Connected: The Hidden Science of Everything (2020) s01e01 Episode Script

1
[Nasser] He's still this way.
Monkey's this way.
[boy] Why monkey is monkey is this way?
I don't know.
-No.
-Okay, let's look at a different one.
Now the monkey is [gasps]
What's the monkey doing?
I think it's nap time.
I'm going to wrap you up
like a sushi roll, okay?
-No!
-Yeah.
[Nasser]
I love putting my two-year-old to bed,
but it's also kind of strange.
Goodnight, bud.
It's not dark.
[Nasser] I'm leaving him there,
behind bars,
in solitary confinement,
under closed-circuit surveillance.
And then I watch.
It used to be out of concern,
but now it's mostly out of curiosity.
It's become the reality TV show
I can't stop watching.
[boy] Is the monitor going to talk?
What's the camera going to say?
This is an episode about surveillance,
but not in the way you think.
[Nasser] It's an episode about watching.
Watching people, watching animals,
and the technologies we use to do so.
And about how much all this watching
can really tell us.
An incredible amount,
but also a scary amount
about stuff we didn't even know
we were looking for:
the taste of bacon,
songbirds who can see the future,
forty thousands selfies,
and my cousin Nadim's love life.
Turns out, watching is so fundamental
that our survival as a species
depends on it.
[Nasser laughs]
Whoa!
[static]
[beeping]
[upbeat music playing]
[birds chirping]
[soft music playing]
I'm Latif Nasser,
and this is a show about
the astonishing connections all around us.
Connections between you and me
and our world
that'll make you see that world
in a whole new way.
It's kind of cliché and obvious to say,
but it smells so nice out here.
[Hecksher] Okay, so the nets are clear.
We're just going to try to make this
less visible to the birds.
So, the whole idea is that the birds
are flying through the forest,
they can't see the net, they hit the net
and they fall down into this shelf.
I can barely see the net,
and I'm standing right in front of it.
[Nasser] To study the creature he studies,
bird scientist Christopher Heckscher
has to be tricky.
They're called veeries.
And they're "veery" elusive.
[Nasser whispers] Alright.
-So I'm going to try to bring the bird in.
-Okay.
Play the song of the veery.
[bird chirping]
You ever switch it up
and just try the marimba ringtone?
[Christopher laughs]
[playful music playing]
-[researcher] We've got a veery.
-Awesome. We got one.
We got it.
-We got a veery.
-[researcher] Yeah.
[Nasser] It looks plain, right?
Like some ordinary brown bird.
But it's not.
It's like watching surgery or something.
-Nice.
-This bird has secrets.
Well, for several years,
I was out here studying them,
and wondering,
"Where do they go in the winter?"
-So nobody knew that?
-No, we didn't. We really didn't know.
[Nasser] Chris knew they were headed south
but he wanted to know exactly where.
And that's where surveillance comes in.
Chris and his team
made tiny GPS tracker backpacks,
weighing no more
than a couple of paperclips.
[Hecksher] Let's take him to the net.
-[researcher] Here?
-[Hecksher] Yup.
[Hecksher] Okay.
[Nasser] And then,
they sent the veeries on their way.
And what happened? What did you find out?
[Hecksher] With the GPS technology,
we were able to pinpoint
within ten meters,
where the bird's spending its time.
So, you can see the exact tree
where that bird was
when that GPS point was taken.
[Nasser] And here's what's wild:
that tree, that the veery ended up in,
is 4,000 miles away
in Brazil.
And the most remarkable thing
is they're leaving Delaware,
going down to the southern Amazon basin,
and they're flying all the way back,
and I've even found the same bird
singing in the same tree.
-Wow.
-It's mind-blowing.
[Nasser] But as Chris solved
this one mystery, he noticed another.
Each year, the veeries were nesting
and leaving Delaware at different times.
So, some birds will shorten
their nesting season in some years.
Sometimes they'll stop nesting
at the end of June.
-Okay.
-Other years they'll go into mid-July.
But that's presumably
a big difference for
Yeah, it's a big difference, um--
-running an ultra-marathon or something?
-Right.
[Nasser] The earlier they left,
the less chance they had
of successfully raising chicks,
which seemed like a big price to pay.
So, I was trying to think
what would be advantageous
of shortening your nesting season?
