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

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

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

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Speaker 1: production of I Heart Radio. Hello, welcome back to the show.

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Speaker 1: My name is Matt, my name is not They call

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Speaker 1: me Ben. We're joined as always with our super producer

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Speaker 1: Paul Mission Control Decond. Most importantly, you are you. You

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Speaker 1: are here, and that makes this the stuff they don't

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Speaker 1: want you to know. It's the top of the week.

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Speaker 1: It's a strange week, so what better time for some

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Speaker 1: strange news. Alex Jones is back in hot water. We'll

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Speaker 1: see how that shakes out. Steve Benden might actually have

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Speaker 1: to answer for some things in court. We'll see how

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Speaker 1: that shakes out. But this is news you have probably

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Speaker 1: already heard about. And that's not the point of strange news.

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Speaker 1: We want to bring you the weird stuff from the

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Speaker 1: dark corners of the Internet. We want to bring you

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Speaker 1: the stuff that, if you looked on a map of

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Speaker 1: the news, would be from an area saying here be serpents. UH.

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Speaker 1: Today we're diving into some stories from the world of technologies,

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Speaker 1: stories that UH may frighten you stories that may inspire

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Speaker 1: you if you are a more vigilante minded person, and UM,

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Speaker 1: one story we really want to see what you think about.

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Speaker 1: And it may not be appropriate for everybody, but I

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Speaker 1: believe the three of us are are quite excited as

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Speaker 1: always to hear your opinions. So stay tuned. We'll give

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Speaker 1: you a heads up for that last one as it

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Speaker 1: may as it definitely is not appropriate for all listeners.

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Speaker 1: But let's let's start with the scary stuff. So a

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Speaker 1: lot of people, your faithful correspondence included, have been working

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Speaker 1: from home for the majority of the pandemic. Many many

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Speaker 1: people have UM. At first it was a safety measure

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Speaker 1: for a lot of folks, and then eventually for many

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Speaker 1: folks it evolved into a personal preference. I mean, before

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Speaker 1: we begin, I think that's that's a good place to

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Speaker 1: check in. UM. We don't talk about it too much

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Speaker 1: with each other because we hang out all the time.

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Speaker 1: But what has you guys experienced been working from home? Primarily?

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Speaker 1: I know we've all been popping in the office every

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Speaker 1: so often. Yeah, I do like the occasional office pop

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Speaker 1: just to change things up. And they've been restocking the

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Speaker 1: snacks again, which is a silent civilization is returning to

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Speaker 1: normal it is. I don't mind it. I think we've

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Speaker 1: gotten used to the flow of doing things this way. Um.

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Speaker 1: I do like doing it in person every now and again,

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Speaker 1: but I really quite enjoy I mean, I have a

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Speaker 1: separate space, so I think that makes a big difference,

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Speaker 1: because in my last house I did not, and that

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Speaker 1: was not fun at all, which is why I moved.

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Speaker 1: But in general I'm good with it. Yeah. I think

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Speaker 1: setting my own hours has become a new thing. It's

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Speaker 1: always kind of been a thing for us at this company,

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Speaker 1: but actually having a place that I can walk a

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Speaker 1: couple of steps to and get some work done rather

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Speaker 1: than drive, you know, in my case it was fifteen

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Speaker 1: to thirty minutes to a place then come home. It's

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Speaker 1: really changed my ability to to work on different kinds

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Speaker 1: of hours. Um, get well and seriously and and actually

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Speaker 1: it's weird and hopefully I'm not going to get in

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Speaker 1: trouble for saying this, but to be able to take

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Speaker 1: an hour during the day to actually get an errand done,

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Speaker 1: like go to a place that's not open when I'm

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Speaker 1: usually not working, and then work an extra hour later

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Speaker 1: in the night or something. Um, I think a lot

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Speaker 1: of people have been taking advantage of that. It's just

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Speaker 1: the question is how much of an advantage is an

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Speaker 1: individual taking with their workday hours. That's where employers are asking, Yeah,

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Speaker 1: there we go. But we're in a results driven space, right,

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Speaker 1: not sound to you know, producerly. I mean, I think

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Speaker 1: it's going to be clear to our employers if we're

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Speaker 1: not pulling our way. Uh So, at the end of

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Speaker 1: the day, no one's police saying exactly how we spend

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Speaker 1: every second of our time, which I think for some people,

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Speaker 1: continuing to work from home is a problem because there's

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Speaker 1: all this kind of nanny state technology that's being implemented

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Speaker 1: that's policing how many clicks people are logging on their

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Speaker 1: keyboards and like, you know, all that kind of stuff.

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Speaker 1: To me, that would be a nightmare. I'd rather just

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Speaker 1: go to the damn office if that was happening. But

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Speaker 1: we're very lucky that's not the case for us as

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Speaker 1: far as we know not to be too or welly

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Speaker 1: inter paranoid. Yeah. For me personally, um, I I prefer

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Speaker 1: it because I keep late hours. I don't sleep as

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Speaker 1: much as um the average person. So uh my my

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Speaker 1: schedules all over the place, and I quite enjoy it. Um.

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Speaker 1: We've known we're a special case. Probably we have known

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Speaker 1: for a long time that if anybody bothered to check

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Speaker 1: our search histories on our work machinery, uh, they would

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Speaker 1: they would find some dark, disturbing things because of the

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Speaker 1: nature of our vocation. But I love what I love

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Speaker 1: the way that we are segueing here to the big,

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Speaker 1: big fear. Uh. Someone recently told me something quite insightful.

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Speaker 1: They said, when you are working from home, you're not

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Speaker 1: really what what what's happening is that you're just now

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Speaker 1: having your job invade your home. And for millions of

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Speaker 1: people around the world, some of whom are listening today,

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Speaker 1: that's not just uh, you know, a thought experiment anymore.

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Speaker 1: And this leads us to the story that you dug up, Matt, which,

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Speaker 1: to your credit, my old dear friend, is something that

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Speaker 1: you have been talking about since like the week we've

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Speaker 1: been met, all those years ago. I guess it's true.

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Speaker 1: We did, yeah, and it was when we were originally

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Speaker 1: searching for the weird stuff that, as you said, shows

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Speaker 1: up in our search histories, and the why the websites

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Speaker 1: that we end up on uh for way too long

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Speaker 1: because we have to read the entire thing. Sometimes other

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Speaker 1: parts of that website are probably not safe for work,

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Speaker 1: but hey, too bad, that's what we do. So with

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Speaker 1: all with this in mind, the concept of work invading

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Speaker 1: our home, it's the question is is it just your

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Speaker 1: work that is now being done at your home or

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Speaker 1: is it somebody from your work or something from your

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Speaker 1: work that is physically looking at what you're doing, watching

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Speaker 1: like like Noel said, key strokes that you type in clicks,

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Speaker 1: maybe the number of clicks, maybe even having access to

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Speaker 1: your camera or your microphone. That would be a little

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Speaker 1: disturbing to imagine that somebody in some group at your

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Speaker 1: office is keeping track of that. Well, that's what the

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Speaker 1: Joint Research Councils and the European Commission and all kinds

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Speaker 1: of other things like the UK's All Party Parliamentary Group,

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Speaker 1: they want to know. They're looking into it. They're interested

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Speaker 1: because there's lots and lots of concern from the collective

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Speaker 1: of individuals who are aware of this kind of technology

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Speaker 1: existing um and they put out some reports and that's

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Speaker 1: what we're talking about today. You can go to the

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Speaker 1: Register dot com and you can read an article titled

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Speaker 1: Workplace surveillance booming during pandemic, destroying trust in employers. That's

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Speaker 1: trust in employers. Uh, the the trust of the employee

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Speaker 1: is what's being destroyed here. Um. Okay, So if you

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Speaker 1: if you go through this, you can read about the

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Speaker 1: Joint Research Councils who have put out a report titled

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Speaker 1: Electronic Monitoring and Surveillance in a Workplace. This is something

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Speaker 1: you can access. It's something you can download in PDF format.

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Speaker 1: You can find it, especially if you go to that

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Speaker 1: Register article. You can just click through and actually look

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Speaker 1: at the thing. You can see an abstract to download

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Speaker 1: the sucker and within this report. Guys, it's way more

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Speaker 1: prevalent than I thought. We've discussed before the monitoring of

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Speaker 1: the kind of the gig economy, just by the sheer

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Speaker 1: nature of what that means. Right, It's a task that

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Speaker 1: goes into a marketplace. Then someone from the gig economy

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Speaker 1: comes in and picks up that task. So the person

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Speaker 1: who sent the task in wants to know what's happening.

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Speaker 1: You know, how of these ten steps, which one are

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Speaker 1: we at right now? How long has it taken? How

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Speaker 1: efficient is this? Um? Now that one person from the

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Speaker 1: gig economy, we can now rate that person depending on

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Speaker 1: how well they did with that one task, and we

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Speaker 1: can predict how they will function on the next task

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Speaker 1: that they're assigned, right, and so on and so forth,

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Speaker 1: building an entire structure of just people trying to get

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Speaker 1: things done because everybody needs to make a little bit

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Speaker 1: of money or as much money as we can. Right,

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Speaker 1: got to get those coupons, Man, who gets those coupons

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Speaker 1: before you die? You know what I mean? Neck and neck?

