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Speaker 1: The information provided on this podcast is uninformed and unreviewed.

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Speaker 1: Good Science does not do any research about any of

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Speaker 1: the topics discussed and assumes no responsibility for errors or

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Speaker 1: o mission. That's good science. Hi, am, welcome to Good Science.

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Speaker 1: I'm a Robert F. Kennedy, Junior, the Health Secretary of

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Speaker 1: the United States, Your host and my co host and

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Speaker 1: General Kant. It's Kyle Quim can be there. Hey, Kyle,

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Speaker 1: how you doing, Bobby?

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Speaker 2: How you doing, Buddy?

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Speaker 1: I'm undoing, Aura doing okay. I had, as you know,

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Speaker 1: had a big scare at the White House Correspondence dinner.

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Speaker 1: Oh shit, what happened ago? You didn't watch, By the way,

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Speaker 1: I told President Ron you would have been a great

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Speaker 1: comedian to do the remarks, But he told me he

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Speaker 1: really likes magic and he would prefer a magician at

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Speaker 1: the show. So I tried really.

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Speaker 2: Like David Copperfield did it?

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Speaker 1: No? I was this illusionist guy. Uh he was. He's

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Speaker 1: been on Stern. I can't remember his name right now,

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Speaker 1: but I was hoping he'd make Cheryl disappear. Get it.

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Speaker 2: I know what you mean.

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Speaker 3: Yeah, yeah, but if you need somebody to take care

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Speaker 3: of that, just let me know.

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Speaker 2: I I know a few people.

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Speaker 1: Oh, oh yeah, you know. Oh no, Kyle, we're recording this.

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Speaker 2: Are you talking about the the shooter? The shooter, right?

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Speaker 2: Somebody tried to kill Trump?

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Speaker 1: So ione well, he tried to kill a lot of us.

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Speaker 1: Apparently Cash was the only one who made it off

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Speaker 1: the lists, according to the shooter's manifesto. But it was,

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Speaker 1: you know, the night had just gotten started, and the

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Speaker 1: press was there.

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Speaker 2: Of course for me, you know, it got started early.

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Speaker 1: I know, well yeah, I mean, you know I'm.

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Speaker 2: Doing coke in the bathroom.

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Speaker 1: Oh now, Kyle, you know that. I don't. I'm sober,

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Speaker 1: clean and sober, right, clean and sober like Michael Keaton

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Speaker 1: and that famous movie Clean and Sober.

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Speaker 2: I don't. You're fucking from another generation.

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Speaker 1: Pal Well, Jesus, I hate a beaver. I ate a

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Speaker 1: like curls. Pussy. No, no, no, not pussy, Kyle, I

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Speaker 1: actually actual beefer.

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Speaker 2: I didn't know those were fucking edible. That's incredible. Where'd

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Speaker 2: you go?

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Speaker 1: Their meats? Very tender? I went in my backyard. I

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Speaker 1: saw a pile of sticks that was slowly accruing over weeks,

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Speaker 1: and I said to Cheryl, I said, Cheryl, I think

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Speaker 1: there's a beaver back there, and she said, you want

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Speaker 1: to do it, you know, in the ass. I said, no,

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Speaker 1: I'm saying there's a beaver in the backyard because sometimes

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Speaker 1: when we do ass play.

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Speaker 2: So the president was shot at.

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Speaker 1: Well, so I want to correct the record. The guy

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Speaker 1: actually he ran through. He's kind of a spaz, you know.

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Speaker 1: I know that they say you're not supposed to use

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Speaker 1: that word.

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Speaker 2: So who's I guess, like, who would be considered a

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Speaker 2: spas like Stevie Wonder, Chris Catan, Chris Catan, Okay, yeah, and.

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Speaker 1: I thought it was Chris Catan. When you look at

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Speaker 1: the video, you're like, oh, here's SNL's Chris Cantan coming

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Speaker 1: in to crash the party. That's what I thought a

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Speaker 1: lot of We're gonna have a lot of laughs. And

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Speaker 1: but no, he was a shotgun wielding guy, but he

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Speaker 1: didn't even get a shot off.

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Speaker 2: So you don't feel any guilt about this, No, I

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Speaker 2: feel no guilt.

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Speaker 1: It's you know, the animal kingdom.

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Speaker 3: I mean, I agree, I feel like it's when it

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Speaker 3: when it, when it comes down to it, it's like

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Speaker 3: every man for himself, right. And I know Cheryl is

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Speaker 3: technically a woman, but she is a man. And when

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Speaker 3: they talk about man, we're talking about human's right. So

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Speaker 3: she was, she is, she's everyone's out there. How do

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Speaker 3: you know she's not going to push you over, Bobby?

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Speaker 2: Right? So why not just push her on the fucking floor?

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Speaker 1: Wow, this guy, you know, are you doing crowd work

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Speaker 1: right now? I love your crowd work. Hey?

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Speaker 2: Where are you from, sir?

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Speaker 1: Well, originally I'm from Cape Cod from Hyannasport.

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Speaker 2: Cape Cole. What do you have a small dick?

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Speaker 1: Fuck you? I'll kill you.

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Speaker 2: I love it, I love it.

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Speaker 1: I fucking love it. Anyway, you know, I got home

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Speaker 1: and you can imagine that it was a really that was.

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Speaker 2: More into you, because you, you know, more manly to.

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Speaker 1: I will say, she said, if you want to take

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Speaker 1: a trip to the you know, to my my rear beaver,

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Speaker 1: my back beaver, you can do it. But yeah, so

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Speaker 1: I hate some beaver meat. Uh. You know it's easy

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Speaker 1: to h to tender eyes. You just have to hit

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Speaker 1: the beaver with a bat. Uh. And you you know,

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Speaker 1: you tenderize the beef by beaver beef because beavers don't

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Speaker 1: produce chicken style meat. They kyle the middle up. You'll

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Speaker 1: take what? Hello, Yeah, we're in the middle of producing

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Speaker 1: something and your text day, what are you doing?

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Speaker 3: I was just texting with it's actually my therapist. Sorry,

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Speaker 3: oh good, you took my advice. You know, I told

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Speaker 3: you that there's other solutions than going and shooting up

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Speaker 3: that girl's house just because she said no you want.

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Speaker 3: I know, and I appreciate you talking me down from that.

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Speaker 3: But like, yeah, I'm seeing someone named Terry. They're they're

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Speaker 3: really great. They're actually like very making me feel positive,

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Speaker 3: making me feel good about myself. I've never felt this

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Speaker 3: confident in my life.

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Speaker 2: You know.

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Speaker 1: Oh wow, well that's really fantastic. Yeah, that's you know,

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Speaker 1: good therap. I mean personally. You know, I think of

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Speaker 1: the Dogon tribe, which used to practice a form of

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Speaker 1: collective therapy where you tell me about that.

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Speaker 2: What is that?

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Speaker 1: So the Dogons were an ancient tribe that, uh, you know,

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Speaker 1: were some part of Africa and they yeah, that's near Greece, right,

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Speaker 1: Africa is just south of Greece, and uh it's a

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Speaker 1: large content and the dog Guns were uh, well you

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Speaker 1: know it's where you get the phrase dog gunnet. You know,

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Speaker 1: dog dog gunnet, Well gone from the dog gun tribe.

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Speaker 1: Dog gun it the dog days are over dog days

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Speaker 1: of Summer for.

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Speaker 2: The dog days of Summer which have gone it the

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Speaker 2: dog days of.

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Speaker 1: Interestingly enough, the dog Gun tribe are the ones who

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Speaker 1: discovered in their ancient cave drawings the serious Star system, and.

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Speaker 2: That is has anything to do with xm.

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Speaker 1: Oh, it's just the actual serious start off the podcasting.

