(piano music)
- [Voiceover] In the near future,
every object on earth will be generating data,
including our homes, our cars,
even our bodies.
- Do you see it?
Yeah, right up there.
- [Voiceover] Almost everything we do today
leaves a trail of digital exhaust,
a perpetual stream of texts, location data,
and other information that will live on
well after each of us is long gone.
We are now being exposed to as much information
in a single day as our 15th century ancestors
were exposed to in their entire lifetime.
But we need to be very careful
because in this vast ocean of data
there's a frighteningly complete picture of us,
where we live, where we go,
what we buy, what we say,
it's all being recorded and stored forever.
This is the story of an extraordinary revolution
that's sweeping almost invisibly through our lives
and about how our planet is beginning to develop
a nervous system with each of us acting as human sensors.
This is the human face of big data.
- All these devices and machines and everything
we're building these days, whether it's phones or computers
or cars or refrigerators, are throwing off data.
- Information is being extracted out of toll booths,
out of parking spaces,
out of Internet searches,
out of Facebook, out of your phone,
tablets, photographs, videos.
- Every single thing that you do leaves a digital trace.
- The exhaust or evidence of humans
interacting with technology and what side effect that has
and that's literally, it's just this massive amount of data.
- What we're doing is we're measuring things
more than we ever have.
It's that active measurement that produces data.
- If you were some omniscient god
and you could look at the footprints of electric devices,
you could kind of see the world.
If the whole world is being recorded in real time,
you could see everything that is going on in the world
through the footprints.
I think it's a lot like written language, right,
it's just at some point they got to the point
where you had to start writing stuff down.
You just got to the point where it wouldn't work
unless we wrote it down, which is making the same point
where well it ain't gonna work unless we write
all the data down and then look at it.
- And all that data coming in is big data.
- We estimate that by 2020
the data volumes will be at about 40 zigabytes.
Just to put it in perspective,
if you were to add up every single grain of sand
on the planet and multiply that by 75,
that would be 40 zigabytes of information.
- All the data processing we did in the last two years
is more than all the data processing
we did in the last 3,000 years.
- And so the more information we get,
the larger the problems will be that we solve.
- Every powerful tool has a dark side, every last one.
Anything that's going to change the world,
by definition has to be able to change it for the worse
as much as for the better.
It doesn't work one way without the other.
- When it comes to big data, a lot of people
are very nervous.
Data can be used in any number of ways
that you're either aware of or you're not.
The less aware of the use of that data that you are,
the less power you have in the coming society
we're going to live.
- Well sort of just in the beginning of this big data thing,
you don't know how it's going to change it,
but you just know it is.
(dramatic music)
- The first real data set to change everything in the world
was the astronomical data set,
meticulously collected over tens of years by Copernicus
that ultimately revealed, even though the sun seemed to be
moving over the sky every morning and every night,
the sun is not moving, it is we who are moving,
it is we who are spinning.
It happened again when we suddenly could see
beneath the visible level
and the microscope in the 1650s and 60s,
opened up the invisible world
and we for the first time were seeing cells and bacteria
and creatures that we couldn't imagine were there.
It then happened again when we revealed the atomic world,
when we said wait a second, there's a level
below the optical microscope where we could begin
to see things at billionths of a meter at a nanometer scale,
where we imagined the atom and the nucleus
and the electron, where we understood that light
is electromagnetic frequencies.
But now, there's actual a supervisible world
coming into play.
Ironically, big data is a microscope.
We're now collecting exabytes and petabytes of data
and we're looking through that microscope
using incredibly powerful algorithms
to see what we would never see before.
- Before what we did was we
thought of things and then we wrote it down
and that became knowledge.
Big data's kind of the opposite.
You have a pile of data that isn't knowledge really
until you start looking at it and noticing wait,
maybe if you shift it this way and you shift it this way,
this turns into this interesting piece of information.
- I think that the BDAD moment,
you know, before data, after data moment,
is really Search.
(tapping)
That was the moment at which we got a tool
that was used by hundreds of millions of people
within a few years,
where we could navigate an incredible amount
of information.
We took all of human knowledge that was in text, right,
and we put it on the web
and we thought to ourselves, "Well we're done.
"Wow that was hard."
And now we realize that was the first minute
of the first inning of the game, right,
because that was just the knowledge we already had
and the knowledge that we continue to add to the web
at a relatively slow pace, you know.
But there is so much more information
that we have not digitized and so much more
information that we're about to take advantage of.
(piano music)
- [Voiceover] In recent years, our technology has allowed us
to store and process mass quantities of data.
