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

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

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Speaker 1: learn this stuff they don't want.

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Speaker 2: You to know.

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Speaker 1: A production of iHeartRadio.

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Speaker 3: Hello, welcome back to the show. My name is Men,

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Speaker 3: my name is noelh.

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Speaker 4: They call me Ben.

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Speaker 2: We're joined as always with our super producer Alexis, code

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Speaker 2: named Doc Holliday Jackson. Most importantly, you are you. You

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Speaker 2: are here. That makes this the stuff they don't want

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Speaker 2: you to know. It is the top of the week,

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Speaker 2: which means it's time for some strange news, and we're

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Speaker 2: going to talk about some crime. We're going to talk

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Speaker 2: about some surveillance. We're going to talk about, of course,

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Speaker 2: a couple of heist gone wrong. We are going to

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Speaker 2: ask what rough beast slouches towards the Bethlehem of the

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Speaker 2: Dead Internet theory before we do any of that. Fellow

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Speaker 2: conspiracy realist, we are fans of music in all form,

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Speaker 2: and Noel, you found a pretty amazing story.

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Speaker 4: Well yeah, I mean, you know, a lot of the

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Speaker 4: conversation around AI, especially as it pertains to corporations using AI,

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Speaker 4: sort of feels like this desperate attempt to capitalize on

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Speaker 4: the hot new technology or whatever, not even to mention

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Speaker 4: the dystopian aspects of AI the job depriving the future

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Speaker 4: aspects of AI or a machine learning to be a

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Speaker 4: little more accurate. So it's kind of rare even when

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Speaker 4: you see one that seems a little innocuous, Like you know,

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Speaker 4: the Beatles released a pretty nothing burger of a song

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Speaker 4: that I've already literally forgot in the name of where

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Speaker 4: they used AI to clean up the audio on a

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Speaker 4: John Lennon demo. Essentially that was like I think him

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Speaker 4: playing a tune on the piano, and they used AI

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Speaker 4: as you know, an interesting tool. It wasn't like to

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Speaker 4: resurrect John Lennon's from the Dead, you know, to fully

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Speaker 4: create this thing that never actually happened. It was you know,

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Speaker 4: using you know, machine learning as a forensic tool of

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Speaker 4: audio reconstruction.

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Speaker 2: I do like John Lennon's.

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Speaker 4: By the way, John Lennons, there's there's there could be

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Speaker 4: more than one. If hey if if, if big corporate

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Speaker 4: America has their way, there will be many John Lennons

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Speaker 4: fed by machine learning. But you know, there was a

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Speaker 4: lot made of that story when it came out because

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Speaker 4: there were a lot of people saying, oh gross, using

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Speaker 4: AI to you know, dishonor the dead and create this song.

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Speaker 4: Not cool beatles, but a lot of those stories buried

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Speaker 4: the leader at the very least people chose not to

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Speaker 4: look into a little bit deeper beyond the headlines to

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Speaker 4: determine yes and fact. That was just a use of

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Speaker 4: that kind of software, that kind of technology as a tool.

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Speaker 4: And you know, Matt and I work pretty extensively with

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Speaker 4: audio and stuff and using tools like isotope RX to

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Speaker 4: you know, clean up noisy recordings, get rid of things

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Speaker 4: like room tone or the drone of an air conditioning.

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Speaker 4: And while I know a lot of audio plugins are

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Speaker 4: starting to use more machine learning algorithms to figure that

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Speaker 4: kind of stuff out, it's sort of still kind of,

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Speaker 4: you know, the early days for that kind of stuff,

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Speaker 4: and you're really just starting to see some of those

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Speaker 4: tools incorporating this technology. I think we could all agree

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Speaker 4: that's pretty cool. That's a use of that tech that's

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Speaker 4: neat and that actually is a value and is still

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Speaker 4: requiring engineers to implement the tool and to use it,

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Speaker 4: you know, to create great audio, whether it be in

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Speaker 4: podcasts or in music. You know, with so many people

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Speaker 4: recording records at home. Being able to clean up audio

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Speaker 4: that maybe wasn't recorded in the most optimal situations is

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Speaker 4: a really cool thing.

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Speaker 3: Well, just really quickly on that, think about all of

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Speaker 3: the amazing tools that exist for video editing and how

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Speaker 3: far that's come. Right, being able to isolate elements back

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Speaker 3: the image and like do all that kind of stuff.

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Speaker 4: Adaptive whatever it can sense, the edges it can sense,

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Speaker 4: you can replace elements to create really cool video effects,

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Speaker 4: not just deep fakes.

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Speaker 2: Yeah, it's super good at this, by the way, folks.

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Speaker 3: But what I'm saying is even before any of this

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Speaker 3: AI integration, video effects and editing and the way you

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Speaker 3: could manipulate a visual image became was becoming extremely powerful

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Speaker 3: the things you could do right. But with audio for

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Speaker 3: a long time, I think it has to do with

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Speaker 3: the nature of the wave and the signal and what

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Speaker 3: you actually get when you record right. With microphones and things,

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Speaker 3: it's very hard to separate pieces, right. Let's say, if

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Speaker 3: there's a sound that occurs at the same time as

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Speaker 3: another sound. For a long time, there was almost no

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Speaker 3: way besides isolating specific frequencies to do anything.

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Speaker 4: I used to work at a guitar shop. You probably

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Speaker 4: have an idea or can't imagine how often people came

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Speaker 4: in and said, hey, you got to miss That'll cut

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Speaker 4: the vocal out of this scene so I can make

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Speaker 4: it into a karaoke track. I kid you not. It

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Speaker 4: happened once every couple of days. So now to your point, Matt,

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Speaker 4: through machine learning, that is possible, and I couldn't exactly

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Speaker 4: explain to you how it's done, but there are quite

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Speaker 4: a few tools that can do just that. And the

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Speaker 4: long winded way of getting to today's story that I brought,

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Speaker 4: which is about the country singer Randy Travis who in

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Speaker 4: twenty thirteen suffered a severe stroke that left him unable

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Speaker 4: to speak, which of course means unable to sing. He

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Speaker 4: also had had issues with mobility due to that and

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Speaker 4: has you know, largely faded from the public spotlight since

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Speaker 4: that point. You might know Randy Travissey. I think he

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Speaker 4: had hits like as far back as like the early eighties.

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Speaker 4: My girlfriend, who's a bit of a country enthusiast, it's

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Speaker 4: not my particular genre of choice. I think I like

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Speaker 4: classic country. I like outlawk country, that kind of stuff.

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Speaker 4: More of the schmaltzy kind of radio country stuff like

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Speaker 4: that doesn't really do it for me, but you could

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Speaker 4: argue Randy Travis sort of straddles those genres. And I

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Speaker 4: think one of his famous songs is called like for

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Speaker 4: I Love You Forever and Ever a man as long

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Speaker 4: as Old Women talk about Old Man or something like that,

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Speaker 4: and didn't quite bail it, but it's apparently a really

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Speaker 4: really sweet song. And he was known for having a

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Speaker 4: very kind of non traditional, very laid back delivery, and

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Speaker 4: that was sort of his you know, stocking trade, and

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Speaker 4: so he was very beloved, not necessarily known as like

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Speaker 4: a belter per se or having like this some kind

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Speaker 4: of crazy range, much more of an everyman kind of singer.

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Speaker 4: You know.

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Speaker 2: On the other hand, that's worth it for like twelve people.

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Speaker 4: On the other hand, is that a ready Travis dress.

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Speaker 2: On the other head, of course, he is a we're

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Speaker 2: talking about this off air. He's kind of a comfortable,

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Speaker 2: hangout singer. That's exactly right, right.

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Speaker 4: So essentially what's happened here, and again, this is an

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Speaker 4: initiative through this record company, Warner you know, Warner Brothers,

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Speaker 4: Warner Media whatever their record wing of that giant media conglomerate.

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Speaker 4: But I would argue a pretty good one of like, hey,

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Speaker 4: we're going to try it out the use of this

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Speaker 4: AI technology to give Randy Travis his voice back, with

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Speaker 4: full cooperation from Randy himself, from his legacy producer of

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Speaker 4: many years, and a fellow country singer named James Dupree

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Speaker 4: who kind of laid down what you might call the

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Speaker 4: guide vocal for the track. So basically, you sing the

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Speaker 4: song kind of knowing how Randy's cadences might go and

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Speaker 4: the way he might deliver the song, and then by

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Speaker 4: going through forensically scraping I guess, I mean, I think

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Speaker 4: that terms kind of become a little gross too, but

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Speaker 4: you get what I'm saying. His back catalog. I think

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Speaker 4: they took forty two of his isolated vocal tracks, which

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Speaker 4: you might call stems in the parlance of recording. It's

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Speaker 4: just the vocal part solo. No AI required, right, because

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Speaker 4: you have the original master tapes, which is multi track,

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Speaker 4: so all of those things can be then split out out.

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Speaker 4: So forty two of his vocal isolated recordings were then analyzed,

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Speaker 4: let's say, and overlaid on top of this James Dupree vocal,

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Speaker 4: and apparently the results A it's charting, it's debuted where

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Speaker 4: that came from the song, the new song from Randy Travis.

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Speaker 4: And in this situation where there's agency, I think you

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Speaker 4: can call it by Randy Travis. What do you guys

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Speaker 4: think about that? Let let's start with that first of all,

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Speaker 4: if there is co signed by the artist himself, not

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Speaker 4: Drake dropping an AI rap verse on a you know,

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Speaker 4: disk track from Tupac of course, who is not with us,

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Speaker 4: and also from Snoop Dogg. Weirdly, we actually didn't talk

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Speaker 4: about that in our Drake segment. That's one of the

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Speaker 4: weirdest parts of that whole rap u. That is no

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Speaker 4: agency that is done without the permission of the people

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Speaker 4: living or deceased, their families that have that have been

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Speaker 4: left behind, and you know, Tupac's estate had a real

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Speaker 4: problem with that and had it taken down. But in

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Speaker 4: this case there has been all of the things and

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Speaker 4: Randy himself. So can you really call this a Randy

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Speaker 4: Travis song? This is really kind of a new frontier

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Speaker 4: of media, Like he didn't actually sing it. This other

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Speaker 4: guy sang it, but then they took his voice and

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Speaker 4: they overlaid it and he was involved. You know, it's

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Speaker 4: very interesting question. I don't know what you guys think

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Speaker 4: about that, And then we'll get to some other stuff

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Speaker 4: with some other aspects of this story.