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Speaker 1: That's right. So you can imagine something we've discussed before

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Speaker 1: on this podcast very recently, Ben, I believe we just

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Speaker 1: talked about Amazon's mechanical Turk system, which which is the

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Speaker 1: thing that they have in place, uh that monitors that

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Speaker 1: very thing, right, how well is an employee doing. The

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Speaker 1: only problem is this is human directed AI functional technology,

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Speaker 1: where it is a computer that is actually looking at

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Speaker 1: all of the data and then sometimes making a decision

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Speaker 1: about how well someone is performing. And as is the

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Speaker 1: case there with some uh some workforces, the AI is

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Speaker 1: actually making the decision to send an email to an

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Speaker 1: actual human employee that says you are no longer needed here. Uh.

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Speaker 1: That is not always the case with many of these

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Speaker 1: because we're also talking about Uber, we're talking about up work,

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Speaker 1: We're talking about lots and lots of different companies and systems. Right,

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Speaker 1: So that so that exists, right, that whole thing monitoring

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Speaker 1: how someone within the gig economy is functioning and like

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Speaker 1: really looking at what everything they're doing while they're on

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Speaker 1: the job. The other thing is employees like us Ben

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Speaker 1: Noel and myself and Paul and alexis looking at us

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Speaker 1: and seeing what we're doing on a day to day

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Speaker 1: basis by monitoring things like Microsoft Teams and Google Workspace,

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Speaker 1: hen Slack and checking to see how often we are responding,

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Speaker 1: how long it takes for us to respond to an email,

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Speaker 1: let's say, how many different meetings we have, what our

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Speaker 1: schedules look like, keeping track of when we are logged in,

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Speaker 1: if our computer is actually on, or if our screen

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Speaker 1: is locked. It's insane the amount of stuff that can

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Speaker 1: be tracked and looked at from somebody who essentially holds

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Speaker 1: the keys to one of one of those enterprise accounts. Sorry,

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Speaker 1: it's just really freaks me up. Yeah, it's super freaky.

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Speaker 1: I don't know about you guys, but I mean do

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Speaker 1: a decent amount of work on my personal phone. I

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Speaker 1: research a lot on my phone, my personal iPad. I

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Speaker 1: do emails on my phone, I do our inter company chat.

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Speaker 1: I don't really go into my office or my studio

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Speaker 1: unless I'm needing to do stuff that requires recording or

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Speaker 1: full screen situation or like I mean, I often will,

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Speaker 1: you know, kind of split my time between several different devices, Uh,

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Speaker 1: some of which belong to me. And you know, we

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Speaker 1: use Microsoft Teams, but not like a lot. It doesn't

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Speaker 1: I don't know, like I just do the companies have

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Speaker 1: to acknowledge that they're doing this or is it just

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Speaker 1: something that's been kind of discovered a little by little.

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Speaker 1: Often in your contract and it will be a simple,

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Speaker 1: like a simple paragraph that just states the company the

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Speaker 1: company reserves the right to monitor what you do with

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Speaker 1: the equipment that it gives you. Yeah, and this is

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Speaker 1: this is more important for some industries than others. Consider

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Speaker 1: industries wherein individual employees may generate a lot of patents

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Speaker 1: or new research issue, or an engineer who creates something

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Speaker 1: new and innovative and you did it on the company

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Speaker 1: machine with company equipment, then of course they're gonna want

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Speaker 1: you know, they're gonna want their big uh, and uh,

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Speaker 1: they rightly deserve it, But yeah, it's usually lumped in

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Speaker 1: in a contract. If you are going to be quote

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Speaker 1: unquote told, it's going to be lumped in with that

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Speaker 1: kind of statement, Which is like the reason why if

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Speaker 1: I if I, for instance, this is the opposite of

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Speaker 1: something productive like engineering. But if I, for instance, wrote

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Speaker 1: a novel on work machine and it became like the

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Speaker 1: next Song of Ice and Fire or something, then there's

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Speaker 1: a pretty valid legal argument that could be made if

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Speaker 1: our job were to be kind of evil, that they

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Speaker 1: own a piece of it because these weren't their ideas,

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Speaker 1: but these were their pieces of equipment. This is real,

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Speaker 1: the camera thing especially, I believe in the uk UM

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Speaker 1: first Microsoft will snitch at you at work like never before.

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Speaker 1: Like Matt said, you got no idea, but I know

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Speaker 1: that in the UK UM pretty pretty comprehensive study or

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Speaker 1: pretty thorough study found that camera technology in particular had exploded.

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Speaker 1: Six months ago, about five per cent of people were

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Speaker 1: being monitored if they were working from home by their

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Speaker 1: company via camera. Now that number is attent and uh

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Speaker 1: and now in the UK thirty two percent of people

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Speaker 1: are being monitored in some way if not cameras, and

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Speaker 1: it isn't the takeaway. Have some of this this probe

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Speaker 1: that like this is bad, this is actually anti productive? Yeah, yeah,

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Speaker 1: because all of this is a way for the employer

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Speaker 1: to see which employees are doing great, which ones need improvement,

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Speaker 1: which ones are going to get those sweet bonuses for

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Speaker 1: Christmas and Boxing Day, which ones are you know, maybe

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Speaker 1: on the outs or they need a performance enhancement plan

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Speaker 1: or whatever the hell companies call something whatever. Yeah, I mean,

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Speaker 1: it's just like that's the concept, right, But when each

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Speaker 1: individual human employee knows that this kind of thing is happening,

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Speaker 1: it may in fact make our productivity take a hit,

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Speaker 1: because it's almost like there's there Oh no, it's not.

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Speaker 1: Almost like there is a resentment towards that kind of

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Speaker 1: helicopter parenting that's being done through our via our computers,

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Speaker 1: by our computers. Um. And you know, we're in a

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Speaker 1: movement right now. Keep thinking back to several voicemails that

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Speaker 1: we've gotten. We're in a movement right now where people

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Speaker 1: are choosing not to go back to these specific office

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Speaker 1: jobs for various reasons. But you know, this is just

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Speaker 1: you can just add this one to the to the

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Speaker 1: mix tape of the reasons that people don't want to

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Speaker 1: do that crap anymore. Um. And I mean again, we're

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Speaker 1: really lucky us in particular in our type of industry

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Speaker 1: because it is results driven. We have specific metrics that

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Speaker 1: we have to meet. We have certain pieces of content

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Speaker 1: that were scheduled to produce every single week, and until

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Speaker 1: the moment we like fail to deliver, no one's really

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Speaker 1: gonna mess with us. At least that's my perhaps naive perception.

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Speaker 1: But to me, that's I kind of bank on that. Uh.

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Speaker 1: And it makes me feel like that's fair, I mean,

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Speaker 1: of course, but it also to me from the perspective

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Speaker 1: of like, you know, we oversee people guys were executive

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Speaker 1: producers and we'd work with teams, and I wouldn't want

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Speaker 1: anybody on my team to feel like I'm up there,

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Speaker 1: you know, up in their business and that way. Um.

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Speaker 1: In fact, Portugal as a country just passed a law

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Speaker 1: that makes it illegal for a boss to text or

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Speaker 1: call an employee after hours. Um. And I think that's

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Speaker 1: that's something that's a little more empowering for the employee

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Speaker 1: than this kind of crap. Well, that's really agree. That's

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Speaker 1: that's work life balanced stuff, right, Um. And that's that's

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Speaker 1: the kind of thing that makes a job enjoyable or not.

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Speaker 1: If you know, you can turn the thing off and

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Speaker 1: go on with your life. And I think, I mean, yes,

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Speaker 1: I think that's generally true for most people, but for

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Speaker 1: me personally, UM, I like, uh, I don't know. Well,

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Speaker 1: I'm probably the worst example, but I'm pretty into this stuff,

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Speaker 1: like I kind of I kind of live it, so

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Speaker 1: I want to know what happens when it happens. I

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Speaker 1: don't save stuff for this win back points, right, yeah,

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Speaker 1: and that's your Yeah. And so what Portugal is saying is, uh,

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Speaker 1: it quickly can become abusive, which is absolutely true, and

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Speaker 1: that should be a matter of personal choice. No, we

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Speaker 1: also have to point out for anybody who is listening

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Speaker 1: is not currently in the US or familiar with it. Uh,

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Speaker 1: the US is policy towards things like vacation is is

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Speaker 1: it's cartoonish, you know what I mean. My friends in

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Speaker 1: Europe are horrified when they hear the way vacations or

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Speaker 1: things like paternity or maternity or parental leave work. So

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Speaker 1: this is probably even more of a necessary law in

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Speaker 1: the US than it is in Portugal. But still, but still,

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Speaker 1: it's it's the future and the monitoring thing is you know,

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Speaker 1: it's funny that it not ha ha funny, but it's

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Speaker 1: it's odd that it becomes a contentious issue when we're

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Speaker 1: talking about workplace monitoring or quote unquo workplace monitoring, because

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Speaker 1: it's only another extension, a smaller extension of the constant

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Speaker 1: surveillance and monitoring that's occurring regardless of what laws are

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Speaker 1: on the books. Cointelpro never ended. Uh. Mass surveillance is

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Speaker 1: just too powerful to not do it. It's the hottest thing. Yep.

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Speaker 1: I yes, And I know we could. I could talk

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Speaker 1: about this for a long time. I know you could too.

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Speaker 1: I want to leave you with a couple of statements

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Speaker 1: from the actual report that we mentioned at the top

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Speaker 1: here that this registered article is referring to, just to

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Speaker 1: kind of leave us with this, have us think about it,

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Speaker 1: and we can hear hopefully from you just what you've experienced,

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Speaker 1: perhaps when it comes to surveillance in your own workplace.

