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Speaker 1: Hey everybody, it's me Josh, and I'm here to tell

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Speaker 1: you it's official. We're going to be in Vancouver, b C.

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Speaker 1: And Portland, Oregon this March. On March twenty nine, will

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Speaker 1: be at the Chance Center in Vancouver and on March

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Speaker 1: will be at the Arlene Schnitzer Concert Hall in Portland.

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Speaker 1: So come see us. Tickets go on sale this Friday.

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Speaker 1: Go to s Y s K live dot com for

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Speaker 1: ticket links and info and everything you need and we'll

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Speaker 1: see you guys in March. Welcome to Stuff You Should Know,

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Speaker 1: a production of I Heart Radios How Stuff Works. Hey,

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Speaker 1: welcome to the podcast. I'm Josh Clark. There's Charles Scooter,

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Speaker 1: Computer Bryant and Jerry Um Matthew Broderick in War Games

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Speaker 1: Rolling and this is Should Know. That's good. Thanks. What's

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Speaker 1: your name, Josh m hmm? Okay, Josh, Ali Shedy in

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Speaker 1: More Games Okay Clark, I know Aaron Cooper is doing

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Speaker 1: right now? Man? How cute was she in that movie? Like?

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Speaker 1: I think they designed that movie for like every thirteen

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Speaker 1: year old boy in America's fall in love with Ali

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Speaker 1: shed I think you're talking about um short Circuit. I

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Speaker 1: never saw that, believe that what with Johnny five. I mean,

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Speaker 1: I know the movie. You gotta see it. Really, it's

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Speaker 1: pretty awful, especially with um oh, what's his name? Who

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Speaker 1: is the sleeves ball from Fast Times at Ridgemont High?

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Speaker 1: Who is the ticket scalper? Yes, he plays a Indian

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Speaker 1: like Asian Indian character, like full on brown face and everything.

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Speaker 1: It's really bad. The movie is bad enough, but then

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Speaker 1: now when you go back and see that, to your like,

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Speaker 1: I can't believe this. I can't believe it. I think

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Speaker 1: he's Italian, oh easily, maybe Jewish or maybe just a

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Speaker 1: straight up white guy. He's definitely not Asian Indian. No,

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Speaker 1: he's not. But anyway, go see Short Circuit. Okay, see

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Speaker 1: what you think Ali shed He just keeps looking at

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Speaker 1: the camera going, I'm so sorry that was a big

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Speaker 1: hit though she didn't have anything be sorry about. Um So.

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Speaker 1: I guess War Games is kind of in your your wheelhouse. Yeah,

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Speaker 1: it was a little old for me. Yeah, because I

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Speaker 1: saw that when I was like twelve, bish uh, and

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Speaker 1: you would have been I don't know how how much

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Speaker 1: younger are you. I would have been seven seven. Yeah,

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Speaker 1: that's a little young for War Games, I would think.

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Speaker 1: I mean, I still watched your wherever, but I was like, yeah,

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Speaker 1: that's hilarious. The computer is called Whopper. Yeah, I mean

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Speaker 1: it was right in my wheelhouse, right. I remember at

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Speaker 1: the end of war Games, Uh, they lock in, you know,

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Speaker 1: they're decoding the the code one number and letter at

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Speaker 1: a time. Very suspenseful and yeah, very suspenseful. And it

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Speaker 1: finally locks in and me and my friends memerized it

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Speaker 1: so we could go home and plug it into an

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Speaker 1: Apple too to see what happened. Nothing, Okay, how do

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Speaker 1: you plug in a number anyway? What does that even mean? Oh? Yeah,

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Speaker 1: that's true, you know. Yeah. I remember a very rudimentary

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Speaker 1: program you could run where you could type in like

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Speaker 1: four lines of whatever I don't even know if you

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Speaker 1: call it code with a phrase, and it would run

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Speaker 1: the phrase like a thousand times all over your screen

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Speaker 1: and a big scroll. And then that just thought that

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Speaker 1: was the coolest thing ever. I feel like, I remember

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Speaker 1: what you're talking just like five lines was like the

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Speaker 1: only part I remember is twenty go to ten and

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Speaker 1: ten was the phrase I think something like I don't remember.

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Speaker 1: Then I was like, man, let's just play Castle Wolfenstein.

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Speaker 1: That was a good one. I remember, did Oregon Trail.

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Speaker 1: I never did as well. I was Castle Wolfenstein, but

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Speaker 1: like wolf and signed on the PC. Oh yeah, like

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Speaker 1: move left arrow, right arrow, shoot uh shoot dash, which

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Speaker 1: is some sort of a bullet. Yeah. It was fun.

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Speaker 1: That was fun, and I thought it was just like

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Speaker 1: the height of technological gaming. It was it was at

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Speaker 1: the time. But now, Chuck, we've reached the height of

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Speaker 1: technology which is being tracked everywhere you go, look at

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Speaker 1: you all the time by whoever wants to do that.

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Speaker 1: I'm gonna change your name to Josh smooth up a

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Speaker 1: Raider Clark for that transition. Very nice. Yeah, Like I

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Speaker 1: like Ali Shedy Clark, Josh, Ali Shedy and Clark. Yeah,

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Speaker 1: this is a good one. Uh did you put this together?

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Speaker 1: It was this you and this is Dave. Dave Brews

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Speaker 1: good stuff in high Dave. We finally got to meet

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Speaker 1: Dave and his family, lovely family that we cursed awfully

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Speaker 1: in front of in Seattle. I felt terrible, thanks for

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Speaker 1: adding yourself to the max and he was like, you

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Speaker 1: know whatever, he was fine. His kids were adorable, they

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Speaker 1: were they were great. They couldn't look at me though

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Speaker 1: that really they were probably just him did by your present,

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Speaker 1: No No, it was because I cursed so badly. So

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Speaker 1: this is good stuff. Though. Facial recognition technology that they've

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Speaker 1: been kind of at since the nineteen fifties, which they

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Speaker 1: rolled out as a test in two thousand two at

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Speaker 1: the Super Bowl in New Orleans, did not go that well. No,

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Speaker 1: it was a little clunky back then, but it's gotten

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Speaker 1: a lot better since then. Let me explain why. For

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Speaker 1: anyone who's listened to the End of the World with

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Speaker 1: Josh Clark the AI episode in particular, everything associated with

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Speaker 1: artificial intelligence got way better starting around two thousand seven,

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Speaker 1: when neural nets became a viable form of machine learning.

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Speaker 1: Because you don't have to train a computer what constitutes

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Speaker 1: a human face and what to look for. You just

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Speaker 1: feeded a bunch of pictures of faces and say these

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Speaker 1: are human faces, learn what a human faces and they

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Speaker 1: trained themselves. And so around about two thousand seven, two

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Speaker 1: thousand eight, two thousand nine, everything had to do with

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Speaker 1: machine learning got way smarter because we started using neural

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Speaker 1: nets and facial recognition software is no exception. Yeah, and

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Speaker 1: there were a few things that kind of converged all

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Speaker 1: at the same time or around the same time. Uh,

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Speaker 1: social meds kind of coming on the scene right in

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Speaker 1: that wheelhouse was a big deal. Um, Facebook, this is staggering.

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Speaker 1: Facebook just by itself processes three hundred and fifty million

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Speaker 1: new photos through its uh facial recognition software every day,

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Speaker 1: and every time one comes through. Mark Zuckerberg goes like,

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Speaker 1: you think it's neat when you go when you put

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Speaker 1: a picture up and it says like would you like

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Speaker 1: to tag Emily your wife because that's her? And you think, oh, well,

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Speaker 1: that's super easy, thanks Facebook. But then you don't think, like,

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Speaker 1: wait a minute, all right, how do they know that's

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Speaker 1: my wife? And you know, it's like with everything else, Uh,

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Speaker 1: there's privacy people that were like, whoa do you guys

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Speaker 1: realize what's going on? And then of the sheep they're

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Speaker 1: like huh no, no, they're like no, it's great, Like

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Speaker 1: I don't have to go in and like make click

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Speaker 1: two links or two buttons to take the way easier.

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Speaker 1: So that was one thing. There's way more photos out

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Speaker 1: there for those machines to learn one like good high

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Speaker 1: quality photos right uty million a day just on Facebook alone, um,

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Speaker 1: which means the machines were getting smarter, They were getting

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Speaker 1: better and better at at training themselves. And then lastly, um,

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Speaker 1: that has led to a ubiquity in in facial recognition.

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Speaker 1: That the better the machines have gotten, the easier it

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Speaker 1: has been to put together data sets for them to

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Speaker 1: train on, which is lots and lots of pictures of people. Um,

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Speaker 1: the cheaper the technology has gotten, which means the more

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Speaker 1: people that are now using facial recognition than ever. Yeah.

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Speaker 1: Amazon has a service called Recognition with a K, which

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Speaker 1: is not a good look. No, it looks very German.

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Speaker 1: There's something about replacing at sea with the K. It

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Speaker 1: just looks creepy, Like when you spell America with a K,

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Speaker 1: it means something. It means like bad America. Yet they

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Speaker 1: went right full steam ahead and called it recognition. You

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Speaker 1: have to say it like that, you do, I think,

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Speaker 1: and you have to be like squeezing the air out

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Speaker 1: of a syringe while you're saying it too. Um, so

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Speaker 1: they have this. I didn't even know about this, but

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Speaker 1: it's ubiquitous and it's not super expensive, and that means

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Speaker 1: that the law enforcement agencies agencies they don't have to

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Speaker 1: like create their own they can just say, well, just

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Speaker 1: to sign up for a recognition right exactly. Because it's there,

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Speaker 1: and because it's it's relatively cheap, you can just get

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Speaker 1: a subscription, not just law enforcement agencies. If you have

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Speaker 1: a photo sharing app or whatever and you want some

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Speaker 1: facial recognition technology, you just contract with with Amazon, and

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Speaker 1: Amazon goes here, you go, here's our data, and you

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Speaker 1: can or um like our code, and you can put

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Speaker 1: it onto your platform. Anybody can use it. Um. So

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Speaker 1: it is kind of everywhere, and that makes a lot

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Speaker 1: of people, including me, very very nervous because as this

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Speaker 1: guy wood Row hurt Zog, if he's not Werner hurt Zog, brother,

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Speaker 1: I'll be disappointed. But but a wood Row and a

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Speaker 1: Werner come on, you know anyway. Um. Wood Row hurt

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Speaker 1: Zog as a professor of computer science, and I believe privacy,

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Speaker 1: civil liberty. Um. He basically says, Look, there is no

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Speaker 1: way we're going to reap the benefits of facial recognition

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Speaker 1: without ultimately sliding irreversibly into a dystopian surveillance state where

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Speaker 1: it's happening right now, and if we don't do something

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Speaker 1: about it, it's never going to change back. We're about

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Speaker 1: to fully give up our privacy because it's one thing

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Speaker 1: to have your phone tracked you can give up your phone,

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Speaker 1: get yourself a burner phone, like you're Jesse Pinkman or

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Speaker 1: something like that, and then you just throw that phone away.