[Nasser] Chris wondered:
Was it about the availability of food?
Or when predators showed up?
But none of his theories matched the data.
Then he thought of something that felt
almost too absurd to say out loud.
[Hecksher] These birds are crossing
the Gulf of Mexico
at the peak of hurricane season,
so they've got to worry about hurricanes,
and they've got to worry about storms.
You could be halfway across the gulf
and you could run into a hurricane,
you're in trouble.
[Nasser] What if the veeries
changed when they left,
based on how bad
the hurricane season was gonna be?
So Chris looked back over
nearly 20 years of data,
put the veery timelines
next to the hurricane timelines,
and it looked like they matched up.
The length of the nesting season
in individuals
is correlated
with tropical storm activity.
[Nasser] It seemed like the earlier
the veeries left in the summer
the worse the hurricane season
would be that fall.
-That is crazy.
-It is crazy.
For them to be able to nest
at a certain time
because they are anticipating, uh,
bad weather.
Like they would have to know that
ahead of time somehow. That seems
It seems incredible.
It just seems impossible.
[Nasser] So in the summer of 2018,
Chris decided to test
his hypothesis out for real.
He would try to predict the severity
of the next hurricane season
using only his data
about when the veeries left Delaware.
The meteorologists were calling for
a below average hurricane season.
[Nasser] But Chris's veery data
seemed to say something else.
The birds left Delaware early.
-Based on what I was seeing out here
-Yeah.
watching these birds,
the data showed me
that we were going to have
a bad hurricane season in 2018.
So, they were in a hurry.
Yeah, to get done breeding fast
and get to South America.
[Nasser] So, it was a showdown.
Weather forecasters
on Team Not-so-hurricaney,
veeries on Team Extra-hurricaney.
[bell dings]
So, June happens, no hurricanes.
July happens
Like, walk me through the months.
Yeah. So, July happens, August happens,
still looks like
the meteorological models, most of them,
were still calling
for a below average season in September.
[Nasser] But then
in September and October
there were six hurricanes,
including Category Four Florence
and Category Five Michael.
In the end, 2018 saw
one of the most active hurricane seasons
on record.
[birds chirping]
The veeries actually are as good or better
than meteorological predictions
that have happened over the last 20 years.
[Nasser] Like how could this tiny bird
with this walnut-sized brain
intuit something months in advance
that, you know, a giant network
of scientists and supercomputers
can't figure out days in advance?
I have no idea how they do that.
No idea how they do that.
Clearly, they're cued in to something
that's linked up with the global climate.
It's a real mystery.
[Nasser] Okay, so show me again.
Hold out your hand
with two fingers Yeah, just like that.
I'm gonna slide her in there.
[Nasser] Just by watching,
we can learn way more than we think.
Even invisible things
that we didn't even know
we were looking for.
I think it's really cool that we can learn
from an animal like a bird.
I think it's a great example
of how if we just observe nature,
what can we learn?
It's one thing to see a wild species
migrate across continents,
but what if we could zoom in even further
on captive animals?
Could we understand what's going on
inside of them as individuals?
[rooster crows]
[sheep bleating]
[soft music playing]
I feel like this is
the least halal thing I've ever done.
My parents are going to be very upset
when they see this, but let's go.
-Okay, well, we'll do it anyway.
-Okay.
Why not upset parents?
That's what they're there for.
[indistinct chatter]
Step over.
If I open the gate they'll come out.
I'll get in with you to distract them.
-[Nasser] Hi. Hi, there. Hello.
-[pigs snorting]
-[Baxter] So, pigs are pretty inquisitive.
-Yeah, they've set up a perimeter here.
[Baxter] Yeah.
[laughs] They're sussing you out.
[Nasser] Sniffing, yeah.
Whoa. Hey, there.
Did that guy try to bite me?
[Baxter] Yeah. Not an intentional bite.
They just like to taste.
-There's a kind of irony there.
-[chuckles]
[Nasser] This may look and smell
like a typical pig farm,
but it's oh so much more.
It's a test farm,
and Emma Baxter is a researcher here.
Emma is the first person to tell you
that the livestock industry
has its problems,
which is why she's trying to find ways
to make it work better,
both for the farmers and the pigs.