Visualizing that data will allow us to see
complex systems function,
see patterns and meaning in ways
that were previously impossible.
Almost everything is measurable and quantifiable.
- So when I look at data, what's exciting to me
is kind of recontextualizing that data
and taking it and putting it back into a form
that we can perceive, understand, talk about,
think about.
- [Voiceover] This is the data for airplane traffic
over North America for a 24-hour period.
When it's visualized, you see everything starts
to fade to black as everyone goes to sleep,
then on the West Coast, planes start moving across
on red-eye flights to the east
and you see everyone waking up on the East Coast,
followed by European flights in the upper right-hand corner.
I think it's one thing to say that there's 140,000 planes
being monitored by the federal government at any one time
and it's another thing to see that system
as it ebbs and flows in front of you.
These are text messages being sent in the city of Amsterdam
on December 31st.
You're seeing the daily flow of text messages
from different parts of the city until we approach midnight,
where everyone says--
- [Voiceover] Happy New Year!
- It takes people or programs or algorithms
to connect it all together to make sense of it
and that's what's important.
We have every single action that we do in this world
is triggering off some amount of data
and most of that data is meaningless
until someone adds some interpretation of it,
someone adds a narrative around it.
- Often, we sort of think of data as stranded numbers,
but they're tethered to things
and if we follow those tethers in the right ways,
then we can find the real-world objects
and the real-world stories that were there.
So a lot of the work is that kind of work.
It's almost investigative work of trying to follow
that trail from the data to what actually happened.
- Sometimes the power of large data sets
isn't immediately obvious.
Google flu trends is a great example
of taking a look at a massive corpus of data
and deriving somewhat tangential information
that can actually be really valuable.
- [Voiceover] Until recently, the only way to detect
a flu epidemic was by accumulating information
submitted by doctors about patient visits,
a process that took about two weeks to reach the CDC.
So the researchers turned it around.
They asked themselves if they could predict a flu outbreak
in real time simply using data from online searches.
So they set out to do the near impossible,
searching the searches, billions of them,
spanning five years to see if user queries
could tell them something.
- When we do searches on Google,
we all think of it as a one-way street,
that we're going into Google and extracting information
from Google, but one of the things we don't really
think about very much is we're actually contributing
information back simply by doing the search.
- [Voiceover] And that's where the breakthrough occurred.
In looking at all the data, they saw that not only
did the number of flu-related searches correlate
with the people who had the flu,
but they also could identify the search terms
that could let them accurately predict flu outbreaks
up to two weeks before the CDC.
- The CDC system takes about a week or two
for the numbers to sort of fully flow in.
What Google could do is to say based on our model,
we'll have it on the spot.
We'll just run the algorithm
based on how people are searching right now.
- And now we have, for the first time,
this real-time feedback loop where we can see
in real time what's going on and respond to it.
- Now there is a flip side to this though
and that is there was a big story this year that
there was a lot of media attention about
what an intense flu season this was.
And so what did that do?
That drove up search.
That drove people who were more interested
in what's going on with this flu
or might have made more people think I must have it
and so they were off, they got it way wrong.
- So you know, one way to think about big data
and all of the computational tools
that we wrap around that big data
to let us discover patterns that are in the data
is when we point all that machinery at ourselves.
- [Voiceover] At MTI, Deb Roy and his colleagues
wanted to see if they could understand
how children acquire language.
- And we realize that no one really knew
for a simple reason, there was no data.
- [Voiceover] After he and his wife Rupal
brought their newborn son home from the hospital,
they did what every normal parent would do,
mount a camera in the ceiling of each room in their home
and record every moment of their lives for two years,
a mere 200 gigabytes of data recorded every day.
- [Deb] We ended up transcribing somewhere between
eight and nine million words of speech.
- [Voiceover] Ga ga ga.
- And as soon as we had that, we could go and identify
the exact moment where my son first said a new word.
- [Deb] We started calling them births.
- We took this idea of a word birth and we started
thinking about why don't we trace back in time
and look at the gestation period for that word.
One example of this was water.
So we looked at every time my son heard the word water,
what was happening, where in the house were they,
how were they moving about
and using that visual information
to capture something about the context
within which the words are used.
We call them wordscapes.
Then we could ask the question
how does the wordscape associated with a word
predict when my son will actually start using that word?
- [Voiceover] What they learned from watching Deb's son
was that the texture of the wordscapes had predictive power.
If most of the previous research had indicated
that the way language was learned was through repetition,
then this analysis of the data showed that it wasn't
actually repetition that generated learning, but context.
