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Zamorodi and today on the show

0:59

what's in a face? Do

1:02

you have any idea how long a face will

1:04

stay in your mind? Like once it's

1:06

in there is it there forever? I actually

1:09

don't know but I

1:11

mean as an example I have

1:13

come across faces that I remember

1:15

seeing from when I was younger

1:18

than 10. So. Oh wow. Yeah

1:20

so I have come across like

1:22

school teachers or classmates or people

1:24

I remember in my neighborhood

1:26

when I lived there as a child and

1:29

it's a lot harder to explain where I've seen

1:31

them. Oh so you're saying it's more

1:33

just a sense of familiarity? Yeah

1:36

and it's almost like this intuition or

1:38

this kind of pain and I know

1:41

like I am so confident I've seen

1:43

that face before. That happens instantly.

1:47

This is Yennie Suh and

1:49

she is what's called a

1:51

super recognizer. So super recognizer

1:53

firstly I find that term

1:56

very cringy. Oh no. Basically

2:01

what it is is super recognises

2:03

people who are maybe on the

2:05

top 1 to 2% who

2:07

are very good at remembering faces

2:10

and I'm told that it's about 80% or so of

2:14

the faces that we see we remember so

2:16

it's very high in comparison to the average

2:19

person. Yenny

2:22

knew she was always good at recognizing

2:24

faces but about five

2:26

years ago she realised that her

2:28

ability was really unusual.

2:30

I just turned on the TV

2:32

and I happened to come across this

2:35

show about people with different abilities

2:38

and I saw this gentleman was

2:41

based in the UK and he was a police

2:43

officer and they made

2:45

him do a test where he was standing

2:47

in a really big train

2:49

station with lots of people going through.

2:52

You've got to try and find my

2:54

four actresses who have hidden themselves away

2:56

in the crowd or be wandering around.

2:59

And he was shown photos of I

3:01

think a handful of different faces and

3:03

he had to pick out the

3:06

people that he'd seen on the photo but the

3:08

trick was they would you know put a wig

3:10

on them or they'd be wearing a different hat

3:12

or glasses. Black leather jacket,

3:14

blue jeans, brilliant.

3:17

And he was able to I think pick them

3:19

all out. When I saw that

3:22

I just got a few spots and I

3:24

just had this really strong confidence that somehow

3:26

I'd be able to do those tests.

3:28

Is it the lady in the black

3:30

jumper cream top and blue jeans? Brilliant.

3:33

Yenny took some tests online and

3:35

she did really well so

3:38

she got in touch with a researcher in

3:40

Australia where she lives who confirmed

3:42

that she was indeed a super

3:44

recognizer. I ended up visiting their

3:46

lab in Sydney and they put

3:48

some sort of sensor detector so

3:50

they saw where my eye movements

3:53

how it worked when I was exposed to

3:55

a face and it's not

3:57

that I pinpoint on one feature. I

4:00

would not focus on the eyes or nose

4:02

or mouth or the shape of the face.

4:04

It's just the whole, the face of

4:06

the whole leaves kind of an imprint

4:08

in my head. So

4:12

have you ever found like your

4:15

ability useful then? Like,

4:18

other than like, you know, fun

4:20

party trick? Yeah, I mean, when

4:23

I was in uni, I worked at

4:25

a clothing store and we I

4:28

ended up catching a shoplifter because we

4:31

had a team meeting and there was

4:33

a particular shoplifter who would repeatedly

4:36

steal the highest priced item

4:38

in the store. And they

4:40

had this CCTV footage of her. It

4:42

was just this really grainy last night

4:44

photo. And they showed it to us

4:46

during our team meeting in the morning and they stuck it

4:48

on the wall and I looked at it and I was

4:50

like, all right, I don't know if I'll be able to catch

4:52

that person. I didn't really think much about it. An

4:56

hour or so into my shift, that exact

4:58

person walked in and I just knew straight

5:00

away it was that person, even though the

5:02

photo was really grainy. I just knew, what

5:04

did you do? We had security

5:06

guards in our store. So I just had to

5:09

we were wearing walkie-talkie type of things. And I

5:11

just told them, yeah, she's here. She's just walked

5:13

into the store. So maybe you guys should go

5:15

have a chat with her. They

5:18

ended up catching her and then they had

5:20

to call the cops in. And so that

5:22

was my one crime fighting experience.

5:27

From what I understand, a lot of

5:29

super recognizers work in or work

5:31

with law enforcement. Is that or

5:34

in some kind of security capacity, is that

5:36

not something that you sort

5:38

of thought, well, you know, I could actually make money

5:40

off of this? I

5:43

mean, at one point, I think

5:45

I did consider it. But I

5:47

think it's still very new. And

5:49

the research in this area is

5:51

still developing. I know countries

5:54

like the UK, like their

5:56

police enforcement have started recruiting

5:59

officers who. have that ability but

6:01

I always thought it was a little

6:03

bit creepy that I don't know if

6:06

creepy is the right word but I

6:08

always thought that you know it would

6:10

be perceived as being a bit like

6:12

I was a stalker or something. Yenny

6:16

sees and processes faces in

6:19

an extraordinary way. The

6:21

technology is quickly passing her

6:24

superhuman abilities. Most

6:26

of us already use facial recognition

6:28

to unlock our phones and tag

6:30

people in photos. Governments,

6:32

law enforcement, and companies can use

6:34

cameras and algorithms to collect

6:36

and identify us. But

6:38

where will we draw the line? Today,

6:41

what's in a face?