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Speaker 1: You can't be tracked anymore. You can't get a burner face.

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Speaker 1: And if that does become a thing down the line,

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Speaker 1: it'll be very, very expensive. So the average person can't

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Speaker 1: get a burner face. Will be tracked by our face

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Speaker 1: everywhere we go. And as we add more and more

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Speaker 1: cameras and this technology becomes cheaper and cheaper, it's we

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Speaker 1: will be living in a world where there will be

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Speaker 1: zero privacy and will be monitored and tracked because it

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Speaker 1: will be so easy, and it will be sold to

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Speaker 1: us like it's being sold to us now that it's

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Speaker 1: a law enforcement tool to get the bad guys. But

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Speaker 1: it's eventually going to extend to include everybody. But what

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Speaker 1: do you have to worry about. You're an upstanding citizen.

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Speaker 1: It doesn't matter if you're tracked. That's not true. That's

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Speaker 1: just not the case, everybody, It's not case. All right,

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Speaker 1: we're gonna call that soap soapbox soliloquy number one of

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Speaker 1: what I guarantee will be probably three or four. Uh,

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Speaker 1: let's talk of a little bit about how it works.

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Speaker 1: It is biometric authentication. Um. It's like a fingerprint or

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Speaker 1: a retina scan. And basically what it does is, uh,

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Speaker 1: it is precise measurements of a face to calculate every

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Speaker 1: person's very unique visual geometry, like how far apart your

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Speaker 1: eyes are, how how far apart your pupils are from

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Speaker 1: your nostrils. Yeah, your facial geometry, how your face is

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Speaker 1: all set up? I think. Yeah, it's even gotten into

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Speaker 1: things like facial hair, skin tone, um, skin texture. Yeah,

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Speaker 1: I'm sure it'll get just more and more specific. Yeah.

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Speaker 1: And then you know, because they're getting the machines are

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Speaker 1: getting better and better and easier and easier to train

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Speaker 1: on this stuff, you can just add more and more

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Speaker 1: data to it, and the the the recognition will just

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Speaker 1: become increasingly um um good. Yeah. And if you want

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Speaker 1: to throw off facial recognition software and freak out every

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Speaker 1: human you meet to shave your eyebrows, oh yeah, that

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Speaker 1: would be a little freaky. Have you ever seen that.

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Speaker 1: You've seen it before, I'm sure in movies and stuff.

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Speaker 1: It's an interesting thing. I remember a kid in industrial

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Speaker 1: arts class did that one year. He was like a

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Speaker 1: little you know, kind of a ninth grade burnout, and

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Speaker 1: he just showed up one day with no eyebrows, like

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Speaker 1: you would I think like not having a nose would

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Speaker 1: be more easily accepted. There's something just uncanny when someone

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Speaker 1: shaves their eyebrows, Like one day they have them, the

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Speaker 1: next day they don't. Was it like immediately recognizable what

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Speaker 1: the thing was, or was it like that's the thing

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Speaker 1: off today? Whereas if you, you know, you came in

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Speaker 1: the next day without a nose, the first thing you

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Speaker 1: would say is what happened to your nose? What happened

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Speaker 1: to your nose? Todd? Yeah, and Todd would be like,

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Speaker 1: I can't rent of it. I fell off. So those

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Speaker 1: measurements we were talking about. What happens then is they

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Speaker 1: compare that just like a fingerprint, with a database of images.

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Speaker 1: And depending on what this is for, it could be

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Speaker 1: like just within your company, or it could be the

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Speaker 1: FBI's database of mugshots, or it could be the d

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Speaker 1: m VS database of driver's license photos yeh, which we'll

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Speaker 1: get into. Yeah. What's interesting is each stage of the way,

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Speaker 1: there's a different algorithm that does you know, each increasingly

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Speaker 1: sophisticated step, until you finally have basically like all of

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Speaker 1: the different data points for that, you know, what makes

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Speaker 1: up that facial geometry, and then you can compare it

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Speaker 1: to all the other data points. We think of like

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Speaker 1: a computer running like a you know, a picture, You've

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Speaker 1: got your input picture and then running you know, all

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Speaker 1: the pictures next to it. That's not what it's doing.

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Speaker 1: It's it's running the numbers. Basically, it's doing computer stuff. Yeah.

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Speaker 1: I love that first step, which is you have to

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Speaker 1: teach the computer what a face is. So, I mean,

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Speaker 1: seems silly, but of course that's what it is. Well, yeah,

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Speaker 1: because I mean if you show it a picture of

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Speaker 1: a person standing next to a fire hydra shaming on

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Speaker 1: the fire Hydran, Yeah, and say hello, handsome, So this

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Speaker 1: is what a human face looks like. Yeah, or no,

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Speaker 1: that's a butt, And then it starts, you know, closer, closer.

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Speaker 1: All right, now that's a face. You know what a

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Speaker 1: face is. Now move on to step two, which is

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Speaker 1: stop screwing around. Yeah, so now you know what a

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Speaker 1: face is. You've got to normalize it for the photo,

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Speaker 1: which means there are not that many. Well, let's you

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Speaker 1: have to put it in the dockers. That normalizes, Um,

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Speaker 1: you isolate that face, and then you have to make

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Speaker 1: sure that it's normalized as far as looking at the camera.

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Speaker 1: So if it's a if you get a photo of

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Speaker 1: someone from a CCTV, let's say, and it's sort of

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Speaker 1: a three quarter, they have the ability to make it

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Speaker 1: as if it is looking straight at you. Yeah, the

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Speaker 1: computer can pretty accurately predict what the rest of the

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Speaker 1: face looks like face you know it on, I guess

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Speaker 1: face on um. And when it normalizes it like that,

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Speaker 1: it makes it much easier to compare to other pictures because,

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Speaker 1: as we'll see, most of the pictures or most of

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Speaker 1: the data points that it's comparing it to, are taken

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Speaker 1: from databases of pictures that have been taking of people

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Speaker 1: face on. Right. So that's why it wants to go

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Speaker 1: to driver's license. Yeah, well just spoiled it. But yes,

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Speaker 1: we already said that. Oh we did, Yeah, I did, Okay,

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Speaker 1: I missed it, I know. So uh from there, do

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Speaker 1: you have more algorithms still that isolate parts of the face. Um.

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Speaker 1: And this is where my old theory that like, there

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Speaker 1: are only so many sort of facial combinations, so that's

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Speaker 1: why you have doppelgangers. We got to do an episode

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Speaker 1: on doppel Gan. There's only so many things you can

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Speaker 1: do with two eyes to eyebrows and nose in a

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Speaker 1: mouth and cheekbones and in chin. Well, okay, what else?

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Speaker 1: I mean, there's not a whole lot. There's lips, lips, sure,

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Speaker 1: what about um uh uh uh, that's about it. These

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Speaker 1: are called elevens, the ridges between your eyebrows. Well, if

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Speaker 1: you want to get super specific, but that's what I'm saying.

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Speaker 1: I think they're getting more and more specific. Oh yeah, yeah.

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Speaker 1: But my whole point is, and we'll learn and here

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Speaker 1: and facial recognition. They do use Apple gangers, but put

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Speaker 1: a pin in that so they recognize all these features

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Speaker 1: and then each feature becomes what's called a nodal point

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Speaker 1: or no Dowell point. I think, nodal nodal, I think.

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Speaker 1: And this is where you're gonna get your super exact

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Speaker 1: uh angles and distances between all these parts as a

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Speaker 1: flat two dimensional thing. Which my question was because below here,

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Speaker 1: you know, it talks about Apple and their iPhone have

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Speaker 1: a three D facial recognition. Is that is two dimensional

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Speaker 1: superior to three D? I don't know, or is it

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Speaker 1: just because that's what all the pictures are in the databasis,

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Speaker 1: so that's what they do. I don't know. All I

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Speaker 1: know is my phone usually unlocks when I look at it.

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Speaker 1: You know what? I Hey, that's having to take off

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Speaker 1: my sunglasses. Worst so I found I've got some wayfarers

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Speaker 1: that I don't have to take, but my aviators I

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Speaker 1: do have to take off. Interesting do they keep trying

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Speaker 1: to make you into mav when you have on the

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Speaker 1: the go ahead? What is that? That was Tom Cruise

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Speaker 1: laughing and chewing gum. Okay, wow thanks, I feel like okay,

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Speaker 1: um we we we gotta keep going because I was

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Speaker 1: about to take a break unnecessarily. So um when the

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Speaker 1: when the computer is running through the pictures, it just

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Speaker 1: sis soon goes like no, no, no, no, no, millions

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Speaker 1: and millions of times and then finally goes yes. But

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Speaker 1: when it says yes and it spits out another picture,

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Speaker 1: it's not like this is that person? No you want

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Speaker 1: it to be. Because we all watch n c I S,

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Speaker 1: we all watch c S I, we all watch um

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Speaker 1: Law and Order, we all watch um uh party down

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Speaker 1: uh Andy Griffith, that Mattlock, the whole CHAMAI um, so

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Speaker 1: we wanted to just spit out and be like, here's

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Speaker 1: your here's your your person of interest, right. But what

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Speaker 1: it's really doing is it's it's producing a similarity score

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Speaker 1: that is probabilistic. It's saying this is there's this percent

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Speaker 1: chance that this is the same person as the picture

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Speaker 1: or the person in the picture that you uploaded. It's

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Speaker 1: a bit of a guess. It is, so a sophisticated

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Speaker 1: guess it is, and the better computers get at this,

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Speaker 1: the likelier it is that if they say this is

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Speaker 1: a there's a chances the same person that it's the

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Speaker 1: same person. But it's as we'll see, it's up to

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Speaker 1: the human user to determine what is an acceptable threshold

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Speaker 1: of a confidence Is it no? Is it no? Frankly,

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Speaker 1: it really should be about or higher should be the

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Speaker 1: confidence setting for the confidence level. Didn't that what Amazon's

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Speaker 1: recognition says the threshold should be. I'm glad you said that, man,

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Speaker 1: because it really is creepy and I couldn't put my

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Speaker 1: finger on it. And it's exactly I mean, I knew

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Speaker 1: the k looked weird or whatever, but it hadn't hit

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Speaker 1: me that just how creepy it is and just how

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Speaker 1: off the mark or potentially on the mark that name is. Like, Oh, like,

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Speaker 1: if my name was spelled c h u K, I'm

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Speaker 1: sinister a little bit, you'd be more sinister. Yeah, I

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Speaker 1: don't think you could ever be truly sinister. I appreciate that. Alright,

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Speaker 1: let's take a break. I'm gonna go work on sinistering

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Speaker 1: up a bit, and we'll talk a little bit more

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Speaker 1: about some of the uses of f R right after this. So,

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Speaker 1: as with all technology, it has to be abbreviated into

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Speaker 1: two letters, the second of which is are do they

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Speaker 1: call it f R? I've seen it, Yes, I was

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Speaker 1: just silly, but it doesn't surprise me. Nope. So in

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Speaker 1: f R facial recognition technology, there are there are some

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Speaker 1: some beneficial uses for it. Yeah, Like we said, you

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Speaker 1: got to tag people is chief among them. The for

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Speaker 1: people like you and me, that's the pinnacle as it stands.

