Latest / Stuff You Should Know / How AI Facial Recognition Works

Transcript

How AI Facial Recognition Works · Stuff You Should Know

0:00
Speaker 1: Hey everybody, it's me Josh, and I'm here to tell
0:03
Speaker 1: you it's official. We're going to be in Vancouver, b C.
0:06
Speaker 1: And Portland, Oregon this March. On March twenty nine, will
0:10
Speaker 1: be at the Chance Center in Vancouver and on March
0:13
Speaker 1: will be at the Arlene Schnitzer Concert Hall in Portland.
0:17
Speaker 1: So come see us. Tickets go on sale this Friday.
0:21
Speaker 1: Go to s Y s K live dot com for
0:24
Speaker 1: ticket links and info and everything you need and we'll
0:26
Speaker 1: see you guys in March. Welcome to Stuff You Should Know,
0:30
Speaker 1: a production of I Heart Radios How Stuff Works. Hey,
0:39
Speaker 1: welcome to the podcast. I'm Josh Clark. There's Charles Scooter,
0:45
Speaker 1: Computer Bryant and Jerry Um Matthew Broderick in War Games
0:52
Speaker 1: Rolling and this is Should Know. That's good. Thanks. What's
0:58
Speaker 1: your name, Josh m hmm? Okay, Josh, Ali Shedy in
1:04
Speaker 1: More Games Okay Clark, I know Aaron Cooper is doing
1:08
Speaker 1: right now? Man? How cute was she in that movie? Like?
1:11
Speaker 1: I think they designed that movie for like every thirteen
1:15
Speaker 1: year old boy in America's fall in love with Ali
1:16
Speaker 1: shed I think you're talking about um short Circuit. I
1:21
Speaker 1: never saw that, believe that what with Johnny five. I mean,
1:25
Speaker 1: I know the movie. You gotta see it. Really, it's
1:27
Speaker 1: pretty awful, especially with um oh, what's his name? Who
1:35
Speaker 1: is the sleeves ball from Fast Times at Ridgemont High?
1:38
Speaker 1: Who is the ticket scalper? Yes, he plays a Indian
1:45
Speaker 1: like Asian Indian character, like full on brown face and everything.
1:53
Speaker 1: It's really bad. The movie is bad enough, but then
1:57
Speaker 1: now when you go back and see that, to your like,
1:59
Speaker 1: I can't believe this. I can't believe it. I think
2:02
Speaker 1: he's Italian, oh easily, maybe Jewish or maybe just a
2:06
Speaker 1: straight up white guy. He's definitely not Asian Indian. No,
2:09
Speaker 1: he's not. But anyway, go see Short Circuit. Okay, see
2:12
Speaker 1: what you think Ali shed He just keeps looking at
2:14
Speaker 1: the camera going, I'm so sorry that was a big
2:18
Speaker 1: hit though she didn't have anything be sorry about. Um So.
2:20
Speaker 1: I guess War Games is kind of in your your wheelhouse. Yeah,
2:25
Speaker 1: it was a little old for me. Yeah, because I
2:28
Speaker 1: saw that when I was like twelve, bish uh, and
2:32
Speaker 1: you would have been I don't know how how much
2:35
Speaker 1: younger are you. I would have been seven seven. Yeah,
2:38
Speaker 1: that's a little young for War Games, I would think.
2:40
Speaker 1: I mean, I still watched your wherever, but I was like, yeah,
2:42
Speaker 1: that's hilarious. The computer is called Whopper. Yeah, I mean
2:45
Speaker 1: it was right in my wheelhouse, right. I remember at
2:47
Speaker 1: the end of war Games, Uh, they lock in, you know,
2:51
Speaker 1: they're decoding the the code one number and letter at
2:55
Speaker 1: a time. Very suspenseful and yeah, very suspenseful. And it
2:58
Speaker 1: finally locks in and me and my friends memerized it
3:01
Speaker 1: so we could go home and plug it into an
3:03
Speaker 1: Apple too to see what happened. Nothing, Okay, how do
3:08
Speaker 1: you plug in a number anyway? What does that even mean? Oh? Yeah,
3:11
Speaker 1: that's true, you know. Yeah. I remember a very rudimentary
3:16
Speaker 1: program you could run where you could type in like
3:21
Speaker 1: four lines of whatever I don't even know if you
3:24
Speaker 1: call it code with a phrase, and it would run
3:27
Speaker 1: the phrase like a thousand times all over your screen
3:30
Speaker 1: and a big scroll. And then that just thought that
3:33
Speaker 1: was the coolest thing ever. I feel like, I remember
3:36
Speaker 1: what you're talking just like five lines was like the
3:39
Speaker 1: only part I remember is twenty go to ten and
3:42
Speaker 1: ten was the phrase I think something like I don't remember.
3:47
Speaker 1: Then I was like, man, let's just play Castle Wolfenstein.
3:49
Speaker 1: That was a good one. I remember, did Oregon Trail.
3:52
Speaker 1: I never did as well. I was Castle Wolfenstein, but
3:55
Speaker 1: like wolf and signed on the PC. Oh yeah, like
3:57
Speaker 1: move left arrow, right arrow, shoot uh shoot dash, which
4:02
Speaker 1: is some sort of a bullet. Yeah. It was fun.
4:04
Speaker 1: That was fun, and I thought it was just like
4:06
Speaker 1: the height of technological gaming. It was it was at
4:10
Speaker 1: the time. But now, Chuck, we've reached the height of
4:13
Speaker 1: technology which is being tracked everywhere you go, look at
4:17
Speaker 1: you all the time by whoever wants to do that.
4:20
Speaker 1: I'm gonna change your name to Josh smooth up a
4:23
Speaker 1: Raider Clark for that transition. Very nice. Yeah, Like I
4:27
Speaker 1: like Ali Shedy Clark, Josh, Ali Shedy and Clark. Yeah,
4:31
Speaker 1: this is a good one. Uh did you put this together?
4:35
Speaker 1: It was this you and this is Dave. Dave Brews
4:37
Speaker 1: good stuff in high Dave. We finally got to meet
4:40
Speaker 1: Dave and his family, lovely family that we cursed awfully
4:45
Speaker 1: in front of in Seattle. I felt terrible, thanks for
4:48
Speaker 1: adding yourself to the max and he was like, you
4:51
Speaker 1: know whatever, he was fine. His kids were adorable, they
4:55
Speaker 1: were they were great. They couldn't look at me though
4:58
Speaker 1: that really they were probably just him did by your present,
5:00
Speaker 1: No No, it was because I cursed so badly. So
5:03
Speaker 1: this is good stuff. Though. Facial recognition technology that they've
5:08
Speaker 1: been kind of at since the nineteen fifties, which they
5:12
Speaker 1: rolled out as a test in two thousand two at
5:15
Speaker 1: the Super Bowl in New Orleans, did not go that well. No,
5:19
Speaker 1: it was a little clunky back then, but it's gotten
5:21
Speaker 1: a lot better since then. Let me explain why. For
5:24
Speaker 1: anyone who's listened to the End of the World with
5:25
Speaker 1: Josh Clark the AI episode in particular, everything associated with
5:31
Speaker 1: artificial intelligence got way better starting around two thousand seven,
5:36
Speaker 1: when neural nets became a viable form of machine learning.
5:41
Speaker 1: Because you don't have to train a computer what constitutes
5:46
Speaker 1: a human face and what to look for. You just
5:49
Speaker 1: feeded a bunch of pictures of faces and say these
5:51
Speaker 1: are human faces, learn what a human faces and they
5:54
Speaker 1: trained themselves. And so around about two thousand seven, two
5:57
Speaker 1: thousand eight, two thousand nine, everything had to do with
6:00
Speaker 1: machine learning got way smarter because we started using neural
6:04
Speaker 1: nets and facial recognition software is no exception. Yeah, and
6:08
Speaker 1: there were a few things that kind of converged all
6:10
Speaker 1: at the same time or around the same time. Uh,
6:14
Speaker 1: social meds kind of coming on the scene right in
6:17
Speaker 1: that wheelhouse was a big deal. Um, Facebook, this is staggering.
