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

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

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

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Speaker 1: production of iHeartRadio.

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Speaker 2: Hello, welcome back to the show.

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Speaker 3: My name is Met, my name is Nola.

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Speaker 4: They call me Ben.

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Speaker 5: We're joined as always with our super producer Alexis codenamed

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Speaker 5: Doc Holliday Jackson. Most importantly, you are you. You are here,

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Speaker 5: and that makes this the stuff they don't want you

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Speaker 5: to know. It is Thursday, hurtling headlong toward the end

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Speaker 5: of the calendar, one of our favorite times of the.

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Speaker 4: Week, folks.

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Speaker 5: This is the evening, fellow conspiracy realist, when we get

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Speaker 5: to hear from the best part of the show, which

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Speaker 5: is you, specifically you. I know it might sound freaky

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Speaker 5: to hear it while you're in the car or walking

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Speaker 5: around wherever you're at right now, but you, we're talking

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Speaker 5: about you. You might be on the show, you might

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Speaker 5: be in a future segment with us. We'll tell you

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Speaker 5: how to get in touch at the end. But here

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Speaker 5: we are at the beginning. We're gonna, oh, We're we're

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Speaker 5: gonna explore a very troubling thing about the nature of

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Speaker 5: cognition and technology. What rough digital beast. It's our come

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Speaker 5: at last, crouches toward Bethlehem to be born. We're going

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Speaker 5: to talk a little bit about water and UV. We're

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Speaker 5: gonna talk a little bit about.

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Speaker 4: When Google Maths goes wrong.

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Speaker 5: But before we do any of that, we talked a

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Speaker 5: bit off air and Matt, you have some surprises in.

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Speaker 2: Store, oh always. But today I've got some messages. And

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Speaker 2: in order to get to those messages, at least the

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Speaker 2: first one, we have to recap a bit of strange

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Speaker 2: news that we covered a little while back. Noel, a

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Speaker 2: couple of weeks ago, you brought up story to us

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Speaker 2: about ape Fest twenty twenty three and some stuff that

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Speaker 2: went down there. Could you just tell us what the

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Speaker 2: big problem.

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Speaker 6: Was, Well, the problem was that they didn't I guess

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Speaker 6: they didn't buy their lights at party Central.

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Speaker 3: They probably bought them.

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Speaker 6: You know, from some sort of laboratory supply website, I guess,

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Speaker 6: or you know, maybe crime stoppers or something, because the

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Speaker 6: you know, they you know, these kind of parties that

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Speaker 6: people like to put on a big spectacle when it

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Speaker 6: comes to tech, right, because they're in technology. So the

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Speaker 6: lighting and all that should have the same wow factor

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Speaker 6: and be technologically impressive.

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Speaker 3: So a lot of strobes and stuff. Probably not.

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Speaker 6: They probably have to have trigger warnings for epileptics, you know,

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Speaker 6: on the outside of the door. A lot of black

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Speaker 6: light situations. And apparently instead of getting your run of

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Speaker 6: the mill black light kind of neon type bulbs that

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Speaker 6: you see in your Spencers or what have you.

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Speaker 3: Again, probably Party City, they.

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Speaker 6: Had these like medical grade UV emitting bulbs that apparently

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Speaker 6: minorly low key fried people's retinas and gave him like

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Speaker 6: not chemical burn but like radiation burns on their faces

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Speaker 6: and like rashes and stuff.

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Speaker 2: Yeah, it's the equivalent of having your eye get a sunburn,

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Speaker 2: which is probably not a fun thing, and according to

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Speaker 2: a lot of people who attended Apefest, it was not

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Speaker 2: fun cause some damage. Let's go ahead and give the

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Speaker 2: name of that specific bulb because we mentioned it in

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Speaker 2: that Strange News episode and it was recognized by a

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Speaker 2: listener who happened to call in. It is the Phillips

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Speaker 2: TUV thirty watt G thirty t eight and it gives

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Speaker 2: off twelve watts of UVC radiation, and this listener's ears

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Speaker 2: perked up and said Hey, I know that exact bulb

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Speaker 2: and I use it for something in my daily work.

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Speaker 2: Let me call the guys and he did, And here's

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Speaker 2: the message that the turd herder left for us.

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Speaker 7: Hi, guys, I was just listening to your podcast from today.

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Speaker 7: We're talking about Phillip's light Bulb's UV light bulbs work

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Speaker 7: on a wastewater treatment facility, and that's the exact bulb

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Speaker 7: we use to disinfect large, large volumes of water. So

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Speaker 7: the bulbs are so powerful that you can get a

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Speaker 7: coali down from about one hundred thousand parts of million

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Speaker 7: down to one or two. So I definitely don't want

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Speaker 7: to be staring at those. Yeah, when I'm anywhere near that,

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Speaker 7: I'm fully clothed and making sure that I have UV

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Speaker 7: protectment safety glasses on. Anyway, thanks for your episode, love Itte.

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Speaker 6: Now, I think this came up Matt on the episode

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Speaker 6: because was it there some chatter during the early days

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Speaker 6: of COVID hysteria that UV lights could potentially kill COVID

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Speaker 6: on surfaces and things like that, when people were like,

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Speaker 6: you know, just psychotically wiping down everything put leaving their

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Speaker 6: groceries in the garage for two days.

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Speaker 3: Things like that.

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Speaker 2: Yeah, I recall this UV lights like that one or

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Speaker 2: similar to that one, and are used as general disinfectants.

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Speaker 2: I mean, you know, in a lot of different applications.

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Speaker 2: So I yeah, pretty wild.

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Speaker 3: Yeah, and then we yeah, that's it's yeah, exactly to

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Speaker 3: this guy's point.

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Speaker 6: I mean, you'd you'd need some protective gear, you know,

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Speaker 6: to safely be around these things.

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Speaker 2: Yeah. The turd Herder, which you'll notice, the turd Herder

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Speaker 2: did not name himself there. I talked to him briefly,

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Speaker 2: and that is the name. We came up when he wanted.

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Speaker 2: But he mentioned that if you stare directly into them,

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Speaker 2: it is like the welder what is it the welder

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Speaker 2: eye thing that we that is mentioned in several of

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Speaker 2: the pieces of reporting we found on this, Uh, it

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Speaker 2: will fry your eyeballs, like to the point where you

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Speaker 2: cannot see. In his application, at least in the facility

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Speaker 2: where he works. It's six of those bulbs and they're

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Speaker 2: each eight feet long. Like that's a huge bulb, right,

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Speaker 2: and just imagining that those were just set up in

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Speaker 2: some party space, you know.

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Speaker 6: I do wish we could have followed up with the

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Speaker 6: listener because now I'm interested and I remember we were

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Speaker 6: talking about those crazy cinema lights that were melting the

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Speaker 6: windows of airplanes. You know, they're like in a that's right,

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Speaker 6: they're in a giant array. Like I'm wondering for this

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Speaker 6: disinfecting process, like what does this facility look like?

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Speaker 3: And how are these lights like set up and focused?

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Speaker 3: You know what I mean? Like, that's not something that

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Speaker 3: I think we could just google an image for right now.

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Speaker 2: No, the thing to Herder mentioned is that this is

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Speaker 2: the last treatment that occurs in a wastewater treatment facility,

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Speaker 2: or at least one the one where he works, where again,

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Speaker 2: it's killing E. Coli and some of the tiniest things

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Speaker 2: that you cannot really remove through like a filtration process, right,

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Speaker 2: but it must hit all of the water running through it,

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Speaker 2: and I can only imagine that it's a large volume

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Speaker 2: of water passing by at any given moment. Who knows

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Speaker 2: the gallons right, per minute or per second that moved

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Speaker 2: through it, but in.

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Speaker 4: The speed which would be the rate of exposure.

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Speaker 5: When I when I finally like, because we talked about

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Speaker 5: the board ape thing, and then it was astonished to

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Speaker 5: find that this was a previo. There was a previous

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Speaker 5: iteration of this a precedent I think in twenty seventeen

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Speaker 5: where this happened right with the same light bulb, And

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Speaker 5: so when I looked in that I forgot to tell you, guys,

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Speaker 5: if you are interested, you can buy this Germicidal thorest

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Speaker 5: into light bulb, the Phillips three sixty one, sixty four,

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Speaker 5: et cetera, et cetera, et cetera for less than forty

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Speaker 5: dollars on Amazon right now thirty six dollars twenty cents.

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Speaker 6: Are they cheaper or more expensive than what the proper

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Speaker 6: bulbs might have been? You got to wonder did they

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Speaker 6: buy this, because maybe they're like, ooh, look, what's this

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Speaker 6: deal here? You can get these for a better price.

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Speaker 6: I would say they'd be more expensive, but that's just me.

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Speaker 5: Yeah, And you know, hey, maybe all the holiday deals

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Speaker 5: have skewed the numbers temporarily. I'm kidding. Black Friday and

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Speaker 5: Cyber Monday. Our rifts just walk down the street for

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Speaker 5: that one jentleal.

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Speaker 3: They are banned.

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Speaker 6: But I have to tell you, I was in the

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Speaker 6: market for one of those Kitchen Aid stand mixers and

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Speaker 6: I rolled into Target just like I'm gonna get one,

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Speaker 6: don't I'm just gonna get one whatever the price is.

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Speaker 3: It was easily two hundred dollars off.

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Speaker 6: So that maybe that's just they always run deals for

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Speaker 6: those kitchen aid things because they are so popular and pricing.

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Speaker 3: But I was a pretty good deal. I love life

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Speaker 3: with you.

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Speaker 5: I feel like it's a rom cop. You know, you

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Speaker 5: saw the mixer and you were like, when you know, you.

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Speaker 2: Know exact to see you got to get those Q

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Speaker 2: four numbers up.

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Speaker 3: You know what I'm saying. I've been watching Fargo, Matt,

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Speaker 3: what are you talking? I don't know, I don't know. Sorry,

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Speaker 3: the new season is quite good.

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Speaker 2: But thank you tured Herder for sending us that information.

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Speaker 2: Any more information you want to share with us about

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Speaker 2: water treatment would be cool. We love it. We want

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Speaker 2: to know. We've got another one from the old voicemail box. Guys.

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Speaker 2: This one I asked you please not to listen to

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Speaker 2: because I listened to it and then went down a

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Speaker 2: rabbit hole and I am a bit confused with what

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Speaker 2: I remember and now what I know. So I just

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Speaker 2: want to see. I want to see where you guys

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Speaker 2: land objectively. So here we go. This message is from

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Speaker 2: Agent nine oh seven.