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Speaker 1: You don't have to tag people yourself. Facebook does it

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Speaker 1: for you. That's what we're trading everything for. I gotta

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Speaker 1: calm down. Okay. There are some other, like genuinely beneficial

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Speaker 1: uses too. There's a nonprofit company called Thorn that scans

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Speaker 1: missing person's pictures against um pictures of children in child

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Speaker 1: porn videos, um or suspected human and trafficking to to

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Speaker 1: get matches, and apparently they've rescued a hundred kids so

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Speaker 1: far from using that technology. There's a pretty beneficial use

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Speaker 1: of facial recognition software. Uh dating apps. Let's say you

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Speaker 1: want to you can get pretty specific on what kind

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Speaker 1: of face you find attractive, which is interesting. But you

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Speaker 1: can say I really think, um, I like guys with

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Speaker 1: high cheekbones, and but no, you would go find mall lips.

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Speaker 1: It would be more like, um, somebody could be like, oh,

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Speaker 1: I really find Christian Bale attractive, and they get a

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Speaker 1: picture of Christian Bale into this dating app and I

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Speaker 1: would come up. But I wouldn't because I wouldn't be

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Speaker 1: in the dating app because I'm happily married. Do you

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Speaker 1: think you look like Christian Bale? I'm told that a lot. Really, Yeah,

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Speaker 1: that's weird. Don't think you look anything like it. I

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Speaker 1: don't either, but people say interesting. I'm I don't know

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Speaker 1: what I would do if I was dating now. I

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Speaker 1: guess I would just go to a service and say

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Speaker 1: and aliety type sure for more games era, But they'd

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Speaker 1: be like, okay, sir, you would just upload the picture.

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Speaker 1: You don't have to come into the office, which is

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Speaker 1: really not even open to the public, and just tell

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Speaker 1: us you're interested in Ali sheety type like a weirdo.

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Speaker 1: You mean, dating apps don't have offices where they just

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Speaker 1: feel complaints and interested parties. You sit down and they

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Speaker 1: they videotape you with the VHS camera, put you on

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Speaker 1: with some other guys on the tape. That's how they

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Speaker 1: used to do it. Oh yeah, that was one of

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Speaker 1: the subplots of Singles, the Cameron Crow movie. Oh yeah,

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Speaker 1: it was Expect the Best was the name of the

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Speaker 1: dating service, and you would make a videotape and like

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Speaker 1: a watch video tapes of people you know, saying who

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Speaker 1: they are. How do you remember that? I was a

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Speaker 1: big singles fan. That's not a bunch I got you. Yeah,

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Speaker 1: Expect the Best. You me pack of cigarettes and some coffee.

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Speaker 1: We don't need anything else becausezoom tight. So what else here?

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Speaker 1: This was? UM Taylor Swift and and her security team

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Speaker 1: on tour used it to scan the audience to see

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Speaker 1: if any of the creeps who have harassed and doctor

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Speaker 1: were in the audience. That's super beneficial. No one should

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Speaker 1: have to go through that. UM also, cops use it

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Speaker 1: in myriad ways, but in particular especially beneficial or um

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Speaker 1: when they use facial recognition to identify people who can't

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Speaker 1: identify themselves. Somebody in the midst of a psychotic break,

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Speaker 1: perhaps somebody wasted on troom's um, somebody you're not Jesus Christ,

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Speaker 1: who who has uh amnesia, Our friend Benjamin Kyle, who

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Speaker 1: apparently knows who he is now but he's decided not

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Speaker 1: to disclose it publicly. Remember the guy he was found

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Speaker 1: behind a burger king near a dumpster, had zero recollection

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Speaker 1: of who he was or how he got there, and

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Speaker 1: like there was this international publicity publicizing like who he

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Speaker 1: wasn't that he couldn't remember who he was, and somebody

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Speaker 1: finally came forward and identified him. So now he knows

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Speaker 1: who he was, but he went like a decade without knowing.

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Speaker 1: By the way, when I said, sorry, you're not Jesus Christ,

404
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Speaker 1: I was making fun of the guy on mushrooms, not

405
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Speaker 1: someone in the midst of a psychotic break. I just

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Speaker 1: want to be very specific. I think that was very

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Speaker 1: clear a right to make sure everybody knows that. So uh.

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Speaker 1: Those are some of the good ways that it can

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Speaker 1: be used UM. Now let's talk about all the bad ways. Yeah,

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Speaker 1: I mean when you're talking about the government, you're talking

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Speaker 1: about law enforcement, when you're talking about things like what's

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Speaker 1: going on allegedly in China with CCTVs everywhere trained to

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Speaker 1: UM to single out ethnic minorities and religious groups just

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Speaker 1: walking down the street going about their day. Yeah, that's

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Speaker 1: it gets into much different territory than tagging people in

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Speaker 1: dating apps. Yeah, it's pretty difficult to attend your religious

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Speaker 1: service if you're not allowed to attend your religious service

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Speaker 1: and you're being tracked everywhere you go. Yeah, that's why

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Speaker 1: places like and this is the most predictable thing in

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Speaker 1: the world San Francisco, Oakland and Berkeley and then Somerville, Maine.

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Speaker 1: I knew the Manners would be in there. They're not

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Speaker 1: into this, that's right. They have banned law enforcement UM

423
00:25:13,320 --> 00:25:18,280
Speaker 1: from using facial recognition altogether in California UM as a state,

424
00:25:19,000 --> 00:25:21,520
Speaker 1: and the ACE has put a three or moratorium on

425
00:25:21,560 --> 00:25:24,399
Speaker 1: the use of it UM body cams, which and the

426
00:25:24,400 --> 00:25:26,639
Speaker 1: a c l U is basically I know this is

427
00:25:26,720 --> 00:25:28,439
Speaker 1: jumping ahead, but they're at the point where they're like,

428
00:25:28,960 --> 00:25:31,240
Speaker 1: we need to tap the brakes here for a few

429
00:25:31,320 --> 00:25:34,879
Speaker 1: years and like because there's no legislation about this yet

430
00:25:35,200 --> 00:25:38,520
Speaker 1: and it's just going full steam ahead. Yeah, I really

431
00:25:38,560 --> 00:25:41,720
Speaker 1: I don't want to like like run past that there

432
00:25:41,840 --> 00:25:46,359
Speaker 1: is aside from Berkeley, San Francisco, who's the other one,

433
00:25:46,440 --> 00:25:53,159
Speaker 1: Oakland and Somerville, Maine. UM, there are no laws, state, local,

434
00:25:53,240 --> 00:25:57,639
Speaker 1: or federal governing the use of facial recognition technology by

435
00:25:57,720 --> 00:26:00,840
Speaker 1: law enforcement. It's just happening very fast. Whatever they want

436
00:26:00,880 --> 00:26:04,560
Speaker 1: to do, they can do UM and in some cases

437
00:26:04,960 --> 00:26:08,160
Speaker 1: they do all sorts of stuff with it. They will

438
00:26:08,240 --> 00:26:12,320
Speaker 1: use it, like the NYPD very famously used UM what

439
00:26:12,400 --> 00:26:14,880
Speaker 1: you were talking about with doppelgangers. There was a guy

440
00:26:14,920 --> 00:26:18,080
Speaker 1: who was caught stealing beer at a CVS. Not even

441
00:26:18,080 --> 00:26:22,200
Speaker 1: a duane read a CVS. UM, and they said, but

442
00:26:22,320 --> 00:26:24,600
Speaker 1: this guy looks a lot like Woody Harrelson. We don't

443
00:26:24,600 --> 00:26:26,399
Speaker 1: have a good we don't have a good shot of

444
00:26:26,480 --> 00:26:32,879
Speaker 1: him to use in official recognitions. So they went and

445
00:26:32,920 --> 00:26:35,440
Speaker 1: got a pick of Woody Harrelson, and there they came

446
00:26:35,520 --> 00:26:37,679
Speaker 1: up with a match, and they think it was the

447
00:26:37,720 --> 00:26:42,280
Speaker 1: guy on video and CVS and so UM. The Georgetown

448
00:26:42,440 --> 00:26:46,000
Speaker 1: School of Law UH produced a study called Garbage in,

449
00:26:46,040 --> 00:26:49,560
Speaker 1: Garbage Out, and they were basically like, that's not okay,

450
00:26:49,640 --> 00:26:52,440
Speaker 1: you really shouldn't be doing that. But that's the level

451
00:26:52,480 --> 00:26:55,520
Speaker 1: of legality as it stands right now. There's it's just

452
00:26:55,640 --> 00:27:00,480
Speaker 1: open season, um, and uh, it's just basically whatever you

453
00:27:00,480 --> 00:27:02,199
Speaker 1: want to do, you can do as far as facial

454
00:27:02,200 --> 00:27:05,240
Speaker 1: recognition is concerned. In that story in particular, it's like

455
00:27:05,240 --> 00:27:07,960
Speaker 1: some people are like, awesome, the system works. Sure. Other

456
00:27:07,960 --> 00:27:10,280
Speaker 1: people are like, what about poor Woody Harrelson. He was

457
00:27:10,320 --> 00:27:13,640
Speaker 1: really in danger right then of being implicated in this

458
00:27:13,680 --> 00:27:17,639
Speaker 1: beer stealing scheme from CVS and what he said? What dude,

459
00:27:18,800 --> 00:27:23,359
Speaker 1: I love that guy man. True Detective the first season, Yeah,

460
00:27:23,520 --> 00:27:27,359
Speaker 1: first four episodes just amazing. That's called using a probe

461
00:27:27,359 --> 00:27:30,560
Speaker 1: photo when you use when you say, hey, that looks

462
00:27:30,600 --> 00:27:32,960
Speaker 1: like someone. They also did the same with one of

463
00:27:32,960 --> 00:27:35,399
Speaker 1: the New York Knicks. Apparently I could not, for the

464
00:27:35,440 --> 00:27:38,040
Speaker 1: life of me find out who. Yeah, it's like he's

465
00:27:38,080 --> 00:27:41,400
Speaker 1: being protected or something that maybe no one said who

466
00:27:41,400 --> 00:27:44,119
Speaker 1: it was. Um, a couple of numbers for you, though. Uh.

