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Speaker 1: School of Humans. Hello, welcome to this week's chaotic episode

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Speaker 1: of Cadaver Gal's the Podcaster, where we talk about all

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Speaker 1: of the ways that people have died to cope with

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Speaker 1: our own mortality. But really we're all just afraid. Now.

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Speaker 1: I'm your host, Taylor, along with Nika Oh hello, and Gabby. Hey,

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Speaker 1: why did you say it was chaotic right at the beginning?

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Speaker 1: We don't even know yet, Taylor. Okay, that's fair, guys.

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Speaker 1: It's so hard being as cute as I am. Like,

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Speaker 1: I'm just looking at this picture I took of myself

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Speaker 1: and thank you. Lord. Honestly, you're welcomes like you reject

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Speaker 1: your chaos. See that is chaos. Okay, Okay, it's called

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Speaker 1: having self esteem. But no, that's fine. But we're already

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Speaker 1: off topic. Okay, great, what are we talking about today?

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Speaker 1: Today we're going to talk about robots and the infamous

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Speaker 1: death of Graham Parsons. Today's trigger warnings are drugs, drug overdose, AI, robots,

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Speaker 1: burning death, dying bodies, all of the things that we

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Speaker 1: all love. We m all right, let's just go, let's

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Speaker 1: just get it over with the goal. Okay, great, welcome back,

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Speaker 1: I mean not welcome back, that was our music. I'm delirious. Okay,

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Speaker 1: you know, I'm not sure. Are you about to cry?

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Speaker 1: I like it, I want more. I don't want crying,

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Speaker 1: but if it happens, I think that makes good audio. Okay, great, Gabby,

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Speaker 1: that's Gabby's podcasting method. She'll be like, hey, like, if

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Speaker 1: you cry, that's okay. Yeah. You know some people have,

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Speaker 1: specifically men have this thing where they like seeing women cry,

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Speaker 1: and this was actually popularized and I think Japan or

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Speaker 1: South Korea where basically women would create realism themselves crying

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Speaker 1: and then they would sell them for a lot of

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Speaker 1: money to men who like to watch that. That's crazy, Nika,

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Speaker 1: because you do that for free on Instagram. Okay, you bitch,

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Speaker 1: I do. I do. But sometimes, like if I'm crying

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Speaker 1: over something stupid, I'll take a picture to remind myself

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Speaker 1: that I look stupid and I am stupid. So anyway, Nika, Yes,

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Speaker 1: oh my story right? That self confidence? Yeah, you know,

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Speaker 1: I'm all here about the self confidence today. We're on

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Speaker 1: the opposite poles of self confidence right now. I am

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Speaker 1: borderline like manic, I am god right, and Taylor is like,

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Speaker 1: oh I am stupid? Okay, cool, So Nica tell us

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Speaker 1: everything about Graham Parsons. First of all, Taylor, I want

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Speaker 1: to thank you for giving me this idea for the story.

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Speaker 1: It was fascinating and I love learning about these things. Specifically,

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Speaker 1: I love learning about American culture because I did not

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Speaker 1: grow up here and so it's always interesting to find

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Speaker 1: research things like this. Okay, so actually came from my dad.

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Speaker 1: My dad was telling me about it. He's like this huge,

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Speaker 1: like he's a music dude. He was in the radio

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Speaker 1: his whole life DJ you know, so, so he knows

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Speaker 1: all about all of the musicians at I hope he

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Speaker 1: doesn't fact check me. Okay, wait, I just really delayed

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Speaker 1: myself with this joke because he said opposite poles, and

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Speaker 1: so I feel like Taylor's the South pole and then

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Speaker 1: you're the Strip pole. Anyway, Sorry, Sorry, the way you

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Speaker 1: were attacking me today, it's gonna be bad anyway, I'm

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Speaker 1: sorry is a threat. The silence is the threat. I

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Speaker 1: don't think you understand. Gabby's like, how do you know

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Speaker 1: this show's going to be chaos today? Okay, you can

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Speaker 1: we focus? Can we focus? Focused? Thank you? Also, I

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Speaker 1: got my new nails done, and so Gabby, I'm literally

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Speaker 1: gonna go out your eyes up. Okay, so let's focus up.