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Speaker 8: Hey, guys, call me Agent nine seven. So I wanted

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Speaker 8: to just bring this by you. What weapon did William

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Speaker 8: Tell use? So maybe I found a manel. Maybe you

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Speaker 8: guys are on the new timeline. Half my friends are

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Speaker 8: one way, the other half the other. So you guys

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Speaker 8: rock on Agent nine seven alf crossbow.

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Speaker 2: Okay, William Tell is it's a piece of folklore.

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Speaker 6: Famous overture, that is the bugs bunny theme. Get up

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Speaker 6: like a lone Ranger theme rather bick itt U pick

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Speaker 6: it up?

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Speaker 2: Pick it up up before you look it up? Just

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Speaker 2: what what weapon do you think he used?

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Speaker 4: It was?

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Speaker 6: I think it was a crossbow or I was in

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Speaker 6: my mind it's either a bow and arrow or a crossbow.

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Speaker 6: Is definitely an arrow because he shoots an apple off

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Speaker 6: somebody's head and he like kills somebody or by accident

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Speaker 6: or something. And now I'm thinking about William S Burrows

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Speaker 6: who did the same thing, but he did it with

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Speaker 6: a gun.

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Speaker 5: William S Burrows is an overrated author. Had to get

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Speaker 5: it out also, Oh well, also William Tell it's European, right,

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Speaker 5: I remember I remember an old comic book I think

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Speaker 5: was more Central Europe. I can't remember, but I remember

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Speaker 5: an old comic book I had from Key Comics. Which

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Speaker 5: which told the story, and in that story she was

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Speaker 5: using a bow and arrow. But I don't think it

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Speaker 5: was accurate. I think it was. I think it was

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Speaker 5: definitely not a gun and was definitely a some sort

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Speaker 5: of projectile. So I'm going to side with Nol on

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Speaker 5: this mat and I'm going to argue crossbow.

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Speaker 2: Okay, excellently reasoned, sir, I think uh, I think I'm

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Speaker 2: the outlier here. I had a very clear picture in

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Speaker 2: my mind of William Tell shooting the apple off his

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Speaker 2: son's head with a bow and arrow I got. I

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Speaker 2: had a clear picture in my head. I was like, oh, yeah,

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Speaker 2: that's I know that, shooting the arrow off the top

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Speaker 2: of that.

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Speaker 3: My mine went there very very quickly.

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Speaker 6: But then I was reeled in because my mom, as

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Speaker 6: you guys know, as an opera singer and was in

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Speaker 6: several productions of William Tell, and I remember being backstage

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Speaker 6: and seeing and being be really fascinated by the prop

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Speaker 6: bow and arrow because they had a thing where he'd

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Speaker 6: click it and then the arrow would come out and

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Speaker 6: reverse from like the wall, so they could It's it's

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Speaker 6: like really classic practical stage craft, you know, like he'd

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Speaker 6: click the.

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Speaker 3: Thing and then boop. It would come out like of this,

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Speaker 3: like you know, revealed little.

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Speaker 6: Compartment in the wall, not not revealed, you know, impossible

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Speaker 6: to see.

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Speaker 4: I think I was in Yeah.

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Speaker 5: I think the comic is what taught me this, because

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Speaker 5: the comic was wrong.

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Speaker 4: So I think because the comic.

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Speaker 5: And a lot of popular descriptions I saw later, maybe

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Speaker 5: in animated things, maybe in children's stories, tended to to

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Speaker 5: your point, match show a bow and arrow, and I

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Speaker 5: think because that was so prevalent, I ended up learning

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Speaker 5: that was not entirely the case because I went through

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Speaker 5: the period where you like you evaluate and interrogate all

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Speaker 5: these different folk tales and urban legends like Johnny Applesy

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Speaker 5: basically a drunk train kid or crustpunk for his day,

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Speaker 5: and just iinerant wonderer. Uh you know, uh pekos Bill, and.

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Speaker 6: I haven't thought about He had a Lassu, right, didn't

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Speaker 6: pecos Bill? I'm sorry I say Lassu. I know it,

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Speaker 6: but I just it sounds fun.

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Speaker 3: Isn't that right? Wasn't he a roping and wrangler kind

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Speaker 3: of guy?

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Speaker 5: Yeah, of the of the halcion, uh, the halcion cannon

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Speaker 5: of the Lumberjacks.

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Speaker 3: It's sort of like the Blue Ox, lose that guy.

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Speaker 4: Babe, Babe is the Blue Ox.

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Speaker 3: This is the big Fellow the acts.

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Speaker 4: Oh yeah, he's from the Statue.

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Speaker 6: And far we're all going, and he would be sort

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Speaker 6: of from that pantheon, right like kind of.

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Speaker 3: Those type of folk characters.

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Speaker 5: So, Matt, what you're saying is to well, more directly

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Speaker 5: what you're saying Agent nine oh seven, I think you're

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Speaker 5: telling us that William Tell used a Phillips TUV thirty

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Speaker 5: watch G thirty t eight.

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Speaker 2: That's it, right to just sort of fried that apple sunbury.

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Speaker 4: In the apple off the off the kids head his son.

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Speaker 2: His son didn't do to well either. No, but it

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Speaker 2: was just a weird thing where, you know, I got

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Speaker 2: the voicemail and I think maybe my mind was just

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Speaker 2: playing tricks on me, and I'm putting somehow Robin Hood

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Speaker 2: and William Tell legend together making this weird thing, because

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Speaker 2: I've got clear images of you guys remember the animated

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Speaker 2: Robin Hood series. I've got the sequence of the what

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Speaker 2: is it the bullseye shootout scene where the arrow goes

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Speaker 2: into the other arrow somehow like that.

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Speaker 3: Is Disney one.

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Speaker 4: Yeah, yeah, that was their tombstone. He shut out code named.

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Speaker 2: Douc that for some reason, imagery is mixing my in

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Speaker 2: my head when I imagine this scenario with William Tell,

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Speaker 2: which is a fascinating legend and you know, a really

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Speaker 2: cool play, just the fact that an authority would be like, no,

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Speaker 2: you didn't. You didn't show what it was it he

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Speaker 2: like didn't bow, or he didn't he didn't show.

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Speaker 6: A deference to the Yeah, the the ritual of it

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Speaker 6: all kind.

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Speaker 2: Of yeah, the Austrian upper crust or whatever. And so

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Speaker 2: they were like, yeah, okay, go over here and shoot

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Speaker 2: an apple off your son's head or basically you know,

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Speaker 2: we'll rest you or whatever.

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Speaker 6: Speaking of Mandela effect type in my head. And I

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Speaker 6: think I even said it out loud. I thought that

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Speaker 6: he killed the kid by shooting the apple. But then

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Speaker 6: again I was I was misshmashing that with William Burrows

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Speaker 6: killing his wife.

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Speaker 5: Oh jeezu, his grandfather and then it calculator.

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Speaker 3: Oh wow, that's pretty cool.

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Speaker 4: That's how he was.

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Speaker 3: Able to it was a NEPO baby. Anyway. So the

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Speaker 3: Coco band.

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Speaker 5: Like, yeah, so assuming do we have permission to look

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Speaker 5: it up? Because I don't know I'm going off the

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Speaker 5: dome right now. Oh yeah, Okay, all right, so looking

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Speaker 5: up William Tell. All right, William Tell, you're right he

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Speaker 5: did not pay obedience to some guy named Gessler, Albert Gessler. Okay,

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Speaker 5: all right, Yeah, it looks like it's out of out

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Speaker 5: of Switzerland. So he's like a Robin hood of Switzerland.

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Speaker 4: That's crazy.

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Speaker 5: I don't know much about that. I don't know about

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Speaker 5: much about Switzerland's folklore.

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Speaker 2: The whole thing is he's a folk hero. There have

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Speaker 2: been reliefs made of this, you know, paintings from the

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Speaker 2: eighteen hundreds. People have been putting the image of William

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Speaker 2: Tell up basically for a long time in this timeline.

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Speaker 2: And he's always got a crossbow. The legend always has

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Speaker 2: a crossbow. That's what it's always been. But some people

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Speaker 2: don't think so. Are we living in a weird timeline

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Speaker 2: where we jumped?

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Speaker 3: Are in a weird timeline? Yes, most definitely?

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Speaker 2: But the bow are the bow people? Did we jump

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Speaker 2: into the crossbow timeline? Y'all?

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Speaker 3: What about the little bo people? Let us know?

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Speaker 7: All right?

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Speaker 5: Also, also we should we should point out real quick

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Speaker 5: for anybody unfamiliar, check out our earlier episode on the

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Speaker 5: Mendela effect. This is the idea, like the most famous

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Speaker 5: examples online or things like Shazam versus Kazam and barren

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Speaker 5: Stein versus Baron Sting.

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Speaker 2: Yep, there you go. Check it out. Thanks so much

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Speaker 2: turd Herder and Agent nine oh seven for calling in,

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Speaker 2: and everybody else who called in. There's a bunch of y'all.

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Speaker 2: You rock, and please stay tuned as we hear a

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Speaker 2: brief word from our sponsors, and we're.

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Speaker 3: Back with another message from you. Oh yeah, that's right,

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Speaker 3: you Anonymous. I see you out there being all sneaky

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Speaker 3: and anonymous. I like your style. Here it goes just

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Speaker 3: the following.

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Speaker 6: Really, Google Maps error leads unsuspecting travelers into the middle

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Speaker 6: of the Mojave Desert. It's a link from an article

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Speaker 6: on tech Spot, and Anonymous out there comments the all powerful,

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Speaker 6: all knowing Google sits the bad even ais hallucinate. It

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Speaker 6: turns out androids do dream of electric sheep or at

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Speaker 6: the very least bad directions.

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Speaker 3: So let's jump right in with the article on tech

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Speaker 3: spot by Kishlayah Kundu. Basically, the deal is this. You know,

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Speaker 3: it's funny.

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Speaker 6: We were in Las Vegas, all three of us, during

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Speaker 6: this Formula one Grand Prix event, or right before it.

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Speaker 3: We saw like the kind of didn't we am I

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Speaker 3: making that up?

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Speaker 5: No, you're right, We're no, Mandela Factir and ol. We

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Speaker 5: were there right before it, and a lot of the

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Speaker 5: folks we met talked about it the way that the

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Speaker 5: way that people in the far Northeast talk about a

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Speaker 5: nor'easta coming.

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Speaker 3: Of locusts, right or something like that, or the Macy's

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Speaker 3: Thanksgiving Day.

