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Speaker 1: From UFOs to psychic powers and government conspiracies. History is

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Speaker 1: riddled with unexplained events. You can turn back now or

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Speaker 1: learn this stuff they don't want you to know. A

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Speaker 1: production of iHeartRadio.

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Speaker 2: Hello, welcome back to the show. My name is Matt, my.

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Speaker 3: Name is Noah. They call me Ben. We're joined as

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Speaker 3: always with our super producer Paul. Mission control decands. Most importantly,

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Speaker 3: you are you. You are here, and that makes this

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Speaker 3: stuff they don't want you to know here in the

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Speaker 3: age of social media. Right, we're recording this as longtime

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Speaker 3: listeners know on a virtual interface. Right, we're mentally and

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Speaker 3: soulfully together, but not physically together, and that may be

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Speaker 3: the case for some time. However, tech is allowing us

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Speaker 3: to feel closer. Right, And this the crazy thing is

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Speaker 3: this stuff is so cheap. Now we're talking about the

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Speaker 3: proliferation of cheap imaging technology. You guys, remember when not

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Speaker 3: everyone could afford a camera, editing software and stuff like that.

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Speaker 4: I mean, hell, I remember when not every phone had

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Speaker 4: a camera on it or it was like, who's got

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Speaker 4: a digital camera?

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Speaker 5: Go find one, you know, And now it's like a

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Speaker 5: non issue.

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Speaker 6: You know, well, and if you were going to take

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Speaker 6: a video with one of those, it was this tiny

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Speaker 6: little MPEG file that you'd end up getting. Even though

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Speaker 6: you had this powerful camera, the digital camera you could use,

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Speaker 6: your video was so terrible.

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Speaker 3: Yeah, yeah, and you just had a nice curated set

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Speaker 3: of pixels that would move and what appeared to look

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Speaker 3: like a face exactly. But now we have you know,

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Speaker 3: now it's it's so affordable to buy these things. And

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Speaker 3: on balance that's a good thing. But combine that with

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Speaker 3: the massive growth of social media and the massive growth

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Speaker 3: there's some of you people don't talk about as often,

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Speaker 3: the massive growth of archival capacity. This means, if you

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Speaker 3: want to be sweet nostalgic about it, there are days

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Speaker 3: of cherishing a few like glamour photographs from your from

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Speaker 3: your local photography studio, or a few expensive polaroids. Those

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Speaker 3: days are all but gone. Now you can post photos

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Speaker 3: of yourself, your friends, or strangers, and you can post

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Speaker 3: them anywhere you please. There are some laws about it,

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Speaker 3: but they're they're not really good because legislation has always

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Speaker 3: been technology's slower sibling, and so those those laws often

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Speaker 3: don't have a lot of teeth to them. And with

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Speaker 3: the rise of all this technology and the rise of

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Speaker 3: all these capabilities. We also see the rise of technology

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Speaker 3: that is exclusively meant to exploit the vast amount of

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Speaker 3: stuff that we've been making just from like live journal, Tumblr, MySpace, friends,

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Speaker 3: their face book, all you know, all the hits, all

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Speaker 3: that jazz. So what do you guys think, I mean,

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Speaker 3: optimists and pessimists have a different idea of what this

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Speaker 3: means for the world.

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Speaker 6: Well, yeah, on the optimist side, I would just say

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Speaker 6: the number of photographs and captured memories I have of

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Speaker 6: my son is probably more, you know, it's certainly thousands

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Speaker 6: of times greater than that which exists for me as

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Speaker 6: a child and my growth, my you know, my parents,

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Speaker 6: and then going back and back and back and back.

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Speaker 6: It's just exponentially a larger amount of memories that are

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Speaker 6: captured throughout his life, my son. So that's great, that's wonderful.

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Speaker 6: We can look back on those, you know, until the

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Speaker 6: the machines all turn off or we lose electricity. Those

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Speaker 6: those images will exist for my family and people that

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Speaker 6: care about me and my son.

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Speaker 4: But on the flip side of that, Matt, you know,

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Speaker 4: like folks like my mom for example, she doesn't consider

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Speaker 4: those re like she's like if it's not printed out

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Speaker 4: and it's not in a frame on my mantle, then

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Speaker 4: that is not a real memory. Which is interesting because

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Speaker 4: to your point about like the machines turning off, there's

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Speaker 4: all these companies now that will take your Instagram photos

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Speaker 4: and print them out and send you framed copies of them,

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Speaker 4: and the proliferation of like instas, cameras and like more

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Speaker 4: analog technology is sort of a response, almost a backlash

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Speaker 4: to this over digitization of like our memories and our

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Speaker 4: collective kind of you know, unconscious really in a way

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Speaker 4: with all the stuff that's out there. It's like there's

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Speaker 4: this sense of is it really ours once it's out

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Speaker 4: there in the ether. And I think that's a big

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Speaker 4: part of today's question.

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Speaker 3: Yeah, who owns the art, the artists or the audience.

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Speaker 3: It's a very old question, but it's one that has

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Speaker 3: continually been relevant, right and Yeah.

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Speaker 6: But the other question, Ben, is is it art or

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Speaker 6: is it just data?

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Speaker 3: Right? Right there we go, That's a that's a good distinction.

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Speaker 3: It's a tough distinction to make. Optimists are saying this

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Speaker 3: is great, you guys, the world's going to be a

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Speaker 3: safer place, and think how convenient everything's gonna be you'll

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Speaker 3: never lose your favorite photo again, you know, and criminals

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Speaker 3: can no longer disappear into a crowd. And then pessimists say,

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Speaker 3: you know, in a world beyond privacy, every person is

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Speaker 3: monitored continually throughout their lives. Your image is logged countless times,

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Speaker 3: and all the data from that image and all the

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Speaker 3: other images of you, it's analyzed. It's bought and sold

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Speaker 3: without your knowledge or your permission. A world's careening towards

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Speaker 3: sci fi, dystopia, pre crime, pre existing medical conditions. Advertising

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Speaker 3: so invasive and so bespoke that it might as well

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Speaker 3: be reading your mind, because it's doing something very close

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Speaker 3: to that. But before you get all blade round around

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Speaker 3: minority report, let's let's start closer to the beginning. There

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Speaker 3: is a revolution happening in the world of faith recognition

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Speaker 3: right now and its name is Clearview AI. Here are

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Speaker 3: the facts.

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Speaker 4: Yeah, I mean, the first time I heard of Clearview

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Speaker 4: AI was on a recent in PR piece where the

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Speaker 4: founder was interviewed. So this is a very relatively new company,

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Speaker 4: at least to me. But as far as the background,

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Speaker 4: it is a research tool that is being used by

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Speaker 4: law enforcement, specifically in the company. It makes it very

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Speaker 4: clear that this is something that's designed exclusively for use

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Speaker 4: by law enforcement in order to identify perpetrators and victims

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Speaker 4: of crimes.

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Speaker 3: It's a little bit pretty very.

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Speaker 5: Vague side I would say, yeah.

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Speaker 3: Yeah, I mean that's you know, we have to be honest.

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Speaker 3: That's the high level like logline or pitch from the

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Speaker 3: company on its own website. So we can't blame them

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Speaker 3: for not wanting to get too into the weeds about algorithms.

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Speaker 3: But if we look at how it actually works, we

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Speaker 3: can we can get a high level understanding. It's a startup.

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Speaker 3: It has a massive database of faces, just specifically faces,

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Speaker 3: somewhere north of three billion images, and as we're recording this,

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Speaker 3: that number is likely still growing, so that number is

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Speaker 3: going to be a little bit higher by the time

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Speaker 3: you get to the end of this episode.

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Speaker 4: Yeah, and it was that the gentleman who founded the

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Speaker 4: startup hann thought was who I heard interviewed on I

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Speaker 4: believe it was The Daily actually, So if you want

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Speaker 4: to hear directly from this gentleman's mouth what he believes

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Speaker 4: the mission of this technology in this company is a

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Speaker 4: very fascinating interview that kind of delves into some of

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Speaker 4: the slippery slope sides of this technology that we're going

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Speaker 4: to get into. But as far as the end users concerned,

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Speaker 4: which in this case would be law enforcement, because currently

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Speaker 4: this is not available to civilians. You take a picture

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Speaker 4: and it identifies who that person is. Is that by

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Speaker 4: by matching it with all of this information that they've collected,

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Speaker 4: and they've collected it, you know, not through any really

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Speaker 4: nefarious means. It's all that information that we talked about

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Speaker 4: that's already out there. All they've got to do is

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Speaker 4: reach out and grab it.

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Speaker 3: It's a it's a search engine for faces, and it

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Speaker 3: sounds innocuous at first. I also if it was The

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Speaker 3: Daily I think I listened to that interview, but I

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Speaker 3: also listened to an excellent interview you sent Matt an

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Speaker 3: extended CNN Business conversation. So if you want to see

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Speaker 3: them visual, like if you want to see the founder

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Speaker 3: using the app a couple of different times, I think

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Speaker 3: he does it on himself, he does it on the interviewer,

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Speaker 3: and then he does it on one of their producers,

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Speaker 3: and it seems to work. It's it's not a sensationalized

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Speaker 3: thing either, at least the way they're presenting it.

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Speaker 2: Yeah, but we're we're gonna get it.

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Speaker 6: We're gonna maybe talk about that a little more, just

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Speaker 6: just if you do want to look at that video.

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Speaker 6: So it's an interview with Donnie O'Sullivan with CNN Business

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Speaker 6: and it is where we're going to reference it several

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Speaker 6: times in this episode.

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Speaker 3: Yeah. So, like you said, nol Clearview has taken this

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Speaker 3: from publicly available sources, your Facebook's, your youtubes, your venmos,

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Speaker 3: which yes, are publicly available, and the federals and local

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Speaker 3: law enforcement admit that they only have a limited knowledge

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Speaker 3: of how the app actually works mechanically, the nuts and

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Speaker 3: bolts of it, but they do confirm they've already used

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Speaker 3: it to help solve shoplifting cases, identity theft cases, credit

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Speaker 3: card fraud, child abuse, even and even several homicides. This

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Speaker 3: technology goes, by the way, far beyond anything constructed by

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Speaker 3: Silicon Valley giants or anything officially created or used by

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Speaker 3: the US government.

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Speaker 2: Yeah.

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Speaker 6: In that that interview that we just mentioned, Juan mentions

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Speaker 6: that they had this image or this video file of

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Speaker 6: a child abuse incident and the person of interest walks

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Speaker 6: past in the background at one point, and there were

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Speaker 6: only two usable frames of this person's face. And again,

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Speaker 6: like imagine in the background of a video, they were

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Speaker 6: able to take a still from that video of this

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Speaker 6: person's face and identify him through other social media websites

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Speaker 6: where they've got all this data from and make an

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Speaker 6: arrest on this person like.

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Speaker 1: That is.

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Speaker 6: It's mind blowing that you could do that, and thank

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Speaker 6: goodness that they were able to do that. But it's

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Speaker 6: the implication of being able to do that in that

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Speaker 6: one instance and then applying it across everything is what

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Speaker 6: makes it feel a little scary because.

