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Speaker 1: Friends and neighbors, fellow conspiracy realist, It is a government

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Speaker 1: and AI week here on stuff they don't want you

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Speaker 1: to know. So for Tonight's classic, we looked back through

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Speaker 1: the archives and we found some stuff that we had

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Speaker 1: been sort of flum mixed about for a while, for

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Speaker 1: more than five years now.

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Speaker 2: Oh yeah, we talk a lot about the technology that

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Speaker 2: was shown off in that certain Tom Cruise movie. You

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Speaker 2: remember the one, the one with the psychics, uh huh,

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Speaker 2: the ones that could pre crime everybody. Well, one of

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Speaker 2: the big things that was shown off in that movie

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Speaker 2: that struck all of us at the time when it

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Speaker 2: came out is this concept of facial recognition everywhere you

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Speaker 2: go in a city. Well, there's a company that's been

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Speaker 2: trying to do this for a long time, and some

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Speaker 2: stuff went down in twenty twenty, and that's when we

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Speaker 2: made this episode.

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Speaker 1: Yes, this is where we introduce you to the concept

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Speaker 1: of clearview AI. The argument of supporters would be this

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Speaker 1: can prevent crime, This can make the world a safer

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Speaker 1: place through the power of pattern recognition and super juiced

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Speaker 1: up algorithms. But then, as we learn there are a

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Speaker 1: lot of detractors for technology like this being rolled out

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Speaker 1: and clear view stories they originally tell. It doesn't always

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Speaker 1: match the things people found out later.

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Speaker 3: Yeah, I mean this is a story that is more

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Speaker 3: relevant now than ever. So why don't we take a

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Speaker 3: quick break here we're from our sponsor and then jump

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Speaker 3: into this classic.

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Speaker 4: From UFOs to psychic powers and government conspiracies. History is

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Speaker 4: riddled with unexplained events. You can turn back now or

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Speaker 4: learn the stuff they don't want you to know. A

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Speaker 4: production of iHeartRadio.

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

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Speaker 2: my name is Noah.

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Speaker 3: They call me Ben.

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Speaker 1: We're joined as always with our super producer Paul, Mission

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Speaker 1: control decand most importantly, you are you. You are here,

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Speaker 1: and that makes this stuff they don't want you to know.

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Speaker 3: Here in the.

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Speaker 1: Age of social media, right, we're recording this as longtime

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Speaker 1: listeners know on a virtual interface. Right, we're mentally and

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Speaker 1: soulfully together, but not physically together, and that may be

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Speaker 1: the case for some time. However, technology is allowing us

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Speaker 1: to feel closer.

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Speaker 3: Right.

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Speaker 1: And the crazy thing is this stuff is so cheap.

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Speaker 1: Now we're talking about the proliferation of cheap imaging technology.

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Speaker 1: You guys, remember when not everyone could afford a camera

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Speaker 1: editing software and stuff like that.

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Speaker 3: I mean, hell, I remember when not every phone had

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Speaker 3: a camera on it or it was like, who's got

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Speaker 3: a digital camera? Find one? You know, Now it's like

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Speaker 3: a non issue.

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Speaker 2: You know, Well, if you were going to take a

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Speaker 2: video with one of those, it was this tiny little

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Speaker 2: MPEG file that you'd end up getting. Even though you

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Speaker 2: had this powerful camera, the digital camera you could use,

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Speaker 2: your video was so terrible.

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Speaker 1: Yeah, yeah, and you just had a nice curated set

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Speaker 1: of pixels that would move and.

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Speaker 3: What appeared to look like a face exactly.

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Speaker 1: But but now we have you know, now it's it's

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Speaker 1: so affordable to buy these things, and and on balance

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Speaker 1: that's a good thing. But combine that with the massive

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Speaker 1: growth of social media and the massive growth there's some

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Speaker 1: of you people don't talk about as often, the massive

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Speaker 1: growth of archival capacity.

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Speaker 3: This means, if you.

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Speaker 1: Want to be sweet nostalgic about it, that are days

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Speaker 1: of cherishing a few like glamour photographs from your from

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Speaker 1: your local photography studio, or a few expensive polaroids. Those

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Speaker 1: days are all but gone. Now you can post photos

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Speaker 1: of yourself, your friends, or strangers, and you can post

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Speaker 1: them anywhere you please. There are some laws about it,

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Speaker 1: but they're they're not really good because legislation has always

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Speaker 1: been technology's slower sibling, and so those those laws often

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Speaker 1: don't have a lot of teeth to them. And with

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Speaker 1: the rise of all this technology and the rise of

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Speaker 1: all these capabilities, we also see the rise of technology

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Speaker 1: that is exclusively meant to exploit the vast amount of

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Speaker 1: stuff that we've been making just from like live journal, Tumblr, MySpace, friends,

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Speaker 1: their Facebook, all, you know, all the hits, all that jazz.

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Speaker 1: So what do you guys think, I mean, optimists and

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Speaker 1: pessimists have a different idea of what this means for

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Speaker 1: the world.

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Speaker 2: Well, yes, on the optimist side, I would just say

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Speaker 2: the number of photographs and captured memories I have of

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Speaker 2: my son is probably more, you know, it's certainly thousands

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Speaker 2: of times greater than that which exist for me as

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Speaker 2: a child in my growth, my you know, my parents,

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Speaker 2: and then going back and back and back and back,

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Speaker 2: it's just exponentially a larger amount of memories that are

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Speaker 2: captured throughout his life, my son. So that's great, that's wonderful.

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Speaker 2: We can look back on those, you know, until the

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Speaker 2: machines all turn off or we lose electricity, those those

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Speaker 2: images will exist for my family and people that care

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Speaker 2: about me and my son.

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Speaker 3: But on the flip side of that, Matt, you know,

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Speaker 3: like folks like my mom, for example, she doesn't consider

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Speaker 3: those real Like she's like, if it's not printed out

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Speaker 3: and it's not in a frame on my mantle, then

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Speaker 3: that is not a real memory. Which is interesting because

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Speaker 3: to your point about like the machines turning off, there's

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Speaker 3: all these companies now that will take your Instagram photos

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Speaker 3: and print them out and send you framed copies of them,

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Speaker 3: and the proliferation of like instacs, cameras and like more

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Speaker 3: analog technology is sort of a response, almost a backlas

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Speaker 3: to this over digitization of like our memories and our

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Speaker 3: collective kind of you know, unconscious really in a way

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Speaker 3: with all the stuff that's out there, it's like there's

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Speaker 3: this sense of is it really ours once it's out

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Speaker 3: there in the ether. And I think that's a big

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Speaker 3: part of today's question.

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Speaker 1: Yeah, who owns the art? The artist or the audience.

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Speaker 1: It's a very old question, but it's one that has

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Speaker 1: continually been relevant.

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Speaker 2: Right And yeah, but but the other question, Ben, is

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Speaker 2: is it art or is it just data?

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Speaker 3: Right?

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Speaker 1: Right there we go. That's a good distinction. It's a

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Speaker 1: tough distinction to make. Optimists are saying this is great,

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Speaker 1: you guys, the world's going to be a safer place,

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Speaker 1: and think how convenient everything's gonna be. You'll never lose

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Speaker 1: your favorite photo again, you know, and criminals can no

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Speaker 1: longer disappear into a crowd. And then pessimists say, you know,

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Speaker 1: in a world beyond privacy, every person is monitored continually

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Speaker 1: throughout their lives. Your image is logged countless times, and

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Speaker 1: all the data from that image and all the other

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Speaker 1: images of you, it's analyzed. It's bought and sold without

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Speaker 1: your knowledge or your permission. A world careening towards sci

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Speaker 1: fi dystopia, pre crime, pre existing medical conditions. Advertising so

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Speaker 1: invasive and so bespoke that it might as well be

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Speaker 1: reading your mind, because it's doing something very close to that.

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Speaker 1: But before you get all blade round round minority report,

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Speaker 1: let's start closer to the beginning. There is a revolution

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Speaker 1: happening in the world of facial recognition right now, and

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Speaker 1: its name is Clearview AI.

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Speaker 3: Here are the facts. Yeah, I mean, the first time

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Speaker 3: I heard of Clearview AI was on a recent in

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Speaker 3: PR piece where the founder was interviewed. So this is

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Speaker 3: a very relatively new company, at least to me. But

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Speaker 3: as far as the background, it is a research tool

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Speaker 3: that is being used by law enforcement, specifically in the company.

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Speaker 3: It makes it very clear that this is something that's

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Speaker 3: designed exclusively for use by law enforcement in order to

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Speaker 3: identify perpetrators and victims of crimes. It's a little bit

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Speaker 3: pretty very vague side I would say, yeah.

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Speaker 1: Yeah, I mean that's you know, we have to be honest.

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Speaker 1: That's that's the high level like logline or pitch from

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Speaker 1: the company on its own website. So we can't blame

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Speaker 1: them for not wanting to get too into the weeds

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Speaker 1: about algorithms. But if we look at how it actually works,

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Speaker 1: we can we can get a high level understanding.

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Speaker 3: It's a startup.

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Speaker 1: It has a massive database of faces, just specifically faces,

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Speaker 1: somewhere north of three billion images and as we're as

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Speaker 1: we're recording this, that number is likely still growing. So

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Speaker 1: that number is going to be a little bit higher

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Speaker 1: by the time you get to the end of this episode.

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Speaker 3: Yeah, and there was the gentleman who found the startup. Hann

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Speaker 3: thought was who I heard interviewed on I believe it

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Speaker 3: was The Daily. Actually, so if you want to hear

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Speaker 3: directly from this gentleman's mouth what he believes the mission

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Speaker 3: of this technology in this company is it a very

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Speaker 3: fascinating interview that kind of delves into some of the

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Speaker 3: slippery slope sides of this technology that we're going to

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Speaker 3: get into. But as far as the end users concerned,

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Speaker 3: which in this case would be law enforcement, because currently

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Speaker 3: this is not available to civilians. You take a picture

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Speaker 3: and it identifies who that person is, simple as that,

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Speaker 3: by matching it with all of this information that they've collected,

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Speaker 3: and they've collected it, you know, not through any really

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Speaker 3: nefarious means. It's all that information that we talked about

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Speaker 3: that's already out there. All they've got to do is

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Speaker 3: reach out and grab.

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Speaker 1: It's a it's a search engine for faces, and it

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Speaker 1: sounds innocuous at first. I also, if it was The Daily,

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Speaker 1: I think I listened to that interview, but I also

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Speaker 1: listen to an excellent interview you sent Matt an extended

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Speaker 1: CNN business conversation. So if you want to see them,

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Speaker 1: like if you want to see the founder using the

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Speaker 1: app a couple of different times, I think he does

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Speaker 1: it on himself, he does it on the interviewer, and

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Speaker 1: then he does it on one of their producers, and

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Speaker 1: it seems to work. It's not a sensationalized thing either,

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Speaker 1: at least the way they're presenting it.

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

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Speaker 2: about that a little more. Just if you do want

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Speaker 2: to look at that video. It's an interview with Donnie

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Speaker 2: O'Sullivan with CNN Business and it is where we're going

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Speaker 2: to reference it several times in this episode.

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Speaker 3: Yeah.