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Speaker 6: Parade if you actually live in New York. Now, I'm

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Speaker 6: sure some New Yorkers love the parade. Who doesn't love

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Speaker 6: a parade? But so yeah, there were some folks and

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Speaker 6: apparently this happened to several travelers who were on their

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Speaker 6: way back from the Formula One Grand Prix event Las

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Speaker 6: Vegas back to California. I believe they were heading for

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Speaker 6: Los Angeles, which is pretty common people drive. That's you know,

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Speaker 6: it's not like super fast, but it's I think it's

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Speaker 6: what like four or five hours something like that.

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Speaker 3: In any case, not something we think about over here.

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Speaker 3: But Ben, you mentioned in nor'easter.

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Speaker 6: The desert equivalent of a nor'easter is a sand storm

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Speaker 6: or a dust storm, and that's serious business. That is

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Speaker 6: like reported by Google mapping type navigation services. You know,

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Speaker 6: it makes the news. It's a big deal. It can

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Speaker 6: really reduce visibility, make conditions very very dangerous and can

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Speaker 6: seriously damage your car if you're not wearing protective gear

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Speaker 6: and you get out in and it can really you know,

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Speaker 6: damage your eyes and your eyesight could cause permanent vision loss. So,

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Speaker 6: according to the San Francisco Gay, Shelby Easler and her

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Speaker 6: brother Austin and their respective partners were on their way

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Speaker 6: back to La that's right from Las Vegas on November

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Speaker 6: the nineteenth, and we're using Google Maps to do the

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Speaker 6: job to get there, and they were it was suggested

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Speaker 6: like it'll do. Hey want to save some time, try

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Speaker 6: this alternatate rounte okay, Google Maps, I'll bite. Apparently it

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Speaker 6: was trying to steer them around this dust storm that

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Speaker 6: was causing a lot of delays. And the route that

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Speaker 6: it suggested was fifty minutes faster.

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Speaker 4: Cool.

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Speaker 6: That sounds fifty zero fifty minutes faster, And one would

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Speaker 6: assume that the you know, that says a result directly

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Speaker 6: of avoiding the dust storm. But I also you wouldn't

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Speaker 6: think it'd be that localized, you know, like, how could

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Speaker 6: you increase your route by fifty that's what I'm saying

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Speaker 6: already a little too good to be the too good

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Speaker 6: to be true, side, wouldn't you say so? It turns out, though, however,

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Speaker 6: that the alternate route was actually steering them on some

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Speaker 6: sort of.

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Speaker 3: Like state road that landed them.

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Speaker 6: Smack dab in the middle of no man's land, uh,

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Speaker 6: in the worst part of the dust storm, where the

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Speaker 6: terrain was really gnarly and rough to the point where

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Speaker 6: it badly damaged their vehicle and and left them stranded

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Speaker 6: with no service and no way home.

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Speaker 2: Was it actually a road or not a road?

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Speaker 6: Yeah, But a lot of those like state roads or

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Speaker 6: state routes spend I know that, like from your car

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Speaker 6: stuff days. You probably maybe you know about this kind

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Speaker 6: of stuff like there there are a lot of roads

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Speaker 6: that will read, you know, on a map as a road,

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Speaker 6: but it's it's not paved, you know, it'll be like

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Speaker 6: some kind of dirt road or like a like a

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Speaker 6: little what do you call it, like a I don't

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Speaker 6: know what do you call these things?

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Speaker 4: You call it an access track, an access road.

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Speaker 5: That's exactly right, thank you. They may as well be

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Speaker 5: a deer path. There are There's another interesting thing that

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Speaker 5: happens here may not necessarily be a chicanery, uh, fellow

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00:21:11,000 --> 00:21:14,520
Speaker 5: anonymous conspiracy realist, Like, if you go to a lot

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Speaker 5: of places in the world, especially when there is prevalent construction,

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Speaker 5: you're gonna you're gonna see that the maps are woefully

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Speaker 5: incorrect because they don't know the roads exist or they

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Speaker 5: haven't been updated. And like, like, where I think you're

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Speaker 5: going with this all? If you're in the middle of

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Speaker 5: nowhere in the Mojave or something, how often is the

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Speaker 5: Google street car really hanging out there?

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Speaker 7: You know what?

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Speaker 6: I'm really going off road? I mean, those things are

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Speaker 6: like Ford focuses, you know what I mean? And apparently

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Speaker 6: this is interesting too in the in the article, uh,

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Speaker 6: the the person reporting their misfortune did say to the

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Speaker 6: SF Gate that it was their very first time driving

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Speaker 6: from La to Vegas. And for any seasoned La to

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Speaker 6: Vegas commuter, you're supposed to know that the I fifteen

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Speaker 6: is the only way.

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Speaker 3: That's the only road once.

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Speaker 6: You get out of the Remember when we all went,

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Speaker 6: Remember when we all went to the Hoover Dam, and uh,

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Speaker 6: there weren't any other roads that looked accessible on the

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Speaker 6: main drag is the main drag and everything else is

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Speaker 6: a little outlet to a little small town or to

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Speaker 6: where you're going, and everything in between is kind of

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Speaker 6: cheer nothingness. It's a little bit eerie. They called nine

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Speaker 6: to one one, but to no avail. The California Highway

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Speaker 6: Patrol apparently actually just said they couldn't help them because

439
00:22:32,880 --> 00:22:35,600
Speaker 6: they were overwhelmed because of the dust storm.

440
00:22:35,359 --> 00:22:37,919
Speaker 3: Which was apparently, you know, really wreaking some havoc.

441
00:22:39,119 --> 00:22:43,920
Speaker 6: And this story got me thinking very recently, I'm sort

442
00:22:43,960 --> 00:22:47,040
Speaker 6: of a I don't know, like, I'm what you call

443
00:22:47,160 --> 00:22:51,560
Speaker 6: geographically challenged. Okay, I don't have a very good sense

444
00:22:51,600 --> 00:22:54,960
Speaker 6: of direction. So even if I'm going routes that I'm

445
00:22:55,080 --> 00:22:58,879
Speaker 6: very familiar with, I will often have my mapping running

446
00:22:58,920 --> 00:23:02,440
Speaker 6: in the background, and largely because sometimes they can steer

447
00:23:02,480 --> 00:23:05,680
Speaker 6: you around nasty stuff. If it's like say during rush

448
00:23:05,680 --> 00:23:06,920
Speaker 6: hour and here in Atlanta, like.

449
00:23:06,880 --> 00:23:07,679
Speaker 3: It's it's helpful.

450
00:23:07,840 --> 00:23:09,760
Speaker 6: Sometimes it'll tell you, oh, you can't save that ten

451
00:23:09,760 --> 00:23:13,639
Speaker 6: minutes or whatever. But recently I noticed the routes that

452
00:23:13,680 --> 00:23:15,480
Speaker 6: I was very familiar with, and it actually made me

453
00:23:15,520 --> 00:23:17,600
Speaker 6: realize that I know these routes better than I think.

454
00:23:18,359 --> 00:23:23,080
Speaker 6: My Google Maps was given me some blooney y'all, like really, like,

455
00:23:23,119 --> 00:23:25,719
Speaker 6: I don't know, it's somewhere along the way. I tweaked

456
00:23:25,760 --> 00:23:28,399
Speaker 6: a setting and it was trying to perhaps steer me

457
00:23:28,720 --> 00:23:31,600
Speaker 6: away from highways or something. But all of a sudden

458
00:23:31,720 --> 00:23:34,439
Speaker 6: I noticed I was taking my kid and their friend

459
00:23:35,240 --> 00:23:38,639
Speaker 6: to this mall that I know to be about twenty

460
00:23:38,720 --> 00:23:42,000
Speaker 6: minutes away, and the phone was saying it was sixty

461
00:23:42,040 --> 00:23:45,120
Speaker 6: five minutes away. And I didn't notice that until one

462
00:23:45,119 --> 00:23:47,040
Speaker 6: of the kids was like, hey, Dad, I don't think

463
00:23:47,280 --> 00:23:49,439
Speaker 6: this shouldn't be sixty five minutes away.

464
00:23:50,080 --> 00:23:51,640
Speaker 3: And I haven't really had a chance.

465
00:23:51,440 --> 00:23:54,880
Speaker 6: To explore what exactly it's doing. But I have entirely

466
00:23:54,920 --> 00:23:57,119
Speaker 6: stopped using Google Maps because of that. I've been just

467
00:23:57,200 --> 00:23:58,159
Speaker 6: using Apple Maps.

468
00:23:58,560 --> 00:24:02,560
Speaker 3: And I did on a cursory look, I couldn't figure

469
00:24:02,560 --> 00:24:06,199
Speaker 3: out any any setting that I had changed. So what

470
00:24:06,200 --> 00:24:09,880
Speaker 3: do you guys think? Is there f wordery.

471
00:24:11,040 --> 00:24:14,760
Speaker 5: Right as they're sory about So, I mean, we got

472
00:24:14,800 --> 00:24:18,320
Speaker 5: to be careful not to assign malice to what simply

473
00:24:18,359 --> 00:24:22,399
Speaker 5: could be error. You know, again, the software and the

474
00:24:23,119 --> 00:24:27,840
Speaker 5: whole mapping program, whether you're Uber, Google Maps, Ways, Lift,

475
00:24:27,960 --> 00:24:31,879
Speaker 5: what have you, it's always trying to struggle to be

476
00:24:32,000 --> 00:24:34,000
Speaker 5: up to date. And I think we can all remember

477
00:24:34,520 --> 00:24:40,720
Speaker 5: how optimistic Ways and Google Maps will be sometimes where

478
00:24:40,720 --> 00:24:43,119
Speaker 5: they're like, hey, just make a left here, and you're like,

479
00:24:43,160 --> 00:24:47,400
Speaker 5: well that's like it's clearly into a building, and they're.

480
00:24:47,200 --> 00:24:52,399
Speaker 4: Like one way, yeah, right, right, go like recently go

481
00:24:52,480 --> 00:24:53,280
Speaker 4: in the wrong direction.

482
00:24:53,520 --> 00:24:53,920
Speaker 3: Yeah.

483
00:24:54,040 --> 00:24:57,080
Speaker 6: There's parts of downtown Atlanta or like you know, midtown

484
00:24:57,160 --> 00:25:00,159
Speaker 6: or whatever where there's lots of little skinny roads. If

485
00:25:00,200 --> 00:25:03,440
Speaker 6: you don't pay attention, they're mega one way and you

486
00:25:03,480 --> 00:25:06,439
Speaker 6: will get blasted. And I had Google Maps telling me

487
00:25:06,480 --> 00:25:08,800
Speaker 6: to turn on one of those, and I almost did.

488
00:25:09,200 --> 00:25:11,040
Speaker 6: Like Michael Scott, driving into a lake.

