Transcriber: Fran Ontanaya Reviewer: Capa Girl
I want to talk to you about two
of the most exciting possible things.
You probably guess what they are -- data and history.
Right? So, I'm not a historian.
I'm not going to give you a definition of history,
but let's think instead of history within a framework.
So when we are making history
or when we are creating historical documents
we are taking things that have happened in the past
and we are stitching them together into a story.
So let me start with a little bit of my own story.
Like anybody my age who works creatively with computers
I was a popular socially well-adjusted young man --
(Laughter)
And sporty! Sporty young man.
And like a lot of people my age
in the kind of business that I'm in
I was influenced tremendously by Apple.
But notice my choice of logo here, right?
The Apple on the left, not the Apple on the right.
I am influenced as much by the Apple on the right
as the next person. But the apple on the left
I mean look at that logo, it's a rainbow --
It's not even in the right order!
That's how crazy Apple was. (Laughter)
But I don't want to talk too much about the company.
I'll start talking about a machine though.
How amazing it is to think about this,
and I go back and I think about this --
Wednesday, one Wednesday when I was
about 12 years old, I didn't have a computer.
On Thursday, I had a computer.
Can you imagine that change?
It's so drastic, I can't even think about anything
that could change our lives that way.
But I'm actually not even going to talk about the computer.
I'm going to talk about a program
that came loaded on that computer.
And it was build by, not the guy on the left,
but the guy on the right.
Does anybody know who the guy on the right is?
Nobody ever knows the answer to this question!
This is Bill Atkinson.
And Bill Atkinson was responsible for tons of things
that you see on your computer everyday.
But I want to talk about one program
that Bill Atkinson wrote called Hypercard.
Yeah -- someone is cheering over there.
(Laughter)
So, Hypercard was a program that shipped with the Mac
and it was designed for users of the computer
to make programs on their computers.
Crazy idea today. And these programs were not
the apps that we think about today
with their large budgets and their big distribution.
These were small things. People making applications
to keep track of their local basketball team scores
or to organize their research
or to teach people about classical music
or to calculate weird astronomical dates.
And then, of course, there were some art projects.
This is my favourite one, it's called 'If monks had Macs'
and it's a non-linear kind of exploratory environment.
I thank the stars for Hypercard all of the time.
And I thank the stars for putting me in this era
where I got to use Hypercard.
Hypercard was the last program to ship
on a public computer, that was designed
for the users of the computer to make programs with it.
If you talked to the people who invented the computer
and you told them there would be a day,
a magical day, when everybody had a computer
but none of them knew how to program,
they would think you were crazy!
So let's get forward a few years.
I'm starting my career as an artist
and I'm building things with my computer --
small scale things. Investigating things
like the growth systems of plants.
Or, in this example, I'm building a simulated economy
in which pixels are trading colour with one another.
Trying to investigate how these types of systems work
and just kind of having fun.
And then this project led me to start working with data.
So I'm building graphics like this,
which compare "communism" -
the frequency of usage of the word "communism"
in the New York Times to "terrorism" at the top.
So you see "terrorism" kind of appears
just as "communism" is going away.
And with these graphics I was really interested
in the aesthetic of the graphs.
So, this is Iran and Iraq, it reads
like a clock, it's called the "timepiece graph"
And this is another timepiece graph
overlaying despair, I think, over hope
and there's only three times -- that's it, crisis
over hope -- There's only three times when crisis
eclipses hope. We are in the middle of one of them
right now -- but don't think about that too much.
(Laughter)
And finally the culmination of this work
with the New York Times' data a few years ago
was the attempt to combine an entire year's
news cycle into a single graphic.
So these graphics actually show us a full year
of news, all the people, and how they are connected
into a single graphic. And, from there
I started to be interested again
in more active systems. So, here's a project
called "Just landed" where I'm looking at people
tweeting on Twitter "Hey! I just landed in Hawaii!"
You know how people just casually try to sneak that
into their twitter conversation. "I'm not showing off!
Really! But I did just land in Hawaii."
And then I'm plotting those people's trips
in the hopes that maybe we can use social network
and the data that it leaves behind
to provide a model of how people move
which would be valuable to epidemiologists
among other people. And, more fun --
this is a similar project looking at people
saying "Good morning!" to each other
all around the world. Which taught me
by the way, that it is true that people
in Vancouver on the West Coast wake up
much later and say "good morning" much later
than the people on the East Coast
who are more adventurous.