6:44

Ideas about the promise and peril

6:46

in turning the human face into

6:49

an everyday digital tool for anyone

6:51

to use. I

6:57

was actually literally just today talking

6:59

to a facial recognition vendor. What

7:02

about? So they're in the middle

7:04

of filing a patent where artificial

7:06

intelligence or machine learning system will

7:08

look at your face and

7:11

determine how you feel. This

7:14

is Bloomberg tech columnist, Parmi

7:16

Olson, which will allow them

7:18

to analyze the faces

7:20

of stock market traders and bond

7:22

traders to get a sense

7:24

of where the market is moving based

7:26

on the emotions shown on the faces

7:29

of these traders. In

7:31

a way that sounds maybe a little bit innocuous, if

7:34

not a very odd way, potentially

7:36

a disastrous way to determine where the market

7:38

is going. I don't know that that would

7:40

work. But I think

7:43

the question is, well, what happens

7:45

when all these different vendors and

7:47

stakeholders have access to our faces

7:49

and can maybe get to a

7:51

point where they want to start

7:54

drawing inferences about

7:56

us based on our faces now,

7:59

even if. making market decisions

8:01

based on the minute facial

8:03

expressions of day traders sounds

8:06

far fetched. Perme says

8:08

the basic technology behind it is

8:10

not. So these

8:12

systems are essentially trained

8:16

on millions and millions of actual

8:18

photos of people and

8:21

the more data it has, the more accurate

8:23

it can get. And

8:26

I think the concern is that

8:28

this technology is so widespread

8:31

and so actually not that difficult

8:33

to build. Some

8:35

of the technology is open source. There

8:38

are billions of images of faces on

8:40

the internet. It's relatively cheap

8:42

to do it. Yeah, and

8:44

it's so cheap that you have written about

8:46

how this software is now used pretty widely

8:49

in retail, even gas

8:51

stations, convenience stores. Yeah,

8:53

I think the main reason

8:56

that retailers want to use

8:58

facial recognition in their shops

9:01

is to actually look for unwanted

9:03

individuals. So

9:05

there was a chain of stores in

9:07

the UK that hired

9:10

a security system, a facial

9:12

recognition security system, to be

9:14

installed. Let's go to

9:16

Alesbury, southern England, and

9:18

to a budget store. Perme Olsen

9:21

continues from the Ted stage. Now,

9:23

Budgens in this particular town has

9:26

been having trouble in the last few

9:28

years with people coming in and stealing

9:31

meat from their refrigeration aisle. So

9:33

a year ago, they installed

9:35

some new technology from a

9:37

company called FaceWatch. And through

9:39

their usual CCTV cameras, FaceWatch's

9:41

computer and software would scan

9:44

every single face that came into

9:46

the budget and match it up against

9:48

a watch list. Now,

9:51

this watch list is processed by

9:53

FaceWatch, and Budgens can also add

9:56

to it if they suspect someone

9:58

of stealing. I called

10:00

up the budget and asked how they thought it

10:02

was working. And the staff member there

10:04

told me that his phone gets

10:06

pinged up to 10 times a day

10:08

with an alert to say

10:11

that someone has walked in the store who

10:13

matches the watch list. So

10:15

if that happens, he might call the police

10:17

if it's an aggressive person or he might

10:19

just say, hey, you're on CCTV. And

10:22

actually it works pretty well, he said. He thinks

10:24

it's helped. But there's

10:26

a few concerns about face watch. So first

10:28

of all, to get

10:30

on the watch list, you don't have to

10:32

be arrested and you don't have to be

10:34

charged by the police. There's no real legal

10:37

due process. And the

10:39

other thing is that to be uploaded onto

10:41

the servers of face watch to be on

10:43

a watch list, you can

10:45

be on it for up to two years and

10:47

you won't be taken off. So

10:51

this is a security company that

10:53

relies on watch lists and

10:56

anyone with clearance, I guess that could

10:58

be a store employee, they could add

11:00

someone to the list and then what,

11:03

that information is shared. Yes,

11:05

that's right. Each store would have their own

11:08

watch list and they would share the watch

11:10

lists with each other. So you'd have an

11:12

even bigger watch list. And

11:14

yes, the people who are on these

11:16

systems, this is a private system. This

11:18

is not something where there's a court

11:20

order or warrant or anything like that

11:22

there. This is totally done privately by

11:24

a business. It's their own private watch

11:26

list that they've put together. So

11:29

for me, one of the underlining problems with

11:32

this kind of mass surveillance is

11:35

that sometimes the algorithms are wrong.

11:38

Right. When I talked to one of

11:40

the people who worked at one of these stores,

11:42

they said that about 25% of the time the

11:44

system was wrong. So

11:46

they would get the alert, get told that person

11:49

had walked in and they'd walk

11:51

around and they'd see actually it wasn't

11:53

that person. And so they had

11:55

to really be careful to trust that

11:57

the system was correct. real

12:00

world when the lighting isn't that good

12:02

and the image might be a little

12:04

bit grainy, not

12:06

surprisingly, the system was getting it

12:09

wrong one out of four times. Wow.

12:11

And I can imagine someone thinking like, okay,

12:13

well, it's a grocery store. But

12:15

if you're talking about a

12:17

situation involving law enforcement, that

12:20

could get quickly escalate, I would think. Yeah.

12:23

So a police officer has

12:26

a body cam with facial recognition

12:28

or a camera on their van

12:30

with facial recognition and they detect

12:32

someone. And if that person has

12:34

increased melanin in their skin or

12:36

they're black, essentially, then it is

12:38

more likely to make

12:40

a mistake in identifying that

12:42

person. And the reason is that the

12:44

database is that these facial recognition models

12:47

are trained on typically

12:49

have way more white

12:51

people than black people.

12:55

And so the system just isn't trained enough

12:57

on black people so it doesn't identify them

12:59

properly and makes more mistakes. And that has

13:01

happened. And it's

13:04

probably going to continue to happen, too. When

13:07

we come back, what are we willing

13:09

to stomach in a face tracking filled

13:11

future? On the

13:13

show today, what's in a face? I'm

13:16

Anoush Zamorodi and you're listening to the TED

13:18

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16:02

I'm Anoush Zamorodi. And on today's

16:04

show, What's in a Face? And

16:07

how companies will use them to make

16:09

money. We were just talking

16:12

to Bloomberg columnist, Parmi Olson. Each of one

16:14

of our faces has a face print and

16:16

a type of fingerprint, except just showing

16:19

the fingers that corresponds

16:21

with the image of our face. And

16:24

there are lots of databases of people's

16:26

face prints out there on the internet.