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Speaker 1: So, like you said, Noel Clearview has taken this from

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Speaker 1: publicly available sources, your Facebook's, your YouTube's, your venmos, which yes,

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Speaker 1: are publicly available, and the Federals and local law enforcement

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Speaker 1: admit that they only have a limited knowledge of how

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Speaker 1: the app actually works mechanically, the nuts and bolts of it,

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Speaker 1: but they do confirm they've already used it to help

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Speaker 1: solve shoplifting cases, identity theft cases, credit card fraud, child abuse,

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Speaker 1: even and even several homicides. This technology goes, by the way,

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Speaker 1: far beyond anything constructed by Silicon Valley giants or anything

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Speaker 1: officially created or used by the US government.

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Speaker 2: Yeah, in that interview that we just mentioned. Juan mentions

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Speaker 2: that they had this image or this video file of

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Speaker 2: a child abuse incident and the person of interest walks

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Speaker 2: past in the background at one point, and there were

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Speaker 2: only two usable frames of this person's face. And again,

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Speaker 2: like imagine in the background of a video, they were

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Speaker 2: able to take still from that video of this person's

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Speaker 2: face and identify him through other social media websites where

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Speaker 2: they've got all this data from and make an arrest

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Speaker 2: on this person like that. That is it's mind blowing

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Speaker 2: that you could do that, and thank goodness that they

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Speaker 2: were able to do that. But it's the implication of

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Speaker 2: being able to do that in that one instance and

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Speaker 2: then applying it across everything is what makes it feel

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Speaker 2: a little scary because.

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Speaker 3: For the critics, for the critics, well.

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Speaker 2: Yeah, for the critics, well for the people who are

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Speaker 2: looking at it big picture. So if you're you're thinking

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Speaker 2: about it just in a law enforcement application, then it's amazing, right,

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Speaker 2: and it's the best thing ever. But if you apply

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Speaker 2: it to everything, to everyone at all times outside of

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Speaker 2: law enforcement, then well.

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Speaker 3: And just strictly on like a technological level, it's fascinating

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Speaker 3: and clearly a huge leap forward in this kind of

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Speaker 3: technology because I mean we've seen these videos they're talking about,

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Speaker 3: these grainy surveillance videos or ATM camera videos. I mean

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Speaker 3: it's hard to as a human being, even if you

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Speaker 3: knew that person, it'd be difficult for you to identify them.

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Speaker 3: So this using this algorithm and you know, analyzing and

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Speaker 3: comparing it to all these different subjects I imagine using

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Speaker 3: points of articulation like on the faces and the structures

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Speaker 3: or whatever, it's able to come back with positive matches.

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Speaker 3: I think that's really fascinating and the really, you know,

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Speaker 3: pretty innovative use of technology. For sure, as a nerd

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Speaker 3: speaking you know, exclusively on that level. But you're right, Matt,

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Speaker 3: when you start to apply on a larger scale, it

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Speaker 3: gets really nineteen eighty four ish and kind of scary.

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Speaker 3: I appreciate that point, Noel.

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Speaker 1: It's something that's going to come into play later and

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Speaker 1: it may surprise some of our fellow listeners to learn

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Speaker 1: a little bit more about this technology and some of

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Speaker 1: the other topics that you raise there. I want to

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Speaker 1: say this, their pitch is very law enforcement based on

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Speaker 1: the website. They're quick to point out, they're quick to

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Speaker 1: claim I should say that they've helped law enforcement track

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Speaker 1: down hundreds of at large criminals. They mention the real

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Speaker 1: the things that make people very emotional, like child abusers, terrorists,

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Speaker 1: sex traffickers, but they also say this technology is used

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Speaker 1: to help exonerate the innocent and identify victims. And it

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Speaker 1: seems that they're they might be embellishing a little bit

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Speaker 1: in terms of the degree of help they're providing, but

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Speaker 1: they are providing help. The authorities have used this, it's

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Speaker 1: happening now. They've done so without much public scrutiny, or

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Speaker 1: they were for most of twenty nineteen doing so without

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Speaker 1: much public scrutiny. In early twenty twenty, Clearview said more

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Speaker 1: than six hundred different law enforcem agencies started using our

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Speaker 1: app just in the last year, but they refuse to

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Speaker 1: provide more specifics because they're very protective of their client base.

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Speaker 1: So the badger is out of the bag. Metaphorically, this

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Speaker 1: is not a what if scenario.

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Speaker 3: This exists.

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Speaker 1: This is in use right now, and that means it

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Speaker 1: can therefore be used on you and every single person

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Speaker 1: you know.

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Speaker 3: Is that a bad thing?

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Speaker 2: Ah?

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Speaker 1: Here's the interesting part. This technology is maybe innovative in application,

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Speaker 1: but very much not innovative in theoretical terms. It has

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Speaker 1: been possible for a while. People in the tech world

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Speaker 1: knew about this possibility for a long long time, and

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Speaker 1: some tech companies this is so weird. Tech companies barely

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Speaker 1: ever do this. Some tech companies like Giants said, you know,

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Speaker 1: we can build something like this. They realized it years ago,

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Speaker 1: and they actually decide to stop researching it. They decided

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Speaker 1: not to deploy this because of the massive potential privacy concerns.

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Speaker 1: Even Google wouldn't do it.

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Speaker 3: It's like that thing in The Batman, which which one

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Speaker 3: is it. It's the one with Keith Keith Fledger, where

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Speaker 3: there's that technology that turns every cell phone in the

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Speaker 3: world into a listening device, and you know he has

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Speaker 3: to destroy it after using it once because it's just

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Speaker 3: such problematic tech that, you know, Morgan Freeman's character basically

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Speaker 3: builds a safeguard device that causes it to you destroy itself,

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Speaker 3: because it's that's the thing, Ben about the badger being

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Speaker 3: out of the bag. Once it's out, you can say

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Speaker 3: all day long, this is just for law enforcement. But

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Speaker 3: like you said, Ben, the capability has been there, so

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Speaker 3: who's to stop somebody from doing a kind of copycat

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Speaker 3: version of this once it's out there and available to

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Speaker 3: check out, even if it's only quote unquote just for

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Speaker 3: law enforcement, you know. I mean, so were certain kinds

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Speaker 3: of radios and tasers and things like that, you know,

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Speaker 3: and people in the public of those now too, don't they.

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Speaker 3: It's a very good point.

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Speaker 1: Yeah, I mean back in twenty eleven, think about how

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Speaker 1: long ago.

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Speaker 3: That was.

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Speaker 1: Almost ten years ago, the chairman of Google said, facial

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Speaker 1: recognition technology, this kind of stuff that Clearview is doing,

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Speaker 1: is the one technology that Google is holding back on

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Speaker 1: because we feel like it could be used in a

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Speaker 1: very bad way. And now this is a fascinating point here.

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Speaker 1: Some cities have even preemptively banned police from using any

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Speaker 1: kind of facial recognition technology. And one of the most

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Speaker 1: notable examples of a city that's banned this San Francisco,

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Speaker 1: at the very heart of technological innovation here in the US,

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Speaker 1: so they definitely knew what was coming. Their lawmakers, their

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Speaker 1: constituents gave it a big not nope. And here's the

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Speaker 1: other thing, Clearview AI. If we think about the future

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Speaker 1: of it, the code behind it is fascinating.

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Speaker 2: If anyone out there has been watching the latest season

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Speaker 2: of Westworld. You may be familiar with the glasses that

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Speaker 2: several characters wear, and just think about that. If you

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Speaker 2: are familiar with that, think about that as we describe

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Speaker 2: what this thing can do.

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Speaker 1: Yeah, think about it carefully, because you see Clearview AI.

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Speaker 1: The code behind it isn't just facial recognition. It also

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Speaker 1: includes programming language that would allow this tool to pair

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Speaker 1: with AR or augmented reality glasses.

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Speaker 3: What does this mean.

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Speaker 1: This means that if the sunglasses I'm wearing now, we're

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Speaker 1: functioning with Clearview AI, I could walk down the street

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Speaker 1: and I could identify almost every person I saw, and

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Speaker 1: not just other pictures of them, but breadcrumbs that would

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Speaker 1: tell me where they lived, you know, what their children's

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Speaker 1: identities were, what their name was, they did for a living,

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Speaker 1: and so it would automatically play the six Degrees of

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Speaker 1: Kevin Bacon. Actually, that's a really great idea for an

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Speaker 1: add on game for these classes. You can see how

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Speaker 1: close people are to Kevin Bacon.

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Speaker 3: You heard it here.

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Speaker 1: First, But but it's scary because then you would like

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Speaker 1: you would see you would see Matt Frederick, and then

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Speaker 1: you would you would know his friend's Paul. Mission Control Deckett,

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Speaker 1: Noel Brown, Ben Bollen, and you would know everyone he

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Speaker 1: worked with. It just gets real sticky really quickly. And

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Speaker 1: can you hear the FEDS drooling in the background? Is

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Speaker 1: that just my cat? I don't know at this point,

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Speaker 1: but that's the high level look.

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Speaker 2: Or spies, I mean, just any spy. Imagine that, well,

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Speaker 2: you guys, I mean.

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Speaker 3: I think we're forgetting. An even more classic sci fi

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Speaker 3: example of this, that the kind of heads up display

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Speaker 3: you'd see in a movie like The Terminator. You know,

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Speaker 3: where you're seeing from the perspective of this, you know,

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Speaker 3: assassin robot who's looking for a target and being able

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Speaker 3: to see who everybody is and get all these stats

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Speaker 3: and metrics. You could apply this to any number of

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Speaker 3: you know, let's call them parameters surrounding an individual. You

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Speaker 3: could then go deeper with this and start pulling stats

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Speaker 3: from their social media and be served up with information

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Speaker 3: about their height and or you know whatever, like any

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Speaker 3: like let's say they have pre existing condition or something

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Speaker 3: like that, or they have some kind of you know

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Speaker 3: what's the word I'm looking for, like compromand on them.

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Speaker 3: You could even go deeper and find pictures of them,

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Speaker 3: like maybe smoking when they've claimed that they don't smoke,

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Speaker 3: and they are, you know, committing insurance fraud. I don't know.

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Speaker 3: I'm just saying, there's all kinds of ways you could

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Speaker 3: deep dive scrape this data and apply these points to

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Speaker 3: an individual and then get that information served up to

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Speaker 3: you very quickly in this display.

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Speaker 1: And yeah, an optimists are so quick to point out

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Speaker 1: the potential benefits of clearview AI recognition in general. It's

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Speaker 1: true there are benefits. Pessimists, however, claim the disadvantages here

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Speaker 1: in this specific case, far outweigh any of those benefits,

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Speaker 1: outweigh them, in fact, by a vast and disturbing margin.

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Speaker 1: We'll pause for word from our sponsor and return to

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Speaker 1: dive down the rabbit hole.

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Speaker 3: Here's where it gets crazy.

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Speaker 1: There are a ton of problems we've you know, we've

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Speaker 1: done some foreshadowing on these, but let's talk a little

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Speaker 1: bit more about them first. You know, you can categorize

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Speaker 1: these right. So first, there are not many strong operational

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Speaker 1: restrictions on how this technology is actually used. That means

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Speaker 1: that it can not only be easily misused, but that

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Speaker 1: there will probably not be repercussions for people, at least

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Speaker 1: not not legal repercussions.

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Speaker 3: It's like a point you made earlier, been off off, Mike.

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Speaker 3: It's like when you used to go into those tobacco

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Speaker 3: shops and uh, you know, you are not allowed to

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Speaker 3: refer to certain smoking apparatuses as bongs. They are water

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Speaker 3: pipes exclusively because that absolves the distributor, the retailer of

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Speaker 3: any responsibility if you choose to use them in any

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Speaker 3: illegal manner. It's the same thing. It's like saying, hey,

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Speaker 3: whatever you do with my thing is up to you.