Words with more distinct wordscapes,
that is words heard in many varied locations,
would be learned first.
- Not only is that true,
but the wordscapes are far more predictive
of when a word will be learned
than the frequency, the number of times it's actually heard.
It's like we're building a new kind of instrument,
like we're building a microscope
and we're able to examine something that is around us,
but it has a structure and patterns and beauty
that are invisible without the right instruments
and all of this data is opening up
to our ability to perceive things around us.
(giggling)
- He's walking.
(beeping)
- A lot of people don't realize
that when a baby is born premature,
it can develop infection in the hospital
and it can kill them.
In our research, we started to just look at infection.
By the time the baby is physically showing signs
of having infection, they are very, very unwell.
So the very first time that I went into a neonatal
intensive care unit, I was amazed
by the sights, the sound, the smell,
just the whole environment,
but mainly for me, the data.
What shocked me was the amount of data lost.
They showed me the paper chart
that the information's recorded onto.
One number every hour for the baby's heart rate,
the respiration, the blood oxygen.
Now in that time, the baby's heart has beaten
more than 7,000 times,
they breathe more than 2,000 times,
and the monitor showing the blood oxygen level
has showed that more than three and a half thousand times.
I said, "Well, where's all the data going
"that's in those machines?"
And they said, "Oh it scrolls out of the memory."
So we have an enormous amount of data lost.
So we're trying to gather that information
and use it over a longer time
in much more complex ways than before
and we try and write computing code
to look at the trends in the monitors
and the trends in the data
to see how that can tell us when a baby's becoming unwell.
- [Voiceover] So Dr. McGregor did what data scientists do,
she looked for the invisible.
She and her team analyzed the data
from thousands of heart beats and what they discovered
were minute fluctuations that could predict
the onset of life-threatening infections
long before physical symptoms appeared.
- When the body first starts dealing with infection,
there are these subtle changes
and that's why we have to watch every single heart beat.
And what we're finding is that when you're starting
to become unwell, the heart's ability to react,
to speed up and slow down, gets subdued.
The human body has always been
exhibiting these certain things.
The difference is we've started to gather
more information about the body now
so that we can build this virtual person.
The better we have the virtual representation,
then the better we can start to understand
what will happen to them in the future.
Back in 1999 I was pregnant with my first child.
She was born premature and she passed away.
There was no other viable outcome for her.
But there are so many others who have just been born early
and they just need that opportunity to grow and develop.
We want to let the computers monitor a baby
as it breathes, as its heart beats, as it sleeps,
so that these algorithms are watching for certain behaviors
and if something starts to go wrong for that baby,
we have the ability to intervene.
If we can just save one life,
then for me personally, it's already worthwhile.
- Everybody understands what it takes to digitize
photography, a movie, a magazine, newspaper,
but they haven't yet grasped what it means
to digitize the medical essence of a human being.
Everything about us now that's medically relevant
can be captured.
With sensors, we can digitize all of our metrics
and with imaging, we can digitize our anatomy
and with our sequence of our DNA,
we can digitize our biology.
- The data story in the genome is the fact that
we have six billion data points sitting in our genomes
that we've never had access to before.
When you sequence a person's genome,
there are known differences in the human genome
that can predict a risk for a disease,
or that you're a carrier for a disease,
or that you have a certain ancestry.
There's a lot of information packed in the genome
that we're starting to learn more and more about.
Getting your own personal information through your genome
would not have been possible
even 10 years ago because of cost.
The technologies that have enabled this
have dropped precipitously and now we're able to
get a really good look at your genome for under $500.
- And when it becomes 100 bucks or 10 bucks,
we're going to have everyone's genome as data.
- The results came back on Tuesday,
it was October 2nd, 1996.
I was diagnosed that day with breast cancer.
A year out of treatment, I found a lump on the other breast
in the exact same position and I went in
and they told me that I had breast cancer again.
Sedona's known about me being tested for the BRCA gene,
she's known my sister has tested,
she knows my other sister tested
and was negative for the gene mutation
and so she actually told me, "When I'm 18, I want to test,
"you know, and see if I have this gene mutation or not."
I am gonna be completely distraught
if I hand this gene down to my kid.
- Do you know what your chances are of having the mutation
that your mom has?
- I'd say 50/50.
- You're exactly right.
BRCA2 is a gene that we all have,
it's called tumor suppressor gene,
but women, if you have a mutation in the gene
it causes the gene not to function like it should.
So the risk mainly of breast and ovarian cancer
is a lot higher than in the general population.