6:22
Speaker 1: Facebook just by itself processes three hundred and fifty million
6:29
Speaker 1: new photos through its uh facial recognition software every day,
6:34
Speaker 1: and every time one comes through. Mark Zuckerberg goes like,
6:39
Speaker 1: you think it's neat when you go when you put
6:41
Speaker 1: a picture up and it says like would you like
6:43
Speaker 1: to tag Emily your wife because that's her? And you think, oh, well,
6:46
Speaker 1: that's super easy, thanks Facebook. But then you don't think, like,
6:49
Speaker 1: wait a minute, all right, how do they know that's
6:51
Speaker 1: my wife? And you know, it's like with everything else, Uh,
6:56
Speaker 1: there's privacy people that were like, whoa do you guys
6:59
Speaker 1: realize what's going on? And then of the sheep they're
7:03
Speaker 1: like huh no, no, they're like no, it's great, Like
7:05
Speaker 1: I don't have to go in and like make click
7:08
Speaker 1: two links or two buttons to take the way easier.
7:12
Speaker 1: So that was one thing. There's way more photos out
7:15
Speaker 1: there for those machines to learn one like good high
7:19
Speaker 1: quality photos right uty million a day just on Facebook alone, um,
7:25
Speaker 1: which means the machines were getting smarter, They were getting
7:27
Speaker 1: better and better at at training themselves. And then lastly, um,
7:32
Speaker 1: that has led to a ubiquity in in facial recognition.
7:37
Speaker 1: That the better the machines have gotten, the easier it
7:41
Speaker 1: has been to put together data sets for them to
7:43
Speaker 1: train on, which is lots and lots of pictures of people. Um,
7:46
Speaker 1: the cheaper the technology has gotten, which means the more
7:49
Speaker 1: people that are now using facial recognition than ever. Yeah.
7:54
Speaker 1: Amazon has a service called Recognition with a K, which
7:59
Speaker 1: is not a good look. No, it looks very German.
8:02
Speaker 1: There's something about replacing at sea with the K. It
8:06
Speaker 1: just looks creepy, Like when you spell America with a K,
8:10
Speaker 1: it means something. It means like bad America. Yet they
8:15
Speaker 1: went right full steam ahead and called it recognition. You
8:20
Speaker 1: have to say it like that, you do, I think,
8:21
Speaker 1: and you have to be like squeezing the air out
8:24
Speaker 1: of a syringe while you're saying it too. Um, so
8:27
Speaker 1: they have this. I didn't even know about this, but
8:30
Speaker 1: it's ubiquitous and it's not super expensive, and that means
8:34
Speaker 1: that the law enforcement agencies agencies they don't have to
8:39
Speaker 1: like create their own they can just say, well, just
8:41
Speaker 1: to sign up for a recognition right exactly. Because it's there,
8:45
Speaker 1: and because it's it's relatively cheap, you can just get
8:48
Speaker 1: a subscription, not just law enforcement agencies. If you have
8:52
Speaker 1: a photo sharing app or whatever and you want some
8:56
Speaker 1: facial recognition technology, you just contract with with Amazon, and
9:00
Speaker 1: Amazon goes here, you go, here's our data, and you
9:04
Speaker 1: can or um like our code, and you can put
9:06
Speaker 1: it onto your platform. Anybody can use it. Um. So
9:10
Speaker 1: it is kind of everywhere, and that makes a lot
9:12
Speaker 1: of people, including me, very very nervous because as this
9:18
Speaker 1: guy wood Row hurt Zog, if he's not Werner hurt Zog, brother,
9:23
Speaker 1: I'll be disappointed. But but a wood Row and a
9:28
Speaker 1: Werner come on, you know anyway. Um. Wood Row hurt
9:31
Speaker 1: Zog as a professor of computer science, and I believe privacy,
9:38
Speaker 1: civil liberty. Um. He basically says, Look, there is no
9:44
Speaker 1: way we're going to reap the benefits of facial recognition
9:48
Speaker 1: without ultimately sliding irreversibly into a dystopian surveillance state where
9:55
Speaker 1: it's happening right now, and if we don't do something
9:58
Speaker 1: about it, it's never going to change back. We're about
10:01
Speaker 1: to fully give up our privacy because it's one thing
10:04
Speaker 1: to have your phone tracked you can give up your phone,
10:07
Speaker 1: get yourself a burner phone, like you're Jesse Pinkman or
10:10
Speaker 1: something like that, and then you just throw that phone away.
10:13
Speaker 1: You can't be tracked anymore. You can't get a burner face.
10:16
Speaker 1: And if that does become a thing down the line,
10:19
Speaker 1: it'll be very, very expensive. So the average person can't
10:22
Speaker 1: get a burner face. Will be tracked by our face
10:26
Speaker 1: everywhere we go. And as we add more and more
10:29
Speaker 1: cameras and this technology becomes cheaper and cheaper, it's we
10:34
Speaker 1: will be living in a world where there will be
10:36
Speaker 1: zero privacy and will be monitored and tracked because it
10:39
Speaker 1: will be so easy, and it will be sold to
10:42
Speaker 1: us like it's being sold to us now that it's
10:45
Speaker 1: a law enforcement tool to get the bad guys. But
10:48
Speaker 1: it's eventually going to extend to include everybody. But what
10:51
Speaker 1: do you have to worry about. You're an upstanding citizen.
10:54
Speaker 1: It doesn't matter if you're tracked. That's not true. That's
10:57
Speaker 1: just not the case, everybody, It's not case. All right,
11:00
Speaker 1: we're gonna call that soap soapbox soliloquy number one of
11:06
Speaker 1: what I guarantee will be probably three or four. Uh,
11:10
Speaker 1: let's talk of a little bit about how it works.
11:12
Speaker 1: It is biometric authentication. Um. It's like a fingerprint or
11:16
Speaker 1: a retina scan. And basically what it does is, uh,
11:20
Speaker 1: it is precise measurements of a face to calculate every
11:25
Speaker 1: person's very unique visual geometry, like how far apart your
11:29
Speaker 1: eyes are, how how far apart your pupils are from
11:32
Speaker 1: your nostrils. Yeah, your facial geometry, how your face is
11:36
Speaker 1: all set up? I think. Yeah, it's even gotten into
11:38
Speaker 1: things like facial hair, skin tone, um, skin texture. Yeah,
11:43
Speaker 1: I'm sure it'll get just more and more specific. Yeah.
11:45
Speaker 1: And then you know, because they're getting the machines are
11:48
Speaker 1: getting better and better and easier and easier to train
11:50
Speaker 1: on this stuff, you can just add more and more
11:52
Speaker 1: data to it, and the the the recognition will just
11:56
Speaker 1: become increasingly um um good. Yeah. And if you want
12:02
Speaker 1: to throw off facial recognition software and freak out every
12:06
Speaker 1: human you meet to shave your eyebrows, oh yeah, that
12:10
Speaker 1: would be a little freaky. Have you ever seen that.
12:13
Speaker 1: You've seen it before, I'm sure in movies and stuff.
12:15
Speaker 1: It's an interesting thing. I remember a kid in industrial
12:17
Speaker 1: arts class did that one year. He was like a
12:19
Speaker 1: little you know, kind of a ninth grade burnout, and
12:21
Speaker 1: he just showed up one day with no eyebrows, like
12:24
Speaker 1: you would I think like not having a nose would
12:27
Speaker 1: be more easily accepted. There's something just uncanny when someone
12:32
Speaker 1: shaves their eyebrows, Like one day they have them, the
12:34
Speaker 1: next day they don't. Was it like immediately recognizable what
12:39
Speaker 1: the thing was, or was it like that's the thing
12:42
Speaker 1: off today? Whereas if you, you know, you came in
12:46
Speaker 1: the next day without a nose, the first thing you
12:48
Speaker 1: would say is what happened to your nose? What happened
12:49
Speaker 1: to your nose? Todd? Yeah, and Todd would be like,
12:52
Speaker 1: I can't rent of it. I fell off. So those
12:56
Speaker 1: measurements we were talking about. What happens then is they
12:58
Speaker 1: compare that just like a fingerprint, with a database of images.
13:05
Speaker 1: And depending on what this is for, it could be
13:08
Speaker 1: like just within your company, or it could be the
13:11
Speaker 1: FBI's database of mugshots, or it could be the d
13:14
Speaker 1: m VS database of driver's license photos yeh, which we'll
13:17
Speaker 1: get into. Yeah. What's interesting is each stage of the way,
13:21
Speaker 1: there's a different algorithm that does you know, each increasingly
13:24
Speaker 1: sophisticated step, until you finally have basically like all of
13:29
Speaker 1: the different data points for that, you know, what makes
13:32
Speaker 1: up that facial geometry, and then you can compare it
13:35
Speaker 1: to all the other data points. We think of like
13:38
Speaker 1: a computer running like a you know, a picture, You've
13:43
Speaker 1: got your input picture and then running you know, all
13:46
Speaker 1: the pictures next to it. That's not what it's doing.