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Speaker 1: UM here it is. Then you're gonna love one of

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Speaker 1: these statements because it's basically some it's your mantra like

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Speaker 1: when that it adds been every time we talk about

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Speaker 1: anything like this, it's in this report here we go

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Speaker 1: quote pervasive monitoring. Target setting technologies in particular are associated

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Speaker 1: with pronounced negative impacts on mental and physical well being

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Speaker 1: as workers experience the extreme pressure of constant, real time

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Speaker 1: micromanagement and automated assessment. AI is transforming work and working

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Speaker 1: lives across the country in ways that have plainly outpaced

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Speaker 1: or avoid the existing regimes for regulation. With increasing reliance

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Speaker 1: on technology to drive economic recovery at home and provide

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Speaker 1: a leadership role abroad, it is clear that the government

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Speaker 1: must bring forward robust proposals for AI regulation to meet

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Speaker 1: these challenges. So they're saying, what is it? Then technology

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Speaker 1: always outpaces legislation and uh, you know that's the UK

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Speaker 1: saying we got to do something about this. We know

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Speaker 1: we do, but right, let's let's form some committees to

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Speaker 1: figure out the meetings. You know what I mean? That

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Speaker 1: is um an accurate quote, And we'll see, We'll see again,

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Speaker 1: speaking of mantras, we'll see again. Before civilization gets to

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Speaker 1: a post work environment, there is a very high probability

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Speaker 1: that it will become what is called a post worker environment,

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Speaker 1: meaning that the robots and the AI can do amazing things,

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Speaker 1: or the machine consciousness can do amazing things for people

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Speaker 1: but no one can afford to engage with it because

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Speaker 1: no one has a job anymore. Imagine the story, John

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Speaker 1: Henry writ large for almost eight billion people at once.

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Speaker 1: That's cool, let's hang out in the metaverse. Some play

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Speaker 1: pinogle with our robot pals. Yeah, we're a lot of parties,

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Speaker 1: all right with that? Imagine the party where it is

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Speaker 1: the three of us and you, what are you gonna

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Speaker 1: say about this stuff? What are you gonna say? Send

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Speaker 1: it our way? Right now, we're gonna take a quick break.

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Speaker 1: Her word from our sponsor, and we'll be right back

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Speaker 1: with more strange news. And we're back with more strange news. Um,

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Speaker 1: I am here to talk about a story that is

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Speaker 1: definitely in line with some of the ones that I've

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Speaker 1: been bringing lately. I'm very fascinated with this new wild

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Speaker 1: West that is the cryptocurrency space, you know, and all

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Speaker 1: of the decentralized finance projects and n f t s

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Speaker 1: and all of that. I think it's interesting on a

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Speaker 1: couple of levels. One that it sort of signals this

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Speaker 1: weird new kind of gray area of the law when

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Speaker 1: it comes to finance that and has been mentioned, technology

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Speaker 1: will always outpace the law. Um, there was just talk

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Speaker 1: of passing a law in three that will allow the

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Speaker 1: government to tax and f t s those non fungible

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Speaker 1: tokens UM, which is a big deal because people were

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Speaker 1: looking at that as a kind of like a free

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Speaker 1: money tax loophole. Uh. Sorry, guys, not gonna be the case. Though.

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Speaker 1: This is probably gonna be decided more in the courts

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Speaker 1: than it is with like one instant law, because there's

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Speaker 1: gonna be new ways around it with technology. UM. Today

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Speaker 1: I'm not talking about that, though I just wanted to

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Speaker 1: mention it. Today, I'm talking about the rise of cryptocurrency

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Speaker 1: scams uh that are getting more and more elaborate and bizarre. UM.

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Speaker 1: I've got kind of two stories. The second one will

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Speaker 1: just be like a quick kind of follow up to

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Speaker 1: the first one. But the first story I want to

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Speaker 1: talk about is this particular scam that is like something

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Speaker 1: out of a horror movie. Honestly. UM. Vice reported on

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Speaker 1: this scam where individuals are hacking people's Instagram accounts UM

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Speaker 1: and using them to communicate with people in their close

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Speaker 1: friends circle. Uh in very you know conversation language, saying

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Speaker 1: hey buddy, long time UM, but been missing you. Hey,

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Speaker 1: I gotta tell you. I just found out about this

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Speaker 1: incredible opportunity where you can spend five dollars on ethereum

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Speaker 1: and you get ten thousand dollars back. Isn't that insane?

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Speaker 1: It's real and legit bro totes um. And then to that,

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Speaker 1: you're you're like, you know, I would probably do it.

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Speaker 1: I might look into it a a little bit or have

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Speaker 1: a little back and forth. But you know, you'd be

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Speaker 1: more likely to be taken in by someone reaching out

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Speaker 1: to you as a friend giving you an inside scoop.

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Speaker 1: Then you would be, you know, from some cold call

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Speaker 1: or some kind of like email phishing scam or whatever.

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Speaker 1: Like nobody reads their emails anymore and friends very rarely

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Speaker 1: email each other conversational It would be something you do

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Speaker 1: through Instagram or or WhatsApp or you know, any kind

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Speaker 1: of peer to peer chat. But here's the creepy part. Um.

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Speaker 1: What they're doing is the people are investing the money,

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Speaker 1: they're giving them instructions how to do it, and then

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Speaker 1: realizing it's a scam. Oh crap, that wasn't Johnny at all,

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Speaker 1: that was some kind of sock puppet or some kind

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Speaker 1: of bought And so then the this person, Yerry Henfield,

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Speaker 1: who is a victim of the scam reached out to

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Speaker 1: the person who had clearly taken control of this friend's

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Speaker 1: Instagram and said, hey, what gives totally messed up? Not cool, bro,

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Speaker 1: give you my money back, to which the hacker responds, Okay,

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Speaker 1: I'll give you your money back, but first you have

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Speaker 1: to make a video where you sound super straight faced

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Speaker 1: and sincere promoting my crypto scam. But I am then, yeah,

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Speaker 1: like a hostage style video of you saying I am

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Speaker 1: so and so and I am of sound mind. The

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00:24:45,160 --> 00:24:47,520
Speaker 1: date is blah blah blah, and I think that this

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00:24:47,720 --> 00:24:50,480
Speaker 1: crypto No, no, no, you seem too shell shocked. Dude,

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Speaker 1: Do it again, Do it again, do it again til

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Speaker 1: it's right, and then I will give you your money back.

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Speaker 1: Oh yeah, and when I give your money back, it'll

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Speaker 1: be conf firms to you via a text that will

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Speaker 1: come to your phone that you need to accept. Okay,

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00:25:06,440 --> 00:25:09,560
Speaker 1: it sounds good. What do you think that text going

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Speaker 1: through the phone was. It was one of those third

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Speaker 1: factor authentication texts that allowed this person access to change

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Speaker 1: the individual's Instagram passwords and repeat no, rinse and repeat

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Speaker 1: cheezd Isn't that sinister? Yea? That's uh this guy Hen's Field. Um,

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Speaker 1: he got scammed, you told Motherboard, uh quote, these people

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Speaker 1: are the definition of sin. It makes me so sick

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Speaker 1: thinking who else they scammed. My Instagram page is also

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Speaker 1: a shrine to my girlfriend who passed away almost six

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Speaker 1: years ago. The page is now a disgrace and it's

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Speaker 1: you know, it's not something that has been shut down yet.

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Speaker 1: No one has uh has fully figured out who the

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Speaker 1: individual is. Let's see, unbeknownst to me, Um, you know

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Speaker 1: is this is this is the part um where they

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Speaker 1: realized what was happening with the text. Unbeknownst to me,

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Speaker 1: it was my Instagram request to gain access and change

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Speaker 1: my password. And then the scammer takes that video and

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Speaker 1: post it as a story to the compromised Instagram account,

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Speaker 1: to which you know, individuals and that person's friend group

429
00:26:21,240 --> 00:26:27,040
Speaker 1: see that and follow up potentially um. And the article

430
00:26:27,119 --> 00:26:29,480
Speaker 1: goes on to say the hacker um had gone from

431
00:26:29,680 --> 00:26:32,560
Speaker 1: not only taking money from Hinsfield, but using his own

432
00:26:32,600 --> 00:26:36,000
Speaker 1: Instagram account and connections to continue the scam on down

433
00:26:36,040 --> 00:26:38,800
Speaker 1: the line. Um. Even though the video was only were

434
00:26:38,800 --> 00:26:41,240
Speaker 1: twenty four hours, it did seem to have converted a

435
00:26:41,240 --> 00:26:43,360
Speaker 1: few people. He says he doesn't know why they took

436
00:26:43,359 --> 00:26:45,960
Speaker 1: it down. Maybe it wasn't so convincing. But stories only

437
00:26:46,040 --> 00:26:47,960
Speaker 1: last for twenty four hours, so I think I don't

438
00:26:47,960 --> 00:26:52,840
Speaker 1: know if he's confused. Um. But even the original like

439
00:26:52,920 --> 00:26:55,800
Speaker 1: patient zero account that they traced this back to Jane

440
00:26:55,840 --> 00:27:00,159
Speaker 1: and uh Jane everything. Uh, they realized even that was

441
00:27:00,200 --> 00:27:05,879
Speaker 1: a hijacked account. Um. And this is something that's generating

442
00:27:06,119 --> 00:27:10,280
Speaker 1: lots and lots of money. Um, and it's just gonna

443
00:27:10,320 --> 00:27:13,560
Speaker 1: get more and more elaborate and more you know, hard

444
00:27:13,640 --> 00:27:16,520
Speaker 1: to track. And these are these are not you know,

445
00:27:17,359 --> 00:27:20,520
Speaker 1: non tech savvy people that are getting you know, scammed

446
00:27:20,560 --> 00:27:23,399
Speaker 1: by this. These are like zoomers and and uh you know,

447
00:27:23,640 --> 00:27:26,560
Speaker 1: um gen x folks that are like pretty savvy about

448
00:27:26,560 --> 00:27:30,120
Speaker 1: this stuff. Um. Even if someone who isn't like into

449
00:27:30,160 --> 00:27:33,840
Speaker 1: crypto at all and they hear someone that they know saying, hey, dude,

450
00:27:33,880 --> 00:27:36,080
Speaker 1: I spend five hundred bucks, you get a thousand bucks back.