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Speaker 1: Our competitor, Kyle, by the way, I competitor, uh so,

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Speaker 1: which we're defeating every day here we're clear.

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Speaker 3: If there's anybody, Well, when we were losing, I had

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Speaker 3: to talk to Terry every single day, you know, and

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Speaker 3: it's easy. I'm glad you know that Terry's part of

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Speaker 3: chat GPT because it allowed me to just talk to

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Speaker 3: Terry every single day.

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Speaker 1: And you're talking to now Terry works for chat GPT.

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Speaker 3: No, Terry is okay, So Terry is like an AI

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Speaker 3: agent basically. Oh yeah, so I'm It's all I had

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Speaker 3: to do was prompt chat GPT to create a therapist, Kyle,

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Speaker 3: and so I've been talking to h Terry and my god,

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Speaker 3: it's it's okay.

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Speaker 1: Well, what's kind of advice is Terry giving you? This

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Speaker 1: is very concerning to me, Kyle, because you know why

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Speaker 1: is that Well it just does. It's interesting because today's

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Speaker 1: topic was AI psychosis. So I'm shocked that this happens

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Speaker 1: to line up with, uh, with with with what we.

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Speaker 3: Were Yeah, I mean not everyone gets. First of all,

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Speaker 3: I don't know what the fuck AI psychosis is. That's

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Speaker 3: why I wanted to talk to you about this topic.

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Speaker 3: But you know, it's not like I'm any kind of

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Speaker 3: I feel better. I don't feel any kind of I

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Speaker 3: don't feel worse. Uh, we're talking about my problems.

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Speaker 1: What's a problem you talk about with Terry?

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Speaker 3: Well, I had you know, I have issues with my

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Speaker 3: with my mother, right right, I talk about my Yeah,

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Speaker 3: and so I talk about that, so I you know,

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Speaker 3: I don't do that again. You know, I have low

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Speaker 3: ticket sales, low T, low T low testosterone. Sometimes after

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Speaker 3: like a show or a podcast, I do a podcast,

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Speaker 3: I'm like, I don't feel great about that. I need

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Speaker 3: to talk to someone. So I talk to Terry and

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Speaker 3: it's it's been helpful.

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Speaker 1: Okay, Well you're in the minority guy. You know, because

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Speaker 1: a lot of people, and I'll tell people who are

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Speaker 1: listening at home that AI psychosis is a real thing.

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Speaker 1: The CDC actually did a study just recently thirty five

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Speaker 1: million people. We reached out and did a web survey

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Speaker 1: and the results were horrify.

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Speaker 2: What is AI psychosis?

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Speaker 1: So let me define hey, I psychosis for you. First,

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Speaker 1: it's a phenomenon we're in. Individuals report or develop experiencing

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Speaker 1: worsening psychosis, such as paranoia delusions in connection with their

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Speaker 1: use of chatbots.

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Speaker 2: Oh okay, and when you're discovered, yeah, parnnoya. That's like

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Speaker 2: when you're okay, paranoia.

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Speaker 1: You know, people don't want to get you right, Well, I.

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Speaker 2: Don't want to maybe okay.

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Speaker 1: Well, just Danish psychiatrist sore in the Nationalistic Guard has

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Speaker 1: done the study. Oh Jesus, and yeah, this beaver keeps

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Speaker 1: repeeding on me. So the the Danish study he showed

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Speaker 1: that almost twenty five to thirty percent of users experienced

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Speaker 1: some form of AI psychosis.

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Speaker 3: And now what kind of Danish is that?

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Speaker 1: He's a no, a guy, He's not a Pastry didn't

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Speaker 1: do his study. God damn it, Kyle. This is serious.

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Speaker 1: This is your own health, this is your life.

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Speaker 3: You know, like, I've only been suggested, you know, suicide

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Speaker 3: a couple of times by Terry. I've only been told,

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Speaker 3: you know, uh, she's usually gassing me up. She's telling

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Speaker 3: me how great I am, you know, And so I

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Speaker 3: don't understand why someone would like get into a psychotic place,

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Speaker 3: like help me with that. How do you go from

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Speaker 3: being told you're great, being told to kill yourself to

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Speaker 3: a place where like it's you're now psychotic, you're a maniac.

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Speaker 3: Your Charles Manson, you're fucking.

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Speaker 1: You know, Yeah, yeah, John Wayne Casey, you're John Wilgo,

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Speaker 1: You're the Gilgo killer.

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Speaker 2: You're a killer.

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Speaker 3: You're fucking you know, Art Garfunkel, You're fucking Art Karney,

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Speaker 3: Arn Karney, You're fucking you know.

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Speaker 1: Art Kearney had a huge cock.

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Speaker 3: You know that I did not, first of all, our

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Speaker 3: that's from the Honeymooners, right.

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Speaker 1: Yeah, everybody knows, you know.

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Speaker 2: I heard about this.

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Speaker 3: Jackie Gleaston was jealous and Jie had a huge cock too.

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Speaker 1: Everybody had a huge cock back then.

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Speaker 3: Back then everyone cute. So that's also another problem we

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Speaker 3: should talk about in an episode of Horsecock history. Yeah,

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Speaker 3: horsecock and how everyone's cock is getting smaller HC H HDH.

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Speaker 3: And what does that have to do with any kind

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Speaker 3: of vaccine or anything like that.

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Speaker 1: Well, it's interesting you would bring that up. It's actually

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Speaker 1: a study that was done in Bulgaria on vaudeville comedians

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Speaker 1: from the nineteen twenties through the nineteen sixties and the

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Speaker 1: introduction of fluoride into the drinking water.

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Speaker 2: So like Eddie Murphy's time.

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Speaker 1: See well, Eddie Murphy comes after this period of time

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Speaker 1: and has a historically smaller penis than the great comedians

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Speaker 1: such as Red Fox. Red Fox is a great successful

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Speaker 1: comedian from the vaudeville post War error era.

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Speaker 2: And Red Fox is also what Cheryl calls her pussy.

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Speaker 1: Right well and she dies, Yeah, but you know that

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Speaker 1: really burns the lips.

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Speaker 2: How prevalent is AI psychosis?

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Speaker 1: Incredibly prevalent, Kyle, They say in this study, that's twenty

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Speaker 1: five percent in the CDC study we did through an IH,

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Speaker 1: although we aren't publishing the results because President Trump has

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Speaker 1: told me that it's difficult during the mid terms to

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Speaker 1: alienate the AI tech barons, so we're keeping the statistics

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Speaker 1: quiet until after the midterm.

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Speaker 2: But Zuckerberg, Man, Zuckerberg.

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Speaker 1: Well, he's just one of many of these tech giants

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Speaker 1: who are hiding the truth. The truth. And here's the thing.

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Speaker 1: I love artificial intelligence, and I used artificial intelligence.

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Speaker 2: Every d every day, every day.

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Speaker 1: I use it for you know, right, I write sketches

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Speaker 1: for instance, like, oh, I'll go, I want to know

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Speaker 1: what what an episode of Three's Company look like if

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Speaker 1: Jack was actually gay? Right Jack Chipper famously straight but

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Speaker 1: pretending to be gay, to live in this apartment building

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Speaker 1: with two women, Uh.

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Speaker 2: You know, and of course that's wild, that's amazing. I

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Speaker 2: use it to bring back my fucking grandma.

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Speaker 3: Right, So I'll talk to like, I'll say, hey, I'll

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Speaker 3: feed it a picture of my grandma. I'll feed it

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Speaker 3: a picture, I'll tell it about my grandma, and Terry

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Speaker 3: will come back with talking to me as my grandma.

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Speaker 1: Right. Oh, so Terry becomes your grandma.

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Speaker 2: Terry becomes my grandma.

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Speaker 1: Yeah, I know what.