Here's a more useful -- maybe -- project
where I took all the information
from the Kepler project and try to put it
into some visual form that made sense to me.
And I should say that everything I've shown you
up to now -- these are all things
that I just did for fun. It may seem weird
but this comes back from Hypercard.
I'm building tools for myself.
I may share them with a few other people
but they are for fun, they are for me.
So all these tools I showed you
that kind of occupy this weird space --
somewhere between science, art and design
and that's where my practice lies.
And still today from my experience with Hypercard
what I'm doing is I'm building visual tools
to help me understand systems.
So today I work at the New York Times --
I'm the data artist in residence at the NY Times --
and I've had the opportunity at the Times
to work on a variety of really interesting projects,
two of which I'm going to share with you today.
The first one I've been working on, in conjunction
with Mark Hansen. Mark Hansen is a professor
of statistics at the UCLA. He's also a media artist.
And Mark came to the Times with a very interesting question
to what may seem like an obvious problem --
When people share content on the Internet, how does
that content get from person A to person B?
Or, maybe person A to person B to person C
to person D. We know that people share content
in the Internet, but what we don't know
is what happens in that gap
between one person to the other.
So we decided to build the tool to explore that
and this tool is called 'cascade'.
So if we look at these systems that start
with one event that leads to other events.
We call that structural cascade.
And these cascades actually happen over time
so we can model these things over time.
Now, the New York Times has a lot of people
who share our contents, so the cascades
do not look like that one, they look more like this --
So here is the typical cascade.
At the bottom left, the very first event.
And then as people are sharing the content
from one person to another we go up
in the Y axis - degrees of separation -
and over on the X axis for time.
So we are able to look at that conversation
in a couple different views. This one
which shows us the threads of conversation.
And this one which combines that stacked view
with a view that lets us see the threads.
Now the Times publishes about seven thousand pieces
of content every month. So it was important for us,
when we were building this tool
to make it an exploratory one.
So the people could dig through this vast terrain
of data -- I think of it as a vehicle
that we are giving people to traverse
this really big terrain of data.
So here is what it really looks like
and here is a cascade playing in real time.
I have to say, this was a tremendous moment.
We had been working with canned data, fake data
for so long, that when we saw this
for the first moment, it was like an archaeologist
who had just dusted off these dinosaur bones.
And we discovered this thing
and we were seeing it for the first time.
These sharing structures that underlie the Internet.
And maybe the dinosaur analogy is a good one
because we are actually making some probabilistic
guesses about how these things link.
So we are looking at some of these pieces
and making some guesses but we try to make sure
that those are as statistically rigorous as possible.
Now, tweets in this case, they become
parts of stories, they become parts of narratives.
So we are building histories here, but they are
very short term histories. And sometimes
these very large cascades are the most
interesting ones, but sometimes the small ones
are also interesting. This is one of my favourites
- we call this "The Rabbi Cascade."
It's a conversation amongst rabbis about this article
in the New York Times about the fact that religious
workers don't get a lot of time off.
I guess Saturdays and Sundays are bad days
for them to take off.
So in this cascade there is a group of rabbis
having a conversation about a New York Times' story.
One of them has the best Twitter name ever --
he's called the 'VelveteenRabbi'.
(Laughter)
But we wouldn't ever found this if it weren't
for this exploratory tool.
This would just be sitting somewhere
and we would have never been able to see that.
But this exercise of taking single pieces
of information and building narrative structures,
building histories out of them,
I find it tremendously interesting.
Now, I moved to New York about two years ago.
And in New York everybody has a story
that surrounds this tremendously impactful event
that happened on September 11th of 2001.
And my own story with September 11th
has really become a more intricate one
because I spent a great deal of time working
on a piece of the 9/11 memorial in Manhattan.
So the central idea about the 9/11 memorial
is that the names in the memorial are not laid out
in alphabetical order, or chronological order,
but instead they are laid out in a way
in which the relationships between the people
who were killed are embodied in the memorial.