13:48
Speaker 1: It's it's running the numbers. Basically, it's doing computer stuff. Yeah.
13:51
Speaker 1: I love that first step, which is you have to
13:55
Speaker 1: teach the computer what a face is. So, I mean,
14:00
Speaker 1: seems silly, but of course that's what it is. Well, yeah,
14:02
Speaker 1: because I mean if you show it a picture of
14:04
Speaker 1: a person standing next to a fire hydra shaming on
14:07
Speaker 1: the fire Hydran, Yeah, and say hello, handsome, So this
14:10
Speaker 1: is what a human face looks like. Yeah, or no,
14:13
Speaker 1: that's a butt, And then it starts, you know, closer, closer.
14:16
Speaker 1: All right, now that's a face. You know what a
14:18
Speaker 1: face is. Now move on to step two, which is
14:21
Speaker 1: stop screwing around. Yeah, so now you know what a
14:24
Speaker 1: face is. You've got to normalize it for the photo,
14:26
Speaker 1: which means there are not that many. Well, let's you
14:29
Speaker 1: have to put it in the dockers. That normalizes, Um,
14:34
Speaker 1: you isolate that face, and then you have to make
14:37
Speaker 1: sure that it's normalized as far as looking at the camera.
14:42
Speaker 1: So if it's a if you get a photo of
14:44
Speaker 1: someone from a CCTV, let's say, and it's sort of
14:47
Speaker 1: a three quarter, they have the ability to make it
14:50
Speaker 1: as if it is looking straight at you. Yeah, the
14:52
Speaker 1: computer can pretty accurately predict what the rest of the
14:56
Speaker 1: face looks like face you know it on, I guess
15:00
Speaker 1: face on um. And when it normalizes it like that,
15:05
Speaker 1: it makes it much easier to compare to other pictures because,
15:09
Speaker 1: as we'll see, most of the pictures or most of
15:11
Speaker 1: the data points that it's comparing it to, are taken
15:14
Speaker 1: from databases of pictures that have been taking of people
15:17
Speaker 1: face on. Right. So that's why it wants to go
15:20
Speaker 1: to driver's license. Yeah, well just spoiled it. But yes,
15:24
Speaker 1: we already said that. Oh we did, Yeah, I did, Okay,
15:27
Speaker 1: I missed it, I know. So uh from there, do
15:32
Speaker 1: you have more algorithms still that isolate parts of the face. Um.
15:38
Speaker 1: And this is where my old theory that like, there
15:40
Speaker 1: are only so many sort of facial combinations, so that's
15:43
Speaker 1: why you have doppelgangers. We got to do an episode
15:45
Speaker 1: on doppel Gan. There's only so many things you can
15:47
Speaker 1: do with two eyes to eyebrows and nose in a
15:49
Speaker 1: mouth and cheekbones and in chin. Well, okay, what else?
15:54
Speaker 1: I mean, there's not a whole lot. There's lips, lips, sure,
15:57
Speaker 1: what about um uh uh uh, that's about it. These
16:01
Speaker 1: are called elevens, the ridges between your eyebrows. Well, if
16:04
Speaker 1: you want to get super specific, but that's what I'm saying.
16:07
Speaker 1: I think they're getting more and more specific. Oh yeah, yeah.
16:09
Speaker 1: But my whole point is, and we'll learn and here
16:12
Speaker 1: and facial recognition. They do use Apple gangers, but put
16:16
Speaker 1: a pin in that so they recognize all these features
16:20
Speaker 1: and then each feature becomes what's called a nodal point
16:24
Speaker 1: or no Dowell point. I think, nodal nodal, I think.
16:28
Speaker 1: And this is where you're gonna get your super exact
16:32
Speaker 1: uh angles and distances between all these parts as a
16:36
Speaker 1: flat two dimensional thing. Which my question was because below here,
16:41
Speaker 1: you know, it talks about Apple and their iPhone have
16:43
Speaker 1: a three D facial recognition. Is that is two dimensional
16:47
Speaker 1: superior to three D? I don't know, or is it
16:50
Speaker 1: just because that's what all the pictures are in the databasis,
16:53
Speaker 1: so that's what they do. I don't know. All I
16:55
Speaker 1: know is my phone usually unlocks when I look at it.
16:59
Speaker 1: You know what? I Hey, that's having to take off
17:01
Speaker 1: my sunglasses. Worst so I found I've got some wayfarers
17:06
Speaker 1: that I don't have to take, but my aviators I
17:09
Speaker 1: do have to take off. Interesting do they keep trying
17:11
Speaker 1: to make you into mav when you have on the
17:16
Speaker 1: the go ahead? What is that? That was Tom Cruise
17:20
Speaker 1: laughing and chewing gum. Okay, wow thanks, I feel like okay,
17:26
Speaker 1: um we we we gotta keep going because I was
17:29
Speaker 1: about to take a break unnecessarily. So um when the
17:34
Speaker 1: when the computer is running through the pictures, it just
17:36
Speaker 1: sis soon goes like no, no, no, no, no, millions
17:40
Speaker 1: and millions of times and then finally goes yes. But
17:42
Speaker 1: when it says yes and it spits out another picture,
17:46
Speaker 1: it's not like this is that person? No you want
17:49
Speaker 1: it to be. Because we all watch n c I S,
17:51
Speaker 1: we all watch c S I, we all watch um
17:55
Speaker 1: Law and Order, we all watch um uh party down
18:00
Speaker 1: uh Andy Griffith, that Mattlock, the whole CHAMAI um, so
18:07
Speaker 1: we wanted to just spit out and be like, here's
18:09
Speaker 1: your here's your your person of interest, right. But what
18:12
Speaker 1: it's really doing is it's it's producing a similarity score
18:17
Speaker 1: that is probabilistic. It's saying this is there's this percent
18:21
Speaker 1: chance that this is the same person as the picture
18:25
Speaker 1: or the person in the picture that you uploaded. It's
18:27
Speaker 1: a bit of a guess. It is, so a sophisticated
18:30
Speaker 1: guess it is, and the better computers get at this,
18:35
Speaker 1: the likelier it is that if they say this is
18:38
Speaker 1: a there's a chances the same person that it's the
18:41
Speaker 1: same person. But it's as we'll see, it's up to
18:45
Speaker 1: the human user to determine what is an acceptable threshold
18:50
Speaker 1: of a confidence Is it no? Is it no? Frankly,
18:55
Speaker 1: it really should be about or higher should be the
18:58
Speaker 1: confidence setting for the confidence level. Didn't that what Amazon's
19:02
Speaker 1: recognition says the threshold should be. I'm glad you said that, man,
19:07
Speaker 1: because it really is creepy and I couldn't put my
19:10
Speaker 1: finger on it. And it's exactly I mean, I knew
19:13
Speaker 1: the k looked weird or whatever, but it hadn't hit
19:15
Speaker 1: me that just how creepy it is and just how
19:18
Speaker 1: off the mark or potentially on the mark that name is. Like, Oh, like,
19:23
Speaker 1: if my name was spelled c h u K, I'm
19:26
Speaker 1: sinister a little bit, you'd be more sinister. Yeah, I
19:29
Speaker 1: don't think you could ever be truly sinister. I appreciate that. Alright,
19:32
Speaker 1: let's take a break. I'm gonna go work on sinistering
19:35
Speaker 1: up a bit, and we'll talk a little bit more
19:37
Speaker 1: about some of the uses of f R right after this. So,
20:04
Speaker 1: as with all technology, it has to be abbreviated into
20:07
Speaker 1: two letters, the second of which is are do they
20:10
Speaker 1: call it f R? I've seen it, Yes, I was
20:14
Speaker 1: just silly, but it doesn't surprise me. Nope. So in
20:17
Speaker 1: f R facial recognition technology, there are there are some
20:22
Speaker 1: some beneficial uses for it. Yeah, Like we said, you
20:25
Speaker 1: got to tag people is chief among them. The for
20:30
Speaker 1: people like you and me, that's the pinnacle as it stands.