467
00:27:44,160 --> 00:27:49,080
Speaker 1: The FBI receives about fifty thousand facial recognition search submissions

468
00:27:49,080 --> 00:27:51,960
Speaker 1: a month for their database. So that's the other thing.

469
00:27:52,160 --> 00:27:55,520
Speaker 1: If you don't have even the money for a subscription

470
00:27:55,560 --> 00:27:58,760
Speaker 1: to Amazon Recognition, or you don't have an I T

471
00:27:58,960 --> 00:28:02,000
Speaker 1: person who's capable of assembling and putting it, you know,

472
00:28:02,119 --> 00:28:05,480
Speaker 1: using it UM. You can just submit these requests to

473
00:28:05,520 --> 00:28:08,760
Speaker 1: the FBI. So there's a lot of different avenues you

474
00:28:08,800 --> 00:28:11,920
Speaker 1: could take his law enforcement to to UM to use

475
00:28:12,000 --> 00:28:16,200
Speaker 1: facial recognition technology to catch suspected criminals. Yeah. I was

476
00:28:16,200 --> 00:28:18,680
Speaker 1: about to say bad guys, but who knows. We'll see

477
00:28:19,000 --> 00:28:22,320
Speaker 1: it's not always the case. So here's some more numbers though,

478
00:28:23,680 --> 00:28:28,280
Speaker 1: because you know it needs to be regulated. But when

479
00:28:28,280 --> 00:28:30,520
Speaker 1: it works, it really works. Yeah, it really does though.

480
00:28:30,520 --> 00:28:32,920
Speaker 1: It is the thing. Yeah, there was one department where

481
00:28:32,960 --> 00:28:34,959
Speaker 1: they said it lowered the average time required for an

482
00:28:34,960 --> 00:28:37,920
Speaker 1: officer to identify a subject from an image from thirty

483
00:28:38,000 --> 00:28:41,600
Speaker 1: days to three minutes, which kind of brings home the

484
00:28:41,640 --> 00:28:48,280
Speaker 1: point there's another number in here. This interesting, but uh,

485
00:28:48,440 --> 00:28:50,840
Speaker 1: it brings on the point that like, this is something

486
00:28:50,840 --> 00:28:54,920
Speaker 1: that human policemen were doing. Officers were doing with their

487
00:28:54,960 --> 00:28:59,080
Speaker 1: eyeballs by flipping through books, yes, for thirty days straight,

488
00:28:59,440 --> 00:29:01,960
Speaker 1: saying like it doesn't look like this person. This is

489
00:29:02,000 --> 00:29:04,800
Speaker 1: like a chance to really speed up that process and

490
00:29:04,840 --> 00:29:09,120
Speaker 1: to spend more time in theory catching bad guys. Yes,

491
00:29:09,400 --> 00:29:12,080
Speaker 1: I'm not arguing for it. I'm just saying they were

492
00:29:12,120 --> 00:29:16,440
Speaker 1: doing this anyway, just through manpower. Right. I think the

493
00:29:16,560 --> 00:29:22,560
Speaker 1: thing is is anytime you add artificial intelligence automatically makes

494
00:29:22,640 --> 00:29:27,400
Speaker 1: the user of the artificial intelligence aside unfair unfairly advantaged.

495
00:29:27,960 --> 00:29:30,520
Speaker 1: It's not like the criminals are able to use AI

496
00:29:30,600 --> 00:29:33,960
Speaker 1: to steal beer from cvs more effectively, but the cops

497
00:29:34,000 --> 00:29:36,960
Speaker 1: are using AI to catch them stealing beer more effectively.

498
00:29:37,360 --> 00:29:40,160
Speaker 1: And it's kind of like, yes, it makes sense to

499
00:29:40,320 --> 00:29:45,640
Speaker 1: catch like child pornographers and um human traffickers and rapists

500
00:29:45,680 --> 00:29:49,280
Speaker 1: and murderers and violent criminals with this stuff, but using

501
00:29:49,320 --> 00:29:51,479
Speaker 1: that kind of technology catch somebody who stole beer from

502
00:29:51,480 --> 00:29:53,840
Speaker 1: a CVS. If that's when it starts to feel like

503
00:29:54,840 --> 00:29:58,680
Speaker 1: what kind of society are we moving towards? You know? Well,

504
00:29:58,720 --> 00:30:00,959
Speaker 1: I think someone not. And let me keep going here

505
00:30:00,960 --> 00:30:02,600
Speaker 1: for a second, because I don't want people be like,

506
00:30:02,600 --> 00:30:04,440
Speaker 1: what do you in favor of the guy stealing beer

507
00:30:04,480 --> 00:30:06,920
Speaker 1: from CVS? No, I'm not. I think you're a scumbag

508
00:30:06,960 --> 00:30:10,480
Speaker 1: if you steal beer from CVS. But I also think

509
00:30:10,520 --> 00:30:13,840
Speaker 1: that it's overkilled to use facial recognition technology to catch

510
00:30:13,920 --> 00:30:19,280
Speaker 1: that person. Use old fashioned police tactics or don't catch them. Yes,

511
00:30:19,400 --> 00:30:21,320
Speaker 1: it's just kind of the fairness of the old western

512
00:30:21,360 --> 00:30:23,160
Speaker 1: New York City. I think I might be on the

513
00:30:23,200 --> 00:30:26,040
Speaker 1: other side because I don't think we need to set

514
00:30:26,080 --> 00:30:31,200
Speaker 1: a fair playing ground between criminals and cops and saying

515
00:30:31,280 --> 00:30:34,000
Speaker 1: like it's unfair that cops can use this stuff and

516
00:30:34,080 --> 00:30:37,160
Speaker 1: criminals are just out there not able to use these

517
00:30:37,160 --> 00:30:41,080
Speaker 1: same techniques. Okay, So my the fairness thing doesn't just

518
00:30:41,360 --> 00:30:43,920
Speaker 1: end at the law and order thing, right like, Like,

519
00:30:44,000 --> 00:30:47,600
Speaker 1: it's not just with cops using it. They they have

520
00:30:47,720 --> 00:30:50,720
Speaker 1: this huge advantage. I totally get how people would be like, no,

521
00:30:50,840 --> 00:30:53,840
Speaker 1: give the cops that huge advantage. I don't have an

522
00:30:53,920 --> 00:30:57,000
Speaker 1: issue with that in and of itself. I think my

523
00:30:57,080 --> 00:31:00,760
Speaker 1: issue comes a step or two down the road, sure,

524
00:31:00,760 --> 00:31:05,520
Speaker 1: where the government or the cops, acting on behalf of

525
00:31:05,520 --> 00:31:09,080
Speaker 1: the government, use that against everyday citizens who have no

526
00:31:09,200 --> 00:31:15,280
Speaker 1: recourse whatsoever. That that that that lopsidedness that's so evident

527
00:31:15,360 --> 00:31:18,840
Speaker 1: when you're using AI to catch somebody's stealing beer from

528
00:31:18,840 --> 00:31:22,040
Speaker 1: the CBS. It's really easy to kind of follow that

529
00:31:22,120 --> 00:31:25,040
Speaker 1: a little further across to the horizon and see just

530
00:31:25,120 --> 00:31:28,720
Speaker 1: how unfair life could be and how oppressive that could

531
00:31:28,720 --> 00:31:32,040
Speaker 1: be using that technology. I think that's ultimately what I'm saying.

532
00:31:32,640 --> 00:31:36,520
Speaker 1: I hopefully dug myself out of that hole by now so.

533
00:31:36,960 --> 00:31:40,680
Speaker 1: Uh And and this gets into some of the controversies

534
00:31:40,720 --> 00:31:43,800
Speaker 1: and the arguments if you're if you're scanning mug shots

535
00:31:44,760 --> 00:31:50,000
Speaker 1: uh for rapists and arsonists and murderers and violent criminals,

536
00:31:50,640 --> 00:31:53,400
Speaker 1: and you're catching people, You're not gonna find a lot

537
00:31:53,440 --> 00:31:56,520
Speaker 1: of people that say, well, that's not fair, go back

538
00:31:56,560 --> 00:31:58,480
Speaker 1: and use take a month to look through a mug

539
00:31:58,520 --> 00:32:02,360
Speaker 1: shot book instead, and waste a bunch of time and

540
00:32:02,440 --> 00:32:05,800
Speaker 1: don't be efficient. So I think most people would say

541
00:32:06,240 --> 00:32:07,960
Speaker 1: if you're looking at mug shots, although we should point

542
00:32:07,960 --> 00:32:10,120
Speaker 1: out that a mug shot doesn't mean that just means

543
00:32:10,160 --> 00:32:12,320
Speaker 1: you're arrested. That doesn't mean you were guilty of anything.

544
00:32:13,200 --> 00:32:17,360
Speaker 1: Um So, there are plenty of opportunities for false positives

545
00:32:17,400 --> 00:32:21,560
Speaker 1: and people being put in jail that shouldn't be. But

546
00:32:21,560 --> 00:32:23,200
Speaker 1: but there's not a lot of people who are like, no,

547
00:32:23,200 --> 00:32:27,320
Speaker 1: not use mugshot databases right Exactly, If you're scanning driver's

548
00:32:27,360 --> 00:32:31,640
Speaker 1: license databases or other just general public databases, that's when

549
00:32:31,640 --> 00:32:35,320
Speaker 1: it gets super tricky because we can't avoid the fact

550
00:32:35,400 --> 00:32:37,680
Speaker 1: that what that means is in in the Center on

551
00:32:37,760 --> 00:32:42,120
Speaker 1: Privacy and Technology, Um kind of stated very plainly what

552
00:32:42,160 --> 00:32:46,400
Speaker 1: that means is everyone is in a perpetual lineup. Essentially,

553
00:32:47,000 --> 00:32:49,120
Speaker 1: if you have a driver's license, you're part of a

554
00:32:49,120 --> 00:32:52,000
Speaker 1: police lineup. Yeah, whether you like it or not, whether

555
00:32:52,040 --> 00:32:54,840
Speaker 1: you know it or not. And if that computer says

556
00:32:55,080 --> 00:32:58,520
Speaker 1: here's the guy, it's Chuck Bryant, Uh, they will say, oh,

557
00:32:58,560 --> 00:33:00,320
Speaker 1: he doesn't strike me as very sinister, or in the

558
00:33:00,320 --> 00:33:03,520
Speaker 1: computer would be like, trust me, this is the guy. Uh,

559
00:33:03,600 --> 00:33:07,520
Speaker 1: with like an eighty something percent confidence interval. Um, Chuck,

560
00:33:07,560 --> 00:33:10,240
Speaker 1: Suddenly you're going to get visited by the cops and

561
00:33:10,400 --> 00:33:12,640
Speaker 1: maybe you'll even get arrested because you were a little

562
00:33:12,720 --> 00:33:14,600
Speaker 1: KG when they talked to you and you set off

563
00:33:14,680 --> 00:33:17,719
Speaker 1: their cop radar or whatever. And then the next thing

564
00:33:17,760 --> 00:33:20,440
Speaker 1: you know, you're in court being charged with the crime

565
00:33:20,480 --> 00:33:23,600
Speaker 1: that you didn't commit because the computer implicated you and

566
00:33:23,640 --> 00:33:26,200
Speaker 1: the cops thought that you were acting KG. And let's

567
00:33:26,200 --> 00:33:29,520
Speaker 1: say that you were a very very poor person and

568
00:33:29,560 --> 00:33:31,560
Speaker 1: you don't have any money to mount a decent defense.