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Speaker 3: For the critics, for the critics.

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Speaker 6: Oh yeah, for the critics, well, for the people who

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Speaker 6: are looking at it big picture. So if you're you're

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Speaker 6: thinking about it just in a law enforcement application, then

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Speaker 6: it's amazing, right, and it's the best thing ever. But

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Speaker 6: if you apply it to everything, to everyone at all

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Speaker 6: times outside of law enforcement, then.

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Speaker 4: Well, and just strictly on like a technological level, it's

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Speaker 4: fascinating and clearly a huge leap forward in this kind

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Speaker 4: of technology, because i mean, we've seen these videos they're

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Speaker 4: talking about, these grainy surveillance videos or ATM camera videos.

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Speaker 4: I mean, it's hard to as a human being, even

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Speaker 4: if you knew that person, it'd be difficult for you

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Speaker 4: to identify them. So this using this algorithm and you know,

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Speaker 4: analyzing and comparing it to all these different subjects. I

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Speaker 4: imagine using points of articulation like on the faces and

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Speaker 4: the structures or whatever, it's able to come back with

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Speaker 4: positive matches. I think that's really fascinating, and they're really,

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Speaker 4: you know, pretty innovative use of technology. For sure, as

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Speaker 4: a nerd speaking you know, exclusively on that level. But

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Speaker 4: you're right, Matt, when you start to apply on a

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Speaker 4: larger scale, it gets really nineteen eighty four ish and

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Speaker 4: kind of scary.

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Speaker 3: I appreciate that point, Noel. It's something that's going to

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Speaker 3: come into play later and it may surprise some of

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Speaker 3: our fellow listeners to learn a little bit more about

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Speaker 3: this technology and some of the other topics that you

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Speaker 3: raise there. I want to say this. Their pitch is

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Speaker 3: very law enforcement based on the website. They're quick to

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Speaker 3: point out, they're quick to claim, I should say that

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Speaker 3: they've helped law enforcement track down hundreds of at large criminals.

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Speaker 3: They mention the real the things that make people very emotional,

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Speaker 3: like child abusers, terrorists, sex traffickers, but they also say,

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Speaker 3: this technology is used to help exonerate the innocent and

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Speaker 3: identify victims, and it seems that there they might be

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Speaker 3: embellishing a little bit in terms of the degree of

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Speaker 3: help they're providing, but they are providing help. The authorities

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Speaker 3: have used this, it's happening now. They've done so without

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Speaker 3: much public scrutiny, or they were for most of twenty

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Speaker 3: nineteen doing so without much public scrutiny. In early twenty twenty,

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Speaker 3: Clearview said more than six hundred different law enforcement agencies

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Speaker 3: started using our app just in the last year, but

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Speaker 3: they refuse to provide more specifics because they're very protective

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Speaker 3: of their client base. So the badger is out of

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Speaker 3: the bag. Metaphorically, this is not a what if scenario.

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Speaker 3: This exists. This is in use right now, and that

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Speaker 3: means it can therefore be used on you and every

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Speaker 3: single person you know. Is that a bad thing? A

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Speaker 3: Here's the interesting part. This technology is maybe in ave

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Speaker 3: an application, but very much not innovative in theoretical terms.

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Speaker 3: It has been possible for a while. People in the

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Speaker 3: tech world knew about this possibility for a long long time,

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Speaker 3: and some tech companies this is so weird. Tech companies

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Speaker 3: barely ever do this. Some tech companies like Giants said,

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Speaker 3: you know, we can build something like this. They realized

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Speaker 3: it years ago, and they actually decided to stop researching it.

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Speaker 3: They decided not to deploy this because of the massive

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Speaker 3: potential privacy concerns. Even Google wouldn't do it.

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Speaker 4: It's like that thing in The Batman, Which which one

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Speaker 4: is it? It's the one with Keith Heith Ledger where

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Speaker 4: there's that technology that turns every cell phone in the

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Speaker 4: world into a listening device, and you know he has

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Speaker 4: to destroy it after using it once because it's just

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Speaker 4: such problematic tech that you know, Morgan Freeman's character basically

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Speaker 4: builds a safeguard device that causes it to you know,

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Speaker 4: destroy itself, because it's that's the thing been about the

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Speaker 4: Badger of being out of the bag. Once it's out,

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Speaker 4: you can say all day long, this is just for

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Speaker 4: law enforcement. But like you said, Ben, the capability has

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Speaker 4: been there, So who's to stop somebody from doing a

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Speaker 4: kind of copycat version of this once it's out there

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Speaker 4: and available to check out, even if it's only quote

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Speaker 4: unquote just for law enforcement, you know, I mean, so

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Speaker 4: were certain kinds of radios and tasers and things like that.

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Speaker 4: You know, and people in the public have those now too,

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Speaker 4: don't they.

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Speaker 3: It's a very good point. Yeah, I mean back in

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Speaker 3: twenty eleven, think about how long ago. That was. Almost

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Speaker 3: ten years ago. The chairman of Google said, facial recognition technology,

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Speaker 3: this kind of stuff that Clearview is doing, is the

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Speaker 3: one technology that Google is holding back on because we

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Speaker 3: feel like it could be used in a very bad way.

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Speaker 3: And now this is a fascinating point here. Some cities

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Speaker 3: have even preemptively banned police from using any kind of

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Speaker 3: facial recognition technology. And one of the most notable example

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Speaker 3: of a city that's banned this San Francisco, at the

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Speaker 3: very heart of technological innovation here in the US, so

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Speaker 3: they definitely knew what was coming. Their lawmakers, their constituents

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Speaker 3: gave it a big nope. And here's the other thing,

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Speaker 3: Clearview AI. If we think about the future of it,

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Speaker 3: the code behind it is fascinating.

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Speaker 6: If anyone out there has been watching the latest season

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Speaker 6: of Westworld, you may be familiar with the glasses that

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Speaker 6: several characters wear, and just think about that. If you

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Speaker 6: are familiar with that, think about that as we describe

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Speaker 6: what this thing can do.

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Speaker 3: Yeah, think about it carefully because you see Clearview AI.

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Speaker 3: The code behind it isn't just facial recognition. It also

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Speaker 3: includes programming language that would allow this tool to pair

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Speaker 3: with AR or augmented reality glasses. What does this mean.

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Speaker 3: This means that if the sunglasses I'm wearing now, we're

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Speaker 3: functioning with Clearview AI, I could walk down the street

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Speaker 3: and I could identify almost every person I saw, and

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Speaker 3: not just other pictures of them, but breadcrumbs that would

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Speaker 3: tell me where they lived, you know, what their children's

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Speaker 3: identities were, what their name was, what they did for

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Speaker 3: a living, and so it would automatically play the six

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Speaker 3: Degrees of Kevin Bacon. Actually, that's a really great idea

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Speaker 3: for an add on game for these glasses. You can

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Speaker 3: see how close people are to Kevin Bacon. You heard

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Speaker 3: it here first. But it's scary because then you would

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Speaker 3: like you would see you would see Matt Frederick, and

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Speaker 3: then you would you would know his friend's Paul Mission

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Speaker 3: Control Deckat and Nold Brown, Ben Bolin, and you would

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Speaker 3: know everyone he worked with. It just it gets real

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Speaker 3: sticky really quickly. And can you hear the fan heads

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Speaker 3: drooling in the background? Is that just my cat I

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Speaker 3: don't know at this point, but that's the high level look.

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Speaker 6: Or spies, I mean, just any spy. Imagine that, well,

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Speaker 6: you guys, I mean, I think we're forgetting an even

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Speaker 6: more classic sci fi example of this, the kind of

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Speaker 6: heads up display you'd see in a movie like The Terminator.

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Speaker 4: You know, where you're seeing from the perspective of this,

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Speaker 4: you know, assassin robot who's looking for a target and

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Speaker 4: being able to see who everybody is and get all

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Speaker 4: these stats and metrics. You could apply this to any

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Speaker 4: number of you know, let's call them parameters surrounding an individual.

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Speaker 4: You could then go deeper with this and start pulling

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Speaker 4: stats from their social media and be served up with

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Speaker 4: information about their height and or you know whatever, like

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Speaker 4: any like let's say they have pre existing condition or

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Speaker 4: something like that, or they have some kind of you

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Speaker 4: know what's the word I'm looking for, like compromand on them.

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Speaker 4: You could even go deeper and find pictures of them,

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Speaker 4: like maybe smoking when they've claimed that they don't smoke,

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Speaker 4: and they are, you know, committing insurance fraud.

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Speaker 5: I don't know. I'm just saying there's all kinds.

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Speaker 4: Of ways you could deep dive, scrape this data and

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Speaker 4: apply these points to an individual and then get that

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Speaker 4: information served up to you very quickly in this display.

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Speaker 3: And yeah, an optimists are so quick to point out

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Speaker 3: the potential benefits of clearview AI and facial recognition in general.

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Speaker 3: It's true there are benefits. Pessimists, however, claim the disadvantages

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Speaker 3: here in this specific case far outweigh any of those

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Speaker 3: benefits outweigh them, in fact, by a vast and disturbing margin.

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Speaker 3: We'll pause for word from our sponsor and return to

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Speaker 3: dive down the rabbit hole. Here's where it gets crazy.

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Speaker 3: There are a ton of problems we've you know, we've

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Speaker 3: done some foreshadowing on these, but let's talk a little

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Speaker 3: bit more about them first. You know you can categorize

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Speaker 3: these right. So first, there are not many strong operational

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Speaker 3: restrictions on how this technology is actually used. That means

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Speaker 3: that it can not only be easily misused, but that

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Speaker 3: there will probably not be repercussions for people, at least

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Speaker 3: not legal repercussions. It's like a point you made earlier,

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Speaker 3: been off off, Mike.

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Speaker 4: It's like when you used to go into those tobacco

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Speaker 4: shops and you know you are not allowed to refer

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Speaker 4: to certain smoking apparatuses as bongs. They are water pipes exclusively,

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Speaker 4: because that absolves the distributor, the retailer of any responsibility

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Speaker 4: if you choose to.

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Speaker 5: Use them in any illegal manner.

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Speaker 3: It's the same thing.

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Speaker 4: It's like saying, hey, whatever you do with my thing

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Speaker 4: is up to you. I know, it's design and exclusively

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Speaker 4: for a legal purpose. It's a real sticky situation, isn't it,

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Speaker 4: Because I mean it's it not only puts all of

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Speaker 4: the burden on the user, it absolves the creator of

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Speaker 4: any kind of responsibility at all.

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Speaker 3: Or does he do this table up, Hey, hey buddy,

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Speaker 3: that's a tobacco water pipe.

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Speaker 6: All right, okay, yeah, yeah, say, you know, saying something

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Speaker 6: like that the way the founder did in that interview

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Speaker 6: with CNN where he was just saying, you know, the

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Speaker 6: thing says, just use it in this way.

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Speaker 2: It's it's we've seen it over and over and over again.

