Gary Flake: is Pivot a turning point for web exploration?

翻译人员: 周 宇轩 校对人员: dahong zhang

If I can leave you with one big idea today,

如果今天我可以留给你们一个理念

it's that the whole of the data

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，

in which we consume

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，

is greater that the sum of the parts,

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，

and instead of thinking about information overload,

并且，与其思考信息爆炸

what I'd like you to think about is how

我希望你想一想怎样使用

we can use information so that patterns pop

这些信息，显示其中的规律

and we can see trends that would otherwise be invisible.

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

那么我们在这里看到的是一个典型的死亡率图表

organized by age.

按照年龄分组

This tool that I'm using here is a little experiment.

我在这里使用的工具是一个小实验

It's called Pivot, and with Pivot what I can do

它称为Pivot，我能用Pivot

is I can choose to filter in one particular cause of deaths -- say, accidents.

选择过滤特殊死亡原因，譬如事故

And, right away, I see there's a different pattern that emerges.

立刻，我看到一个不同的模式展现出来

This is because, in the mid-area here,

这是因为，在中间这里

people are at their most active,

人们在他们最活跃的年龄

and over here they're at their most frail.

而在这里他们也是最体弱多病的时候

We can step back out again

我们可以退回来

and then reorganize the data by cause of death,

根据死亡原因重组数据

seeing that circulatory diseases and cancer

我们看到循环系统疾病和癌症

are the usual suspects, but not for everyone.

是致死的主要病症，但这个规律并非适用于每个人

If we go ahead and we filter by age --

如果我们按照年龄过滤，

say 40 years or less --

譬如40岁以下

we see that accidents are actually

我们可以看到

the greatest cause that people have to be worried about.

意外事故变成了人们需要格外小心的杀手

And if you drill into that, it's especially the case for men.

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

好了，你大概明白这个工具的作用了

that viewing information, viewing data in this way,

通过这种方式查看信息，数据

is a lot like swimming

很像在

in a living information info-graphic.

鲜活的信息资料图片中遨游。

And if we can do this for raw data,

如果我们能够对原始数据这样做

why not do it for content as well?

为什么不也在内容上做呢？

So what we have right here

因此我们在这里展示

is the cover of every single Sports Illustrated

有史以来的

ever produced.

每一期体育画报的封面

It's all here; it's all on the web.

全部在这里，全部在网络上。

You can go back to your rooms and try this after my talk.

你可以在我演讲结束后回到你的房间试试看。

With Pivot, you can drill into a decade.

使用Pivot，你能够以十年为单位查看。

You can drill into a particular year.

你能够深入指定的某一年。

You can jump right into a specific issue.

你能直接进入一个某一期

So I'm looking at this; I see the athletes

比如我看到这个；我看见曾经出现在这期中的

that have appeared in this issue, the sports.

运动员，体育。

I'm a Lance Armstrong fan, so I'll go ahead and I'll click on that,

我是兰斯·阿姆斯特朗的粉丝，所以我继续点击

which reveals, for me, all the issues

它就给我展示了所有

in which Lance Armstrong's been a part of.

这只是兰斯·阿姆斯特朗所有问题中的一部分

(Applause)

（掌声欢呼）

Now, if I want to just kind of take a peek at these,

现在，如果我仅仅是想取样这些数据的高峰

I might think,

我会想，

"Well, what about taking a look at all of cycling?"

好吧，看看所有自行车运动员如何？

So I can step back, and expand on that.

因此，我可以退一步，并扩大这一点

And I see Greg LeMond now.

我现在看见了格雷格·莱蒙德

And so you get the idea that when you

因此你要明白

navigate over information this way --

当你用这种方式浏览信息时

going narrower, broader,

狭窄的，宽阔的，

backing in, backing out --

后退，反向，

you're not searching, you're not browsing.

你不是在搜寻，不是在浏览。

You're doing something that's actually a little bit different.

你做的事实际上有点不同。

It's in between, and we think it changes

介于两者之间，并且我们认为

the way information can be used.

这改变了信息的使用方式

So I want to extrapolate on this idea a bit

因此，我推断在这个想法上

with something that's a little bit crazy.

有些东西是有点疯狂。

What we're done here is we've taken every single Wikipedia page

我们在这儿做的是摘取每个维基百科的页面

and we reduced it down to a little summary.

然后压缩为一个小小的摘要

So the summary consists of just a little synopsis

摘要包含了简介

and an icon to indicate the topical area that it comes from.

一个图标显示它来自专业领域。

I'm only showing the top 500

我仅仅在这里展示前500个

most popular Wikipedia pages right here.

最受欢迎的维基百科页面

But even in this limited view,

但即使在这些有限的浏览中，

we can do a lot of things.

我们依然可以做很多事情。

Right away, we get a sense of what are the topical domains

立刻，我们得知

that are most popular on Wikipedia.