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Speaker 1: We're taking a musical trip to the nineteen We're going

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Speaker 1: on a trip in my favorite rocket. We're going to

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Speaker 1: the nineteen sixties, and this talented artist known as Graham

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Speaker 1: Parsons was living his life blending country music and psychedelic

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Speaker 1: California rock into this fascinating sound, very cosmic, very weird,

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Speaker 1: very country too. Like I think there's banjo's in his music,

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Speaker 1: which I found so fun. I love that, I know, right,

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Speaker 1: I highly encourage listening to some of his records today

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Speaker 1: as we talk about this. One of his singles is

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Speaker 1: called Hickory Wind and it was so very country. And

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Speaker 1: then also my favorite song of his is Hot Burrito

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Speaker 1: number one by the Flying Burrito Brothers. Yes, it was amazing.

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Speaker 1: I had so much fun researching this. Anyway, So he

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Speaker 1: was born in Florida and grew up in Georgia, of

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Speaker 1: all places, which that is where we are all based.

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Speaker 1: So it's really fun in this very very small town.

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Speaker 1: He left his small town living to go to Harvard

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Speaker 1: and he didn't love it there, so he kind of

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Speaker 1: stayed for like five months and just played music and chilled.

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Speaker 1: I guess as you do in the sixties when you

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Speaker 1: go to an Ivy League school. I wouldn't know. Maybe, Gabby,

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Speaker 1: maybe you know more about this than I do. No,

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Speaker 1: I was very stressed the twenty ten so it was

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Speaker 1: a different We actually had to like do homework. We

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Speaker 1: couldn't just be white, not just do drugs and music. Okay, gotcha? Um? Well,

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Speaker 1: he ended up just kind of hanging out on campus

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Speaker 1: for a little bit, playing music, meeting with other artists

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Speaker 1: in Massachusetts, and then this kind of music trajectory had

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Speaker 1: him bop around the country for a while. He became

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Speaker 1: well known. He joined the band The Birds and really

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Speaker 1: helped infuse more country into their sound. And he was

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Speaker 1: just living his like late sixties traveling musician life, for which,

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Speaker 1: by the way, that kind of life back then, I

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Speaker 1: cannot even imagine the insanity of what must have went

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Speaker 1: downe of illegal substances, and the amount of really cool stories.

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Speaker 1: I just I find it fascinating. So he ended up

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Speaker 1: recording a solo album, and then a little bit after

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Speaker 1: he started to have kind of a hard time. He

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Speaker 1: and his wife were in the process of getting divorced.

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Speaker 1: His house in Laurel Canyon went on fire, which is terrible.

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Speaker 1: And so he was kind of living with a friend

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Speaker 1: for a while, and so he kind of just threw

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Speaker 1: himself back into the studio to start recording his second album,

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Speaker 1: just Get Away from It all right. And so before this, he,

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Speaker 1: as I said, lots of drugs, lots of illegal substances,

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Speaker 1: partied really hard stuff like that. But it seems like

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Speaker 1: during the recording of his second album, he kind of

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Speaker 1: mellowed out, and his manager explained that he just saw

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Speaker 1: a different side of Graham. You know, he was very

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Speaker 1: all about the music, not really you know, drinking or

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Speaker 1: doing drugs or whatever, and it was just good for him.

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Speaker 1: So things were kind of on the up. He also

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Speaker 1: rekindled a romance while still in the process of getting

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Speaker 1: separated with his high school sweetheart, which that's cute, I guess.

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Speaker 1: So yes, I mean, if you don't mean depends on

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Speaker 1: your high school sweetheart. Literally, I feel like I was

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Speaker 1: a very different person in high school than when I

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Speaker 1: am now two years ago. Okay, anyway, and to think

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Speaker 1: of the person that I was with in high school,

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Speaker 1: if I ever do that, like, please ship me off

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Speaker 1: to an island and put me in quarantine, because I

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Speaker 1: must have like, you know, lost moment a little bit,

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Speaker 1: but apparently he was living. He loved it. He was happy,

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Speaker 1: which is good and again kind of sweet. Sorry for

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Speaker 1: being symical. That's cute. Now he's happy is so cute.