- An average woman would have a 12% risk
of getting breast cancer in a lifetime
and most women aren't going out there,
getting preventive mastectomies,
but when you're faced with an 87% risk
of getting breast cancer in your lifetime,
it kind of makes that a possible choice.
- [Voiceover] You'll need to swish this mouth wash
for 30 seconds.
- We are definitely moving into a world
where the patient or the person is at the center of things
and hopefully also at the controls.
People will have access to the data
that is informative around the type of disease they have
and that data then can point much more directly
to proper treatments,
but the data can also say that a treatment
works for a person or it doesn't work for a person
based on their genetic profile
and we're gonna start moving more and more
into this notion of personalized medicine
as we learn more about the genome
and the study of pharmacogenetics,
which is how do our genes influence the drugs we take.
Ultimately, instead of treating disease,
is there data that could really help us
move away from contracting these illnesses to begin with
and go more toward a preventive model?
(mellow music)
- Now you can't talk about information separate from health.
How you feel is information,
how you respond to a drug is information,
your genetic code is information.
What's really happening is when we start collecting it,
we're going to start seeing it
and we're going to start interpreting it.
We're beginning the age of collecting information
from sensors that are cheap and ubiquitous
that we can process continuously
and we can actually start knowing things.
- If we monitored our health throughout the day,
continuously every second, what would that really enable?
- And there's now a lot of really great technology
coming out around this sense of tracking and monitoring
and we have all kinds of sensor companies and devices.
- We're actually collecting a lot of physiological
information, you know, heart rate, breathing,
in real-time, you know, every minute, every second.
- [Linda] People wanting to measure their daily activities
and being able to track your own sleep,
being able to watch and monitor your own food uptake,
being able to track your own movement.
- It's almost like looking down at our lives
from 30,000 feet.
There's a company right now in Boston
that can actually predict that you're going to get depressed
two days before you get depressed
and the gentleman who created it said
if you actually watch any one of us,
most people have a very discernible pattern of behavior.
And for the first week, our software basically determines
what your normal pattern is
and then two days before you're showing
any outward signs of depression,
the amount of Tweets and emails that you're sending
go down, your radius of travel starts shrinking,
the amount of time that you spend at home goes up.
- You can look to see if how you exercise
changes your social behavior,
if what you eat changes how you sleep
and how that impacts your medical claims.
- All kinds of data and information
are sitting inside the world you do every day.
- Now, with all these devices,
we have real-time information, real-time understanding.
- Now that might sound interesting,
might help you shed a few pounds,
realize you're eating too many potato chips
and sitting around too much perhaps
and that's useful to you individually,
but if hundreds of millions of people do that,
you have a big cloud of data
about people's behavior that can be crawled through
by pattern recognition algorithm.
And doctors and health policy officials
can start to see patterns that change the way,
collectively as a society, we understand
not just our health, but every single area
where data can be applied
because we start to understand how we might,
collectively as a culture, change our behavior.
- And if you look at the future of this,
we're gonna be embedded in a sea of information services
that are connected to massive databases in the cloud.
(rhythmic electronic music)
- If you take a look at everything that you touch
in everyday life, the majority of these things
were invented many, many, many, many, many years ago
and they're ripe for reinvention
and when they get reinvented,
they're gonna be connected,
they're gonna be connected in some way
that data that comes off of these devices that you touch
is gonna be collected and stored in a central location
and people are gonna run big data algorithms on this data
and then you're gonna get the feedback
of the collective whole rather than the individual.
- So it's taking people who are already out there,
who already have these devices,
and turning all these people into contributors
of information back to the system.
You become one of the nodes on the network.
I think the Internet, as wondrous as it's been
over the last 20 years, was like a layer
that needed to be in place for all these sensors
and devices to be able to communicate with each other.
- You know, we're building this global brain
that has these new functions and we're accessing them
primarily now through our mobile devices,
or obviously also on our desktops,
but increasingly mobile.
- I think this data revolution has a strange impact really
of people feeling like there's somebody listening to them
and that could mean listening in the sense of Big Brother,
someone's listening in,
or it could be someone's really hearing me.
This device in my hand knows who I am,
it can somewhat anticipate what I want
or where I'm going and react to that.
The implications of that are huge
for the decisions that we make
and for the systems that we're part of.
I think about living in a city
and how you're experience of living in that city
would be, in 10 or 15 years.
You've got places like Chicago
where they're being hugely innovative
and they're taking massive data sets,
combining them in interesting ways,
running interesting algorithms on them
and figuring out ways that they can intervene
in this system to sort of see patterns
and be able to react to those patterns.