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Speaker 3: I know, it's designed exclusively for a legal purpose. It's

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Speaker 3: a real sticky situation, isn't it, Because I mean it's

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Speaker 3: it not only puts all of the burden on the user,

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Speaker 3: it absolves the creator of any kind of responsibility at all.

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Speaker 1: Or does he do this ta hey, hey buddy, that's

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Speaker 1: a tobacco water pipe, all right?

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Speaker 2: Okay, yeah, yeah, say, you know, saying something like the

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Speaker 2: way the founder did in that interview with CNN where

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Speaker 2: he was just saying, you know, the thing says, just

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Speaker 2: use it in this way. It's it's we've seen it

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Speaker 2: over and over and over again. There there are a

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Speaker 2: couple of good things that he does speak about in

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Speaker 2: that CNN Business Interview, and one of the major things,

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Speaker 2: you know, if you put it at the top, it's

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Speaker 2: that the software costs around fifty thousand dollars to have

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Speaker 2: a license for a you know, for a department or

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Speaker 2: something for a year, years for two years, and so

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Speaker 2: at least there's a buy in level there that just

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Speaker 2: your average person if they're getting if they're obtaining it legally,

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Speaker 2: wouldn't be able to use. And then there are you know,

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Speaker 2: ways to verify if you know an end user is

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Speaker 2: actually if that license is is actually that person they

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Speaker 2: should be using it? That's good. And the other thing

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Speaker 2: is if you're a manager within the software of a

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Speaker 2: you know, a licensee of this software, you get audits

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Speaker 2: of every time the software has been used, which is

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Speaker 2: really nice. So at least at least there could be

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Speaker 2: some oversight to it and they could check to see

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Speaker 2: if you are quote, using it in a way they're

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Speaker 2: not supposed to.

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Speaker 1: Yeah, yeah, there's any there's an example of that. I

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Speaker 1: think they'll come into play here later, which is kind

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Speaker 1: of it's pretty weird.

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Speaker 3: It's it's a little it's a.

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Speaker 1: Little spooky, but I want to go back to that warning.

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Speaker 1: It's it's literally something. It's it's like that old FBI

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Speaker 1: VHS tape warning that we mentioned in earlier episode. You

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Speaker 1: go on to the homepage essentially of the app, which

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Speaker 1: you can use on desktop or you can use on

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Speaker 1: your phone, and basically all it says is like, don't

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Speaker 1: use this in a bad way.

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Speaker 3: And that's it's.

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Speaker 1: It's like as effective as those tags on mattresses that

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Speaker 1: say don't remove this tag, or or the the little

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Speaker 1: note on your boxy Q tips that's like, hey, don't

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Speaker 1: put these in your ear. You know it feels awesome. Uh,

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Speaker 1: and I know that's what everybody does with it, but

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Speaker 1: don't don't put them in your ear.

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Speaker 3: Uh. And then like you said, no that absolves them.

439
00:25:09,160 --> 00:25:11,359
Speaker 3: Or the warning about seizures at the beginning of when

440
00:25:11,400 --> 00:25:13,480
Speaker 3: you start up your PlayStation, you know what I mean,

441
00:25:13,560 --> 00:25:16,399
Speaker 3: it's like, yeah, okay, maybe I get seizures, but I'm

442
00:25:16,440 --> 00:25:18,240
Speaker 3: gonna roll the dice because I sure want to play

443
00:25:18,280 --> 00:25:20,320
Speaker 3: me some Borderlands three, you know.

444
00:25:20,840 --> 00:25:23,240
Speaker 1: Yeah, because you know, now that I think about it,

445
00:25:23,840 --> 00:25:26,919
Speaker 1: we might be at a bit of a glasshouse situation here, uh,

446
00:25:27,320 --> 00:25:30,840
Speaker 1: my friends, because our show literally starts with the warning

447
00:25:30,880 --> 00:25:33,440
Speaker 1: that says you can turn back now we're not We're

448
00:25:33,440 --> 00:25:34,919
Speaker 1: not as bad as Q tips.

449
00:25:35,000 --> 00:25:37,960
Speaker 3: You can you can turn back. Now that's definitely as

450
00:25:38,000 --> 00:25:41,359
Speaker 3: as is our prerogative to follow said warnings and not

451
00:25:41,440 --> 00:25:43,600
Speaker 3: stick Q tips in our ears. But who wants to

452
00:25:43,640 --> 00:25:45,359
Speaker 3: do that? What are you gonna use them for? Like

453
00:25:45,680 --> 00:25:49,400
Speaker 3: putting on eyeshadowers? I mean, I don't know the back

454
00:25:49,440 --> 00:25:51,320
Speaker 3: of your ear. Really do you need it to do?

455
00:25:51,359 --> 00:25:52,240
Speaker 1: You a C tip for that?

456
00:25:52,280 --> 00:25:55,040
Speaker 3: Can't you just use like a like a like a tissue.

457
00:25:55,520 --> 00:25:57,480
Speaker 3: Some people are living wild man. I don't know what

458
00:25:57,560 --> 00:26:00,440
Speaker 3: to tell you. You tips are literally probes. I don't

459
00:26:00,440 --> 00:26:03,320
Speaker 3: know anyway. I just feel like they're very clearly designed

460
00:26:03,320 --> 00:26:05,359
Speaker 3: to shove in your ear and it does feel great,

461
00:26:05,359 --> 00:26:08,080
Speaker 3: but yeah, it can. It's pretty scary when you pull

462
00:26:08,080 --> 00:26:10,040
Speaker 3: out some blood, you know, just saying.

463
00:26:10,520 --> 00:26:14,640
Speaker 2: So, yes, I've never done that. Actually I'm gonna I'm

464
00:26:14,640 --> 00:26:17,879
Speaker 2: gonna start trying. Uh no, we get gotten blood like

465
00:26:18,040 --> 00:26:21,600
Speaker 2: gone in for wax out with blood. But you know,

466
00:26:21,680 --> 00:26:24,840
Speaker 2: maybe I'm just fortunate. I want to bring up something

467
00:26:24,960 --> 00:26:29,000
Speaker 2: that is possible with this software and just how it

468
00:26:29,119 --> 00:26:31,919
Speaker 2: uses it before we jump into the next thing we're

469
00:26:31,920 --> 00:26:35,000
Speaker 2: going to talk about. And that's just that when your

470
00:26:35,280 --> 00:26:39,439
Speaker 2: face ends up on a social media website somewhere, if

471
00:26:39,520 --> 00:26:42,080
Speaker 2: you even if you did not take that picture, even

472
00:26:42,080 --> 00:26:44,280
Speaker 2: if you were in the background of a selfie that

473
00:26:44,320 --> 00:26:47,560
Speaker 2: someone took at Bonaru, let's say, or something like that,

474
00:26:48,400 --> 00:26:54,240
Speaker 2: your face is still searchable with facial recognition software if

475
00:26:54,240 --> 00:26:56,560
Speaker 2: it is visible in the background of an image, if

476
00:26:56,600 --> 00:26:59,680
Speaker 2: you're in a crowd, and if anywhere you are anywhere

477
00:26:59,680 --> 00:27:02,120
Speaker 2: you've been in where a photograph has been taken where

478
00:27:02,119 --> 00:27:06,840
Speaker 2: you can are visible somewhere in the background. This thing

479
00:27:07,560 --> 00:27:12,359
Speaker 2: clearview AI can may very much likely has that image

480
00:27:12,359 --> 00:27:16,520
Speaker 2: within its database and your face will be recognizable to it.

481
00:27:16,640 --> 00:27:17,400
Speaker 2: Just let's put that.

482
00:27:17,440 --> 00:27:21,600
Speaker 1: There, so it doesn't matter if you're on social media exactly.

483
00:27:21,840 --> 00:27:24,439
Speaker 3: Guys Devil's advocate here. Isn't that one of the situations

484
00:27:24,480 --> 00:27:28,479
Speaker 3: where if you're in public you're sort of giving up

485
00:27:28,520 --> 00:27:31,320
Speaker 3: that right to privacy, Or if you go to a

486
00:27:31,400 --> 00:27:34,440
Speaker 3: bon Aroo, there's usually signs that said when you pass

487
00:27:34,520 --> 00:27:37,800
Speaker 3: these gates, you are subject to being filmed, etc. And

488
00:27:37,840 --> 00:27:40,960
Speaker 3: that's usually for more like documentary purposes of the festival,

489
00:27:41,040 --> 00:27:42,960
Speaker 3: but I would think it would also apply if you're

490
00:27:42,960 --> 00:27:46,639
Speaker 3: in public and you're caught on you know, one of

491
00:27:46,680 --> 00:27:49,560
Speaker 3: these millions of little surveillance devices that people carry around

492
00:27:49,560 --> 00:27:53,960
Speaker 3: with them. Is that fair game? Can you sue over

493
00:27:54,119 --> 00:27:56,720
Speaker 3: use of your of misuse of your image? I'm gonna

494
00:27:57,160 --> 00:27:58,520
Speaker 3: I wouldn't have think. I wouldn't have thought.

495
00:27:58,520 --> 00:28:01,320
Speaker 2: So I'm certainly not arguing about, you know, the legality

496
00:28:01,359 --> 00:28:03,919
Speaker 2: of it. I'm just saying the reality of it is

497
00:28:04,040 --> 00:28:08,280
Speaker 2: that wherever you go, if there is a camera taking

498
00:28:08,280 --> 00:28:11,600
Speaker 2: a picture, whether it's cct well, CCTV generally wouldn't show

499
00:28:11,640 --> 00:28:13,479
Speaker 2: up in one of these things unless it was placed

500
00:28:13,480 --> 00:28:14,679
Speaker 2: into the system.

501
00:28:14,960 --> 00:28:19,760
Speaker 1: At the At the risk of sounding to I don't know,

502
00:28:19,920 --> 00:28:23,639
Speaker 1: to extreme about this, maybe too prescient, I think the

503
00:28:23,680 --> 00:28:27,159
Speaker 1: best for people concerned about this, the best way to

504
00:28:27,240 --> 00:28:31,320
Speaker 1: handle it, which is maddening, is to is to apply

505
00:28:31,440 --> 00:28:33,879
Speaker 1: some of the rules that some of the rules that

506
00:28:33,920 --> 00:28:38,600
Speaker 1: we apply to guns to cameras. So you always assume

507
00:28:38,600 --> 00:28:41,200
Speaker 1: a gun is loaded, right, you never want to point

508
00:28:41,200 --> 00:28:44,400
Speaker 1: it at you, So always assume a camera is recording.

509
00:28:44,800 --> 00:28:47,240
Speaker 1: And if you don't want to be with that assumption,

510
00:28:47,400 --> 00:28:51,000
Speaker 1: if you don't want to be in the footage or

511
00:28:51,040 --> 00:28:54,960
Speaker 1: in the photo, then don't have it pointed at you.

512
00:28:54,960 --> 00:28:58,800
Speaker 1: You guys, remember, I was very uncomfortable with Pokemon Go

513
00:28:59,320 --> 00:29:02,400
Speaker 1: when it came out, it was like, no, they're putting poke,

514
00:29:02,520 --> 00:29:06,440
Speaker 1: They're putting these ar little cute things to collect because

515
00:29:06,480 --> 00:29:07,240
Speaker 1: you're filming.

516
00:29:07,600 --> 00:29:08,720
Speaker 3: You're filming for them.