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Speaker 6: There there are a couple of good things that he

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Speaker 6: does speak about in that CNN Business interview, and one

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Speaker 6: of the major things, you know, if you put it

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Speaker 6: at the top, it's that the software costs around fifty

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Speaker 6: thousand dollars to have a license for a you know,

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Speaker 6: for a department or something for a year, years for

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Speaker 6: two years, and so at least there's a buy in

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Speaker 6: level there that just your average person if they're getting

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Speaker 6: if they're obtaining it legally, wouldn't be able to use.

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Speaker 6: And then there are you know, ways to verify if

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Speaker 6: you know, an end user is actually if that license

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Speaker 6: is is actually that person they should be using it?

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Speaker 2: That's good.

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Speaker 6: And the other thing is if you're a manager within

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Speaker 6: the software of a you know, a licensee of this software,

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Speaker 6: you get audits of every time the software has been used,

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Speaker 6: which is really nice. So at least at least there

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Speaker 6: could be some oversight to it and they could check

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Speaker 6: to see if you are quote using it in a

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Speaker 6: way they're not supposed to.

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Speaker 3: Yeah, yeah, there's any there's an example of that. I

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Speaker 3: think that they'll come into play here later, which is

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Speaker 3: kind of it's pretty weird. It's it's a little it's

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Speaker 3: a little spooky, uh, but I want to go back

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Speaker 3: to that warning. It's it's literally something. It's it's like

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Speaker 3: that old FBI VHS tape warning that we mentioned in

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Speaker 3: earlier episode. You go on to the homepage essentially of

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Speaker 3: the app, which you can use on desktop or you

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Speaker 3: can use on your phone, and basically all it says

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Speaker 3: is like, don't use this in a bad way, and

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Speaker 3: that's it's It's like as effective as those tags on

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Speaker 3: mattresses that say don't remove this tag, or the little

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00:23:21,000 --> 00:23:24,320
Speaker 3: note on your boxy Q tips that's like, hey, don't

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Speaker 3: put these in your ear. I know it feels awesome,

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Speaker 3: and I know that's what everybody does with it, but

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Speaker 3: don't put them in your ear. And then like you said, no,

400
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Speaker 3: that absolves them.

401
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Speaker 4: Or they're warning about seizures at the beginning of when

402
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Speaker 4: you start up your PlayStation, you know what I mean,

403
00:23:39,359 --> 00:23:42,240
Speaker 4: It's like, yeah, okay, maybe I get seizures, but I'm

404
00:23:42,240 --> 00:23:44,040
Speaker 4: gonna roll the dice because I sure want to play

405
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Speaker 4: me some Borderlands three, you know.

406
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Speaker 3: Yeah, Because you know, now that I think about it,

407
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Speaker 3: we might be in a bit of a glasshouse situation here,

408
00:23:53,119 --> 00:23:56,639
Speaker 3: my friends, because our show literally starts with the warning

409
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Speaker 3: that says you can turn back now. We're not as

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Speaker 3: bad a Q tips. You can you can turn back now.

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Speaker 4: That's definitely as as is our prerogative to follow said

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Speaker 4: warnings and not stick cue tips in our ears.

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Speaker 3: But who wants to do that.

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Speaker 4: What are you gonna use them for? Like putting on eyeshadowers?

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Speaker 4: I mean, I don't know, Like I feel like the

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Speaker 4: back of your ear you need it to do need

417
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Speaker 4: a cue tip for that? Can't you just use like

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Speaker 4: a like a like a tissue.

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Speaker 3: Some people are living wild man. I don't know what

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Speaker 3: to tell you. You tips are literally probes. I don't

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Speaker 3: know anyway.

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Speaker 4: I just feel like they're very clearly designed to shove

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Speaker 4: in your ear and it does feel great, but yeah,

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Speaker 4: it can. It's pretty scary when you pull out some blood,

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Speaker 4: you know, just saying.

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Speaker 6: So, yes, I've never done that. Actually, I'm gonna start

427
00:24:41,160 --> 00:24:44,160
Speaker 6: keep trying. Uh No we get gotten blood like gone

428
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Speaker 6: in for wax out with blood. But you know, maybe

429
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Speaker 6: I'm just fortunate.

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Speaker 4: Uh.

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Speaker 6: I want to bring up something that is possible with

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Speaker 6: this software and just how it uses it before we

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Speaker 6: jump into, uh, the next thing we're going to talk about,

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Speaker 6: and that's just that when your face ends up on

435
00:25:02,440 --> 00:25:06,199
Speaker 6: a social media website somewhere, even if you did not

436
00:25:06,280 --> 00:25:09,159
Speaker 6: take that picture, even if you were in the background

437
00:25:09,280 --> 00:25:12,440
Speaker 6: of a selfie that someone took at Bonnaroo, let's say,

438
00:25:12,520 --> 00:25:18,080
Speaker 6: or something like that, your face is still searchable with

439
00:25:18,240 --> 00:25:21,520
Speaker 6: facial recognition software if it is visible in the background

440
00:25:21,520 --> 00:25:23,760
Speaker 6: of an image, if you're in a crowd, and if

441
00:25:23,840 --> 00:25:27,199
Speaker 6: anywhere you are, anywhere you've been where a photograph has

442
00:25:27,240 --> 00:25:31,360
Speaker 6: been taken, where you can are visible somewhere in the background,

443
00:25:32,040 --> 00:25:37,520
Speaker 6: this thing clearview AI can may very much likely has

444
00:25:37,600 --> 00:25:41,199
Speaker 6: that image within its database and your face will be

445
00:25:41,320 --> 00:25:42,320
Speaker 6: recognizable to it.

446
00:25:42,480 --> 00:25:43,720
Speaker 2: Just let's put that there.

447
00:25:43,920 --> 00:25:47,399
Speaker 3: So it doesn't matter if you're on social media exactly.

448
00:25:47,640 --> 00:25:50,240
Speaker 4: Guys Devil's advocate here, isn't that one of the situations

449
00:25:50,280 --> 00:25:54,320
Speaker 4: where if you're in public, you're sort of giving up

450
00:25:54,359 --> 00:25:57,760
Speaker 4: that right to privacy. Or if you go to a Bonnaroo,

451
00:25:58,119 --> 00:26:00,959
Speaker 4: there's usually signs that said when you pass these gates,

452
00:26:01,040 --> 00:26:03,840
Speaker 4: you are subject to being filmed, et cetera. And that's

453
00:26:03,920 --> 00:26:06,919
Speaker 4: usually for more like documentary purposes of the festival. But

454
00:26:07,200 --> 00:26:08,879
Speaker 4: I would think it would also apply if you're in

455
00:26:08,960 --> 00:26:12,720
Speaker 4: public and you're caught on you know, one of these

456
00:26:12,800 --> 00:26:16,000
Speaker 4: millions of little surveillance devices that people carry around with them.

457
00:26:16,359 --> 00:26:20,240
Speaker 4: Is that fair game? Can you sue over use of

458
00:26:20,240 --> 00:26:23,399
Speaker 4: your of misuse of your image? I'm gonna I wouldn't

459
00:26:23,400 --> 00:26:24,320
Speaker 4: have think I wouldn't have thought.

460
00:26:24,320 --> 00:26:27,159
Speaker 6: So I'm certainly not arguing about you know, the legality

461
00:26:27,160 --> 00:26:29,720
Speaker 6: of it. I'm just saying the reality of it is

462
00:26:29,840 --> 00:26:34,080
Speaker 6: that wherever you go, if there is a camera taking

463
00:26:34,080 --> 00:26:37,400
Speaker 6: a picture, whether it's cct well, CCTV, generally wouldn't show

464
00:26:37,440 --> 00:26:39,280
Speaker 6: up in one of these things unless it was placed

465
00:26:39,320 --> 00:26:40,400
Speaker 6: into the system.

466
00:26:40,760 --> 00:26:45,400
Speaker 3: At the At the risk of sounding to I don't

467
00:26:45,440 --> 00:26:49,280
Speaker 3: know too extreme about this, maybe too prescient. I think

468
00:26:49,320 --> 00:26:52,840
Speaker 3: the best for people concerned about this, the best way

469
00:26:52,880 --> 00:26:56,720
Speaker 3: to handle it, which is maddening, is to is to

470
00:26:56,800 --> 00:26:59,560
Speaker 3: apply some of the rules that some of the rules

471
00:26:59,600 --> 00:27:04,399
Speaker 3: that we apply guns to cameras. So you always assume

472
00:27:04,400 --> 00:27:07,000
Speaker 3: a gun is loaded, right, you never want to point

473
00:27:07,000 --> 00:27:10,240
Speaker 3: it at you. So always assume a camera is recording.

474
00:27:10,600 --> 00:27:13,040
Speaker 3: And if you don't want to be with that assumption,

475
00:27:13,200 --> 00:27:16,800
Speaker 3: if you don't want to be in the footage or

476
00:27:16,880 --> 00:27:20,800
Speaker 3: in the photo, then don't have it pointed at you.

477
00:27:20,800 --> 00:27:24,600
Speaker 3: You guys, remember I was very uncomfortable with Pokemon Go

478
00:27:25,160 --> 00:27:28,200
Speaker 3: when it came out. I was like, no, they're putting poke,

479
00:27:28,320 --> 00:27:32,240
Speaker 3: They're putting these ar little cute things to collect because

480
00:27:32,280 --> 00:27:35,280
Speaker 3: you are filming, you're filming for them. You guys are

481
00:27:35,280 --> 00:27:39,840
Speaker 3: like Ben big Brother does not give a like the

482
00:27:39,880 --> 00:27:46,199
Speaker 3: faintest hint of a about charizards or whatever. Right, but

483
00:27:46,480 --> 00:27:48,160
Speaker 3: still that stuff freaks me out.

484
00:27:48,240 --> 00:27:48,320
Speaker 2: Ye.

485
00:27:48,480 --> 00:27:51,200
Speaker 4: Well no, but we did a whole story about that company,

486
00:27:51,280 --> 00:27:55,119
Speaker 4: Nantec and like how it had roots and intelligence cathering.

487
00:27:55,200 --> 00:27:58,159
Speaker 4: I mean, you're not wrong, Ben and I remember that

488
00:27:58,280 --> 00:28:00,760
Speaker 4: we'd be out hanging out and people would be doing

489
00:28:00,800 --> 00:28:03,600
Speaker 4: their little capturing their pokemons and they'd be on your

490
00:28:03,600 --> 00:28:05,840
Speaker 4: cell phone or something, and you would like cover your face,

491
00:28:06,119 --> 00:28:08,160
Speaker 4: and that is your prerogative to do. And I completely

492
00:28:08,280 --> 00:28:10,480
Speaker 4: understand where you're coming from, because it's like with the

493
00:28:10,520 --> 00:28:14,040
Speaker 4: whole zoom phenomenon that we're obviously we're using it right now.

494
00:28:14,440 --> 00:28:17,520
Speaker 4: You can turn off that camera as far as you're

495
00:28:17,560 --> 00:28:20,040
Speaker 4: concerned where it doesn't show it to the group, but

496
00:28:20,160 --> 00:28:23,720
Speaker 4: I guarandamn to you, it's still capturing what's going on

497
00:28:24,119 --> 00:28:28,720
Speaker 4: and storing it somewhere. So you know, it's a great technology.