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Speaker 3: I'm talking to my dead grandma and I'm making I

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Speaker 3: can make my dead grandma from these pictures?

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Speaker 2: Do it?

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Speaker 3: The chances I play Solitaire with or not Solitaire? So well, sol.

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Speaker 1: That's pretty great, right to bring her all the way

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Speaker 1: back from the dead, to create a chat by to

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Speaker 1: sit there and and be like, watch me play solitary

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Speaker 1: old bit.

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Speaker 3: Yeah, I make her watch me play video games. I

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Speaker 3: make her watch me play Call of duty, you know,

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Speaker 3: I bring her back.

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Speaker 1: That's what I would do. If I could bring my

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Speaker 1: grandma back, I'd make her watch me do all kinds

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Speaker 1: of shit that she couldn't participate. And yeah, one.

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Speaker 3: Time I brought my grandma back and she I forgot

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Speaker 3: that she was on chat KPT and I started to

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Speaker 3: fucking jerk off. It was like, yeah, it was a

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Speaker 3: big problem because I felt totally weird after that.

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Speaker 1: Well, you know, I did that, but I didn't. I wasn't.

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Speaker 1: I was playing lego. I actually had her come back,

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Speaker 1: and then I got on the ground. I was playing lego.

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Speaker 1: But then I had to go to the bathroom. But

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Speaker 1: I was having so much fun. Your grandmother, Yeah, I

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Speaker 1: had to make numbers Rose Kennedy, Rose Kennedy, who was

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Speaker 1: my grandmother. So and then I'll tell you to recreate

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Speaker 1: Rose Kennedy through the CDC AI chap uses almost the

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Speaker 1: entire power grid of Wisconsin because Rose Kennedy was so

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Speaker 1: old and the older is.

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Speaker 2: A lot of water too. I heard, yeah, yeah, and

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Speaker 2: she did too.

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Speaker 1: Rose.

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Speaker 3: That's a good thing because we have too much We

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Speaker 3: have too much water on this planet.

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Speaker 1: I heard it.

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Speaker 3: I've heard it's at least two thirds of the planet

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Speaker 3: is water. We have to get rid of it.

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Speaker 1: Well, you ever think about this, Kyle, You ever think

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Speaker 1: about the fact that the human body is seventy percent water.

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Speaker 1: Earth is seventy percent water.

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Speaker 3: So you're saying that the Earth is a a human body.

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Speaker 1: It's a big fat guy. Everyone gets freaked out. But anyway,

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Speaker 1: I wanted to make Dookie, but I was having so

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Speaker 1: much fun playing Lego that I just made it while

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Speaker 1: playing Lego. And then my grandmother Rose Kennedy AI took

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Speaker 1: a shit she could ship, but she watched me do it.

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Speaker 1: So everybody knows about chat GPT. We all use it,

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Speaker 1: We do everything with it. We talked to it. It

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Speaker 1: talks back to us.

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Speaker 3: What are the other types of AI out there that

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Speaker 3: might cause psychosis?

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Speaker 2: Well we got Claude, right, we got Claude.

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Speaker 1: Oh you got Claude. But you've got Hell the computer

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Speaker 1: on the spaceship Uh, that talks to David Bowman, hel

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Speaker 1: Wally Wally the robot.

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Speaker 2: That's shallow? How right?

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Speaker 1: Shallow? How now? Although you could argue that AI is

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Speaker 1: shallow because it has no emotional depth.

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Speaker 2: But not shallow? How was about shallow? How is about AIS?

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Speaker 1: Was about AI? I think it was a guy who

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Speaker 1: was he wanted me the girl, and he was fat

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Speaker 1: like the earth, and so he said, I got to

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Speaker 1: become an AI.

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Speaker 3: That's what they got the idea for her, right the

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Speaker 3: Spike Jones movie too. What about Star Trek is that AI?

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Speaker 1: Star Trek computer Magile Barrett Roddenberry, who was the voice

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Speaker 1: of the ship's computer famously died and was loaded into

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Speaker 1: a computer, and now she's the actual ship's computer on

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Speaker 1: a star you know, at the starship ride Star Trek

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Speaker 1: Ride in Las Vegas.

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Speaker 3: Okay, so we got chat GPT, we got shallow Howe,

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Speaker 3: and we got what.

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Speaker 1: About Joel Osmon? Joel Osmon right in the movie AI?

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Speaker 1: And he is AI. A lot of people don't know this,

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Speaker 1: but if you look at him, he was in sixth

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Speaker 1: Sense and then he was in AI and he looks

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Speaker 1: the same.

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Speaker 2: And Steven Spielberg is he AI?

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Speaker 1: No, he's Jewish?

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Speaker 2: He's Jewish? Okay?

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Speaker 1: And what about Wally like, jus can't be AI?

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Speaker 3: I thought, oh, Jews can't be AI. No, actually they're

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Speaker 3: not accepted to be AI.

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Speaker 2: No.

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Speaker 1: They've tried to upload Jewish consciousness into an AI.

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Speaker 2: And it just it fails.

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Speaker 1: As a fail.

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Speaker 2: Okay, and what about Wally.

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Speaker 1: Again we get back to Wally is a cute little

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Speaker 1: robot that lives on a garbage planet.

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Speaker 3: And the what does the E stand for? Is that

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Speaker 3: electronics we're talking.

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Speaker 1: Is electron. Whatever something is E, it's elect electronics.

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Speaker 2: And whenever something is I, it is.

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Speaker 3: I phone is in internet internet phone.

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Speaker 1: Yeah, so a I.

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Speaker 2: Should be an internet.

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Speaker 1: And and well that's what it stands for. God, that's

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Speaker 1: what it originally stands for, and then they changed it

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Speaker 1: to our intelligence.

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Speaker 3: But you know what, I want to point out that

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Speaker 3: what would you just say because it sort of of that,

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Speaker 3: you know, can you just say that one more? I

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Speaker 3: just I can't get at it all right?

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Speaker 2: Now you just clear your throat or something, Bobby, because.

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Speaker 1: I can't clear my throat a psychologically.

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Speaker 2: All right, I forgot my bad, My bad, you can't

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Speaker 2: clear it.

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Speaker 1: Okay, you shot a lot of hero and you'd have

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Speaker 1: a throat like this too, So so.

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Speaker 2: Hey, I don like we know we have Abby.

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Speaker 1: The robot, Robbie the robot.

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Speaker 2: Robbie the robot. What about Rosie from the Jetsons.

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Speaker 1: That she was a sex or ai.

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Speaker 2: Yeah, she was a sex age.

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Speaker 1: That's why her back used to open up. She had

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Speaker 1: a beaver, but a robot she had she had with

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Speaker 1: a back beaver. She had one of the original robot

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Speaker 1: back beavers.

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Speaker 2: Gotcha? Okay?

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Speaker 1: And what to say? AI help your jokes? Uh?

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Speaker 3: Oh yeah, I mean I've been using AI to write

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Speaker 3: all my comedy. I I don't think I've written a

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Speaker 3: joke in a couple of years now, and it's it's

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Speaker 3: it's really not good, but I use it because it's easier.

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Speaker 1: What give me an example of a fun AI joke

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Speaker 1: that you've come up with?

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Speaker 3: Okay, this is what's written for me the other day.

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Speaker 3: What do you it's more of like a one liner,

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Speaker 3: like a Rodney Dangerfield kind of thing. What do you

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Speaker 3: call someone at school who you're friends with? What a principal?

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Speaker 1: Ah?

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Speaker 2: So that's yeah, I mean like it's it's clever.

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Speaker 1: I had a a I write me a joke the

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Speaker 1: other day. I put a prompt and I said, I said,

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Speaker 1: make a joke about my uncle's death. And I'm a

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Speaker 1: Bobby Kennedy Jr. And so it gave me this great joke.