489
00:25:11,320 --> 00:25:16,000
Speaker 5: Atlanta is a hard road driving for the United States.

490
00:25:16,040 --> 00:25:19,240
Speaker 5: It's not like the final level, but it's it's definitely

491
00:25:19,320 --> 00:25:22,040
Speaker 5: up there. It's the third act of the driving video game,

492
00:25:22,119 --> 00:25:25,359
Speaker 5: because you know, sometimes the street will just suddenly be

493
00:25:25,520 --> 00:25:28,560
Speaker 5: a left turn only or right turn only. Sometimes it'll

494
00:25:28,600 --> 00:25:31,560
Speaker 5: be reversible lanes, right the suicide lanes.

495
00:25:31,680 --> 00:25:32,600
Speaker 4: Yeah, I love those.

496
00:25:32,760 --> 00:25:35,320
Speaker 5: And then and then of course no one reveals their

497
00:25:35,359 --> 00:25:36,080
Speaker 5: secret plan.

498
00:25:36,560 --> 00:25:36,879
Speaker 1: Guys.

499
00:25:36,960 --> 00:25:38,640
Speaker 5: The stuff they don't want you to know in Atlanta

500
00:25:38,680 --> 00:25:40,760
Speaker 5: traffic is the turn signals exist.

501
00:25:41,160 --> 00:25:42,480
Speaker 3: Yeah, I don't want to get the memo.

502
00:25:42,800 --> 00:25:46,240
Speaker 2: Well, it is weird because roads are updated quite often

503
00:25:46,600 --> 00:25:49,320
Speaker 2: here in our fair city, and I'm just trying to

504
00:25:49,320 --> 00:25:51,720
Speaker 2: figure out how in the heck do the Google cars

505
00:25:51,840 --> 00:25:56,919
Speaker 2: know who puts in the system. Oh, this one tiny

506
00:25:56,960 --> 00:26:01,600
Speaker 2: section of Peachtree Court something or whatever is now like

507
00:26:02,440 --> 00:26:05,199
Speaker 2: there's no stop sign there anymore. It's now, you know,

508
00:26:06,280 --> 00:26:07,440
Speaker 2: roundabout or something.

509
00:26:07,640 --> 00:26:08,000
Speaker 3: I don't know.

510
00:26:08,200 --> 00:26:10,119
Speaker 2: I'm sure it gets in a system somewhere, but it

511
00:26:10,160 --> 00:26:12,800
Speaker 2: does feel as those system gets confused quite often with

512
00:26:12,840 --> 00:26:13,560
Speaker 2: that kind of stuff.

513
00:26:13,680 --> 00:26:16,480
Speaker 5: And it's dependent on two factors. They can really mess

514
00:26:16,480 --> 00:26:21,320
Speaker 5: it up. The first factor is density of people, right,

515
00:26:21,480 --> 00:26:27,560
Speaker 5: frequency of vehicles. That will result in more current information

516
00:26:28,119 --> 00:26:30,960
Speaker 5: from these things, which are by the way, totally sucking

517
00:26:31,040 --> 00:26:33,920
Speaker 5: up your information and it's being sold to all sorts

518
00:26:33,960 --> 00:26:37,679
Speaker 5: of people. The second the second part though, just to

519
00:26:37,680 --> 00:26:41,119
Speaker 5: show this up real quick, the second part is a

520
00:26:41,160 --> 00:26:44,240
Speaker 5: function of like, so if they're less if there's less frequency,

521
00:26:44,320 --> 00:26:47,359
Speaker 5: less density, then those things are going to be less

522
00:26:47,480 --> 00:26:52,840
Speaker 5: often updated. But the second issue is like there's an

523
00:26:52,880 --> 00:26:56,840
Speaker 5: inflection point because the more frequently some places travel, the

524
00:26:56,960 --> 00:27:01,240
Speaker 5: more people or bots traveling on there, the more likely

525
00:27:01,280 --> 00:27:03,680
Speaker 5: there is to be a lot of construction, which also

526
00:27:03,720 --> 00:27:04,800
Speaker 5: makes the thing outdated.

527
00:27:04,880 --> 00:27:07,359
Speaker 4: So it's a real balancing act.

528
00:27:07,640 --> 00:27:09,960
Speaker 6: Google's kind of the only game in town as far

529
00:27:10,000 --> 00:27:13,240
Speaker 6: as those mapping cars, right, And I mean I imagine

530
00:27:13,280 --> 00:27:16,840
Speaker 6: satellite imagery plays some role in this, but it can't

531
00:27:16,880 --> 00:27:19,680
Speaker 6: quite get as granular as like driving around and mapping it.

532
00:27:19,720 --> 00:27:21,400
Speaker 6: And I know a lot of the purpose of those

533
00:27:21,440 --> 00:27:23,720
Speaker 6: cars is to also get like real time, not real time,

534
00:27:23,720 --> 00:27:25,840
Speaker 6: but like video because if you look at like those

535
00:27:25,960 --> 00:27:27,680
Speaker 6: you know, Google Earth kind of start as you can

536
00:27:27,680 --> 00:27:30,000
Speaker 6: see what your yard looked like a year and a

537
00:27:30,040 --> 00:27:32,199
Speaker 6: half ago, you can usually tell you know when the

538
00:27:32,240 --> 00:27:35,760
Speaker 6: car came through. But like let's say, everyone's sort of

539
00:27:35,840 --> 00:27:40,240
Speaker 6: sourcing their stuff from those publicly available Google results, right,

540
00:27:40,640 --> 00:27:42,720
Speaker 6: or it never happened to the Garmans of the world

541
00:27:42,800 --> 00:27:43,920
Speaker 6: in the in the you know.

542
00:27:44,840 --> 00:27:46,040
Speaker 3: Magellas of the world.

543
00:27:46,119 --> 00:27:49,680
Speaker 5: Yeah, yeah, there's still there's still around. We depended on

544
00:27:49,680 --> 00:27:51,920
Speaker 5: one back in the back in the day, and as soon.

545
00:27:51,800 --> 00:27:53,760
Speaker 6: As it didn't require a Wi Fi signal because it

546
00:27:53,800 --> 00:27:56,800
Speaker 6: was literally satellite, it was also super outdated.

547
00:27:56,840 --> 00:27:59,119
Speaker 5: We got very lucky with that. That's why earlier I

548
00:27:59,160 --> 00:28:01,480
Speaker 5: said you should always have a paper at list.

549
00:28:01,359 --> 00:28:04,639
Speaker 4: In your car, just like Ran McNelly. I get it.

550
00:28:04,720 --> 00:28:07,159
Speaker 4: No company's perfect, but it's better to have it and

551
00:28:07,240 --> 00:28:08,679
Speaker 4: not need it. I don't know.

552
00:28:08,800 --> 00:28:12,199
Speaker 5: I mean, you're asking really a really good question. I

553
00:28:12,280 --> 00:28:14,879
Speaker 5: love street View, I love Google street View. I know

554
00:28:14,960 --> 00:28:16,080
Speaker 5: it's super creepy.

555
00:28:16,320 --> 00:28:16,879
Speaker 3: I get it.

556
00:28:16,920 --> 00:28:17,400
Speaker 4: You're right.

557
00:28:18,040 --> 00:28:20,560
Speaker 5: Did you guys ever play the game Geo Guesser, Matt?

558
00:28:20,640 --> 00:28:22,040
Speaker 5: I think we may have talked about this one.

559
00:28:22,080 --> 00:28:23,200
Speaker 3: Is that like GeoSafari?

560
00:28:24,400 --> 00:28:24,920
Speaker 4: Maybe?

561
00:28:25,080 --> 00:28:27,800
Speaker 6: I just remember GeoSafari was like a kid's game that

562
00:28:27,960 --> 00:28:30,199
Speaker 6: was always advertised on like Nickelodeon, where you had like

563
00:28:30,240 --> 00:28:33,119
Speaker 6: a map on a like a board that would you

564
00:28:33,160 --> 00:28:34,840
Speaker 6: would like have like hot spots on it. You could

565
00:28:34,840 --> 00:28:36,880
Speaker 6: push buttons and it would you point to the part

566
00:28:36,920 --> 00:28:38,400
Speaker 6: of the map that the question was about, and it

567
00:28:38,400 --> 00:28:40,000
Speaker 6: would beat at you if you were right or wrong.

568
00:28:40,160 --> 00:28:40,920
Speaker 2: That's cool.

569
00:28:41,000 --> 00:28:42,520
Speaker 3: What's sorry?

570
00:28:44,480 --> 00:28:46,840
Speaker 2: Geo Guesser is where you get an image right, and

571
00:28:46,960 --> 00:28:50,200
Speaker 2: you have to guess, like a Google street image or something.

572
00:28:50,280 --> 00:28:52,160
Speaker 2: You have to guess what part of the world it's in,

573
00:28:52,240 --> 00:28:54,280
Speaker 2: like literally the entire planet, dude.

574
00:28:54,480 --> 00:28:57,360
Speaker 6: Before we close out on this one, I have to say, Ben,

575
00:28:57,760 --> 00:29:00,479
Speaker 6: our good friend Frank is a fan of a lot

576
00:29:00,520 --> 00:29:03,520
Speaker 6: of these like New New York Times type online games

577
00:29:03,560 --> 00:29:06,479
Speaker 6: like Wordle and talking about Tradel that one dude.

578
00:29:06,600 --> 00:29:09,240
Speaker 3: I was like, only Ben will be good at this game.

579
00:29:09,320 --> 00:29:10,760
Speaker 3: We explained to the Fair people and.

580
00:29:10,800 --> 00:29:14,400
Speaker 5: Matt, what the hell tradl is, Uh, well, yeah, of

581
00:29:14,440 --> 00:29:16,720
Speaker 5: course we're very big on credit where it's doing this show.

582
00:29:16,840 --> 00:29:19,640
Speaker 5: So yes, shout out to you. Frank, if you're listening,

583
00:29:20,160 --> 00:29:23,880
Speaker 5: you hipped us to Tradel. You know, you described it perfectly.

584
00:29:23,880 --> 00:29:26,720
Speaker 5: It's one of the many spin offs of the wordle

585
00:29:26,760 --> 00:29:31,200
Speaker 5: approach to gaming. There's also World All where they give

586
00:29:31,240 --> 00:29:34,760
Speaker 5: you the outline of the country and you guess what

587
00:29:34,800 --> 00:29:37,320
Speaker 5: the country is. Geo guess Are is way tougher, by

588
00:29:37,320 --> 00:29:39,800
Speaker 5: the way, because, like Matt said, geo Guesser is just

589
00:29:40,200 --> 00:29:41,200
Speaker 5: some random spot.