451
00:27:36,119 --> 00:27:38,800
Speaker 1: It's a trusted source who wouldn't give it a shot.

452
00:27:39,400 --> 00:27:40,440
Speaker 1: And then at the end of the day it's just

453
00:27:40,480 --> 00:27:42,919
Speaker 1: five bucks that you've lost. That's like not nothing. That's

454
00:27:42,960 --> 00:27:44,920
Speaker 1: certainly a lot of money too many folks. It's a

455
00:27:44,920 --> 00:27:48,840
Speaker 1: lot of money to me, UM probably not worth, you know,

456
00:27:49,920 --> 00:27:52,960
Speaker 1: going to the mattress is over. Well, it's also not

457
00:27:53,040 --> 00:27:56,280
Speaker 1: worth taking money out of the proverbial mattress. I don't

458
00:27:56,320 --> 00:27:58,479
Speaker 1: mean to sound like a jerk. I think the world

459
00:27:59,000 --> 00:28:02,679
Speaker 1: of UM most of my friends and family, but I

460
00:28:04,440 --> 00:28:06,879
Speaker 1: very short list of people that I would take financial

461
00:28:06,920 --> 00:28:12,000
Speaker 1: advice from, and would would prefer that to be completely transactional.

462
00:28:12,320 --> 00:28:14,919
Speaker 1: I want to want to pay a pro instead of

463
00:28:14,960 --> 00:28:19,200
Speaker 1: instead of here and here in the next hot drift UM.

464
00:28:19,359 --> 00:28:24,040
Speaker 1: But sometimes sometimes it does work out. Our very good

465
00:28:24,080 --> 00:28:27,680
Speaker 1: friend Casey Pegram, friend of the show, if you listen

466
00:28:27,680 --> 00:28:29,800
Speaker 1: in Ridiculous History, you know him, you love him, if

467
00:28:29,800 --> 00:28:31,719
Speaker 1: you've heard him on a couple of other things, very

468
00:28:31,920 --> 00:28:35,320
Speaker 1: friend of ours. He was right about bitcoin. There's no

469
00:28:35,400 --> 00:28:38,240
Speaker 1: walking away from that one. Casey nailed it. But that

470
00:28:38,440 --> 00:28:41,480
Speaker 1: is that is an exception, and you're right. The leveraging

471
00:28:41,520 --> 00:28:45,440
Speaker 1: of trust is so very effective. That's because it's it's

472
00:28:45,440 --> 00:28:49,200
Speaker 1: social engineering, That's what it is. It's just on a

473
00:28:49,280 --> 00:28:54,200
Speaker 1: new platform. I would strongly advise UM, and very rarely

474
00:28:54,240 --> 00:28:59,000
Speaker 1: do advice, but I would strongly advise assuming that any

475
00:28:59,160 --> 00:29:03,240
Speaker 1: link you get sent as a cold call link is

476
00:29:03,240 --> 00:29:05,840
Speaker 1: a scamp. Just think of it that way and then

477
00:29:06,560 --> 00:29:09,239
Speaker 1: contact him another way if you think it's legit, like

478
00:29:09,280 --> 00:29:13,400
Speaker 1: a completely different way. And asked them about the link

479
00:29:13,480 --> 00:29:15,200
Speaker 1: and asked them if they can resend it and if

480
00:29:15,200 --> 00:29:18,320
Speaker 1: it's literally they will. And my mom even you know,

481
00:29:18,360 --> 00:29:20,440
Speaker 1: my mom is is older, she's in her seventies, and

482
00:29:20,480 --> 00:29:23,680
Speaker 1: it's not good at computers. Um. She doesn't have an

483
00:29:23,680 --> 00:29:26,440
Speaker 1: Amazon Prime account. But she got a telephone call saying

484
00:29:26,480 --> 00:29:29,640
Speaker 1: that she owed thousands of dollars on Amazon Prime or

485
00:29:29,640 --> 00:29:33,120
Speaker 1: that someone was you know, using her Amazon Prime account.

486
00:29:33,280 --> 00:29:35,080
Speaker 1: And she called me and asked him that. I'm like, Mom,

487
00:29:35,120 --> 00:29:37,480
Speaker 1: you don't have an Amazon Prime account. You use mine?

488
00:29:37,840 --> 00:29:40,960
Speaker 1: Like she just doesn't have it. So it's not a thing. Um,

489
00:29:41,000 --> 00:29:43,320
Speaker 1: but that you know, it's like older generations are being

490
00:29:43,320 --> 00:29:46,000
Speaker 1: scanned with the phone because they still kind of trust

491
00:29:46,040 --> 00:29:49,400
Speaker 1: the phone. You know, people in our generation couldn't trust

492
00:29:49,440 --> 00:29:52,840
Speaker 1: anything less. I mean, I I am like suspicious when

493
00:29:52,880 --> 00:29:55,360
Speaker 1: I get a phone call. You know, I think most

494
00:29:55,360 --> 00:29:57,520
Speaker 1: of us feel that way. Everyone's like, you know, just

495
00:29:57,640 --> 00:30:00,840
Speaker 1: texting or or social media. Um. But here's my follow

496
00:30:00,920 --> 00:30:02,600
Speaker 1: up really quickly. It's gonna wrap it up with some

497
00:30:02,640 --> 00:30:07,120
Speaker 1: good news. The good news is that UM the blockchain,

498
00:30:07,200 --> 00:30:10,520
Speaker 1: which is the you know, open source record that tracks

499
00:30:10,560 --> 00:30:14,280
Speaker 1: all of these transactions. It it does just that it

500
00:30:14,440 --> 00:30:18,120
Speaker 1: is like a breadcrumb trail of all of these transactions

501
00:30:18,160 --> 00:30:20,520
Speaker 1: that if you know, in the right hands, through the

502
00:30:20,600 --> 00:30:24,960
Speaker 1: right lens, can lead to the perpetrator. And there are

503
00:30:25,040 --> 00:30:28,000
Speaker 1: folks or a community of folks on Twitter that are

504
00:30:28,040 --> 00:30:32,680
Speaker 1: like you know, becoming these modern day digital vigilantees who

505
00:30:32,680 --> 00:30:38,680
Speaker 1: are hunting down and exposing these crypto scammers. One digitalantes,

506
00:30:38,800 --> 00:30:42,320
Speaker 1: can we do digital? That was a missed opportunity. Thank

507
00:30:42,360 --> 00:30:45,600
Speaker 1: you Ben for for for saving that one. UM one

508
00:30:45,600 --> 00:30:49,640
Speaker 1: in particular, there's a really great article about and wired Uh.

509
00:30:49,720 --> 00:30:55,760
Speaker 1: The article is by gian m Volk pickeli Um talks

510
00:30:55,800 --> 00:30:59,800
Speaker 1: about a particular one of these digitalantees goes by the

511
00:31:00,000 --> 00:31:04,240
Speaker 1: handle I'm thrilled about Gabba google f which is a

512
00:31:04,320 --> 00:31:10,200
Speaker 1: reference to the h very popular um cured meat uh

513
00:31:10,320 --> 00:31:13,200
Speaker 1: that is made famous, you know, in Italian cuisine, but

514
00:31:13,320 --> 00:31:16,080
Speaker 1: also in the Sopranos. That's how they say it's a

515
00:31:16,120 --> 00:31:18,440
Speaker 1: gobba goule. It's actually I think a capa cola maybe

516
00:31:18,480 --> 00:31:21,240
Speaker 1: would be how a layman would say it, but it's

517
00:31:21,240 --> 00:31:24,440
Speaker 1: gobba goule in in the Sopranos parlance UM and E

518
00:31:24,840 --> 00:31:27,280
Speaker 1: is a is a reference to ethereum, which is the

519
00:31:27,360 --> 00:31:30,520
Speaker 1: type of cryptocurrency that's typically used in UM. These n

520
00:31:30,560 --> 00:31:34,680
Speaker 1: f T exchanges UM and a lot of decentralized finance

521
00:31:35,240 --> 00:31:38,440
Speaker 1: UM operations and just really quickly decentralized finances. These like

522
00:31:38,760 --> 00:31:41,640
Speaker 1: a peer to peer middleman gets cut out kind of

523
00:31:41,680 --> 00:31:45,360
Speaker 1: like funding projects that we could it could be around

524
00:31:45,400 --> 00:31:47,560
Speaker 1: a particular type of token, it could be around a

525
00:31:47,560 --> 00:31:52,040
Speaker 1: particular type of you know, charitable contribution or something like that.