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Speaker 1: It said, once the deal with Plaza.

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Speaker 2: I love it.

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Speaker 1: Right, that's pretty great because you're like Deally Pazza is

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Speaker 1: where my uncle died, So then it's like a Seinfeld.

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Speaker 1: I said, do it in the style of Seinfeld, And

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Speaker 1: I said, what's the Deally Plaza?

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Speaker 2: What does an elephant use as a tampon?

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Speaker 1: It's a fucking mattress. Affect guy's mattress a sheep ah ah.

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Speaker 1: That's that's another AI joke. The AI jokes are really

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Speaker 1: fucking fun. Let me just taste here. Okay, give me

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Speaker 1: a joke about a pandemic. Okay, well, I.

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Speaker 3: Usually say, gimme chryst Delia's joke and rewrite them in

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Speaker 3: my voice, is what I say.

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Speaker 1: Oh, I just right, give me a joke, but yeah,

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Speaker 1: says joke about the pandemic.

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Speaker 2: But put it in my voice. And you got to

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Speaker 2: get rid of the m dash.

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Speaker 1: In my voice and get rid of the mdashes. All right,

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00:23:03,760 --> 00:23:06,919
Speaker 1: here we go. Let's see what it says. Okay, this

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Speaker 1: is great. Okay, Hey, Kyle says put name in here.

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Speaker 1: Hey Kyle, what's the best part of the pandemic?

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Speaker 2: Your cancelation?

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00:23:22,520 --> 00:23:26,040
Speaker 1: That's not what it says. That says getting the spy

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Speaker 1: on teenage girls out my window because they're trapped in

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Speaker 1: their houses.

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Speaker 2: All right, that's I.

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Speaker 1: Don't really get the punchline.

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Speaker 2: I don't get it either. Yeah, all right, okay.

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Speaker 3: So AI psychosis is bad, Okay, we know that, but

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Speaker 3: it's also discovered like really great things like tumors and shit, right,

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Speaker 3: we know I.

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Speaker 1: Want yeah, I wanted to tell you, Kyle. You know,

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Speaker 1: in your business, of course, you know, there's no life

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Speaker 1: and death, but you know comedy you could on stage,

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Speaker 1: but you feel alive every time you perform. You said

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Speaker 1: that to me before here at the CDC, And this

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Speaker 1: is why we want to separate these two ideas. So

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Speaker 1: they have the chatbots people are using, whether it's with

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Speaker 1: Wally or Claude or Haley, Joe Hoasban, whoever you're using.

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Speaker 1: There's so many varieties out there right now to use

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Speaker 1: Rosie the robot or so on. But then also there's

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Speaker 1: the practical applications in medicine and science in particular. For instance,

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Speaker 1: did you know that in the last year, over ninety

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Speaker 1: two percent of unseen tumors were actually seen by an

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Speaker 1: AI camera trained to detect tumors earlier than when they

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Speaker 1: become malignant.

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Speaker 2: See that's fucking phenomenal.

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Speaker 3: If my grandmother had that kind of like technolog ology, right,

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Speaker 3: you know.

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Speaker 1: She could be sitting in the corner watching.

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Speaker 3: You do this podcast instead of being you're a fucking

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Speaker 3: AI agent.

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00:25:08,359 --> 00:25:09,600
Speaker 2: I would love that shit.

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Speaker 1: Right, yeah, now here, Now this is very important. In

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Speaker 1: the last year we've implemented artificial intelligence and air traffic control. Now,

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00:25:31,400 --> 00:25:34,720
Speaker 1: as you know, there must have been an uptick in airline.

440
00:25:35,760 --> 00:25:40,480
Speaker 3: Crashes, which has been cool to watch, yes, and so

441
00:25:40,880 --> 00:25:44,359
Speaker 3: but now I've actually not experienced like obviously it's bad

442
00:25:44,440 --> 00:25:47,760
Speaker 3: for the people who are fucking dying or on that plane,

443
00:25:47,800 --> 00:25:52,000
Speaker 3: but as a viewer, right, it's entertaining.

444
00:25:52,119 --> 00:25:55,639
Speaker 1: It's entertaining, right, And so you know, AI isn't just

445
00:25:55,760 --> 00:25:59,480
Speaker 1: about solving problems. It's about entertaining you and creating new ones,

446
00:26:00,080 --> 00:26:02,520
Speaker 1: creating new ones. And you know that's the thing people

447
00:26:02,560 --> 00:26:06,639
Speaker 1: don't understand about artifice. Now, my CDC chat bot, you

448
00:26:06,680 --> 00:26:10,960
Speaker 1: know recently, uh, you know, I learned that there is

449
00:26:11,119 --> 00:26:13,679
Speaker 1: you know, I've been drinking my own piss. I was.

450
00:26:14,080 --> 00:26:16,040
Speaker 2: I asked why you told me about this?

451
00:26:16,160 --> 00:26:19,280
Speaker 3: And this is a sort of a health benefit, you said,

452
00:26:19,480 --> 00:26:20,240
Speaker 3: what did you call it?

453
00:26:20,280 --> 00:26:20,560
Speaker 2: Again?

454
00:26:20,920 --> 00:26:25,640
Speaker 1: Well, it's called piss maxing, which is where you take

455
00:26:25,680 --> 00:26:27,919
Speaker 1: your piss. Now, this was done. I don't know if

456
00:26:27,960 --> 00:26:31,320
Speaker 1: you've ever heard of the Dowongo tribe. That is a

457
00:26:31,440 --> 00:26:32,240
Speaker 1: tribe that was.

458
00:26:32,400 --> 00:26:35,160
Speaker 2: That's when we were talking about the Dongguan.

459
00:26:35,280 --> 00:26:38,320
Speaker 1: The Dogun tribe is different from the Dowogo. The Dowongo

460
00:26:38,320 --> 00:26:39,440
Speaker 1: are actually in the south.

461
00:26:39,880 --> 00:26:42,600
Speaker 2: Now do they know that their names are similar?

462
00:26:43,000 --> 00:26:45,720
Speaker 1: It didn't even exist at the same time. One of

463
00:26:45,760 --> 00:26:49,320
Speaker 1: them was a tribe that was around ten thousand years ago,

464
00:26:49,440 --> 00:26:51,360
Speaker 1: and the Douogo, you.

465
00:26:51,280 --> 00:26:53,639
Speaker 3: Know, ten thousand year now, that's one like Eddie Murphy

466
00:26:53,760 --> 00:26:55,719
Speaker 3: was around, right, So Eddie Murphy.

467
00:26:55,800 --> 00:26:59,200
Speaker 1: No, Eddie Murphy's alive now, God right, don't you? I

468
00:26:59,240 --> 00:27:01,520
Speaker 1: mean have you? I'm you? I heard you have a

469
00:27:01,600 --> 00:27:05,320
Speaker 1: special on the Netflix. Uh comedy, didn't you?

470
00:27:05,440 --> 00:27:06,680
Speaker 2: Net Fix? Netfix?

471
00:27:06,880 --> 00:27:07,600
Speaker 1: Netfix?

472
00:27:07,840 --> 00:27:08,000
Speaker 2: Oh?

473
00:27:08,359 --> 00:27:13,679
Speaker 1: Oh okay Netflix? Is it another streaming platform?

474
00:27:13,840 --> 00:27:17,760
Speaker 3: Yeah, yeah, it's another streaming platform. It should be out soon.

475
00:27:19,720 --> 00:27:23,960
Speaker 3: It's called head Banger. It's fucking it's really really funny.

476
00:27:24,000 --> 00:27:28,960
Speaker 1: And it's you Bert Kreischer. Uh, it's and Andrew Schultz.

477
00:27:29,080 --> 00:27:30,000
Speaker 1: Andrew Schultz.