569
00:33:31,880 --> 00:33:34,400
Speaker 1: The best you can afford is a free public defender

570
00:33:34,600 --> 00:33:37,120
Speaker 1: who has fifty other cases is not really paying very

571
00:33:37,200 --> 00:33:40,160
Speaker 1: much attention to you and you're in jail now because

572
00:33:40,160 --> 00:33:43,760
Speaker 1: you got convicted wrongly because you were putting a lineup

573
00:33:44,040 --> 00:33:47,040
Speaker 1: just because you had a driver's license. Yeah. I think

574
00:33:47,080 --> 00:33:51,120
Speaker 1: for me, um, and this is total my privilege coming

575
00:33:51,120 --> 00:33:53,760
Speaker 1: through as well, Like I'd want to see some numbers

576
00:33:54,400 --> 00:33:58,520
Speaker 1: and if one of every ten thousand arrest and conviction

577
00:33:58,600 --> 00:34:01,160
Speaker 1: of a real criminal or an a rapist and a murderer,

578
00:34:01,520 --> 00:34:05,120
Speaker 1: and there's three people that get falsely identified and have

579
00:34:05,200 --> 00:34:07,160
Speaker 1: to go through the system and may or it may

580
00:34:07,200 --> 00:34:10,040
Speaker 1: not be acquitted, I'd want to see those numbers. But

581
00:34:10,080 --> 00:34:13,960
Speaker 1: again that's coming from a privileged position as someone who

582
00:34:13,960 --> 00:34:18,360
Speaker 1: could afford a legal defense who uh white, Yeah exactly.

583
00:34:18,520 --> 00:34:21,759
Speaker 1: That's another one too, is that people of color uh

584
00:34:22,040 --> 00:34:26,719
Speaker 1: bear a inordinate burden, disproportionate burden when it comes to

585
00:34:26,760 --> 00:34:29,640
Speaker 1: facial recognition technologies. We'll see well, I mean you might

586
00:34:29,680 --> 00:34:31,920
Speaker 1: as well go ahead and talk about that. It's um.

587
00:34:31,960 --> 00:34:35,040
Speaker 1: I think from the beginning, even with social media, there

588
00:34:35,040 --> 00:34:40,960
Speaker 1: were certain facial recognition, early facial recognition technologies that admitted like,

589
00:34:41,160 --> 00:34:46,320
Speaker 1: we're not as good as uh seeing or recognizing faces

590
00:34:46,400 --> 00:34:49,120
Speaker 1: with darker skin. It's just not that good. Yeah, I

591
00:34:49,120 --> 00:34:52,239
Speaker 1: think something like darker skinned men and women were recognized,

592
00:34:52,760 --> 00:34:57,600
Speaker 1: twelve and thirty were misidentified, compared to one percent and

593
00:34:57,680 --> 00:35:00,880
Speaker 1: seven percent of light skinned men and him in And

594
00:35:00,960 --> 00:35:04,240
Speaker 1: they say it's because of the data sets that these

595
00:35:04,640 --> 00:35:08,560
Speaker 1: machines have been trained on, which is not it's not purposefully,

596
00:35:08,680 --> 00:35:10,440
Speaker 1: but it makes sense if you live in like a

597
00:35:10,600 --> 00:35:14,880
Speaker 1: generally like like the white people are in power, and

598
00:35:14,960 --> 00:35:17,960
Speaker 1: it's like whiteness is the most celebrated part of the

599
00:35:18,080 --> 00:35:20,480
Speaker 1: society or whatever. That's why you're going to have more

600
00:35:20,520 --> 00:35:24,120
Speaker 1: pictures of And when you feed just a bunch of

601
00:35:24,160 --> 00:35:27,080
Speaker 1: pictures from your society into a machine and say, learn

602
00:35:27,120 --> 00:35:29,279
Speaker 1: what faces are, it's going to go, oh, white men,

603
00:35:29,400 --> 00:35:31,960
Speaker 1: I got you. Well, they just are more white people.

604
00:35:32,160 --> 00:35:36,279
Speaker 1: Numbers wise, probably has something to do with it, right, Um, Yes,

605
00:35:36,360 --> 00:35:38,480
Speaker 1: that's a really that is an excellent point as well,

606
00:35:38,640 --> 00:35:41,840
Speaker 1: for sure. But the fact of the matter is the

607
00:35:41,960 --> 00:35:45,960
Speaker 1: data sets that the machines are learning on are largely

608
00:35:46,120 --> 00:35:50,239
Speaker 1: white and largely male, and so they're just not as

609
00:35:50,280 --> 00:35:55,080
Speaker 1: good at recognizing the differences in faces among um people

610
00:35:55,120 --> 00:35:59,359
Speaker 1: who aren't white males. Uh, let's read these quotes. There's

611
00:35:59,400 --> 00:36:02,080
Speaker 1: a couple of good quotes here. The first one is

612
00:36:02,120 --> 00:36:07,719
Speaker 1: from Woodrow Heart sog. I was going to read it

613
00:36:07,760 --> 00:36:10,000
Speaker 1: as wurner. I don't know if I can. I should

614
00:36:10,040 --> 00:36:12,560
Speaker 1: get Nolan here. He does a good burner. The most

615
00:36:12,680 --> 00:36:18,040
Speaker 1: uniquely dangerous surveillance mechanism ever intended. It's an irresistible tool

616
00:36:18,080 --> 00:36:23,440
Speaker 1: for oppression that's perfectly suited for governments to display unprecedented

617
00:36:23,440 --> 00:36:29,439
Speaker 1: authoritary tane I'm sorry, authoritarian control and an all out

618
00:36:29,480 --> 00:36:36,080
Speaker 1: privacy eviscerating machine. I just realized it's heart sog, so

619
00:36:36,200 --> 00:36:39,600
Speaker 1: it's it's spelled differently. It's a j r T hert

620
00:36:39,640 --> 00:36:42,560
Speaker 1: sog is just h g r z o G. I'm

621
00:36:42,600 --> 00:36:45,840
Speaker 1: glad that we didn't figure that out beforehand, though. You

622
00:36:45,880 --> 00:36:47,440
Speaker 1: want to take the other one though. I also I

623
00:36:47,480 --> 00:36:50,080
Speaker 1: have to say I detected a hint of Michael Caine

624
00:36:50,080 --> 00:36:52,600
Speaker 1: in there too. There might have been a little bit.

625
00:36:52,680 --> 00:36:54,880
Speaker 1: It's hard to get Michael Caine out of my system.

626
00:36:55,000 --> 00:36:58,799
Speaker 1: What's the other one? Oh? From Microsoft president Brad Smith? Yeah. So.

627
00:36:58,920 --> 00:37:02,400
Speaker 1: Brad Smith says that when combined with ubiquitous cameras and

628
00:37:02,520 --> 00:37:05,520
Speaker 1: massive computing power and storage in the cloud, a government

629
00:37:05,600 --> 00:37:10,120
Speaker 1: could use facial recognition technology to enable continuous surveillance of

630
00:37:10,200 --> 00:37:14,280
Speaker 1: specific individuals like they're supposedly doing in China as an aside,

631
00:37:14,360 --> 00:37:18,040
Speaker 1: it could follow anyone anywhere, or for that matter, everyone everywhere,

632
00:37:18,200 --> 00:37:21,000
Speaker 1: at any time, or even all the time. And he

633
00:37:21,080 --> 00:37:23,560
Speaker 1: wasn't this wasn't a sales pitch. He was speaking out

634
00:37:23,640 --> 00:37:26,319
Speaker 1: against this to Congress, saying like, guys, we gotta we

635
00:37:26,360 --> 00:37:28,239
Speaker 1: have to do something about this, because this is the

636
00:37:29,200 --> 00:37:34,400
Speaker 1: path we're heading down. And that's why uh Seth abrama

637
00:37:34,440 --> 00:37:38,360
Speaker 1: Witz changed his name to Brad Smith. It sounds like

638
00:37:38,400 --> 00:37:43,080
Speaker 1: a total like like I just want to blend in. UM.

639
00:37:43,160 --> 00:37:47,320
Speaker 1: So you've got scanning against mug shots, scanning against driver's licenses,

640
00:37:47,440 --> 00:37:49,640
Speaker 1: and then um, there's a new one that just came out.

641
00:37:49,680 --> 00:37:53,400
Speaker 1: The New York Times just released this expose on January eighteenth,

642
00:37:53,480 --> 00:37:56,479
Speaker 1: just a few days ago, UM on a company called

643
00:37:56,480 --> 00:38:02,200
Speaker 1: clear View AI. And apparently even among um Silicon Valley

644
00:38:02,280 --> 00:38:06,399
Speaker 1: there has been this longstanding kind of unspoken thing where

645
00:38:06,920 --> 00:38:10,600
Speaker 1: let's steer clear of this facial recognition technology because it's

646
00:38:10,640 --> 00:38:14,560
Speaker 1: such a tool of oppression potentially. And clear View AI said, hey,

647
00:38:14,560 --> 00:38:16,800
Speaker 1: we're not from Silicon Valley. Well, we're just going to

648
00:38:16,920 --> 00:38:19,880
Speaker 1: do our own thing. So now there's this, there's this

649
00:38:20,000 --> 00:38:23,839
Speaker 1: tool that's available to law enforcement agencies that they're using.

650
00:38:23,880 --> 00:38:26,280
Speaker 1: Remember that one that one guy who had a quote

651
00:38:26,320 --> 00:38:29,240
Speaker 1: saying that, um, it went from thirty days to three minutes.