498
00:28:28,840 --> 00:28:31,240
Speaker 4: It works real well, which is why it's having this

499
00:28:31,359 --> 00:28:35,080
Speaker 4: moment right now. But I just would wouldn't trust it

500
00:28:35,119 --> 00:28:36,320
Speaker 4: too implicitly, you know.

501
00:28:36,720 --> 00:28:39,480
Speaker 6: You know, the best technology, it's what Ben, Ben and

502
00:28:39,480 --> 00:28:44,479
Speaker 6: I it's a sticker or some tape just go poop

503
00:28:45,600 --> 00:28:46,600
Speaker 6: at all times.

504
00:28:47,080 --> 00:28:51,240
Speaker 3: Yeah, but that's uh, that's that's still that's that's a

505
00:28:51,280 --> 00:28:53,600
Speaker 3: low tech hack. But you have to do it because

506
00:28:53,640 --> 00:28:56,560
Speaker 3: this stuff can be weaponized. And uh, there's a guy

507
00:28:56,640 --> 00:29:00,120
Speaker 3: named Eric Goldman at the High Tech Law Institute at

508
00:29:00,160 --> 00:29:03,959
Speaker 3: Santa Clara University. I like the way you put it.

509
00:29:04,000 --> 00:29:09,120
Speaker 3: He said, the weaponization possibilities of this are endless. Imagine

510
00:29:09,160 --> 00:29:13,400
Speaker 3: a rogue law enforcement officer who wants to stalk someone

511
00:29:13,520 --> 00:29:15,920
Speaker 3: that they want to hit on later, or maybe a

512
00:29:15,960 --> 00:29:19,520
Speaker 3: foreign government is doing similar to what you described, Noel.

513
00:29:19,640 --> 00:29:22,560
Speaker 3: They're digging up secrets about people to blackmail them or

514
00:29:22,600 --> 00:29:25,520
Speaker 3: to throw members of an opposition party in jail. This

515
00:29:25,560 --> 00:29:30,800
Speaker 3: stuff can happen. And you know, Clearview is also notoriously

516
00:29:30,960 --> 00:29:34,640
Speaker 3: secretive even now that's the second big issue. It leaves

517
00:29:34,680 --> 00:29:38,760
Speaker 3: a lot of critics ill at ease. We know some

518
00:29:38,840 --> 00:29:44,640
Speaker 3: stuff because of some excellent research from earlier reporters. Let

519
00:29:44,720 --> 00:29:47,560
Speaker 3: me tell you, guys, this weird story. So there was

520
00:29:47,600 --> 00:29:50,320
Speaker 3: a New York Times journalist who was working on a

521
00:29:50,400 --> 00:29:53,720
Speaker 3: story about Clearview that really brought it to international attention.

522
00:29:54,520 --> 00:29:59,600
Speaker 3: And while this journalist is working with clear is trying

523
00:29:59,600 --> 00:30:03,000
Speaker 3: to do this story, Clearview isn't really answering him. They're

524
00:30:03,040 --> 00:30:06,680
Speaker 3: like ducking attempts to contact via text, email, phone call.

525
00:30:07,160 --> 00:30:12,080
Speaker 3: And so this reporter knows that a lot of law

526
00:30:12,160 --> 00:30:16,800
Speaker 3: enforcement agencies have clearview access, and so this reporter asks

527
00:30:16,880 --> 00:30:21,000
Speaker 3: some contacts in the police department to run his photo

528
00:30:21,160 --> 00:30:25,360
Speaker 3: through the app and they do a couple times. So

529
00:30:25,600 --> 00:30:30,280
Speaker 3: the first approach, the first thing he learns about clearview

530
00:30:30,560 --> 00:30:34,160
Speaker 3: approaching outside forces is not when they come to him.

531
00:30:34,320 --> 00:30:37,160
Speaker 3: It's when they go to the police that he worked

532
00:30:37,200 --> 00:30:40,959
Speaker 3: with and they ask the police, Hey, are you talking

533
00:30:41,000 --> 00:30:43,160
Speaker 3: to the media. How shady is that?

534
00:30:43,720 --> 00:30:46,800
Speaker 2: Wow? Yes, that is whoa.

535
00:30:47,120 --> 00:30:50,440
Speaker 3: They have an explanation, right, oh, absolutely, and just really quickly.

536
00:30:50,480 --> 00:30:52,360
Speaker 4: That's the piece that was on the Daily because the

537
00:30:52,440 --> 00:30:54,160
Speaker 4: Daily is affiliate with the New York Times, so this

538
00:30:54,320 --> 00:30:57,120
Speaker 4: was after the fact of this reporter whose name was

539
00:30:57,120 --> 00:30:59,960
Speaker 4: trying to defind, had done all this research and gone

540
00:31:00,280 --> 00:31:03,400
Speaker 4: through this whole run around to even get a sit down.

541
00:31:04,160 --> 00:31:06,600
Speaker 4: The reporter eventually found the office and it was like

542
00:31:06,720 --> 00:31:08,680
Speaker 4: very shady, like there weren't with that many people there.

543
00:31:08,680 --> 00:31:11,680
Speaker 4: It was a skeleton crew, and finally got access to

544
00:31:11,720 --> 00:31:14,640
Speaker 4: the to the founder. But that is all, you know,

545
00:31:14,880 --> 00:31:17,600
Speaker 4: described in great detail on that piece by The Daily.

546
00:31:17,640 --> 00:31:19,000
Speaker 5: I highly recommend checking it out.

547
00:31:19,440 --> 00:31:23,080
Speaker 3: Yeah, Kashmir Hill, the secretive company that might end privacy

548
00:31:23,120 --> 00:31:26,480
Speaker 3: as we know it is. The is the Times article. Yeah,

549
00:31:26,560 --> 00:31:29,680
Speaker 3: I liked what you're saying about. When when when Hill

550
00:31:29,760 --> 00:31:34,320
Speaker 3: finally figured out where the company was originally on LinkedIn,

551
00:31:34,720 --> 00:31:37,760
Speaker 3: they just had like one employee listed and it turned

552
00:31:37,800 --> 00:31:40,440
Speaker 3: out to be a pseudonym or a fake name that

553
00:31:40,480 --> 00:31:44,360
Speaker 3: the founder was using. So they're they're aware of the

554
00:31:44,440 --> 00:31:48,320
Speaker 3: dangers here. But but when they did eventually speak to

555
00:31:48,360 --> 00:31:52,920
Speaker 3: the reporter, they had and they had reasonable explanations for

556
00:31:53,080 --> 00:31:57,160
Speaker 3: both of these things. First, the founder said, look, we

557
00:31:57,160 --> 00:32:00,680
Speaker 3: were avoiding speaking to the media, not because we're like

558
00:32:00,840 --> 00:32:04,200
Speaker 3: super villains or something. It's because we are a tiny

559
00:32:04,280 --> 00:32:06,840
Speaker 3: startup and a lot of times when you're a tiny startup,

560
00:32:06,840 --> 00:32:09,560
Speaker 3: you're in you're in stealth mode, right, And that makes

561
00:32:09,600 --> 00:32:12,640
Speaker 3: sense because the NDAs on those things are pretty ironclad.

562
00:32:13,000 --> 00:32:15,360
Speaker 3: You don't want anyone to scoop you. And then he

563
00:32:15,400 --> 00:32:18,360
Speaker 3: additionally said, here's why we talked to the police about

564
00:32:18,400 --> 00:32:22,920
Speaker 3: your search terms. We actually monitor all the searches that

565
00:32:22,960 --> 00:32:26,280
Speaker 3: are done because we want to make sure that they're

566
00:32:26,320 --> 00:32:29,240
Speaker 3: being used correctly. In other words, you know, we want

567
00:32:29,280 --> 00:32:33,200
Speaker 3: to make sure nothing like like, there's never a situation

568
00:32:33,280 --> 00:32:37,360
Speaker 3: where a police officer is a jilted is jilted by

569
00:32:37,360 --> 00:32:40,080
Speaker 3: a romantic partner and then they find out their X

570
00:32:40,120 --> 00:32:42,000
Speaker 3: has a new lover and they're like, well, I'm gonna

571
00:32:42,000 --> 00:32:46,280
Speaker 3: find out about this. God, I'm even afraid to make

572
00:32:46,320 --> 00:32:47,720
Speaker 3: up a name because I don't want to put it

573
00:32:47,760 --> 00:32:54,520
Speaker 3: out there. Marcus mcgilly cutty. Oh that's swinging a miss.

574
00:32:54,440 --> 00:32:56,800
Speaker 4: Funny thing about that, though, Ben is like, you don't

575
00:32:56,800 --> 00:33:00,520
Speaker 4: even need technology this robust to dig in this somebody

576
00:33:00,760 --> 00:33:04,080
Speaker 4: that closely removed from you know, a person you really know,

577
00:33:04,720 --> 00:33:08,040
Speaker 4: they're probably already like posting tagging each other on Instagram

578
00:33:08,080 --> 00:33:09,640
Speaker 4: posts and you just, you know, go and.

579
00:33:09,560 --> 00:33:11,000
Speaker 5: Just do a couple of clicks and you're there.

580
00:33:12,040 --> 00:33:13,840
Speaker 4: But no, it's just a really good point and that's

581
00:33:13,880 --> 00:33:16,720
Speaker 4: what what's set off those alarm bells. But yeah, the

582
00:33:16,760 --> 00:33:19,600
Speaker 4: way it's described in the in the in the Daily piece,

583
00:33:20,080 --> 00:33:23,280
Speaker 4: it was very it felt very like, Okay, A, they

584
00:33:23,360 --> 00:33:28,640
Speaker 4: know they're onto something, and B they don't want to

585
00:33:28,680 --> 00:33:31,200
Speaker 4: be found. And I can see to your point been

586
00:33:31,280 --> 00:33:33,360
Speaker 4: the whole startup mode. They don't want to get scooped,

587
00:33:33,360 --> 00:33:36,959
Speaker 4: They don't want anybody stealing their proprietary in their you know,

588
00:33:37,000 --> 00:33:40,520
Speaker 4: technology and trying to misuse it or you know, create

589
00:33:40,640 --> 00:33:42,800
Speaker 4: a copycat kind of version of it. So that does

590
00:33:42,960 --> 00:33:44,720
Speaker 4: make sense, but just it's the whole thing is a

591
00:33:44,720 --> 00:33:49,600
Speaker 4: little strange. But this app clearview claims. Uh, you know,

592
00:33:49,760 --> 00:33:53,840
Speaker 4: is is up to security industry standards in terms of

593
00:33:53,920 --> 00:33:57,720
Speaker 4: I guess in terms of you know, hackability or like

594
00:33:57,880 --> 00:34:01,160
Speaker 4: firewalls security or you know, being not being vulnerable to

595
00:34:01,560 --> 00:34:07,200
Speaker 4: being infiltrated, but who is actually watching them? Who is

596
00:34:07,280 --> 00:34:11,880
Speaker 4: actually monitoring them because it's it's essentially untested and already

597
00:34:11,920 --> 00:34:14,759
Speaker 4: being put out into the wild in cases that can

598
00:34:14,800 --> 00:34:18,600
Speaker 4: have huge effects on people's lives. We'll talk more about

599
00:34:18,600 --> 00:34:20,280
Speaker 4: that after a quick word from our sponsor.