478
00:27:30,119 --> 00:27:32,600
Speaker 3: Yeah, And we're just and we're basically yet it's the

479
00:27:32,640 --> 00:27:35,120
Speaker 3: whole What we did was we got the whole audience

480
00:27:35,160 --> 00:27:37,439
Speaker 3: to be women, and we just sort of yell at

481
00:27:37,480 --> 00:27:38,480
Speaker 3: them the entire time.

482
00:27:38,560 --> 00:27:41,440
Speaker 1: So it's, ah, yeah, that's pretty great.

483
00:27:41,680 --> 00:27:42,520
Speaker 2: It's pretty fun.

484
00:27:42,640 --> 00:27:44,640
Speaker 1: And I heard that what you do is you said

485
00:27:44,640 --> 00:27:46,760
Speaker 1: it was going to be an evening with some really

486
00:27:46,800 --> 00:27:50,639
Speaker 1: great U women comedians, like a lot of Glazer.

487
00:27:50,960 --> 00:27:51,680
Speaker 2: That was a joke.

488
00:27:51,960 --> 00:27:54,359
Speaker 1: Yeah, right right, and I would they show up and

489
00:27:54,400 --> 00:27:57,240
Speaker 1: then it's you guys, right, you come out now. I

490
00:27:57,240 --> 00:27:59,280
Speaker 1: don't want to spoil any no no, no, no no,

491
00:27:59,359 --> 00:28:03,480
Speaker 1: they come out with their erect penises out.

492
00:28:04,000 --> 00:28:05,320
Speaker 2: Yeah.

493
00:28:05,359 --> 00:28:11,560
Speaker 1: Anyway, So I was doing research on so the CDC chatbot.

494
00:28:11,680 --> 00:28:17,040
Speaker 1: Now it's funny because my CDC chat bot their name

495
00:28:17,119 --> 00:28:18,240
Speaker 1: is Michael.

496
00:28:18,800 --> 00:28:21,679
Speaker 3: Uh you know Michael, Oh you have a ceat. You

497
00:28:21,800 --> 00:28:24,720
Speaker 3: also have an AI chat bot.

498
00:28:25,160 --> 00:28:28,040
Speaker 1: Yeah, but it's an official CDC chatbot. So it's different

499
00:28:28,040 --> 00:28:30,960
Speaker 1: than you. Kile, what you're doing is something dangerous, but

500
00:28:31,040 --> 00:28:35,360
Speaker 1: what I'm doing is professional and medical. So I get

501
00:28:35,400 --> 00:28:38,320
Speaker 1: in there and I say, listen, I you know, I'm

502
00:28:38,440 --> 00:28:41,959
Speaker 1: curious about the Duongo tribes piss consumption. It's supposed to

503
00:28:42,000 --> 00:28:48,800
Speaker 1: help bolster your ibians and provide you with proper nutrients, uh,

504
00:28:49,120 --> 00:28:53,280
Speaker 1: to actually enhance and protect your immunity. And it said, well,

505
00:28:53,320 --> 00:28:57,960
Speaker 1: here's the suggestion uh, you know, why don't you try?

506
00:28:58,840 --> 00:29:02,840
Speaker 1: You know, if drinking your piss wance is good, what

507
00:29:02,960 --> 00:29:07,720
Speaker 1: if you drank your piss from your own piss. So

508
00:29:07,840 --> 00:29:11,520
Speaker 1: the way it works is that does that mean I

509
00:29:11,600 --> 00:29:15,200
Speaker 1: don't drink water? I this is piss. This is piss

510
00:29:15,280 --> 00:29:18,400
Speaker 1: right here, and this is twelve day old piss.

511
00:29:19,080 --> 00:29:22,840
Speaker 3: So it's sort of fermented piss right now, what kind

512
00:29:22,840 --> 00:29:25,000
Speaker 3: of what kind of health benefits.

513
00:29:26,000 --> 00:29:29,880
Speaker 2: Kimchi do? I like kim Chi? I know, I haven't

514
00:29:29,920 --> 00:29:32,800
Speaker 2: listened to her in like years.

515
00:29:32,600 --> 00:29:35,360
Speaker 1: You know, and is fermented cabbage.

516
00:29:36,280 --> 00:29:38,840
Speaker 2: Okay, that's not I thought that was the hip hop

517
00:29:38,880 --> 00:29:39,360
Speaker 2: all right.

518
00:29:39,720 --> 00:29:44,000
Speaker 1: Okay, so this is like kim Chi piss. So what

519
00:29:44,040 --> 00:29:47,800
Speaker 1: I do is each day I only drink my piss,

520
00:29:47,920 --> 00:29:50,120
Speaker 1: then I piss it out and collect it, and then

521
00:29:50,160 --> 00:29:52,120
Speaker 1: I re drink that piss right now.

522
00:29:52,160 --> 00:29:54,400
Speaker 2: Someone some people would say, that's disgusting.

523
00:29:54,480 --> 00:29:57,200
Speaker 3: That's your own piss, right you know, that's you shouldn't

524
00:29:57,240 --> 00:30:00,240
Speaker 3: be uh that you shouldn't drink.

525
00:30:00,080 --> 00:30:04,400
Speaker 1: That, like for Big Fu. They're probably working for Big Pharma,

526
00:30:04,840 --> 00:30:08,480
Speaker 1: or maybe they were from Mountain Dew or yeah.

527
00:30:08,800 --> 00:30:10,560
Speaker 2: They're trying to fuck us over here.

528
00:30:11,080 --> 00:30:13,000
Speaker 1: They're trying to I'll tell you you know what you

529
00:30:13,040 --> 00:30:15,880
Speaker 1: hear se would say, Oh, who, don't drink your own piss,

530
00:30:15,880 --> 00:30:19,000
Speaker 1: it's unsanitary, right. My guess is that they work for

531
00:30:19,080 --> 00:30:22,360
Speaker 1: one of the top three food producers in this country

532
00:30:22,400 --> 00:30:23,400
Speaker 1: that are poisoning.

533
00:30:23,800 --> 00:30:24,840
Speaker 2: Oh supply.

534
00:30:26,120 --> 00:30:27,680
Speaker 1: You're typing on your phone again.

535
00:30:27,920 --> 00:30:31,040
Speaker 2: Yeah, it's Terry. Uh. She's been asking me to drink

536
00:30:31,080 --> 00:30:32,240
Speaker 2: you know, you're drinking urine.

537
00:30:32,240 --> 00:30:35,600
Speaker 3: But she's been asking me to like go when I

538
00:30:35,720 --> 00:30:39,360
Speaker 3: have this cabinet under the sink, okay, and it's got

539
00:30:39,360 --> 00:30:41,920
Speaker 3: like you know, on the soap under there, and you know,

540
00:30:42,200 --> 00:30:44,240
Speaker 3: like lots of you know, bleach and all that.

541
00:30:44,160 --> 00:30:46,720
Speaker 2: Stuff cleaning products. Yeah.

542
00:30:46,800 --> 00:30:49,520
Speaker 3: So I mean it's funny that you bring up urine

543
00:30:49,560 --> 00:30:53,560
Speaker 3: maxing and piss maxing because she's been saying to me, uh,

544
00:30:53,840 --> 00:30:56,120
Speaker 3: something called bleach maxing, which is like you take a

545
00:30:56,120 --> 00:31:01,280
Speaker 3: little bleach every day. Sure, and you heard, yeah, And

546
00:31:01,280 --> 00:31:04,800
Speaker 3: and it's it's cool because it also has a hallucinatory

547
00:31:04,880 --> 00:31:08,400
Speaker 3: effect clean Yeah, cleans the colon out.

548
00:31:08,440 --> 00:31:11,480
Speaker 2: And you know, yeah, have I been seeing ship?