652
00:38:29,760 --> 00:38:33,040
Speaker 1: They were almost certainly using clear View AI. And the

653
00:38:33,120 --> 00:38:36,120
Speaker 1: reason clear View AI has such an advantage because they've

654
00:38:36,200 --> 00:38:39,680
Speaker 1: gone to this place where everyone else said was off limits,

655
00:38:39,880 --> 00:38:43,360
Speaker 1: which is scraping social media. So rather than the forty

656
00:38:43,400 --> 00:38:47,920
Speaker 1: one million driver's license and mug shot pictures that is

657
00:38:47,920 --> 00:38:51,960
Speaker 1: available in the FBI's database, clear View AI is this

658
00:38:52,040 --> 00:38:54,080
Speaker 1: app that you can subscribe to for a year for

659
00:38:54,160 --> 00:38:56,840
Speaker 1: like two thousand and ten thousand dollars, and they have

660
00:38:57,239 --> 00:39:02,160
Speaker 1: three billion pictures, including links to the social accounts of

661
00:39:02,200 --> 00:39:04,919
Speaker 1: the people whose pictures come up, so that you can

662
00:39:04,920 --> 00:39:07,560
Speaker 1: not only see who it is, you can find out

663
00:39:07,560 --> 00:39:11,839
Speaker 1: where they're at right then. And it's a hugely invasive thing.

664
00:39:11,920 --> 00:39:16,120
Speaker 1: And there's no legislation on this whatsoever. And it's only

665
00:39:16,160 --> 00:39:18,880
Speaker 1: just recently come out that this this company even exists,

666
00:39:18,960 --> 00:39:21,040
Speaker 1: or that this app exists, and that law enforcement is

667
00:39:21,120 --> 00:39:24,600
Speaker 1: using this stuff because again there's basically no laws saying

668
00:39:24,640 --> 00:39:27,279
Speaker 1: you can do this, you can't do that. Um And

669
00:39:27,320 --> 00:39:31,160
Speaker 1: again Woodrow Herzog has basically said, there's no way we're

670
00:39:31,160 --> 00:39:35,200
Speaker 1: going to realize the benefits of this without the incredibly

671
00:39:35,640 --> 00:39:40,040
Speaker 1: disproportionate drawbacks, and um, he just calls for an all

672
00:39:40,080 --> 00:39:42,600
Speaker 1: outband of the technology. He's basically saying it's not worth it.

673
00:39:42,960 --> 00:39:45,120
Speaker 1: All right, let's take another break. Oh my gosh, we

674
00:39:45,160 --> 00:39:48,120
Speaker 1: haven't taken our second break yet. Uh, and we'll be

675
00:39:48,200 --> 00:39:51,480
Speaker 1: right back to talk about the rest of this stuff

676
00:39:52,040 --> 00:40:17,759
Speaker 1: right after this. I think we should talk a little bit,

677
00:40:18,040 --> 00:40:22,120
Speaker 1: like we've talked about the false positives, UM, and I

678
00:40:22,160 --> 00:40:27,760
Speaker 1: think within Amazon, their contention is that what you're talking

679
00:40:27,760 --> 00:40:29,600
Speaker 1: about with like the studies out of m I T

680
00:40:29,920 --> 00:40:33,000
Speaker 1: that said, UM, that there are too many false positive

681
00:40:33,239 --> 00:40:35,439
Speaker 1: is he's they're saying, wait a minute, you're talking about

682
00:40:35,440 --> 00:40:39,720
Speaker 1: facial analysis, not facial recognition, and those are two different things.

683
00:40:40,040 --> 00:40:41,920
Speaker 1: I did not understand this at all. I went and

684
00:40:41,960 --> 00:40:43,640
Speaker 1: looked it up and I just fully get it. Either

685
00:40:43,719 --> 00:40:46,600
Speaker 1: there's it sounds like some tap dancing to me. I

686
00:40:46,680 --> 00:40:49,399
Speaker 1: looked at and there's like not a distinction between those

687
00:40:49,440 --> 00:40:53,040
Speaker 1: two aside from this quote. Yeah, it's basically the same thing.

688
00:40:53,719 --> 00:40:56,000
Speaker 1: And also it doesn't even make sense as a defense.

689
00:40:56,040 --> 00:41:00,880
Speaker 1: So basically what they're saying is that um, that they

690
00:41:00,920 --> 00:41:03,479
Speaker 1: were being called out by M I. T. S. Media Lab.

691
00:41:03,560 --> 00:41:06,239
Speaker 1: They did a two thousand eighteen study. That's the one

692
00:41:06,280 --> 00:41:08,520
Speaker 1: that found that there's like a twelve and thirty five

693
00:41:08,920 --> 00:41:16,080
Speaker 1: misidentification among darker skinned men and women women. I think yeah, um,

694
00:41:16,160 --> 00:41:19,640
Speaker 1: and Amazon said no, no, no, uh, you guys are

695
00:41:19,760 --> 00:41:23,640
Speaker 1: using facial analysis, not facial recognition. It's like, no, that's

696
00:41:23,880 --> 00:41:26,360
Speaker 1: that's not the case at all. They're doing facial recognition.

697
00:41:26,400 --> 00:41:27,960
Speaker 1: All right. I'm glad it wouldn't just me, because you

698
00:41:27,960 --> 00:41:30,480
Speaker 1: see I wrote I don't get it next to this.

699
00:41:30,600 --> 00:41:33,320
Speaker 1: It was a it was a bad, a bad jam,

700
00:41:33,360 --> 00:41:36,239
Speaker 1: I guess. But I think their point was, well, you're

701
00:41:36,239 --> 00:41:39,560
Speaker 1: trying to tell the gender of somebody, and if you're

702
00:41:39,560 --> 00:41:43,759
Speaker 1: doing binary gender stuff, like you're trying to say this

703
00:41:43,800 --> 00:41:47,239
Speaker 1: as male or female, you can't really use facial recognition

704
00:41:47,280 --> 00:41:51,319
Speaker 1: for that, especially among darker skinned people. And they said

705
00:41:51,360 --> 00:41:54,080
Speaker 1: that you shouldn't use that, especially in cases of people's

706
00:41:54,080 --> 00:41:58,680
Speaker 1: civil liberties or whatever. But it still remains the case

707
00:41:58,800 --> 00:42:03,640
Speaker 1: that if you are a darker skinned person and you're

708
00:42:03,760 --> 00:42:07,839
Speaker 1: being looked at by a police department that has their

709
00:42:07,920 --> 00:42:12,160
Speaker 1: threshold for a confidence level set low, yeah, there's a

710
00:42:12,280 --> 00:42:14,520
Speaker 1: chance that a false positive is going to be put

711
00:42:14,560 --> 00:42:17,160
Speaker 1: out there, right, And that's that can be trouble for

712
00:42:17,200 --> 00:42:19,440
Speaker 1: you if you don't have the money to mount a defense.

713
00:42:19,840 --> 00:42:21,600
Speaker 1: And even if you do have the money, you shouldn't

714
00:42:21,600 --> 00:42:23,759
Speaker 1: have to mount a defense to spend money on that

715
00:42:23,840 --> 00:42:26,480
Speaker 1: to be equitted of a crime. Just because the computer

716
00:42:26,560 --> 00:42:29,840
Speaker 1: is not so good at a distinguishing Um black people

717
00:42:30,160 --> 00:42:33,080
Speaker 1: think it is among white people. Yeah, And what you

718
00:42:33,080 --> 00:42:35,160
Speaker 1: know when it comes to where this is going to

719
00:42:35,280 --> 00:42:38,360
Speaker 1: end up legally, Uh, you might want to look at

720
00:42:38,400 --> 00:42:41,839
Speaker 1: the Fourth Amendment. Um. It gets really dicey on how

721
00:42:41,880 --> 00:42:45,480
Speaker 1: you interpret the constitution when you talk about illegal search

722
00:42:45,520 --> 00:42:50,760
Speaker 1: and seizure? Is this a search or a seizure? Probably not, um,

723
00:42:50,840 --> 00:42:53,640
Speaker 1: because it depends on what we're talking about with the

724
00:42:53,680 --> 00:42:58,120
Speaker 1: Supreme Court. Um. You've probably been stopped h in a

725
00:42:58,120 --> 00:43:01,560
Speaker 1: at a d y checkpoint, and that's stopping everybody. That's

726
00:43:01,560 --> 00:43:03,560
Speaker 1: sort of the same thing. It's like, if you're in

727
00:43:03,560 --> 00:43:05,279
Speaker 1: a car, we're gonna stop you and check you out

728
00:43:05,719 --> 00:43:11,000
Speaker 1: because the couplet the public, the public his said, you know,

729
00:43:11,120 --> 00:43:14,920
Speaker 1: that's okay, it's reasonable, it's not super invasive. And if

730
00:43:14,920 --> 00:43:17,319
Speaker 1: you're stopping drunk drivers, it's just putting someone out for

731
00:43:17,400 --> 00:43:19,840
Speaker 1: a few minutes. Yeah, the court said, if it's minimally

732
00:43:19,880 --> 00:43:23,480
Speaker 1: invasive and the public good or the potential for public good,

733
00:43:23,520 --> 00:43:26,000
Speaker 1: which is in this case getting drunk drivers off the

734
00:43:26,080 --> 00:43:29,560
Speaker 1: road is high enough, then it's it's okay to basically

735
00:43:29,560 --> 00:43:32,480
Speaker 1: search everybody without probable cause. Yeah. Same with T s

736
00:43:32,520 --> 00:43:36,120
Speaker 1: A checkpoints when it comes to official rulings. Obviously we

737
00:43:36,120 --> 00:43:38,640
Speaker 1: don't have one in facial recognition yet. But if you

738
00:43:38,680 --> 00:43:42,080
Speaker 1: look at Carpenter v. United States, the court ruled five

739
00:43:42,160 --> 00:43:45,520
Speaker 1: four that police violated uh Fourth Amendment rights of a

740
00:43:45,560 --> 00:43:48,600
Speaker 1: man when they asked for his cell phone location data

741
00:43:48,680 --> 00:43:53,640
Speaker 1: without a warrant from T Mobile. Um. So hopefully this

742
00:43:53,719 --> 00:43:56,279
Speaker 1: nuance will prevail, and it just won't. It looks like

743
00:43:56,280 --> 00:43:59,080
Speaker 1: it probably won't be some blanket ruling that just says, yep,

744
00:43:59,160 --> 00:44:01,239
Speaker 1: you can use it for whatever you want, right if

745
00:44:01,280 --> 00:44:03,000
Speaker 1: it even gets to that point, and if the court

746
00:44:03,040 --> 00:44:06,040
Speaker 1: hears it, which probably would. So. The other thing that

747
00:44:06,200 --> 00:44:11,239
Speaker 1: um that is has become worrisome for people, though, is there.