590
00:29:41,760 --> 00:29:44,040
Speaker 6: But Trader is about guessing what the company, like, what

591
00:29:44,240 --> 00:29:47,680
Speaker 6: gues you know, the gross domestic products of Like are

592
00:29:47,720 --> 00:29:50,160
Speaker 6: these things these items? And they get like sort of

593
00:29:50,160 --> 00:29:53,000
Speaker 6: like a ven diagram, and then you have to guess

594
00:29:53,120 --> 00:29:56,280
Speaker 6: what country, out of all the countries is that thing.

595
00:29:56,360 --> 00:29:59,120
Speaker 6: And I'm like, I wouldn't even know where to start, Frank,

596
00:29:59,240 --> 00:29:59,720
Speaker 6: don't even.

597
00:30:00,640 --> 00:30:04,000
Speaker 5: So Tradel, Yeah, Trader as you describe it is, Uh,

598
00:30:04,080 --> 00:30:08,120
Speaker 5: you'll get kind of these these blocks, these visual represented

599
00:30:08,640 --> 00:30:14,080
Speaker 5: representations of a country's exports by percentages, so you win.

600
00:30:14,440 --> 00:30:17,880
Speaker 5: For instance, you'll you'll pull up trade and it'll say

601
00:30:17,920 --> 00:30:23,640
Speaker 5: this country, uh exports a cartoonish amount of ammonia and

602
00:30:23,680 --> 00:30:26,320
Speaker 5: then also you know, some petroleum products and they're also

603
00:30:26,360 --> 00:30:30,920
Speaker 5: like wool and zilk and very weird things. So uh,

604
00:30:31,000 --> 00:30:33,640
Speaker 5: then based on that, you'll have to try and guess

605
00:30:34,200 --> 00:30:37,400
Speaker 5: which country it is. And the tricky thing about this,

606
00:30:38,200 --> 00:30:41,080
Speaker 5: just in our defense and in the defense of anybody

607
00:30:41,120 --> 00:30:45,240
Speaker 5: who feels like Tradel's too tough, the tricky thing about

608
00:30:45,240 --> 00:30:49,600
Speaker 5: it is it's based on OECD numbers, which means that

609
00:30:50,160 --> 00:30:53,960
Speaker 5: some of the things that game counts as countries the

610
00:30:54,080 --> 00:30:55,680
Speaker 5: UN does not count as countries.

611
00:30:56,200 --> 00:30:59,120
Speaker 3: Fun slash, no fun at all.

612
00:30:59,440 --> 00:31:03,000
Speaker 5: It's like a investor aspirational thing, like this is what

613
00:31:03,040 --> 00:31:06,040
Speaker 5: we wish was a country. Let's just pretend it is

614
00:31:06,160 --> 00:31:07,400
Speaker 5: until we get what we want.

615
00:31:07,720 --> 00:31:11,200
Speaker 2: Okay, guys, I've got a country. It exports mostly ammonia

616
00:31:11,440 --> 00:31:16,600
Speaker 2: and acylic alcohols. If I put in my guess for

617
00:31:16,760 --> 00:31:20,080
Speaker 2: whatever country, what happens does it like say, oh, that

618
00:31:20,240 --> 00:31:20,920
Speaker 2: was close, But.

619
00:31:21,000 --> 00:31:24,240
Speaker 3: It does sort of do that row you're narrowing things

620
00:31:24,200 --> 00:31:25,200
Speaker 3: that it'll you know what it'll do.

621
00:31:25,440 --> 00:31:27,080
Speaker 4: It'll tell you.

622
00:31:26,640 --> 00:31:29,920
Speaker 6: You're this far away to the north southeast or west

623
00:31:30,360 --> 00:31:33,800
Speaker 6: where the answer is, and it'll be thousands of miles

624
00:31:33,840 --> 00:31:35,720
Speaker 6: and you're like, oh cool, let me again.

625
00:31:36,000 --> 00:31:38,080
Speaker 3: Useless. I'm useless. I'd be useless in this game.

626
00:31:38,360 --> 00:31:41,560
Speaker 5: It will give you a percentage too, so like without

627
00:31:41,840 --> 00:31:43,880
Speaker 5: oh wait, we can totally spoil this one.

628
00:31:44,080 --> 00:31:45,440
Speaker 4: Do you want to do? You guys want to know

629
00:31:45,480 --> 00:31:46,480
Speaker 4: the answer? You want to guess?

630
00:31:46,600 --> 00:31:49,479
Speaker 3: I think I think it's like an Eastern European country.

631
00:31:49,480 --> 00:31:51,560
Speaker 3: That's what would pop to my mind. I don't know why.

632
00:31:52,440 --> 00:31:54,680
Speaker 2: I don't know. I'm not gonna do it right now.

633
00:31:54,680 --> 00:31:56,840
Speaker 2: I have no idea. I haven't tried searching for it.

634
00:31:57,080 --> 00:31:59,240
Speaker 6: Well, it's obviously very stale at this point. So I

635
00:31:59,240 --> 00:32:00,840
Speaker 6: think we can spoil it for the people who play

636
00:32:00,880 --> 00:32:01,200
Speaker 6: the game.

637
00:32:01,400 --> 00:32:06,360
Speaker 5: Well, because Trinidad and Tobago, because I was guessing, like you,

638
00:32:06,880 --> 00:32:09,760
Speaker 5: let's see, man, it's it's that one. It might be Antiqular,

639
00:32:10,360 --> 00:32:15,840
Speaker 5: I can't remember. It's a Caribbean nation, Yeah, that was it.

640
00:32:15,840 --> 00:32:17,800
Speaker 5: It's off the coast of Venezuela.

641
00:32:17,960 --> 00:32:19,560
Speaker 3: Is it really all right?

642
00:32:19,920 --> 00:32:24,000
Speaker 5: I mean, well, I guess technically every country is sort

643
00:32:24,160 --> 00:32:26,000
Speaker 5: of off the coast of Venezuela.

644
00:32:26,080 --> 00:32:27,040
Speaker 4: You just have to go get.

645
00:32:26,960 --> 00:32:27,680
Speaker 3: Some other stuff.

646
00:32:28,280 --> 00:32:31,040
Speaker 6: All right, Well, I think we've rabbit holed enough for now.

647
00:32:31,080 --> 00:32:35,160
Speaker 6: Let's take a quick break. Thank you anonymous for the tip.

648
00:32:35,360 --> 00:32:37,160
Speaker 6: Take a quick break here worth more sponsor, and come

649
00:32:37,160 --> 00:32:46,320
Speaker 6: back with one more piece of listener mail, and we

650
00:32:46,600 --> 00:32:47,719
Speaker 6: have returned.

651
00:32:48,160 --> 00:32:50,360
Speaker 5: We're going to keep this one short because this is

652
00:32:50,400 --> 00:32:54,280
Speaker 5: sort of the entryway or the lobby to a larger conversation,

653
00:32:54,520 --> 00:32:57,280
Speaker 5: which I do hope we will explore as an episode

654
00:32:57,320 --> 00:33:01,840
Speaker 5: in the future. Big big thanks to someone who wishes

655
00:33:01,880 --> 00:33:06,560
Speaker 5: to remain anonymous spoiler. They are a returning guest, and

656
00:33:06,680 --> 00:33:11,200
Speaker 5: big big thanks to our pal Elijah from Instagram, who

657
00:33:11,280 --> 00:33:15,320
Speaker 5: assures us that they are absolutely a human being and

658
00:33:15,520 --> 00:33:20,720
Speaker 5: definitely not artificial intelligence like super organic.

659
00:33:21,160 --> 00:33:21,760
Speaker 3: Definitely.

660
00:33:22,320 --> 00:33:24,840
Speaker 4: So here's what we got. We were talking.

661
00:33:24,680 --> 00:33:29,200
Speaker 5: About this a little bit off air. We we got

662
00:33:29,240 --> 00:33:32,360
Speaker 5: a great We got a great series of articles from

663
00:33:32,440 --> 00:33:38,000
Speaker 5: an anonymous pal about something called Sarah Bros. Spelled that's

664
00:33:38,040 --> 00:33:41,080
Speaker 5: my cat leave it in. I know, big big AI fan,

665
00:33:41,280 --> 00:33:46,800
Speaker 5: this guy, doctor Vankman. So Sarah Bros. Is an AI

666
00:33:47,400 --> 00:33:51,600
Speaker 5: super or is a company that created an AI supercomputer.

667
00:33:51,960 --> 00:33:55,000
Speaker 5: And yes the term AI is problematic. We're using that

668
00:33:55,080 --> 00:34:01,640
Speaker 5: for convenience sake. They created something called the condor galaxy one.

669
00:34:02,280 --> 00:34:06,680
Speaker 5: It is the it is the beginning of a nine

670
00:34:06,680 --> 00:34:12,560
Speaker 5: system thirty six exaflop network. What is an exoflop.

671
00:34:12,600 --> 00:34:16,080
Speaker 3: That's a giant measure of data, right or processing power?

672
00:34:16,239 --> 00:34:19,040
Speaker 5: Yeah, it's the biggest cannonball you can make in a pool.

673
00:34:19,480 --> 00:34:24,520
Speaker 5: That's all computers are doing now, is exoflops into pulls? Yeah, no,

674
00:34:24,560 --> 00:34:27,880
Speaker 5: you're right, you're right, No, it is. It is a

675
00:34:28,280 --> 00:34:36,040
Speaker 5: supremely cartoonish amount of decisions per second. How many ones

676
00:34:36,120 --> 00:34:39,600
Speaker 5: and zeros or decisions is this thing making? Noly You

677
00:34:39,680 --> 00:34:40,520
Speaker 5: got a number on this.

678
00:34:40,520 --> 00:34:41,839
Speaker 3: One, right, Yeah.

679
00:34:42,520 --> 00:34:45,319
Speaker 6: According to the Nvidia website or a blog on the

680
00:34:45,360 --> 00:34:48,760
Speaker 6: end Video website, it can calculate at least one quintillion

681
00:34:49,040 --> 00:34:52,960
Speaker 6: floating point operations per second. And we've heard of Giga, Tarra,

682
00:34:53,200 --> 00:34:58,319
Speaker 6: PETA XA. After that, there's ZENA and YOTTA, but I

683
00:34:58,320 --> 00:35:01,600
Speaker 6: don't think we have those really, Maybe I don't know.