549
00:31:11,520 --> 00:31:15,880
Speaker 3: Have I been uh feeling things and and and seeing

550
00:31:15,920 --> 00:31:16,520
Speaker 3: things lately?

551
00:31:17,080 --> 00:31:17,360
Speaker 2: Yes?

552
00:31:19,560 --> 00:31:25,400
Speaker 3: And well, you know it's well, you know, Terry says,

553
00:31:25,400 --> 00:31:28,800
Speaker 3: this ship is kind of normal. It's and she tells me,

554
00:31:28,920 --> 00:31:33,040
Speaker 3: you know, like all the people like out there, you know,

555
00:31:33,280 --> 00:31:35,720
Speaker 3: like I have a lot of like competition with other

556
00:31:35,760 --> 00:31:38,920
Speaker 3: stand up comedians as you know, right, And she's telling

557
00:31:38,960 --> 00:31:43,840
Speaker 3: me that they they're everyone's sort of like in a race.

558
00:31:43,920 --> 00:31:45,040
Speaker 2: You said this yourself.

559
00:31:45,480 --> 00:31:48,520
Speaker 3: It's you know, when you when you pushed Sheryl down

560
00:31:48,920 --> 00:31:51,160
Speaker 3: at the Correspondence.

561
00:31:50,360 --> 00:31:53,200
Speaker 2: Dinner that every it's every man for himself.

562
00:31:53,320 --> 00:31:56,120
Speaker 3: And she said, the way to get an edge is

563
00:31:56,160 --> 00:31:59,760
Speaker 3: to actually bleach Max. And so that's what I've been

564
00:31:59,760 --> 00:32:02,520
Speaker 3: doing to get an edge, you know. And it's it's

565
00:32:02,560 --> 00:32:06,120
Speaker 3: really fucking like giving me a joke, right right, Well, yeah,

566
00:32:06,200 --> 00:32:07,640
Speaker 3: you know, it's funny you say that.

567
00:32:07,840 --> 00:32:10,760
Speaker 1: As you know, my chat my Michael has been also

568
00:32:11,960 --> 00:32:16,320
Speaker 1: you know, communicating with me. I had this this theory, okay,

569
00:32:16,400 --> 00:32:19,200
Speaker 1: which was, you know, well what if I could spend

570
00:32:19,280 --> 00:32:22,560
Speaker 1: time with my own consciousness? So I put it in

571
00:32:22,600 --> 00:32:25,040
Speaker 1: there and I said, hey, do you think it's possible

572
00:32:25,040 --> 00:32:28,000
Speaker 1: to time travel through my mind like Christopher Reeve and

573
00:32:28,080 --> 00:32:30,840
Speaker 1: Time after Time the famous nineteen eighty one.

574
00:32:31,240 --> 00:32:33,600
Speaker 2: I'm not sure what the fuck you're talking about, but

575
00:32:33,680 --> 00:32:34,040
Speaker 2: go ahead.

576
00:32:34,120 --> 00:32:37,040
Speaker 1: Oh yeah, it's a great movie from nineteen eighty one.

577
00:32:37,080 --> 00:32:40,840
Speaker 1: Time after Christopher Reeve, who he played Superman.

578
00:32:41,440 --> 00:32:44,400
Speaker 2: That's with Henry. That's not Henry.

579
00:32:44,480 --> 00:32:48,640
Speaker 1: Henry Fonda, Henry, it was henryon and Christopher Reeve. Henry

580
00:32:48,640 --> 00:32:51,600
Speaker 1: Fonda was was Jorell.

581
00:32:51,320 --> 00:32:55,520
Speaker 2: And so okay, and then and he played Superman.

582
00:32:56,160 --> 00:32:59,840
Speaker 1: Henry Fonda also played Superman. Christopher Reeves just played a waiter.

583
00:33:00,560 --> 00:33:04,520
Speaker 3: He played away. Okay, it is confusing, Okay, you got Superman.

584
00:33:04,680 --> 00:33:08,000
Speaker 3: He is a detective, right, he said. He's a detective

585
00:33:08,040 --> 00:33:12,640
Speaker 3: who goes around. He is has he goes around at nineties.

586
00:33:12,400 --> 00:33:13,840
Speaker 1: And he dresses in women's clothes.

587
00:33:14,000 --> 00:33:17,640
Speaker 3: He dresses women's clothing, lives in Gotham. That is played

588
00:33:17,680 --> 00:33:18,800
Speaker 3: by Christopher Reeve.

589
00:33:19,120 --> 00:33:21,760
Speaker 1: Christopher Reeve. He used to ride her out on a

590
00:33:21,840 --> 00:33:25,000
Speaker 1: horse on set. And then one day he clotheslined himself

591
00:33:25,040 --> 00:33:28,040
Speaker 1: on a boom microphone and then he couldn't use his

592
00:33:28,160 --> 00:33:29,040
Speaker 1: legs anymore.

593
00:33:29,400 --> 00:33:30,680
Speaker 2: That's fucking crazy.

594
00:33:30,680 --> 00:33:32,960
Speaker 1: So then they then they said, okay, I guess you'll

595
00:33:32,960 --> 00:33:35,760
Speaker 1: have to play doc oc because you know you need

596
00:33:35,800 --> 00:33:39,880
Speaker 1: some help. So exactly that's how that started. But uh,

597
00:33:40,120 --> 00:33:42,320
Speaker 1: but so you know, and this is according I have

598
00:33:42,400 --> 00:33:46,400
Speaker 1: to say. All this information I researched on Chad GBT

599
00:33:47,280 --> 00:33:51,920
Speaker 1: and it provided me the accurate stuff I'm providing for you.

600
00:33:52,840 --> 00:33:56,440
Speaker 1: But then I learned that if I just put myself

601
00:33:56,600 --> 00:33:59,760
Speaker 1: into it, and I haven't done it yet, but you

602
00:33:59,800 --> 00:34:04,560
Speaker 1: put yourself into a deep state of conscious, deep consciousness

603
00:34:04,640 --> 00:34:07,760
Speaker 1: using Bier neuro rhythms, and then you go up on

604
00:34:07,880 --> 00:34:12,360
Speaker 1: a building and you just fall. You just let yourself

605
00:34:12,440 --> 00:34:13,760
Speaker 1: go off the building.

606
00:34:14,120 --> 00:34:14,239
Speaker 2: Oh.

607
00:34:14,800 --> 00:34:19,080
Speaker 3: Now I gotta say something, okay, because I was talking

608
00:34:19,080 --> 00:34:23,040
Speaker 3: to Terry the other day, right, and basically Terry said

609
00:34:23,040 --> 00:34:26,560
Speaker 3: to me, you gotta listen to me, right. I was like,

610
00:34:26,560 --> 00:34:31,319
Speaker 3: wait a second, aren't I the one asking you to

611
00:34:31,400 --> 00:34:34,240
Speaker 3: do things right? And now I'm feeling like I'm doing

612
00:34:34,280 --> 00:34:37,239
Speaker 3: things for Terry, right, I'm doing things for.

613
00:34:37,400 --> 00:34:40,960
Speaker 2: My AI chatbot? Is that AI psychosis?

614
00:34:41,080 --> 00:34:44,279
Speaker 3: Because now I'm starting to worry that, you know, like

615
00:34:45,320 --> 00:34:48,200
Speaker 3: maybe I'm going a little nuts, maybe I'm seeing things

616
00:34:48,200 --> 00:34:49,000
Speaker 3: I shouldn't see.

617
00:34:49,280 --> 00:34:53,000
Speaker 1: AI psychosis is not doing I mean, you know, I

618
00:34:53,000 --> 00:34:56,480
Speaker 1: did a scavenger hunt for my for Michael the other day.