526
00:31:52,040 --> 00:31:54,719
Speaker 1: There there's different tons of different ones, and many of

527
00:31:54,760 --> 00:31:59,040
Speaker 1: them exist in these UM smart chains that are like

528
00:31:59,120 --> 00:32:02,080
Speaker 1: kind of separate you know, sub block chains of different

529
00:32:02,120 --> 00:32:05,120
Speaker 1: current different cryptocurrencies. But the point is a lot of

530
00:32:05,160 --> 00:32:07,360
Speaker 1: scams are going on. I talked recently about a rug

531
00:32:07,400 --> 00:32:10,880
Speaker 1: poll operation where like you know, these influencers in the

532
00:32:10,960 --> 00:32:14,800
Speaker 1: UK where I think it was called um uh crypto eats.

533
00:32:14,880 --> 00:32:17,080
Speaker 1: It was like, you know, an answer to uber eats

534
00:32:17,120 --> 00:32:19,280
Speaker 1: that allows you to pay with crypto. The whole thing

535
00:32:19,360 --> 00:32:22,680
Speaker 1: was absolute sham all of the influencers. They were scamed too,

536
00:32:22,760 --> 00:32:25,520
Speaker 1: but it also showed that how a lot of influencers

537
00:32:25,520 --> 00:32:28,560
Speaker 1: will just take the money and and and shill for whatever,

538
00:32:28,920 --> 00:32:31,240
Speaker 1: you know, not even really thinking through if it's real

539
00:32:31,320 --> 00:32:32,960
Speaker 1: or not, or if they even care. And now you

540
00:32:33,040 --> 00:32:34,920
Speaker 1: made a good point that you know, it sounded cool

541
00:32:35,320 --> 00:32:37,680
Speaker 1: if someone was reached out to and and and all

542
00:32:37,760 --> 00:32:41,160
Speaker 1: everything seemed you know, legit. Um, maybe it wouldn't be

543
00:32:41,360 --> 00:32:44,520
Speaker 1: such a stretched to to take the money and do

544
00:32:44,560 --> 00:32:47,440
Speaker 1: a little positive video about it. But I think that's

545
00:32:47,480 --> 00:32:49,320
Speaker 1: not I think that's the exception and not the rule.

546
00:32:49,320 --> 00:32:51,080
Speaker 1: I think a lot of folkses take money and talk

547
00:32:51,120 --> 00:32:54,040
Speaker 1: about whatever, not even caring who it affects or you know,

548
00:32:54,080 --> 00:32:57,480
Speaker 1: who it hurts. Um. And that's a big problem in

549
00:32:57,480 --> 00:33:01,600
Speaker 1: in the decentralized finance where and there's one in particular

550
00:33:01,720 --> 00:33:04,960
Speaker 1: where a company some of these projects do these things

551
00:33:04,960 --> 00:33:07,680
Speaker 1: called token drops, where like if you've invested a certain

552
00:33:08,120 --> 00:33:11,240
Speaker 1: a certain amount of of of capital into these projects,

553
00:33:11,440 --> 00:33:15,680
Speaker 1: you will be rewarded occasionally with free tokens that have

554
00:33:15,800 --> 00:33:18,440
Speaker 1: a like a time limit on like what when you

555
00:33:18,480 --> 00:33:20,880
Speaker 1: can cash them out, um to keep them from like

556
00:33:21,120 --> 00:33:23,400
Speaker 1: messing with the value of the tokens. But it's just

557
00:33:23,440 --> 00:33:26,400
Speaker 1: a way of rewarding people that are kind of early adopters.

558
00:33:26,760 --> 00:33:31,120
Speaker 1: And there's this one situation where gaba Ghoul noticed that

559
00:33:31,200 --> 00:33:34,040
Speaker 1: a company called ribbon or a defied project called ribbon

560
00:33:34,440 --> 00:33:36,960
Speaker 1: um did a token drop, and then one particular user

561
00:33:37,400 --> 00:33:40,840
Speaker 1: was able to cash out all of those uh those

562
00:33:41,000 --> 00:33:47,440
Speaker 1: tokens um from duplicate accounts and then convert them to ethereum.

563
00:33:47,560 --> 00:33:49,120
Speaker 1: I think it was somewhere in the neighborhood of two

564
00:33:49,160 --> 00:33:52,760
Speaker 1: point three million dollars that they stole well quote unquote

565
00:33:52,880 --> 00:33:56,360
Speaker 1: stole um. They you know, they broke the rules. They

566
00:33:56,440 --> 00:34:00,960
Speaker 1: essentially had, you know, advanced knowledge of this token drop,

567
00:34:01,160 --> 00:34:03,080
Speaker 1: so they were able to time it right and gain

568
00:34:03,160 --> 00:34:06,040
Speaker 1: the kind of system essentially what the SEC would call

569
00:34:06,560 --> 00:34:11,560
Speaker 1: instead of trading, which is illegal. Two point three million,

570
00:34:11,800 --> 00:34:15,000
Speaker 1: which isn't like a ton of money, considering that defied

571
00:34:15,000 --> 00:34:18,560
Speaker 1: projects are now worth two hundred and fifty billion dollars total.

572
00:34:18,920 --> 00:34:22,000
Speaker 1: But um, they actually this person traced it back to

573
00:34:22,040 --> 00:34:25,360
Speaker 1: an individual who worked for the parent company quote unquote

574
00:34:25,520 --> 00:34:31,759
Speaker 1: of this uh, this DeFi project UM, and they essentially said, Okay,

575
00:34:31,800 --> 00:34:34,240
Speaker 1: we didn't really break the law, because there's a lawless

576
00:34:34,280 --> 00:34:37,560
Speaker 1: world out there. Um, but we did cross a line. Uh.

577
00:34:37,600 --> 00:34:40,200
Speaker 1: The employee has been reprimanded and we will now return

578
00:34:40,880 --> 00:34:45,719
Speaker 1: those funds. Um. But again, that all happened because of

579
00:34:45,800 --> 00:34:49,759
Speaker 1: this you know, blockchain sleuthing from Gobba gool and there's

580
00:34:49,800 --> 00:34:54,080
Speaker 1: other ones zach sysiphus um as a handful of these,

581
00:34:54,120 --> 00:34:58,040
Speaker 1: you know Twitter kind of you know, crypto detectives that

582
00:34:58,080 --> 00:35:00,399
Speaker 1: are out there doing God's work. And I think it's cool.

583
00:35:00,760 --> 00:35:03,879
Speaker 1: I agreed. So um, now there you have it. I'm

584
00:35:03,880 --> 00:35:06,359
Speaker 1: gonna keep an eye on still, but I'm glad. I'm

585
00:35:06,360 --> 00:35:09,600
Speaker 1: glad to know that these uh Internet superheroes are out there,

586
00:35:09,719 --> 00:35:13,319
Speaker 1: you know, trying to keep people honest, a balance for

587
00:35:13,360 --> 00:35:18,920
Speaker 1: all things. Indeed, let's take a break and then we'll

588
00:35:18,920 --> 00:35:28,640
Speaker 1: be back with one more bit of strange news. Welcome

589
00:35:28,680 --> 00:35:31,600
Speaker 1: back to the show, folks. This is the part we

590
00:35:31,719 --> 00:35:34,920
Speaker 1: told you about at the top. This is the juice.

591
00:35:35,560 --> 00:35:40,120
Speaker 1: This is the u umami, this is the creepy stuff. UH.

592
00:35:40,120 --> 00:35:42,680
Speaker 1: This is something uh, this is something that I was

593
00:35:42,719 --> 00:35:46,799
Speaker 1: on the fence about bringing and uh checked checked with

594
00:35:46,800 --> 00:35:49,720
Speaker 1: the team beforehand. But perhaps one of the most important

595
00:35:49,719 --> 00:35:52,160
Speaker 1: things at the top here is to say the following.

596
00:35:52,719 --> 00:35:56,280
Speaker 1: If you are listening along to this show with your children,

597
00:35:56,480 --> 00:36:00,680
Speaker 1: fellow conspiracy realist, this may not be appropriate it for them,

598
00:36:00,800 --> 00:36:03,680
Speaker 1: but also thank you for starting them young. It's very

599
00:36:03,680 --> 00:36:09,719
Speaker 1: important thoughts. UH. If you are somewhat skittish about graphic material. Uh.

600
00:36:09,960 --> 00:36:13,680
Speaker 1: If you are not the kind of person who thinks

601
00:36:13,719 --> 00:36:17,560
Speaker 1: gross things are funny, then this maybe where you pause

602
00:36:17,640 --> 00:36:20,840
Speaker 1: the podcast. This is why we wanted this story to

603
00:36:20,920 --> 00:36:25,279
Speaker 1: be the story at the very end today. And then, um,

604
00:36:25,320 --> 00:36:27,600
Speaker 1: even if you're not interested in it, fast forward a

605
00:36:27,600 --> 00:36:29,960
Speaker 1: little bit because there is a special message I have

606
00:36:30,040 --> 00:36:33,759
Speaker 1: at the end. But here's the here's the headline. Matt Noll.

607
00:36:33,880 --> 00:36:37,040
Speaker 1: You guys know about this. We have talked a lot

608
00:36:37,080 --> 00:36:42,880
Speaker 1: about deep learning, about neural networks, about applying AI in

609
00:36:42,960 --> 00:36:47,600
Speaker 1: a generative sense. Uh. The probably the version of this

610
00:36:47,719 --> 00:36:50,840
Speaker 1: that's most familiar to people is actually a really cool

611
00:36:51,000 --> 00:36:57,040
Speaker 1: meme slash joke, wherein someone says, hey, I've fed thousands

612
00:36:57,040 --> 00:37:01,320
Speaker 1: of hours of Batman or sign fell Old, or um,

613
00:37:01,320 --> 00:37:05,280
Speaker 1: it's always sunny in Philadelphia or a president's speeches into

614
00:37:05,760 --> 00:37:09,080
Speaker 1: this AI program and it generated its own thing, and

615
00:37:09,120 --> 00:37:12,000
Speaker 1: then waca, waca waka. Look how wacky this is. There

616
00:37:12,040 --> 00:37:15,719
Speaker 1: are great examples of it. There are many are quite enjoyable.