When you take in data, it affects you as an individual
and then you affect the system
and that affects the data again
and this round trip that you start to see yourself part of
makes me understand that I'm an actor in a larger system.
For instance, if you know by looking at the data,
and you have to put different data sets together
to be able to see this, that some of the street lights,
you know, when they go out, they cause higher crime
in that particular block,
(siren blares)
you start to see ways that if you can query that data
in intelligent ways, that you can prioritize
the limited resources that you have in a city
to take care of the things that have, you know,
follow along effects and follow along costs.
- In the end, you know, you're going to hope that
this is just our reaction as a species
to this scale problem, right,
how do you get another, you know,
two billion people on the planet?
You can't do it unless you start instrumenting
every little thing and dialing it in just right.
- And you know, right now you wait for the bus
because the bus is coming on a particular schedule
and it's great, we're now at the point where
your phone will tell you when the bus is really coming,
not just when the bus is scheduled to come.
You know, take that a little bit forward.
What about when there's more use
on one line than the other?
Well instead of sticking with the schedule,
does the system start to understand
that maybe this route doesn't need 10 buses today
and automatically shift those resources
over to the lines where the buses are full.
- Boston just created a new smartphone app
which uses the accelerometer in your phone.
So if you're driving through the streets of south Boston
and all of a sudden there's a big dip in the street,
the phone realizes it.
So anybody in the city of Boston
that has this up and running
is feeding real-time data on the quality of the roads
to the city of Boston.
- Then you start to feel that your city
is sort of a responsive organism
just like your body puts your blood where it needs it.
Think about ways that we could live in cities
when they're that responsive to our needs
and think about the implications of that for the planet
because really cities are also really
how we're going to survive the 21st century.
You can live in a city with a far smaller footprint
than anywhere else in the world
and I think data and sort of the responsive systems
will play an enormous role in that.
- I think one of the most exciting things about data
is that, you know, it's giving us extra senses,
it's expanding upon, you know,
our ability to perceive the world
and it actually ends up giving us the opportunity
to make things tangible again
and to actually get a perspective on ourselves,
both as individuals and also as society.
- And there's always that moment in data visualization
when you're looking at, you know,
tons and tons and tons of data.
The point is not to look at the tons and tons
and tons of data, but what are the stories
that emerge out of it.
- If you said look, give me the home street address
of everyone who entered New York State prison last year
and the home street address of everyone
who left New York State prison last year
and we said look, let's get the numbers, put it on a map
and actually show it to people.
And when we first produced our Brooklyn map,
which was the first one we did,
they hit the floor, not because nobody knew this.
You know, everyone knew anecdotally
how concentrated the effect of incarceration was,
but no one had actually seen it based on actual data.
We started to show these remarkably intensive
concentrations of people going in and out of prison,
highly disproportionately located
in very small areas around the city.
- [Voiceover] And what we found is that the home addresses
of incarcerated people correlates very highly
with poverty and with people of color.
- You have a justice system, which by all accounts
is supposed to be essentially based on
a case-by-case, individual decision of justice.
Well when you looked at the map over time,
what you really were seeing was this mass population
movement out and mass population resettlement back,
this cyclical movement of people.
- So once we had mapped the data,
we quantified it in terms of how much it cost
to house those same people in prison.
- And that's where we started to think
about million dollar blocks.
We found over 35 individual city blocks in Brooklyn alone
for which the state was spending
more than a million dollars every year
to remove and return people to prison.
We needed to reframe that conversation
and what immediately emerged out of this was
this idea of justice reinvestment.
We weren't building anything in those places
for those dollars.
How can we demand sort of more equity
for that investment
to extract those neighborhoods
from what decades of criminalization has done?
And that shift had to come from the data
and a new way of thinking about information.
These maps did that.
- The amount of data that now is being collected
about those areas that are stuck in cycles of poverty,
cycles of famine, cycles of war,
gives people or governments and NGOs
an opportunity to do good.
Understanding on the ground, information on the ground,
data on the ground can change the way
people apply resources
which are intended to try to help.
- We really fundamentally believe
that data has intrinsic value
and we also fundamentally believe
that the individuals who create that data
should be able to benefit from that data.
But we're working with one of the big mobile phone
operators in Kenya, we're looking at the dynamics
of these mobile phone subscribers.
Millions of phones in Kenya.
We're looking at how the population
was moving over the country.
And we're overlaying that movement data
with data about parasite prevalence
from household surveys and data from hospitals.
We can start identifying these malaria hot spots,
regions within Kenya that desperately needed
the eradication dollars.
