特定的問題,也許你不會問 我也不會問,但是他們會 哪些蛋白質參與信號轉導 並且也和錐體神經元相關? 當你將這個問題放到Google上搜索 自然沒有回答結果的頁面 因為之前沒有人問過這樣的問題 雖然你得到了223,000個結果 但是沒有一個你用得上 當你查詢關聯資料 -- 現在他們已經被放到了一起 命中32個結果,每一個結果都是與特性相關的蛋白質 並且你可以查看 做為一個科學家, 詢問那些問題的能力 那些問題基本上都是跨學科的問題 是非常徹底的重大改變 這是非常非常重要的 科學家們那時完全陷入了困境 因為其他科學家搜集的資料,其價值被鎖起來了 我們需要將之解鎖,以便處理那些重大問題
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.
現在,如果我繼續像這樣講,你們會覺得這些數據都是從大機構得來的 和你沒有一點關係 但是,這種想法並不對 事實上,數據關乎我們的生活 你剛剛登陸了你的社交網站 你最喜歡的一個,你說“這是我朋友” 叮!關聯,資料 你說“這副照片,是這個人的” 叮!那是數據。數據,數據,數據 每次你在社交網站上做的事 社交網站就獲取資料並利用它 重新設計資料的目的是為了讓這個網站的其他人過得更有趣 但是,當你上另一個關聯資料網站 假設是一個旅遊網站 你說“我想把這張照片發給那個組裡的所有人” 但你卻無法翻過這些牆 經濟學家曾經寫了一篇關於這個問題的文章,並且許多人也發了相關部落格表示出 巨大的挫敗感 打破孤島的方式是實現交互操作 在這些社交網站之間 我們需要通過關聯資料做這件事
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.
最後一種我將要談到的資料,也許是最令人激動的 在我來這之前,我通過OpenStreetMap查找了一下 OpenStreetMap是一個地圖,但同樣也是一個維琪 放大這個方塊,這是一個劇場 -- 就是我們現在所處的地方 -- 特羅斯劇場(位於加州長灘市)。它現在還沒有被標上名字 所以我可以到編輯模式,選擇劇場 然後在底下填上名字,然後保存它 現在你再去訪問OpenStreetMap.org 你找到這個地方,你會發現它現在有名字了 是我做的,是我! 我在地圖上標的,剛剛做的 我把它標注在那裡。嗨,你知道嗎 如果除了我,每個人都在這個地圖上標注一點 將會產生難以置信的資源 因為其他每個人都做了 這就是關聯資料 每個人都做一點 生成一點內容,然後把它們連接起來 關聯資料就是這樣工作的 你做一些,每個人都做一些 也許你的資料在關聯資料中只是很小一部分 但你知道你需要它 我們已經在實踐了
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.
關聯資料 -- 是非常巨大的 我只能告訴你很小一部分 我們生活的每個方面 工作和快樂的每個方面 不管是資料出處的有多少 關鍵是把它聯繫起來 當你把數據聯繫起來 你能從這樣的方式中獲取在網路或文檔中無法獲取的力量 你能從中得到巨大的力量 現在我們處在一個階段 我們必須要做的階段 -- 那些認為這是個偉大想法的人們 而且所有人 -- 我想在 TED 的大部分人 他們做事情並不是為了要使投資得到立即的回報 因為只有當每個人都這麼做了才會有所回報 他們將會這麼做,因為他們是那類人 那類希望每個人都參與進來而讓事情變好的人 OK,這就是關聯資料 我希望你參與 我希望你需要它 我也認為這個想法值得宣揚
Thanks.
謝謝
(Applause)
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