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Speaker 3: Well, I haven't heard it, guys. Does it sound like

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Speaker 3: it's just him recording in a booth?

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Speaker 4: I think so. I mean, you know this kind of

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Speaker 4: very polished country. It's got a certain sheen to it.

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Speaker 4: You know a lot of theah, there's auto tune used

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Speaker 4: in big country artists, and auto tune again as a tool,

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Speaker 4: he's gotten more and more transparent where it doesn't sound

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Speaker 4: like auto tune. There's even this other auto tune is

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Speaker 4: actually these days, in my experience, used more to sound

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Speaker 4: like auto tune, where it is very much meant to

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Speaker 4: be an effect, like in trap music or or you know,

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Speaker 4: a Polish pop. It's almost like that robot voice that you.

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Speaker 2: Know he being could actually singul to know that. Check

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Speaker 2: out the Tiny Desk concert. The guy has a voice

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Speaker 2: of an angel.

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Speaker 4: Picasso could paint landscapes. The guy it was classically trained artists.

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Speaker 4: He just chose to paint like a like a small child.

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Speaker 4: I'm kidding. I love Picasso, but I'm saying people often

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Speaker 4: joke about like, how, oh man, anybody could do that?

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Speaker 4: Blah blah blah. No, a lot of these artists, they

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Speaker 4: made those choices. That is often the case with auto

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Speaker 4: tune these days. It's a choice, it's an aesthetic choice.

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Speaker 4: Melodine is another tool that is much more for correcting mistakes,

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Speaker 4: correcting little warbles, and it's real precise and I've used

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Speaker 4: it and it's it's miraculous. You've probably seen uh sort

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Speaker 4: of jokey videos online where someone will take a song

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Speaker 4: and retune it and like make it sound completely different.

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Speaker 4: That's all using melodye. So in my opinion, it does

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Speaker 4: sound like a real Randy Travis on me not being

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Speaker 4: a you know, Randy Travis super fan, I couldn't say

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Speaker 4: for sure, but by all accounts, he loved it. Apparently

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Speaker 4: he you know, when they played it for him. There's

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Speaker 4: a video of him kind of taking it all in

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Speaker 4: and you see him sort of go through this range

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Speaker 4: of emotions, and I believe the emotion that he lands

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Speaker 4: on is gratitude. He uses the word grace, you know,

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Speaker 4: in his discussion of this song, and he believes this

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Speaker 4: was a form of a divine second chance.

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Speaker 2: So like when a Frank Sinatra watched Pavati singing some

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Speaker 2: of his hits.

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Speaker 4: That's a good point, man, That's my question to you, guys.

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Speaker 4: Though it is like that sort of but it's also

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Speaker 4: different because it is charting as a new song from

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Speaker 4: the country megastar Randy Travis. But it's also that ship

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Speaker 4: of thesis kind of question, isn't it guys? Like, is

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Speaker 4: it really him? If it's actually sung by someone else

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Speaker 4: in the overlaid with pieces of him? I don't know.

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Speaker 4: I think it's that's a philosophical question, right, I don't

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Speaker 4: really have the answer.

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Speaker 2: Well, it's licensing at that point.

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Speaker 4: It is licensing, that's right, it's But at the same time,

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Speaker 4: it's a little different than Abba or Kiss selling their

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Speaker 4: likenesses to the the Hologram factory, you know, for constant

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Speaker 4: perpetual touring. You know, that's on rails. They've created this show.

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Speaker 4: There's see this is kind of on rails too. I

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Speaker 4: don't know, guys, I'd love to hear from conspiracy realists

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Speaker 4: out there. Is this really Randy Travis? Is that? Even

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Speaker 4: the point? I don't know. His producer, Kyle Lemming is

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Speaker 4: the one who went through the old recordings. The lyrics

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Speaker 4: are ten years old. So this is something that I

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Speaker 4: think was meant for him in the first place. I'm

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Speaker 4: not one hundred percent sure, but it does have three

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Speaker 4: point three million streams in its very first week, which

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Speaker 4: is pretty neat. I just think this is sort of

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Speaker 4: like a feel good, a high story in the midst

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Speaker 4: of a lot of real scary, doom and gloom AI stories.

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Speaker 4: And I think I don't think anybody could argue that

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Speaker 4: this is a pretty interesting use of this tool, you know,

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Speaker 4: and other AI related I guess positive news or you

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Speaker 4: know where AI with agency news If anyone's familiar with

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Speaker 4: the singer and dancer, pretty amazing artist fka Twigs. She

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Speaker 4: actually spoke before like a house committee, I believe, on

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Speaker 4: the use of AI, and she has developed an AI

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Speaker 4: deep fake avatar of herself that she with agency from

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Speaker 4: her and her team want to be able to have

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Speaker 4: interact with fans. So, you know, is it a genie

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Speaker 4: out of the bottle kind of situation? If it's done correctly,

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Speaker 4: can you put the reins on it? Can it be

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Speaker 4: rained in? I guess there's a better way of putting that.

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Speaker 4: I don't know. I'd like to think so, but I

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Speaker 4: want to end. But I want to end this segment

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Speaker 4: unless you guys had any other stuff to add.

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Speaker 3: No, I can't wait to listen to the song.

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Speaker 4: I also want to give it another listen.

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Speaker 2: I will point out again broken record style. Hope me

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Speaker 2: on this. It is fundamentally philosophically fraudulent to describe any

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Speaker 2: intelligence as artificial.

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Speaker 4: It's fair. I think that's fair and a good reminder.

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Speaker 4: So this is really quick, and this is really just

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Speaker 4: for for laws, y'all. I think this is a really

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Speaker 4: sweet story. I recently came across a kind of a

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Speaker 4: new I guess burgeoning hilarious sort of joke scene on

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Speaker 4: the internet. We talked about dead Internet theory. A lot

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Speaker 4: of that is being perpetuated and really escalated by AI

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Speaker 4: content creation, right, but a lot of that stuff, I mean,

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Speaker 4: it's gonna get more advanced, like it does require prompts

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Speaker 4: from human beings. And I stumbled upon a treasure trove

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Speaker 4: of AI generated country songs. And this one here was

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Speaker 4: entirely AI generated. A human had to have prompted it.

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Speaker 4: Because you're gonna hear some some real themes here, y'all. Well,

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Speaker 4: let's just this is here real quick. This is a

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Speaker 4: big red cups. I got beer in my boots, dirn

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Speaker 4: on the truck, corn on the mone and a god

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Speaker 4: of my butt.

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Speaker 5: It's a stop town, leister's dirt, bleister's beer trucks.

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Speaker 4: And sure.

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Speaker 2: This is not bo Burnham, by the way.

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Speaker 4: Consent butts, red cups, dirty beer, folks.

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Speaker 2: I wish you could see Matt's pace right now. I

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Speaker 2: wish you could see an old dancing to this one.

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Speaker 5: The butts are proud, y'all, biscuit in my boot?

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Speaker 4: Sorry, all right enough, Oh my goodness, gracious, what do

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Speaker 4: you guys think about that?

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Speaker 3: I mean, guys, I live out in the country.

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Speaker 4: Now, yeah, you got big, you got boots and boots, trucks,

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Speaker 4: get mud, and there are any guns and butts out there?

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Speaker 3: Of course that's all. It's all true. No, no, it's

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Speaker 3: I feel offended on behalf of everybody that lives within

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Speaker 3: with several miles Listen.

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Speaker 4: That's why I point out that someone didn't just right

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Speaker 4: AI make a country song. Someone said make a country

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Speaker 4: song about guns and butts. Okay, that didn't come out

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Speaker 4: of whole cloth, I guarantee, because as we know, with

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Speaker 4: these tools, they require prompts, and sometimes these prompts can

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Speaker 4: be very targeted and focused, whether it be for visual

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Speaker 4: stuff or what have you. I thought that was really funny.

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Speaker 4: It made me laugh. I shared it with anyone who

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Speaker 4: would listen. Now I shared it with you.

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Speaker 2: AI is o Henry's Monkey Paul, also coming from parts

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Speaker 2: of rural Appalachia. I think they would dig this song.

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Speaker 4: I think they would do. It's a banger of a chorus, yeah,

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Speaker 4: and it certainly does when you when you when you

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Speaker 4: step away from the jokiness of it. If an AI

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Speaker 4: did compose the melody and hook of that, whether the

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Speaker 4: prompt you know that probably Again, I haven't messed with

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Speaker 4: one of these audio AI things like that, but pretty

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Speaker 4: impressive and a little scary in terms of what that

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Speaker 4: means for pop music and artists like James Blake. I've

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Speaker 4: really come out saying how the record industries are really

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Speaker 4: pushing this narrative of like ten second clips for a TikTok,

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Speaker 4: five second, fifteen second clips from TikTok, which is pushing

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Speaker 4: the way music is consumed much more towards a world

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Speaker 4: where AI generated stuff would fly.

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Speaker 2: And that's not coolish. I would dig this song.

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Speaker 4: It's a real fist pumper, y'all, and I just think

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Speaker 4: it's gonna get weirder from there. So I use that

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Speaker 4: kind of jokey bit just to point out some of

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Speaker 4: the scary parts of this stuff too. Weren't you weren't

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Speaker 4: going to get an AI story on stuff that I

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Speaker 4: want you to know without a little bit of doom

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Speaker 4: sprinkled in there for good measure. So at least we

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Speaker 4: could wrap that doom. And you know, guns and butts.

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Speaker 3: Big red cups, dirty trucks, dirty truck dirty but.

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Speaker 4: A biscuit in my boot, corn on my mind. You know,

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Speaker 4: you know, I'm always out there thinking about that corn. Wow.

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Speaker 4: I don't know, guys, this is fun and scary and sweet.

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Speaker 4: I'm feeling a lot of emotions. Let's take a quick

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Speaker 4: break here, a word from our sponsor, and then come

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Speaker 4: back with more strange news.

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Speaker 3: And we've returned. Guys, We've got a bit of a

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Speaker 3: mystery that's going to take us down let's say a

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Speaker 3: forking path, and we'll explore it as it forks. The

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Speaker 3: first story comes from CBS News or k COW News.