617
00:37:15,920 --> 00:37:18,560
Speaker 1: The vast majority of those, of course, are written by

618
00:37:19,360 --> 00:37:24,360
Speaker 1: clever comedic people rather than machines. At this point, to

619
00:37:24,480 --> 00:37:30,040
Speaker 1: get into this, UM, I'll say something that is UH.

620
00:37:30,280 --> 00:37:31,920
Speaker 1: I don't know if we talked about too much on

621
00:37:31,920 --> 00:37:34,880
Speaker 1: the show. So we know that war drives innovation, right.

622
00:37:35,000 --> 00:37:40,160
Speaker 1: War drives science, war drives medicine, war drives transport, war

623
00:37:40,280 --> 00:37:45,880
Speaker 1: drives economies. UM. Sex also, in a very real way,

624
00:37:46,080 --> 00:37:51,319
Speaker 1: drives technology. For anybody old enough to remember VHS tapes, uh,

625
00:37:51,400 --> 00:37:56,280
Speaker 1: the reason VHS became like a world standard is because

626
00:37:56,400 --> 00:38:00,720
Speaker 1: that's what pornographic studios went with. And so we didn't

627
00:38:00,760 --> 00:38:03,440
Speaker 1: say this out loud when we've been talking about Ai.

628
00:38:03,480 --> 00:38:05,440
Speaker 1: We're talking about oh AI. It's kind of like a

629
00:38:05,520 --> 00:38:09,080
Speaker 1: child soldier, which is disturbing and true. We didn't talk

630
00:38:09,120 --> 00:38:11,960
Speaker 1: about it when we talked about all the beautiful potential

631
00:38:12,000 --> 00:38:16,919
Speaker 1: it has for art or the weird way it explores ethics.

632
00:38:16,960 --> 00:38:19,800
Speaker 1: But it was only a matter of time, folks, before

633
00:38:19,920 --> 00:38:24,719
Speaker 1: someone gazed upon the new exciting realm of the synthetic

634
00:38:24,840 --> 00:38:27,600
Speaker 1: mind and said, can we use this to make porn?

635
00:38:28,280 --> 00:38:34,200
Speaker 1: And that's what happens. That is exactly what has happened. Ah.

636
00:38:34,320 --> 00:38:38,920
Speaker 1: We have a guy named Eric shardcore Drafts to thank

637
00:38:39,080 --> 00:38:42,960
Speaker 1: for this. Uh. He is the creator of something called

638
00:38:43,040 --> 00:38:48,719
Speaker 1: the machine Gaze, which uses machine learning tools uh to

639
00:38:48,719 --> 00:38:55,640
Speaker 1: to create to generate. Um, what this software thinks is pornography,

640
00:38:56,000 --> 00:38:58,720
Speaker 1: just like c g I creatures, like cd I figures.

641
00:38:59,719 --> 00:39:02,280
Speaker 1: I was show you, I'll show you, Okay, okay, alright,

642
00:39:02,360 --> 00:39:06,560
Speaker 1: So I am sending you guys in the chat here,

643
00:39:06,800 --> 00:39:10,000
Speaker 1: I am sending you a very short video. It's about

644
00:39:10,400 --> 00:39:12,160
Speaker 1: three minutes. We can just have it played on the

645
00:39:12,160 --> 00:39:19,600
Speaker 1: background of UM. What the machine learning algorithms seem to

646
00:39:19,760 --> 00:39:23,160
Speaker 1: think is intercourse. And I just want to be honest,

647
00:39:23,200 --> 00:39:25,200
Speaker 1: I don't know who the audience is. I don't think

648
00:39:25,200 --> 00:39:31,719
Speaker 1: it's humans. The music does not help. But uh but

649
00:39:31,920 --> 00:39:38,480
Speaker 1: it is hearing a heartbeat, yes, yeah, a drone. Uh huh.

650
00:39:38,880 --> 00:39:45,160
Speaker 1: It's a little for David nch right, Oh my god, playboy.

651
00:39:45,360 --> 00:39:48,600
Speaker 1: I would totally project this like behind like a psychedelic

652
00:39:48,680 --> 00:39:52,440
Speaker 1: rock band. This is like seeing like a gallery, you know,

653
00:39:52,560 --> 00:39:54,520
Speaker 1: like it's called four Hands to It even looks like

654
00:39:54,560 --> 00:40:02,040
Speaker 1: a digital art project. Yeah, I want to No, I

655
00:40:02,280 --> 00:40:06,560
Speaker 1: don't see anything sexy about this at all. It's just cool. Yeah,

656
00:40:06,680 --> 00:40:10,960
Speaker 1: we're we're not the target audience again, I think, but

657
00:40:11,120 --> 00:40:13,680
Speaker 1: it was only it's weird because it was only a

658
00:40:13,719 --> 00:40:19,040
Speaker 1: matter of time before something like this happened, right, I mean,

659
00:40:19,080 --> 00:40:24,359
Speaker 1: that's just how people work. So here's what Here's what

660
00:40:24,480 --> 00:40:27,520
Speaker 1: draft finds interesting about this. He says, by turning the

661
00:40:27,800 --> 00:40:30,440
Speaker 1: AI on the domain of poor and you make imagery

662
00:40:30,480 --> 00:40:34,200
Speaker 1: that's not pornography but is fleshy. You can see the

663
00:40:34,320 --> 00:40:37,440
Speaker 1: roots that it's come from. He says, it starts to

664
00:40:37,480 --> 00:40:40,480
Speaker 1: trigger the similar parts of your brain, but in slightly

665
00:40:40,600 --> 00:40:43,720
Speaker 1: the wrong way. So a bit of a uncanny Valley

666
00:40:43,920 --> 00:40:49,440
Speaker 1: dreamlike quality. Matt, your facial expression here is priceless, dude.

667
00:40:52,360 --> 00:40:55,600
Speaker 1: I am why I'm strangely finding myself becoming titillated and

668
00:40:55,680 --> 00:41:02,600
Speaker 1: aroused watching this video. I'm so yeah, okay, So here

669
00:41:02,640 --> 00:41:05,600
Speaker 1: for him. The underlying question, there's another quote, is what

670
00:41:05,680 --> 00:41:09,600
Speaker 1: new machines with no concept of sexuality or sexual behavior

671
00:41:10,000 --> 00:41:14,239
Speaker 1: see when they look at this content. And he fed

672
00:41:14,320 --> 00:41:19,240
Speaker 1: a lot of content will call it into these programs. Uh,

673
00:41:19,239 --> 00:41:22,120
Speaker 1: it's for me. What's strange about is it's it's a

674
00:41:22,200 --> 00:41:25,359
Speaker 1: raw shack of flesh, is really, I think a way

675
00:41:25,400 --> 00:41:32,040
Speaker 1: to describe it. And the the dreamlike quality is a

676
00:41:32,120 --> 00:41:35,200
Speaker 1: huge part of it is due to the music. I

677
00:41:35,239 --> 00:41:40,040
Speaker 1: wonder whether Drafts had experimented with generating audio as well,

678
00:41:40,120 --> 00:41:43,280
Speaker 1: and that was just too weird like these not quite

679
00:41:43,360 --> 00:41:49,439
Speaker 1: human kaleidoscopes and sonic fractals of moans and exclamations. That's

680
00:41:49,480 --> 00:41:54,080
Speaker 1: what would have occurred, I imagine, and this UM that

681
00:41:54,200 --> 00:42:02,560
Speaker 1: sounds amazing AI generated sounds from those that same two. Well,

682
00:42:02,200 --> 00:42:04,520
Speaker 1: I will tell you watching this, we don't have the

683
00:42:04,560 --> 00:42:07,680
Speaker 1: sound off, UM, but if it had like a sexy

684
00:42:07,840 --> 00:42:09,960
Speaker 1: saxophone on top of it, I think that might really

685
00:42:09,960 --> 00:42:12,719
Speaker 1: take it to the next level. Listen, we're we're just

686
00:42:12,760 --> 00:42:14,520
Speaker 1: gonna do this real time for a second, just to

687
00:42:14,520 --> 00:42:17,200
Speaker 1: get the listener an idea. Okay, we're just gonna sample.

688
00:42:17,200 --> 00:42:18,719
Speaker 1: You're ready to see if you can hear this from

689
00:42:18,719 --> 00:42:21,480
Speaker 1: my headphone? You ready, know? Man? Yeah, go for it.

690
00:42:27,760 --> 00:42:32,000
Speaker 1: Very David Lynch, that's very David Lynch. All the flesh,

691
00:42:32,440 --> 00:42:38,400
Speaker 1: all the flesh must be sacrificed. Is the new flesh.

692
00:42:38,440 --> 00:42:41,359
Speaker 1: They were talking about, what doest thou like to live deliciously?

