If I can leave you with one big idea today , it &apos;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 &apos;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 .
So what we &apos;re looking at right here is a typical mortality chart organized by age .
This tool that I &apos;m using here is a little experiment .
It &apos;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 &apos;s a different pattern that emerges .
This is because , in the mid-area here , people are at their most active , and over here they &apos;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 &apos;s especially the case for men .
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 &apos;s all here .
It &apos;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 &apos;m looking at this ; I see the athletes that have appeared in this issue , the sports .
I &apos;m a Lance Armstrong fan , so I &apos;ll go ahead and I &apos;ll click on that , which reveals , for me , all the issues in which Lance Armstrong &apos;s been a part of .
Now , if I want to just kind of take a peek at these , I might think , &quot; Well , what about taking a look at all of cycling ? &quot;
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 &apos;re not searching , you &apos;re not browsing .
You &apos;re doing something that &apos;s actually a little bit different .
It &apos;s in between , and we think it changes the way information can be used .
So I want to extrapolate on this idea a bit something that &apos;s a little bit crazy .
What we &apos;re done here is we &apos;ve taken every single Wikipedia page and reduced it down to a little summary .
So the summary consisted of just little synopsis and an icon to indicate the topical area that it comes from .
I &apos;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 &apos;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 &apos;s only possible to see that insight when you step back and look at all of them .
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 &apos;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 &apos;re navigating the web for the first time as if it &apos;s actually a web , not page to page , but at a higher level of abstraction .
And so I want to show you one other thing that may catch you a little bit by surprise .
I &apos;m just showing the New York Times website here .
So Pivot , this application -- I don &apos;t want to call it a browser ; it &apos;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 in to 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 in to what I &apos;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 &apos;s one metaphor repurposed multiple times , and in each case it makes the whole greater than the sum of the parts with the data .
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 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 &apos;s wisdom to be found .
So with that , I thank you .