517
00:29:08,920 --> 00:29:11,840
Speaker 1: You guys are like Ben big Brother does not give

518
00:29:12,040 --> 00:29:18,800
Speaker 1: a like the faintest hint of a about Charis Arts

519
00:29:18,960 --> 00:29:22,560
Speaker 1: or whatever. Right, But still that stuff freaks me out yet,

520
00:29:22,680 --> 00:29:23,720
Speaker 1: you know, But we did.

521
00:29:23,640 --> 00:29:26,720
Speaker 3: A whole story about that company, Nantek, and like how

522
00:29:26,760 --> 00:29:31,240
Speaker 3: it had roots and intelligence gathering. I mean, you're not wrong, Ben,

523
00:29:31,400 --> 00:29:33,960
Speaker 3: And I remember that we'd be out hanging out and

524
00:29:34,040 --> 00:29:37,040
Speaker 3: people would be doing their little capturing their pokemons and

525
00:29:37,080 --> 00:29:38,960
Speaker 3: they'd be on your cell phone or something, and you

526
00:29:38,960 --> 00:29:41,400
Speaker 3: would like cover your face, and that is your prerogative

527
00:29:41,440 --> 00:29:43,720
Speaker 3: to do. And I completely understand where you're coming from,

528
00:29:43,840 --> 00:29:46,520
Speaker 3: because it's like with the whole zoom phenomenon that we're

529
00:29:46,560 --> 00:29:49,360
Speaker 3: obviously we're using it right now. You can turn off

530
00:29:49,400 --> 00:29:52,920
Speaker 3: that camera as far as you're concerned where it doesn't

531
00:29:52,920 --> 00:29:55,760
Speaker 3: show it to the group, but I guarandamn to you,

532
00:29:56,000 --> 00:30:00,280
Speaker 3: it's still capturing what's going on and storing it somewhere.

533
00:30:00,520 --> 00:30:04,280
Speaker 3: So you know, it's a great technology. It works real well,

534
00:30:04,360 --> 00:30:07,120
Speaker 3: which is why it's having this moment right now. But

535
00:30:07,400 --> 00:30:10,160
Speaker 3: I just would wouldn't trust it too implicitly.

536
00:30:10,240 --> 00:30:13,200
Speaker 2: You know, you know, the best technology it's what ben

537
00:30:13,360 --> 00:30:17,920
Speaker 2: ben and it's a sticker or some tape just go

538
00:30:19,760 --> 00:30:20,800
Speaker 2: at all times.

539
00:30:21,240 --> 00:30:25,640
Speaker 1: Yeah, but that's that's that's still that's that's a low

540
00:30:25,720 --> 00:30:27,920
Speaker 1: tech hack. But you have to do it because this

541
00:30:27,960 --> 00:30:31,040
Speaker 1: stuff can be weaponized. And uh, there's a guy named

542
00:30:31,120 --> 00:30:34,680
Speaker 1: Eric Goldman at the High Tech Law Institute at Santa

543
00:30:34,720 --> 00:30:36,920
Speaker 1: Clara University.

544
00:30:37,240 --> 00:30:38,160
Speaker 3: I like the way he put it.

545
00:30:38,160 --> 00:30:43,320
Speaker 1: He said, the weaponization possibilities of this are endless. Imagine

546
00:30:43,360 --> 00:30:47,600
Speaker 1: a rogue law enforcement officer who wants to stalk someone

547
00:30:47,680 --> 00:30:50,120
Speaker 1: that they want to hit on later, or maybe a

548
00:30:50,160 --> 00:30:53,560
Speaker 1: foreign government is doing similar to what you described. No,

549
00:30:53,800 --> 00:30:56,760
Speaker 1: they're digging up secrets about people to blackmail them or

550
00:30:56,800 --> 00:30:59,280
Speaker 1: to throw members of an opposition party in jail.

551
00:30:59,560 --> 00:31:00,680
Speaker 3: This stuff can happen.

552
00:31:01,040 --> 00:31:06,520
Speaker 1: And you know, Clearview is also notoriously secretive. Even now

553
00:31:06,600 --> 00:31:09,240
Speaker 1: that's the second big issue. It leaves a lot of

554
00:31:09,280 --> 00:31:14,520
Speaker 1: critics ill at ease. We know some stuff because of

555
00:31:14,560 --> 00:31:19,720
Speaker 1: some excellent research from earlier reporters. Let me tell you, guys,

556
00:31:19,720 --> 00:31:22,040
Speaker 1: this weird story. So there's a there was a New

557
00:31:22,120 --> 00:31:25,160
Speaker 1: York Times journalist who was working on a story about

558
00:31:25,160 --> 00:31:30,280
Speaker 1: Clearview that really brought it to international attention. And while

559
00:31:30,440 --> 00:31:34,040
Speaker 1: this journalist is working with clear is trying to do

560
00:31:34,120 --> 00:31:37,840
Speaker 1: this story, Clearview isn't really answering him. They're like ducking

561
00:31:38,120 --> 00:31:40,880
Speaker 1: attempts to contact via text, email, phone call.

562
00:31:41,360 --> 00:31:43,640
Speaker 3: And so this reporter.

563
00:31:44,000 --> 00:31:48,840
Speaker 1: Knows that a lot of law enforcement agencies have Clearview access,

564
00:31:48,960 --> 00:31:52,880
Speaker 1: and so this reporter asks some contacts in the police

565
00:31:52,880 --> 00:31:57,520
Speaker 1: department to run his photo through the app, and they

566
00:31:57,560 --> 00:32:02,280
Speaker 1: do a couple times. So the first approach, the first

567
00:32:02,600 --> 00:32:07,320
Speaker 1: thing he learns about Clearview approaching outside forces is not

568
00:32:07,440 --> 00:32:09,680
Speaker 1: when they come to him. It's when they go to

569
00:32:09,840 --> 00:32:14,160
Speaker 1: the police that he worked with and they ask the police, Hey,

570
00:32:14,360 --> 00:32:15,840
Speaker 1: are you talking to the media.

571
00:32:16,280 --> 00:32:17,360
Speaker 3: How shady is that?

572
00:32:17,920 --> 00:32:20,960
Speaker 2: Wow? Yes, that is whoa.

573
00:32:21,320 --> 00:32:24,200
Speaker 3: They have an explanation right, oh absolutely, And just really

574
00:32:24,280 --> 00:32:26,480
Speaker 3: quickly that's the piece that was on the Daily because

575
00:32:26,520 --> 00:32:28,200
Speaker 3: the Daily is affiliate with the New York Times, So

576
00:32:28,280 --> 00:32:31,120
Speaker 3: this was after the fact of this reporter whose name

577
00:32:31,120 --> 00:32:33,800
Speaker 3: I'm trying to find, had done all this research and

578
00:32:33,920 --> 00:32:36,880
Speaker 3: gone through this whole run around to even get a

579
00:32:37,000 --> 00:32:40,480
Speaker 3: sit down. The reporter eventually found the office and it

580
00:32:40,560 --> 00:32:42,360
Speaker 3: was like very shady like there weren't with that many

581
00:32:42,400 --> 00:32:45,360
Speaker 3: people there. It was a skeleton crew and finally got

582
00:32:45,400 --> 00:32:48,800
Speaker 3: access to the founder. But that is all, you know,

583
00:32:49,040 --> 00:32:51,800
Speaker 3: described in great detail on that piece by The Daily.

584
00:32:51,840 --> 00:32:53,200
Speaker 3: I highly recommend checking it out.

585
00:32:53,640 --> 00:32:57,280
Speaker 1: Yeah, Kashmir Hill, the secretive company that might end privacy

586
00:32:57,280 --> 00:33:00,680
Speaker 1: as we know it is the is the Times article. Yeah,

587
00:33:00,760 --> 00:33:03,840
Speaker 1: I like what you're saying about. When when when Hill

588
00:33:03,960 --> 00:33:08,520
Speaker 1: finally figured out where the company was originally on LinkedIn,

589
00:33:08,920 --> 00:33:11,960
Speaker 1: they just had like one employee listed and it turned

590
00:33:11,960 --> 00:33:14,600
Speaker 1: out to be a pseudonym or a fake name that

591
00:33:14,680 --> 00:33:18,560
Speaker 1: the founder was using. So they're they're aware of the

592
00:33:18,640 --> 00:33:22,520
Speaker 1: dangers here. But but when they did eventually speak to

593
00:33:22,560 --> 00:33:27,120
Speaker 1: the reporter, they had and they had reasonable explanations for

594
00:33:27,240 --> 00:33:31,360
Speaker 1: both of these things. First, the founder said, look, we

595
00:33:31,360 --> 00:33:34,800
Speaker 1: were avoiding speaking to the media, not because we're like

596
00:33:35,040 --> 00:33:38,360
Speaker 1: super villains or something. It's because we are a tiny

597
00:33:38,480 --> 00:33:41,000
Speaker 1: startup and a lot of times when you're a tiny startup,

598
00:33:41,040 --> 00:33:43,760
Speaker 1: you're in you're in stealth mode, right, And that makes

599
00:33:43,800 --> 00:33:46,840
Speaker 1: sense because the NDAs on those things are pretty ironclad.

600
00:33:47,200 --> 00:33:49,560
Speaker 1: You don't want anyone to scoop you. And then he

601
00:33:49,600 --> 00:33:52,560
Speaker 1: additionally said, here's why we talked to the police about

602
00:33:52,560 --> 00:33:57,120
Speaker 1: your search terms. We actually monitor all the searches that

603
00:33:57,160 --> 00:34:00,480
Speaker 1: are done because we want to make sure that they're

604
00:34:00,480 --> 00:34:03,440
Speaker 1: being used correctly. In other words, you know, we want

605
00:34:03,440 --> 00:34:07,360
Speaker 1: to make sure nothing like like there's never a situation

606
00:34:07,440 --> 00:34:11,600
Speaker 1: where a police officer is jilted, is jilted by a

607
00:34:11,680 --> 00:34:14,440
Speaker 1: romantic partner and then they find out their X has

608
00:34:14,480 --> 00:34:15,640
Speaker 1: a new lover and they're like.

609
00:34:15,600 --> 00:34:17,680
Speaker 3: Well, I'm gonna find out about this.

610
00:34:19,160 --> 00:34:21,200
Speaker 1: God, I'm even afraid to make up a name because

611
00:34:21,200 --> 00:34:26,080
Speaker 1: I don't want to put it out there. Uh, Marcus

612
00:34:26,160 --> 00:34:28,640
Speaker 1: mcgilly cutty. Oh that's swinging a miss.

613
00:34:28,600 --> 00:34:30,960
Speaker 3: Funny thing about that, though, Ben is like, you don't

614
00:34:31,000 --> 00:34:35,040
Speaker 3: even need technology this robust to dig into somebody that

615
00:34:35,239 --> 00:34:38,279
Speaker 3: closely removed from you know, a person you really know

616
00:34:38,920 --> 00:34:42,239
Speaker 3: they're probably already like posting tagging each other on Instagram

617
00:34:42,280 --> 00:34:43,920
Speaker 3: posts and you just you know, go and just do

618
00:34:43,960 --> 00:34:47,000
Speaker 3: a couple of clicks and you're there. But no, it's

619
00:34:47,040 --> 00:34:48,560
Speaker 3: just a really good point and that's what what's set

620
00:34:48,560 --> 00:34:51,800
Speaker 3: off those alarm bells. But yeah, the way it's described

621
00:34:51,800 --> 00:34:54,560
Speaker 3: in the in the in the Daily piece, it was

622
00:34:54,840 --> 00:34:57,959
Speaker 3: very it felt very like okay, a they know they're

623
00:34:57,960 --> 00:35:03,560
Speaker 3: onto something and be they don't want to be found.