20:34
Speaker 1: You don't have to tag people yourself. Facebook does it
20:37
Speaker 1: for you. That's what we're trading everything for. I gotta
20:40
Speaker 1: calm down. Okay. There are some other, like genuinely beneficial
20:45
Speaker 1: uses too. There's a nonprofit company called Thorn that scans
20:50
Speaker 1: missing person's pictures against um pictures of children in child
20:56
Speaker 1: porn videos, um or suspected human and trafficking to to
21:02
Speaker 1: get matches, and apparently they've rescued a hundred kids so
21:04
Speaker 1: far from using that technology. There's a pretty beneficial use
21:08
Speaker 1: of facial recognition software. Uh dating apps. Let's say you
21:13
Speaker 1: want to you can get pretty specific on what kind
21:16
Speaker 1: of face you find attractive, which is interesting. But you
21:20
Speaker 1: can say I really think, um, I like guys with
21:24
Speaker 1: high cheekbones, and but no, you would go find mall lips.
21:28
Speaker 1: It would be more like, um, somebody could be like, oh,
21:32
Speaker 1: I really find Christian Bale attractive, and they get a
21:34
Speaker 1: picture of Christian Bale into this dating app and I
21:37
Speaker 1: would come up. But I wouldn't because I wouldn't be
21:39
Speaker 1: in the dating app because I'm happily married. Do you
21:41
Speaker 1: think you look like Christian Bale? I'm told that a lot. Really, Yeah,
21:45
Speaker 1: that's weird. Don't think you look anything like it. I
21:46
Speaker 1: don't either, but people say interesting. I'm I don't know
21:51
Speaker 1: what I would do if I was dating now. I
21:52
Speaker 1: guess I would just go to a service and say
21:54
Speaker 1: and aliety type sure for more games era, But they'd
21:57
Speaker 1: be like, okay, sir, you would just upload the picture.
22:00
Speaker 1: You don't have to come into the office, which is
22:02
Speaker 1: really not even open to the public, and just tell
22:05
Speaker 1: us you're interested in Ali sheety type like a weirdo.
22:08
Speaker 1: You mean, dating apps don't have offices where they just
22:10
Speaker 1: feel complaints and interested parties. You sit down and they
22:13
Speaker 1: they videotape you with the VHS camera, put you on
22:17
Speaker 1: with some other guys on the tape. That's how they
22:19
Speaker 1: used to do it. Oh yeah, that was one of
22:21
Speaker 1: the subplots of Singles, the Cameron Crow movie. Oh yeah,
22:26
Speaker 1: it was Expect the Best was the name of the
22:29
Speaker 1: dating service, and you would make a videotape and like
22:32
Speaker 1: a watch video tapes of people you know, saying who
22:35
Speaker 1: they are. How do you remember that? I was a
22:37
Speaker 1: big singles fan. That's not a bunch I got you. Yeah,
22:40
Speaker 1: Expect the Best. You me pack of cigarettes and some coffee.
22:45
Speaker 1: We don't need anything else becausezoom tight. So what else here?
22:49
Speaker 1: This was? UM Taylor Swift and and her security team
22:53
Speaker 1: on tour used it to scan the audience to see
22:56
Speaker 1: if any of the creeps who have harassed and doctor
23:00
Speaker 1: were in the audience. That's super beneficial. No one should
23:03
Speaker 1: have to go through that. UM also, cops use it
23:06
Speaker 1: in myriad ways, but in particular especially beneficial or um
23:11
Speaker 1: when they use facial recognition to identify people who can't
23:14
Speaker 1: identify themselves. Somebody in the midst of a psychotic break,
23:18
Speaker 1: perhaps somebody wasted on troom's um, somebody you're not Jesus Christ,
23:25
Speaker 1: who who has uh amnesia, Our friend Benjamin Kyle, who
23:31
Speaker 1: apparently knows who he is now but he's decided not
23:34
Speaker 1: to disclose it publicly. Remember the guy he was found
23:37
Speaker 1: behind a burger king near a dumpster, had zero recollection
23:41
Speaker 1: of who he was or how he got there, and
23:43
Speaker 1: like there was this international publicity publicizing like who he
23:49
Speaker 1: wasn't that he couldn't remember who he was, and somebody
23:52
Speaker 1: finally came forward and identified him. So now he knows
23:55
Speaker 1: who he was, but he went like a decade without knowing.
23:59
Speaker 1: By the way, when I said, sorry, you're not Jesus Christ,
24:01
Speaker 1: I was making fun of the guy on mushrooms, not
24:03
Speaker 1: someone in the midst of a psychotic break. I just
24:06
Speaker 1: want to be very specific. I think that was very
24:08
Speaker 1: clear a right to make sure everybody knows that. So uh.
24:13
Speaker 1: Those are some of the good ways that it can
24:15
Speaker 1: be used UM. Now let's talk about all the bad ways. Yeah,
24:19
Speaker 1: I mean when you're talking about the government, you're talking
24:21
Speaker 1: about law enforcement, when you're talking about things like what's
24:27
Speaker 1: going on allegedly in China with CCTVs everywhere trained to
24:32
Speaker 1: UM to single out ethnic minorities and religious groups just
24:39
Speaker 1: walking down the street going about their day. Yeah, that's
24:43
Speaker 1: it gets into much different territory than tagging people in
24:47
Speaker 1: dating apps. Yeah, it's pretty difficult to attend your religious
24:50
Speaker 1: service if you're not allowed to attend your religious service
24:53
Speaker 1: and you're being tracked everywhere you go. Yeah, that's why
24:56
Speaker 1: places like and this is the most predictable thing in
25:00
Speaker 1: the world San Francisco, Oakland and Berkeley and then Somerville, Maine.
25:05
Speaker 1: I knew the Manners would be in there. They're not
25:08
Speaker 1: into this, that's right. They have banned law enforcement UM
25:13
Speaker 1: from using facial recognition altogether in California UM as a state,
25:19
Speaker 1: and the ACE has put a three or moratorium on
25:21
Speaker 1: the use of it UM body cams, which and the
25:24
Speaker 1: a c l U is basically I know this is
25:26
Speaker 1: jumping ahead, but they're at the point where they're like,
25:28
Speaker 1: we need to tap the brakes here for a few
25:31
Speaker 1: years and like because there's no legislation about this yet
25:35
Speaker 1: and it's just going full steam ahead. Yeah, I really
25:38
Speaker 1: I don't want to like like run past that there
25:41
Speaker 1: is aside from Berkeley, San Francisco, who's the other one,
25:46
Speaker 1: Oakland and Somerville, Maine. UM, there are no laws, state, local,
25:53
Speaker 1: or federal governing the use of facial recognition technology by
25:57
Speaker 1: law enforcement. It's just happening very fast. Whatever they want
26:00
Speaker 1: to do, they can do UM and in some cases
26:04
Speaker 1: they do all sorts of stuff with it. They will
26:08
Speaker 1: use it, like the NYPD very famously used UM what
26:12
Speaker 1: you were talking about with doppelgangers. There was a guy
26:14
Speaker 1: who was caught stealing beer at a CVS. Not even
26:18
Speaker 1: a duane read a CVS. UM, and they said, but
26:22
Speaker 1: this guy looks a lot like Woody Harrelson. We don't
26:24
Speaker 1: have a good we don't have a good shot of
26:26
Speaker 1: him to use in official recognitions. So they went and
26:32
Speaker 1: got a pick of Woody Harrelson, and there they came
26:35
Speaker 1: up with a match, and they think it was the
26:37
Speaker 1: guy on video and CVS and so UM. The Georgetown
26:42
Speaker 1: School of Law UH produced a study called Garbage in,
26:46
Speaker 1: Garbage Out, and they were basically like, that's not okay,
26:49
Speaker 1: you really shouldn't be doing that. But that's the level
26:52
Speaker 1: of legality as it stands right now. There's it's just
26:55
Speaker 1: open season, um, and uh, it's just basically whatever you
27:00
Speaker 1: want to do, you can do as far as facial
27:02
Speaker 1: recognition is concerned. In that story in particular, it's like
27:05
Speaker 1: some people are like, awesome, the system works. Sure. Other
27:07
Speaker 1: people are like, what about poor Woody Harrelson. He was
27:10
Speaker 1: really in danger right then of being implicated in this
27:13
Speaker 1: beer stealing scheme from CVS and what he said? What dude,
27:18
Speaker 1: I love that guy man. True Detective the first season, Yeah,
27:23
Speaker 1: first four episodes just amazing. That's called using a probe
27:27
Speaker 1: photo when you use when you say, hey, that looks
27:30
Speaker 1: like someone. They also did the same with one of
27:32
Speaker 1: the New York Knicks. Apparently I could not, for the
27:35
Speaker 1: life of me find out who. Yeah, it's like he's
27:38
Speaker 1: being protected or something that maybe no one said who
27:41
Speaker 1: it was. Um, a couple of numbers for you, though. Uh.