An example: on giant farms
with thousands of pigs,
farmers can't give those pigs
individualized treatment
because it's so hard to track
which pig is which.
At the moment,
the standard way to do that is ear tags.
[Baxter] When you're tagging an animal,
you're causing a mutilation, right?
You're putting a hole in its ear.
And so, it would be great
if you could not have to do anything
to that animal.
Oh. Hey. Ow. I got a little bite.
I got some teeth there.
[Baxter] We're gotta get out of this pen.
[Nasser] Emma and her crew are testing out
an ingenious way to tell who's who.
So, we adapted a drinker,
and then we put
a very basic web cam behind that,
connected that to some specific software
that was run on motion detect,
and just get thousands and thousands
of images each day,
taking a picture of the pigs' faces.
So, every time any of them drink,
you're getting a picture.
Yeah, and you're capturing it
on this machine.
[Nasser] That's right.
It's facial recognition.
Piggy facial recognition.
That's a pretty good shot
you're getting there.
[Baxter] Yeah, it's pretty good.
Perfect selfie there.
[Nasser] And it works.
I mean, it really works.
She is able to identify the right pig
97 percent of the time.
She could literally tell the difference
between the Three Little Pigs
by the hairs on their chinny chin chins.
-It'll just capture that now?
-Yeah, it's just taking those as stills.
-It did it already?
-Yeah, it did.
So, it's taking several images.
It'll learn, as the animal grows,
that its face will change,
and that will make it a far more powerful
tool than us, actually.
So, in a way,
it's not just a way to identify them,
it's also a way to track them.
-Yeah, to track them, actually. Yes.
-Oh, interesting.
[Nasser] And tracking these pigs' faces
means you can watch them
over the course of their whole lives:
see how much attention
their mom gives them,
who their friends are.
[Emma] But also, you could then attach
its welfare experience,
its health experience.
That would be the next step.
So, that's part
of our next program of work
to look at facial expressions
and measuring the welfare.
Wait. Facial expressions?
If it works, it'd be cool. Pretty cool.
[chuckles]
She doesn't want to just recognize
the pigs' faces.
She wants to read them.
How would you say you're feeling today?
Happy?
Angry?
Intrigued?
[imitates pig snort]
[Nasser] Now, this is
where Mel Smith comes in.
His background
has nothing to do with pigs:
he's a professor of machine vision.
[pig snorts]
We'd spent quite a lot of time
looking at bathroom tiles, floor tiles,
to look for defects
in these manufactured surfaces.
And we developed
a technique able to do that.
Then, we started thinking,
"Well, what else could we use it for?"
[Nasser] So he tried pig faces?
[Smith] They are quite expressive,
and they communicate with each other,
so that would seem to indicate
that maybe they're using facial expression
for communication.
Yeah. The way we are.
The way we are, yeah.
In exactly the same way we are.
So, for things like being fearful,
being in pain,
if the animal's having its tail docked
or being castrated,
then you'll have a pained face,
and that's well-known.
What does the pained face look like?
It's like a grimace.
It's very much like a human face,
so eyes shut and looking as though
it's not enjoying the experience.
[Nasser] This is a happy piglet.
This is a piglet in pain.
Or wait, is this the one in pain?
Anyway. That's exactly
what Mel's trying to figure out.
What we're trying to do,
to be blunt about it,
is we're detecting the expression,
and then we're inferring
that the expression
is related to their mood.
Is it stressed? Is it fearful?
Is it in pain?
If we can do that sort of thing,
then it's going to be really useful.
It's like you want to read
the inner emotional life of the pig.
-Yeah.
-Off of their face.
[Smith]
We're trying to be like the pig, really.
So, the pig has its expression.
It's just that we're not very good
at recognizing it.
And the computer maybe can.
A computer can learn better
how to see like a pig than we can.
Yeah. That's right, yeah.
[pig snorting]
[Nasser] Is that mud
or poop that she rolled in?
[Baxter] It's kind of poop, yeah.
[chuckles]
I'm not going to lie to you. [chuckles]
-She is big.
-She's a big lady. Yup.
Come on then.
[Nasser] Yeah. How's that?
-[pig snorts]
-[Baxter chuckles]
So, you can see
this an ideal shot for the pig.
[Nasser] Yeah.