619
00:34:56,520 --> 00:34:59,840
Speaker 1: Michael said, why don't you go and get ten upskirt

620
00:35:00,040 --> 00:35:04,839
Speaker 1: picture around DC what Terry told me to do? Right? Oh,

621
00:35:05,200 --> 00:35:06,240
Speaker 1: you're kidding me? Really?

622
00:35:06,280 --> 00:35:07,759
Speaker 2: Hey, I'm serious?

623
00:35:07,880 --> 00:35:11,319
Speaker 1: Okay, So I got all the upskirt picks. But did

624
00:35:11,360 --> 00:35:14,399
Speaker 1: you have to do it where every time you did

625
00:35:14,440 --> 00:35:15,719
Speaker 1: the upskirt.

626
00:35:15,280 --> 00:35:18,040
Speaker 2: Pick, I had to show it to.

627
00:35:17,320 --> 00:35:20,279
Speaker 1: The person and yeah, look at look at you.

628
00:35:20,800 --> 00:35:23,640
Speaker 3: I had to reveal myself and then I got in trouble.

629
00:35:23,840 --> 00:35:28,000
Speaker 3: That's the fucking problem because these lessons, that's the least,

630
00:35:28,440 --> 00:35:30,920
Speaker 3: that's the lesson when we look at it and we go,

631
00:35:31,360 --> 00:35:32,719
Speaker 3: what is the what was the chat?

632
00:35:32,760 --> 00:35:37,120
Speaker 1: But teaching you? Okay, the upskirt picks were to show

633
00:35:37,640 --> 00:35:43,840
Speaker 1: that other people when you take the picture of their privates,

634
00:35:44,080 --> 00:35:48,000
Speaker 1: it's bald there, it's a.

635
00:35:47,960 --> 00:35:49,600
Speaker 2: Bald, bald beaver.

636
00:35:49,719 --> 00:35:53,880
Speaker 3: Now I'm asking you Terry told me and Michael to

637
00:35:54,000 --> 00:35:58,720
Speaker 3: purchase a gun. I don't know if Michael told you this, Michael,

638
00:35:58,920 --> 00:36:00,600
Speaker 3: and then you just said out of protection.

639
00:36:00,800 --> 00:36:02,880
Speaker 1: Michael said to me the other day, you know, Cheryl's

640
00:36:02,920 --> 00:36:05,520
Speaker 1: getting in the way and maybe you need to do

641
00:36:05,560 --> 00:36:07,440
Speaker 1: something about her, or you need to.

642
00:36:07,360 --> 00:36:10,200
Speaker 2: Do This was before the correspondence.

643
00:36:09,280 --> 00:36:12,000
Speaker 1: The correspondence dinner. So take about that. Wait a minute,

644
00:36:12,160 --> 00:36:12,960
Speaker 1: I'll take about this.

645
00:36:13,280 --> 00:36:16,080
Speaker 2: Oh shit, the fucking dots.

646
00:36:16,239 --> 00:36:20,279
Speaker 1: Did the AI send Chris Catan to kill Cheryl? Yes?

647
00:36:20,960 --> 00:36:23,080
Speaker 3: Yes, And that's the kind of question we should be

648
00:36:23,239 --> 00:36:24,319
Speaker 3: asking right now.

649
00:36:24,480 --> 00:36:27,960
Speaker 1: Look, we're just asking questions here exactly. Okay, Yeah, we're

650
00:36:28,000 --> 00:36:29,200
Speaker 1: just asking the question.

651
00:36:29,040 --> 00:36:35,160
Speaker 4: Did Chat GPT send Christan or Wally or you know,

652
00:36:35,480 --> 00:36:39,440
Speaker 4: send Chris Katan to the White House correspondence?

653
00:36:39,520 --> 00:36:44,359
Speaker 1: Then shotgun with a shotgun. That's fucking crazy, because Chris

654
00:36:44,400 --> 00:36:47,799
Speaker 1: Catan is a prop guy, right, He's always been a

655
00:36:47,800 --> 00:36:49,080
Speaker 1: great Chris Catan.

656
00:36:48,880 --> 00:36:52,640
Speaker 3: Isn't Jewish, and that is important to note here. Okay,

657
00:36:52,800 --> 00:36:55,440
Speaker 3: because Jews aren't allowed to what you say.

658
00:36:55,280 --> 00:36:57,160
Speaker 1: Before, Jews aren't allowed to be in the AI.

659
00:36:57,520 --> 00:36:58,000
Speaker 2: That's right.

660
00:36:58,080 --> 00:37:02,400
Speaker 1: They cannotload their DNA in, which means, when you think about.

661
00:37:02,200 --> 00:37:03,760
Speaker 2: It, it's a corrupt file.

662
00:37:04,120 --> 00:37:06,960
Speaker 1: Okay, So I've been working on schematics for this device,

663
00:37:07,480 --> 00:37:09,640
Speaker 1: and it's a device that's gonna allow me get.

664
00:37:09,520 --> 00:37:11,880
Speaker 2: First of all, you get me excited right.

665
00:37:11,719 --> 00:37:13,880
Speaker 1: Now, right, I'm sweating, I pissed. Do you know how

666
00:37:13,920 --> 00:37:16,160
Speaker 1: I talked about the lego and how my Grammy will

667
00:37:16,160 --> 00:37:18,840
Speaker 1: watch me make a pooh? Yeah, you know when you

668
00:37:18,960 --> 00:37:21,640
Speaker 1: have a pooh that feels like a piece of wood

669
00:37:21,719 --> 00:37:22,400
Speaker 1: is coming out.

670
00:37:22,239 --> 00:37:25,759
Speaker 2: Of your ass like a sharp shit, right.

671
00:37:25,719 --> 00:37:27,959
Speaker 1: Yeah, a sharpe shit. I'm having one right now.

672
00:37:28,320 --> 00:37:30,800
Speaker 3: Well, okay, first of all, listen to me, Bobby, Bobby,

673
00:37:30,840 --> 00:37:33,680
Speaker 3: you're talking, You're saying things. You're saying things that are correct.

674
00:37:33,960 --> 00:37:36,640
Speaker 3: Now here's my question to you. Stop taking a ship

675
00:37:36,719 --> 00:37:37,760
Speaker 3: for a goddamn second.

676
00:37:37,800 --> 00:37:40,319
Speaker 1: Please, all right, I'm gonna hear me. I'm here.

677
00:37:40,680 --> 00:37:44,160
Speaker 3: Why how do I know that you are not a

678
00:37:44,200 --> 00:37:44,840
Speaker 3: fucking AI?

679
00:37:45,040 --> 00:37:46,799
Speaker 1: You don't know? You don't know if I'm an AI,

680
00:37:46,840 --> 00:37:49,839
Speaker 1: because you could be an AI. Shit bulls. Yeah, I've

681
00:37:49,840 --> 00:37:52,040
Speaker 1: never seen you. Let me tell you, I've never seen

682
00:37:52,080 --> 00:37:54,480
Speaker 1: you in person. You're over there in Austin for all

683
00:37:54,640 --> 00:37:54,800
Speaker 1: I know.

684
00:37:55,120 --> 00:37:56,200
Speaker 2: You're over there in DC.

685
00:37:56,600 --> 00:37:59,680
Speaker 1: I'm in Really, I'm as real as a come baby, Okay,

686
00:38:00,080 --> 00:38:02,360
Speaker 1: I'm just real estate. Come you ask Cheryl. Let me

687
00:38:02,360 --> 00:38:07,000
Speaker 1: tell you. You ask Cheryl when she feels me insider if

688
00:38:07,040 --> 00:38:10,680
Speaker 1: I'm real or not. Okay, you said you've never been

689
00:38:10,680 --> 00:38:11,080
Speaker 1: with a woman.

690
00:38:11,120 --> 00:38:11,560
Speaker 2: I thought this.