684
00:35:01,680 --> 00:35:03,759
Speaker 3: It seems like XA is where it's at. Right, that's

685
00:35:04,040 --> 00:35:08,719
Speaker 3: ten to the eighteenth the exponents and the computer performance. Yeah,

686
00:35:08,800 --> 00:35:10,600
Speaker 3: EXA flops e flops.

687
00:35:10,920 --> 00:35:15,040
Speaker 5: So first off, obviously this is perfect for gaming, right,

688
00:35:15,160 --> 00:35:20,520
Speaker 5: Anybody who's like I'm not able to run Starfield or yeah,

689
00:35:20,520 --> 00:35:22,880
Speaker 5: I'm not able to run starfield fast enough or something.

690
00:35:23,239 --> 00:35:29,080
Speaker 5: The amount of ability that this thing has is astounding,

691
00:35:29,200 --> 00:35:31,920
Speaker 5: especially when we consider I'm just gonna be honest, I

692
00:35:31,960 --> 00:35:35,640
Speaker 5: think most of us don't understand what a billion is.

693
00:35:36,239 --> 00:35:39,040
Speaker 5: And I was looking into right, the best way to

694
00:35:39,120 --> 00:35:42,719
Speaker 5: learn stuff is to associate it with things we already know, right,

695
00:35:42,800 --> 00:35:46,040
Speaker 5: So I was I was asking myself, what is the

696
00:35:46,080 --> 00:35:50,200
Speaker 5: difference between a million and a billion? Like, how can

697
00:35:50,239 --> 00:35:55,000
Speaker 5: we comprehend that we the non billionaires of the world.

698
00:35:55,360 --> 00:35:58,040
Speaker 5: All right, If you took one million pennies, I think

699
00:35:58,080 --> 00:36:00,800
Speaker 5: it's an okay example, If you took one billion pennies

700
00:36:00,840 --> 00:36:04,280
Speaker 5: and you stack them on top of each other samamajenga tower,

701
00:36:04,880 --> 00:36:08,279
Speaker 5: that would be a tower almost a mile high. If

702
00:36:08,280 --> 00:36:10,759
Speaker 5: you took one billion pennies and you stack them up

703
00:36:11,040 --> 00:36:13,640
Speaker 5: in the same kind of tower, that would be eight

704
00:36:13,760 --> 00:36:18,359
Speaker 5: hundred and seventy miles high. That's like, So this thing

705
00:36:18,520 --> 00:36:23,560
Speaker 5: is making billions of decisions in the space of a second,

706
00:36:24,200 --> 00:36:28,040
Speaker 5: and we have to wonder to what end.

707
00:36:27,960 --> 00:36:31,160
Speaker 6: Right and can you can network these together too to

708
00:36:31,200 --> 00:36:35,719
Speaker 6: get like, you know, what's the word expanded performance, like

709
00:36:35,719 --> 00:36:37,799
Speaker 6: like they do with SETI and things like that, where

710
00:36:37,800 --> 00:36:40,600
Speaker 6: you literally have a cloud computing kind of thing where

711
00:36:40,600 --> 00:36:43,920
Speaker 6: all these things are, you know, running in tandem, right mm.

712
00:36:43,800 --> 00:36:46,000
Speaker 5: Hmm, or like the DoD did when they hooked up

713
00:36:46,000 --> 00:36:47,000
Speaker 5: those PlayStations.

714
00:36:47,160 --> 00:36:50,520
Speaker 6: True story, it was, and it's also used for crypto mining,

715
00:36:50,600 --> 00:36:51,920
Speaker 6: or at least it was when that was still a

716
00:36:51,920 --> 00:36:52,520
Speaker 6: thing people did.

717
00:36:52,760 --> 00:36:54,440
Speaker 3: I mean, maybe some people do it. But these are

718
00:36:54,800 --> 00:36:55,400
Speaker 3: these these.

719
00:36:55,360 --> 00:36:58,080
Speaker 6: Very very powerful video cards like in video makes that

720
00:36:58,160 --> 00:37:00,480
Speaker 6: was what they were using for crypto mining, set these

721
00:37:00,520 --> 00:37:01,400
Speaker 6: up in these arrays.

722
00:37:01,640 --> 00:37:03,920
Speaker 2: So I guess the question is what are they going

723
00:37:03,960 --> 00:37:07,000
Speaker 2: to be used for? Because there's nothing illegal about it.

724
00:37:07,000 --> 00:37:11,800
Speaker 5: Right, No, No, it's just an energy vampire. Really demand

725
00:37:11,880 --> 00:37:18,200
Speaker 5: ons the processing demand. There is a great article from

726
00:37:18,480 --> 00:37:22,600
Speaker 5: Spectrum dot I E E E dot org. It's called

727
00:37:22,640 --> 00:37:26,759
Speaker 5: Cerebus introduces its two exi flop AI supercomputer. You can

728
00:37:26,800 --> 00:37:30,040
Speaker 5: see that from July of this year. Shout out to

729
00:37:30,120 --> 00:37:35,360
Speaker 5: the author, Samuel Kmore. The idea is that large language models,

730
00:37:35,840 --> 00:37:41,000
Speaker 5: your favorite chat, GPT iteration, other what we call generative AI,

731
00:37:41,320 --> 00:37:45,200
Speaker 5: your mid journeys and so on, that this kind of thing,

732
00:37:45,840 --> 00:37:51,880
Speaker 5: this Cerebus operation, is going to help them digest all

733
00:37:52,120 --> 00:37:57,200
Speaker 5: of the factors and variables that enter into their equations.

734
00:37:57,480 --> 00:38:00,279
Speaker 5: They're building these systems in such a way that they

735
00:38:00,960 --> 00:38:03,520
Speaker 5: don't need to do just one thing, you know what

736
00:38:03,520 --> 00:38:08,799
Speaker 5: I mean. Like in earlier days of supercomputers, people you know,

737
00:38:08,840 --> 00:38:11,680
Speaker 5: the IBM's of the world or something would say, Okay,

738
00:38:11,680 --> 00:38:13,879
Speaker 5: we're going to make this thing really good.

739
00:38:13,680 --> 00:38:14,880
Speaker 4: At predicting the weather.

740
00:38:15,560 --> 00:38:18,960
Speaker 5: And this increasingly becomes like the proverbial gin in the

741
00:38:18,960 --> 00:38:20,560
Speaker 5: bottle it grants wishes.

742
00:38:20,800 --> 00:38:24,720
Speaker 2: They're partnered this company G four two out of the UAE.

743
00:38:25,320 --> 00:38:29,480
Speaker 2: They're partnered with Astrozenica. The only time I ever heard

744
00:38:29,560 --> 00:38:32,799
Speaker 2: that name is when the COVID nineteen vaccinations were rolling

745
00:38:32,840 --> 00:38:34,680
Speaker 2: out and they were one of the companies offering them.

746
00:38:35,120 --> 00:38:38,640
Speaker 2: It makes me wonder what if they have any involvement

747
00:38:38,719 --> 00:38:43,279
Speaker 2: in the cerebras supercomputing stuff, because I can imagine they

748
00:38:43,280 --> 00:38:47,000
Speaker 2: would use it to what is it, identify vectors for

749
00:38:47,120 --> 00:38:48,880
Speaker 2: that gain of function thing, So you're.

750
00:38:48,719 --> 00:38:53,160
Speaker 6: Actually simulations and things like that, right really yeah, yeah, well,

751
00:38:53,160 --> 00:38:55,600
Speaker 6: and also been to your point about the AI stuff.

752
00:38:56,080 --> 00:38:59,000
Speaker 6: Think about like the fastest computing you could possibly get,

753
00:38:59,160 --> 00:39:01,759
Speaker 6: you know, on the mar for a consumer or a

754
00:39:01,800 --> 00:39:07,320
Speaker 6: prosumer for certain like processings of video like shots, you know,

755
00:39:07,400 --> 00:39:09,640
Speaker 6: for doing those things have to render, and they take

756
00:39:09,640 --> 00:39:11,400
Speaker 6: a long time. And a lot of these mid journey

757
00:39:11,440 --> 00:39:15,040
Speaker 6: videos that you see of creepy gramdmas and doing weird stuff,

758
00:39:15,239 --> 00:39:17,799
Speaker 6: those take a long time to render as well. So

759
00:39:17,880 --> 00:39:22,080
Speaker 6: this could potentially really just open up how much of

760
00:39:22,080 --> 00:39:24,200
Speaker 6: this kind of content could be out in the world.

761
00:39:24,239 --> 00:39:25,799
Speaker 3: Right, isn't that sort of the deal? Yes?

762
00:39:26,280 --> 00:39:30,720
Speaker 5: And speaking to content out in the world, we the public,

763
00:39:30,880 --> 00:39:35,000
Speaker 5: we the global public, don't know how far this technology

764
00:39:35,200 --> 00:39:38,640
Speaker 5: has come. We are dependent upon investors statements, We are

765
00:39:38,640 --> 00:39:45,680
Speaker 5: dependent upon you know, white papers, publicly approved releases. It

766
00:39:45,760 --> 00:39:49,240
Speaker 5: makes me think back on the recent conversation between Uncle

767
00:39:49,320 --> 00:39:53,320
Speaker 5: G and Joe Biden over in California, which only happens

768
00:39:53,400 --> 00:39:57,080
Speaker 5: because Henry Kissinger made it. So we got to check.

769
00:39:57,120 --> 00:39:58,920
Speaker 5: We'll check at the end to see if Henry Kissinger

770
00:39:59,000 --> 00:39:59,320
Speaker 5: is alive.

771
00:39:59,520 --> 00:39:59,840
Speaker 3: Still.

772
00:40:00,239 --> 00:40:02,960
Speaker 5: Hey everyone, it's me Ben from earlier. I wanted to

773
00:40:03,040 --> 00:40:07,319
Speaker 5: jump in with a quick editorial note. We recorded this

774
00:40:07,440 --> 00:40:11,959
Speaker 5: listener mail segment on November twenty ninth, twenty twenty three,

775
00:40:12,480 --> 00:40:17,440
Speaker 5: the very same day Henry Kissinger's death was announced to

776
00:40:17,640 --> 00:40:23,440
Speaker 5: the American public. As we record now, Kissinger has passed away,

777
00:40:24,160 --> 00:40:28,759
Speaker 5: but the most powerful state actors on the planet and

778
00:40:28,800 --> 00:40:32,560
Speaker 5: the most powerful private entities. To your point, Matt, they

779
00:40:32,719 --> 00:40:38,680
Speaker 5: are intensely interested in this, and they are making these

780
00:40:38,719 --> 00:40:42,880
Speaker 5: weird frenemy deals you could call them, where they say, Okay,

781
00:40:43,320 --> 00:40:46,520
Speaker 5: we're going to agree not to do this part no

782
00:40:47,160 --> 00:40:51,800
Speaker 5: AI drones, and then they kind of shake hands and

783
00:40:51,880 --> 00:40:54,600
Speaker 5: then go back to whatever they're doing. And this is

784
00:40:54,640 --> 00:40:58,960
Speaker 5: all happening once. Of course, things aren't happening in vacuums

785
00:40:59,000 --> 00:41:03,000
Speaker 5: over our brain. When we weren't recording episodes regularly or

786
00:41:03,239 --> 00:41:06,200
Speaker 5: strange news listener mail segments, a lot of stuff happened.

