If I can leave you with one big idea today, it's that the whole of the data in which we consume is greater that the sum of the parts, and instead of thinking about information overload, what I'd like you to think about is how we can use information so that patterns pop and we can see trends that would otherwise be invisible.
如果我今天的演講可以留給你們一個新概念, 那就是我們所消費的 資料整體是 大於其各個部分相加的總和的。 然而,與其擔心資訊爆炸, 不如思考一下怎樣使用 這些資訊, 使其中的規律顯現, 幫助我們看見原本不可見的趨勢。
So what we're looking at right here is a typical mortality chart organized by age. This tool that I'm using here is a little experiment. It's called Pivot, and with Pivot what I can do is I can choose to filter in one particular cause of deaths -- say, accidents. And, right away, I see there's a different pattern that emerges. This is because, in the mid-area here, people are at their most active, and over here they're at their most frail. We can step back out again and then reorganize the data by cause of death, seeing that circulatory diseases and cancer are the usual suspects, but not for everyone. If we go ahead and we filter by age -- say 40 years or less -- we see that accidents are actually the greatest cause that people have to be worried about. And if you drill into that, it's especially the case for men.
在這裡,我們看到的是一個典型的死亡率表, 根據年齡排列。 我現在用的這個工具是一個小小的實驗, 這個工具叫「樞紐」 (Pivot), 使用「樞紐」, 我可以選擇過濾出某個特殊的死因, 例如說:事故身亡。 然後,馬上我就看見一組不同的模式顯現。 這是因爲, 中間這裡 人們處於他們最活躍的年齡, 而在這裡, 人們是最體弱多病的時候。 我們可以退回來, 重新根據死因來排列資料, 我們可以看到循環系統疾病和癌症 是常見的致死因素,但並不適用於每一個人。 如果我們繼續過濾年齡, 比如說 40 歲以下的人群, 我們會發現意外事故是 人們最需要小心的殺手 如果你進一步挖掘, 這個準則對男人尤其適用。
So you get the idea that viewing information, viewing data in this way, is a lot like swimming in a living information info-graphic. And if we can do this for raw data, why not do it for content as well? So what we have right here is the cover of every single Sports Illustrated ever produced. It's all here; it's all on the web. You can go back to your rooms and try this after my talk. With Pivot, you can drill into a decade. You can drill into a particular year. You can jump right into a specific issue. So I'm looking at this; I see the athletes that have appeared in this issue, the sports. I'm a Lance Armstrong fan, so I'll go ahead and I'll click on that, which reveals, for me, all the issues in which Lance Armstrong's been a part of.
所以,你對這個東西有點概念了, 用這種方法來瀏覽資訊、數據 很像是在一個 鮮活的資訊圖片裡游泳。 如果我們可以對原始資料這麼做, 爲什麼不將內容也比照辦理呢? 所以,我們在這裡顯示的 是過去發表過的每一期 運動畫刊的封面。 都在這裡了, 都在網路上 演講完,你回到飯店房間後,可以試試這個工具。 用「樞紐」, 你可以深入某一個世代, 深入到具體的某一年, 你也可以直接進入某一期。 所以當我看著這個的時候, 我看到出現在該期雜誌中的各種運動以及運動員們。 我是蘭斯•阿姆斯壯迷, 所以我就點擊選取這一期, 然後它為我展示了所有刊登過有關蘭斯•阿姆斯壯 內容的所有期數。
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Now, if I want to just kind of take a peek at these, I might think, "Well, what about taking a look at all of cycling?" So I can step back, and expand on that. And I see Greg LeMond now. And so you get the idea that when you navigate over information this way -- going narrower, broader, backing in, backing out -- you're not searching, you're not browsing. You're doing something that's actually a little bit different. It's in between, and we think it changes the way information can be used.
現在, 如果我只是簡單的瀏覽一眼這些內容 我可能會想 「好, 那把所有有關自行車運動的期刊都找出來如何?」 所以我可以退回去, 然後在著重在那些內容。 現在我看到 Greg Lemond 了。 你現在應該已經知道, 當你 用這種方法在大量資訊中領航, 你可以縮小、擴大、 深入、淺出, 你不是在搜索,你也不是在瀏覽 你所做的事情跟這兩者都有些不同。 界於兩者之間, 我們認爲這改變了 資訊可以被使用的方法。
So I want to extrapolate on this idea a bit with something that's a little bit crazy. What we're done here is we've taken every single Wikipedia page and we reduced it down to a little summary. So the summary consists of just a little synopsis and an icon to indicate the topical area that it comes from. I'm only showing the top 500 most popular Wikipedia pages right here. But even in this limited view, we can do a lot of things. Right away, we get a sense of what are the topical domains that are most popular on Wikipedia. I'm going to go ahead and select government. Now, having selected government, I can now see that the Wikipedia categories that most frequently correspond to that are Time magazine People of the Year. So this is really important because this is an insight that was not contained within any one Wikipedia page. It's only possible to see that insight when you step back and look at all of them.