Why, to you, do you think
this is a worthwhile thing to do?
If we could use automation
to identify individuals
and know about individuals,
how individuals are feeling,
it seems that we could create
a better life for the animal,
improve its welfare.
Hmm, it's more like a
almost a humanitarian
piganitarian
Also, there's a commercial side as well.
If the animal's in pain and suffering,
it's likely not to be so productive,
so from a purely commercial sense,
it makes sense to be able to identify
these things early on.
[Nasser] Imagine if,
when you went to buy pork,
it wouldn't just say "free range"
or "no hormones,"
it'd also say, "certified happy pig."
Happy, healthy animals, generally,
are more efficient animals.
Weird question:
can you tell by the taste of it 
if it's, like, a healthier, happier pig?
[chuckles] There is work to show
that higher welfare standards
produce better tasting meat.
-Really?
-Yeah.
It's so weird that you can
It's like you're tasting happiness.
-[chuckles] Well, potentially.
-That's so funny.
[Nasser] And it's not just pigs.
Other scientists are working on cow faces
and sheep faces.
-Even chicken body language.
-[chicken clucking]
Between the facial recognition
and the pigs,
it makes me think of George Orwell,
between Animal Farm
and Nineteen Eighty-Four.
It's like somehow, we're in
a mash-up of those two novels.
[Smith] I tend to think that as well,
actually.
[Nasser]
It's George Orwell's worst nightmare:
Pig Brother is watching.
[Mel] In theory, rather like Big Brother,
this system could be
looking at the animals all the time.
But it's looking at them, hopefully,
in a caring way.
[Nasser] In a way, Mel and Emma's
facial recognition software
feels less like Big Brother
and more like baby brother:
still learning how to recognize faces
and tell them apart.
It's getting really good at it,
but they want to train it
to be even better.
And the only way to train
these kinds of algorithms
is by showing them thousands of portraits.
But not necessarily pig portraits.
The facial recognition software
that was being used on the pigs,
it wasn't made for pigs.
It was made for us.
Most of the cameras around us,
at the mall, at the airport,
walking down the street,
they're in that same toddler phase.
They're learning how to watch us.
What, I can't get like a happiness or a
Wrongdoer-offender?
That feels, like, judgey.
[suspenseful music playing]
[Nasser] I'm at an exhibition in Milan
where the walls are papered
with thousands of portraits.
These are some of the faces
that we're using
to train our cameras to recognize us.
You know how they say,
"You don't want to see
what goes into the sausage"?
Well, I'm about to get an up-close look
at the ingredients that go into
the sausage of facial recognition.
[Nasser] Alright.
Okay, where should we start?
Well, this is always
one of my favorite places to start.
[Nasser] My guide is Kate Crawford,
an expert on artificial intelligence.
Woody Bledsoe was one of the first people
to really start thinking
about segmenting the human face.
[Nasser] Right here
is the origin of facial recognition.
Back in the '60s,
a guy named Woody Bledsoe
was trying to figure out
how to turn a face,
with all its curves and details,
into a set of numbers.
His team would physically measure
key features.
You can see what he's doing
is really looking at distances
between, say, the eyes
and the nose bridge and the mouth.
-Like right here, this measurement.
-Exactly like that.
[Nasser]
Who was Woody making this for again?
[Kate] He's making this for the CIA.
Way back in the '60s,
they were doing that?
-1963, that's right.
-Huh.
Even though all this work dates back
to a time before artificial intelligence,
when computers were the size of rooms,
Bledsoe's work
remains classified to this day.
These images
were uploaded to the internet anonymously.
Wow. Weird. So weird.
[Kate] This is how we train
artificial intelligence.
[Nasser] Man.
In the early days,
scientists trying to create
facial recognition systems
relied on mugshots.
But then, in the early 2000s,
that started to change.
-[Nasser] Whoa.
-[Crawford] Welcome to the internet.
-[in a vibrating voice] Whoa.
-[laughs]
Everything changes from here.
[Nasser] The internet was like a
never-ending sushi conveyor belt of faces.
And researchers could gather
thousands of them at a time
and clump them into giant data sets.
This is, basically, you know,
lots of politicians, lots of celebrities.
It was images
of the notable and newsworthy.
-Because they have lots of pictures.