693
00:42:41,960 --> 00:42:47,520
Speaker 1: A um? All the boundaries? Yeah, so so we predicted Yeah,

694
00:42:48,040 --> 00:42:52,279
Speaker 1: I think civilization predicted the emergence of deep fakes, and

695
00:42:52,320 --> 00:42:57,000
Speaker 1: there's still kind of emergent. But this is something different

696
00:42:57,480 --> 00:43:02,680
Speaker 1: because in a way, this is asked UM. This is

697
00:43:02,719 --> 00:43:10,160
Speaker 1: like asking someone who's never never seen something, someone who's

698
00:43:10,200 --> 00:43:13,080
Speaker 1: been who hasn't been cited since birth. This is like

699
00:43:13,200 --> 00:43:17,520
Speaker 1: asking them to describe an image in a dream, right,

700
00:43:17,600 --> 00:43:21,120
Speaker 1: there's not a frame of reference. And it immediately makes

701
00:43:21,160 --> 00:43:24,080
Speaker 1: me think of the deep dream software and the neural

702
00:43:24,120 --> 00:43:28,239
Speaker 1: network kind of uncanny Valley, psychedelic kind of you know,

703
00:43:28,400 --> 00:43:31,600
Speaker 1: hell Escape. That was those videos, which I love. I

704
00:43:31,600 --> 00:43:33,759
Speaker 1: think those are super cool. They's sort of gotten a

705
00:43:33,800 --> 00:43:35,879
Speaker 1: little played out and almost like a little cliche at

706
00:43:35,880 --> 00:43:37,840
Speaker 1: this point, but at the time I thought it was

707
00:43:37,920 --> 00:43:39,759
Speaker 1: really neat, And this really makes me think of that,

708
00:43:39,800 --> 00:43:44,560
Speaker 1: only it's all just flesh and intertwined kind of bodies. Yeah,

709
00:43:44,600 --> 00:43:48,719
Speaker 1: and this is um. I I do think that this

710
00:43:48,960 --> 00:43:55,320
Speaker 1: is important work because reproduction is biologically the primary goal

711
00:43:55,400 --> 00:43:57,960
Speaker 1: of the human species. Everything else is just sort of

712
00:43:58,760 --> 00:44:03,040
Speaker 1: extracurricular stuff built around that. Right, And uh, it makes

713
00:44:03,040 --> 00:44:08,840
Speaker 1: sense that this fundamental, this potentially fundamental shift in civilization

714
00:44:09,320 --> 00:44:14,359
Speaker 1: would address one of the fundamental bedrocks of civilization as

715
00:44:14,400 --> 00:44:19,000
Speaker 1: it is commonly understood. Uh, the question is does the

716
00:44:20,120 --> 00:44:23,719
Speaker 1: do the programs to the algorithms understand what they're doing.

717
00:44:24,000 --> 00:44:27,160
Speaker 1: That's that verges into philosophy, and we're already kind of

718
00:44:27,160 --> 00:44:29,919
Speaker 1: in the deep water of poetry and poetic thought. Here

719
00:44:30,160 --> 00:44:34,279
Speaker 1: are poetic science, as Ada Lovelace would say. The next um,

720
00:44:34,480 --> 00:44:38,839
Speaker 1: the next question is is this is this truly generative?

721
00:44:39,800 --> 00:44:44,680
Speaker 1: Is it only referential? Has drops been steering it? Like?

722
00:44:44,800 --> 00:44:50,080
Speaker 1: What kind of did did this human individual's predilections play

723
00:44:50,200 --> 00:44:53,040
Speaker 1: any role in it? Like? Uh, I don't think it's

724
00:44:53,080 --> 00:44:55,040
Speaker 1: offensive for me to point out, since we are in

725
00:44:55,080 --> 00:44:58,280
Speaker 1: the adult proportion of today's show, that it was based

726
00:44:58,400 --> 00:45:01,759
Speaker 1: entirely on Quentin Tarantino taste. We might just see a

727
00:45:01,800 --> 00:45:05,359
Speaker 1: bunch of like feet, right. That was that too low

728
00:45:05,400 --> 00:45:07,640
Speaker 1: a blow, not at all. I mean feat picks are

729
00:45:07,680 --> 00:45:11,839
Speaker 1: like internet currency, so weird. Well, you know, if you're

730
00:45:11,840 --> 00:45:15,960
Speaker 1: not hurting anybody, go forth, do your thing. Uh, live

731
00:45:16,000 --> 00:45:20,280
Speaker 1: your life. But this, this was fascinating to me because

732
00:45:20,560 --> 00:45:24,839
Speaker 1: of what it means for the future. This means that

733
00:45:24,880 --> 00:45:28,600
Speaker 1: there is a world in which I'm bringing I'm going

734
00:45:28,640 --> 00:45:30,799
Speaker 1: somewhere here that's not gross. This means there is a

735
00:45:30,840 --> 00:45:36,400
Speaker 1: world in which my dream of AI generated films and

736
00:45:36,560 --> 00:45:42,319
Speaker 1: scripts becomes even more possible, increasingly plausible. There could be

737
00:45:42,360 --> 00:45:46,799
Speaker 1: I like, think past the pornography and whatever silly taboos

738
00:45:46,920 --> 00:45:49,920
Speaker 1: humans have around that stuff, and think about the world

739
00:45:49,920 --> 00:45:53,719
Speaker 1: in which you could say, I want I want to

740
00:45:53,880 --> 00:45:59,520
Speaker 1: film computer. I want to film with Kianu Reeves and

741
00:46:00,280 --> 00:46:04,400
Speaker 1: uh Marilyn Monroe, and I wanted to be about the

742
00:46:04,560 --> 00:46:08,319
Speaker 1: night that Edgar Allan Poe died, and I wanted to

743
00:46:08,480 --> 00:46:13,359
Speaker 1: be um a musical kind of like you know, West

744
00:46:13,400 --> 00:46:15,680
Speaker 1: Side Story or Hamilton's And then there could be a

745
00:46:15,680 --> 00:46:19,560
Speaker 1: program that did that. This is a within our lifetimes

746
00:46:19,600 --> 00:46:24,520
Speaker 1: possibly thing if humanity keeps it together. Um yeah, well

747
00:46:25,080 --> 00:46:27,880
Speaker 1: do you think stuff like this can replace like human

748
00:46:27,920 --> 00:46:31,200
Speaker 1: imagination and like like like, do you think we'll have

749
00:46:31,280 --> 00:46:33,320
Speaker 1: a I that will be smart enough to mean? I

750
00:46:33,360 --> 00:46:36,000
Speaker 1: think it requires a shift in what we expect out

751
00:46:36,040 --> 00:46:38,719
Speaker 1: of a story. Like I think, on a long enough timeline,

752
00:46:39,040 --> 00:46:43,319
Speaker 1: maybe a computer could generate a story that the society

753
00:46:43,320 --> 00:46:46,439
Speaker 1: of that day would consider acceptable. But I don't think

754
00:46:46,480 --> 00:46:49,279
Speaker 1: they could ever be as robust and imaginative as the

755
00:46:49,320 --> 00:46:52,280
Speaker 1: stuff we I mean, even like content has gotten dumbed

756
00:46:52,280 --> 00:46:55,920
Speaker 1: down over time in our lifetime and like it's it's

757
00:46:55,920 --> 00:46:59,640
Speaker 1: it's much more remarkable when something truly original and thoughtful

758
00:46:59,760 --> 00:47:02,720
Speaker 1: comes up, whether in music or film or television or whatever,

759
00:47:02,920 --> 00:47:07,319
Speaker 1: because so much stuff is basically algorithmically generated, you know,

760
00:47:07,520 --> 00:47:10,040
Speaker 1: by the Netflix machine, Like what kind of content do

761
00:47:10,120 --> 00:47:14,240
Speaker 1: people want? It's what you just describe, Ben, perhaps, Um,

762
00:47:14,440 --> 00:47:17,440
Speaker 1: do you think this is just going to create base

763
00:47:18,000 --> 00:47:21,440
Speaker 1: kind of broad stuff that would just be like the

764
00:47:21,440 --> 00:47:23,000
Speaker 1: lowest common doom there? Do you think this has the

765
00:47:23,000 --> 00:47:26,719
Speaker 1: potential for like creative expression? I'm very glad that you

766
00:47:26,760 --> 00:47:32,440
Speaker 1: brought up the concept of imagination, because when humans think

767
00:47:32,640 --> 00:47:36,200
Speaker 1: of how to define success for the creation of a

768
00:47:36,280 --> 00:47:41,759
Speaker 1: machine consciousness, there's often a very easy, very self referential

769
00:47:41,920 --> 00:47:45,360
Speaker 1: error to make, and that error is thinking that the

770
00:47:45,480 --> 00:47:49,000
Speaker 1: aim should be for very similitude, you know, for something

771
00:47:49,000 --> 00:47:51,919
Speaker 1: that seems very much like the human mind, very much

772
00:47:51,960 --> 00:47:55,279
Speaker 1: capable of the human imagination. But I pause it, that

773
00:47:55,400 --> 00:47:59,480
Speaker 1: is not success. Success if success is to be defined

774
00:47:59,520 --> 00:48:04,640
Speaker 1: in this old is a completely original imagination, you know

775
00:48:04,640 --> 00:48:07,200
Speaker 1: what I mean. It's a I that is making stories

776
00:48:07,239 --> 00:48:12,120
Speaker 1: for AI that do not necessarily involve humans or the

777
00:48:12,200 --> 00:48:17,520
Speaker 1: human mind. And if civilization can make a truly distinct

778
00:48:17,640 --> 00:48:22,560
Speaker 1: creative imagination, then you know, maybe, uh, maybe all the

779
00:48:22,640 --> 00:48:26,919
Speaker 1: horrible stuff between the first time people discovered fire and

780
00:48:27,200 --> 00:48:30,000
Speaker 1: the day that happens will be in some way worth it,

781
00:48:30,640 --> 00:48:33,319
Speaker 1: which I'm trying to be optimistic. I'm not super great

782
00:48:33,320 --> 00:48:35,759
Speaker 1: at this totally And that was beautifully said Ben, It

783
00:48:35,880 --> 00:48:38,000
Speaker 1: really was. But now I'm thinking we need to do

784
00:48:38,080 --> 00:48:44,560
Speaker 1: Ben is, get images of motherboards and CPUs and feed

785
00:48:44,880 --> 00:48:47,279
Speaker 1: all of that into the system and then see what

786
00:48:47,400 --> 00:48:53,279
Speaker 1: kind of real machine gaze we can get us in

787
00:48:53,400 --> 00:49:00,280
Speaker 1: weird places? Yeah, what would what would like pleasurable onton?