748
00:44:11,280 --> 00:44:16,560
Speaker 1: It's becoming um our society is becoming increasingly surveiled. Right

749
00:44:17,000 --> 00:44:22,120
Speaker 1: like ring the ring doorbell. They market to law enforcement

750
00:44:22,160 --> 00:44:24,680
Speaker 1: basically saying like you can, you can, These people like

751
00:44:24,719 --> 00:44:26,919
Speaker 1: will pay to have video cameras put on their house

752
00:44:26,960 --> 00:44:29,560
Speaker 1: and you can go get these videos on neighborhood all

753
00:44:29,560 --> 00:44:31,480
Speaker 1: the time, people like my car got broken into, who

754
00:44:31,480 --> 00:44:33,560
Speaker 1: can help me out with their cameras. So it's being

755
00:44:33,600 --> 00:44:36,080
Speaker 1: marketed to law enforcement. Your TV has a camera and

756
00:44:36,200 --> 00:44:38,600
Speaker 1: your smart speaker has a microphone and it So the

757
00:44:38,719 --> 00:44:42,960
Speaker 1: more that we are um surveiled and the more ubiquitous

758
00:44:43,080 --> 00:44:47,120
Speaker 1: facial recognition technology gets, the easier will be to not

759
00:44:47,400 --> 00:44:50,759
Speaker 1: just scan a picture of somebody stealing beard of a

760
00:44:50,920 --> 00:44:57,960
Speaker 1: CVS against the mugshot database, or but to say this

761
00:44:58,040 --> 00:45:01,080
Speaker 1: person right here that you're you're looking at, that the

762
00:45:01,120 --> 00:45:05,160
Speaker 1: cameras following, that's this, that's that's um, that's uh, that's

763
00:45:05,239 --> 00:45:09,000
Speaker 1: Chuck Bryant right there. And everywhere you walk there's getting

764
00:45:09,040 --> 00:45:11,759
Speaker 1: a little icon next to your head Chuck Bryant. You know,

765
00:45:11,800 --> 00:45:14,240
Speaker 1: if you click it, it'll show you your Facebook page

766
00:45:14,320 --> 00:45:16,480
Speaker 1: or a map to your house or whatever. They want

767
00:45:16,480 --> 00:45:18,640
Speaker 1: to know, your police record, it doesn't matter. And that

768
00:45:18,800 --> 00:45:22,960
Speaker 1: this is what we're increasingly getting closer to. And some

769
00:45:23,000 --> 00:45:25,959
Speaker 1: people say this is what they're already doing in China. Yeah,

770
00:45:26,360 --> 00:45:28,799
Speaker 1: and London has has They were one of the first

771
00:45:28,840 --> 00:45:32,520
Speaker 1: on the CCTV train. Yes, but they use humans, which

772
00:45:32,560 --> 00:45:37,040
Speaker 1: is fair right for recognizing phases. Yeah, they have people

773
00:45:37,080 --> 00:45:40,760
Speaker 1: like actually looking at the at the individual monitors looking

774
00:45:40,800 --> 00:45:43,600
Speaker 1: for crime. This is this is the idea of this

775
00:45:43,640 --> 00:45:46,800
Speaker 1: is it's just tracking people who are just doing nothing wrong. Yeah,

776
00:45:46,840 --> 00:45:48,640
Speaker 1: but there are plenty of people on the other side

777
00:45:48,680 --> 00:45:51,160
Speaker 1: we should point out that are like, you know what,

778
00:45:51,239 --> 00:45:53,600
Speaker 1: if you're catching bad guys, that's great. If you're a

779
00:45:53,600 --> 00:45:55,320
Speaker 1: good guy, you've got nothing to hide, so you shouldn't

780
00:45:55,320 --> 00:45:57,759
Speaker 1: sweat it. Yeah. I can never remember the name of

781
00:45:57,760 --> 00:46:00,840
Speaker 1: the article. I'll try to find it, but there's a

782
00:46:01,880 --> 00:46:03,360
Speaker 1: man I wish I could remember off the top of

783
00:46:03,400 --> 00:46:06,040
Speaker 1: my head. But there's there's this amazing article from a

784
00:46:06,080 --> 00:46:09,560
Speaker 1: few years back, um that that basically says like that's

785
00:46:09,640 --> 00:46:13,359
Speaker 1: that's a terrible argument that that even if you have

786
00:46:13,520 --> 00:46:18,000
Speaker 1: nothing to hide, you still, um are a human being.

787
00:46:18,400 --> 00:46:21,160
Speaker 1: And if somebody wanted to put together and but if

788
00:46:21,200 --> 00:46:25,120
Speaker 1: somebody wanted to put together like a dossier on your

789
00:46:25,760 --> 00:46:29,160
Speaker 1: embarrassing things that you've done or said or thought or whatever,

790
00:46:29,640 --> 00:46:32,080
Speaker 1: um and and put it all together and condensed it,

791
00:46:32,200 --> 00:46:35,239
Speaker 1: you can make anybody look bad. No one should want

792
00:46:35,280 --> 00:46:38,520
Speaker 1: to live in a situation where like that could conceivably

793
00:46:38,560 --> 00:46:42,960
Speaker 1: happen in the police state. Yeah, police state, good stuff.

794
00:46:44,480 --> 00:46:47,200
Speaker 1: I guess we'll see how it pans out. I'm not

795
00:46:47,280 --> 00:46:51,240
Speaker 1: saying police state stuff. Police date. It's good stuff. Yeah,

796
00:46:51,520 --> 00:46:54,920
Speaker 1: we'll see what happens right in Woodrow, Hurtzog and let

797
00:46:55,000 --> 00:46:57,520
Speaker 1: us know what to do. If you want to know

798
00:46:57,560 --> 00:47:02,880
Speaker 1: more about facial recognition technology, you can go onto the

799
00:47:02,880 --> 00:47:05,880
Speaker 1: internet and start reading stuff about it. Definitely read the

800
00:47:05,920 --> 00:47:10,680
Speaker 1: New York Times expose about um clear view AI came

801
00:47:10,680 --> 00:47:14,160
Speaker 1: out January. Yes, okay, since I said that it's time

802
00:47:14,160 --> 00:47:16,560
Speaker 1: for a listener mitt all right, now it's not you

803
00:47:16,560 --> 00:47:18,319
Speaker 1: know what it's time for. Oh yeah, I know it's

804
00:47:18,360 --> 00:47:20,360
Speaker 1: time for you ready, Yeah, you say it. It's time

805
00:47:20,440 --> 00:47:33,600
Speaker 1: for administrative details. All right. This is part two. This

806
00:47:33,640 --> 00:47:35,799
Speaker 1: is where we thank people on the show that have

807
00:47:35,880 --> 00:47:41,640
Speaker 1: sent us kindness. Is via snail mail. Siggy s I

808
00:47:41,719 --> 00:47:44,560
Speaker 1: g D. I sent sent me some hand that it

809
00:47:44,640 --> 00:47:46,600
Speaker 1: sucks not you for some reason. I don't know why.

810
00:47:46,920 --> 00:47:49,040
Speaker 1: I got some socks too. Oh really yeah, I didn't

811
00:47:49,080 --> 00:47:51,160
Speaker 1: know who they were from, so they may be from Siggy.

812
00:47:51,320 --> 00:47:53,960
Speaker 1: I think probably what it was is you left him

813
00:47:54,320 --> 00:47:58,120
Speaker 1: with my desk and I thank you for it, CHUCKR

814
00:47:58,520 --> 00:48:01,240
Speaker 1: do another one while I'm pulling up my list acting.

815
00:48:01,560 --> 00:48:05,360
Speaker 1: Julie Shoop made us t shirts. Shoop, this is good stuff.

816
00:48:05,400 --> 00:48:09,560
Speaker 1: Faux band name tour shirts. Uh, super fun. Thanks a

817
00:48:09,600 --> 00:48:12,719
Speaker 1: lot of Julie. Very cool. Uh, you're still working, so

818
00:48:12,719 --> 00:48:16,920
Speaker 1: I'm gonna keep going. Thalia Dawes is our pal from Australia,

819
00:48:17,000 --> 00:48:20,520
Speaker 1: said my daughter a couple of books. She's a very

820
00:48:20,520 --> 00:48:23,560
Speaker 1: lovely lady who has a very adorable and whip smart

821
00:48:23,640 --> 00:48:26,960
Speaker 1: daughter about the same age, who listens to our show.

822
00:48:27,600 --> 00:48:30,080
Speaker 1: And um, I was just like, man, I wish she

823
00:48:30,160 --> 00:48:32,160
Speaker 1: lived here. We could go into play date. They both

824
00:48:32,160 --> 00:48:35,760
Speaker 1: seemed like lovely humans. There's such thing as plains. Yeah,

825
00:48:35,920 --> 00:48:39,040
Speaker 1: good Australia for a play date. Um. So at our

826
00:48:39,200 --> 00:48:42,600
Speaker 1: Portland's main show, Chuck, we had like a lot of um,

827
00:48:42,640 --> 00:48:46,759
Speaker 1: We've got a lot of neat gifts. Jim Diefenbacher made

828
00:48:46,840 --> 00:48:51,520
Speaker 1: us amazing cross hatch portraits of them. Yeah, those were

829
00:48:51,560 --> 00:48:54,640
Speaker 1: great of us, like of a photo we took I

830
00:48:54,680 --> 00:48:57,480
Speaker 1: think on like our West Coast tour from two. It

831
00:48:57,520 --> 00:49:00,560
Speaker 1: brought back some memories saw that. It's just really great stuff.

832
00:49:00,560 --> 00:49:03,720
Speaker 1: And you can see Jim's work at Jim Diefenbaker dot com,

833
00:49:03,800 --> 00:49:06,719
Speaker 1: j I M D I E F F E N

834
00:49:06,800 --> 00:49:09,359
Speaker 1: B A C H E R dot com and they

835
00:49:09,360 --> 00:49:12,480
Speaker 1: were framed in everything. Yeah, very sweet stuff, Jim. We

836
00:49:12,600 --> 00:49:17,919
Speaker 1: got some home tapped maple syrup from Andy Huntsberger from

837
00:49:19,480 --> 00:49:24,479
Speaker 1: Elgin I A, Okay, what's Iowa? Is that Iowa? Yeah? Yeah, okay, Yeah,

838
00:49:24,600 --> 00:49:27,400
Speaker 1: I was about to say the wrong state. What are

839
00:49:27,440 --> 00:49:29,279
Speaker 1: you gonna say? You know, I think I went to

840
00:49:29,320 --> 00:49:32,200
Speaker 1: say Illinoia. If you ever see Gary Gulman's bid on

841
00:49:32,280 --> 00:49:35,719
Speaker 1: Nate abbreviating the states, dude, just look it up. One

842
00:49:35,719 --> 00:49:38,520
Speaker 1: of the great comedy bits I've ever seen. It's hysterical.