所以我想對這個觀點做進一步的闡釋, 是一些稍微有點瘋狂的想法。 我們在這裡所做的是,我們將每一頁維基百科, 縮簡成一小段摘要。 這摘要只包括了一些簡介 和一個顯示標題範圍來源的圖示。 我這裡只顯示維基百科中 最熱門的 500 頁。 但即使在這有限的展示中, 我們也可以做很多事情。 我們馬上可以知道,哪些主題 在維基百科中最熱門。 我這裡選擇「政府」, 選了「政府」以後, 我可以看到維基百科中 與之對應最頻繁的類別是, 時代雜誌的年度風雲人物。 這真的很重要,因爲這種洞見, 並不包含在任何維基百科的網頁中。 唯一可以看出這個關係的方法是, 退後一步,縱觀全局。
Looking at one of these particular summaries, I can then drill into the concept of Time magazine Person of the Year, bringing up all of them. So looking at these people, I can see that the majority come from government; some have come from natural sciences; some, fewer still, have come from business -- there's my boss -- and one has come from music. And interestingly enough, Bono is also a TED Prize winner. So we can go, jump, and take a look at all the TED Prize winners. So you see, we're navigating the web for the first time as if it's actually a web, not from page-to-page, but at a higher level of abstraction.
看著這些不同摘要的其中一個, 我可以接著深入探索 時代雜誌年度風雲人物這個概念, 把他們都帶出來。 看著這些人, 我發現他們中的多數來自政府, 有些來自自然科學, 有些,很少數,是商業人士。 這是我老闆。 其中一個是音樂界人士, 而有趣的是, Bono 也是 TED 大獎得主。 所以我們可以直接跳進去,看看所有的 TED 大獎得主。 所以你看, 我們第一次在網路上航行了, 好像它真的是一大張網,不是一張張的頁面, 而是一種更高層次抽象的概念。
And so I want to show you one other thing that may catch you a little bit by surprise. I'm just showing the New York Times website here. So Pivot, this application -- I don't want to call it a browser; it's really not a browser, but you can view web pages with it -- and we bring that zoomable technology to every single web page like this. So I can step back, pop right back into a specific section. Now the reason why this is important is because, by virtue of just viewing web pages in this way, I can look at my entire browsing history in the exact same way. So I can drill into what I've done over specific time frames. Here, in fact, is the state of all the demo that I just gave. And I can sort of replay some stuff that I was looking at earlier today. And, if I want to step back and look at everything, I can slice and dice my history, perhaps by my search history -- here, I was doing some nepotistic searching, looking for Bing, over here for Live Labs Pivot. And from these, I can drill into the web page and just launch them again. It's one metaphor repurposed multiple times, and in each case it makes the whole greater than the sum of the parts with the data.
我還想給你們看另一樣東西, 那可能會讓你覺得有點驚訝。 我現在顯示的是紐約時報的網頁, 所以「樞紐」,這個應用程式, 我不想稱它為瀏覽器,因爲它並不是一個瀏覽器, 但是你可以用它來看網頁。 我們引進了可縮放技術, 運用到每一個網頁。 所以我可以退出, 快速回到一個特定的部分。 為什麼這很重要?因為, 這樣看網頁的好處是, 我可以將我的整個瀏覽歷史, 完整重現。 所以我可以深入探索, 在過去某段時間內,我曾經做過的事。 這邊所顯示的,事實上, 就在剛剛 我做過的所有的示範。 我可以重播一些今天前些時間我在搜尋的東西。 而如果我想退後一步,縱觀所局, 我可以層層切割我的歷史紀錄, 例如我的搜尋紀錄。 我在這裡做一些相關的搜尋, 搜尋 Bing,這裡是 Live 實驗室的 Pivot。 從那裡,我可以進入網頁, 只要再打開就可以了。 這是同樣的原始資料,因不同目的被多次組合使用, 而每一次的重新組合使得它 比各個部分的總和更爲強大。
So right now, in this world, we think about data as being this curse. We talk about the curse of information overload. We talk about drowning in data. What if we can actually turn that upside down and turn the web upside down, so that instead of navigating from one thing to the next, we get used to the habit of being able to go from many things to many things, and then being able to see the patterns that were otherwise hidden? If we can do that, then instead of being trapped in data, we might actually extract information. And, instead of dealing just with information, we can tease out knowledge. And if we get the knowledge, then maybe even there's wisdom to be found.
所以, 現在, 在這個世界上 我們說到數據的時候常常提到這個詛咒, 我們會提到資訊爆炸, 我們會提到淹沒在資料中。 如果我們能夠顛覆這些想法, 顛覆網路世界, 相對於一個東西連接到另一個東西, 讓我們開始來習慣從多樣向多樣的轉換, 然後能夠看到 隱藏其中的規律。 如果我們能做到,那麼,我們將不再被困於大量的資料, 我們或許可以真的從中萃取出有用的資訊。 而,除了單純地處理資訊, 我們可以獲取知識。 而如果我們得到了知識, 也許我們就會找到智慧。
So with that, I thank you.
這就是我的總結, 謝謝大家。
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