27:44
Speaker 1: The FBI receives about fifty thousand facial recognition search submissions
27:49
Speaker 1: a month for their database. So that's the other thing.
27:52
Speaker 1: If you don't have even the money for a subscription
27:55
Speaker 1: to Amazon Recognition, or you don't have an I T
27:58
Speaker 1: person who's capable of assembling and putting it, you know,
28:02
Speaker 1: using it UM. You can just submit these requests to
28:05
Speaker 1: the FBI. So there's a lot of different avenues you
28:08
Speaker 1: could take his law enforcement to to UM to use
28:12
Speaker 1: facial recognition technology to catch suspected criminals. Yeah. I was
28:16
Speaker 1: about to say bad guys, but who knows. We'll see
28:19
Speaker 1: it's not always the case. So here's some more numbers though,
28:23
Speaker 1: because you know it needs to be regulated. But when
28:28
Speaker 1: it works, it really works. Yeah, it really does though.
28:30
Speaker 1: It is the thing. Yeah, there was one department where
28:32
Speaker 1: they said it lowered the average time required for an
28:34
Speaker 1: officer to identify a subject from an image from thirty
28:38
Speaker 1: days to three minutes, which kind of brings home the
28:41
Speaker 1: point there's another number in here. This interesting, but uh,
28:48
Speaker 1: it brings on the point that like, this is something
28:50
Speaker 1: that human policemen were doing. Officers were doing with their
28:54
Speaker 1: eyeballs by flipping through books, yes, for thirty days straight,
28:59
Speaker 1: saying like it doesn't look like this person. This is
29:02
Speaker 1: like a chance to really speed up that process and
29:04
Speaker 1: to spend more time in theory catching bad guys. Yes,
29:09
Speaker 1: I'm not arguing for it. I'm just saying they were
29:12
Speaker 1: doing this anyway, just through manpower. Right. I think the
29:16
Speaker 1: thing is is anytime you add artificial intelligence automatically makes
29:22
Speaker 1: the user of the artificial intelligence aside unfair unfairly advantaged.
29:27
Speaker 1: It's not like the criminals are able to use AI
29:30
Speaker 1: to steal beer from cvs more effectively, but the cops
29:34
Speaker 1: are using AI to catch them stealing beer more effectively.
29:37
Speaker 1: And it's kind of like, yes, it makes sense to
29:40
Speaker 1: catch like child pornographers and um human traffickers and rapists
29:45
Speaker 1: and murderers and violent criminals with this stuff, but using
29:49
Speaker 1: that kind of technology catch somebody who stole beer from
29:51
Speaker 1: a CVS. If that's when it starts to feel like
29:54
Speaker 1: what kind of society are we moving towards? You know? Well,
29:58
Speaker 1: I think someone not. And let me keep going here
30:00
Speaker 1: for a second, because I don't want people be like,
30:02
Speaker 1: what do you in favor of the guy stealing beer
30:04
Speaker 1: from CVS? No, I'm not. I think you're a scumbag
30:06
Speaker 1: if you steal beer from CVS. But I also think
30:10
Speaker 1: that it's overkilled to use facial recognition technology to catch
30:13
Speaker 1: that person. Use old fashioned police tactics or don't catch them. Yes,
30:19
Speaker 1: it's just kind of the fairness of the old western
30:21
Speaker 1: New York City. I think I might be on the
30:23
Speaker 1: other side because I don't think we need to set
30:26
Speaker 1: a fair playing ground between criminals and cops and saying
30:31
Speaker 1: like it's unfair that cops can use this stuff and
30:34
Speaker 1: criminals are just out there not able to use these
30:37
Speaker 1: same techniques. Okay, So my the fairness thing doesn't just
30:41
Speaker 1: end at the law and order thing, right like, Like,
30:44
Speaker 1: it's not just with cops using it. They they have
30:47
Speaker 1: this huge advantage. I totally get how people would be like, no,
30:50
Speaker 1: give the cops that huge advantage. I don't have an
30:53
Speaker 1: issue with that in and of itself. I think my
30:57
Speaker 1: issue comes a step or two down the road, sure,
31:00
Speaker 1: where the government or the cops, acting on behalf of
31:05
Speaker 1: the government, use that against everyday citizens who have no
31:09
Speaker 1: recourse whatsoever. That that that that lopsidedness that's so evident
31:15
Speaker 1: when you're using AI to catch somebody's stealing beer from
31:18
Speaker 1: the CBS. It's really easy to kind of follow that
31:22
Speaker 1: a little further across to the horizon and see just
31:25
Speaker 1: how unfair life could be and how oppressive that could
31:28
Speaker 1: be using that technology. I think that's ultimately what I'm saying.
31:32
Speaker 1: I hopefully dug myself out of that hole by now so.
31:36
Speaker 1: Uh And and this gets into some of the controversies
31:40
Speaker 1: and the arguments if you're if you're scanning mug shots
31:44
Speaker 1: uh for rapists and arsonists and murderers and violent criminals,
31:50
Speaker 1: and you're catching people, You're not gonna find a lot
31:53
Speaker 1: of people that say, well, that's not fair, go back
31:56
Speaker 1: and use take a month to look through a mug
31:58
Speaker 1: shot book instead, and waste a bunch of time and
32:02
Speaker 1: don't be efficient. So I think most people would say
32:06
Speaker 1: if you're looking at mug shots, although we should point
32:07
Speaker 1: out that a mug shot doesn't mean that just means
32:10
Speaker 1: you're arrested. That doesn't mean you were guilty of anything.
32:13
Speaker 1: Um So, there are plenty of opportunities for false positives
32:17
Speaker 1: and people being put in jail that shouldn't be. But
32:21
Speaker 1: but there's not a lot of people who are like, no,
32:23
Speaker 1: not use mugshot databases right Exactly, If you're scanning driver's
32:27
Speaker 1: license databases or other just general public databases, that's when
32:31
Speaker 1: it gets super tricky because we can't avoid the fact
32:35
Speaker 1: that what that means is in in the Center on
32:37
Speaker 1: Privacy and Technology, Um kind of stated very plainly what
32:42
Speaker 1: that means is everyone is in a perpetual lineup. Essentially,
32:47
Speaker 1: if you have a driver's license, you're part of a
32:49
Speaker 1: police lineup. Yeah, whether you like it or not, whether
32:52
Speaker 1: you know it or not. And if that computer says
32:55
Speaker 1: here's the guy, it's Chuck Bryant, Uh, they will say, oh,
32:58
Speaker 1: he doesn't strike me as very sinister, or in the
33:00
Speaker 1: computer would be like, trust me, this is the guy. Uh,
33:03
Speaker 1: with like an eighty something percent confidence interval. Um, Chuck,
33:07
Speaker 1: Suddenly you're going to get visited by the cops and
33:10
Speaker 1: maybe you'll even get arrested because you were a little
33:12
Speaker 1: KG when they talked to you and you set off
33:14
Speaker 1: their cop radar or whatever. And then the next thing
33:17
Speaker 1: you know, you're in court being charged with the crime
33:20
Speaker 1: that you didn't commit because the computer implicated you and
33:23
Speaker 1: the cops thought that you were acting KG. And let's
33:26
Speaker 1: say that you were a very very poor person and
33:29
Speaker 1: you don't have any money to mount a decent defense.
33:31
Speaker 1: The best you can afford is a free public defender
33:34
Speaker 1: who has fifty other cases is not really paying very
33:37
Speaker 1: much attention to you and you're in jail now because
33:40
Speaker 1: you got convicted wrongly because you were putting a lineup
33:44
Speaker 1: just because you had a driver's license. Yeah. I think
33:47
Speaker 1: for me, um, and this is total my privilege coming
33:51
Speaker 1: through as well, Like I'd want to see some numbers
33:54
Speaker 1: and if one of every ten thousand arrest and conviction
33:58
Speaker 1: of a real criminal or an a rapist and a murderer,
34:01
Speaker 1: and there's three people that get falsely identified and have
34:05
Speaker 1: to go through the system and may or it may
34:07
Speaker 1: not be acquitted, I'd want to see those numbers. But
34:10
Speaker 1: again that's coming from a privileged position as someone who
34:13
Speaker 1: could afford a legal defense who uh white, Yeah exactly.
34:18
Speaker 1: That's another one too, is that people of color uh
34:22
Speaker 1: bear a inordinate burden, disproportionate burden when it comes to
34:26
Speaker 1: facial recognition technologies. We'll see well, I mean you might
34:29
Speaker 1: as well go ahead and talk about that. It's um.