600
00:34:26,760 --> 00:34:30,640
Speaker 3: Yes, you know, that was a perfect ad. Break bill

601
00:34:31,360 --> 00:34:34,360
Speaker 3: also gave me time to get this cat out of

602
00:34:34,480 --> 00:34:36,920
Speaker 3: out of my recording studio. I heard you had a

603
00:34:36,920 --> 00:34:43,640
Speaker 3: little me hour. Yeah, yeah, Well we're all dealing with

604
00:34:43,719 --> 00:34:44,960
Speaker 3: new coworkers, right.

605
00:34:44,960 --> 00:34:47,360
Speaker 4: Can I just do a quick aside, A quick quarantine

606
00:34:47,360 --> 00:34:50,600
Speaker 4: aside had a crisis yesterday we I don't know if

607
00:34:50,640 --> 00:34:53,080
Speaker 4: I've mentioned the show that my daughter got a hamster

608
00:34:53,239 --> 00:34:55,800
Speaker 4: right before quarantine kicked in and I had to inherit

609
00:34:55,840 --> 00:34:59,360
Speaker 4: it because her mom was allergic. She found out hamster's

610
00:34:59,400 --> 00:35:02,520
Speaker 4: name is Hannah, which is like an anime character. But anyway,

611
00:35:02,719 --> 00:35:06,640
Speaker 4: we realized yesterday afternoon that Honakoh was not in her cage.

612
00:35:07,040 --> 00:35:10,040
Speaker 3: Uh, so we had a hamster.

613
00:35:09,719 --> 00:35:12,960
Speaker 4: Search party all up in this house, UH freaking out

614
00:35:12,960 --> 00:35:15,520
Speaker 4: and I had just these horrible visions of like finding

615
00:35:15,560 --> 00:35:18,600
Speaker 4: a dead, decaying hamster, you know, two weeks later in

616
00:35:18,680 --> 00:35:21,960
Speaker 4: my sock drawer or something. So uh, luckily, the little

617
00:35:22,040 --> 00:35:24,920
Speaker 4: the little critter found herself back to where she belonged

618
00:35:24,920 --> 00:35:26,240
Speaker 4: on her own, which was great.

619
00:35:27,120 --> 00:35:27,400
Speaker 3: Great.

620
00:35:27,719 --> 00:35:29,680
Speaker 4: Yeah, she just popped out and was right there, even

621
00:35:29,719 --> 00:35:31,640
Speaker 4: thought it was a sock and it was. Turns out

622
00:35:31,680 --> 00:35:33,839
Speaker 4: it was a hamster. And it wasn't long. I read

623
00:35:33,840 --> 00:35:36,440
Speaker 4: all this horror stories about how you can only search

624
00:35:36,520 --> 00:35:38,960
Speaker 4: late at night and you gotta bait it with peanut

625
00:35:38,960 --> 00:35:41,080
Speaker 4: butter and all this stuff, and it she just came

626
00:35:41,160 --> 00:35:41,799
Speaker 4: came right back.

627
00:35:41,840 --> 00:35:42,359
Speaker 2: It was great.

628
00:35:42,680 --> 00:35:43,640
Speaker 3: That's fantastic.

629
00:35:43,800 --> 00:35:44,040
Speaker 6: Yeah.

630
00:35:44,200 --> 00:35:47,200
Speaker 3: I had a Uh, I had a Gerbil in my

631
00:35:47,320 --> 00:35:51,799
Speaker 3: younger days that escaped and lived and died in the

632
00:35:51,840 --> 00:35:55,040
Speaker 3: walls of a house in my Uh tried to tried

633
00:35:55,040 --> 00:35:59,120
Speaker 3: to move the oven and appliances to get to it. Uh,

634
00:35:59,160 --> 00:36:03,000
Speaker 3: but then we couldn't, and my father thought it would

635
00:36:03,000 --> 00:36:04,799
Speaker 3: be a good lesson in mortality.

636
00:36:05,280 --> 00:36:08,440
Speaker 4: Oh boy, I was hoping to avoid that particular lesson

637
00:36:08,480 --> 00:36:12,080
Speaker 4: for my kid, especially during these trying times when things

638
00:36:12,120 --> 00:36:14,920
Speaker 4: have been mainly pretty positive for us here at the house.

639
00:36:15,200 --> 00:36:15,960
Speaker 3: I'm so glad.

640
00:36:16,200 --> 00:36:19,680
Speaker 4: Yeah, thanks guys, But no, you're right, Ben, tell us

641
00:36:19,719 --> 00:36:23,120
Speaker 4: a little bit about the security of this app and

642
00:36:23,480 --> 00:36:30,040
Speaker 4: the idea of it being tested for you know, vulnerabilities.

643
00:36:29,239 --> 00:36:32,680
Speaker 3: Right, Yeah, this is a very interesting point. So a

644
00:36:32,719 --> 00:36:36,880
Speaker 3: lot of the reporting that came up about Clearview Ai

645
00:36:37,640 --> 00:36:43,440
Speaker 3: came before late February of this year. In late February,

646
00:36:44,520 --> 00:36:48,759
Speaker 3: Clearview app security was actually tested in the form of

647
00:36:48,800 --> 00:36:52,040
Speaker 3: a hack. On February twenty six, the public learned that

648
00:36:52,080 --> 00:36:56,440
Speaker 3: the company had been breached, their security been compromised. Hackers

649
00:36:56,480 --> 00:37:01,160
Speaker 3: had stolen Clearview AI's entire customer list, which was coveted

650
00:37:01,360 --> 00:37:06,080
Speaker 3: and very much secret right the customer list, adding to

651
00:37:06,480 --> 00:37:09,359
Speaker 3: the troubling facts about this company, The customer list did

652
00:37:09,400 --> 00:37:13,320
Speaker 3: not jibe with what Clearview had said earlier. It spans

653
00:37:13,400 --> 00:37:17,640
Speaker 3: thousands of government entities and private businesses across the world.

654
00:37:17,960 --> 00:37:21,960
Speaker 3: So the US Department of Justice, Immigration and Customs, but

655
00:37:22,040 --> 00:37:24,960
Speaker 3: it also includes banks, and he does allude to banks

656
00:37:25,040 --> 00:37:27,520
Speaker 3: in a couple of different interviews the founders. It also

657
00:37:27,520 --> 00:37:32,160
Speaker 3: includes Macy's and best Buy and Interpool. And then on

658
00:37:32,320 --> 00:37:36,800
Speaker 3: the list you also see credentialed users at the FBI.

659
00:37:37,719 --> 00:37:40,280
Speaker 3: You know, we said hundreds of police departments. But here's

660
00:37:40,280 --> 00:37:44,399
Speaker 3: something very interesting. It also includes users in Saudi Arabia

661
00:37:44,440 --> 00:37:48,800
Speaker 3: and the United Arab Emirates. These are countries that are,

662
00:37:48,880 --> 00:37:53,920
Speaker 3: you know, not particularly known for their progressive policies or

663
00:37:53,200 --> 00:37:56,840
Speaker 3: their First Amendment rights. The founder, by the way, says

664
00:37:57,200 --> 00:38:01,319
Speaker 3: that they're exercising First Amendment right and they scrape all

665
00:38:01,360 --> 00:38:02,000
Speaker 3: these images.

666
00:38:02,520 --> 00:38:05,399
Speaker 6: Yeah, they're they're exercising a First Amendment right because they're

667
00:38:05,400 --> 00:38:08,840
Speaker 6: all publicly available and out there. And if the argument

668
00:38:08,920 --> 00:38:12,400
Speaker 6: that they make generally is that if Google can scrape

669
00:38:12,440 --> 00:38:16,279
Speaker 6: all the websites and create search terms for all those

670
00:38:16,320 --> 00:38:19,359
Speaker 6: things and then allow you to search that information. If

671
00:38:20,000 --> 00:38:23,439
Speaker 6: you know, YouTube has you know, when you upload something

672
00:38:23,480 --> 00:38:26,040
Speaker 6: to YouTube, it's all publicly available. They can scrape all

673
00:38:26,080 --> 00:38:28,319
Speaker 6: the information for all the data, and then the other

674
00:38:28,400 --> 00:38:31,600
Speaker 6: companies intertwine and use that data to help each other

675
00:38:31,680 --> 00:38:34,160
Speaker 6: find other things. Well, hey, why can't we do that too,

676
00:38:35,120 --> 00:38:37,720
Speaker 6: So we're going to However, a lot of the scrutiny

677
00:38:37,760 --> 00:38:41,080
Speaker 6: that's been happening because of that hack that was reported,

678
00:38:41,120 --> 00:38:43,920
Speaker 6: as well as just the tech in general and how

679
00:38:44,000 --> 00:38:47,120
Speaker 6: these other giant tech companies, how their data is being used.

680
00:38:47,560 --> 00:38:51,520
Speaker 6: Tech giants like Twitter, Google, and YouTube same thing have

681
00:38:51,640 --> 00:38:55,719
Speaker 6: sent cease and desist letters to Clearview orders saying do

682
00:38:55,840 --> 00:38:59,680
Speaker 6: not do this anymore. And even like because of this,

683
00:39:00,080 --> 00:39:04,000
Speaker 6: some police departments and other users have just discontinued use

684
00:39:04,160 --> 00:39:07,919
Speaker 6: of the software. And when the founder, you know, responds

685
00:39:08,480 --> 00:39:11,600
Speaker 6: to this kind of thing, he says, just you know,

686
00:39:11,640 --> 00:39:14,120
Speaker 6: our legal team is on it. They're working right now

687
00:39:14,640 --> 00:39:18,719
Speaker 6: to fight that battle, using the First Amendment as our

688
00:39:18,840 --> 00:39:19,640
Speaker 6: major argument.

689
00:39:20,160 --> 00:39:25,560
Speaker 3: Yeah, and importantly we should note that they said the

690
00:39:25,640 --> 00:39:28,480
Speaker 3: customer list was the only thing compromised, so the hackers

691
00:39:28,520 --> 00:39:33,280
Speaker 3: don't have that three billion plus and growing image archive.

692
00:39:33,800 --> 00:39:37,800
Speaker 3: But you're right, Matt, Legal troubles gather like storm clouds

693
00:39:37,840 --> 00:39:42,880
Speaker 3: on the horizon. Clearview has said it's complied with things

694
00:39:42,920 --> 00:39:47,000
Speaker 3: like policies proposed by the ACLU. The ACLU has said

695
00:39:47,000 --> 00:39:50,600
Speaker 3: that they don't comply. And I think there's there's a

696
00:39:50,680 --> 00:39:54,600
Speaker 3: case coming up against Clearview in Illinois and it's one

697
00:39:54,640 --> 00:39:59,160
Speaker 3: of what may be many arguably legal troubles should be

698
00:39:59,239 --> 00:40:03,080
Speaker 3: gathering on the horizon and the storm should hit, especially

699
00:40:03,120 --> 00:40:06,480
Speaker 3: when we look at the implications of this technology, and

700
00:40:06,520 --> 00:40:11,360
Speaker 3: before we continue, we want to be like cartoonishly transparent here.