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Speaker 3: That's a California. It was posted on May eighth, twenty

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Speaker 3: twenty four, and it is regarding one woman's experience. The

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Speaker 3: title is hidden Camera Found planted outside of Chino Hills

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Speaker 3: woman's home. And I'm just going to read a couple

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Speaker 3: of the details here and then let's discuss A woman

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Speaker 3: living in Chino Hills, California, was disturbed to learn that

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Speaker 3: a camera disguised as a rock had been planted outside

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Speaker 3: of her home. The camera was planted in the ground

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Speaker 3: across the street from her house. It's believed that the

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Speaker 3: camera was placed on April twenty ninth, where it sat

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Speaker 3: for several days before being spotted by a neighbor. It

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Speaker 3: was a camera wrapped in clay to make it look

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Speaker 3: like a rock, and it was attached via cable to

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Speaker 3: a very large power bank that was actually larger than

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Speaker 3: the entire you know, fake rock itself. So like a

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Speaker 3: long term yeah, just sitting there across the street from

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Speaker 3: this house. The neighbor who found it was able to

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Speaker 3: actually download some of the images from it, so it's

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Speaker 3: not as though it was attached to Wi Fi. Well

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Speaker 3: maybe it was, but if it was attached to Wi Fi,

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Speaker 3: it was also capturing images locally to the device. So

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Speaker 3: the neighbor downloaded an image from the camera that showed

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Speaker 3: the moment the hand places it there. Somebody was wearing

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Speaker 3: a glove like looks like a gardener or something, places

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Speaker 3: it down there. But then the neighbors, knowing the date

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Speaker 3: and time, looked at their own security cameras and found

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Speaker 3: the moment that somebody rides up on a scooter and

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Speaker 3: just kind of, you know, stops for a moment, goes down,

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Speaker 3: puts something or messes with the ground in some way, right,

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Speaker 3: and then just rides right off. And that's it. That's

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Speaker 3: all they know. Uh So everybody is just trying to

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Speaker 3: figure out why, and the woman obviously is going, oh, okay,

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Speaker 3: what am I dealing with here? We've got several options, guys,

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Speaker 3: what do you think is most likely that this could be.

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Speaker 2: It's tough to guess without more information.

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Speaker 3: Okay, no, right options. I'll throw the first one out.

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Speaker 3: What if it is, and this one's way out there.

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Speaker 3: What if it is a BTK style person that is

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Speaker 3: casing a human being, watching a human being to see

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Speaker 3: their movements, their patterns and everything like that, like a serial.

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Speaker 2: Killer, a predator scoping like the bomb torture killed Dennis

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Speaker 2: Radar guy. Cinematic possible, but maybe not plausible. Those fates

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Speaker 2: are kind of rare.

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Speaker 3: Okay, Well, what would be more common then maybe uh,

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Speaker 3: burglar or someone who's casing the joint just to steal

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Speaker 3: from it.

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Speaker 2: Yeah, that's I almost said a good one. That's a

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Speaker 2: more plausible one.

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Speaker 4: It's also a commitment to the bit, right, I mean,

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Speaker 4: you know.

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Speaker 2: The possibility of a stalker without necessarily serial killer level

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Speaker 2: predation at this point. You know, Matt, in the notes

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Speaker 2: you you said, this could also just be someone trying

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Speaker 2: to rob a house or chasing the joint for the

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Speaker 2: worst hoa ever.

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Speaker 3: Yeah, it could be that. What if it's a private

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Speaker 3: investigator that was hired by some outside party that is

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Speaker 3: trying to capture something specific or activity going on?

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Speaker 4: Oh? Well, have you guys been watching this show Sugar

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00:21:42,480 --> 00:21:46,800
Speaker 4: on on Apple TV. It's a private investigator type show.

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Speaker 4: It's set in the modern day, but it's kind of

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Speaker 4: got these old school you know, Noir detective advised. But

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Speaker 4: dude is constantly placing hidden cameras on stuff, right all

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Speaker 4: the time.

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Speaker 2: Oh yeah, bigtim We'll get in situations.

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Speaker 3: Well, that's a common thing, right, especially if you're in

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Speaker 3: a cheating spouse situation, A PI will place camera us

404
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Speaker 3: in front.

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Speaker 4: To be completely wireless, it would have to have a

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Speaker 4: big old power supply like that you couldn't how would

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Speaker 4: it would be such a giveaway if you tapped somebody's

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00:22:13,480 --> 00:22:15,680
Speaker 4: electricity or you know, it have to have a big

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Speaker 4: old battery too, and then you probably have to go

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Speaker 4: back in and replace it with a charge one.

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Speaker 2: And not to victim blame at all in any way,

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Speaker 2: but to understand this story, Matt, it sounds like we

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Speaker 2: need to learn more about the woman in Chino Hills

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Speaker 2: she identified, Like is there do we know anything about

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Speaker 2: her background or possible like malevolent forces that might have interacted.

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Speaker 3: With No, we have, We have no information. This is

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Speaker 3: like a one off story that at least to my knowledge,

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Speaker 3: came out last week and it's that's all I've seen

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Speaker 3: about it. And I haven't even pursued it further because

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Speaker 3: I kind of don't want to know. That's that lady's business.

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Speaker 3: I don't want to whatever she's getting into. The reason

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Speaker 3: I'm bringing to you guys is to kind of go

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Speaker 3: down this pathway of why would someone place a hidden

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Speaker 3: camera across the street from a house. The only other

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Speaker 3: thing I have like thinking about it would be some

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00:23:07,160 --> 00:23:11,159
Speaker 3: kind of law enforcement issue where they're trying to watch

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Speaker 3: either a house or a specific walkway where people are traveling.

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Speaker 3: But usually for law enforcement, you would have to have

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Speaker 3: a human being monitoring that stuff. So you've got that

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Speaker 3: chain of custody thing.

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Speaker 4: Right, Yeah, in a windowless van, right.

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Speaker 3: Yeah, usually a human being on hand somewhere making sure

433
00:23:30,040 --> 00:23:32,560
Speaker 3: that at all times people know what is happening in

434
00:23:32,680 --> 00:23:33,760
Speaker 3: time stamping things.

435
00:23:34,119 --> 00:23:35,959
Speaker 2: So and also I hope she's okay.

436
00:23:36,240 --> 00:23:38,280
Speaker 4: Oh absolutely, I was joking about what did she do?

437
00:23:38,960 --> 00:23:40,840
Speaker 4: No victim? Let me hear at all. I just want

438
00:23:40,840 --> 00:23:42,399
Speaker 4: to add, Matt. I think one thing that piques all

439
00:23:42,400 --> 00:23:45,639
Speaker 4: of our interests about this is the very clever, you know,

440
00:23:45,840 --> 00:23:48,880
Speaker 4: camouflaging of the camera. It's not something that I've really

441
00:23:48,920 --> 00:23:51,160
Speaker 4: thought about it or seen before. It's like a fake

442
00:23:51,240 --> 00:23:53,560
Speaker 4: rock looking thing. Somebody puts some thoughts or time and

443
00:23:53,560 --> 00:23:54,399
Speaker 4: some energy into.

444
00:23:54,280 --> 00:23:56,040
Speaker 3: This, Yeah, but then they just kind of covered it

445
00:23:56,080 --> 00:24:00,639
Speaker 3: with leaves with a giant metal power thing attached to it.

446
00:24:00,680 --> 00:24:03,800
Speaker 4: Okay, that's like a tracking compliment, You're right.

447
00:24:04,320 --> 00:24:08,640
Speaker 3: It's just it's a weird combination of things, right, which

448
00:24:08,680 --> 00:24:09,640
Speaker 3: just makes you go, what.

449
00:24:09,720 --> 00:24:11,000
Speaker 4: Is going on? Laziness?

450
00:24:11,440 --> 00:24:14,840
Speaker 2: Yeah, I guess so it is possible to be stupidly smart.

451
00:24:15,160 --> 00:24:17,879
Speaker 3: Hey, there you go. There you go. So, guys, the

452
00:24:17,920 --> 00:24:20,240
Speaker 3: next story that I wanted to jump to from this

453
00:24:20,359 --> 00:24:24,360
Speaker 3: thinking about this story is a story here from Boston

454
00:24:24,440 --> 00:24:27,080
Speaker 3: twenty five News from a while ago. This is April

455
00:24:27,119 --> 00:24:30,040
Speaker 3: twenty sixth, But it made me think about this story.

456
00:24:30,720 --> 00:24:33,880
Speaker 3: There was a group of four men who were recently

457
00:24:34,040 --> 00:24:40,640
Speaker 3: arrested for casing and burglarizing forty three homes. These four

458
00:24:40,720 --> 00:24:45,640
Speaker 3: men carried out forty three successful burglaries and stole more

459
00:24:45,680 --> 00:24:49,520
Speaker 3: than four million dollars worth of that's an estimate worth

460
00:24:49,560 --> 00:24:53,760
Speaker 3: of valuables. It was across twenty five towns in Massachusetts

461
00:24:53,760 --> 00:24:55,480
Speaker 3: from twenty eighteen to twenty twenty four.

462
00:24:55,680 --> 00:24:57,199
Speaker 2: Oh that's a long time window.