624
00:35:04,040 --> 00:35:05,840
Speaker 3: And I can see to your point been the whole

625
00:35:05,920 --> 00:35:07,799
Speaker 3: startup mode. They don't want to get scooped. They don't

626
00:35:07,800 --> 00:35:11,799
Speaker 3: want anybody stealing their proprietary in their you know, technology

627
00:35:11,840 --> 00:35:14,920
Speaker 3: and trying to misuse it or you know, create a

628
00:35:15,040 --> 00:35:17,560
Speaker 3: copycat kind of version of it. So that does make sense,

629
00:35:17,560 --> 00:35:19,600
Speaker 3: but just it's the whole thing is a little strange.

630
00:35:19,960 --> 00:35:24,640
Speaker 3: But this app clearview claims you know, is is up

631
00:35:24,680 --> 00:35:28,600
Speaker 3: to security industry standards in terms of I guess in

632
00:35:28,719 --> 00:35:33,480
Speaker 3: terms of you know, hackability or like firewalls security or

633
00:35:33,520 --> 00:35:37,279
Speaker 3: you know, being not being vulnerable to being infiltrated, but

634
00:35:37,880 --> 00:35:42,560
Speaker 3: who is actually watching them? Who is actually monitoring them

635
00:35:42,600 --> 00:35:46,839
Speaker 3: because it's it's essentially untested and already being put out

636
00:35:46,840 --> 00:35:50,120
Speaker 3: into the wild in cases that can have huge effects

637
00:35:50,120 --> 00:35:53,239
Speaker 3: on people's lives. We'll talk more about that after a

638
00:35:53,320 --> 00:36:02,480
Speaker 3: quick word from our sponsor. Yes, you know that was

639
00:36:03,000 --> 00:36:04,280
Speaker 3: a perfect ad break.

640
00:36:04,560 --> 00:36:08,480
Speaker 1: Bill also came be time to get this cat out

641
00:36:08,480 --> 00:36:10,160
Speaker 1: of out of my recording studio.

642
00:36:10,480 --> 00:36:11,719
Speaker 3: I heard you had a little me hour.

643
00:36:13,040 --> 00:36:19,080
Speaker 1: Yeah uh yeah. Well we're all dealing with new coworkers, right.

644
00:36:19,160 --> 00:36:21,520
Speaker 3: Can I just do a quick aside, a quick quarantine

645
00:36:21,560 --> 00:36:24,799
Speaker 3: aside had a crisis yesterday. We I don't know if

646
00:36:24,800 --> 00:36:27,280
Speaker 3: I've mentioned the show, that my daughter got a hamster

647
00:36:27,440 --> 00:36:30,000
Speaker 3: right before quarantine kicked in, and I had to inherit

648
00:36:30,000 --> 00:36:33,560
Speaker 3: it because her mom was allergic. She found out hamster's

649
00:36:33,600 --> 00:36:36,680
Speaker 3: name is Honakah, which is like an anime character. But anyway,

650
00:36:36,920 --> 00:36:40,799
Speaker 3: we realized yesterday afternoon that Honakoh was not in her cage.

651
00:36:41,560 --> 00:36:45,480
Speaker 3: So we had a hamster search party all up in

652
00:36:45,560 --> 00:36:48,200
Speaker 3: this house, freaking out, and I had just these horrible

653
00:36:48,280 --> 00:36:51,920
Speaker 3: visions of like finding a dead, decaying hamster, you know,

654
00:36:51,960 --> 00:36:55,480
Speaker 3: two weeks later in my sock drawer or something. So luckily,

655
00:36:55,719 --> 00:36:58,480
Speaker 3: the little the little critter found herself back to where

656
00:36:58,520 --> 00:37:02,200
Speaker 3: she belonged on her own, which was great. What great? Yeah,

657
00:37:02,239 --> 00:37:04,080
Speaker 3: she just popped out and was right there, even thought

658
00:37:04,120 --> 00:37:05,960
Speaker 3: it was a sock and it was turns out it

659
00:37:05,960 --> 00:37:08,160
Speaker 3: was a hamster and it wasn't long. I read all

660
00:37:08,160 --> 00:37:10,879
Speaker 3: this horror stories about how you can only search late

661
00:37:10,920 --> 00:37:13,399
Speaker 3: at night and you gotta bait it with peanut butter

662
00:37:13,480 --> 00:37:15,520
Speaker 3: and all this stuff, and it she just came came

663
00:37:15,600 --> 00:37:16,560
Speaker 3: right back. It was great.

664
00:37:16,880 --> 00:37:20,640
Speaker 1: That's fantastic. Yeah, I had a I had a gerbil

665
00:37:21,040 --> 00:37:24,960
Speaker 1: and in my younger days that escaped and lived and

666
00:37:25,040 --> 00:37:28,640
Speaker 1: died in the walls of a house in my tried

667
00:37:28,680 --> 00:37:31,759
Speaker 1: to tried to move the oven and appliances to get

668
00:37:31,800 --> 00:37:36,839
Speaker 1: to it, but then we couldn't, and my father thought

669
00:37:36,840 --> 00:37:38,960
Speaker 1: it would be a good lesson in mortality.

670
00:37:39,440 --> 00:37:42,640
Speaker 3: Oh boy, I was hoping to avoid that particular lesson

671
00:37:42,640 --> 00:37:46,319
Speaker 3: for my kid, especially during these trying times when things

672
00:37:46,320 --> 00:37:49,080
Speaker 3: have been mainly pretty positive for us here at the house.

673
00:37:49,360 --> 00:37:53,279
Speaker 3: I'm so glad. Yeah, thanks guys, but no, you're right, Ben,

674
00:37:53,480 --> 00:37:56,879
Speaker 3: tell us a little bit about the security of this

675
00:37:56,960 --> 00:38:00,960
Speaker 3: app and the idea of it being tested for.

676
00:38:02,480 --> 00:38:05,840
Speaker 1: Vulnerabilities, right, Yeah, this is a very interesting point. So

677
00:38:06,800 --> 00:38:10,640
Speaker 1: a lot of the reporting that came up about Clearview

678
00:38:10,760 --> 00:38:17,600
Speaker 1: Ai came before late February of this year. In late February,

679
00:38:18,719 --> 00:38:22,920
Speaker 1: clearview app security was actually tested in the form of

680
00:38:22,960 --> 00:38:26,200
Speaker 1: a hack. On February twenty six, the public learned that

681
00:38:26,239 --> 00:38:30,640
Speaker 1: the company had been breached, their security been compromised. Hackers

682
00:38:30,640 --> 00:38:35,360
Speaker 1: had stolen Clearview AI's entire customer list, which was coveted

683
00:38:35,560 --> 00:38:40,279
Speaker 1: and very much secret right the customer list, adding to

684
00:38:40,680 --> 00:38:43,560
Speaker 1: the troubling facts about this company, The customer list did

685
00:38:43,600 --> 00:38:47,480
Speaker 1: not jibe with what Clearview had said earlier. It spans

686
00:38:47,560 --> 00:38:51,800
Speaker 1: thousands of government entities and private businesses across the world,

687
00:38:52,160 --> 00:38:56,160
Speaker 1: so the US Department of Justice, Immigration, and Customs. But

688
00:38:56,239 --> 00:38:59,120
Speaker 1: it also includes banks, and he does allude to banks

689
00:38:59,200 --> 00:39:01,400
Speaker 1: in a couple of different and interviews the founders. It

690
00:39:01,440 --> 00:39:06,120
Speaker 1: also includes Macy's and best Buy and Interpool. And then

691
00:39:06,320 --> 00:39:10,960
Speaker 1: on the list you also see credentialed users at the FBI.

692
00:39:11,920 --> 00:39:14,439
Speaker 1: You know, we said hundreds of police departments. But here's

693
00:39:14,480 --> 00:39:18,440
Speaker 1: something very interesting. It also includes users in Saudi Arabia

694
00:39:18,640 --> 00:39:22,959
Speaker 1: and the United Arab Emirates. These are countries that are,

695
00:39:23,080 --> 00:39:27,360
Speaker 1: you know, not particularly known for their progressive policies or

696
00:39:27,400 --> 00:39:30,560
Speaker 1: their their First Amendment rights. The founder, by the way,

697
00:39:30,680 --> 00:39:34,520
Speaker 1: says that they're exercising a First Amendment right when they

698
00:39:34,880 --> 00:39:36,200
Speaker 1: scrape all these images.

699
00:39:36,719 --> 00:39:39,560
Speaker 2: Yeah, they're they're exercising a First Amendment right because they're

700
00:39:39,600 --> 00:39:43,040
Speaker 2: all publicly available and out there. And if the argument

701
00:39:43,080 --> 00:39:46,640
Speaker 2: that they make generally is that if Google can scrape

702
00:39:46,640 --> 00:39:50,479
Speaker 2: all the websites and create search terms for all those

703
00:39:50,480 --> 00:39:53,560
Speaker 2: things and then allow you to search that information. If

704
00:39:54,200 --> 00:39:57,640
Speaker 2: you know, YouTube has you know, when you upload something

705
00:39:57,640 --> 00:40:00,279
Speaker 2: to YouTube, it's all publicly available, they can scrap all

706
00:40:00,280 --> 00:40:02,480
Speaker 2: the information for all the data, and then the other

707
00:40:02,560 --> 00:40:05,799
Speaker 2: companies intertwine and use that data to help each other

708
00:40:05,880 --> 00:40:08,359
Speaker 2: find other things. Well, hey, why can't we do that too?

709
00:40:09,320 --> 00:40:11,920
Speaker 2: So we're going to However, a lot of the scrutiny

710
00:40:11,920 --> 00:40:15,280
Speaker 2: that's been happening because of that hack that was reported,

711
00:40:15,280 --> 00:40:18,120
Speaker 2: as well as just the tech in general and how

712
00:40:18,200 --> 00:40:21,320
Speaker 2: these other giant tech companies how their data is being used.

713
00:40:21,760 --> 00:40:25,719
Speaker 2: Tech giants like Twitter, Google, and YouTube same thing have

714
00:40:25,840 --> 00:40:29,880
Speaker 2: sent cease and desist letters to Clearview orders saying do

715
00:40:30,000 --> 00:40:33,839
Speaker 2: not do this anymore. And even like because of this,

716
00:40:34,160 --> 00:40:38,200
Speaker 2: some police departments and other users have just discontinued use

717
00:40:38,320 --> 00:40:42,080
Speaker 2: of the software. And when the founder, you know, responds

718
00:40:42,680 --> 00:40:45,759
Speaker 2: to this kind of thing, he says, just you know,

719
00:40:45,800 --> 00:40:48,320
Speaker 2: our legal team is on it. They're working right now

720
00:40:48,800 --> 00:40:52,920
Speaker 2: to fight that battle using the First Amendment as our

721
00:40:53,040 --> 00:40:53,800
Speaker 2: major argument.

722
00:40:54,320 --> 00:40:54,560
Speaker 3: Yeah.