34:31
Speaker 1: I think from the beginning, even with social media, there
34:35
Speaker 1: were certain facial recognition, early facial recognition technologies that admitted like,
34:41
Speaker 1: we're not as good as uh seeing or recognizing faces
34:46
Speaker 1: with darker skin. It's just not that good. Yeah, I
34:49
Speaker 1: think something like darker skinned men and women were recognized,
34:52
Speaker 1: twelve and thirty were misidentified, compared to one percent and
34:57
Speaker 1: seven percent of light skinned men and him in And
35:00
Speaker 1: they say it's because of the data sets that these
35:04
Speaker 1: machines have been trained on, which is not it's not purposefully,
35:08
Speaker 1: but it makes sense if you live in like a
35:10
Speaker 1: generally like like the white people are in power, and
35:14
Speaker 1: it's like whiteness is the most celebrated part of the
35:18
Speaker 1: society or whatever. That's why you're going to have more
35:20
Speaker 1: pictures of And when you feed just a bunch of
35:24
Speaker 1: pictures from your society into a machine and say, learn
35:27
Speaker 1: what faces are, it's going to go, oh, white men,
35:29
Speaker 1: I got you. Well, they just are more white people.
35:32
Speaker 1: Numbers wise, probably has something to do with it, right, Um, Yes,
35:36
Speaker 1: that's a really that is an excellent point as well,
35:38
Speaker 1: for sure. But the fact of the matter is the
35:41
Speaker 1: data sets that the machines are learning on are largely
35:46
Speaker 1: white and largely male, and so they're just not as
35:50
Speaker 1: good at recognizing the differences in faces among um people
35:55
Speaker 1: who aren't white males. Uh, let's read these quotes. There's
35:59
Speaker 1: a couple of good quotes here. The first one is
36:02
Speaker 1: from Woodrow Heart sog. I was going to read it
36:07
Speaker 1: as wurner. I don't know if I can. I should
36:10
Speaker 1: get Nolan here. He does a good burner. The most
36:12
Speaker 1: uniquely dangerous surveillance mechanism ever intended. It's an irresistible tool
36:18
Speaker 1: for oppression that's perfectly suited for governments to display unprecedented
36:23
Speaker 1: authoritary tane I'm sorry, authoritarian control and an all out
36:29
Speaker 1: privacy eviscerating machine. I just realized it's heart sog, so
36:36
Speaker 1: it's it's spelled differently. It's a j r T hert
36:39
Speaker 1: sog is just h g r z o G. I'm
36:42
Speaker 1: glad that we didn't figure that out beforehand, though. You
36:45
Speaker 1: want to take the other one though. I also I
36:47
Speaker 1: have to say I detected a hint of Michael Caine
36:50
Speaker 1: in there too. There might have been a little bit.
36:52
Speaker 1: It's hard to get Michael Caine out of my system.
36:55
Speaker 1: What's the other one? Oh? From Microsoft president Brad Smith? Yeah. So.
36:58
Speaker 1: Brad Smith says that when combined with ubiquitous cameras and
37:02
Speaker 1: massive computing power and storage in the cloud, a government
37:05
Speaker 1: could use facial recognition technology to enable continuous surveillance of
37:10
Speaker 1: specific individuals like they're supposedly doing in China as an aside,
37:14
Speaker 1: it could follow anyone anywhere, or for that matter, everyone everywhere,
37:18
Speaker 1: at any time, or even all the time. And he
37:21
Speaker 1: wasn't this wasn't a sales pitch. He was speaking out
37:23
Speaker 1: against this to Congress, saying like, guys, we gotta we
37:26
Speaker 1: have to do something about this, because this is the
37:29
Speaker 1: path we're heading down. And that's why uh Seth abrama
37:34
Speaker 1: Witz changed his name to Brad Smith. It sounds like
37:38
Speaker 1: a total like like I just want to blend in. UM.
37:43
Speaker 1: So you've got scanning against mug shots, scanning against driver's licenses,
37:47
Speaker 1: and then um, there's a new one that just came out.
37:49
Speaker 1: The New York Times just released this expose on January eighteenth,
37:53
Speaker 1: just a few days ago, UM on a company called
37:56
Speaker 1: clear View AI. And apparently even among um Silicon Valley
38:02
Speaker 1: there has been this longstanding kind of unspoken thing where
38:06
Speaker 1: let's steer clear of this facial recognition technology because it's
38:10
Speaker 1: such a tool of oppression potentially. And clear View AI said, hey,
38:14
Speaker 1: we're not from Silicon Valley. Well, we're just going to
38:16
Speaker 1: do our own thing. So now there's this, there's this
38:20
Speaker 1: tool that's available to law enforcement agencies that they're using.
38:23
Speaker 1: Remember that one that one guy who had a quote
38:26
Speaker 1: saying that, um, it went from thirty days to three minutes.
38:29
Speaker 1: They were almost certainly using clear View AI. And the
38:33
Speaker 1: reason clear View AI has such an advantage because they've
38:36
Speaker 1: gone to this place where everyone else said was off limits,
38:39
Speaker 1: which is scraping social media. So rather than the forty
38:43
Speaker 1: one million driver's license and mug shot pictures that is
38:47
Speaker 1: available in the FBI's database, clear View AI is this
38:52
Speaker 1: app that you can subscribe to for a year for
38:54
Speaker 1: like two thousand and ten thousand dollars, and they have
38:57
Speaker 1: three billion pictures, including links to the social accounts of
39:02
Speaker 1: the people whose pictures come up, so that you can
39:04
Speaker 1: not only see who it is, you can find out
39:07
Speaker 1: where they're at right then. And it's a hugely invasive thing.
39:11
Speaker 1: And there's no legislation on this whatsoever. And it's only
39:16
Speaker 1: just recently come out that this this company even exists,
39:18
Speaker 1: or that this app exists, and that law enforcement is
39:21
Speaker 1: using this stuff because again there's basically no laws saying
39:24
Speaker 1: you can do this, you can't do that. Um And
39:27
Speaker 1: again Woodrow Herzog has basically said, there's no way we're
39:31
Speaker 1: going to realize the benefits of this without the incredibly
39:35
Speaker 1: disproportionate drawbacks, and um, he just calls for an all
39:40
Speaker 1: outband of the technology. He's basically saying it's not worth it.
39:42
Speaker 1: All right, let's take another break. Oh my gosh, we
39:45
Speaker 1: haven't taken our second break yet. Uh, and we'll be
39:48
Speaker 1: right back to talk about the rest of this stuff
39:52
Speaker 1: right after this. I think we should talk a little bit,
40:18
Speaker 1: like we've talked about the false positives, UM, and I
40:22
Speaker 1: think within Amazon, their contention is that what you're talking
40:27
Speaker 1: about with like the studies out of m I T
40:29
Speaker 1: that said, UM, that there are too many false positive
40:33
Speaker 1: is he's they're saying, wait a minute, you're talking about
40:35
Speaker 1: facial analysis, not facial recognition, and those are two different things.
40:40
Speaker 1: I did not understand this at all. I went and
40:41
Speaker 1: looked it up and I just fully get it. Either
40:43
Speaker 1: there's it sounds like some tap dancing to me. I
40:46
Speaker 1: looked at and there's like not a distinction between those
40:49
Speaker 1: two aside from this quote. Yeah, it's basically the same thing.
40:53
Speaker 1: And also it doesn't even make sense as a defense.
40:56
Speaker 1: So basically what they're saying is that um, that they
41:00
Speaker 1: were being called out by M I. T. S. Media Lab.
41:03
Speaker 1: They did a two thousand eighteen study. That's the one
41:06
Speaker 1: that found that there's like a twelve and thirty five
41:08
Speaker 1: misidentification among darker skinned men and women women. I think yeah, um,
41:16
Speaker 1: and Amazon said no, no, no, uh, you guys are
41:19
Speaker 1: using facial analysis, not facial recognition. It's like, no, that's
41:23
Speaker 1: that's not the case at all. They're doing facial recognition.
41:26
Speaker 1: All right. I'm glad it wouldn't just me, because you
41:27
Speaker 1: see I wrote I don't get it next to this.