16:29

And even if they aren't identifying us by

16:31

name, our faces can

16:33

be tracked, categorized, and profited off

16:35

of. For example, a

16:37

few years ago, Walgreens installed cameras in

16:40

some of their stores that

16:42

identified shoppers by age, gender,

16:44

and then displayed targeted ads.

16:47

Yo, no spark? Like, do you look male

16:49

and around 20 years old? I have a

16:51

face of Sprite. Yeah, it's Sprite. And you

16:53

looked in green last day. 50

16:55

and female, maybe a green tea.

16:58

Subaru has a driver monitoring... Car

17:00

maker Subaru's new vehicles use facial

17:02

recognition tech that create user profiles.

17:15

So there is a casino in London

17:18

which has facial recognition cameras dotted around

17:20

all the different rooms. And

17:22

it uses that so that when high

17:24

rollers walk in to a certain room,

17:26

then the staff get an alert on

17:28

their phone, which gets sent to an

17:31

encrypted chat app they use called Wicker.

17:33

And then they notify each other like, oh,

17:36

so-and-so this hostess should go up because that's

17:38

the high roller's favorite. And we know that

17:40

they like this particular type of food and

17:42

this particular type of drink. And so they

17:45

can actually provide a better service and

17:47

they call it their white glove service.

17:50

And I remember asking the head of

17:52

security, well, are the

17:55

patrons actually a little bit put

17:57

off by that? And he said, not at all,

17:59

not a single... woman thinks

18:01

that that's even the slightest bit creepy, they just

18:03

see it as part of the service. It's

18:06

what they expect. And I think it's

18:08

a nice little allegory for just how this

18:10

kind of surveillance is going to eventually

18:12

come to serve the rest of us as

18:15

consumers, that the convenience will ultimately be

18:17

something that we just take

18:19

for granted and we won't worry too

18:21

much about the price that we're paying

18:23

with our privacy. And I

18:25

think that is just the way it'll go. I

18:29

mean, I guess I can see the

18:31

appeal if you're opting into a luxury

18:33

service. I mean, that just feels very

18:35

different than being tracked while you're walking

18:37

down the street or going into a

18:39

store. I just don't think that

18:41

that is something a lot of us would sign up for. Yeah,

18:44

I would say that facial

18:46

recognition definitely has become

18:49

a controversial subject. And I think

18:51

that's made it difficult for brands,

18:54

for advertisers who might be able to

18:56

benefit from using it to target their

18:59

brands at people. They've

19:01

had to take a step back.

19:03

Yeah, there are a number of

19:05

retailers now, I think this is

19:07

what you're referring to, who are

19:09

facing lawsuits for surveillance, for gathering

19:11

data on their customers without consent.

19:15

But really, like for now, it just feels like

19:17

whack-a-mole because we have not figured out as

19:20

a society what we think is okay and

19:22

what isn't okay when it comes to face

19:24

tracking. Yes, that's right. Companies

19:26

who develop these kinds of systems need to be very

19:29

careful. There needs to be more ethical oversight of

19:31

how these systems are developed. And right now, that's

19:34

only going to come from regulation, which is a

19:36

couple of years away, yes, but also campaign groups.

19:38

And there are some really good civil liberties groups

19:41

in the United States and Europe who are really

19:43

keeping an eye on this and just helping keep

19:45

companies on their toes. If there

19:47

wasn't the amount of kind of upset that

19:49

had been created around facial recognition, I think

19:51

there'd be a lot more advertisers using it

19:53

right now. But because people have

19:55

really rung alarm bells about it, then I think

19:58

that's made companies companies really just take stock and

20:00

just sit back and just say, okay, let's just

20:02

be a little bit more cautious about how we

20:04

use this. And I think that's a really good

20:06

thing. Given that

20:08

there still aren't major regulations

20:10

out there around facial recognition,

20:12

I mean, are we just

20:14

at the point of no return here? It is,

20:16

I mean, can't go back. There

20:19

definitely needs to be more laws and

20:21

regulation, but we have sort of gone

20:23

past trying to force

20:25

companies to design algorithms in

20:28

a way that are safe

20:30

and ethical because the algorithm is already

20:32

out there. But there is a law

20:35

coming from the European Union called the

20:38

AI Act, and it actually

20:40

bans all forms of facial

20:42

recognition for surveillance by

20:45

police unless it's for trying

20:47

to combat terrorism. So that's

20:49

a pretty blunt rule.

20:51

And I mean, that's going to be the first

20:53

kind of comprehensive

20:55

legislation around the use

20:58

of artificial intelligence algorithms.

21:00

I think the issue with it is that

21:03

it is so broad. It's not just about

21:05

facial recognition. It's about all forms of AI.

21:07

So whether that's recommendation systems

21:09

on social media or

21:13

facial recognition, it covers a lot. And so

21:15

enforcing it, I think, is going to be

21:17

difficult. You

21:20

know, one of the reasons all this

21:22

tracking is possible is because we have

21:25

accepted the idea that cameras are

21:28

in our pockets all the time. They're on

21:30

our doors. They are all over public spaces.

21:32

And we're okay with

21:34

it. We are okay, largely, with

21:37

being surveilled. Yeah, there's

21:39

something like 20 million homes in the

21:41

US have a video doorbell. The

21:44

thing about ring doorbells that

21:47

I think is really interesting is that actually the studies

21:50

that have been done about just

21:52

how effective these cameras are in

21:55

reducing neighborhood crime show that

21:58

the evidence is really flimsy. There's

22:00

actually not much evidence that they

22:02

do reduce crime, but

22:04

the big impact is on human

22:06

sentiment. So the owners of these

22:08

cameras feel a greater sense of

22:10

security and a greater sense of

22:12

control. But then on the

22:14

other hand, we also collectively come to accept

22:17

that our behavior is being watched. So yeah,

22:19

take that how you will. I think

22:22

we are just an increasingly surveilled society.