维基百科上最流行的是什么。

I'm going to go ahead and select government.

我继续前进并选择政府。

Now, having selected government,

现在，通过选定后的政府，

I can now see that the Wikipedia categories

我可以看到在维基百科类别中

that most frequently correspond to that

最常对应的

are Time magazine People of the Year.

是时代周刊年度风云人物

So this is really important because this is an insight

这是很重要的 因为这是一项

that was not contained within any one Wikipedia page.

不属于任何一个维基网页所载述的内容。

It's only possible to see that insight

只有你退后几步俯瞰全局

when you step back and look at all of them.

才有可能看的透彻

Looking at one of these particular summaries,

看看这些特殊摘要中的一种，

I can then drill into the concept of

随后我能深入

Time magazine Person of the Year,

时代杂志年度风云人物

bringing up all of them.

深入他们。

So looking at these people,

所以，看看这些人

I can see that the majority come from government;

我可以看到大多数来自政府。

some have come from natural sciences;

有一部分来自自然科学界。

some, fewer still, have come from business --

更少的部分来自商界。

there's my boss --

其中有我的老板。

and one has come from music.

一个来自音乐界。

And interestingly enough,

而有趣的是，

Bono is also a TED Prize winner.

波诺也是TED大奖得主。

So we can go, jump, and take a look at all the TED Prize winners.

因此我们能够继续，跳转，看看所有的TED大奖得主。

So you see, we're navigating the web for the first time

所以你看，这是我们第一次在网络上遨游

as if it's actually a web, not from page-to-page,

仿佛的确是一张网，不仅一页一页的。

but at a higher level of abstraction.

而且是在更高的抽象层次上的网。

And so I want to show you one other thing

所以我想告诉你另一件事

that may catch you a little bit by surprise.

可能会让你吃惊。

I'm just showing the New York Times website here.

我只是在这展示纽约时报网站。

So Pivot, this application --

Pivot，这个应用程序——

I don't want to call it a browser; it's really not a browser,

我不想称之为浏览器，它确实不仅是一个浏览器，

but you can view web pages with it --

你能用它浏览网页——

and we bring that zoomable technology

并且我们给每个像这样的网页引入了可缩放技术。

to every single web page like this.

并且我们给每个像这样的网页引入了可缩放技术。

So I can step back,

因此我可以退后，

pop right back into a specific section.

退后到特定的地方

Now the reason why this is important is because,

为什么这个是重要的是因为，

by virtue of just viewing web pages in this way,

由于通过这种方式浏览网页的好处，

I can look at my entire browsing history

我能用完全相同的方式

in the exact same way.

看到我的全部浏览历史。

So I can drill into what I've done

因此我能深入

over specific time frames.

具体时间段的具体事件。

Here, in fact, is the state

这里，事实上，

of all the demo that I just gave.

是我刚才所有演示的情况。

And I can sort of replay some stuff that I was looking at earlier today.

我可以在某种程度上重放我今天早些时候看到的东西。

And, if I want to step back and look at everything,

如果我想退后一步看所一切东西，

I can slice and dice my history,

我可以切割我的历史

perhaps by my search history --

也许是我的搜索历史。

here, I was doing some nepotistic searching,

这里，我做了一些相关搜索，

looking for Bing, over here for Live Labs Pivot.

搜寻Bing，在这里有关微软Live Labs的Pivot。

And from these, I can drill into the web page

从这些，我可以深入网页

and just launch them again.

仅仅重启他们。

It's one metaphor repurposed multiple times,

这是同样的信息，因不同目的被多次组合使用，

and in each case it makes the whole greater

每次重新组合，使得它比组合起来的整体所含信息更多。

than the sum of the parts with the data.

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,

现在，在这个世界上，

we think about data as being this curse.

我们谈到数据常常提到这个诅咒。

We talk about the curse of information overload.

我们谈论信息爆炸魔咒。

We talk about drowning in data.

我们谈论淹没在信息海洋中。

What if we can actually turn that upside down

假设我们能把这种观念颠覆

and turn the web upside down,

把网络上下颠覆，

so that instead of navigating from one thing to the next,

相对于单线信息相互链接的情况，

we get used to the habit of being able to go from many things to many things,

让我们开始习惯多样信息链接多样信息，

and then being able to see the patterns

然后看到除此方式外无法看到的，隐藏规律？

that were otherwise hidden?

然后看到除此方式外无法看到的，隐藏规律？

If we can do that, then instead of being trapped in data,

如果我们能做到这一点，而不是被困在数据中，

we might actually extract information.

我们才能真正的获取信息。

And, instead of dealing just with information,

并且，不仅仅是处理信息，

we can tease out knowledge.

我们还可以梳理知识。

And if we get the knowledge, then maybe even there's wisdom to be found.

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。

(Applause)

（掌声欢呼）