Original subtitles

(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)