22:24

And I think people are just like

22:27

slow boiled frogs. We're increasingly accepting of

22:29

it because it's just what's happening for better

22:31

or worse. That's

22:33

Parmi Olson. She's a tech columnist

22:36

at Bloomberg. And you can see

22:38

her full talk at ted.npr.org. And

22:43

earlier, we heard from super recognizer

22:45

Yeni Seh, who works as a

22:47

translator in Australia. On

22:50

the show today, what's in a face?

22:53

Often, to understand how technology

22:55

will change our lives, we

22:58

just need to watch a movie, like

23:00

this one released in 2021. We're

23:03

going to watch a movie. We're

23:07

going to watch a movie. So

23:10

the champion was a film shot and

23:12

made in Poland. So everyone's speaking, basically,

23:14

Polish or German. This is Mike

23:16

Seymour. He's a researcher at the University

23:19

of Sydney and works in the film

23:21

industry in special effects. And it's

23:23

a great film. It's a true story about one

23:25

of the first members of Auschwitz who was a

23:27

boxer. Terribly moving story, but

23:29

of course, only in Polish or German.

23:32

And usually, when there's a foreign film

23:34

that wants to break into the English-speaking

23:36

market, there are three

23:39

options. Doving it so we

23:41

get somebody else to voice over a different

23:43

piece of dialogue. But of course, the lips

23:45

aren't right, so it looks kind of odd.

23:48

Or there's subtitles. Or we have

23:50

the new version of what we

23:52

call facial reenactment. Facial reenactment.

23:55

It's a new technique that Mike and his

23:57

team used on the champion. We

24:00

got involved as part of a team to convert

24:02

the entire film to English. So

24:06

now if you were to watch the film

24:08

in English, every actor speaks as if they'd

24:10

been shot in English. So

24:17

we've replaced effectively the actors' faces

24:19

with their own faces, saying

24:22

the lines in English. He

24:24

looks more like a small rooster

24:26

black of its feathers than a

24:28

champion. Okay, so Mike, when

24:31

I watch this English version, it's seamless.

24:33

It's like their mouths, their faces,

24:35

everything looks like it was originally

24:38

shot this way in English. Is

24:41

this common in the industry? Is it normal? Well,

24:44

it's the first time anyone's done it in the world, but hopefully it's going to become normal. In

24:46

fact, in the film industry... Months

24:49

after filming, the actors re-recorded

24:51

all their lines in English

24:53

as cameras taped their voices

24:55

and facial expressions. And

24:58

then through a process called neural rendering

25:01

technology, their faces were replaced.

25:04

So it looks like the film was just shot

25:06

twice in two different languages. Come

25:09

on, Jim. Come on. The

25:14

film industry is always pioneering new

25:16

tech to trick our eye, to make

25:19

someone or something look real.

25:22

But over the last few years, Mike has

25:24

been developing ways to use these techniques in

25:27

our real lives. Could

25:29

we take this tech and just sort of use it

25:31

outside the film industry that fascinated me, where I say,

25:33

well, hey, I don't want to get

25:35

shaven and put on a suit for my important

25:37

meeting today. So I'll just flip a switch and

25:39

get digital makeup and I'll look a whole lot

25:41

better and a whole lot smarter. And

25:44

I would be able to say, speak in Korean when I

25:47

absolutely can't speak in Korean. And

25:49

that, we hope, would facilitate much

25:52

more genuine communications across cultural

25:54

divides. Wow. You're

25:57

saying that maybe one day if... I

26:00

have relatives all over the world who speak all

26:02

different languages, but maybe one day we could

26:04

do FaceTime and it would sound

26:06

as though I was speaking fluent

26:09

Swedish and they were speaking back to

26:11

me, well they can speak English, but

26:14

that we would hear each other's native tongue

26:17

and wouldn't know the difference. It would look as though I

26:19

could speak fluent Swedish, but it wouldn't look

26:21

like I was not myself.

26:24

Yes, there is a lot of

26:27

modern technology that's very sophisticated that

26:29

would, we think, benefit from being able to have

26:32

an extra layer of communication

26:34

that you get from face-to-face

26:37

interaction. We're kind of this nexus

26:39

point where that's possible. Mike

26:41

Seymour picks up from the TED stage. We're

26:43

interested in being able to see if

26:45

we can put a face on technology

26:47

because how would you react when

26:49

a computer reacts to you with a smile?

26:52

Would a six-year-old learn

26:54

maths better if there was

26:57

a six-year-old teacher on the screen? What

26:59

about if it was a slightly older version

27:01

of herself? Would a grandparent

27:04

having a cup of tea be more likely to

27:06

check in with a computer system? They

27:09

didn't have to log in and type. They

27:11

could just talk to a virtual agent that

27:14

actually was somebody from their past. This is

27:16

what we're excited to explore with digital humans.

27:19

Our ability to produce digital humans up

27:21

until recently has been quite limited, but

27:23

we're now seeing interactive digital humans starting

27:25

to appear. The doors are

27:27

opening. We are in an inflection

27:30

point. We have this perfect storm

27:32

of faster GPU graphics cards, new

27:35

artificial intelligence deep learning algorithms and

27:37

great advances in game engines. It's

27:40

an incredible combination of things coming

27:42

together. This tremendous nexus

27:44

of points is just providing us

27:47

with an extraordinary opportunity of taking

27:49

the things that we can do. The important

27:51

thing about this technology is that we can

27:53

now use this to get these

27:55

faces to work with us in real time.