463
00:24:57,359 --> 00:25:00,280
Speaker 3: It is a really long time window. And here is

464
00:25:00,359 --> 00:25:03,080
Speaker 3: the part that astounded me in reading the story. You guys,

465
00:25:04,000 --> 00:25:06,320
Speaker 3: think about all the ring cameras out there, all of

466
00:25:06,359 --> 00:25:09,080
Speaker 3: the simply safe and blah blah blah, all the r

467
00:25:09,119 --> 00:25:12,560
Speaker 3: low cameras. These guys were only caught on camera out

468
00:25:12,600 --> 00:25:17,560
Speaker 3: of forty three burglaries one time. One time. Isn't that

469
00:25:17,680 --> 00:25:20,359
Speaker 3: kind of astounding. It makes you wonder how in the

470
00:25:20,400 --> 00:25:23,280
Speaker 3: heck they could do that. And I learned about something

471
00:25:23,320 --> 00:25:27,080
Speaker 3: through this article. I didn't know that you could jam

472
00:25:27,200 --> 00:25:30,640
Speaker 3: Wi Fi signals in a local area as somebody who

473
00:25:30,720 --> 00:25:33,120
Speaker 3: was not law enforcement or not, you know, and in

474
00:25:33,280 --> 00:25:36,479
Speaker 3: working for an intelligence company, you can just get a

475
00:25:36,520 --> 00:25:41,760
Speaker 3: device that will jam Wi Fi signals, essentially making all

476
00:25:41,800 --> 00:25:45,200
Speaker 3: of these camera systems inert because if they're not connected

477
00:25:45,240 --> 00:25:48,280
Speaker 3: to the Wi Fi generally they don't function unless there

478
00:25:48,280 --> 00:25:50,360
Speaker 3: are a specific type of camera. Of some of them

479
00:25:50,400 --> 00:25:52,679
Speaker 3: will function whether they have signal or not, and they

480
00:25:52,760 --> 00:25:54,119
Speaker 3: store locally and then upload.

481
00:25:54,200 --> 00:25:56,119
Speaker 4: Yeah, really popular one is called the camera.

482
00:25:56,160 --> 00:25:58,320
Speaker 3: Mamma jamma, camera, mamma jamma.

483
00:25:58,440 --> 00:25:59,680
Speaker 4: I just made that up. That's not true.

484
00:25:59,720 --> 00:26:01,719
Speaker 3: Oh I thought it is.

485
00:26:01,760 --> 00:26:05,520
Speaker 2: Now we just lack mirrored it. Also, also, it is,

486
00:26:06,000 --> 00:26:10,000
Speaker 2: to be fair, folks, it is completely legal to buy

487
00:26:10,040 --> 00:26:13,160
Speaker 2: those products or to manufacture them on your own.

488
00:26:13,720 --> 00:26:18,640
Speaker 4: Is that because it's all public air, like where you're

489
00:26:18,680 --> 00:26:21,320
Speaker 4: jamming at like like I don't know, like who owns

490
00:26:21,600 --> 00:26:22,000
Speaker 4: the air?

491
00:26:22,320 --> 00:26:28,080
Speaker 2: Okay, it's like it's illegal to shoot a person in general,

492
00:26:28,440 --> 00:26:30,840
Speaker 2: but it is not illegal to buy a gun. It

493
00:26:30,960 --> 00:26:35,200
Speaker 2: is legal to buy these sorts of things, these contraptions

494
00:26:35,240 --> 00:26:39,119
Speaker 2: and the components that compose those contraptions, but the way

495
00:26:39,240 --> 00:26:44,280
Speaker 2: you use them determines the legality or the illegality and.

496
00:26:44,320 --> 00:26:45,560
Speaker 4: These great architectors.

497
00:26:45,640 --> 00:26:49,080
Speaker 2: Right, Yeah, it's a good comparison. I don't know why

498
00:26:49,119 --> 00:26:51,600
Speaker 2: I say comparison, Like I don't speak this language. I'm

499
00:26:51,640 --> 00:26:52,680
Speaker 2: still learning it.

500
00:26:52,760 --> 00:26:54,680
Speaker 4: But no, it's one of those things that, like you know,

501
00:26:54,760 --> 00:26:57,639
Speaker 4: has intended for law enforcement or what have you, and

502
00:26:57,640 --> 00:26:59,159
Speaker 4: then it made its way to the public sector. I

503
00:26:59,160 --> 00:27:03,119
Speaker 4: think it's technically a legal to use them for nefarious purposes,

504
00:27:03,160 --> 00:27:06,840
Speaker 4: but it's a pretty gray area about that kind of stuff.

505
00:27:06,880 --> 00:27:09,560
Speaker 2: Yeah, that's the right color for it. It's a great area.

506
00:27:09,680 --> 00:27:12,840
Speaker 2: And these So these guys did get caught though, right Matt,

507
00:27:12,920 --> 00:27:16,320
Speaker 2: despite only being sited on camera once like some sort

508
00:27:16,359 --> 00:27:19,240
Speaker 2: of true crime cryptid. They did get popped.

509
00:27:19,680 --> 00:27:23,120
Speaker 3: They did get caught. They're currently charged. So I guess

510
00:27:23,200 --> 00:27:25,560
Speaker 3: this stuff is they allegedly did all these things. They

511
00:27:25,560 --> 00:27:28,640
Speaker 3: haven't gone through the whole process, right, but they did

512
00:27:28,680 --> 00:27:31,439
Speaker 3: get caught. There was one other thing. This was a

513
00:27:31,440 --> 00:27:36,000
Speaker 3: sophisticated group of folks doing this because they also never

514
00:27:36,040 --> 00:27:38,720
Speaker 3: brought cell phones with them when they were, you know,

515
00:27:38,800 --> 00:27:41,240
Speaker 3: casing a joint or going into a place. They were

516
00:27:41,320 --> 00:27:45,359
Speaker 3: very careful with their signals intelligence. Basically they almost wouldn't

517
00:27:45,400 --> 00:27:48,399
Speaker 3: have gotten caught, probably just because they really were moving

518
00:27:48,480 --> 00:27:50,960
Speaker 3: pretty effectively in the shadows, I think, unless there were

519
00:27:51,000 --> 00:27:53,160
Speaker 3: some kind of sting or something like that. But hey,

520
00:27:53,200 --> 00:27:55,359
Speaker 3: this is a weird one. They got caught. That's good.

521
00:27:55,920 --> 00:27:57,840
Speaker 3: I don't know how you really protect yourself against that

522
00:27:57,960 --> 00:28:01,680
Speaker 3: stuff unless I don't know, you just super upgrade your

523
00:28:01,760 --> 00:28:05,439
Speaker 3: security systems and think about all the potential holes and

524
00:28:05,480 --> 00:28:08,119
Speaker 3: then go crazy and paranoid and maybe don't do that.

525
00:28:08,480 --> 00:28:11,359
Speaker 2: Spend your life unhappily anxious.

526
00:28:11,640 --> 00:28:14,200
Speaker 3: Yeah, let's not do that. That's not a great idea.

527
00:28:14,480 --> 00:28:16,679
Speaker 3: Take it from me somebody who has dealt with that

528
00:28:16,800 --> 00:28:19,080
Speaker 3: and is always and is still dealing with that.

529
00:28:19,160 --> 00:28:21,000
Speaker 2: All right, maybe we should get some coffee.

530
00:28:21,640 --> 00:28:26,399
Speaker 3: Hey, there we go. Uh well, so here's another potential

531
00:28:26,720 --> 00:28:29,160
Speaker 3: reason for that camera to be out there. By way

532
00:28:29,160 --> 00:28:32,480
Speaker 3: of another story that was in The Guardian on May eleventh,

533
00:28:32,600 --> 00:28:36,800
Speaker 3: titled US woman charged with concealing bleach in husband's coffee

534
00:28:37,119 --> 00:28:40,880
Speaker 3: avoids jail. Okay, so here's one to get all our

535
00:28:40,960 --> 00:28:47,160
Speaker 3: paranoias up. There was a US Navy serviceman who was

536
00:28:47,400 --> 00:28:52,520
Speaker 3: stationed outside of the United States. He noticed that the

537
00:28:52,560 --> 00:28:54,720
Speaker 3: coffee he was drinking in the mornings that he would

538
00:28:54,720 --> 00:28:58,959
Speaker 3: brew it home, started tasting a little off, so he

539
00:28:59,040 --> 00:29:03,080
Speaker 3: decided to weirdly enough to check it for pool chemicals

540
00:29:03,480 --> 00:29:06,040
Speaker 3: like chlorine in those kinds of things. Sure, so he

541
00:29:06,120 --> 00:29:08,400
Speaker 3: checked it and turns out, oh, there's bleach in there.

542
00:29:08,840 --> 00:29:12,920
Speaker 3: So he thought, how did bleach get in my coffee? Like,

543
00:29:13,040 --> 00:29:15,120
Speaker 3: checks out the coffee maker. Oh yeah, the water in

544
00:29:15,160 --> 00:29:18,800
Speaker 3: there tastes real funny, cleans it out, puts new water in,

545
00:29:19,480 --> 00:29:22,360
Speaker 3: and then all of a sudden, it tastes weird again.

546
00:29:22,960 --> 00:29:26,600
Speaker 3: So he sets up a hidden camera in his kitchen

547
00:29:27,000 --> 00:29:31,080
Speaker 3: and he watches his spouse put something into the coffee

548
00:29:31,120 --> 00:29:34,160
Speaker 3: maker's water holder, right in the back of the coffee maker,

549
00:29:34,680 --> 00:29:36,560
Speaker 3: and he's like, huh, I wonder what that was. That

550
00:29:36,680 --> 00:29:39,680
Speaker 3: was really weird. So then he waits until they move

551
00:29:39,800 --> 00:29:41,960
Speaker 3: back to the United States. He just by the way,

552
00:29:42,000 --> 00:29:44,080
Speaker 3: he pretends like he drinks his coffee in the morning.

553
00:29:44,400 --> 00:29:47,000
Speaker 3: From this point on, he doesn't actually drink it. He

554
00:29:47,040 --> 00:29:48,640
Speaker 3: knows there's something in it, and his wife puts something

555
00:29:48,640 --> 00:29:50,680
Speaker 3: in it. He's not sure what's going on, so he

556
00:29:50,720 --> 00:29:53,040
Speaker 3: waits till he get back to the US. Then he

557
00:29:53,080 --> 00:29:55,880
Speaker 3: sets up another camera in this new kitchen and he

558
00:29:56,040 --> 00:29:59,920
Speaker 3: catches her taking bleach, putting it into container and then

559
00:30:00,080 --> 00:30:03,440
Speaker 3: putting that the contents of that container into the coffee maker,

560
00:30:04,160 --> 00:30:05,600
Speaker 3: and she had to go to jail.

561
00:30:06,320 --> 00:30:09,360
Speaker 4: But wait, didn't her husband like argue in her favor

562
00:30:09,560 --> 00:30:13,080
Speaker 4: against long term jail time And she got off with

563
00:30:13,160 --> 00:30:17,000
Speaker 4: some like probation. And then they I think they're getting

564
00:30:17,040 --> 00:30:19,400
Speaker 4: divorced or they just you know, but they're still living

565
00:30:19,440 --> 00:30:23,200
Speaker 4: together with their kid. Yeah, why do you tell the

566
00:30:23,360 --> 00:30:26,600
Speaker 4: kid mommy and daddy love each other very much, but

567
00:30:27,360 --> 00:30:30,440
Speaker 4: mommy tried to kill daddy or was.