788
00:49:00,320 --> 00:49:05,000
Speaker 1: What would the equivalent of machine learning pornography be? Would

789
00:49:05,040 --> 00:49:08,200
Speaker 1: it be math? Would it be like like sexy math?

790
00:49:09,120 --> 00:49:11,920
Speaker 1: Is that a thing? Would humans even get it? You

791
00:49:11,920 --> 00:49:13,960
Speaker 1: know what I mean? Would you be like, oh no,

792
00:49:14,000 --> 00:49:17,200
Speaker 1: I caught my personal assistant. I caught Alexa doing the

793
00:49:17,560 --> 00:49:21,520
Speaker 1: the dirty calculus again. I don't know. It's a weird

794
00:49:21,560 --> 00:49:24,799
Speaker 1: world to live in. But just like typing sixty nine

795
00:49:24,840 --> 00:49:28,640
Speaker 1: over and over. Yeah exactly again, like, well, what's the

796
00:49:28,680 --> 00:49:30,919
Speaker 1: frame of reference for what it is unless it thinks

797
00:49:30,920 --> 00:49:34,920
Speaker 1: about it in a human world? Um? From Sin to

798
00:49:35,000 --> 00:49:37,839
Speaker 1: Sign and co Sign, Yeah exactly. I heard a really

799
00:49:37,840 --> 00:49:41,600
Speaker 1: good point about sort of this. Um. I've been really

800
00:49:41,640 --> 00:49:44,040
Speaker 1: getting into this podcast called blank Check with Griffin and

801
00:49:44,120 --> 00:49:48,000
Speaker 1: David Um. It's like a film kind of critique podcast.

802
00:49:48,080 --> 00:49:51,600
Speaker 1: But they were talking about the movie, uh, Interstellar, and

803
00:49:51,680 --> 00:49:54,400
Speaker 1: if you'll remember, there's a robot in it called tars.

804
00:49:54,560 --> 00:49:56,279
Speaker 1: That's really neat because it just kind of looks like

805
00:49:56,280 --> 00:49:58,160
Speaker 1: a wall, but that it also like sort of like

806
00:49:58,160 --> 00:50:01,080
Speaker 1: pops out with these little appendages can like do different things.

807
00:50:01,520 --> 00:50:04,879
Speaker 1: And the thing that the point they made was, why

808
00:50:04,880 --> 00:50:07,280
Speaker 1: would if we were going to to build an android,

809
00:50:07,600 --> 00:50:10,200
Speaker 1: as we've seen in every science fiction film ever since

810
00:50:10,239 --> 00:50:12,640
Speaker 1: the beginning of you know, cinema, why would we make

811
00:50:12,680 --> 00:50:14,560
Speaker 1: it look like a person. We already have a person.

812
00:50:14,760 --> 00:50:17,080
Speaker 1: We have people that can do people things. If we

813
00:50:17,080 --> 00:50:20,000
Speaker 1: were going to build an effective android, like for like

814
00:50:20,080 --> 00:50:22,880
Speaker 1: war or something, why we we should be a form

815
00:50:23,280 --> 00:50:26,359
Speaker 1: that we are incapable of replicating with human body, like

816
00:50:26,520 --> 00:50:30,560
Speaker 1: a moving wall that can generate you know, arms, and

817
00:50:30,560 --> 00:50:33,400
Speaker 1: and become different shapes as needed. That kind of outside

818
00:50:33,400 --> 00:50:35,920
Speaker 1: of the box thinking, I think requires I don't know,

819
00:50:36,080 --> 00:50:38,160
Speaker 1: human mind in some ways. This is interesting. I think

820
00:50:38,200 --> 00:50:39,839
Speaker 1: it was. I thought it was a really good point.

821
00:50:39,920 --> 00:50:44,000
Speaker 1: Could also be a medium sized canine, yes, very much so. Uh,

822
00:50:44,200 --> 00:50:48,520
Speaker 1: one capable of carrying a serious firepower on its back. Uh.

823
00:50:48,719 --> 00:50:53,880
Speaker 1: This this is the world in which you listening today

824
00:50:54,000 --> 00:50:57,840
Speaker 1: and your descendants may very well live in soon. I

825
00:50:57,840 --> 00:50:59,480
Speaker 1: would love to hear your take on this. Is this

826
00:50:59,680 --> 00:51:02,880
Speaker 1: over all a good thing. Is this just the ego

827
00:51:03,080 --> 00:51:07,120
Speaker 1: level set that human civilization needs? Or is it the end?

828
00:51:07,880 --> 00:51:11,320
Speaker 1: Is it the end? I I am hoping that machine

829
00:51:11,480 --> 00:51:17,560
Speaker 1: imagination and human imagination will be different enough that both

830
00:51:17,600 --> 00:51:21,760
Speaker 1: can coexist and perhaps even in some way complements one another.

831
00:51:22,239 --> 00:51:24,920
Speaker 1: And I'm trying to be optimistic. Um. And while I'm

832
00:51:24,960 --> 00:51:28,880
Speaker 1: being optimistic, just to close this out on air, I

833
00:51:28,960 --> 00:51:33,160
Speaker 1: wanted to thank everybody, and Matt and Noel and Paul,

834
00:51:33,200 --> 00:51:37,080
Speaker 1: I wanted to thank you guys specifically. I was out

835
00:51:37,400 --> 00:51:41,719
Speaker 1: um previously, and you guys held down the fort with

836
00:51:41,760 --> 00:51:47,600
Speaker 1: a cavalcade of voicemails from a very special listener. Male segment. UM,

837
00:51:48,000 --> 00:51:51,400
Speaker 1: I am going to maybe towards the end of Fear

838
00:51:51,880 --> 00:51:55,799
Speaker 1: talk a little bit more about what went on backstage.

839
00:51:55,920 --> 00:51:59,719
Speaker 1: But everything is fine, as the dog says in the

840
00:52:00,080 --> 00:52:05,160
Speaker 1: Burning House. Uh. And we are all collectively grateful for

841
00:52:05,480 --> 00:52:09,280
Speaker 1: every single one of your conspiracy realists who have tuned in.

842
00:52:09,640 --> 00:52:11,600
Speaker 1: You are, again, the most important part of this show.

843
00:52:11,640 --> 00:52:14,080
Speaker 1: I never get tired of saying that we can't wait

844
00:52:14,120 --> 00:52:17,439
Speaker 1: to hear from you. What's your crypto scam? Don't send

845
00:52:17,520 --> 00:52:21,399
Speaker 1: us the link? What are you? Uh? What are you

846
00:52:21,480 --> 00:52:25,280
Speaker 1: thinking about when you think about workplace surveillance necessary evil

847
00:52:25,960 --> 00:52:29,600
Speaker 1: or just another step towards a big brother society, as

848
00:52:29,640 --> 00:52:32,840
Speaker 1: Matt said, a nanny state. And what is the future

849
00:52:33,320 --> 00:52:36,200
Speaker 1: I mean, once we get past the pornography, what is

850
00:52:36,239 --> 00:52:40,880
Speaker 1: the future of imagination in the mind of a machine.

851
00:52:41,320 --> 00:52:43,160
Speaker 1: We cannot wait to hear from you. We try to

852
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Speaker 1: make it easy to find us online. Oh, the internet

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Speaker 1: is rifeless stuff that I don't want you to know.

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Speaker 1: Communication options. You can find us on Facebook, Twitter, and

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Speaker 1: YouTube at the handle conspiracy stuff or conspiracy Stuff show

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Speaker 1: on Instagram. Yes, you can use your mouth to contact us,

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Speaker 1: and your phone call eight three three st d w

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Speaker 1: y t K. Let us know if we can use

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Speaker 1: your name and voice on the air. Give yourself a

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Speaker 1: cool nickname. And that's a part of the whole scenario too.

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Speaker 1: And you've got three minutes say whatever you'd like anything

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Speaker 1: in the whole wide world. Use it how you will. Well,

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Speaker 1: except for machine prawn, let's not do that. Let's not

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Speaker 1: do that anywhere. No drones, no fleshy naval created knuckles.

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Speaker 1: But it's also hard to do on an audio format.

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Speaker 1: But if you've got too much to say, they won't

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Speaker 1: fit into those three minutes. Please, please, please send us

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Speaker 1: a good old fashioned email. We are conspiracy at iHeart

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Speaker 1: radio dot com. Stuff they don't want you to know.

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Speaker 1: Is a production of I heart Radio. For more podcasts

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Speaker 1: from my heart Radio, visit the i heart radio app,

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Speaker 1: Apple Podcasts, or wherever you listen to your favorite shows.