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.
如果今天我可以留给你们一个理念 那就是我们消费的所有的数据所富含的信息大于各部分相加的总和, 那就是我们消费的所有的数据所富含的信息大于各部分相加的总和, 那就是我们消费的所有的数据所富含的信息大于各部分相加的总和, 并且,与其思考信息爆炸 我希望你想一想怎样使用 这些信息,显示其中的规律 使得我们能看见本来不可见的趋势,
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.
那么我们在这里看到的是一个典型的死亡率图表 按照年龄分组 我在这里使用的工具是一个小实验 它称为Pivot,我能用Pivot 选择过滤特殊死亡原因,譬如事故 立刻,我看到一个不同的模式展现出来 这是因为,在中间这里 人们在他们最活跃的年龄 而在这里他们也是最体弱多病的时候 我们可以退回来 根据死亡原因重组数据 我们看到循环系统疾病和癌症 是致死的主要病症,但这个规律并非适用于每个人 如果我们按照年龄过滤, 譬如40岁以下 我们可以看到 意外事故变成了人们需要格外小心的杀手 如果你进一步挖掘,会发现这一条尤其针对男性适用
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.
好了,你大概明白这个工具的作用了 通过这种方式查看信息,数据 很像在 鲜活的信息资料图片中遨游。 如果我们能够对原始数据这样做 为什么不也在内容上做呢? 因此我们在这里展示 有史以来的 每一期体育画报的封面 全部在这里,全部在网络上。 你可以在我演讲结束后回到你的房间试试看。 使用Pivot,你能够以十年为单位查看。 你能够深入指定的某一年。 你能直接进入一个某一期 比如我看到这个;我看见曾经出现在这期中的 运动员,体育。 我是兰斯·阿姆斯特朗的粉丝,所以我继续点击 它就给我展示了所有 这只是兰斯·阿姆斯特朗所有问题中的一部分
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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.
现在,如果我仅仅是想取样这些数据的高峰 我会想, 好吧,看看所有自行车运动员如何? 因此,我可以退一步,并扩大这一点 我现在看见了格雷格·莱蒙德 因此你要明白 当你用这种方式浏览信息时 狭窄的,宽阔的, 后退,反向, 你不是在搜寻,不是在浏览。 你做的事实际上有点不同。 介于两者之间,并且我们认为 这改变了信息的使用方式
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.
因此,我推断在这个想法上 有些东西是有点疯狂。 我们在这儿做的是摘取每个维基百科的页面 然后压缩为一个小小的摘要 摘要包含了简介 一个图标显示它来自专业领域。 我仅仅在这里展示前500个 最受欢迎的维基百科页面 但即使在这些有限的浏览中, 我们依然可以做很多事情。 立刻,我们得知 维基百科上最流行的是什么。 我继续前进并选择政府。 现在,通过选定后的政府, 我可以看到在维基百科类别中 最常对应的 是时代周刊年度风云人物 这是很重要的 因为这是一项 不属于任何一个维基网页所载述的内容。 只有你退后几步俯瞰全局 才有可能看的透彻
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.
看看这些特殊摘要中的一种, 随后我能深入 时代杂志年度风云人物 深入他们。 所以,看看这些人 我可以看到大多数来自政府。 有一部分来自自然科学界。 更少的部分来自商界。 其中有我的老板。 一个来自音乐界。 而有趣的是, 波诺也是TED大奖得主。 因此我们能够继续,跳转,看看所有的TED大奖得主。 所以你看,这是我们第一次在网络上遨游 仿佛的确是一张网,不仅一页一页的。 而且是在更高的抽象层次上的网。
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.
所以我想告诉你另一件事 可能会让你吃惊。 我只是在这展示纽约时报网站。 Pivot,这个应用程序—— 我不想称之为浏览器,它确实不仅是一个浏览器, 你能用它浏览网页—— 并且我们给每个像这样的网页引入了可缩放技术。 并且我们给每个像这样的网页引入了可缩放技术。 因此我可以退后, 退后到特定的地方 为什么这个是重要的是因为, 由于通过这种方式浏览网页的好处, 我能用完全相同的方式 看到我的全部浏览历史。 因此我能深入 具体时间段的具体事件。 这里,事实上, 是我刚才所有演示的情况。 我可以在某种程度上重放我今天早些时候看到的东西。 如果我想退后一步看所一切东西, 我可以切割我的历史 也许是我的搜索历史。 这里,我做了一些相关搜索, 搜寻Bing,在这里有关微软Live Labs的Pivot。 从这些,我可以深入网页 仅仅重启他们。 这是同样的信息,因不同目的被多次组合使用, 每次重新组合,使得它比组合起来的整体所含信息更多。 每次重新组合,使得它比组合起来的整体所含信息更多。
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.
现在,在这个世界上, 我们谈到数据常常提到这个诅咒。 我们谈论信息爆炸魔咒。 我们谈论淹没在信息海洋中。 假设我们能把这种观念颠覆 把网络上下颠覆, 相对于单线信息相互链接的情况, 让我们开始习惯多样信息链接多样信息, 然后看到除此方式外无法看到的,隐藏规律? 然后看到除此方式外无法看到的,隐藏规律? 如果我们能做到这一点,而不是被困在数据中, 我们才能真正的获取信息。 并且,不仅仅是处理信息, 我们还可以梳理知识。 如果我们获得知识,之后甚至可以发现智慧。
So with that, I thank you.
谢谢。
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