-Because they have lots of pictures.
It really was a standardized benchmark
to test new and emerging
technical systems.
[Nasser] This exact collection,
called "Labeled Faces in the Wild,"
was the one used to train
the pig-recognition cameras
on that Scottish farm.
Similar data sets make it possible
for Facebook to automatically
tag your friends in photos,
for you to unlock your iPhone,
and for Taylor Swift
to track stalkers at her concerts.
And it's more
than just about recognizing faces.
Like with the pigs,
it's also about reading emotions.
Why would this help anybody
to be able to look at someone and say,
"You're happy" or "You're sad"?
Well, interestingly this really began,
again, with funding from the military.
There's a long tradition
of defense agencies
wanting to know more about ourselves
than we might want to reveal.
You can see how people
in intelligence communities
-would want to know that as well.
-Uh.
However, it's not just the military
using this now.
Of course, we've got lots of corporations
that want to use these systems.
Are there examples of private companies
that are watching their employees
with facial recognition?
Yeah. For example, they will be tracking
a camera on your screen at work.
And then if you're looking distracted
or not looking engaged enough,
well, "Maybe you're not really
one of the top performers of our company,
and maybe
you shouldn't stick around."
It does feel like a tool that
Yeah, that sides with the powerful,
because they're the ones with the cameras,
and they're the ones
with the computer programs.
[Kate] And they're the ones who decide
what all of these images mean.
[Nasser] Now, these systems
still have a lot to learn.
For example, they're notoriously bad
at recognizing people of color.
Despite that, they can take
a turn for the dystopian pretty quickly.
In China,
the authorities have drawn major criticism
from the international media
over using facial recognition
to track the Uighurs,
a largely Muslim minority.
[reporter] Authorities there
are building a surveillance state
to be able to track their every move,
including CCTV cameras
that rely on facial recognition software.
[Nasser] And the Chinese government
is exporting its surveillance technologies
to over a dozen other nations,
including some
with histories of human rights abuses.
By doing so,
China gets more and different faces
to train their systems.
In the US,
more than 600 law enforcement agencies
use an app called Clearview
to ID people based on three billion images
scraped from the web.
Just by taking a picture
of someone on the street,
they can know their name,
address, and more.
[Kate] In the vast majority of cases,
people have no idea,
in terms of how the system is built,
whether or not they're actually
being included in these systems,
and how they might
actually impact their lives.
[Nasser] And tech companies
and governments continue to hoover up
countless images of faces
from the internet.
Ones we put there,
through apps like Instagram.
[Kate] You start to see vast numbers,
millions and millions of selfies
that have been captured, extracted,
and classified in sets like this one.
What can you find out about a person,
just by looking at their selfies?
Are they wearing clothes
that are expensive,
or are these clothes more affordable?
And what can we, perhaps, surmise
about how much money they earn?
So, these people
are just people all over the world,
who took selfies
and put them on Instagram.
What you're seeing is really the emergence
of vast-scale scraping of people's images
without any consent or any awareness
that their images were going to be used
for things like facial recognition.
[Nasser] If you have ever taken a selfie
and put it online,
your face may be
in one of these data sets,
and you don't even know it.
[Nasser] It's weird, right?
All these similar technologies
feel so vastly different.
Looking over a bird's shoulder
and learning what it knows
feels totally fine.
Watching a pig and getting in its head
feels probably right, but a little creepy.
Our governments and giant corporations
watching and profiling us
feels downright sinister.
But then weirdly,
when we offer up our selfies
and watch each other,
which feels great by the way,
we're just helping
those giant corporations and governments
watch us even harder.
All those feelings
are about to get even more tangled,
because millions of those photos
come from a realm
where many of us
are at our most vulnerable.
They come
from dating apps.
[Nasser] Oh, you look gorgeous, Nadim.
It's the webcam quality.
Oh yeah. It's really flattering.
[Nasser] Despite being long out of the
dating game, I've volunteered my services
to help my cousin Nadim
set up a new profile.
Okay, let's set up Match.
Okay, show me your photos.
Let's see what we have to work with here.
Dude, you're on a motorcycle.
You do not have a motorcycle.
We're definitely not using
that motorcycle picture.
Oh, this is terrible. This is terrible.
We're not using that. Keep going.