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Speaker 1: It truly is. So he had just finished recording his

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Speaker 1: second album things were on that so he and his

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Speaker 1: high school sweetheart, as I said, Margaret Fisher, plus his

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Speaker 1: assistant Michael, Martin's and Martin's girlfriend all decide to go

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Speaker 1: on a little vacation in September of nineteen seventy three.

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Speaker 1: He had just finished recording his second album. His road

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Speaker 1: manager was like, dude, go take it easy, go to

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Speaker 1: on a vacation. And Grant loved the desert, loved Joshua

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Speaker 1: Tree specifically, would go there all the time. Loved to

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Speaker 1: relax there. I love to stay at the Joshua Tree Inn,

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Speaker 1: which is this iconic motel in twenty nine Palms and

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Speaker 1: Joshua Tree, and so he and Margaret stayed in room eight.

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Speaker 1: He was just kind of hanging out, chilling, definitely started

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Speaker 1: partying again. Loved to go to the bars, played music there,

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Speaker 1: just relaxed, and every one of the group were doing

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Speaker 1: very hard drugs. So sadly he was kind of going

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Speaker 1: back to that lifestyle. But he's on vacation, you know.

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Speaker 1: Oh okay, well he was on vacation. But I think

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Speaker 1: you will eat your words when I tell you what

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Speaker 1: happened September nineteenth. He and Margaret are doing drugs that

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Speaker 1: he got from this guy's ex wife who he ran into.

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Speaker 1: They she got him morphine, which I don't I don't

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Speaker 1: know that people could do morphine as a drug. I mean,

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Speaker 1: I know you get addicted to it, but like I

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Speaker 1: didn't know the back in the sixties, they were generals

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Speaker 1: like what people. Yeah, just like dealing morphine injecting it

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Speaker 1: like that just seems real intense. Yeah, yeah, I have

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Speaker 1: no interest. It's some hard stuff. Yeah, exactly, I almost

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Speaker 1: died on morphine. Not to brag from from your concussion. No, right, No,

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Speaker 1: it's when I was a little kid. I had hip

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Speaker 1: surgery because I'm actually an old person and a young

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Speaker 1: person's body and Benjamin Button vibes. Yeah, and then I

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Speaker 1: was at the I was a kid, but I was

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Speaker 1: at this like adult hospital and they weren't supervising me.

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Speaker 1: So you just had like the morphine drip and I

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Speaker 1: just there's a red button, so I kept hitting it

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Speaker 1: a while and then I stopped breathing. But then my

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Speaker 1: dad was there, was like, she's not breathing, and they're like, oh,

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Speaker 1: oop sees. But that's why I'm like this today, Gabby,

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Speaker 1: Oh Gabby what I'm just like grand Parsons, but different.

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Speaker 1: Did you see did you see heaven at all? Like

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Speaker 1: did you have a near death situation or did you

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Speaker 1: just not remember? No? All I remember is like when

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Speaker 1: I finally woke up for some reason, my butt was

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Speaker 1: out and I was like eleven, and I was like, nurse,

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Speaker 1: why is my butt out? And she didn't have a

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Speaker 1: good response. So I don't know what That's all I'm

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Speaker 1: saying in that sue little hospital gown. They don't make

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Speaker 1: those very secure, all right. I feel like every glad

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Speaker 1: you're okay, Yeah, me too. I feel like every episode

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Speaker 1: we find out more and more about Gaby's tragic pass. Okay,

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Speaker 1: thank you, Okay. So back to the story. So he

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Speaker 1: got them morphine and both of them were very high,

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Speaker 1: and then he lost consciousness and Margaret couldn't wake him up.