568
00:30:30,440 --> 00:30:33,400
Speaker 2: It more of a Munchausen's by proxy situation?

569
00:30:33,760 --> 00:30:36,520
Speaker 4: It's a good question this. Well, I mean, we obviously

570
00:30:36,560 --> 00:30:40,280
Speaker 4: don't know any motive right in terms of abuse or

571
00:30:40,280 --> 00:30:40,920
Speaker 4: anything like that.

572
00:30:41,280 --> 00:30:43,600
Speaker 3: Right, But again, I guess what I'm saying is, I

573
00:30:43,640 --> 00:30:46,200
Speaker 3: guess there are a lot of reasons to want to

574
00:30:46,280 --> 00:30:49,320
Speaker 3: have cameras set up and to conceal those cameras so

575
00:30:49,400 --> 00:30:51,640
Speaker 3: the people you're trying to capture don't know they're there.

576
00:30:51,760 --> 00:30:53,200
Speaker 3: It's just a weird situation.

577
00:30:53,520 --> 00:30:55,240
Speaker 4: Can you imagine the thought process?

578
00:30:55,440 --> 00:30:58,680
Speaker 2: I like that there's a thematic point here too, because

579
00:30:59,600 --> 00:31:04,880
Speaker 2: in general, now one must always assume one is being

580
00:31:04,960 --> 00:31:08,480
Speaker 2: filmed or recorded in some way. It's just the safest

581
00:31:08,520 --> 00:31:12,360
Speaker 2: assumption we're not trying to ruin anybody's day or anything

582
00:31:12,440 --> 00:31:12,760
Speaker 2: like that.

583
00:31:12,920 --> 00:31:16,080
Speaker 4: And I also don't think we're all necessarily proposing that

584
00:31:16,560 --> 00:31:19,960
Speaker 4: we put round the clock hidden cameras in every aspect

585
00:31:19,960 --> 00:31:22,120
Speaker 4: of our life to make sure no one's trying to poisonous.

586
00:31:22,240 --> 00:31:24,520
Speaker 4: What I was gonna ask, though, Matt, was can you guys,

587
00:31:24,560 --> 00:31:27,680
Speaker 4: are both of you can you imagine the thought process

588
00:31:27,840 --> 00:31:30,760
Speaker 4: that would lead to this conclusion? And like the slew thing,

589
00:31:30,840 --> 00:31:33,760
Speaker 4: and like, surely she's not Wait a minute, maybe she is.

590
00:31:34,160 --> 00:31:35,920
Speaker 4: Oh god, she definitely is.

591
00:31:36,880 --> 00:31:39,440
Speaker 2: You immediately test for pool chemistry.

592
00:31:39,480 --> 00:31:43,719
Speaker 4: That's a weird one the context, you know, who like

593
00:31:43,920 --> 00:31:45,280
Speaker 4: have you like we've.

594
00:31:45,040 --> 00:31:47,920
Speaker 2: All had beat me here, doc, and thank you. We've

595
00:31:47,920 --> 00:31:51,239
Speaker 2: all had the drinks at some point, right, you know,

596
00:31:51,400 --> 00:31:53,960
Speaker 2: you get something from a fast food place that soda

597
00:31:54,000 --> 00:31:58,720
Speaker 2: founds off. Who on earth? Like what leads you to

598
00:31:58,760 --> 00:32:01,280
Speaker 2: this point in your life where you sip coffee it

599
00:32:01,400 --> 00:32:04,160
Speaker 2: tastes off and you say, time to test for the

600
00:32:04,200 --> 00:32:05,120
Speaker 2: pool chemicals.

601
00:32:05,440 --> 00:32:08,880
Speaker 4: There's that bit in the pilot episode of twin Peaks

602
00:32:09,120 --> 00:32:13,000
Speaker 4: where the guy goes there was a fish in the percolat.

603
00:32:15,360 --> 00:32:17,560
Speaker 4: That's what it made me think. But guys, I mean

604
00:32:17,600 --> 00:32:20,479
Speaker 4: I don't know if anyone's accidentally tasted a little bleach

605
00:32:20,560 --> 00:32:23,280
Speaker 4: in their life. I haven't, but I know what it

606
00:32:23,360 --> 00:32:25,720
Speaker 4: smells like, and we know that there's a lot of

607
00:32:25,720 --> 00:32:28,520
Speaker 4: connection between taste and smell. I could imagine that he

608
00:32:28,600 --> 00:32:32,000
Speaker 4: tested for pool chemicals because the coffee tasted a little bleachy.

609
00:32:32,240 --> 00:32:34,640
Speaker 3: Well, yeah, and it was a taste that lingered for

610
00:32:34,680 --> 00:32:37,440
Speaker 3: a long time. And again, like you just want to know, Well,

611
00:32:37,520 --> 00:32:40,600
Speaker 3: is it just this batch of coffee that's tainted or

612
00:32:40,760 --> 00:32:42,880
Speaker 3: is something wrong with my coffee maker? What the heck's

613
00:32:42,920 --> 00:32:44,440
Speaker 3: going on? Maybe it was in my cup?

614
00:32:45,480 --> 00:32:48,479
Speaker 4: Yeah, Well the gaslightingness of it all too, is the

615
00:32:48,520 --> 00:32:51,880
Speaker 4: whole Like, am I insane? Like I totally cleaned out

616
00:32:51,920 --> 00:32:54,440
Speaker 4: the coffee maker and now it's back. You know, at

617
00:32:54,480 --> 00:32:57,440
Speaker 4: what point does he decide I better get to the

618
00:32:57,440 --> 00:32:59,920
Speaker 4: bottom of this. This isn't in my head he had.

619
00:33:00,040 --> 00:33:02,000
Speaker 4: You have wrestled with that for at least a minute.

620
00:33:02,040 --> 00:33:02,640
Speaker 4: You know, is he.

621
00:33:02,720 --> 00:33:05,080
Speaker 2: Testing everything for pool chemicals?

622
00:33:05,240 --> 00:33:08,960
Speaker 3: Well, he had test strips, right, and his first thought was, oh, crap,

623
00:33:09,360 --> 00:33:12,160
Speaker 3: something is getting into our water supply because they were

624
00:33:12,200 --> 00:33:14,720
Speaker 3: stationed in Germany, and he was like, maybe there's just

625
00:33:14,720 --> 00:33:16,920
Speaker 3: something I don't know about so we tested the water

626
00:33:17,000 --> 00:33:20,600
Speaker 3: faucet and it was clean. So he's like, that's weird. Well,

627
00:33:21,120 --> 00:33:23,080
Speaker 3: so he tested the water that was sitting there in

628
00:33:23,120 --> 00:33:25,600
Speaker 3: the percolator and it was wrong.

629
00:33:25,960 --> 00:33:30,120
Speaker 4: Hats off to this guy's deductive skills of reasoning and

630
00:33:30,160 --> 00:33:32,760
Speaker 4: then you know, process of elimination. I would say he

631
00:33:33,400 --> 00:33:35,000
Speaker 4: saved his own life. Yep.

632
00:33:35,320 --> 00:33:37,880
Speaker 2: All, don't drink bleach, folks.

633
00:33:37,640 --> 00:33:40,360
Speaker 3: No, good God, please don't do that.

634
00:33:40,600 --> 00:33:44,840
Speaker 2: Not even diet bleach, about the calories. Just don't drink bleach.

635
00:33:45,320 --> 00:33:51,160
Speaker 3: But what if it tastes like midnight cherry midnight zero. Yeah,

636
00:33:51,920 --> 00:33:55,200
Speaker 3: it's my favorite too. It's right there. All right, we're gonna,

637
00:33:55,440 --> 00:33:57,400
Speaker 3: I don't know, think about this stuff, y'all. We're gonna

638
00:33:57,400 --> 00:33:59,160
Speaker 3: take a break and we'll be right back with more

639
00:33:59,320 --> 00:33:59,960
Speaker 3: strange news.

640
00:34:06,760 --> 00:34:10,640
Speaker 2: And we have returned a quick cavalcade of updates. Good

641
00:34:10,760 --> 00:34:15,480
Speaker 2: news for a very specific demographic of people in Indiana.

642
00:34:15,719 --> 00:34:20,200
Speaker 2: An Indiana judge has ruled that tacos and burritos qualify

643
00:34:20,440 --> 00:34:25,640
Speaker 2: as sandwiches. So st yeah, yeah, yeah, the future. Who

644
00:34:25,719 --> 00:34:29,640
Speaker 2: is this a judge in Indiana? A judge by profession,

645
00:34:29,800 --> 00:34:33,080
Speaker 2: He's not like a judge Reinhold or whatever that actor's

646
00:34:33,160 --> 00:34:37,480
Speaker 2: name is in Fort Wayne, Indiana. He ruled as part

647
00:34:37,520 --> 00:34:42,600
Speaker 2: of a commercial development case that burritos and tacos count

648
00:34:42,640 --> 00:34:43,440
Speaker 2: as sandwiches.

649
00:34:43,960 --> 00:34:48,080
Speaker 4: I think this is out of his league, saying, you're lane, judge.

650
00:34:48,080 --> 00:34:50,720
Speaker 2: This is the most conservative opinion I have heard.

651
00:34:50,719 --> 00:34:53,520
Speaker 4: You have nol to do with you.

652
00:34:53,880 --> 00:34:54,759
Speaker 3: Look, we've been in a.

653
00:34:54,640 --> 00:34:57,600
Speaker 4: Matter of legality, it's a matter of common sense, dude.

654
00:34:57,600 --> 00:35:00,359
Speaker 3: We've been asking for like an official ruling on this,

655
00:35:00,440 --> 00:35:01,960
Speaker 3: and this judge gave it to us.

656
00:35:03,200 --> 00:35:07,040
Speaker 2: He wrote, the proposed famous taco restaurant falls within the

657
00:35:07,040 --> 00:35:10,360
Speaker 2: scope of general use approved in the original written commitment.