Brothers are placed next to brothers,
coworkers are placed together. So, this memorial
actually considers all of these myriad connections
that were part of these people's lives.
So I work with a company called Local Projects
to work on an algorithm and a software tool
to help the architects build the layout for the memorial.
Almost three thousand names,
and almost fifteen hundred of these adjacency requests,
these requests for connections. So a very dense story,
a very dense narrative that becomes
an embodied part of this memorial.
So I'm working with Jake Barton, we produce
the software tool, which allows the architects
to, first of all, generate the layout that satisfied
all of those adjacency requests, but then second
make little adjustments where they needed to
to tell the stories that they wanted to tell.
So this memorial, I think,
has an incredibly timely concept in our era
of social networks, because these networks --
these real-life networks that make people's lives
are actually embodied inside of the memorial.
And one of the most tremendously moving experiences
it's to go to the memorial and see how these people
are placed next to each other,
so that this memorial is representing their own lives.
How does this effect our lives?
Well, I don't know if you remember, but,
in the spring, there was a controversy,
because it was discovered that, on the iPhone,
and actually on your computer,
you were storing a tremendous amount
of location data. So, Apple responded saying,
this was not location data about you,
it was location data about wireless networks
that were in the area where you are.
So it's not about you, it's about where you are.
(Laughter) This is very valuable data!
It's like gold to researchers! This mobility data --
human mobility data. So what we thought --
we thought, "Man! How many people have iPhones?"
How many of you have iPhones?
So in this room we have this tremendous database
of location data that researchers would really really like.
So we built this system called Open Paths,
which lets people upload their iPhone data
and broker relationships with researchers
to share that data, to donate that data
to people that can actually put it to use.
So Open Paths was a great success as a prototype.
We received thousands of datasets, and we built
this interface which allows people to actually
see their lives unfolding from this traces
that are left behind on your devices.
Now, what we didn't expect was how moving
this experience would be. When I uploaded my data
I thought, "Big deal. I know where I live.
I know where I work. What am I going to see here?"
Well it turns out what I saw was
that moment I got off the plane
to start my new life in New York.
The restaurant where I had Thai food
that first night, thinking about
this new experience of being in New York.
The day that I met my girlfriend.
This is LaGuardia airport.
This is this Thai restaurant on Amsterdam Avenue.
This is the moment I met my girlfriend.
See how that changes the first time
I told you about those stories,
and the second time I told you about those stories.
Because what we do in the tool inadvertently is --
we put these pieces of data into a human context.
And by placing data into a human context
it gains meaning. And I think this is tremendously
tremendously important. Because these are
our histories that are being stored on these devices.
And by thinking about them that way,
putting them in a human context --
first of all what we do with our own data
is get a better understanding of the type
of information that we are sharing.
But if we can do this with other data, we can put
data into a human context, I think we can change
a lot of things. Because, it builds automatically
empathy for the people involved in these systems.
And that in turn results in a fundamental respect which
I believe is missing in a large part of technology.
And we start to deal with issues like privacy.
By understanding that these numbers
are not just numbers, but instead they are attached
- they are tethered to pieces of the real world.
They carry weight. By understanding that
the dialog becomes a lot different.
How many of you have ever clicked a button
that enables a third party to access
your location data on your phone?
Many people. Lots of you.
So the third party is the developer,
the second party is Apple --
the only party that never gets access
to this information is the first party!
And I think that's because we think about these pieces
of data in a strandred abstract way.
We don't put them into a context which I think
makes them a lot more important.
So what I'm asking you to do is really simple.
Start to think about data in a human context.
Doesn't really take anything. When you read
stock prices, think about them in a human context.
When you think about mortgage reports,
think about them in a human context.
There's no doubt that big data is big business.
There's an industry being developed here.
Think about how well we have done
in previous industries that we have developed
involving resources -- not very well at all.
And I think part of that problem is that we have had
a lack of participation in these dialogues
from multiple pieces of human society.
So the other thing that I'm asking for
is an inclusion in this dialogue from artists,
from poets, from writers -- from people
who can bring a human element into this discussion.
Because I believe that this world of data
is going to be transformative for us.
And unlike our attempts with the resource industry
and our attempts with the financial industry,
by bringing the human element into this story
I think that we can take it to tremendous places.
Thank you.
(Applause)