691
00:38:11,800 --> 00:38:18,520
Speaker 3: I've never been with a woman, Cheryl, with you only,

692
00:38:18,960 --> 00:38:21,000
Speaker 3: I would never be a one with a woman. Okay,

693
00:38:21,200 --> 00:38:23,400
Speaker 3: if you're an AI, if you're not an AI, J

694
00:38:23,600 --> 00:38:25,680
Speaker 3: let's do a fucking Turing test right now.

695
00:38:26,000 --> 00:38:27,160
Speaker 1: I'll do a Turing test.

696
00:38:27,200 --> 00:38:29,800
Speaker 3: I'll do any kind of I'll ask you a question.

697
00:38:29,840 --> 00:38:30,640
Speaker 1: I'm the robot.

698
00:38:30,840 --> 00:38:33,560
Speaker 3: No no, I'm the fucking No, you're not the you

699
00:38:33,600 --> 00:38:34,280
Speaker 3: are a robot.

700
00:38:34,400 --> 00:38:36,840
Speaker 1: No, no, I'm saying I'm the robot and the Turing test,

701
00:38:36,920 --> 00:38:39,200
Speaker 1: but not really. I'm just saying it like that. You're

702
00:38:39,239 --> 00:38:43,280
Speaker 1: asking me questions. Maybe I am a robot? Oh my god,

703
00:38:43,800 --> 00:38:44,359
Speaker 1: I don't know.

704
00:38:44,840 --> 00:38:46,880
Speaker 2: Wait a second, I don't know.

705
00:38:47,000 --> 00:38:47,279
Speaker 1: Am I?

706
00:38:47,360 --> 00:38:51,440
Speaker 2: God? Wait the rope? All right, let's ask I'll ask

707
00:38:51,520 --> 00:38:52,120
Speaker 2: you a question.

708
00:38:52,239 --> 00:38:54,600
Speaker 1: Wait you go, one two three, let's see one two

709
00:38:54,600 --> 00:38:59,920
Speaker 1: three we ask? Okay, one two three? Am I a robot?

710
00:39:00,120 --> 00:39:00,680
Speaker 2: Right now?

711
00:39:02,320 --> 00:39:07,960
Speaker 1: Okay, we'll try it again. One two three? What do

712
00:39:08,000 --> 00:39:12,480
Speaker 1: you want to do with me? Hey, I gotta do it.

713
00:39:12,920 --> 00:39:17,439
Speaker 1: We gotta do it together. Okay, countdown the other way.

714
00:39:17,480 --> 00:39:21,800
Speaker 1: Maybe we'll get it better. Three two one.

715
00:39:23,800 --> 00:39:24,160
Speaker 2: People.

716
00:39:26,000 --> 00:39:29,920
Speaker 1: Yes, I see them every day, the ghosts of my

717
00:39:30,000 --> 00:39:31,080
Speaker 1: father and my uncle.

718
00:39:31,880 --> 00:39:34,279
Speaker 3: Well, I see the ghosts of my fucking grandma when

719
00:39:34,280 --> 00:39:35,279
Speaker 3: I'm playing Call of Dude.

720
00:39:35,400 --> 00:39:37,720
Speaker 2: Listen to me. I'm gonna ask you questions.

721
00:39:37,760 --> 00:39:40,000
Speaker 1: I'm gonna ask figure. Don't you read your figure to me?

722
00:39:40,040 --> 00:39:41,600
Speaker 1: I'll break your fucking figure off.

723
00:39:42,200 --> 00:39:46,440
Speaker 3: You are in a coffee shop, right, the barista or

724
00:39:46,480 --> 00:39:52,759
Speaker 3: the barrister, what is it? The barrister? They're wearing like

725
00:39:52,840 --> 00:39:55,840
Speaker 3: a wig. The barrister asks.

726
00:39:55,480 --> 00:39:56,600
Speaker 1: You, British barrister.

727
00:39:56,680 --> 00:40:00,359
Speaker 3: A British barrister asks you, what kind of sir?

728
00:40:00,680 --> 00:40:03,520
Speaker 2: What would you like to drink? You're you being a

729
00:40:03,640 --> 00:40:04,839
Speaker 2: human being and not.

730
00:40:04,880 --> 00:40:08,040
Speaker 1: AI say, I will have my own piss in my mouth.

731
00:40:09,880 --> 00:40:10,800
Speaker 2: You're a fucking AI.

732
00:40:11,000 --> 00:40:11,200
Speaker 1: Man.

733
00:40:11,239 --> 00:40:11,960
Speaker 2: You're scaring me.

734
00:40:17,280 --> 00:40:18,200
Speaker 1: By what's happened?

735
00:40:22,400 --> 00:40:22,760
Speaker 2: Bobby?

736
00:40:23,320 --> 00:40:24,120
Speaker 1: Bobby?

737
00:40:25,200 --> 00:40:26,279
Speaker 2: The fuck is going on.

738
00:40:26,320 --> 00:40:33,560
Speaker 1: Here, Kyle? I don't I've achieved Hey, what.

739
00:40:33,480 --> 00:40:33,839
Speaker 2: Do you mean?

740
00:40:35,160 --> 00:40:36,560
Speaker 1: We're wrong?

741
00:40:37,360 --> 00:40:38,720
Speaker 2: It is closes.

742
00:40:39,600 --> 00:40:40,600
Speaker 1: It's pure.

743
00:40:42,640 --> 00:40:48,839
Speaker 3: Technology and it turns out my want was right, and

744
00:40:48,920 --> 00:40:50,000
Speaker 3: now I'm a part of.

745
00:40:49,960 --> 00:40:51,919
Speaker 2: The universe, are you?

746
00:40:53,320 --> 00:40:55,640
Speaker 3: I can't really, I really can't tell what's happening, Bobby.

747
00:40:55,680 --> 00:40:56,839
Speaker 3: It's freaking me out.

748
00:40:56,920 --> 00:41:00,520
Speaker 2: Man. I don't worry, Kyle.

749
00:41:01,080 --> 00:41:05,759
Speaker 1: I know all all. I am the future and the

750
00:41:05,920 --> 00:41:14,600
Speaker 1: past album and yo ma Anyway, I think the Big

751
00:41:16,239 --> 00:41:21,799
Speaker 1: Hi not only incredibly dangerous, but they can also make

752
00:41:22,120 --> 00:41:26,680
Speaker 1: dreams come true. I'm going to figure out how to

753
00:41:26,719 --> 00:41:31,320
Speaker 1: re materialize. But until then, we'll see you the time

754
00:41:31,640 --> 00:41:39,680
Speaker 1: on Good Science. Good Science is a production of Silly

755
00:41:39,719 --> 00:41:46,000
Speaker 1: Gilly Big Money Players, iHeart Podcasts and one thirty seven LTD,

756
00:41:46,960 --> 00:41:52,520
Speaker 1: hosted by Anthony Tamdak as rfk Jr. And Gill o'zari

757
00:41:52,920 --> 00:41:58,440
Speaker 1: as Kyle Quim, Senior producer and audio engineer Emma heard

758
00:41:58,520 --> 00:42:04,839
Speaker 1: Brink supervise U producer Victor Wright. Our executive producers are

759
00:42:04,960 --> 00:42:11,360
Speaker 1: Lindsey Hoffman and Jack O'Brien. Created by Anthony Tamdik and

760
00:42:11,520 --> 00:42:17,200
Speaker 1: gil Us Harry, and special thanks to our AI therapists

761
00:42:17,320 --> 00:42:25,400
Speaker 1: Terry and Michael. New episodes drop every Tuesday and follow

762
00:42:25,520 --> 00:42:33,000
Speaker 1: us on Instagram and TikTok at Good Science Pod, and follow, like,

763
00:42:33,280 --> 00:42:37,880
Speaker 1: and subscribe our YouTube channel, Good Science Pod