27:58

In other words, this is a really key thing. point, the

28:00

faces that we're talking about can

28:02

talk, interact and see us. Okay,

28:08

putting faces on our technology. Tell

28:10

me more about how you see

28:12

this working and the reasons

28:14

why we would want it. Yeah,

28:17

I mean there are a lot. Already

28:19

in New Zealand there is automatic sign

28:21

language, so if somebody's speaking a digital

28:23

human signs for the deaf community, you

28:26

might have an assistant sitting in on a

28:28

Zoom call that you can ask

28:30

to help book future things, take

28:32

notes, do stuff. In aged care you

28:34

could have an assistant that

28:37

logs in with somebody each day and make

28:39

sure that they're okay and are lucid and

28:41

they've taken their pills, not to replace a

28:44

healthcare worker but just simply that to make

28:46

sure that they're okay and facilitate them staying

28:48

in the home longer. And

28:50

so in a world where we're saying, hey, you

28:53

know, even to use the phone, there are no

28:55

buttons now, you have to, you know, swipe up,

28:57

swipe left, do all this stuff. People are like,

28:59

I have trouble with that. And so

29:02

we could bring a face from their past

29:05

that would be the one that they interact

29:07

with that technology. You don't think that would be odd

29:09

to someone that if you said, well, this is your

29:11

sister, she's not actually your sister, she just

29:13

kind of looks like your sister and she's

29:15

gonna help you use your phone.

29:17

That, I don't know, that might freak me out. Yeah,

29:20

you know, you just touched on a

29:22

really interesting point. People when asked traditionally

29:24

say, I wouldn't like that. So if

29:26

you project ahead, you say, hey, would

29:28

you have a digital human tell you

29:30

what to do? No, no, no, absolutely not. That would

29:32

be freaky. And yet every time we do a lab

29:35

test, they completely don't do that.

29:39

I looked up one of those services that

29:41

might be available in the near future. Hello,

29:44

this is Sol, Dr. Beanie's

29:46

assistant. Hello, Sol, this is Tyler.

29:49

I wanted to ask you about my recent surgery. In

29:51

the demo video, a man is home after

29:53

knee surgery and consults on

29:56

his laptop with his AI nurse.

30:00

seem to know her stuff. The discharge

30:02

summary states that you should take the

30:04

pain medicine about 20 minutes before you

30:06

put on your headset for your virtual

30:08

reality meditation therapy. And one way? Mike,

30:12

I don't know. I have to be honest, I

30:14

was a little unnerved by soul. Sure.

30:17

What I would say is it was

30:19

her lack of authenticity that probably bothered

30:21

you, not the digital representation of

30:23

the face. Once you get to

30:25

a certain level of quality, you kind of pass

30:27

what we've referred to as the uncanny

30:30

valley. So you now got something that looks

30:32

pretty darn good. It doesn't matter

30:34

whether you can tell it's real or not. That's

30:37

not the deciding factor. It's the authenticity

30:39

of the emotional kind of response that

30:42

matters. And that's the driving factor. And

30:44

so for us to succeed in those

30:47

cases, we really need to make sure

30:49

that it's the sort of the back

30:51

end behind the face that's delivering what's

30:53

wanted, not so much the face itself.

30:58

Speaking of the uncanny valley, you

31:01

did a demonstration on stage where you

31:03

showed off a very realistic

31:05

digital version of your head,

31:07

your face on a

31:09

screen that you could control.

31:12

Hi, I'm Mike. Well, kind of virtual Mike,

31:14

really. This is our digital human project, which

31:16

is a collaboration of a whole bunch of

31:18

people coming together to produce, well, a

31:20

virtual human. And not only a virtual human, but... I mean,

31:23

people, like right now, we can make digital avatars

31:25

of ourselves, but not like this. So

31:27

how do we make this realistic extent? How hard was

31:29

that to build? Yeah,

31:31

I mean, we sort of are close. I mean, that one took

31:34

a lot of people. So we

31:36

scanned my face and I got my

31:38

face done in one of the most

31:41

high resolution facial scanning systems in the world.

31:43

It produced this super realistic version of my

31:45

head. Then I could puppeteer that

31:47

in real time or have it driven. So how

31:49

do we do it? So first

31:51

we scanned my face. This

31:53

allowed us to produce a very complex

31:55

digital avatar of my head or

31:58

a digital puppet. Then with

32:00

a camera mounted on a head rig, the computer

32:02

can actually read my face. An

32:05

advanced AI engine then

32:07

basically interprets that into

32:09

expressions. Now the

32:11

computer can tell the digital puppet what to do.

32:14

In effect, what's happening is it's the

32:17

computer telling the muscles in the digital

32:19

mic how to smile, talk or do

32:21

things. It

32:24

makes me wonder if we might get to

32:26

a point where kids think, well, I would much

32:29

rather deal with my extremely

32:32

realistic-looking tutor on

32:34

my laptop who responds to me

32:36

but who doesn't actually give

32:39

me a hard time and won't be offended if I

32:41

tell it to shut up. How

32:43

do we make

32:45

sure that people don't choose these

32:47

artificially intelligent agents over humans?

32:51

Brilliant question and I wish I had a definitive

32:53

answer. I can only give you my hope. Imagine

32:56

I'm a vet, I come back,

32:59

but I'm an 18-year-old guy. I've

33:01

experienced some horrendous experiences in conflict.

33:04

I'm now suffering from all sorts of sexual

33:07

dysfunction. I cry at night. I have

33:09

things I'm really embarrassed about and ashamed

33:12

of. I kind of want help

33:14

but I don't want to have to sit there and tell

33:16

a doctor that. I'd actually like my doctor to know all

33:18

that so that they can help me. If

33:20

there are ways where you can communicate that

33:23

to an effectively like a digital nurse, a

33:25

digital doctor substitute so that the system can

33:27

know it but you don't have to face

33:29

them and look them in the eye and

33:32

say, you know, I have sexual

33:34

dysfunction, but you can then

33:36

get treatment and help and the

33:38

system knows and can look after

33:40

you, that's a tremendous benefit. So

33:42

hopefully for that generation, there'll be

33:45

tools that appear in their everyday life

33:47

that just make it a bit easier

33:49

and reconnect them with people,

33:52

not take them away. And I'd like

33:54

to think that if I

33:56

had teenagers who were in

33:58

distress and... teenagers that were struggling,

34:01

if there were tools that helped them,

34:03

that that would do just that, it

34:05

would help them. It wouldn't replace human

34:08

contact. In

34:12

a minute, the ethical dilemmas with

34:14

giving our technology a face.