It's fascinating to start extracting models
and plotting graphs of the behavior
of tens of millions of people in Kenya,
but it's meaningful when you can make those insights count,
when you can take the insights that you've gleaned
and put them into practice
and measure what the impact was
and hopefully making the lives of the people
who are generating this data better.
(children yelling)
(siren blaring)
- That afternoon when the earthquake struck in January,
I was watching CNN and saw the breaking news
and I had taken my wife in Port-au-Prince at the time
and for the better part of 12 hours
had no idea whether any one of my friends
were alive or dead.
- [Voiceover] Meier was a Tufts University PhD student
and directed crisis mapping for Ushahidi,
a nonprofit that collects, visualizes,
and then maps crisis data.
- And so I went on social media
and I found dozens and dozens of Haitians
tweeting live about the damage
and a lot of the time they were sharing
where this damage was happening.
So they would say the church on the corner of X and Y
has been destroyed or is collapsed
and they would refer to street names and so on.
So it's about really becoming a digital detector
and then trying to understand where on the map this was.
- [Voiceover] So he called everyone he knew
and put together a mostly volunteer team in Boston
to prioritize the most life and death tweets
and map them for rescue workers.
- For the first time, it wasn't the government
emergency management organization
that had the best data of what was happening,
but it was legions of volunteers that came together
and crowdmapped the location
of buildings that had collapsed,
people that were trapped in rubble,
locations where water was needed,
where physicians were needed and the like.
- I think we've seen, not only in Haiti
but almost every disaster since Haiti,
just an explosion of social media content.
- [Voiceover] Disaster mapping groups like Meier's
realized that there was so much at stake
and so much raw data coming from social media
during natural disasters.
They needed to come up with new algorithms
to sort through the flood of information.
- We are drawing on artificial intelligence,
machine learning, working with data scientists
to develop semi-automated ways
to extract relevant, informative and actionable information
from social media during disasters.
So one of our projects is called
Artificial Intelligence for Disaster Response.
During the Hurricane Sandy,
we collected five million tweets during the first few days.
With the Sandy data, we've been able to show empirically
that we can automatically identify whether or not
a tweet has been written by an eye witness.
So somebody who is writing something
saying the bridge is down,
we can say with a degree of accuracy
of about 80% and higher whether that tweet
has actually been posted by an eye witness,
which is really important for disaster response.
I think that goes to the heart of why
something like social media and Twitter is so important.
Having these millions of eyes and ears on the ground.
It's about empowering the crowd,
it's about empowering those who are effected
and those who want to help.
These are real lives that we're capturing.
This is not abstract information.
These are real people who are affected by disasters
who are trying to either help or seek help.
It doesn't get more real than this.
- Today, technology allows,
in a lot of our communication tools,
allows an idea to be spread instantly
and with the original source of truth.
I can have an idea and I can decide that
I want to bring this around the world
and I can do it almost instantaneously.
- Tunisia's a great example.
There were little uprisings happening all over Tunisia
and each one was brutally squashed
and there was no media attention
so no one knew that any other little village had an issue.
But what happened was in one village
there was the man who self-immolated in protest
and the images were put online
by a distant group onto Facebook
and then Al Jazeera picked it up
and broadcasted the image across their region
and then all of Tunisia realized
wait a second, we're about to have an uprising
and it just went.
(yelling)
So Tunisia was really activists on the ground,
social media and mainstream media working together,
spreading across Tunisia this idea that
you're not the only ones
and it gave everyone the courage to do the uprising.
Technology has fundamentally changed
the way people interact with government.
That's another layer of the stack
that's sort of being opened up.
I think that's one of the key challenges that big data
has so much opportunity for both good
and for also really screwing up our system.
- You can't talk about data without talking about people
because people create the data
and people utilize the data.
(whirring)
- So a handful of years ago there's a guy named Andrew Pole
who is a statistician who gets hired by Target.
He's sitting at his desk and some guys
from the marketing department come by and they say,
"Look, if we wanted to figure out
"which of our customers are pregnant,
"could you tell us that?"
So what Andrew Pole started doing is he said
the women who had signed up for the baby registry,
let's track what they're buying
and see if there's any patterns.
I mean, obviously if someone starts buying a crib
or a stroller, you know they're pregnant.
But by using all of this data they had collected,
they were able to start seeing these patterns
that you couldn't actually guess at.
When women were in their second trimester,
they suddenly stopped buying scented lotion
and started buying unscented lotion
and about at the end of their second trimester,
the beginning of their third trimester, they would start
buying a lot of cotton balls and wash cloths.