701
00:40:11,760 --> 00:40:16,799
Speaker 3: We're not saying that clear View or the people in

702
00:40:16,840 --> 00:40:19,680
Speaker 3: the startup are bad people, and we're not saying even

703
00:40:19,719 --> 00:40:22,799
Speaker 3: that their intentions are bad. We're saying that this technology,

704
00:40:23,239 --> 00:40:28,520
Speaker 3: just like any other technology, has inherent implications. It has

705
00:40:28,600 --> 00:40:32,560
Speaker 3: just like fire. It has it has some positive benefits,

706
00:40:32,600 --> 00:40:37,000
Speaker 3: and it has some very dangerous possible consequences, like how

707
00:40:37,040 --> 00:40:40,560
Speaker 3: could it be misused? The first one just off the

708
00:40:40,640 --> 00:40:42,640
Speaker 3: top here. The first one is the one we talked

709
00:40:42,640 --> 00:40:45,880
Speaker 3: about a couple times in this episode. Somebody with access

710
00:40:45,920 --> 00:40:49,160
Speaker 3: could use it for personal reasons. We did that hypothetical

711
00:40:49,280 --> 00:40:53,560
Speaker 3: cop who's mad at his ex for dating Mark McGillicutty

712
00:40:53,640 --> 00:40:57,520
Speaker 3: or whatever. But we can go beyond the hypothetical examples.

713
00:40:57,600 --> 00:41:01,280
Speaker 3: There's a real life example. There's a billionaire Clearview investor

714
00:41:01,640 --> 00:41:04,919
Speaker 3: who used the app to stalk his daughter's newest bow.

715
00:41:05,239 --> 00:41:08,080
Speaker 3: He wanted to find out about this guy. I imagine

716
00:41:08,160 --> 00:41:13,680
Speaker 3: you know, whether you're a millionaire or a popper. Of course,

717
00:41:13,719 --> 00:41:15,480
Speaker 3: you want to know who your kid's dating, you want

718
00:41:15,480 --> 00:41:18,719
Speaker 3: to know about them. But it's crazy that this guy

719
00:41:18,960 --> 00:41:23,239
Speaker 3: was able to use it and he found out all

720
00:41:23,280 --> 00:41:25,560
Speaker 3: the stuff that we just mentioned, like where the guy lives,

721
00:41:25,600 --> 00:41:29,120
Speaker 3: what his job is, you know, his known connections, his

722
00:41:29,239 --> 00:41:32,480
Speaker 3: social media and stuff. This example is a little weird though,

723
00:41:32,520 --> 00:41:35,200
Speaker 3: because I imagine being a billionaire, you could just hire

724
00:41:35,239 --> 00:41:38,880
Speaker 3: a person to dedicate their time to doing that. Just

725
00:41:38,920 --> 00:41:39,600
Speaker 3: hire a PI.

726
00:41:40,960 --> 00:41:43,160
Speaker 6: This is a This is a really important thing about

727
00:41:43,160 --> 00:41:45,719
Speaker 6: this that I maybe we mentioned, but we didn't get

728
00:41:45,719 --> 00:41:48,600
Speaker 6: into it. The reason why he was able to find

729
00:41:48,640 --> 00:41:52,080
Speaker 6: all that stuff about this person. The way I've seen

730
00:41:52,120 --> 00:41:55,600
Speaker 6: the software work, it's not like you identify this person

731
00:41:55,600 --> 00:41:58,440
Speaker 6: that gives you a giant readout of all of this

732
00:41:58,480 --> 00:42:05,319
Speaker 6: person's information. You identify that person's face, then all of

733
00:42:05,360 --> 00:42:08,919
Speaker 6: the other publicly publicly available on the internet faces show up,

734
00:42:09,560 --> 00:42:11,920
Speaker 6: and you can click on it and it takes you

735
00:42:11,960 --> 00:42:14,680
Speaker 6: to the website where that face is, where that image

736
00:42:14,680 --> 00:42:17,719
Speaker 6: is located. So that means you're then directly connected to

737
00:42:17,760 --> 00:42:21,680
Speaker 6: their social media, to anywhere that that photo has been posted,

738
00:42:22,000 --> 00:42:24,480
Speaker 6: and then through those other websites you can gather all

739
00:42:24,480 --> 00:42:27,360
Speaker 6: that other information at least that's the way it was

740
00:42:27,400 --> 00:42:30,719
Speaker 6: demonstrated in that CNN Business interview and a couple of

741
00:42:30,760 --> 00:42:31,480
Speaker 6: other places.

742
00:42:31,640 --> 00:42:34,279
Speaker 3: That's the way it works for now, but we know

743
00:42:34,440 --> 00:42:38,600
Speaker 3: that that UI could easily be upgraded to maybe pull

744
00:42:39,320 --> 00:42:43,319
Speaker 3: to scrape that information if it's publicly available, right, I mean,

745
00:42:43,360 --> 00:42:46,600
Speaker 3: think about how many things are we've talked about before,

746
00:42:46,640 --> 00:42:49,359
Speaker 3: how easy it is to find a phone number, how

747
00:42:49,400 --> 00:42:52,120
Speaker 3: easy it is to find not just your address, but

748
00:42:52,200 --> 00:42:56,200
Speaker 3: the last few addresses you lived at over the years.

749
00:42:55,760 --> 00:42:59,040
Speaker 3: This is where we should also mention people don't trust

750
00:42:59,080 --> 00:43:02,040
Speaker 3: Clearview AI is a little bit of stereotyping, but they

751
00:43:02,040 --> 00:43:04,560
Speaker 3: don't trust it because one of the investors, Peter Thiel,

752
00:43:05,080 --> 00:43:13,480
Speaker 3: is also big in Facebook. So Facebook. Facebook is good

753
00:43:13,480 --> 00:43:16,200
Speaker 3: at a lot of things and users privacy has never

754
00:43:16,280 --> 00:43:19,520
Speaker 3: been one that is not a bug, that's a feature

755
00:43:19,680 --> 00:43:20,040
Speaker 3: from there.

756
00:43:20,840 --> 00:43:23,200
Speaker 6: And this is just a dovetail on all of that.

757
00:43:26,000 --> 00:43:29,360
Speaker 6: In that interview, Donnia Sullivan does a search of his

758
00:43:29,440 --> 00:43:34,360
Speaker 6: face and it's the official profile picture on the website

759
00:43:35,120 --> 00:43:38,759
Speaker 6: where he works for the CNN Business site, and when

760
00:43:39,000 --> 00:43:41,480
Speaker 6: it scrolled, when they scrolled all the way down, there

761
00:43:41,560 --> 00:43:43,800
Speaker 6: was an image of him because he's in his I

762
00:43:43,840 --> 00:43:46,759
Speaker 6: guess thirties, late thirties or something to that. I don't

763
00:43:46,800 --> 00:43:48,960
Speaker 6: know how old he is, but he's a person about

764
00:43:48,960 --> 00:43:52,560
Speaker 6: our age. Ben A Nol and I they scrolled the

765
00:43:52,600 --> 00:43:55,359
Speaker 6: way down and there's an image of him from a

766
00:43:55,400 --> 00:44:00,239
Speaker 6: local newspaper where he was in Ireland, right when he

767
00:44:00,480 --> 00:44:03,319
Speaker 6: was sixteen years old. And it's a group of you know,

768
00:44:03,400 --> 00:44:06,560
Speaker 6: teenagers holding up a sign and you can just see

769
00:44:06,560 --> 00:44:12,120
Speaker 6: his face and he looks absolutely different. I mean I

770
00:44:12,239 --> 00:44:14,239
Speaker 6: would not have been able to tell that that was him,

771
00:44:14,280 --> 00:44:15,279
Speaker 6: but he recognized the.

772
00:44:15,280 --> 00:44:17,040
Speaker 2: Photo and he knew it was him.

773
00:44:17,400 --> 00:44:20,680
Speaker 6: And this is the kind of thing where you know,

774
00:44:20,760 --> 00:44:23,920
Speaker 6: this is not an allegation on my part, This is

775
00:44:24,080 --> 00:44:29,280
Speaker 6: just me wondering and ruminating. I'm wondering how they connected

776
00:44:29,280 --> 00:44:34,200
Speaker 6: that up, How this face recognition software inserted that picture

777
00:44:34,239 --> 00:44:38,799
Speaker 6: of him where he looks so different. And the founder says, well,

778
00:44:38,840 --> 00:44:41,400
Speaker 6: you know that there's still parts of your face, the

779
00:44:41,440 --> 00:44:44,040
Speaker 6: geometry of it, that remained the same over the years,

780
00:44:44,080 --> 00:44:46,240
Speaker 6: even if you put on weight, even if you're wearing glasses,

781
00:44:46,239 --> 00:44:49,680
Speaker 6: even if you're covering your face. I get that argument,

782
00:44:50,960 --> 00:44:56,920
Speaker 6: but to me, it makes me wonder if there is

783
00:44:56,960 --> 00:45:01,680
Speaker 6: some kind of added like Spochio search that's going on

784
00:45:01,960 --> 00:45:05,960
Speaker 6: within this system where you're looking at. That's just another

785
00:45:06,040 --> 00:45:11,000
Speaker 6: company that you can search social media, any social media

786
00:45:11,040 --> 00:45:13,439
Speaker 6: accounts that somebody has using an email or a phone

787
00:45:13,480 --> 00:45:16,239
Speaker 6: number or a name. And it makes me wonder if

788
00:45:16,239 --> 00:45:20,359
Speaker 6: they're connecting things up there as an extra layer of like.

789
00:45:20,400 --> 00:45:25,000
Speaker 3: A name, like prioritizing an image by other information.

790
00:45:25,400 --> 00:45:27,120
Speaker 6: Yeah, I mean, it makes me wonder if that's happening.

791
00:45:27,200 --> 00:45:31,080
Speaker 6: I guess not, but I would say he didn't sufficiently,

792
00:45:31,400 --> 00:45:35,120
Speaker 6: the founder didn't sufficiently explain how that image got in there,

793
00:45:35,160 --> 00:45:39,400
Speaker 6: and the interviewer is clearly a little weirded out and

794
00:45:39,440 --> 00:45:40,279
Speaker 6: disturbed by it.