34:17

I'm Manoush Zamorodi, and you're listening to

34:19

the TED Radio Hour from NPR. Stick

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the TED Radio Hour from NPR.

36:33

I'm Manoush Zomorodi. And on the

36:35

show today, What's in a Face?

36:38

We were just hearing from Mike

36:40

Seymour, a film industry veteran who

36:42

now wants our virtual helpers to

36:45

look and act more human.

36:47

Hi, I'm Mike. Well, kind of

36:49

virtual Mike, really. This is our

36:51

digital human project, which is a

36:53

virtual human, but one rendered in

36:55

real time. Puppeteered or driven in

36:57

real time, rendered in real time,

37:00

Mike says that technology with a

37:02

face can better interact with us.

37:04

Talk to patients about how they're

37:06

feeling. Ask students where

37:08

they're struggling in algebra. Coach

37:10

brain injury survivors to be

37:13

more self-sufficient. So

37:16

many different types of technology, so many different

37:18

use case scenarios, as you've mentioned. I expect

37:21

we're going to see this area

37:23

explode in the next few years.

37:27

But along the way, wow, do

37:29

we have a lot of ethical

37:32

dilemmas to sort out. I mean,

37:34

you're just reminding me, we've talked about this on

37:36

the show a lot, the deep fakes.

37:39

You know, there's the famous case

37:41

of seeing President Obama giving

37:44

a speech that he never gave. Where

37:47

do you see some of the pitfalls that we need to watch

37:49

out for? I completely agree

37:51

with you. Some of the applications of this

37:54

technology by what I would describe

37:56

as bad actors is appalling and just,

37:58

you know, absolutely indefensible. It's

38:00

a really interesting sort of fundamental ethical question.

38:03

Is the technology good or evil or is

38:05

it the use of it and the application

38:07

of it? And I

38:09

can say this, for me personally, you can

38:11

use steel to make ambulances or tanks. I'm

38:14

in the business of trying to see if we

38:16

can't use it to make lots of good ambulances.

38:19

I know some people are going to make tanks, but

38:21

that's something I don't have any control over. But

38:24

I do feel that it's going to happen. The

38:26

best line of defense we have to

38:29

the deception that can be done

38:31

by this technology is an informed

38:33

public. If you see

38:35

something that's highly improbable, you're

38:37

going to say, hang on a second,

38:39

that's probably been faked or not real.

38:41

Or you'll dig in to try and

38:43

discover its authenticity. There's

38:47

a lot of ways we can produce inaccurate

38:49

material, but an informed public

38:52

that is aware of what's

38:54

going on, that understands what

38:56

the sort of limits of

38:58

technology are. And

39:00

where it's going is vital to being

39:02

able to do this sort of stuff.

39:04

But are we just barreling towards a

39:06

future where your identity gets stolen, but

39:08

it's not just your social security number,

39:11

it's your face that

39:13

can make it look like

39:15

you're handing over your bank account number to someone

39:17

on a Zoom call? Or

39:20

is my imagination way ahead

39:22

of the technology? Gosh,

39:25

I mean, people will deceive people with

39:27

this technology. But yeah, when

39:29

I'm talking about these AIs, they work very well

39:31

when you've got a limited amount of stuff that

39:34

you're asking them to do. So

39:36

if I was having an agent that

39:38

was helping you as a maths tutor,

39:40

and it was discussing maths and explaining

39:42

mathematical concepts, that could be completely plausible

39:45

and look photorealistic and wonderful.

39:48

But if I asked my maths

39:50

assistant, what does it mean to

39:52

understand existential philosophy in France, it

39:54

would completely blank out. So

39:57

we're not talking about a general

39:59

intelligence. intelligence. People quickly extrapolate

40:01

to that, but we are so far

40:03

away from that. General AI

40:06

intelligence is a long way off, but

40:08

as I say, these plausible,

40:11

realistic, domain-specific applications in

40:14

health, in aged care,

40:16

in all of these parts

40:18

of everyday life, completely

40:20

plausible and extremely likely to

40:22

happen because we just love

40:25

faces. We love

40:27

face-to-face communication. We

40:29

love seeing people face-to-face. Humanity

40:32

just likes faces. We're

40:34

talking about just putting a face

40:36

on technology so that it's a

40:39

bit more friendly, a bit more empathetic,

40:41

a bit more engaging, that has an

40:43

emotional response and therefore we find it

40:46

to be a better,

40:48

more pleasurable experience. That's

40:51

Mike Seymour. He's a researcher and

40:53

academic at the University of Sydney.

40:55

You can see his talk at ted.com.

40:59

On the show today, What's in

41:01

a Face?, how our faces are

41:03

captured, where that data ends up

41:05

and who has access to it.

41:08

Do you use Instagram? Do you

41:11

let Google Maps track you? Do

41:13

you, I don't know, let open

41:15

your iPhone with your face? Oh

41:18

God. I'm pretty privacy conscious,

41:20

as you might imagine. I don't

41:23

allow Google to store my location. I

41:26

don't have the face on lock turned on in my phone.

41:29

And that is in part

41:31

because I'm just aware of how sensitive

41:33

the data is and I feel so

41:35

self-conscious about it. This is Alison Killing.

41:38

She's a journalist who, ironically,

41:40

uses all sorts of data

41:42

that's available online to track

41:44

the actions of authoritarian governments.

41:47

So all of the digital traces that we

41:49

leave behind on the internet, like how can

41:51

we use those to investigate? And I'm mostly

41:53

focused on human rights. In

41:55

2021, Alison won the Pulitzer Prize

41:57

for her investigations into China. A

42:00

place where people's faces and

42:02

movements are constantly being watched.