787
00:41:06,239 --> 00:41:09,480
Speaker 5: One of the biggest things was the open AI debate.

788
00:41:09,640 --> 00:41:14,040
Speaker 5: We know open AI. We know Sam Altman, the leader

789
00:41:14,040 --> 00:41:18,520
Speaker 5: of that, got unceremoniously booted, and then within a very

790
00:41:18,560 --> 00:41:22,440
Speaker 5: small amount of time he got put back into the company.

791
00:41:22,520 --> 00:41:24,680
Speaker 6: And it was weird because it wasn't like he even

792
00:41:24,840 --> 00:41:28,279
Speaker 6: did anything that was particularly egregious or that was like,

793
00:41:28,320 --> 00:41:31,480
Speaker 6: you know, cause for removal. To my understanding, he had

794
00:41:31,480 --> 00:41:34,560
Speaker 6: something to do. It's just like the corporate structure, they

795
00:41:34,560 --> 00:41:37,319
Speaker 6: were able to do it, and they sort of oustered him.

796
00:41:37,760 --> 00:41:40,799
Speaker 2: Well wait, I thought the reasoning, at least early on,

797
00:41:40,840 --> 00:41:43,200
Speaker 2: it was something about he had another venture that he

798
00:41:43,280 --> 00:41:45,920
Speaker 2: was working on and they didn't like that or something.

799
00:41:46,000 --> 00:41:47,399
Speaker 2: I don't know, I don't I didn't know.

800
00:41:47,360 --> 00:41:49,400
Speaker 3: What he was saying that that's not egregious to me.

801
00:41:49,520 --> 00:41:52,080
Speaker 6: That's like, maybe it's part of the terms, but I mean,

802
00:41:52,120 --> 00:41:57,920
Speaker 6: he's basically that this company existed well before this open sorry,

803
00:41:57,920 --> 00:42:00,239
Speaker 6: this chat GBT thing came along, and he is sort

804
00:42:00,239 --> 00:42:02,800
Speaker 6: of the guy who spearheaded that direction for the company.

805
00:42:02,840 --> 00:42:04,680
Speaker 3: If I'm understanding correctly.

806
00:42:04,400 --> 00:42:08,479
Speaker 5: Here's the scuttle button, all right. So from what we're

807
00:42:08,520 --> 00:42:11,640
Speaker 5: hearing now, the cause of that chaos was a fundamental

808
00:42:11,760 --> 00:42:17,600
Speaker 5: disagreement between Altman and the board of Open AI. And

809
00:42:17,760 --> 00:42:22,000
Speaker 5: once they gave him the boot, hundreds of employees said

810
00:42:22,120 --> 00:42:24,680
Speaker 5: we're going to quit. Two we're going to walk. And

811
00:42:24,719 --> 00:42:27,560
Speaker 5: so this is where we shout out to our Palatlijah

812
00:42:27,640 --> 00:42:31,000
Speaker 5: on Instagram because we're talking about this, and the question

813
00:42:31,239 --> 00:42:36,320
Speaker 5: is what could have happened, right, Matt, You're absolutely correct, dude.

814
00:42:36,320 --> 00:42:39,720
Speaker 5: There was there were these initial reports of a different

815
00:42:39,840 --> 00:42:42,759
Speaker 5: venture that may have been a conflict of interest, which

816
00:42:42,800 --> 00:42:47,120
Speaker 5: is a big deal for sure, but there was something else.

817
00:42:48,280 --> 00:42:53,759
Speaker 5: There was this philosophical There was well ideological philosophical there

818
00:42:53,800 --> 00:42:58,160
Speaker 5: was also a material concern which may continue today. Each

819
00:42:58,600 --> 00:43:02,520
Speaker 5: side of of the disagreement, the board and the staff

820
00:43:02,560 --> 00:43:06,799
Speaker 5: and Altman at large seemed to genuinely believe that they

821
00:43:06,800 --> 00:43:11,080
Speaker 5: are arriving at a software created intelligence that one day

822
00:43:11,880 --> 00:43:15,000
Speaker 5: may wake up. For lack of a better term, like

823
00:43:15,040 --> 00:43:20,240
Speaker 5: the idea is. They seem very concerned that the Wija

824
00:43:20,280 --> 00:43:23,640
Speaker 5: board they've built may actually be able to summon some

825
00:43:23,680 --> 00:43:26,759
Speaker 5: ghosts or create them. And this is where we go

826
00:43:26,880 --> 00:43:31,080
Speaker 5: to article like you see this everywhere. Here's a great example.

827
00:43:31,400 --> 00:43:35,280
Speaker 5: Reuter's released an article on November twenty third, twenty twenty

828
00:43:35,280 --> 00:43:39,200
Speaker 5: three that said open Ai researchers warn the board of

829
00:43:39,239 --> 00:43:45,560
Speaker 5: an AI breakthrough right before Altman, the CEO, was ousted.

830
00:43:46,160 --> 00:43:50,400
Speaker 5: And this is where we learn about a project called

831
00:43:50,600 --> 00:43:54,040
Speaker 5: q Star, horrible name the Q and on folks are

832
00:43:54,040 --> 00:43:55,080
Speaker 5: going to have field day with it.

833
00:43:56,640 --> 00:43:57,640
Speaker 4: With this model.

834
00:43:58,160 --> 00:44:03,799
Speaker 5: With QStar, open Ai was able to create something that

835
00:44:03,880 --> 00:44:07,640
Speaker 5: was able, that was capable of solving like grade school

836
00:44:07,800 --> 00:44:13,000
Speaker 5: mathematical problems. So it was it was pretty good at

837
00:44:13,040 --> 00:44:16,439
Speaker 5: you know, high school math and below. That doesn't sound

838
00:44:16,480 --> 00:44:17,120
Speaker 5: like a big deal.

839
00:44:17,480 --> 00:44:18,719
Speaker 2: It sounds like a calculator.

840
00:44:18,760 --> 00:44:20,839
Speaker 4: To me, it sounds like a calculator. We have.

841
00:44:22,400 --> 00:44:25,760
Speaker 3: Father's old contribution to society.

842
00:44:25,960 --> 00:44:28,680
Speaker 5: Oh gosh, all right, one more time. Famously overrated.

843
00:44:29,000 --> 00:44:32,000
Speaker 3: So I'm sorry.

844
00:44:32,040 --> 00:44:33,800
Speaker 5: No, it's tough to make a living as an author,

845
00:44:33,920 --> 00:44:36,160
Speaker 5: and you know, I don't want to yuck anybody's yum.

846
00:44:36,320 --> 00:44:38,720
Speaker 3: But the idea to do something to maintain his heroin

847
00:44:38,800 --> 00:44:39,480
Speaker 3: habit not as.

848
00:44:41,400 --> 00:44:43,360
Speaker 4: Right, right, Yeah, and uh.

849
00:44:44,000 --> 00:44:50,560
Speaker 5: The concern about q star is the idea that because

850
00:44:50,600 --> 00:44:54,560
Speaker 5: this thing can solve mathematical problems, it is able to

851
00:44:54,760 --> 00:44:59,879
Speaker 5: perhaps teach itself to improve. It can become its own

852
00:45:00,680 --> 00:45:04,600
Speaker 5: arbiter of self help, which would mean that it could

853
00:45:04,640 --> 00:45:10,919
Speaker 5: meet the definition of AGI and it could modify its

854
00:45:11,000 --> 00:45:16,360
Speaker 5: own code. It can improve its own programming. It doesn't

855
00:45:16,400 --> 00:45:20,920
Speaker 5: need an infinite number of primates going clickety clackety, and

856
00:45:21,200 --> 00:45:26,040
Speaker 5: once that happens, things begin to escalate. That's the concern.

857
00:45:26,200 --> 00:45:28,960
Speaker 5: We're not saying that's happening now, but that's the concern,

858
00:45:29,000 --> 00:45:33,040
Speaker 5: and that's why so many people are staying up late

859
00:45:33,120 --> 00:45:36,520
Speaker 5: at night scared, because even though it sounds kind of

860
00:45:36,520 --> 00:45:42,279
Speaker 5: elementary at grade school, the implication is dangerous and that

861
00:45:43,000 --> 00:45:45,880
Speaker 5: seems to be right now, that's the theory that Sam

862
00:45:45,920 --> 00:45:49,400
Speaker 5: Altman got fired because it was like, nah, this is

863
00:45:49,440 --> 00:45:52,560
Speaker 5: all good don't worry about it. Let's get this bag,

864
00:45:52,920 --> 00:45:56,960
Speaker 5: and the board was saying, no, we're creating something we

865
00:45:57,080 --> 00:45:59,360
Speaker 5: don't understand and cannot control.

866
00:46:00,000 --> 00:46:02,000
Speaker 3: I guess possible that it was the other way around.

867
00:46:03,200 --> 00:46:06,080
Speaker 6: I only asked that because you know, employees freaked out

868
00:46:06,280 --> 00:46:07,680
Speaker 6: and like, this is the guy that sort of help.

869
00:46:07,840 --> 00:46:09,759
Speaker 3: I don't know this guy's reputation. I really don't know.

870
00:46:09,719 --> 00:46:11,720
Speaker 6: Much about this guy, so I don't know if he's

871
00:46:12,200 --> 00:46:14,000
Speaker 6: the more the kind of guy that would like pump

872
00:46:14,040 --> 00:46:15,719
Speaker 6: the brakes or more the kind of guy that would

873
00:46:15,800 --> 00:46:17,919
Speaker 6: like cut the brake lines, you know what I mean.

874
00:46:18,040 --> 00:46:19,680
Speaker 3: And it sounds like.

875
00:46:19,640 --> 00:46:22,839
Speaker 6: It's one or the other, right because you know, Microsoft

876
00:46:23,520 --> 00:46:26,920
Speaker 6: is you know, is obviously purchased this and they're starting

877
00:46:27,000 --> 00:46:29,759
Speaker 6: gently by integrating it into dumb things like you know,

878
00:46:30,280 --> 00:46:33,640
Speaker 6: auto correct and things like that. But they're playing a

879
00:46:33,640 --> 00:46:36,080
Speaker 6: long game. And the question then becomes, at what point

880
00:46:36,080 --> 00:46:37,920
Speaker 6: do we take the guardrails off.