Don't zoom in
on the picture of the two of us, okay?
[chuckles] Oh, God. You're killing me.
You're killing me. Okay.
Okay, this is great! That's totally you.
Well, also, it's funny
that your arm is out
'cause the bird should be on your arm,
but it's on your shoulder.
This is an argument for the photo,
not against.
Okay, so let's move onto the text stuff.
Okay. Like, to me,
it's, like, nerdy, silly, you travel
Oh, you wrote "needy" instead of "nerdy."
That's probably
the exactly wrong way to go.
[Nadim chuckles] Something like,
I was interviewed when I was in Bishkek,
and, like, they made me into some sort
of eligible bachelor of--
Oh, my God, that's what you got to write.
Was voted most eligible bachelor
of Bishkek in 2013 or whenever it was.
-Do people even know where Bishkek is?
-It doesn't matter.
-I knew we'd get to something eventually.
-[laughs]
-And then Kyrgyzstan flag.
-[chuckles] Yeah, okay. Great.
Okay, but I think that's good.
Now it's just you have homework,
which is to swipe yourself silly.
Fantastic. Fantastic. That's easy.
-Let's see what happens.
-Yeah.
Okay. I love you.
-I love you. Bye.
-Bye.
Alright.
Ba-doom.
Nadim seems to be making
a rational exchange:
reveal information about himself,
in return for a chance at love.
But there's something he doesn't realize:
these dating apps are watching him.
And they aren't just seeing
the stuff he voluntarily shares
as part of his profile.
They can see way more than that.
[eerie music playing]
When did you start using Tinder?
It was after a breakup.
I decided to go on Tinder
like everybody does after a breakup.
Okay.
At the beginning,
I was in love with Tinder, you know,
because I'm not so confident
when it comes to love
or seductions or everything.
And I just had so many matches,
and so many men
wanted to connect with me or meet with me.
[Nasser] Judith Duportail is a journalist
based in the City of Love.
Like millions of others, she hoped
Tinder would help her find a match.
Instead,
she found something entirely different,
something virtually no one else on Tinder
even thought to look for.
-So, you're this active user of Tinder.
-Yes. Yes.
It's just sort of going on
in the background of your life.
And then how does this idea
even come to you,
that, "Oh, I want to see
what they have on me,
and that's even possible"?
[Duportail] One day, I read an article,
and I learned that we all have
a secret desirability score on Tinder,
uh to rate your attractiveness
and make you match with people
from the same league as you.
If somebody that is
with a very high desirability likes me,
I earn a lot of points.
But if somebody not so beautiful
rejects me, I lose points.
-Wow. Weird.
-Yeah.
When I read that article,
I started to feel like boiling inside.
[dance music playing]
It would be like if I would go to a party,
and people considered too good-looking
for me or too ugly for me,
I would not even be able to see them.
[Nasser] Is that because they would match
people's desirability scores?
[Duportail] Yes, exactly. They would match
people according to their desirability.
And everything that's unique about you,
like the sound of your voice, your humor,
maybe you have dogs on your shirt,
stuff like that,
just doesn't matter anymore.
It's just based on
a definition of attractiveness
that you don't even know about.
[Nasser] Judith decided
she did want to know:
she wanted to know
what her attractiveness score was.
So she called up Tinder.
And then they would say what?
They would say,
"That's our intellectual property."
-But it's about you.
-Yeah, I know. I know. I know.
Then how did you end up getting it?
-I never actually got my score.
-Oh.
But I found out much more.
[Nasser] After six months of emails
and calls, eventually
-They replied to me with my personal data.
-Okay.
Yeah, that's an 800-page file.
-Eight hundred pages?
-Yes. Yes.
-Oh, my God.
-[Duportail] Wait. That's not it.
-Oh, my God, there's more!
-[laughs]
Yeah. Can you believe it?
-This is just about you?
-Just about me and my crappy dating life.
-Your love life.
-Yeah. [chuckles]
[Nasser] The file contains all of Judith's
personal details, like age and education,
but also all her Tinder swipes
and conversations.
But Tinder doesn't just have
her Tinder data.
They also have her Facebook likes,
her Instagram posts
Yeah, all your apps
are talking to each other about you
behind your back.
Like, you kind of have a sense,
but you don't realize
that you're this exposed.