723
00:40:54,680 --> 00:41:00,280
Speaker 1: And then importantly we should note that they said the Sstomer

724
00:41:00,360 --> 00:41:03,000
Speaker 1: lists was the only thing compromised, so the hackers don't

725
00:41:03,040 --> 00:41:08,080
Speaker 1: have that three billion plus and growing image archive. But

726
00:41:08,400 --> 00:41:12,080
Speaker 1: you're right, Matt, legal troubles gather like storm clouds on

727
00:41:12,200 --> 00:41:17,200
Speaker 1: the horizon. Clearview has said it's complied with things like

728
00:41:17,320 --> 00:41:21,279
Speaker 1: policies proposed by the ACLU. The ACLU has said that

729
00:41:21,320 --> 00:41:25,879
Speaker 1: they don't comply. And I think there's a case coming

730
00:41:25,960 --> 00:41:29,040
Speaker 1: up against Clearview in Illinois, and it's one of what

731
00:41:29,239 --> 00:41:34,319
Speaker 1: may be many arguably legal troubles should be gathering on

732
00:41:34,400 --> 00:41:37,680
Speaker 1: the horizon and the storm should hit, especially when we

733
00:41:37,719 --> 00:41:41,600
Speaker 1: look at the implications of this technology, and before we continue,

734
00:41:41,640 --> 00:41:46,400
Speaker 1: we want to be like cartoonishly transparent here. We're not

735
00:41:46,760 --> 00:41:52,200
Speaker 1: saying that Clearview or the people in the startup are

736
00:41:52,280 --> 00:41:54,719
Speaker 1: bad people, and we're not saying even that their intentions

737
00:41:54,760 --> 00:41:57,919
Speaker 1: are bad. We're saying that this technology, just like any

738
00:41:57,920 --> 00:42:01,720
Speaker 1: other technology, has inherent implications.

739
00:42:01,880 --> 00:42:03,480
Speaker 3: It hasn't just like fire.

740
00:42:04,000 --> 00:42:07,359
Speaker 1: It has it has some positive benefits, and it has

741
00:42:07,400 --> 00:42:12,239
Speaker 1: some very dangerous possible consequences, like how could it be misused?

742
00:42:13,040 --> 00:42:15,720
Speaker 1: The first one just off the top here. The first

743
00:42:15,719 --> 00:42:17,640
Speaker 1: one is the one we talked about a couple times

744
00:42:17,680 --> 00:42:20,719
Speaker 1: in this episode. Somebody with access could use it for

745
00:42:20,800 --> 00:42:24,960
Speaker 1: personal reasons. We did that hypothetical cop who's you know,

746
00:42:25,440 --> 00:42:28,240
Speaker 1: mad at his ex for dating Mark McGillicutty or whatever.

747
00:42:28,520 --> 00:42:32,080
Speaker 1: But we can go beyond the hypothetical examples. There's a

748
00:42:32,120 --> 00:42:36,400
Speaker 1: real life example. There's a billionaire Clearview investor who used

749
00:42:36,400 --> 00:42:39,840
Speaker 1: the app to stalk his daughter's newest bow. He wanted

750
00:42:39,880 --> 00:42:42,600
Speaker 1: to find out about this guy. I imagine, you know,

751
00:42:43,360 --> 00:42:48,000
Speaker 1: whether you're a millionaire or a pauper. Of course you

752
00:42:48,000 --> 00:42:49,759
Speaker 1: want to know who your kid's dating. You want to

753
00:42:49,800 --> 00:42:53,200
Speaker 1: know about them. But it's crazy that this guy was

754
00:42:53,280 --> 00:42:57,560
Speaker 1: able to use it and he found out all the

755
00:42:57,600 --> 00:42:59,839
Speaker 1: stuff that we just mentioned. Where the guy lives, what

756
00:43:00,040 --> 00:43:03,800
Speaker 1: his job is, you know, his known connections, his social

757
00:43:03,800 --> 00:43:06,680
Speaker 1: media and stuff. This example is a little weird though,

758
00:43:06,719 --> 00:43:09,560
Speaker 1: because I imagine being a billionaire, you could just hire a

759
00:43:09,640 --> 00:43:12,640
Speaker 1: person to dedicate their time to doing that.

760
00:43:13,000 --> 00:43:13,800
Speaker 3: Just hire a PI.

761
00:43:15,160 --> 00:43:17,319
Speaker 2: This is a This is a really important thing about

762
00:43:17,320 --> 00:43:19,879
Speaker 2: this that I maybe we mentioned but we didn't get

763
00:43:19,920 --> 00:43:22,799
Speaker 2: into it. The reason why he was able to find

764
00:43:22,840 --> 00:43:26,279
Speaker 2: all that stuff about this person. The way I've seen

765
00:43:26,320 --> 00:43:29,760
Speaker 2: the software work, it's not like you identify this person,

766
00:43:29,800 --> 00:43:32,480
Speaker 2: then it gives you a giant readout of all of

767
00:43:32,520 --> 00:43:37,920
Speaker 2: this person's information. You you identify that that person's face,

768
00:43:38,680 --> 00:43:41,640
Speaker 2: then all of the other publicly publicly available on the

769
00:43:41,640 --> 00:43:45,480
Speaker 2: internet faces show up and you can click on it

770
00:43:45,520 --> 00:43:48,000
Speaker 2: and it takes you to the website where that face is,

771
00:43:48,080 --> 00:43:50,960
Speaker 2: where that image is located, so that means you're then

772
00:43:51,000 --> 00:43:54,200
Speaker 2: directly connected to their social media to anywhere that that

773
00:43:54,520 --> 00:43:57,800
Speaker 2: photo has been posted, and then through those other websites

774
00:43:57,840 --> 00:44:00,719
Speaker 2: you can gather all that other information. At least that's

775
00:44:00,760 --> 00:44:03,920
Speaker 2: the way it was demonstrated in that CNN Business interview

776
00:44:03,920 --> 00:44:06,160
Speaker 2: in a couple of other places.

777
00:44:05,840 --> 00:44:08,440
Speaker 1: That's the way it works for now, But we know

778
00:44:08,640 --> 00:44:12,800
Speaker 1: that that UI could easily be upgraded to maybe pull

779
00:44:13,480 --> 00:44:17,480
Speaker 1: to scrape that information if it's publicly available, right, I mean,

780
00:44:17,560 --> 00:44:20,800
Speaker 1: think about how many things are we've talked about before,

781
00:44:20,840 --> 00:44:23,560
Speaker 1: how easy it is to find a phone number, how

782
00:44:23,560 --> 00:44:26,319
Speaker 1: easy it is to find not just your address, but

783
00:44:26,360 --> 00:44:30,360
Speaker 1: the last few addresses you lived at over the years.

784
00:44:29,960 --> 00:44:33,200
Speaker 1: This is where we should also mention people don't trust

785
00:44:33,239 --> 00:44:36,239
Speaker 1: clearview AI is a little bit of stereotyping, but they

786
00:44:36,239 --> 00:44:38,719
Speaker 1: don't trust it because one of the investors, Peter Thiel,

787
00:44:39,280 --> 00:44:41,160
Speaker 1: is also big in Facebook.

788
00:44:42,320 --> 00:44:44,520
Speaker 3: So Facebook.

789
00:44:46,800 --> 00:44:49,320
Speaker 1: Facebook is good at a lot of things, and users

790
00:44:49,400 --> 00:44:52,880
Speaker 1: privacy has never been one that is not a bug,

791
00:44:52,960 --> 00:44:55,080
Speaker 1: that's a feature from their opinion, and.

792
00:44:55,360 --> 00:45:00,279
Speaker 2: This is just a dovetail on all of that. In

793
00:45:00,320 --> 00:45:03,960
Speaker 2: that interview, Donnia Sullivan does a search of his face

794
00:45:04,600 --> 00:45:09,520
Speaker 2: and it's the official profile picture on the website where

795
00:45:09,560 --> 00:45:13,760
Speaker 2: he works for the CNN business site. And when it scrolled,

796
00:45:13,760 --> 00:45:15,960
Speaker 2: when they scrolled all the way down, there was an

797
00:45:16,000 --> 00:45:18,800
Speaker 2: image of him because he's in his I guess thirties,

798
00:45:19,400 --> 00:45:21,160
Speaker 2: late thirties or something to that. I don't know how

799
00:45:21,200 --> 00:45:23,720
Speaker 2: old he is, but he's a person about our age.

800
00:45:24,280 --> 00:45:27,160
Speaker 2: Ben and Nola and I. They scrolled the way down

801
00:45:27,360 --> 00:45:30,600
Speaker 2: and there's an image of him from a local newspaper

802
00:45:30,800 --> 00:45:35,200
Speaker 2: where he was in Ireland, right when he was sixteen

803
00:45:35,280 --> 00:45:38,239
Speaker 2: years old. And it's a group of you know, teenagers

804
00:45:38,280 --> 00:45:40,840
Speaker 2: holding up a sign and you can just see his

805
00:45:40,880 --> 00:45:46,720
Speaker 2: face and he looks absolutely different. I mean, I would

806
00:45:46,719 --> 00:45:48,439
Speaker 2: not have been able to tell that that was him,

807
00:45:48,440 --> 00:45:50,640
Speaker 2: but he recognized the photo and he knew it was him.

808
00:45:51,600 --> 00:45:54,840
Speaker 2: And this is the kind of thing where you know,

809
00:45:54,920 --> 00:45:58,120
Speaker 2: this is not an allegation on my part. This is

810
00:45:58,280 --> 00:46:03,440
Speaker 2: just me wondering and ruminating. I'm wondering how they connected

811
00:46:03,480 --> 00:46:08,400
Speaker 2: that up, How this face recognition software inserted that picture

812
00:46:08,440 --> 00:46:13,000
Speaker 2: of him where he looks so different. And the founder says, well,

813
00:46:13,040 --> 00:46:15,600
Speaker 2: you know that there's still parts of your face, the

814
00:46:15,640 --> 00:46:18,240
Speaker 2: geometry of it that remained the same over the years,

815
00:46:18,239 --> 00:46:20,440
Speaker 2: even if you put on weight, even if you're wearing glasses,

816
00:46:20,440 --> 00:46:23,880
Speaker 2: even if you're covering your face. I get that argument,

817
00:46:25,120 --> 00:46:31,080
Speaker 2: But to me, it makes me wonder if there is

818
00:46:31,160 --> 00:46:35,840
Speaker 2: some kind of added like Spochio search that's going on

819
00:46:36,120 --> 00:46:40,160
Speaker 2: within this system where you're looking at that's just another

820
00:46:40,200 --> 00:46:45,200
Speaker 2: company that you can search social media, any social media

821
00:46:45,200 --> 00:46:47,600
Speaker 2: accounts that somebody has using an email or a phone

822
00:46:47,680 --> 00:46:50,440
Speaker 2: number or a name, And it makes me wonder if

823
00:46:50,440 --> 00:46:54,560
Speaker 2: they're connecting things up there as an extra layer of like.

824
00:46:54,600 --> 00:46:59,160
Speaker 1: A name, like prioritizing an image by other information.

825
00:46:59,560 --> 00:47:01,960
Speaker 2: Yeah, it makes me wonder if that's happening. I guess not,

826
00:47:02,400 --> 00:47:06,080
Speaker 2: but they I would say he didn't sufficiently, the founder

827
00:47:06,120 --> 00:47:09,360
Speaker 2: didn't sufficiently explain how that image got in there, and

828
00:47:10,200 --> 00:47:14,120
Speaker 2: the interviewer is clearly a little weirded out and disturbed

829
00:47:14,160 --> 00:47:14,440
Speaker 2: by it.