- And then they could start to subtly send you coupons
for things that might be related to your pregnancy.
- The decided to do a little test case.
So they send out some of these ads to a local community
and a couple weeks later this father comes in
to one of the stores and he's furious
and he's got a flyer in his hand that was sent to his house
and he finds the manager and he says to the manager,
he says, "Look, I'm so upset.
"You know, my daughter is 18 years old.
"I don't know what you're doing sending her this trash.
"You sent her these coupons for diapers
"and for cribs and for nursing equipment.
"She's 18 years old
"and it's like you're encouraging her to get pregnant."
Now the manager, who has no idea what's going on
with the pregnancy prediction machine
that Andrew Pole built,
says "Look, I'm so sorry.
"I apologize, it's not going to happen again."
And a couple days later the guy feels so bad about this
that he calls the father at home and he says to the father,
"I just wanted to apologize again.
"I'm so sorry this happened."
And the father kind of pauses for a moment.
He says, "Well, I want you to know
"I had a conversation with my daughter
"and there's been some activities in my household
"that I haven't been aware of
"and she's due in August.
"So I owe you an apology."
And when I asked Andrew Pole about this,
before he stopped talking to me,
before Target told him that he couldn't talk to me anymore,
he said, "Oh look, like you gotta understand,
"like this science is just at the beginning,
"like we're still playing with what we can figure out
"about your life."
(mellow electronic music)
- Everybody who's on Facebook is involved
in a transaction in which they're donating their data
to Facebook, who then sells their data
and in return they get this service
which allows them to post pictures
and connect to their friends
and so on and so on and so on and so on.
That's the transaction,
but nobody knows that's the transaction.
Most people, I think, don't understand that.
They just literally think they're getting Facebook for free
and it's not a free thing,
we're paying for it by allowing them access to our data.
- There are a lot of people on Facebook who don't know,
for example, how much information is really out there
about themselves and probably and apparently don't care
as long as they can put up pictures of their cats.
I think most people, when they think about privacy,
they don't seem to connect
their willingness to share their personal information
with the world, either through social media
or through shopping online or anything else,
they don't seem to equate that with surveillance.
- Every time I receive a text message,
every time I make a phone call,
my location is being recorded.
That data about me is being pushed off to a server
that is owned by my mobile operator.
If I call that mobile phone operator and say
"Hey, I'd like to have my data, please.
"At the minimum, share it with me.
"I'd like to see my locations over time."
They won't give it to me.
- The increased ability of these devices that we have
to become recording and sensing objects,
so data collection devices essentially,
in public space, that changes a lot of things.
- Even if the phone company took away
all of your personal identifying information,
it would know within about 30 centimeters
where you woke up every morning
and where you went to work every day
and the path that you took
and who you were walking with
and so even if they didn't know who you are,
they know who you are.
What I'm really worried about is the cost to democracy.
Now, today, it's nearly impossible to be truly anonymous
and so the ability to everything to be connected to you
and for everything you do in the real world
to be connected to you, everything you're doing
in cyberspace, and then the ability for
whoever it is to take that, put it together,
and turn it into a story.
My fear really is that once there's so much data out there
and once governments and companies
start to be able to use that data to profile people,
to filter them out, everybody is going to start to worry
about their activities.
- We're at a very, very important point
where I think our society has come to realize this fact
and just begun in earnest to debate the implictions of it.
- You have, I think, an attitude in the NSA
that they have a right to every bit of information
they can collect.
We have constructed a world where
the government is collecting secretly
all of the data it can on each individual citizen,
whether that individual citizen has done anything or not.
They have been collecting massive amounts of data
through cell phone providers, Internet providers,
that is then sifted through secretly
by people over whom no democratic institution
has effective control.
There's a feeling that if you're not
communing with terrorists,
what do you care if the government gathers your information.
This is probably the most pernicious,
anti Bill of Rights line of thought that there is
because these are rights we hold in common.
Every violation of somebody else's rights
is a violation of yours.
- What's going to happen, I think, is that we now
have so much information out there about ourselves
and the ability for people to abuse it,
people are going to get hurt,
people are going to lose their jobs,
people are going to get divorced,
people are going to get killed
and it's going to become really painful
and everyone's going to realize
we have to do something about this
and then we're going to start to change.
Now the question is how bad is it.
- [Voiceover] You can't have a secret operation
validated by a secret court based on secret evidence
in a democratic republic.
So the system closes and no information gets out
except it gets leaked or it gets dumped on the world
by outside actors, whether that's WikiLeaks,
or whether that's Bradley Manning,
or whether that's Edward Snowden.