795
00:45:40,920 --> 00:45:43,120
Speaker 4: Well, another thing too, is I mean, you know, in

796
00:45:43,520 --> 00:45:48,319
Speaker 4: the United States, presumably you know, you might be identified

797
00:45:48,480 --> 00:45:51,000
Speaker 4: using this technology, but doesn't mean you are immediately going

798
00:45:51,040 --> 00:45:53,960
Speaker 4: to be convicted of something without some kind of physical

799
00:45:53,960 --> 00:45:57,160
Speaker 4: evidence or without a trial. But in other countries where

800
00:45:57,200 --> 00:45:59,520
Speaker 4: stuff like that isn't as much of a thing, it

801
00:45:59,560 --> 00:46:03,040
Speaker 4: could be use to identify social dissidents or like people

802
00:46:03,120 --> 00:46:06,560
Speaker 4: that are speaking out against the totalitarian government and flag

803
00:46:06,640 --> 00:46:09,360
Speaker 4: them and throw them in the gulag or whatever, you know,

804
00:46:09,480 --> 00:46:13,080
Speaker 4: without ever you know, batting an eye. And that is

805
00:46:13,760 --> 00:46:16,280
Speaker 4: sort of the crux of the problem with this software

806
00:46:16,800 --> 00:46:19,960
Speaker 4: is it depends on the notion of people that use

807
00:46:20,000 --> 00:46:23,640
Speaker 4: it being good actors and and and being good stewards

808
00:46:23,719 --> 00:46:26,200
Speaker 4: of this power that they have. And as we know,

809
00:46:27,120 --> 00:46:30,759
Speaker 4: power corrupts, and we've got a lot of governments that

810
00:46:30,840 --> 00:46:34,560
Speaker 4: are kind of corrupt and out to shut down any

811
00:46:34,640 --> 00:46:39,520
Speaker 4: kind of criticism of what they do. And this would

812
00:46:39,560 --> 00:46:42,279
Speaker 4: be a great way to identify those people and round

813
00:46:42,320 --> 00:46:44,600
Speaker 4: them up and put them away.

814
00:46:45,400 --> 00:46:49,319
Speaker 3: Yeah, and it's it's it's dangerous for that reason. You know,

815
00:46:49,400 --> 00:46:53,600
Speaker 3: you could you could detain people, and you could cut

816
00:46:53,640 --> 00:46:56,680
Speaker 3: around some of their legal protections like what if this

817
00:46:56,880 --> 00:46:59,879
Speaker 3: what if this counted in a court of law as

818
00:47:00,080 --> 00:47:03,359
Speaker 3: definitive identification, then you muld say, well, we don't need

819
00:47:03,400 --> 00:47:06,640
Speaker 3: to figure out if you're the person, right, It could

820
00:47:06,640 --> 00:47:09,400
Speaker 3: get so dangerous so quickly. There's another point we have

821
00:47:09,480 --> 00:47:11,920
Speaker 3: to add. You know, this sounds like some kind of

822
00:47:11,960 --> 00:47:16,680
Speaker 3: Skynet Big Brother stuff. It can also be used inaccurately.

823
00:47:16,840 --> 00:47:21,080
Speaker 3: It is far from perfect. It's one thing that I've

824
00:47:21,120 --> 00:47:24,000
Speaker 3: been talking a lot about with some people online about

825
00:47:25,440 --> 00:47:30,600
Speaker 3: facial recognition has an inherent racial bias that I wish

826
00:47:30,680 --> 00:47:34,440
Speaker 3: more people talked about for non white people, for persons

827
00:47:34,440 --> 00:47:38,680
Speaker 3: of color. This stuff lags in accuracy, not just clearview,

828
00:47:38,719 --> 00:47:42,719
Speaker 3: but in general. Facial recognition is known for this and

829
00:47:42,760 --> 00:47:45,560
Speaker 3: this means that you could be arrested due to a

830
00:47:45,600 --> 00:47:50,120
Speaker 3: computer error because the software decided that you look like

831
00:47:50,480 --> 00:47:52,800
Speaker 3: a guy you never met, who lives in a state

832
00:47:52,840 --> 00:47:55,840
Speaker 3: you've never been to, who committed a crime that occurred

833
00:47:55,880 --> 00:47:59,239
Speaker 3: while you were not alive, and it could happen. It

834
00:47:59,239 --> 00:47:59,960
Speaker 3: could happen here.

835
00:48:00,480 --> 00:48:04,000
Speaker 6: And to continue with that, we mentioned, you know, the

836
00:48:04,000 --> 00:48:07,800
Speaker 6: people that were known to be using this software, the organizations,

837
00:48:07,920 --> 00:48:10,560
Speaker 6: and two of them or three of them, let's just

838
00:48:10,560 --> 00:48:15,200
Speaker 6: point these these out, Department of Justice, US Immigration and

839
00:48:15,320 --> 00:48:16,560
Speaker 6: Customs Enforcement.

840
00:48:16,840 --> 00:48:18,040
Speaker 2: Let's just leave it to those two.

841
00:48:18,600 --> 00:48:21,680
Speaker 6: So take the problems you're talking about ben that are

842
00:48:21,719 --> 00:48:25,319
Speaker 6: inherent to this system. Apply that then to let's say,

843
00:48:25,360 --> 00:48:29,640
Speaker 6: Immigrations and custom looking for people who are in the

844
00:48:29,760 --> 00:48:34,280
Speaker 6: United States illegally, and imagine that you're attempting to round

845
00:48:34,280 --> 00:48:41,640
Speaker 6: everyone up. The implications of this software would be extremely effective,

846
00:48:41,640 --> 00:48:46,840
Speaker 6: i would say, or completely ineffective because they're they're detaining

847
00:48:46,840 --> 00:48:50,560
Speaker 6: and rounding up people incorrectly because of the problems with

848
00:48:50,600 --> 00:48:51,160
Speaker 6: the software.

849
00:48:51,800 --> 00:48:56,600
Speaker 3: Well think about this too. The problems with the software

850
00:48:56,840 --> 00:48:59,239
Speaker 3: so they can pound on the state level, but they

851
00:48:59,239 --> 00:49:02,279
Speaker 3: can pound on the private level too, And this is

852
00:49:02,360 --> 00:49:06,759
Speaker 3: one that I think for the immediate future is at

853
00:49:06,840 --> 00:49:10,479
Speaker 3: least as dangerous as the Orwellian stuff we're talking about

854
00:49:10,560 --> 00:49:14,239
Speaker 3: right now. So that data, when it's mind right, just

855
00:49:14,280 --> 00:49:16,600
Speaker 3: like your data on any other social media, it can

856
00:49:16,640 --> 00:49:21,160
Speaker 3: be sold to third parties, insurance companies, financial institutions, and

857
00:49:21,200 --> 00:49:26,600
Speaker 3: so on. This goes way past ads the phrase. I know,

858
00:49:26,640 --> 00:49:28,919
Speaker 3: I'm kind of churchifying here, but the phrase I think

859
00:49:28,960 --> 00:49:33,680
Speaker 3: of is a longitudinal profile of your face and your

860
00:49:33,719 --> 00:49:38,120
Speaker 3: behavior over time. So it's like watching a real life

861
00:49:38,160 --> 00:49:44,200
Speaker 3: progression of you aging. And let's say that an insurance

862
00:49:44,239 --> 00:49:46,680
Speaker 3: company or an algorithm could start to pick up on

863
00:49:46,719 --> 00:49:50,400
Speaker 3: certain medical conditions that are manifesting in your face in

864
00:49:51,360 --> 00:49:54,640
Speaker 3: very minuscule ways ways of human being, and even a

865
00:49:54,719 --> 00:49:58,880
Speaker 3: human doctor wouldn't sense. The computer picks this up before

866
00:49:58,920 --> 00:50:01,760
Speaker 3: you know anything's wrong, sort of like the way targets

867
00:50:01,800 --> 00:50:05,680
Speaker 3: algorithms figured out that poor girl was pregnant before her

868
00:50:06,400 --> 00:50:12,160
Speaker 3: family knew. So here's the question. Would the insurance company

869
00:50:12,400 --> 00:50:17,239
Speaker 3: legally be required to notify you of this possible health condition, say,

870
00:50:17,280 --> 00:50:21,680
Speaker 3: like there's clearly something happening maybe if they have video

871
00:50:21,719 --> 00:50:26,560
Speaker 3: to in your behavior that is indicative of an early

872
00:50:26,680 --> 00:50:30,640
Speaker 3: on set terminal condition, would they be legally required to

873
00:50:30,920 --> 00:50:34,080
Speaker 3: tell you that they knew you had a let's say,

874
00:50:34,120 --> 00:50:37,040
Speaker 3: eighty percent chance of dying in the next two years

875
00:50:37,320 --> 00:50:40,799
Speaker 3: and give you the assistance you need right now. That's

876
00:50:40,800 --> 00:50:42,360
Speaker 3: a note. That is a big no.

877
00:50:42,600 --> 00:50:42,920
Speaker 5: Chief.

878
00:50:43,120 --> 00:50:45,640
Speaker 3: They don't have to do a damn thing for you.

879
00:50:45,680 --> 00:50:49,439
Speaker 3: What they can do with absolutely no repercussions is say, hey,

880
00:50:49,440 --> 00:50:51,359
Speaker 3: this person's probably going to be dead in two years

881
00:50:51,360 --> 00:50:53,400
Speaker 3: and it's going to be expensive to get them to

882
00:50:53,400 --> 00:50:56,600
Speaker 3: a year two, so let's just drop them. Let's just

883
00:50:56,640 --> 00:50:59,719
Speaker 3: drop them. There's no law that, there's no like ethical

884
00:51:00,360 --> 00:51:03,640
Speaker 3: legal requirement for them to tell you what they know.

885
00:51:04,080 --> 00:51:09,640
Speaker 3: And that's that's mind boggling. That is reprehensible. That is

886
00:51:09,719 --> 00:51:12,439
Speaker 3: like I need, I need to get my good non

887
00:51:12,440 --> 00:51:15,240
Speaker 3: abridged thesaurus to come up with all the different words

888
00:51:15,400 --> 00:51:17,279
Speaker 3: for how bad and disturbing that is.

889
00:51:17,680 --> 00:51:19,239
Speaker 4: Oh, Ben, I would I love it when you do

890
00:51:19,280 --> 00:51:20,719
Speaker 4: that because I always learn a new word.

891
00:51:23,320 --> 00:51:23,440
Speaker 6: Uh.

892
00:51:23,520 --> 00:51:25,799
Speaker 3: Did you guys ever, just to light in the mood,

893
00:51:26,200 --> 00:51:28,799
Speaker 3: did you guys ever hear that old joke about the

894
00:51:28,840 --> 00:51:32,560
Speaker 3: abridge thesaurus It's like I have an abridged thesaurus. Not

895
00:51:32,600 --> 00:51:38,080
Speaker 3: only is it terrible, it's terrible. So I didn't write that,

896
00:51:38,400 --> 00:51:41,160
Speaker 3: but you but you can. The good news is right now,

897
00:51:42,239 --> 00:51:45,160
Speaker 3: depending on where you live, you can opt out of

898
00:51:45,520 --> 00:51:47,320
Speaker 3: clear View. Oh my gosh.