41:30
Speaker 1: It was a it was a bad, a bad jam,
41:33
Speaker 1: I guess. But I think their point was, well, you're
41:36
Speaker 1: trying to tell the gender of somebody, and if you're
41:39
Speaker 1: doing binary gender stuff, like you're trying to say this
41:43
Speaker 1: as male or female, you can't really use facial recognition
41:47
Speaker 1: for that, especially among darker skinned people. And they said
41:51
Speaker 1: that you shouldn't use that, especially in cases of people's
41:54
Speaker 1: civil liberties or whatever. But it still remains the case
41:58
Speaker 1: that if you are a darker skinned person and you're
42:03
Speaker 1: being looked at by a police department that has their
42:07
Speaker 1: threshold for a confidence level set low, yeah, there's a
42:12
Speaker 1: chance that a false positive is going to be put
42:14
Speaker 1: out there, right, And that's that can be trouble for
42:17
Speaker 1: you if you don't have the money to mount a defense.
42:19
Speaker 1: And even if you do have the money, you shouldn't
42:21
Speaker 1: have to mount a defense to spend money on that
42:23
Speaker 1: to be equitted of a crime. Just because the computer
42:26
Speaker 1: is not so good at a distinguishing Um black people
42:30
Speaker 1: think it is among white people. Yeah, And what you
42:33
Speaker 1: know when it comes to where this is going to
42:35
Speaker 1: end up legally, Uh, you might want to look at
42:38
Speaker 1: the Fourth Amendment. Um. It gets really dicey on how
42:41
Speaker 1: you interpret the constitution when you talk about illegal search
42:45
Speaker 1: and seizure? Is this a search or a seizure? Probably not, um,
42:50
Speaker 1: because it depends on what we're talking about with the
42:53
Speaker 1: Supreme Court. Um. You've probably been stopped h in a
42:58
Speaker 1: at a d y checkpoint, and that's stopping everybody. That's
43:01
Speaker 1: sort of the same thing. It's like, if you're in
43:03
Speaker 1: a car, we're gonna stop you and check you out
43:05
Speaker 1: because the couplet the public, the public his said, you know,
43:11
Speaker 1: that's okay, it's reasonable, it's not super invasive. And if
43:14
Speaker 1: you're stopping drunk drivers, it's just putting someone out for
43:17
Speaker 1: a few minutes. Yeah, the court said, if it's minimally
43:19
Speaker 1: invasive and the public good or the potential for public good,
43:23
Speaker 1: which is in this case getting drunk drivers off the
43:26
Speaker 1: road is high enough, then it's it's okay to basically
43:29
Speaker 1: search everybody without probable cause. Yeah. Same with T s
43:32
Speaker 1: A checkpoints when it comes to official rulings. Obviously we
43:36
Speaker 1: don't have one in facial recognition yet. But if you
43:38
Speaker 1: look at Carpenter v. United States, the court ruled five
43:42
Speaker 1: four that police violated uh Fourth Amendment rights of a
43:45
Speaker 1: man when they asked for his cell phone location data
43:48
Speaker 1: without a warrant from T Mobile. Um. So hopefully this
43:53
Speaker 1: nuance will prevail, and it just won't. It looks like
43:56
Speaker 1: it probably won't be some blanket ruling that just says, yep,
43:59
Speaker 1: you can use it for whatever you want, right if
44:01
Speaker 1: it even gets to that point, and if the court
44:03
Speaker 1: hears it, which probably would. So. The other thing that
44:06
Speaker 1: um that is has become worrisome for people, though, is there.
44:11
Speaker 1: It's becoming um our society is becoming increasingly surveiled. Right
44:17
Speaker 1: like ring the ring doorbell. They market to law enforcement
44:22
Speaker 1: basically saying like you can, you can, These people like
44:24
Speaker 1: will pay to have video cameras put on their house
44:26
Speaker 1: and you can go get these videos on neighborhood all
44:29
Speaker 1: the time, people like my car got broken into, who
44:31
Speaker 1: can help me out with their cameras. So it's being
44:33
Speaker 1: marketed to law enforcement. Your TV has a camera and
44:36
Speaker 1: your smart speaker has a microphone and it So the
44:38
Speaker 1: more that we are um surveiled and the more ubiquitous
44:43
Speaker 1: facial recognition technology gets, the easier will be to not
44:47
Speaker 1: just scan a picture of somebody stealing beard of a
44:50
Speaker 1: CVS against the mugshot database, or but to say this
44:58
Speaker 1: person right here that you're you're looking at, that the
45:01
Speaker 1: cameras following, that's this, that's that's um, that's uh, that's
45:05
Speaker 1: Chuck Bryant right there. And everywhere you walk there's getting
45:09
Speaker 1: a little icon next to your head Chuck Bryant. You know,
45:11
Speaker 1: if you click it, it'll show you your Facebook page
45:14
Speaker 1: or a map to your house or whatever. They want
45:16
Speaker 1: to know, your police record, it doesn't matter. And that
45:18
Speaker 1: this is what we're increasingly getting closer to. And some
45:23
Speaker 1: people say this is what they're already doing in China. Yeah,
45:26
Speaker 1: and London has has They were one of the first
45:28
Speaker 1: on the CCTV train. Yes, but they use humans, which
45:32
Speaker 1: is fair right for recognizing phases. Yeah, they have people
45:37
Speaker 1: like actually looking at the at the individual monitors looking
45:40
Speaker 1: for crime. This is this is the idea of this
45:43
Speaker 1: is it's just tracking people who are just doing nothing wrong. Yeah,
45:46
Speaker 1: but there are plenty of people on the other side
45:48
Speaker 1: we should point out that are like, you know what,
45:51
Speaker 1: if you're catching bad guys, that's great. If you're a
45:53
Speaker 1: good guy, you've got nothing to hide, so you shouldn't
45:55
Speaker 1: sweat it. Yeah. I can never remember the name of
45:57
Speaker 1: the article. I'll try to find it, but there's a
46:01
Speaker 1: man I wish I could remember off the top of
46:03
Speaker 1: my head. But there's there's this amazing article from a
46:06
Speaker 1: few years back, um that that basically says like that's
46:09
Speaker 1: that's a terrible argument that that even if you have
46:13
Speaker 1: nothing to hide, you still, um are a human being.
46:18
Speaker 1: And if somebody wanted to put together and but if
46:21
Speaker 1: somebody wanted to put together like a dossier on your
46:25
Speaker 1: embarrassing things that you've done or said or thought or whatever,
46:29
Speaker 1: um and and put it all together and condensed it,
46:32
Speaker 1: you can make anybody look bad. No one should want
46:35
Speaker 1: to live in a situation where like that could conceivably
46:38
Speaker 1: happen in the police state. Yeah, police state, good stuff.
46:44
Speaker 1: I guess we'll see how it pans out. I'm not
46:47
Speaker 1: saying police state stuff. Police date. It's good stuff. Yeah,
46:51
Speaker 1: we'll see what happens right in Woodrow, Hurtzog and let
46:55
Speaker 1: us know what to do. If you want to know
46:57
Speaker 1: more about facial recognition technology, you can go onto the
47:02
Speaker 1: internet and start reading stuff about it. Definitely read the
47:05
Speaker 1: New York Times expose about um clear view AI came
47:10
Speaker 1: out January. Yes, okay, since I said that it's time
47:14
Speaker 1: for a listener mitt all right, now it's not you
47:16
Speaker 1: know what it's time for. Oh yeah, I know it's
47:18
Speaker 1: time for you ready, Yeah, you say it. It's time
47:20
Speaker 1: for administrative details. All right. This is part two. This
47:33
Speaker 1: is where we thank people on the show that have
47:35
Speaker 1: sent us kindness. Is via snail mail. Siggy s I
47:41
Speaker 1: g D. I sent sent me some hand that it
47:44
Speaker 1: sucks not you for some reason. I don't know why.
47:46
Speaker 1: I got some socks too. Oh really yeah, I didn't
47:49
Speaker 1: know who they were from, so they may be from Siggy.
47:51
Speaker 1: I think probably what it was is you left him
47:54
Speaker 1: with my desk and I thank you for it, CHUCKR
47:58
Speaker 1: do another one while I'm pulling up my list acting.
48:01
Speaker 1: Julie Shoop made us t shirts. Shoop, this is good stuff.
48:05
Speaker 1: Faux band name tour shirts. Uh, super fun. Thanks a
48:09
Speaker 1: lot of Julie. Very cool. Uh, you're still working, so
48:12
Speaker 1: I'm gonna keep going. Thalia Dawes is our pal from Australia,
48:17
Speaker 1: said my daughter a couple of books. She's a very
48:20
Speaker 1: lovely lady who has a very adorable and whip smart
48:23
Speaker 1: daughter about the same age, who listens to our show.