Yeah, yeah, yeah, exactly.
[Nasser] Based on her investigation
of Tinder, Judith wrote a book.
Just a few days before it came out,
Tinder announced
they phased out the desirability score.
But they still haven't disputed
what she wrote
about how they collect all that data.
Yeah, so what were the kinds of things
they learned about you?
Of course, there are some sexual things
I Oh, this is like It gets real
It gets real
[chuckles] Maybe we shouldn't.
But it's not the most shameful thing
in the end.
The most shameful thing for me is
they can know exactly when I feel lonely.
[Nasser] Judith's file recorded the times
when she'd been most actively swiping,
searching for love,
or at least a connection.
[Duportail] I remember vividly,
there was one morning
I was coming back from a New Year's party,
and I remember I was feeling a bit lonely,
being like, "I don't have a boyfriend,
and it's a new year starting."
-Yeah.
-And when I received
all my conversations from Tinder,
I had to realize that this precise night
when I came back,
-I was talking to 16 guys simultaneously.
-Wow!
You know, trying desperately,
like, to create something.
And I was using the same line
as an opener.
And when you see it written 16 times, 
-one time after the other, you know?
-Oh, my God!
So, it's like they now have
this almost direct pipeline
into your brain
-Yeah.
-to see when you're feeling vulnerable.
Yeah. And to maybe use it against you
in a way, you know?
[Nasser] It's a trade-off. Theoretically,
we're all signing the waiver.
We all know that this is happening.
They're getting stuff from us,
but we're getting stuff from them.
-I agree.
-Isn't it just a fair deal?
I agree,
but, for me, it would be a fair deal
if I was really knowing
what I was signing for.
Then, for me, it's your choice.
Now having seen this,
what was or what is the cost to you?
It's like, my secrets.
Uh. My most intimate secrets.
Yeah, and for me
that has a huge value, yeah.
Yeah.
-[Nasser] So, are you still on Tinder?
-No, I don't use dating apps anymore.
Sometimes, I relapse. I go look.
I have to be honest.
Wait. So, you said you stopped using it,
but did you delete it from your phone?
Yes, I deleted it from my phone,
but sometimes I reactivate my account.
You re-download it every time?
Yeah. I take a peek
and then I'm like, "Oh, no, not again."
-And I delete them again.
-That's funny.
-Wow, the pull is so strong.
-So strong.
[Nasser] Even Judith, who knows far better
than the rest of us what she's giving up,
even she can't quit the app.
And my cousin Nadim too.
I told him all about how much data
these apps actually keep about him,
but he was totally undeterred.
I don't care how the strangers
in the Tinder office think about me.
There's this thing
called the privacy paradox,
and the idea is
that we all say we value privacy,
but then virtually everything we do online
seems to contradict that.
Like imagine there was some shadowy figure
that was, like, following you around
and taking notes on everything you did.
That would be sketchy as hell.
But if you yourself went online
and divulged all of that same information,
that doesn't [chuckles]
seem strange at all.
So, what if we just took all this
to the extreme,
went to another matchmaking site,
with even higher stakes,
where there's no privacy whatsoever?
And that's why I'm here,
at the zoo,
to learn about another dating app
that you have definitely never heard of
and even more definitely
never been on yourself.
The Living Desert Zoo
recently celebrated a birthday.
Actually, six birthdays.
Six new African wild dog pups.
Can you tell which ones are the parents?
Uh, one of the males, I think,
standing up in the sun back there.
-Oh, that big dog over there?
-Yup.
-And this is Mom right here.
-Oh, that's Mom.
Yeah, Beatrix and Kiraka are Mom and Dad.
And then the smaller ones that aren't
so small anymore are the six puppies.
[Nasser] When a new animal's born
at your local zoo,
there's very little natural about it.
Who mates with who is decided for them,
by these folks.
My job as the studbook keeper
is to keep track of all the individuals.
Wait, what is a studbook?
Studbook sounds like a dating app,
basically.
Essentially, it is.
A studbook is basically a large database
where we keep track of every single animal
that is in human care.
Who's housed with who,
who is housed where
Yeah, so matchmaking
is kind of the other half of that.
So, the data gets collected in ZIMS,
Zoological Information Management System.