658
00:35:10,600 --> 00:35:11,440
Speaker 4: Blah blah blah.

659
00:35:11,239 --> 00:35:17,040
Speaker 2: Blah blah, YadA YadA YadA da da Uh. The original

660
00:35:17,040 --> 00:35:21,640
Speaker 2: written commitment does not restrict potential restaurants to only American

661
00:35:21,800 --> 00:35:25,480
Speaker 2: cuisine style sandwiches. So that's a big win for Big Taco.

662
00:35:27,680 --> 00:35:31,080
Speaker 4: The court. Baby, all right, the justices, deway.

663
00:35:31,080 --> 00:35:34,239
Speaker 2: All right, we'll text them. We'll text them because your

664
00:35:34,320 --> 00:35:37,360
Speaker 2: voice should be heard. Also, if you own a yacht,

665
00:35:37,440 --> 00:35:40,920
Speaker 2: bad news. The Orcas are out to get you. Just

666
00:35:41,000 --> 00:35:45,680
Speaker 2: knocked another way out of the straight to Gibraltar, and uh,

667
00:35:45,960 --> 00:35:46,560
Speaker 2: Team Orca.

668
00:35:46,719 --> 00:35:50,200
Speaker 3: To be quite honest, wasn't a really big nice boat too,

669
00:35:50,280 --> 00:35:51,360
Speaker 3: like a yacht.

670
00:35:51,160 --> 00:35:54,399
Speaker 4: Or something that gets the distinction is on the big

671
00:35:54,440 --> 00:35:57,440
Speaker 4: and or nice side. But this was a bigger yacht

672
00:35:57,640 --> 00:35:58,160
Speaker 4: right above.

673
00:35:59,120 --> 00:36:01,600
Speaker 2: Sorry, I know I saw like Pacino and Devil's Advocate,

674
00:36:01,680 --> 00:36:06,160
Speaker 2: but yeah, Tea Morca. So also, we were talking about

675
00:36:06,160 --> 00:36:08,640
Speaker 2: this a little bit off air, this last one before

676
00:36:08,640 --> 00:36:11,359
Speaker 2: you get to our big piece. Shout out to our

677
00:36:11,440 --> 00:36:15,800
Speaker 2: friend doctor Damian Patrick. Williams was speaking with him a

678
00:36:15,840 --> 00:36:20,840
Speaker 2: little bit earlier. There is a flood of fake science

679
00:36:20,920 --> 00:36:23,880
Speaker 2: hitting academic journals. We talked about this a little bit

680
00:36:23,920 --> 00:36:29,240
Speaker 2: in our previous episode on the academic journal problems, the controversies,

681
00:36:29,280 --> 00:36:33,520
Speaker 2: corruption and conspiracy surrounding that a lot of these journals

682
00:36:33,560 --> 00:36:39,040
Speaker 2: now are closing down because there is an industry using chat,

683
00:36:39,080 --> 00:36:44,400
Speaker 2: GPT and AI to plagiarize papers. They're called paper mills

684
00:36:44,880 --> 00:36:49,000
Speaker 2: and they are so I told myself I wasn't going

685
00:36:49,000 --> 00:36:51,560
Speaker 2: to curse you guys, but Doc, with great thanks, beat

686
00:36:51,600 --> 00:36:55,600
Speaker 2: me again. They are so fucking dumb. They're not good.

687
00:36:56,520 --> 00:37:00,239
Speaker 2: They do this thing where they ran they ran and

688
00:37:00,440 --> 00:37:05,120
Speaker 2: synonyms through so instead of saying something like this study

689
00:37:05,160 --> 00:37:08,680
Speaker 2: is about breast cancer, they say this study is about

690
00:37:08,840 --> 00:37:09,920
Speaker 2: bosom peril.

691
00:37:10,320 --> 00:37:14,600
Speaker 4: It's like the the like kind of bootleg Halloween costumes

692
00:37:14,640 --> 00:37:17,400
Speaker 4: trying to avoid being sued for intellectual property, like for

693
00:37:17,520 --> 00:37:20,560
Speaker 4: Wednesday atoms. It's spooky mid midweek QT.

694
00:37:21,480 --> 00:37:24,680
Speaker 6: Stuff like that is true, It's very true. It's a thing,

695
00:37:24,719 --> 00:37:27,560
Speaker 6: you know, it's a spirit and spooky midweek beauty. Really

696
00:37:27,640 --> 00:37:30,360
Speaker 6: quickly though, I mean just to add, like these types

697
00:37:30,400 --> 00:37:33,239
Speaker 6: of operations have been around as long as there have

698
00:37:33,280 --> 00:37:37,040
Speaker 6: been papers, right, you know, with varying degrees of quality.

699
00:37:37,200 --> 00:37:40,960
Speaker 6: You know, we certainly have heard stories of astute scam

700
00:37:41,040 --> 00:37:44,480
Speaker 6: artists in schools, you know, doing this, going into business

701
00:37:44,480 --> 00:37:47,160
Speaker 6: for themselves, and oftentimes they'll sell the same paper like

702
00:37:47,200 --> 00:37:48,080
Speaker 6: three or four times.

703
00:37:48,200 --> 00:37:50,760
Speaker 4: So this is just sort of an escalation of that, right.

704
00:37:50,719 --> 00:37:54,959
Speaker 2: Yes, just as the Internet itself is an escalation of

705
00:37:55,000 --> 00:37:58,680
Speaker 2: the gregarious nature of human beings. Here you go, another

706
00:37:58,880 --> 00:38:05,440
Speaker 2: couple of examples of these synonym griffs. Fluid dynamics was

707
00:38:05,480 --> 00:38:06,960
Speaker 2: called gooey stream.

708
00:38:08,280 --> 00:38:10,680
Speaker 4: Oh that's yeah, Okay, weird.

709
00:38:10,480 --> 00:38:19,160
Speaker 2: About that one. We're all so Artificial intelligence was also

710
00:38:19,239 --> 00:38:23,080
Speaker 2: referred to as counterfeit consciousness. And I think this is

711
00:38:23,120 --> 00:38:27,399
Speaker 2: a segue to our last story for tonight's program. Did

712
00:38:27,440 --> 00:38:29,800
Speaker 2: you guys ever do online dating?

713
00:38:30,280 --> 00:38:33,799
Speaker 4: Not? Oh? Yeah, my current relationship that I have been

714
00:38:33,840 --> 00:38:36,640
Speaker 4: in for lo these three plus years at this point

715
00:38:37,520 --> 00:38:40,560
Speaker 4: was the product of online dating. You know, I got

716
00:38:40,600 --> 00:38:44,680
Speaker 4: divorced from someone that I did not meet in online dating,

717
00:38:44,840 --> 00:38:46,560
Speaker 4: and then after that it just kind of became the

718
00:38:46,600 --> 00:38:48,319
Speaker 4: way you do it in a bigger city where you

719
00:38:48,320 --> 00:38:51,719
Speaker 4: maybe don't know as many people. So it made sense

720
00:38:51,719 --> 00:38:53,640
Speaker 4: for me. And I've had good and bad experiences.

721
00:38:53,920 --> 00:38:56,879
Speaker 2: Yeah, you know, Matt and I are maybe a little

722
00:38:56,880 --> 00:39:00,520
Speaker 2: more old school. We meet people the old fast and way,

723
00:39:01,040 --> 00:39:03,520
Speaker 2: you know, getting deported at customs.

724
00:39:04,880 --> 00:39:07,360
Speaker 3: I just get into minor car accidents with people I

725
00:39:07,400 --> 00:39:10,719
Speaker 3: find attractive.

726
00:39:15,840 --> 00:39:16,520
Speaker 4: To meet cute.

727
00:39:17,680 --> 00:39:22,680
Speaker 2: So obviously, no shade on online dating. It works very

728
00:39:22,719 --> 00:39:26,680
Speaker 2: well for a lot of people, and it can be

729
00:39:26,880 --> 00:39:31,000
Speaker 2: a difficult speed bump, right Like it's it's already difficult

730
00:39:31,040 --> 00:39:36,279
Speaker 2: to meet folks, to find the person that you want

731
00:39:36,320 --> 00:39:41,280
Speaker 2: to spend time or your life with. And what gets

732
00:39:41,320 --> 00:39:45,120
Speaker 2: me with this is I was always startled by the

733
00:39:45,400 --> 00:39:50,600
Speaker 2: idea of online dating. And I remember when some of

734
00:39:50,640 --> 00:39:56,080
Speaker 2: my friend groups started dating online using the apps of whatever,

735
00:39:56,719 --> 00:40:00,880
Speaker 2: and they eventually told me it was like a series

736
00:40:00,960 --> 00:40:05,399
Speaker 2: of auditions. They felt like they were always auditioning, and

737
00:40:05,760 --> 00:40:08,520
Speaker 2: they felt like they were auditioning for roles they didn't

738
00:40:08,560 --> 00:40:12,000
Speaker 2: necessarily want, but it was Saturday and they had to

739
00:40:12,000 --> 00:40:16,719
Speaker 2: do something. And this brings us to our final story,

740
00:40:16,760 --> 00:40:22,279
Speaker 2: which includes artificial intelligence again fraudulent term as well as

741
00:40:22,400 --> 00:40:26,760
Speaker 2: online dating. You guys. Recently, one of the high ups,

742
00:40:26,800 --> 00:40:31,240
Speaker 2: one of the grand pubas of the dating enterprise Bumble,

743
00:40:31,719 --> 00:40:35,680
Speaker 2: Whitney wolf Heard, said, we're going to take the awkwardness out.

744
00:40:36,080 --> 00:40:41,120
Speaker 2: We are going to eliminate this small talk, eliminate the

745
00:40:41,160 --> 00:40:44,440
Speaker 2: dead ends, the red herrings and so on. We are

746
00:40:44,480 --> 00:40:50,600
Speaker 2: going to create an AI concierge for each user of Bumble,

747
00:40:51,160 --> 00:40:57,200
Speaker 2: and that AI concierge will talk to another AI concierge

748
00:40:57,760 --> 00:41:00,960
Speaker 2: to figure out what the vibe is where the actual

749
00:41:01,040 --> 00:41:02,480
Speaker 2: people ever meet.