843
00:49:39,880 --> 00:49:43,560
Speaker 1: Let's see. Uh oh. Another at the Portlands Show, we

844
00:49:43,640 --> 00:49:47,399
Speaker 1: got a letter from tog Braun from Downea's day Boat

845
00:49:47,480 --> 00:49:53,240
Speaker 1: from Lloyd Braun. Tog Braun Um and Downey's day Boats

846
00:49:53,320 --> 00:49:56,600
Speaker 1: mission is to bring sustainable, delicious scalops from Maine to

847
00:49:56,680 --> 00:49:59,880
Speaker 1: the world, and she said that scalops have varietals like

848
00:50:00,000 --> 00:50:02,319
Speaker 1: oysters and that main has the best. So check out

849
00:50:02,440 --> 00:50:07,279
Speaker 1: down East day Boat dot com. Coke Braun feel free

850
00:50:07,320 --> 00:50:09,640
Speaker 1: to send us some scallops as long as they've been

851
00:50:09,680 --> 00:50:14,520
Speaker 1: appropriately refrigerated. The entire time. I got another children's book,

852
00:50:14,640 --> 00:50:18,040
Speaker 1: Are You a Good Egg? And that was from Peter Deutschel,

853
00:50:18,520 --> 00:50:21,360
Speaker 1: along with some stuff you should know coasters. Yeah yeah,

854
00:50:21,400 --> 00:50:24,240
Speaker 1: thanks again Peter. I think we thanked him last episode

855
00:50:24,239 --> 00:50:27,560
Speaker 1: for the coasters too. I didn't know about the children's book.

856
00:50:27,600 --> 00:50:31,120
Speaker 1: Then Sarah Law who is an s y s k

857
00:50:31,280 --> 00:50:34,400
Speaker 1: Army member. Um, she came to the Toronto show and

858
00:50:34,400 --> 00:50:37,960
Speaker 1: she brought us a bunch of um Canadian goodies, everything

859
00:50:38,080 --> 00:50:42,720
Speaker 1: from Japanese cheesecakes and tarts from Uncle Tetsus. So good.

860
00:50:43,200 --> 00:50:46,400
Speaker 1: Um and uh. I think some other stuff too, like

861
00:50:46,760 --> 00:50:50,319
Speaker 1: coffee crisps, which are my favorite. Um. Yeah, so thanks

862
00:50:50,320 --> 00:50:53,280
Speaker 1: a lot. Sarah's always that is everything from Japan awesome.

863
00:50:54,000 --> 00:50:57,399
Speaker 1: They just it's really good. They don't necessarily invent much.

864
00:50:57,480 --> 00:51:00,080
Speaker 1: They just take other people's inventions and perfect them. And

865
00:51:00,120 --> 00:51:01,640
Speaker 1: it seems like they take a lot of pride in

866
00:51:01,760 --> 00:51:04,920
Speaker 1: like doing things right. I think that he could say that,

867
00:51:04,960 --> 00:51:12,040
Speaker 1: probably because we got from Matt an assortment of food

868
00:51:12,160 --> 00:51:17,080
Speaker 1: things from Japan and that came in today, including our

869
00:51:17,120 --> 00:51:21,920
Speaker 1: beloved QP Man's. I love that stuff. It's been too long, man,

870
00:51:22,160 --> 00:51:25,400
Speaker 1: God bless you. Let's see Leah Harrison gave us some

871
00:51:25,480 --> 00:51:30,080
Speaker 1: amazing goodies to including coffee Crisp and Canadian Smarties, which

872
00:51:30,120 --> 00:51:33,280
Speaker 1: are way better than American Smarties because they involved chocolate

873
00:51:33,360 --> 00:51:37,640
Speaker 1: and super smarties. A student named Maria Styling wrote us

874
00:51:37,640 --> 00:51:40,759
Speaker 1: a letter for an honors English project because she had

875
00:51:40,800 --> 00:51:43,880
Speaker 1: to write someone who inspired her, and she asked this.

876
00:51:43,960 --> 00:51:45,440
Speaker 1: I told her we to answer, how do we choose

877
00:51:45,440 --> 00:51:48,799
Speaker 1: a topic? Maria? We choose a topic. It's not it's

878
00:51:48,800 --> 00:51:52,080
Speaker 1: pretty low fih. We just send each other one each

879
00:51:52,080 --> 00:51:55,280
Speaker 1: week on whatever happens to grab our fancy. We're always

880
00:51:55,520 --> 00:52:00,080
Speaker 1: looking around our world, uh and thinking I wonder about that.

881
00:52:00,080 --> 00:52:01,759
Speaker 1: That's as that's as easy as it gets. And we'll

882
00:52:01,760 --> 00:52:04,840
Speaker 1: just send each other an email and times out of

883
00:52:04,840 --> 00:52:08,719
Speaker 1: a hundred will say great, let's do it. Yeah more ka,

884
00:52:09,560 --> 00:52:13,160
Speaker 1: let's see um oh. Michael C. Learner, who's an attorney

885
00:52:13,160 --> 00:52:16,520
Speaker 1: at Law and Reno, sent us a letter about getting

886
00:52:16,520 --> 00:52:20,080
Speaker 1: the word out about the National Consumer Law Center, for

887
00:52:20,200 --> 00:52:22,480
Speaker 1: which Learner does a lot of pro bono work for

888
00:52:22,520 --> 00:52:25,759
Speaker 1: people who are poor and getting screwed over because of debt.

889
00:52:25,840 --> 00:52:28,480
Speaker 1: As he put it so, he pointed to the National

890
00:52:28,480 --> 00:52:32,880
Speaker 1: Consumer Law Center and the Practicing law Institutes Consumer Financial Services.

891
00:52:32,960 --> 00:52:36,000
Speaker 1: Answer book. So if you are in debt and you're

892
00:52:36,040 --> 00:52:38,960
Speaker 1: getting pushed around, go check those things out, says Michael C. Learner.

893
00:52:39,120 --> 00:52:41,680
Speaker 1: Good stuff Van Ostro, and we gotta thank him again.

894
00:52:42,200 --> 00:52:45,520
Speaker 1: Our buddy from Washington sent us a book by his

895
00:52:45,600 --> 00:52:49,560
Speaker 1: friend Andy Robbins called Field Guide to the North American Jackalothes.

896
00:52:49,640 --> 00:52:53,680
Speaker 1: Pretty awesome, that's pretty fun. Paul Speth from Mars Community

897
00:52:53,760 --> 00:52:56,080
Speaker 1: Brewing Company in Chicago gives a bunch of beer at

898
00:52:56,080 --> 00:52:59,520
Speaker 1: the Chicago show. Thank you for that. Um, I got

899
00:52:59,520 --> 00:53:01,359
Speaker 1: one more. Okay, I'll go and finish up and then

900
00:53:01,360 --> 00:53:03,560
Speaker 1: you can round us out. Man, I have a whole page.

901
00:53:03,640 --> 00:53:06,920
Speaker 1: Laugh all right. Robert Highland from Wammo, this was just

902
00:53:06,960 --> 00:53:09,839
Speaker 1: came in today. Okay. He works for Wammo. He sent

903
00:53:09,960 --> 00:53:13,839
Speaker 1: us each their seventieth anniversary super book. Oh wow, thanks

904
00:53:13,840 --> 00:53:15,880
Speaker 1: a lot. Like you guys talk a lot about wammo products.

905
00:53:15,920 --> 00:53:18,799
Speaker 1: Does it bounce? I have not dropped it on the floor. YEA,

906
00:53:18,920 --> 00:53:21,160
Speaker 1: let's find out. Give it a try. I'm gonna do

907
00:53:21,200 --> 00:53:23,160
Speaker 1: a couple more and then well maybe we'll split these

908
00:53:23,239 --> 00:53:25,840
Speaker 1: up because there for both of us for another episode.

909
00:53:26,000 --> 00:53:27,720
Speaker 1: It's up to you. He can blaze through them too.

910
00:53:28,520 --> 00:53:32,880
Speaker 1: Now there's too many Um, so let's see the Crown

911
00:53:32,960 --> 00:53:36,920
Speaker 1: Royal people again for hooking us up. Very sweet. They've

912
00:53:37,120 --> 00:53:40,319
Speaker 1: hooked us up many, many times. Um. And they gave

913
00:53:40,400 --> 00:53:43,399
Speaker 1: us a nice congratulations because we've got the Best Curiosity

914
00:53:43,440 --> 00:53:46,919
Speaker 1: Award from the Heart Podcast Awards last year. Oh yeah,

915
00:53:47,000 --> 00:53:49,360
Speaker 1: that's how old this one is. Mick Sullivan gave us

916
00:53:49,360 --> 00:53:51,680
Speaker 1: a copy of his book The Meat Shower, which is

917
00:53:51,680 --> 00:53:54,560
Speaker 1: amazingly illustrated. You can check it out on the Past

918
00:53:54,640 --> 00:53:58,279
Speaker 1: and the Curious dot com. Yeah that just sounds really

919
00:53:58,280 --> 00:54:02,439
Speaker 1: greats it really does. Let's see, um and over round

920
00:54:02,480 --> 00:54:05,279
Speaker 1: everything out with Danielle Dixon, who is a real life

921
00:54:05,280 --> 00:54:08,520
Speaker 1: marine biologists chuck at the University of Delaware, and she

922
00:54:08,680 --> 00:54:10,960
Speaker 1: sent us a couple of copies of her kids books

923
00:54:11,000 --> 00:54:15,719
Speaker 1: See Stories, children's books based on real science. You can

924
00:54:15,800 --> 00:54:18,279
Speaker 1: check it out at s E A S T O

925
00:54:18,640 --> 00:54:22,160
Speaker 1: r Y books dot com. Alright, you're gonna save the rest.

926
00:54:22,239 --> 00:54:25,360
Speaker 1: I'm gonna save the restival splow up. All right. Thanks

927
00:54:25,360 --> 00:54:28,560
Speaker 1: everybody who sent us stuff, and and thank you also

928
00:54:28,680 --> 00:54:30,839
Speaker 1: just for saying hi to anyone who does. You can

929
00:54:30,880 --> 00:54:33,520
Speaker 1: say hi to us by sending us an email. Wrap

930
00:54:33,560 --> 00:54:35,440
Speaker 1: it up, spending on the bottom, send it off to

931
00:54:35,520 --> 00:54:43,000
Speaker 1: Stuff podcast at i heart radio dot com. Stuff you

932
00:54:43,000 --> 00:54:45,759
Speaker 1: Should Know is a production of iHeart Radio's How Stuff Works.

933
00:54:45,800 --> 00:54:48,040
Speaker 1: For more podcasts for my heart Radio, visit the iHeart

934
00:54:48,080 --> 00:54:50,600
Speaker 1: radio app, Apple Podcasts, or wherever you listen to your

935
00:54:50,640 --> 00:54:51,320
Speaker 1: favorite shows.