[Nasser] ZIMS is basically
like Tinder for critters.
For ten million of them,
at over a thousand zoos around the world.
Bears, bats, even beetles are on there.
Each of those critters has a file
not unlike Judith's.
But instead of their Facebook likes
and Instagram posts,
the data on them comes from zookeepers.
Initially, it was kind of simple things
like average weights and stuff like that,
and it's just grown and grown and grown.
-Yeah.
-So, think of every animal's runny nose,
every animal either enjoying
or not enjoying
the enrichment they were given that day.
[Nasser] Today ZIMS includes
everything from what the animals eat,
to who they interact with,
how often they poop,
even how in the mood they are
to get down and dirty.
And then zookeepers
look through these profiles,
factoring in age, genetics, location.
And you pick breeding pairs from that.
[Nasser] But ZIMS is
the highest-stakes dating app of all,
because some of these
are endangered species,
on the brink of oblivion.
And if a species dies out,
there can be all kinds
of unexpected consequences.
Look what happened when wolves
and a few other predators
were killed off in Yellowstone
in the 1930s.
[elk squeals]
We started seeing elk and deer
actually starving to death
because their populations grew so much,
they were eating themselves
out of house and home.
[Nasser] The booming numbers of deer
and elk overgrazed the park's young trees.
We started losing forests.
Songbirds went away
'cause they didn't have a place to nest.
We were having erosion because the roots
weren't holding the soil in.
-Our rivers changed courses.
-No.
[Nasser] All of it connected
to losing just a few species.
But captive breeding programs
have successfully brought
many species back from the brink,
including the Mexican gray wolf,
the Arabian oryx
and the California condor.
And that's why Brigid
is using the ZIMS dating app
to match a pair
of slender-horned gazelles.
There's no more
than a few hundred left in the wild,
and their numbers are going down.
If zoos like this one
can't breed a new generation,
the species could disappear forever.
So, what happens? How does it exactly go?
[Randale] So, what we have here
is we've got our two females.
We're introducing our male to them.
They're resident females here,
so they know the lay of the land.
-So the male--
-[Nasser] Oh
-[Randale] You see him pacing back there?
-[Nasser] Yeah.
He knows that there's something cool
out here. He wants to be out here.
[Nasser] How long are they in fence mode?
Slender-horned gazelle
are typically pretty easy going,
so usually these introductions
go decently well.
But you really rely
on your observations of the animal.
So, our keeper has let our male out,
and so he's going to check out the yard
and see that he's now got
physical access to the females.
Oh, wow.
Okay, let's see what happens here.
[Nasser] Wow, I'm shocked that they're
not just way more excited to see him.
[Randale]
We never know how it's going to go
when we give physical contact
to the animals.
Oh, is there some bum sniffing
about to happen?
Yeah!
[Randale] So, he's checking her out,
which is what we definitely want to see.
He's trying to find
as much information as he can about her.
[Nasser] Because for all the work
Brigid has done 
to bring the gazelles together,
in the end, like for Tinder,
it's up to the individuals themselves
to size each other up,
and possibly make a connection.
So, you never know what could crop up,
but we cross that bridge
when we get to it.
Right now we're gonna have fingers crossed
that everything just goes super smooth.
[Nasser] The future of the species
could depend on it.
Animals, including us,
have been watching each other,
to see who's a friend or who's a foe
or who's a mate or who's a threat
since the dawn of time.
It's more than just a yearning
for connection,
it's a baseline survival instinct.
We are born watchers.
But all this newfangled technology
has thrown this into hyperdrive.
It's all so seductive
that we've neglected to protect
a lot of what makes us
singular and special:
our faces, our desires,
-our vulnerabilities.
-[Judith laughs]
And there's a paradox there too.
We're giving governments
and tech companies
more of our secrets than ever before.
But in exchange, we're making
more connections than we ever have before.
It's all so new, that we still don't know
if the trade-off is worth it.
Maybe one day, when we're
on the brink of extinction ourselves,
we'll have to surrender all our privacy
like the gazelles.
But that hasn't happened yet,
and it's our job to make sure
it doesn't happen.
In the meantime,
we watch animals, they watch us,
and we all watch each other.
Right, Ghazi?
That's it.
That's the end of the monologue.
[upbeat music playing]