750
00:41:02,840 --> 00:41:06,560
Speaker 3: Oh god, amazing, right, they just want to double their

751
00:41:06,680 --> 00:41:10,800
Speaker 3: user base by creative users.

752
00:41:11,280 --> 00:41:14,200
Speaker 4: Yeah, so wait a minute. So so the person it's

753
00:41:14,239 --> 00:41:17,120
Speaker 4: sort of like a stand in, like a fill in,

754
00:41:17,320 --> 00:41:19,799
Speaker 4: Like I don't I guess I don't quite understand the technology.

755
00:41:19,960 --> 00:41:23,719
Speaker 3: She's saying. The AIS interact, right, that's what you're saying.

756
00:41:23,480 --> 00:41:24,960
Speaker 2: Ben, Yeah, that's the idea.

757
00:41:25,160 --> 00:41:29,680
Speaker 4: So, like, if your AI avatar interacts with another person's

758
00:41:29,680 --> 00:41:33,680
Speaker 4: AI avatar that has built a profile based on your

759
00:41:33,800 --> 00:41:35,759
Speaker 4: god knows how much data.

760
00:41:35,960 --> 00:41:39,120
Speaker 2: So like an Ai Ben Bullen and an Ai Fran

761
00:41:39,280 --> 00:41:44,520
Speaker 2: Dresser have a series of amazing conversations, and then later

762
00:41:44,680 --> 00:41:48,359
Speaker 2: they notify the human Fran and the human Bed and

763
00:41:48,360 --> 00:41:52,279
Speaker 2: they're like, hey, you guys both like the nanny, you

764
00:41:52,280 --> 00:41:53,120
Speaker 2: should hang out.

765
00:41:53,400 --> 00:41:55,839
Speaker 3: Yeah, But and then the Ais had been hooking up

766
00:41:55,920 --> 00:41:58,000
Speaker 3: for like a week or two, and they're like, I

767
00:41:58,040 --> 00:41:59,040
Speaker 3: guess we should tell them.

768
00:42:01,400 --> 00:42:04,000
Speaker 4: It's doing all your for play chat for you? Is

769
00:42:04,000 --> 00:42:07,799
Speaker 4: that basically the idea here? I'm sorry, I know that

770
00:42:07,800 --> 00:42:09,480
Speaker 4: that's not necessarily the term, you know what I mean?

771
00:42:09,480 --> 00:42:13,560
Speaker 4: Though the precursor getting to know you micro flair. Isn't

772
00:42:13,560 --> 00:42:16,920
Speaker 4: that part of the fun of it? Though, I guess

773
00:42:16,960 --> 00:42:19,200
Speaker 4: maybe there's some people that this is for. I don't.

774
00:42:19,320 --> 00:42:21,360
Speaker 4: I don't know. You can usually tell within one or

775
00:42:21,360 --> 00:42:26,160
Speaker 4: two quick exchanges whether someone gets you or is worth interacting.

776
00:42:25,640 --> 00:42:28,640
Speaker 2: With you say it on air.

777
00:42:28,960 --> 00:42:31,160
Speaker 4: Mean, yeah, I couldn't tell you. I don't have a

778
00:42:31,160 --> 00:42:33,160
Speaker 4: specific one. It's been a long time at this point

779
00:42:33,239 --> 00:42:35,280
Speaker 4: since I've done it. But I just try to be real.

780
00:42:36,000 --> 00:42:37,920
Speaker 4: I don't remember exactly. I never have like a go

781
00:42:37,960 --> 00:42:39,759
Speaker 4: to one. I'm sure I tried different ones out but

782
00:42:40,000 --> 00:42:42,640
Speaker 4: I'm not a lying kind of guy. But when you do,

783
00:42:42,719 --> 00:42:44,960
Speaker 4: there is something to be said though, of the fun

784
00:42:45,360 --> 00:42:49,440
Speaker 4: and sort of spontaneity of engaging with someone who and

785
00:42:49,520 --> 00:42:52,040
Speaker 4: realizing that you, guys have a vibe and that it's cool.

786
00:42:52,480 --> 00:42:56,200
Speaker 4: Is this meant to you? Just get rid of that entirely.

787
00:42:56,280 --> 00:42:59,279
Speaker 4: I'm a little confused. Who's asking for this seems more

788
00:42:59,320 --> 00:43:01,600
Speaker 4: to benefit they than the individuals guys.

789
00:43:01,600 --> 00:43:03,120
Speaker 3: I'm gonna go out there on a limb and say,

790
00:43:03,160 --> 00:43:07,240
Speaker 3: I think this is for a very specific audience. There's

791
00:43:07,280 --> 00:43:09,560
Speaker 3: a quote from one of the articles you shared back.

792
00:43:09,640 --> 00:43:10,759
Speaker 3: Can I do you mind if I read one of

793
00:43:10,800 --> 00:43:14,759
Speaker 3: these please? It says there is a world where your

794
00:43:14,800 --> 00:43:17,520
Speaker 3: dating concierge could go on a date for you with

795
00:43:17,600 --> 00:43:21,480
Speaker 3: other dating concierge. This is from What's what's the person's name?

796
00:43:21,640 --> 00:43:27,359
Speaker 3: Somebody Wolf Witney, Whitney. Yeah, okay, And she says, and

797
00:43:27,480 --> 00:43:30,680
Speaker 3: then you don't have to talk to six hundred people,

798
00:43:30,800 --> 00:43:34,520
Speaker 3: and it'll scan all of the San Francisco area for

799
00:43:34,680 --> 00:43:37,320
Speaker 3: you and say these are the three people you really

800
00:43:37,360 --> 00:43:37,919
Speaker 3: ought to meet.

801
00:43:38,680 --> 00:43:39,040
Speaker 4: Who.

802
00:43:39,280 --> 00:43:41,319
Speaker 3: Look, I'm putting this out there. I've never used one

803
00:43:41,320 --> 00:43:44,600
Speaker 3: of these dating apps, but people I've heard about some

804
00:43:44,719 --> 00:43:48,120
Speaker 3: friends of mine that have been using them don't get

805
00:43:48,239 --> 00:43:52,080
Speaker 3: six hundred matches or whatever. Every once in a while

806
00:43:52,160 --> 00:43:53,720
Speaker 3: get a match and hope for the best.

807
00:43:53,880 --> 00:43:56,200
Speaker 4: Oh that's me, baby, I'm not gonna lie. It is

808
00:43:56,400 --> 00:43:59,880
Speaker 4: not like raining matches. And it's also dudes in a

809
00:44:00,040 --> 00:44:02,359
Speaker 4: in a big way too. In general, they don't care

810
00:44:02,560 --> 00:44:05,120
Speaker 4: as many matches as women. But women that I've heard

811
00:44:05,120 --> 00:44:07,719
Speaker 4: it described as like just having hot dogs thrown at

812
00:44:07,760 --> 00:44:08,120
Speaker 4: your face.

813
00:44:08,840 --> 00:44:12,160
Speaker 2: Horror show. You know, it's it's terrible. I like the

814
00:44:12,160 --> 00:44:15,279
Speaker 2: point that we're bringing up here because it goes back

815
00:44:15,320 --> 00:44:19,120
Speaker 2: to the earlier idea in our episode about dead Internet theory,

816
00:44:19,760 --> 00:44:23,719
Speaker 2: the old parable of Fantasia and The Sorcerer's Apprentice. The

817
00:44:23,800 --> 00:44:29,879
Speaker 2: concept is to automate interactions, initial interactions, and like you said, Matt,

818
00:44:29,920 --> 00:44:35,400
Speaker 2: that that quote from Whitney Wolf heard stands out because

819
00:44:35,920 --> 00:44:42,320
Speaker 2: she's estimating that this AI concierge would talk to not four,

820
00:44:42,960 --> 00:44:46,560
Speaker 2: not eleven, not forty two or forty three or whatever,

821
00:44:46,760 --> 00:44:52,960
Speaker 2: but six hundred different AI representatives avatars. And then, as

822
00:44:52,960 --> 00:44:57,040
Speaker 2: you said, Matt, find the three schlubs in San Francisco

823
00:44:57,560 --> 00:44:58,719
Speaker 2: that might vibrate or.

824
00:44:58,880 --> 00:45:02,160
Speaker 3: No, the three tech bros who are gonna you know,

825
00:45:02,840 --> 00:45:05,879
Speaker 3: make seven figures or whatever. I don't know, man, I

826
00:45:05,920 --> 00:45:08,320
Speaker 3: just don't. I don't like any of the whatever.

827
00:45:08,640 --> 00:45:11,000
Speaker 4: I don't Again, though, I think the question of who

828
00:45:11,040 --> 00:45:13,839
Speaker 4: is this for is still is largely answered by it's

829
00:45:13,880 --> 00:45:16,759
Speaker 4: for the data brokers, you know. And then like if

830
00:45:16,760 --> 00:45:19,239
Speaker 4: somebody thinks this is a good idea, they're like, you know,

831
00:45:19,360 --> 00:45:22,520
Speaker 4: an individual, Yeah, take my data. I love this, have

832
00:45:22,640 --> 00:45:25,400
Speaker 4: it all, give me the better chance at finding the

833
00:45:25,440 --> 00:45:28,520
Speaker 4: right match. But I think unless we see some real,

834
00:45:28,760 --> 00:45:33,000
Speaker 4: insanely believable success stories from this, this is all smoke

835
00:45:33,040 --> 00:45:36,120
Speaker 4: and mirrors just to get more juice out of the

836
00:45:36,120 --> 00:45:40,080
Speaker 4: meat bags that are you know, Internet users, the remaining

837
00:45:40,120 --> 00:45:41,240
Speaker 4: ones the real.

838
00:45:41,239 --> 00:45:44,320
Speaker 2: Indeed tempted to agree with you in that regard, because

839
00:45:45,120 --> 00:45:50,240
Speaker 2: you can see the original conversation over on Bloomberg dot

840
00:45:50,320 --> 00:45:55,920
Speaker 2: com wherein at a tech summit, this this executive is

841
00:45:56,000 --> 00:45:59,440
Speaker 2: speaking about this and also to your point about data

842
00:45:59,560 --> 00:46:04,520
Speaker 2: retechn or data aggregation. Uh, she says, wolf Heard says,

843
00:46:05,239 --> 00:46:10,040
Speaker 2: this will also make you a better person. This AI

844
00:46:10,239 --> 00:46:19,920
Speaker 2: concierge will help you learn about yourself. Can give merizzler.