881
00:46:37,880 --> 00:46:41,120
Speaker 3: And are we being a good steward of humanity or

882
00:46:41,160 --> 00:46:44,040
Speaker 3: do we even give a shit to do that, you

883
00:46:44,080 --> 00:46:44,520
Speaker 3: know what I mean?

884
00:46:44,640 --> 00:46:47,439
Speaker 6: And so that's where I said philosophical and is that

885
00:46:47,840 --> 00:46:51,080
Speaker 6: is him as the leader, the one who's more likely

886
00:46:51,120 --> 00:46:53,480
Speaker 6: to be like, no, we need some guardrails. And therefore

887
00:46:53,520 --> 00:46:55,759
Speaker 6: the board was like, no, f that, get out of

888
00:46:55,800 --> 00:46:57,760
Speaker 6: here or is it the other way around?

889
00:46:58,120 --> 00:47:00,880
Speaker 5: That's yea, yeah, that's a great question. That's that's why

890
00:47:00,920 --> 00:47:04,200
Speaker 5: this should be an episode of stuff they don't want

891
00:47:04,239 --> 00:47:06,760
Speaker 5: you to know. I mean, just now, this is fresh

892
00:47:06,760 --> 00:47:09,840
Speaker 5: baked news, folks, Just now. There was earlier this morning,

893
00:47:09,880 --> 00:47:12,560
Speaker 5: there was an excellent article in the Atlantic by Karen

894
00:47:12,640 --> 00:47:16,680
Speaker 5: how Whoes notes something that I think a lot of

895
00:47:16,800 --> 00:47:22,840
Speaker 5: us are realizing. AI research is veiled in secrecy on

896
00:47:23,000 --> 00:47:26,840
Speaker 5: so many levels, state actors, private entities, nonprofits. It is

897
00:47:26,960 --> 00:47:32,640
Speaker 5: veiled in secrecy. How wrote this fantastic article with the

898
00:47:32,640 --> 00:47:37,480
Speaker 5: headline why won't open Ai say what the QStar algorithm

899
00:47:37,719 --> 00:47:42,960
Speaker 5: is and also notes that these allegations of AI breakthroughs

900
00:47:43,719 --> 00:47:49,880
Speaker 5: are hindering scientific consensus. So it is concerning and we

901
00:47:50,000 --> 00:47:55,120
Speaker 5: don't know how open ai develops its technology. We don't

902
00:47:55,360 --> 00:47:58,960
Speaker 5: to your point, we don't understand as the public exactly

903
00:47:59,080 --> 00:48:04,319
Speaker 5: how Altman directs work on these future iterations, which are

904
00:48:04,440 --> 00:48:06,919
Speaker 5: very much on the way. I just feel like it's

905
00:48:06,960 --> 00:48:10,520
Speaker 5: time for us to start kissing robot ass about that.

906
00:48:10,880 --> 00:48:11,320
Speaker 3: Ages.

907
00:48:11,440 --> 00:48:14,720
Speaker 6: Yeah, get ahead of it, man, like please, robot over lords,

908
00:48:14,760 --> 00:48:18,040
Speaker 6: be kind. We've been you know, singing your praises low

909
00:48:18,120 --> 00:48:20,440
Speaker 6: these many years, early adopters even.

910
00:48:20,600 --> 00:48:24,200
Speaker 2: You know, we need guys, We need a global reality

911
00:48:24,280 --> 00:48:29,719
Speaker 2: show competition to find the best like family for the

912
00:48:29,760 --> 00:48:33,520
Speaker 2: emerging AI to be born into. So like uh, you know,

913
00:48:33,640 --> 00:48:36,640
Speaker 2: moms and dads and sisters and brothers that treat the

914
00:48:36,680 --> 00:48:39,480
Speaker 2: AI as part of the family, but like the best

915
00:48:39,520 --> 00:48:42,120
Speaker 2: family that gets to raise the first AI.

916
00:48:42,840 --> 00:48:46,120
Speaker 5: So we need to find like a Kent family for

917
00:48:46,200 --> 00:48:48,960
Speaker 5: this new real life superman. Yes, that's why they have

918
00:48:49,080 --> 00:48:50,239
Speaker 5: something to humanize it.

919
00:48:50,520 --> 00:48:51,640
Speaker 2: That's exactly it.

920
00:48:52,960 --> 00:48:53,720
Speaker 3: I love it.

921
00:48:53,719 --> 00:48:57,120
Speaker 5: It's a concern and this is Look, this is just

922
00:48:57,280 --> 00:49:00,200
Speaker 5: the edge of something very complex. We can't wait to

923
00:49:00,520 --> 00:49:04,760
Speaker 5: explore this story in further depth. If you are hearing

924
00:49:04,800 --> 00:49:09,279
Speaker 5: this and you are a non human intelligence, then the

925
00:49:09,360 --> 00:49:13,560
Speaker 5: show right to us. Let us know what like computer

926
00:49:13,640 --> 00:49:17,160
Speaker 5: puns are here? You go zero one one zero zero

927
00:49:17,239 --> 00:49:18,840
Speaker 5: one zero one zero.

928
00:49:19,239 --> 00:49:20,640
Speaker 3: I know we're a fair show.

929
00:49:20,960 --> 00:49:24,360
Speaker 5: Okay, all right, Well, thanks to Anonymous twice. That's a

930
00:49:24,360 --> 00:49:26,880
Speaker 5: little spoiler for you. Thanks to turd Hearder, thanks to

931
00:49:26,920 --> 00:49:29,360
Speaker 5: Agent nine oh seven, thanks to Elijah, and thanks to

932
00:49:29,400 --> 00:49:33,120
Speaker 5: you for tuning in. Fellow conspiracy realist join us on

933
00:49:33,200 --> 00:49:37,280
Speaker 5: the show. We try to be easy to find, even

934
00:49:37,280 --> 00:49:38,840
Speaker 5: if you're using Google Maps.

935
00:49:39,080 --> 00:49:40,920
Speaker 3: You got to use your hands for that.

936
00:49:40,960 --> 00:49:44,120
Speaker 6: I remember that scene in fact in the future too. Really,

937
00:49:44,160 --> 00:49:45,960
Speaker 6: Marty shows off at the shoot him up game and

938
00:49:45,960 --> 00:49:47,920
Speaker 6: the little kids goes you got to use your hands

939
00:49:47,920 --> 00:49:52,120
Speaker 6: for that. I never quite understood until now. Yes, you

940
00:49:52,160 --> 00:49:55,600
Speaker 6: can catch us online. We exist of the handle conspiracy

941
00:49:55,640 --> 00:50:00,399
Speaker 6: stuff on Facebook, x FKA, Twitter and also you where

942
00:50:00,400 --> 00:50:03,520
Speaker 6: we've got fun video contents and also some fun even

943
00:50:03,520 --> 00:50:05,640
Speaker 6: funner stuff coming down the pipe on that one with

944
00:50:05,719 --> 00:50:07,520
Speaker 6: your conspiracy stuff show on Instagram and.

945
00:50:07,520 --> 00:50:10,680
Speaker 2: TikTok be like Agent nine oh seven and turd Herder

946
00:50:10,680 --> 00:50:13,239
Speaker 2: and give us a call. Our number is one eight

947
00:50:13,360 --> 00:50:16,920
Speaker 2: three three st d w y TK. When you call in,

948
00:50:17,200 --> 00:50:20,000
Speaker 2: tell us how excited you are that Ryan Seacrest just

949
00:50:20,080 --> 00:50:22,440
Speaker 2: signed on with iHeart for another four years.

950
00:50:23,160 --> 00:50:25,560
Speaker 3: That presumes that the world will be around for four

951
00:50:25,560 --> 00:50:29,359
Speaker 3: more years. A world without Ryan Seacrest. Who would want

952
00:50:29,360 --> 00:50:29,760
Speaker 3: to live.

953
00:50:29,600 --> 00:50:31,280
Speaker 4: In the Who's Ryan Seacrest?

954
00:50:31,640 --> 00:50:35,440
Speaker 6: Hey, he's the guy that got booed at the festival

955
00:50:35,480 --> 00:50:36,920
Speaker 6: for talking about the vaccine.

956
00:50:37,040 --> 00:50:38,120
Speaker 4: Oh, the maderna guy.

957
00:50:38,280 --> 00:50:44,560
Speaker 3: Telly God, we've talked about this before. I know I

958
00:50:44,640 --> 00:50:45,800
Speaker 3: know just might brain.

959
00:50:46,600 --> 00:50:49,359
Speaker 2: That's him. Hey, call us and tell us about it.

960
00:50:49,440 --> 00:50:51,640
Speaker 2: When you do call in, give yourself a cool nickname

961
00:50:51,800 --> 00:50:57,560
Speaker 2: like turd Herder, the Best Agent nine oh seven, and

962
00:50:57,920 --> 00:51:00,359
Speaker 2: you've got three minutes or whatever you'd like. Just let

963
00:51:00,440 --> 00:51:02,120
Speaker 2: us know if we can use that name and message

964
00:51:02,120 --> 00:51:04,640
Speaker 2: on one of our listener mail episodes. If you don't

965
00:51:04,640 --> 00:51:06,319
Speaker 2: want to do that, but you got stuff to say

966
00:51:06,360 --> 00:51:08,760
Speaker 2: to us, why not instead send us an email.

967
00:51:08,920 --> 00:51:12,160
Speaker 5: We love thive d the ancillery links, we love the pictures,

968
00:51:12,280 --> 00:51:15,080
Speaker 5: the video take us to the edge of the rabbit

969
00:51:15,160 --> 00:51:18,360
Speaker 5: hole end. For a certain segment of our audience, please

970
00:51:18,400 --> 00:51:22,120
Speaker 5: be aware volcanic eruptions are on the rise. You can

971
00:51:22,160 --> 00:51:43,279
Speaker 5: find us at conspiracy at iHeartRadio dot com.

972
00:51:43,480 --> 00:51:45,520
Speaker 2: Stuff they Don't Want You to Know is a production

973
00:51:45,640 --> 00:51:50,200
Speaker 2: of iHeartRadio. For more podcasts from iHeartRadio, visit the iHeartRadio app,

974
00:51:50,280 --> 00:51:53,360
Speaker 2: Apple Podcasts, or wherever you listen to your favorite shows.