830
00:47:15,080 --> 00:47:17,479
Speaker 3: Well, another thing too, is I mean, you know, in

831
00:47:17,719 --> 00:47:21,799
Speaker 3: the United States, presumably you you know you might be

832
00:47:21,840 --> 00:47:24,960
Speaker 3: identified using this technology, but doesn't mean you are immediately

833
00:47:24,960 --> 00:47:27,719
Speaker 3: going to be convicted of something without some kind of

834
00:47:27,719 --> 00:47:31,120
Speaker 3: physical evidence or without a trial. But in other countries

835
00:47:31,160 --> 00:47:33,239
Speaker 3: where stuff like that isn't as much of a thing,

836
00:47:33,640 --> 00:47:36,920
Speaker 3: it could be used to identify social dissidents or like

837
00:47:37,000 --> 00:47:40,359
Speaker 3: people that are speaking out against the totalitarian government and

838
00:47:40,400 --> 00:47:43,240
Speaker 3: flag them and throw them in the gulag or whatever,

839
00:47:43,360 --> 00:47:46,480
Speaker 3: you know, without ever you know, batting an eye. And

840
00:47:46,640 --> 00:47:49,759
Speaker 3: that is sort of the crux of the problem with

841
00:47:49,800 --> 00:47:53,640
Speaker 3: this software is it depends on the notion of people

842
00:47:53,719 --> 00:47:57,160
Speaker 3: that use it being good actors and and being good

843
00:47:57,239 --> 00:48:00,440
Speaker 3: stewards of this power that they have. And as we know,

844
00:48:01,280 --> 00:48:04,960
Speaker 3: power corrupts, and we've got a lot of governments that

845
00:48:05,040 --> 00:48:08,759
Speaker 3: are kind of corrupt and out to shut down any

846
00:48:08,840 --> 00:48:13,719
Speaker 3: kind of criticism of what they do. And this would

847
00:48:13,719 --> 00:48:16,439
Speaker 3: be a great way to identify those people and round

848
00:48:16,480 --> 00:48:18,759
Speaker 3: them up and put them away.

849
00:48:19,560 --> 00:48:23,520
Speaker 1: Yeah, and it's it's it's dangerous for that reason. You know,

850
00:48:23,600 --> 00:48:27,799
Speaker 1: you could you could detain people, and you could cut

851
00:48:27,800 --> 00:48:30,839
Speaker 1: around some of their legal protections like what if this,

852
00:48:31,040 --> 00:48:34,080
Speaker 1: what if this counted in a court of law as

853
00:48:34,200 --> 00:48:37,560
Speaker 1: definitive identification, then you would say, well, we don't need

854
00:48:37,600 --> 00:48:40,799
Speaker 1: to figure out if you're the person, right, It could

855
00:48:40,840 --> 00:48:43,600
Speaker 1: get so dangerous so quickly. There's another point we have

856
00:48:43,640 --> 00:48:46,080
Speaker 1: to add. You know, this sounds like some kind of

857
00:48:46,120 --> 00:48:50,880
Speaker 1: Skynet Big Brother stuff. It can also be used inaccurately.

858
00:48:51,000 --> 00:48:54,759
Speaker 1: It is far from perfect. There's It's one thing that

859
00:48:54,840 --> 00:48:58,520
Speaker 1: I've been talking a lot about with some people online about, uh,

860
00:48:59,600 --> 00:49:04,800
Speaker 1: facial recognition has an inherent racial bias that I wish

861
00:49:04,880 --> 00:49:08,640
Speaker 1: more people talked about for non white people, for persons

862
00:49:08,640 --> 00:49:12,880
Speaker 1: of color. This stuff lags in accuracy, not just clearview,

863
00:49:12,880 --> 00:49:16,920
Speaker 1: but in general. Facial recognition is known for this. And

864
00:49:16,960 --> 00:49:19,759
Speaker 1: this means that you could be arrested due to a

865
00:49:19,800 --> 00:49:24,319
Speaker 1: computer error because the software decided that you look like

866
00:49:24,680 --> 00:49:26,960
Speaker 1: a guy you never met, who lives in a state

867
00:49:27,040 --> 00:49:30,040
Speaker 1: you've never been to, who committed a crime that occurred

868
00:49:30,080 --> 00:49:33,400
Speaker 1: while you were not alive, and it could happen. It

869
00:49:33,440 --> 00:49:34,200
Speaker 1: could happen here.

870
00:49:34,680 --> 00:49:38,160
Speaker 2: And to continue with that, we mentioned, you know, the

871
00:49:38,200 --> 00:49:42,000
Speaker 2: people that were known to be using this software, the organizations,

872
00:49:42,080 --> 00:49:44,719
Speaker 2: and two of them or three of them, let's just

873
00:49:44,760 --> 00:49:49,400
Speaker 2: point these these out Department of Justice, US Immigration and

874
00:49:49,480 --> 00:49:52,920
Speaker 2: Customs Enforcement. Let's just leave it to those two. So

875
00:49:53,400 --> 00:49:56,280
Speaker 2: take the problems you're talking about ben that are inherent

876
00:49:56,320 --> 00:49:59,960
Speaker 2: to this system. Apply that then to let's say, Immigration

877
00:50:00,040 --> 00:50:04,320
Speaker 2: and custom looking for people who are in the United

878
00:50:04,360 --> 00:50:09,080
Speaker 2: States illegally, and imagine that you're attempting to round everyone up.

879
00:50:10,080 --> 00:50:15,920
Speaker 2: The implications of this software would be extremely effective, I

880
00:50:15,920 --> 00:50:21,520
Speaker 2: would say, or completely ineffective because they're detaining and rounding

881
00:50:21,560 --> 00:50:25,360
Speaker 2: up people incorrectly because of the problems with the software.

882
00:50:25,960 --> 00:50:30,759
Speaker 1: Well think about this too, the problems with the software.

883
00:50:31,000 --> 00:50:33,399
Speaker 1: So they can pound on the state level, but they

884
00:50:33,440 --> 00:50:36,480
Speaker 1: can pound on the private level too, And this is

885
00:50:36,520 --> 00:50:40,920
Speaker 1: one that I think for the immediate future is at

886
00:50:41,000 --> 00:50:44,680
Speaker 1: least as dangerous as the Orwellian stuff we're talking about

887
00:50:44,760 --> 00:50:48,400
Speaker 1: right now. So that data, when it's mined right, just

888
00:50:48,480 --> 00:50:50,799
Speaker 1: like your data on any other social media, it can

889
00:50:50,840 --> 00:50:55,360
Speaker 1: be sold to third parties, insurance companies, financial institutions, and

890
00:50:55,400 --> 00:51:00,680
Speaker 1: so on. This goes way past ads the phrase. I

891
00:51:00,719 --> 00:51:02,839
Speaker 1: know I'm kind of churchifying here, but the phrase I

892
00:51:02,880 --> 00:51:07,680
Speaker 1: think of is a longitudinal profile of your face and

893
00:51:07,719 --> 00:51:11,959
Speaker 1: your behavior over time. So it's like watching a real

894
00:51:12,040 --> 00:51:17,879
Speaker 1: life progression of you aging. And let's say that an

895
00:51:17,880 --> 00:51:20,759
Speaker 1: insurance company or an algorithm could start to pick up

896
00:51:20,760 --> 00:51:24,120
Speaker 1: on certain medical conditions that are manifesting in your face

897
00:51:24,560 --> 00:51:28,719
Speaker 1: in very minuscule ways ways of human being, and even

898
00:51:28,760 --> 00:51:32,640
Speaker 1: a human doctor wouldn't sense. The computer picks this up

899
00:51:32,719 --> 00:51:35,359
Speaker 1: before you know anything's wrong, sort of like the way

900
00:51:35,480 --> 00:51:39,360
Speaker 1: targets algorithms figured out that poor girl was pregnant before

901
00:51:39,760 --> 00:51:41,720
Speaker 1: her family knew.

902
00:51:42,239 --> 00:51:43,920
Speaker 3: So here's the question.

903
00:51:44,680 --> 00:51:48,759
Speaker 1: Would the insurance company legally be required to notify you

904
00:51:49,160 --> 00:51:54,240
Speaker 1: of this possible health condition? Say, like, there's clearly something happening.

905
00:51:54,760 --> 00:51:57,239
Speaker 1: Maybe if they have video to in your behavior that

906
00:51:57,360 --> 00:52:03,239
Speaker 1: is indicative of an early on set terminal condition, would

907
00:52:03,239 --> 00:52:06,320
Speaker 1: they be legally required to tell you that they knew

908
00:52:06,760 --> 00:52:09,880
Speaker 1: you had a let's say, eighty percent chance of dying

909
00:52:10,120 --> 00:52:12,799
Speaker 1: in the next two years and give you the assistance

910
00:52:12,840 --> 00:52:15,520
Speaker 1: you need right now. That's a nope.

911
00:52:15,640 --> 00:52:18,080
Speaker 3: That is a big no. Chief. They don't have to

912
00:52:18,120 --> 00:52:19,839
Speaker 3: do a damn thing for you.

913
00:52:19,880 --> 00:52:23,600
Speaker 1: What they can do with absolutely no repercussions is say, hey,

914
00:52:23,640 --> 00:52:25,560
Speaker 1: this person's probably going to be dead in two years

915
00:52:25,560 --> 00:52:27,600
Speaker 1: and it's going to be expensive to get them to

916
00:52:27,560 --> 00:52:28,360
Speaker 1: a year two.

917
00:52:28,239 --> 00:52:31,319
Speaker 3: So let's just drop them. You, let's just drop them.

918
00:52:31,480 --> 00:52:35,640
Speaker 1: There's no law that, there's no like ethical legal requirement

919
00:52:36,280 --> 00:52:37,840
Speaker 1: for them to tell you what they know.

920
00:52:38,280 --> 00:52:39,520
Speaker 3: And that's that's.

921
00:52:40,960 --> 00:52:44,760
Speaker 1: Mind boggling, that is reprehensible. That is like I need

922
00:52:44,840 --> 00:52:47,720
Speaker 1: I need to get my good non abridged the source

923
00:52:47,800 --> 00:52:49,960
Speaker 1: to come up with all the different words for how

924
00:52:50,040 --> 00:52:51,440
Speaker 1: bad and disturbing that is.

925
00:52:51,880 --> 00:52:53,520
Speaker 3: Oh, ben, I would I love it when you do that,

926
00:52:53,560 --> 00:52:54,919
Speaker 3: because I always learn a new word.

927
00:52:57,520 --> 00:52:59,959
Speaker 1: Uh, did you guys ever just light in the mood?

928
00:53:00,400 --> 00:53:02,960
Speaker 1: Did you guys ever hear that old joke about the

929
00:53:03,000 --> 00:53:06,719
Speaker 1: abridged thesaurus. It's like I have an abridged thesaurus. Not

930
00:53:06,800 --> 00:53:12,279
Speaker 1: only is it terrible, it's terrible. So I didn't write that,

931
00:53:12,600 --> 00:53:15,359
Speaker 1: but you but you can. The good news is right now,

932
00:53:16,440 --> 00:53:20,279
Speaker 1: depending on where you live, you can opt out of Clearview.

933
00:53:20,800 --> 00:53:21,520
Speaker 3: Oh my gosh.