That's the way that people find out
what their government is up to.
We're living in a future where we've lost
our right to privacy.
We've given it away for convenience sake
in our economic and social lives
and we've lost it for fear's sake vis-a-vis our government.
- Any time you're looking at an ability to segment,
analyze, you've got to think about both sides.
But there's so much good here,
there's so much chance to improve the quality of life
that to basically close the box and say,
"You know what, we're not going to look
"at all this information, we're not going to collect it,"
that's not practical.
What we're going to have to do is think as a community.
- We have cultures that have never been in dialogue
with more than a hundred or 200 or 400 people
now connected to three billion.
(mellow music)
The phone is the on-ramp to the information network.
Once you're on the information network,
you're in, everybody's in.
- Billions and billions of people
who have been excluded from the discussion,
who couldn't afford to step into the world
of being connected,
step into the world of information,
step into the world of being able to learn things
they could never learn are suddenly on the network.
- [Voiceover] The world of the Internet,
from an innovation perspective,
is push innovation out of large institutions
to people on the edges.
- [Voiceover] I suspect as we equip these next billion
consumers with these devices that connect them
with the rest of the world and with the Internet,
we'll have a lot to learn about how they use them.
- All of these people in these countries
are now connecting with each other,
sharing data about prices of crops, prices of parts.
The Africans are talking to the Chinese
who are talking to the Indians
and the world is connected in its nooks and crannies.
- The person that is in Rwanda
that has their first phone that now has access
to an education system
that they never could have dreamed of before
can start finding solutions
for his or her little town,
his or her village.
- Once we have that ability to connect people
and they are able to be connected,
there's gonna be some genius, you know,
in some remote location who would never
have been discovered, who would never have had
the capability to get to the education,
to get to the resources that he or she needs and...
that young woman is going to change the world
rather than just changing her village.
- The idea that that genius will be able to find
his or her way into the greater culture
through the tiny, little two-by-two window
of a feature phone is very exciting.
- A billion people in India, a billion people in China,
you're talking, you know,
500 million to a billion in Africa.
Suddenly the world has a lot more minds
connected in the simplest, least expensive possible way
to make the world better.
- So you look at the agricultural revolution
and the Industrial Revolution.
Is the Internet and then the data revolution
associated with it of that scale?
It's certainly possible.
- I don't think there's any question that
we're at a moment in human history
that we will look back on in 50 or a hundred years
and say right around 2000 or so it all changed.
And I do think we will date
before the explosion of data and after.
I don't think it's an issue of climate change
or health or jobs, I think it's all issues.
Everything has information at its core, everything.
So if information matters, then reorganizing
the entire information network of the planet
is like wiring up the brain of a two-year-old child.
Suddenly that child can talk
and think and act and behave, right.
The world is wiring up a cerebral cortex, if you will,
of billions of connected elements
that are going to exchange billions of ideas,
billions of points of knowledge,
and billions of ways of working together.
- Together, there becomes an enormous wave of change
and that wave of change is going to take us
in directions that we can't begin to imagine.
- The ability to turn that data into actionable insight
is what computers are very good at,
the ability to take action is what we're really good at
and I think it's really important to separate those two
because people conflate them and get scared
and think the computers are taking over.
The computers are this extraordinary tool
that we have at our disposal to accelerate our ability
to solve the problems that, frankly,
we've gotten ourselves into.
- I am fundamentally optimistic,
but I'm not blindly, foolishly optimistic.
You got to remember, the financial crisis was brought to us
by big data people as well because
they weren't actually thinking very hard
about how do they create value for the world.
They were just thinking about
how do they create value for themselves.
You know, we have a fair amount of literature,
a fair amount of understanding that if you take
more out of the ecosystem than you put back in,
the whole thing breaks down.
That's why I think we have to actually earn our future.
We can't just sort of pat ourselves on the back
and think it's just going to fall into our laps.
We have to care about what kind of future we're making
and we have to invest in that future
and we have to make the right choices.
- It is, to me, paramount that a culture understands,
our culture understands
that we must take this data thing as ours,
that we are the platform for it,
humans, individuals are the platform for it,
that it is not something done to us,
but rather it is ours to do with something as we wish.
When I was young, we landed on the moon
and so the future to me meant going further than that.
We looked outward.
Today, I think there's a new energy around
the future and it has much more to do
with looking at where we are now
and the globe we stand on
and solving for that.
The tools that are in our hands now
are going to allow us to do that.
Now it's like no wait a minute, this is our place
and we're going to figure out how to make it blossom.
(dramatic music)
(mid tempo orchestral music)