48:27
Speaker 1: And um, I was just like, man, I wish she
48:30
Speaker 1: lived here. We could go into play date. They both
48:32
Speaker 1: seemed like lovely humans. There's such thing as plains. Yeah,
48:35
Speaker 1: good Australia for a play date. Um. So at our
48:39
Speaker 1: Portland's main show, Chuck, we had like a lot of um,
48:42
Speaker 1: We've got a lot of neat gifts. Jim Diefenbacher made
48:46
Speaker 1: us amazing cross hatch portraits of them. Yeah, those were
48:51
Speaker 1: great of us, like of a photo we took I
48:54
Speaker 1: think on like our West Coast tour from two. It
48:57
Speaker 1: brought back some memories saw that. It's just really great stuff.
49:00
Speaker 1: And you can see Jim's work at Jim Diefenbaker dot com,
49:03
Speaker 1: j I M D I E F F E N
49:06
Speaker 1: B A C H E R dot com and they
49:09
Speaker 1: were framed in everything. Yeah, very sweet stuff, Jim. We
49:12
Speaker 1: got some home tapped maple syrup from Andy Huntsberger from
49:19
Speaker 1: Elgin I A, Okay, what's Iowa? Is that Iowa? Yeah? Yeah, okay, Yeah,
49:24
Speaker 1: I was about to say the wrong state. What are
49:27
Speaker 1: you gonna say? You know, I think I went to
49:29
Speaker 1: say Illinoia. If you ever see Gary Gulman's bid on
49:32
Speaker 1: Nate abbreviating the states, dude, just look it up. One
49:35
Speaker 1: of the great comedy bits I've ever seen. It's hysterical.
49:39
Speaker 1: Let's see. Uh oh. Another at the Portlands Show, we
49:43
Speaker 1: got a letter from tog Braun from Downea's day Boat
49:47
Speaker 1: from Lloyd Braun. Tog Braun Um and Downey's day Boats
49:53
Speaker 1: mission is to bring sustainable, delicious scalops from Maine to
49:56
Speaker 1: the world, and she said that scalops have varietals like
50:00
Speaker 1: oysters and that main has the best. So check out
50:02
Speaker 1: down East day Boat dot com. Coke Braun feel free
50:07
Speaker 1: to send us some scallops as long as they've been
50:09
Speaker 1: appropriately refrigerated. The entire time. I got another children's book,
50:14
Speaker 1: Are You a Good Egg? And that was from Peter Deutschel,
50:18
Speaker 1: along with some stuff you should know coasters. Yeah yeah,
50:21
Speaker 1: thanks again Peter. I think we thanked him last episode
50:24
Speaker 1: for the coasters too. I didn't know about the children's book.
50:27
Speaker 1: Then Sarah Law who is an s y s k
50:31
Speaker 1: Army member. Um, she came to the Toronto show and
50:34
Speaker 1: she brought us a bunch of um Canadian goodies, everything
50:38
Speaker 1: from Japanese cheesecakes and tarts from Uncle Tetsus. So good.
50:43
Speaker 1: Um and uh. I think some other stuff too, like
50:46
Speaker 1: coffee crisps, which are my favorite. Um. Yeah, so thanks
50:50
Speaker 1: a lot. Sarah's always that is everything from Japan awesome.
50:54
Speaker 1: They just it's really good. They don't necessarily invent much.
50:57
Speaker 1: They just take other people's inventions and perfect them. And
51:00
Speaker 1: it seems like they take a lot of pride in
51:01
Speaker 1: like doing things right. I think that he could say that,
51:04
Speaker 1: probably because we got from Matt an assortment of food
51:12
Speaker 1: things from Japan and that came in today, including our
51:17
Speaker 1: beloved QP Man's. I love that stuff. It's been too long, man,
51:22
Speaker 1: God bless you. Let's see Leah Harrison gave us some
51:25
Speaker 1: amazing goodies to including coffee Crisp and Canadian Smarties, which
51:30
Speaker 1: are way better than American Smarties because they involved chocolate
51:33
Speaker 1: and super smarties. A student named Maria Styling wrote us
51:37
Speaker 1: a letter for an honors English project because she had
51:40
Speaker 1: to write someone who inspired her, and she asked this.
51:43
Speaker 1: I told her we to answer, how do we choose
51:45
Speaker 1: a topic? Maria? We choose a topic. It's not it's
51:48
Speaker 1: pretty low fih. We just send each other one each
51:52
Speaker 1: week on whatever happens to grab our fancy. We're always
51:55
Speaker 1: looking around our world, uh and thinking I wonder about that.
52:00
Speaker 1: That's as that's as easy as it gets. And we'll
52:01
Speaker 1: just send each other an email and times out of
52:04
Speaker 1: a hundred will say great, let's do it. Yeah more ka,
52:09
Speaker 1: let's see um oh. Michael C. Learner, who's an attorney
52:13
Speaker 1: at Law and Reno, sent us a letter about getting
52:16
Speaker 1: the word out about the National Consumer Law Center, for
52:20
Speaker 1: which Learner does a lot of pro bono work for
52:22
Speaker 1: people who are poor and getting screwed over because of debt.
52:25
Speaker 1: As he put it so, he pointed to the National
52:28
Speaker 1: Consumer Law Center and the Practicing law Institutes Consumer Financial Services.
52:32
Speaker 1: Answer book. So if you are in debt and you're
52:36
Speaker 1: getting pushed around, go check those things out, says Michael C. Learner.
52:39
Speaker 1: Good stuff Van Ostro, and we gotta thank him again.
52:42
Speaker 1: Our buddy from Washington sent us a book by his
52:45
Speaker 1: friend Andy Robbins called Field Guide to the North American Jackalothes.
52:49
Speaker 1: Pretty awesome, that's pretty fun. Paul Speth from Mars Community
52:53
Speaker 1: Brewing Company in Chicago gives a bunch of beer at
52:56
Speaker 1: the Chicago show. Thank you for that. Um, I got
52:59
Speaker 1: one more. Okay, I'll go and finish up and then
53:01
Speaker 1: you can round us out. Man, I have a whole page.
53:03
Speaker 1: Laugh all right. Robert Highland from Wammo, this was just
53:06
Speaker 1: came in today. Okay. He works for Wammo. He sent
53:09
Speaker 1: us each their seventieth anniversary super book. Oh wow, thanks
53:13
Speaker 1: a lot. Like you guys talk a lot about wammo products.
53:15
Speaker 1: Does it bounce? I have not dropped it on the floor. YEA,
53:18
Speaker 1: let's find out. Give it a try. I'm gonna do
53:21
Speaker 1: a couple more and then well maybe we'll split these
53:23
Speaker 1: up because there for both of us for another episode.
53:26
Speaker 1: It's up to you. He can blaze through them too.
53:28
Speaker 1: Now there's too many Um, so let's see the Crown
53:32
Speaker 1: Royal people again for hooking us up. Very sweet. They've
53:37
Speaker 1: hooked us up many, many times. Um. And they gave
53:40
Speaker 1: us a nice congratulations because we've got the Best Curiosity
53:43
Speaker 1: Award from the Heart Podcast Awards last year. Oh yeah,
53:47
Speaker 1: that's how old this one is. Mick Sullivan gave us
53:49
Speaker 1: a copy of his book The Meat Shower, which is
53:51
Speaker 1: amazingly illustrated. You can check it out on the Past
53:54
Speaker 1: and the Curious dot com. Yeah that just sounds really
53:58
Speaker 1: greats it really does. Let's see, um and over round
54:02
Speaker 1: everything out with Danielle Dixon, who is a real life
54:05
Speaker 1: marine biologists chuck at the University of Delaware, and she
54:08
Speaker 1: sent us a couple of copies of her kids books
54:11
Speaker 1: See Stories, children's books based on real science. You can
54:15
Speaker 1: check it out at s E A S T O
54:18
Speaker 1: r Y books dot com. Alright, you're gonna save the rest.
54:22
Speaker 1: I'm gonna save the restival splow up. All right. Thanks
54:25
Speaker 1: everybody who sent us stuff, and and thank you also
54:28
Speaker 1: just for saying hi to anyone who does. You can
54:30
Speaker 1: say hi to us by sending us an email. Wrap
54:33
Speaker 1: it up, spending on the bottom, send it off to
54:35
Speaker 1: Stuff podcast at i heart radio dot com. Stuff you
54:43
Speaker 1: Should Know is a production of iHeart Radio's How Stuff Works.
54:45
Speaker 1: For more podcasts for my heart Radio, visit the iHeart
54:48
Speaker 1: radio app, Apple Podcasts, or wherever you listen to your
54:50
Speaker 1: favorite shows.