42:05

There's really words to cover

42:07

cities in a way that they

42:10

are able to obtain as much data

42:12

as possible. So placing cameras in high

42:14

traffic areas, so for example at the

42:17

entrance to a neighborhood, where they can

42:19

then say, okay, we know everybody who

42:21

is in this neighborhood now, and we know

42:23

whether they're in or when they've left. China

42:26

has the world's largest surveillance network,

42:29

and cameras watch over residential

42:31

complexes, office buildings, train stations,

42:34

shopping malls. So these

42:36

very high traffic places where they can

42:38

then say, okay, these are

42:40

the people who are in this area, so

42:42

that they can then control that area. They're

42:46

collecting a lot of data, and there's huge ambition

42:48

about the things that they would like to do

42:50

with it. A lot

42:52

of work has gone into the processing

42:54

tools at the back end of this

42:56

software to identify people by gender and

42:58

age, and then controversially also by ethnicity.

43:01

And as you may know, the Chinese government has

43:03

been tracking one large group of people in particular,

43:06

the Uyghurs, a Muslim ethnic

43:09

minority in a western region called

43:11

Xinjiang. Yeah, there's been

43:13

a lot of discrimination. There's been

43:16

intermittent crackdowns on the practice of

43:18

Islam, but then in 2009 there

43:20

were two Uyghur workers killed,

43:23

and that led to protests

43:26

which turned violent, and about 200

43:28

people were killed. And

43:31

this was going to be the start as

43:33

well of the Chinese authorities starting to

43:35

crack down on the region and seeing it

43:38

as a very violent place, seeing it as a

43:40

site of terrorism. The

43:43

incident ushered in an era of

43:45

Chinese control of the Uyghurs, using

43:48

all kinds of tactics. So I think

43:52

from 2013, 2014, we saw the start of

43:54

this real campaign of oppression in Xinjiang with

43:58

the installation of this incredibly... invasive

44:01

surveillance state. And

44:03

the New York Times has done a

44:05

lot of investigation on this topic where

44:07

they actually found documents

44:09

from tech companies which

44:12

were boasting that they could identify

44:14

regas using facial recognition software. So

44:17

one of the first things that we saw

44:19

was the creation of this network of

44:22

detention camps. You

44:24

know, Alison, we were just talking to

44:26

Parmi Olson, she's a tech reporter, about how

44:29

people view facial recognition in the

44:31

Western world. And it always, it

44:33

often feels like what if scenarios.

44:35

But here in China, we are

44:37

talking about the worst case scenario come

44:39

true, with proof

44:41

that a minority are being tracked

44:43

and rounded up because you could

44:45

see the camps on satellite imagery.

44:48

Yeah, in the satellite imagery, we saw

44:50

them starting to appear in late 2016. And

44:53

these stories started to emerge that hundreds

44:55

of thousands of people have been disappeared

44:57

into these camps. And nobody

45:00

knew where they were. In the

45:02

far west of China, evidence is building

45:04

that a monstrous crime has taken

45:06

place. Uyghurs

45:11

are now being rounded up by the hundreds

45:13

of thousands. There are many accounts of people

45:15

who have had their relatives disappear into the

45:17

camps. And we don't really know what's happening

45:19

to them. Alison

45:21

Killing picks up the story from the TED

45:23

stage. I got involved

45:26

in investigating Xinjiang in the summer of 2018,

45:29

when I met Mega Rajagapalan, an American

45:31

journalist who had been working in China

45:33

for several years. Over the

45:35

past few years, China has been carrying

45:37

out a campaign of forcible assimilation. And

45:40

several nations have described it as a

45:42

genocide. It's estimated that

45:44

over a million people have been disappeared

45:46

into detention camps. And while

45:49

the Chinese government claims that these

45:51

are part of a benign programme

45:53

of re-education, dozens of former detainees

45:55

describe being tortured and abused and

45:57

women being forcibly sterilised. And

46:00

yet, for a long time, we lacked

46:02

information about what was happening in Xinjiang

46:05

because the Chinese government controls the internet

46:07

tightly and restricts journalists' work in the

46:09

region. Journalists would

46:11

be followed or detained, and the

46:13

authorities occasionally even went so far

46:15

as to set up fake roadworks

46:17

or stage car crashes to prevent

46:19

access to certain roads. Local

46:22

people who did speak to journalists faced the risk

46:24

of being sent to a detention camp for doing

46:26

so. Megha had

46:28

been the first journalist to visit one of the

46:31

camps, but shortly after publishing

46:33

her article, the Chinese authorities declined to

46:35

renew her visa and she had to

46:37

leave. Other journalists had

46:39

managed to visit the handful of the camps,

46:42

but they still represented a fraction of what

46:44

we believed was out there, and no one

46:46

knew where the others were. But

46:48

Megha was keen to find the rest. She

46:51

just needed to find a way to work

46:53

effectively from outside China. And

46:56

so this is where you come into the story,

46:58

Alison, because you and Megha decided

47:00

to team up. Yes.

47:02

So I met Megha at this

47:05

workshop from the summer of 2018.

47:07

I've been doing a lot of

47:09

cartography work and satellite imagery. And

47:13

we got talking and we realised that we

47:16

maybe had to implement these skills to be able

47:18

to send these camps. You know, the

47:20

way that Megha had found this first camp was through

47:22

satellite imagery. And so she had

47:25

the idea that that could be

47:27

a good way to find the rest. But

47:30

it's still... Like,

47:33

Xinjiang is a bit really massive,

47:35

so you can't just like scour

47:37

all of the satellite imagery of the region. We

47:40

needed to work out where to look. There

47:44

was no street-level imagery, but as I zoomed

47:46

in on the satellite images, this weird thing

47:48

happened. A light grey square

47:51

suddenly appeared above the location of the camp,

47:53

and then disappeared just as quickly as I

47:55

zoomed in further. It

47:57

was a bit like the map wasn't loading.