899
00:51:47,320 --> 00:51:51,520
Speaker 4: But even the way you opt out isn't like it's

900
00:51:51,520 --> 00:51:53,960
Speaker 4: such a burden. It's such a burdensome process. You can't

901
00:51:54,000 --> 00:51:56,920
Speaker 4: just click a box in an email, right you. All

902
00:51:56,960 --> 00:51:59,080
Speaker 4: you have to do is send the headshot. We all

903
00:51:59,080 --> 00:52:03,399
Speaker 4: have those and an image of your government issued ID.

904
00:52:04,280 --> 00:52:08,200
Speaker 4: And this only applies also to residents of California or

905
00:52:08,239 --> 00:52:10,440
Speaker 4: the EU.

906
00:52:09,800 --> 00:52:12,760
Speaker 5: For whatever reason. Sorry, rest of the world.

907
00:52:12,640 --> 00:52:17,120
Speaker 6: Right because of privacy, because of privacy laws that exists there.

908
00:52:18,320 --> 00:52:21,560
Speaker 3: Yeah, we shall also point out, going back to this

909
00:52:21,640 --> 00:52:25,960
Speaker 3: accuracy thing, there are serious questions about how accurate Clearview

910
00:52:26,040 --> 00:52:30,680
Speaker 3: is in general. So BuzzFeed, according to marketing literature they

911
00:52:30,680 --> 00:52:35,040
Speaker 3: found from Clearview, says that the company the company touts

912
00:52:35,080 --> 00:52:37,200
Speaker 3: the ability to find a match out of one million

913
00:52:37,200 --> 00:52:40,200
Speaker 3: phases ninety eight point six percent of the time. But

914
00:52:40,320 --> 00:52:43,320
Speaker 3: when Clearview did finally start talking to The New York Times,

915
00:52:43,800 --> 00:52:46,480
Speaker 3: they said the tool produces a match up to seventy

916
00:52:46,520 --> 00:52:48,839
Speaker 3: five percent of the time, and we don't know how

917
00:52:48,840 --> 00:52:52,080
Speaker 3: many of those are quote unquote true matches. So there's

918
00:52:52,120 --> 00:52:57,480
Speaker 3: some contradictory informations coming out which can make people feel uncomfortable, obviously,

919
00:52:58,360 --> 00:53:01,680
Speaker 3: and that's where we leave today. The battle lines are

920
00:53:01,760 --> 00:53:05,160
Speaker 3: firming up. On one side, you have clear view, its investors,

921
00:53:05,239 --> 00:53:09,680
Speaker 3: its clients, and what I would call the techno optimists.

922
00:53:09,920 --> 00:53:12,680
Speaker 3: Right on the other side, you have privacy advocates, you

923
00:53:12,719 --> 00:53:16,080
Speaker 3: have tech giants like Microsoft, IBM, you even have the Pope.

924
00:53:16,239 --> 00:53:19,480
Speaker 3: The Pope came out against facial recognition and he summed

925
00:53:19,480 --> 00:53:23,239
Speaker 3: it up pretty nicely. He said, quote this asymmetry by

926
00:53:23,239 --> 00:53:25,880
Speaker 3: which a select few know everything about us while we

927
00:53:25,960 --> 00:53:29,280
Speaker 3: know nothing about them Dull's critical thought and the conscious

928
00:53:29,320 --> 00:53:30,760
Speaker 3: exercise of freedom.

929
00:53:31,360 --> 00:53:36,839
Speaker 6: Imagine applying that to a priest who hears confessions from

930
00:53:36,880 --> 00:53:42,719
Speaker 6: everyone every week when they go and tell their dirty secrets.

931
00:53:42,800 --> 00:53:46,680
Speaker 6: The priest is then the one who everybody knows nothing about,

932
00:53:48,400 --> 00:53:50,239
Speaker 6: but he knows everything about them.

933
00:53:50,280 --> 00:53:51,120
Speaker 2: Just putting it out.

934
00:53:50,960 --> 00:53:55,320
Speaker 6: There, especially hey, and also this thing is targeting child

935
00:53:55,360 --> 00:53:58,680
Speaker 6: sex abuse and abusers.

936
00:53:58,719 --> 00:53:59,959
Speaker 2: Just also leaving that there.

937
00:54:00,480 --> 00:54:04,760
Speaker 3: Yeah, Oh, one other thing for anyone listening along and thinking,

938
00:54:05,680 --> 00:54:08,600
Speaker 3: good thing, I made my profile on insert social media

939
00:54:08,600 --> 00:54:12,280
Speaker 3: here private years ago. Sorry, homie. Anything that was public

940
00:54:12,360 --> 00:54:15,160
Speaker 3: at some point is pretty much in the system, even

941
00:54:15,160 --> 00:54:17,719
Speaker 3: if you later made it private. So check your MySpace.

942
00:54:18,360 --> 00:54:21,560
Speaker 6: In that CNN Business interview, as you said, Ben, they

943
00:54:21,800 --> 00:54:24,360
Speaker 6: test out the software on the producer who has a

944
00:54:24,440 --> 00:54:29,400
Speaker 6: private Instagram account, and images from that account show up

945
00:54:29,400 --> 00:54:31,760
Speaker 6: in the search and it is because it was public

946
00:54:31,840 --> 00:54:32,600
Speaker 6: at one point.

947
00:54:33,280 --> 00:54:37,359
Speaker 3: And that's where we leave off today. What do you think,

948
00:54:37,400 --> 00:54:42,360
Speaker 3: fellow conspiracy realists? Do the benefits outweigh the potential consequences

949
00:54:42,400 --> 00:54:47,319
Speaker 3: of this? Do the consequences outweigh the potential benefits? Let

950
00:54:47,400 --> 00:54:49,319
Speaker 3: us know. You can find us on Facebook, you can

951
00:54:49,320 --> 00:54:51,800
Speaker 3: find us on Instagram, you can find us on Twitter.

952
00:54:52,120 --> 00:54:56,000
Speaker 3: We always like to recommend our Facebook community page. Here's

953
00:54:56,080 --> 00:54:57,440
Speaker 3: where it gets crazy.

954
00:54:57,840 --> 00:54:58,000
Speaker 5: Now.

955
00:54:58,000 --> 00:54:59,759
Speaker 6: You can jump on there. You can talk about this

956
00:55:00,440 --> 00:55:03,120
Speaker 6: or any other in the past. You can post some

957
00:55:03,280 --> 00:55:07,720
Speaker 6: dank memes, let's say, some conspiracy memes or facial recognition stuff.

958
00:55:08,239 --> 00:55:13,160
Speaker 6: Maybe you've had access to this before to clearview and

959
00:55:13,200 --> 00:55:14,200
Speaker 6: you want to talk to us about it.

960
00:55:14,200 --> 00:55:16,000
Speaker 2: Don't do that on Facebook. Don't do that.

961
00:55:16,160 --> 00:55:17,640
Speaker 6: But if you do want to tell us about that,

962
00:55:17,680 --> 00:55:20,399
Speaker 6: you can call our number. We are one eight three

963
00:55:20,560 --> 00:55:25,799
Speaker 6: three std WYTK. You can leave a message there, tell

964
00:55:25,840 --> 00:55:27,279
Speaker 6: us about it, let us know if you don't want

965
00:55:27,280 --> 00:55:30,279
Speaker 6: to be identified, if you don't want us to know

966
00:55:31,239 --> 00:55:34,600
Speaker 6: or talk about anything on air, just just let us know,

967
00:55:34,760 --> 00:55:38,080
Speaker 6: give us the information. We'd love to hear from you.

968
00:55:38,520 --> 00:55:40,439
Speaker 3: And I want to add to this to say that

969
00:55:41,360 --> 00:55:48,080
Speaker 3: I finally start despite my strange phobia regarding phones, I

970
00:55:48,200 --> 00:55:52,279
Speaker 3: started diving in Matt You're doing massive, amazing work there

971
00:55:52,520 --> 00:55:56,160
Speaker 3: and I've started listening to these calls as well. Thank

972
00:55:56,200 --> 00:55:59,880
Speaker 3: you so much to everyone who calls in. It's inspiring

973
00:56:00,040 --> 00:56:02,000
Speaker 3: and I don't know about you. I don't know about

974
00:56:02,000 --> 00:56:05,759
Speaker 3: you guys, but it makes me feel like what we're

975
00:56:05,800 --> 00:56:08,279
Speaker 3: doing is worthwhile. If that makes sense.

976
00:56:08,640 --> 00:56:11,080
Speaker 2: Oh, definitely, definitely. I've had you know.

977
00:56:11,600 --> 00:56:14,359
Speaker 6: Hopefully you're gonna if you could get past the phobia, Ben,

978
00:56:15,120 --> 00:56:17,960
Speaker 6: hopefully you can call a few people because you can

979
00:56:18,040 --> 00:56:19,680
Speaker 6: use that app and you should be good to go

980
00:56:21,520 --> 00:56:25,799
Speaker 6: actually speaking to to you know, you know who you are.

981
00:56:25,960 --> 00:56:30,359
Speaker 6: If we've talked on the phone, it means a great deal,

982
00:56:30,400 --> 00:56:32,480
Speaker 6: as Ben is saying, just to us, to know that

983
00:56:33,120 --> 00:56:36,799
Speaker 6: we're not just talking in a darkened room and that

984
00:56:36,880 --> 00:56:39,680
Speaker 6: you guys, you guys care about us as much as

985
00:56:39,680 --> 00:56:42,359
Speaker 6: we care about you. So this is a great relationship.

986
00:56:42,440 --> 00:56:47,319
Speaker 3: Let's keep doing it well, said Matt Well said, and

987
00:56:47,760 --> 00:56:50,360
Speaker 3: we'll be following up with some of those messages in

988
00:56:50,440 --> 00:56:54,640
Speaker 3: the future, so keep them coming. One last thing, if

989
00:56:54,680 --> 00:56:58,960
Speaker 3: you say, look, Ben's right, phones are terrifying and weird,

990
00:56:59,560 --> 00:57:02,200
Speaker 3: but also h social media. You guys just told me

991
00:57:02,239 --> 00:57:06,560
Speaker 3: how dangerous that is. Why on earth? What gives how

992
00:57:06,719 --> 00:57:09,279
Speaker 3: I have a story that my fellow listeners need to

993
00:57:09,320 --> 00:57:12,440
Speaker 3: know about. I have some I have some experience. I

994
00:57:12,480 --> 00:57:14,880
Speaker 3: have some terrible jokes to tell you, but I don't

995
00:57:14,960 --> 00:57:18,400
Speaker 3: know how to contact you. Well, we have good news

996
00:57:18,440 --> 00:57:22,000
Speaker 3: for you. You can always twenty four to seven contact

997
00:57:22,160 --> 00:57:24,560
Speaker 3: us at our good old fashioned email where we.

998
00:57:24,600 --> 00:57:47,560
Speaker 6: Are conspiracy at iHeartRadio dot com. Stuff they don't want

999
00:57:47,600 --> 00:57:50,600
Speaker 6: you to know is a production of iHeartRadio. For more

1000
00:57:50,640 --> 00:57:54,520
Speaker 6: podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts, or

1001
00:57:54,560 --> 00:57:56,760
Speaker 6: wherever you listen to your favorite shows.