845
00:46:20,160 --> 00:46:26,200
Speaker 2: That's the medium plan, Yeah, skibby, And we also we

846
00:46:26,600 --> 00:46:30,920
Speaker 2: got to say this too, uh, There are now, of course,

847
00:46:31,560 --> 00:46:37,759
Speaker 2: multiple other digital things that are normalizing this sort of

848
00:46:39,160 --> 00:46:42,799
Speaker 2: I guess you would call it cognitive cybernetic approach right

849
00:46:43,000 --> 00:46:47,080
Speaker 2: to consciousness, and this, uh, this is perhaps most apparent

850
00:46:47,239 --> 00:46:54,120
Speaker 2: in things like AI girlfriend apps, which I experimented with

851
00:46:54,239 --> 00:47:01,320
Speaker 2: one because I had to find out uh back in college, right, yeah, college,

852
00:47:01,320 --> 00:47:02,360
Speaker 2: in college, right.

853
00:47:03,400 --> 00:47:04,760
Speaker 4: Experimenting college.

854
00:47:04,840 --> 00:47:09,880
Speaker 2: So I think this is first off, they are aggressively mediocre,

855
00:47:10,360 --> 00:47:13,720
Speaker 2: no offense. I'm sure I can imagine technology will improve

856
00:47:13,760 --> 00:47:17,239
Speaker 2: at some point, but I do wonder whether humans are

857
00:47:17,239 --> 00:47:21,279
Speaker 2: ready for this precipice upon which they stand. You know,

858
00:47:21,320 --> 00:47:24,800
Speaker 2: I'm not I'm not necessarily thinking about us listening tonight

859
00:47:24,840 --> 00:47:28,880
Speaker 2: as adults. I'm thinking about your kids, you know, your

860
00:47:28,960 --> 00:47:32,160
Speaker 2: kids who grow up, like, is it that bad because

861
00:47:32,400 --> 00:47:37,080
Speaker 2: a lot of people have imaginary friends growing up. Is

862
00:47:37,120 --> 00:47:40,000
Speaker 2: this not just an imaginary friend that texts you back

863
00:47:40,600 --> 00:47:42,280
Speaker 2: and ask you for your credit card number.

864
00:47:42,600 --> 00:47:44,319
Speaker 3: I guess we'll all have to try it out.

865
00:47:44,360 --> 00:47:44,600
Speaker 4: Ben.

866
00:47:45,840 --> 00:47:49,480
Speaker 2: I don't think it's a good idea. We're not here

867
00:47:49,520 --> 00:47:53,759
Speaker 2: to yuck anybody's yum. But it does feel like it

868
00:47:53,800 --> 00:47:58,000
Speaker 2: does feel like an extension of what we're talking about previously,

869
00:47:58,840 --> 00:48:02,080
Speaker 2: with the idea of the inner accelerating past the point

870
00:48:02,280 --> 00:48:07,840
Speaker 2: of human intervention. Right. We ended that episode spoiler folks,

871
00:48:08,120 --> 00:48:10,719
Speaker 2: with the idea that Earth could end up being a

872
00:48:10,800 --> 00:48:14,880
Speaker 2: haunted house in some way, just a series of algorithms

873
00:48:15,360 --> 00:48:19,520
Speaker 2: repeating and interacting and iterating with each other long after

874
00:48:19,960 --> 00:48:24,520
Speaker 2: humanity had gone the way of the Dodo. However, in

875
00:48:24,600 --> 00:48:28,120
Speaker 2: this case, what if you found out, let's spin it out.

876
00:48:28,160 --> 00:48:32,279
Speaker 2: What if you found out years after the fact that

877
00:48:32,480 --> 00:48:36,879
Speaker 2: you had an AI representative that you were not necessarily

878
00:48:36,920 --> 00:48:40,839
Speaker 2: aware of, that had become in a close relationship, an

879
00:48:40,840 --> 00:48:45,319
Speaker 2: intimate relationship with an AI representative of a person you

880
00:48:45,360 --> 00:48:48,200
Speaker 2: have never met, and what if it's gone on for years?

881
00:48:48,640 --> 00:48:52,160
Speaker 2: The question would you want to meet that person? Soon?

882
00:48:52,160 --> 00:48:54,240
Speaker 4: As topiing for me to even wrap my head around. Guys.

883
00:48:54,920 --> 00:48:57,200
Speaker 3: It reminds me of that Netflix movie from a long

884
00:48:57,200 --> 00:48:59,640
Speaker 3: time ago called I Am Mother. Did you guys ever

885
00:48:59,680 --> 00:49:02,080
Speaker 3: see Yeah? Yeah, I don't want to. Shouldn't spoil it

886
00:49:02,120 --> 00:49:04,080
Speaker 3: for anybody if you haven't seen it. I enjoyed it

887
00:49:04,160 --> 00:49:07,520
Speaker 3: a lot. It's just the wanting to meet that person,

888
00:49:07,600 --> 00:49:10,840
Speaker 3: like that question, Ben, I think feels like that to me,

889
00:49:11,000 --> 00:49:13,439
Speaker 3: like would you want to know the truth or really

890
00:49:13,480 --> 00:49:17,400
Speaker 3: meet the person? Oh the original maybe.

891
00:49:17,120 --> 00:49:19,400
Speaker 4: Oh, before we get out of here, one really important

892
00:49:19,440 --> 00:49:23,640
Speaker 4: final question. If burrito is a sandwich? Is a rap

893
00:49:24,000 --> 00:49:28,600
Speaker 4: also a sandwich? They're all wraps, sandwiches, NEETs.

894
00:49:28,640 --> 00:49:30,520
Speaker 2: You would say there is no God to stop us.

895
00:49:31,239 --> 00:49:32,719
Speaker 4: Jeez, I'm gonna lose sleep over.

896
00:49:32,760 --> 00:49:36,719
Speaker 2: This also shout out to a past Ben who one

897
00:49:36,760 --> 00:49:40,040
Speaker 2: time left a post it note in a freezer that said,

898
00:49:40,360 --> 00:49:42,920
Speaker 2: puh plus a burrito, fa rito.

899
00:49:43,320 --> 00:49:45,880
Speaker 4: Tell no one, I won't.

900
00:49:46,600 --> 00:49:50,200
Speaker 2: These are gems we drop. We end tonight with a

901
00:49:50,239 --> 00:49:55,440
Speaker 2: philosophical quandary similar to how we began. Right, No, we

902
00:49:55,560 --> 00:50:00,160
Speaker 2: are asking ourselves what the role of humans will be

903
00:50:00,600 --> 00:50:03,680
Speaker 2: in the future. We want to hear your thoughts, be

904
00:50:03,800 --> 00:50:07,600
Speaker 2: you human or bought. We can't wait to interact with you.

905
00:50:08,239 --> 00:50:10,759
Speaker 2: If we don't have a I don't think we have

906
00:50:10,840 --> 00:50:14,839
Speaker 2: an AI powered interaction yet. You'll have to find us

907
00:50:15,040 --> 00:50:16,480
Speaker 2: as ourselves online.

908
00:50:16,600 --> 00:50:19,840
Speaker 3: Yes, but Ben, in Dallas, there's a restaurant that serves

909
00:50:19,920 --> 00:50:21,840
Speaker 3: a fa rito. We gotta go.

910
00:50:23,320 --> 00:50:24,800
Speaker 2: We've got a farrito gap.

911
00:50:25,080 --> 00:50:27,080
Speaker 4: Seems like you'd be really wet. We are you gonna

912
00:50:27,080 --> 00:50:28,839
Speaker 4: put you dip it in the broth? Maybe you dip

913
00:50:28,840 --> 00:50:30,280
Speaker 4: it in the brath? I thought I in.

914
00:50:31,760 --> 00:50:33,480
Speaker 3: No, dude, it just looks awesome.

915
00:50:33,560 --> 00:50:35,360
Speaker 4: It looks that it's a good idea.

916
00:50:35,560 --> 00:50:37,440
Speaker 3: It's an all right.

917
00:50:37,680 --> 00:50:41,200
Speaker 4: Parallel thinking is really all. Parallel thinking is real. So

918
00:50:41,239 --> 00:50:44,080
Speaker 4: why don't you let us know about any crazy inventions

919
00:50:44,120 --> 00:50:45,880
Speaker 4: that you may have come up with out there in

920
00:50:45,880 --> 00:50:48,040
Speaker 4: conspiracy real Islam. You can find it to the handle

921
00:50:48,080 --> 00:50:51,920
Speaker 4: Conspiracy Stuff, where we exist on Facebook, on XFK, Twitter,

922
00:50:52,120 --> 00:50:55,399
Speaker 4: and on YouTube, where we've got video content rolling out

923
00:50:55,440 --> 00:50:59,880
Speaker 4: every single week, on Instagram and TikTok where conspiracy Stuff show.

924
00:51:00,160 --> 00:51:02,120
Speaker 3: We have a phone number. You can call it one

925
00:51:02,280 --> 00:51:06,960
Speaker 3: eight three three STDWYTK. When you call in, you've got

926
00:51:07,000 --> 00:51:09,440
Speaker 3: three minutes, give yourself a cool nickname, and let us

927
00:51:09,480 --> 00:51:11,080
Speaker 3: know at some point in the message if we can

928
00:51:11,200 --> 00:51:14,000
Speaker 3: use your message and your voice on the air. If

929
00:51:14,000 --> 00:51:16,080
Speaker 3: you've got more to say, why not instead send us

930
00:51:16,080 --> 00:51:17,360
Speaker 3: a good old fashioned email.

931
00:51:17,560 --> 00:51:39,359
Speaker 2: We are conspiracy at iHeartRadio dot com.

932
00:51:39,520 --> 00:51:41,600
Speaker 3: Stuff they don't want you to know is a production

933
00:51:41,719 --> 00:51:46,239
Speaker 3: of iHeartRadio. For more podcasts from iHeartRadio, visit the iHeartRadio app,

934
00:51:46,320 --> 00:51:49,560
Speaker 3: Apple Podcasts, or wherever you listen to your favorite shows.