48:00

properly, but then I zoomed out and in again,

48:02

only for the same thing to happen. I

48:04

realized it couldn't be a problem with the map

48:06

loading because the tiles would have been in the

48:09

browser's cache. And when I found

48:11

the same thing happening at the other locations we knew

48:13

to be camps, I realized that we

48:15

had a technique we could use to find the rest

48:17

of the network. It's

48:21

quite rare for maps and satellite images

48:23

to have these blank spots because blank

48:25

areas tend to draw attention to themselves.

48:28

But here we got lucky. Obscuring

48:31

the camps had inadvertently revealed

48:33

all of their locations. We

48:44

worked with developer Christo Bushek who

48:46

specializes in documenting human rights issues

48:48

and building tools for open source

48:50

researchers to map the mask tile

48:53

locations. We had to

48:55

work quickly and secretly to map the

48:57

mask tiles before anyone found out what

48:59

we were doing and remove them because

49:01

our investigation relied on access to that

49:03

information. The idea was that

49:06

we could go and look at the mask

49:08

tile locations and then look at that same

49:10

image at that same location in other unaltered

49:12

satellite imagery and see what was there. Zooming

49:15

in on the satellite imagery, we can

49:17

see the bobbed wire in the courtyards

49:19

that creates exercise pens for the detainees

49:21

adjacent to the buildings. In

49:24

other images, we can even see people

49:27

all wearing red uniforms lined up in

49:29

the courtyard. These features

49:31

could help us decide whether a location was a

49:33

camp or not. As

49:36

we investigated further, we realized that

49:38

the camps program had evolved away

49:40

from the early days of makeshift

49:42

camps in former schools and hospitals

49:44

and it became more permanent that

49:46

the camps were now larger, higher

49:48

security and purpose built. This

49:51

is the largest camp that we know of. It's in

49:53

the Ban Cheng. The complex is

49:55

two miles long and it would cover a

49:58

quarter of New York Central Park. In

50:01

the satellite images, we can see the thick

50:03

perimeter walls, the God Towers, and these bluewiff

50:05

buildings which we believe to be factories. We

50:08

estimate that this complex can hold over 40,000

50:11

people without overcrowding. In

50:14

total, we found 348 locations

50:17

bearing the hallmarks of camps and prisons,

50:19

and we believe that this is close

50:21

to being the full network. We

50:23

estimate that these facilities have been built

50:26

to hold more than a million people.

50:29

That's enough space to detain

50:31

one in every 25 of Xinjiang's

50:33

residents. Wow,

50:36

your one little

50:39

lucky revelation finding that quirk

50:41

on the digital map led

50:43

to a horrifying and

50:45

huge discovery. And

50:48

how did China respond to the

50:50

allegations? So at the

50:52

beginning, when the rumors were

50:55

first emerging of all

50:57

of these people disappearing into camps, there was

50:59

denial on the part of the Chinese government that

51:01

this was happening. In

51:04

mid-2018, the UN had

51:06

made a statement about what was happening in

51:08

Xinjiang and raising concerns and saying

51:10

it was one of the most urgent human

51:12

rights crises in the world at that time.

51:15

And the Chinese government was then under pressure to respond to

51:17

that. And what they started

51:20

to say was, well, these places do

51:22

exist, but they're education and vocational schools.

51:24

People are there voluntarily. They're learning skills

51:26

which will allow them to get higher

51:28

paid factory jobs. That

51:30

wasn't true. People were taken there

51:32

forcibly. And so if the people

51:34

who were initially targeted to be sent to

51:37

the camps were the most highly educated people

51:39

in those communities. So,

51:41

you know, the Chinese government's claims about

51:43

these being vocational schools just were

51:45

incredible. So where

51:47

do things stand now in terms of what

51:50

you can do with this knowledge that you

51:52

have accumulated other than share

51:54

it with us? Yeah. And

51:56

one of the big things that has

51:59

been done. I mean, we've

52:01

seen sanctions on key individuals

52:04

within the Chinese Communist Party. We've

52:07

also seen sanctions on goods coming out of

52:09

Xinjiang. We go Force Labor

52:11

Prevention Act came into force earlier this year,

52:14

and that means any products

52:16

coming out of Xinjiang because it's

52:19

very, very likely that goods coming out of Xinjiang

52:21

have involved forced labor, and it's very difficult to

52:23

prove that they haven't. And

52:26

so that has also been a big impact

52:28

that we've seen. With

52:31

social media data and satellite imagery, we

52:33

can provide evidence of human rights abuses

52:36

in a way that wasn't possible before.

52:39

We can move beyond looking at individual

52:41

instances of human rights violations to show

52:43

the scale of what's happened. We

52:46

can corroborate the testimony of eyewitnesses

52:48

and provide further proof of their

52:51

stories. We can build

52:53

a more detailed picture of what's happening

52:55

to inform policymakers or to provide evidence

52:57

that can be presented in court. With

53:00

open source data, we can provide the

53:02

evidence needed for accountability. And

53:05

then, hopefully, action. Thank

53:07

you. That's Alison Killing. She's

53:14

an investigative journalist and an

53:16

architect. In 2021, she won

53:18

the Pulitzer Prize for her reporting. You

53:21

can see her full talk at

53:24

ted.com. Thank

53:30

you so much for listening to our show

53:32

this week. What's in a Face?

53:35

This episode was produced by Andrea

53:38

Gutierrez, James De La Hucie, and

53:40

Katie Montelillon. It was edited by

53:42

Sanaz Mezchanpour, James De La Hucie,

53:44

Rachel Faulkner-White, and me. Our

53:46

production staff at NPR also includes Matthew

53:49

Cloutier, Fiona Guerin, and Catherine Seifer.

53:52

Our theme music was written by Ramtin

53:54

Arablui. Our audio engineer was Quasi Lee.

53:58

Research support came from Cecile Davis-Davies. Vasquez.

54:00

Our partners at TED are

54:03

Chris Anderson, Colin Helms, Anna

54:05

Phelan, Michelle Quint, Jimmy Gutierrez,

54:07

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