934
00:53:21,520 --> 00:53:25,680
Speaker 5: But even the way you opt out isn't like it's

935
00:53:25,719 --> 00:53:28,160
Speaker 5: such a burden. It's such a burdensome process. You can't

936
00:53:28,200 --> 00:53:31,120
Speaker 5: just click a box in an email, right you. All

937
00:53:31,120 --> 00:53:33,239
Speaker 5: you have to do is send a headshot. We all

938
00:53:33,280 --> 00:53:37,560
Speaker 5: have those and an image of your government issued ID.

939
00:53:38,480 --> 00:53:42,359
Speaker 5: And this only applies also to residents of California or

940
00:53:42,440 --> 00:53:44,600
Speaker 5: the EU.

941
00:53:43,960 --> 00:53:47,040
Speaker 3: For whatever reason, sorry, rest of the world.

942
00:53:46,840 --> 00:53:51,279
Speaker 2: Right because of privacy, because of privacy laws that exists there.

943
00:53:52,480 --> 00:53:55,719
Speaker 1: Yeah, we shall also point out, going back to this

944
00:53:55,800 --> 00:53:59,080
Speaker 1: accuracy thing there, there are serious questions about how accurate

945
00:53:59,560 --> 00:54:04,720
Speaker 1: Clearview is in general. So BuzzFeed, according to marketing literature

946
00:54:04,719 --> 00:54:08,880
Speaker 1: they found from Clearview, says that the company the company

947
00:54:08,920 --> 00:54:11,040
Speaker 1: touts the ability to find a match out of one

948
00:54:11,040 --> 00:54:13,880
Speaker 1: million faces ninety eight point six percent of the time.

949
00:54:14,280 --> 00:54:16,799
Speaker 1: But when Clearview did finally start talking to the New

950
00:54:16,880 --> 00:54:19,920
Speaker 1: York Times, they said, the tool produces a match up

951
00:54:19,960 --> 00:54:22,680
Speaker 1: to seventy five percent of the time, and we don't

952
00:54:22,719 --> 00:54:25,520
Speaker 1: know how many of those are quote unquote true matches.

953
00:54:25,640 --> 00:54:29,279
Speaker 1: So there's some contradictory informations coming out which can make

954
00:54:29,400 --> 00:54:34,320
Speaker 1: people feel uncomfortable, obviously, and that's that's where we leave today.

955
00:54:34,560 --> 00:54:37,560
Speaker 1: The battle lines are firming up. On one side, you

956
00:54:37,600 --> 00:54:41,040
Speaker 1: have Clearview, its investors, it's clients, and what I would

957
00:54:41,040 --> 00:54:45,560
Speaker 1: call the techno optimists. Right on the other side, you

958
00:54:45,560 --> 00:54:48,920
Speaker 1: have privacy advocates, you have tech giants like Microsoft IBM,

959
00:54:49,200 --> 00:54:51,880
Speaker 1: you even have the Pope. The Pope came out against

960
00:54:51,920 --> 00:54:55,279
Speaker 1: facial recognition and he summed it up pretty nicely. He said,

961
00:54:55,360 --> 00:54:59,160
Speaker 1: quote this asymmetry by which a select view know everything

962
00:54:59,200 --> 00:55:02,240
Speaker 1: about us while you know nothing about them Doll's critical

963
00:55:02,280 --> 00:55:05,080
Speaker 1: thought and the conscious exercise of freedom.

964
00:55:05,520 --> 00:55:10,799
Speaker 2: Imagine applying that to a priest who hears the confessions

965
00:55:10,800 --> 00:55:16,000
Speaker 2: from everyone every week when they go and tell their

966
00:55:16,040 --> 00:55:20,000
Speaker 2: dirty secrets. The priest is then the one who everybody

967
00:55:20,000 --> 00:55:24,520
Speaker 2: knows nothing about, but he knows everything about them, just

968
00:55:24,560 --> 00:55:27,960
Speaker 2: putting it out there, especially hey, and also this thing

969
00:55:28,040 --> 00:55:33,800
Speaker 2: is targeting child sex abuse and abusers. Just also leaving

970
00:55:33,800 --> 00:55:34,160
Speaker 2: that there.

971
00:55:34,680 --> 00:55:38,920
Speaker 1: Yeah, one other thing for anyone listening along and thinking,

972
00:55:39,880 --> 00:55:40,239
Speaker 1: good thing.

973
00:55:40,280 --> 00:55:40,839
Speaker 3: I made my.

974
00:55:40,960 --> 00:55:44,040
Speaker 1: Profile on insert social media here private years ago.

975
00:55:44,200 --> 00:55:45,000
Speaker 3: Sorry, homie.

976
00:55:45,160 --> 00:55:48,239
Speaker 1: Anything that was public at some point is pretty much

977
00:55:48,320 --> 00:55:50,560
Speaker 1: in the system, even if you later made it private.

978
00:55:50,600 --> 00:55:51,920
Speaker 3: So check your MySpace.

979
00:55:52,520 --> 00:55:55,759
Speaker 2: In that CNN Business interview, as you said, Ben, they

980
00:55:55,960 --> 00:55:58,560
Speaker 2: test out the software on the producer who has a

981
00:55:58,640 --> 00:56:03,560
Speaker 2: private Instagram account, and images from that account show up

982
00:56:03,600 --> 00:56:05,920
Speaker 2: in the search and it is because it was public

983
00:56:06,040 --> 00:56:06,760
Speaker 2: at one point.

984
00:56:07,480 --> 00:56:11,520
Speaker 1: And that's where we leave off today. What do you think,

985
00:56:11,600 --> 00:56:16,560
Speaker 1: fellow conspiracy realists? Do the benefits outweigh the potential consequences

986
00:56:16,560 --> 00:56:20,560
Speaker 1: of this? Do the consequences outweigh the potential benefits?

987
00:56:21,320 --> 00:56:21,840
Speaker 3: Let us know.

988
00:56:22,000 --> 00:56:23,880
Speaker 1: You can find us on Facebook, you can find us

989
00:56:23,880 --> 00:56:26,760
Speaker 1: on Instagram, you can find us on Twitter. We always

990
00:56:26,760 --> 00:56:29,280
Speaker 1: like to recommend our Facebook community page.

991
00:56:29,880 --> 00:56:32,160
Speaker 3: Here's where it gets crazy. Now.

992
00:56:32,200 --> 00:56:34,000
Speaker 2: You can jump on there. You can talk about this

993
00:56:34,080 --> 00:56:36,800
Speaker 2: episode or any other in the past. You can post

994
00:56:37,120 --> 00:56:40,840
Speaker 2: some dank memes, let's say, some conspiracy memes, or facial

995
00:56:40,920 --> 00:56:47,239
Speaker 2: recognition stuff. Maybe you've had access to this before, to clearview,

996
00:56:47,280 --> 00:56:48,560
Speaker 2: and you want to talk to us about it. Don't

997
00:56:48,600 --> 00:56:50,719
Speaker 2: do that on Facebook. Don't do that. But if you

998
00:56:50,719 --> 00:56:52,399
Speaker 2: do want to tell us about that, you can call

999
00:56:52,440 --> 00:56:57,480
Speaker 2: our number. We are one eight three three std WYTK.

1000
00:56:58,719 --> 00:57:00,640
Speaker 2: You can leave a message there, tell about it, let

1001
00:57:00,719 --> 00:57:02,640
Speaker 2: us know. If you don't want to be identified, if

1002
00:57:02,640 --> 00:57:06,560
Speaker 2: you don't want us to know or talk about anything

1003
00:57:06,640 --> 00:57:09,920
Speaker 2: on air, just just let us know, give us the information.

1004
00:57:11,160 --> 00:57:12,279
Speaker 2: We'd love to hear from you.

1005
00:57:12,680 --> 00:57:14,640
Speaker 1: And I want to add to this to say that

1006
00:57:15,520 --> 00:57:22,240
Speaker 1: I finally start despite my strange phobia regarding phones, I

1007
00:57:22,400 --> 00:57:26,479
Speaker 1: started diving in Matt You're doing massive, amazing work there,

1008
00:57:26,720 --> 00:57:30,360
Speaker 1: and I've started listening to these calls as well. Thank

1009
00:57:30,400 --> 00:57:34,080
Speaker 1: you so much to everyone who calls in. It's inspiring

1010
00:57:34,160 --> 00:57:36,160
Speaker 1: and I don't know about you. I don't know about

1011
00:57:36,200 --> 00:57:39,960
Speaker 1: you guys, but it makes me feel like what we're

1012
00:57:39,960 --> 00:57:42,480
Speaker 1: doing is worthwhile, if that makes sense.

1013
00:57:42,800 --> 00:57:46,880
Speaker 2: Oh, definitely, definitely. I've had you know. Hopefully you're gonna

1014
00:57:47,000 --> 00:57:49,840
Speaker 2: if you could get past the phobia, Ben, hopefully you

1015
00:57:49,880 --> 00:57:52,560
Speaker 2: can call a few people because you can use that

1016
00:57:52,600 --> 00:57:56,480
Speaker 2: app and you should be good to go actually speaking

1017
00:57:56,560 --> 00:58:00,440
Speaker 2: to to you know you know who you are. If

1018
00:58:00,480 --> 00:58:04,560
Speaker 2: we've talked on the phone, it means a great deal,

1019
00:58:04,600 --> 00:58:06,680
Speaker 2: as Ben is saying, just to us, to know that

1020
00:58:07,320 --> 00:58:11,000
Speaker 2: we're not just talking in a darkened room and that

1021
00:58:11,080 --> 00:58:13,880
Speaker 2: you guys, you guys care about us as much as

1022
00:58:13,880 --> 00:58:16,560
Speaker 2: we care about you. So this is a great relationship.

1023
00:58:16,640 --> 00:58:21,479
Speaker 1: Let's keep doing it well, said Matt Well said, And

1024
00:58:21,960 --> 00:58:24,560
Speaker 1: we'll be following up with some of those messages in

1025
00:58:24,600 --> 00:58:28,840
Speaker 1: the future, so keep them coming. One last thing, if

1026
00:58:28,840 --> 00:58:33,120
Speaker 1: you say, look, Ben's right, phones are terrifying and weird,

1027
00:58:33,760 --> 00:58:36,560
Speaker 1: but also social media. You guys just told me how

1028
00:58:36,640 --> 00:58:37,440
Speaker 1: dangerous that is.

1029
00:58:37,600 --> 00:58:40,760
Speaker 3: Why Why on earth? What gives how?

1030
00:58:40,880 --> 00:58:43,440
Speaker 1: I have a story that my fellow listeners need to

1031
00:58:43,520 --> 00:58:46,640
Speaker 1: know about. I have some I have some experience. I

1032
00:58:46,680 --> 00:58:49,080
Speaker 1: have some terrible jokes to tell you, but I don't

1033
00:58:49,160 --> 00:58:50,440
Speaker 1: know how to contact you.

1034
00:58:50,520 --> 00:58:53,320
Speaker 3: Well, we have good news for you.

1035
00:58:53,320 --> 00:58:56,840
Speaker 1: You can always twenty four to seven contact us at

1036
00:58:56,840 --> 00:58:58,880
Speaker 1: our good old fashioned email where we.

1037
00:58:58,760 --> 00:59:21,560
Speaker 2: Are conspiracy see at iHeartRadio dot com. Stuff they Don't

1038
00:59:21,560 --> 00:59:24,520
Speaker 2: want you to Know is a production of iHeartRadio. For

1039
00:59:24,600 --> 00:59:28,600
Speaker 2: more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts,

1040
00:59:28,680 --> 00:59:30,520
Speaker 2: or wherever you listen to your favorite shows,