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Speaker 1: That's a problem. That's a problem. That is So she

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Speaker 1: runs to Michael's girlfriend's room, which, by the way, her

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Speaker 1: name is Dale. Okay. She runs to Dale's room, yes,

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Speaker 1: because Michael was off getting more drugs for everyone in

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Speaker 1: La okay, and she's like, Graham is unconscious. I need help,

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Speaker 1: get me some ice too. So Dale does that, gets ice,

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Speaker 1: goes to help in whatever way she can. It seems

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Speaker 1: like Margaret believed that putting ice up your butthole shocks

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Speaker 1: the system into conscienceness, and they did it to Graham,

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Speaker 1: and according to both women, he woke up and was

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Speaker 1: walking around the room and seemed fine, joked around about

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Speaker 1: why his pants were down, the whole thing, and she's

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Speaker 1: very gabby because his pants were also damn wow, weird,

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Speaker 1: I don't they The ice you know method at the

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Speaker 1: Hospital of Revival is Gabby is Gabby, um, the reincarnation

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Speaker 1: of Graham Parsons. Gabby, When when were you born? Not

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Speaker 1: nineteen seventy three? Yeah I wasn't. I was born in

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Speaker 1: ninety one. Okay, never mind. But sometimes with reincarnation it

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Speaker 1: takes a few years, you know. Okay, well, yeah, you

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Speaker 1: never know. Um. Anyway, that's so weird though, Gabby. Okay,

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Speaker 1: So he was walking around and seemed fine. They chilling

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Speaker 1: the room for a little bit, and then Dale goes

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Speaker 1: to her rooms, like, y'all seem to have this handled,

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Speaker 1: so I'm going to go now. Margaret then decides to

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Speaker 1: go and get some food and coffee for the both

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Speaker 1: of them because they're both still off and just like

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Speaker 1: high and stuff, and so she asks Dale to watch

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Speaker 1: Graham while she goes to get food. Dale grabs a

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Speaker 1: book goes to watch Graham. Graham is asleep in the bed.

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Speaker 1: It was like a twin bed situation, and Dale was

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Speaker 1: on one side one bed and Graham was sleeping on

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Speaker 1: the other. When suddenly Dale realizes that Graham is breathing

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Speaker 1: very heavy and like it's very like labored breathing, and

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Speaker 1: then he stops breathing, which is really bad. So obviously

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Speaker 1: she tries to go get help. Dale tries to give

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Speaker 1: him mouth to mouth resuscitation, but it doesn't work, and

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Speaker 1: then Margaret's there by now, so she and Margaret call

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Speaker 1: an ambulance and sadly, he died of an overdose oh

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Speaker 1: on morph So of the morphine specifically, yes, I mean

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00:13:01,725 --> 00:13:04,325
Speaker 1: they were doing a lot of shit, but the substance

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00:13:04,525 --> 00:13:06,885
Speaker 1: named that did it was morphine. He was in the

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Speaker 1: twenty seven club, wasn't he He's like two months away,

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Speaker 1: like a month and a half away of being in

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Speaker 1: the twenty seventh club. Well, he's almost in the twenty

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Speaker 1: seventh club. That counts. I think, first of all, I'm

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Speaker 1: glad that both of you have survived the age of

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Speaker 1: twenty seven, because that's scary. That's scary. It is a

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Speaker 1: scary age. Yeah, when you get there, you're gonna you're

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Speaker 1: gonna be on your toes. Tip talked about it a lot.

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Speaker 1: I was like, oh, twenty seven, it's scary. Could I

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Speaker 1: die every day of age twenty seven? You're like, ah ah, yeah, great,

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Speaker 1: I'm still looking forward to it. Can we go back

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Speaker 1: to Parson tragic death? Yeah, okay, great, thank you. Obviously

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Speaker 1: this is really sad and terrible and tragic, and it

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Speaker 1: gets a bit weird. So Phil Kaufman, who was Graham's

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Speaker 1: road manager, he had attended a funeral with Graham a

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Speaker 1: little bit before the josh Wittree vacation, and they were

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Speaker 1: talking about death, and Graham specifically that he did not

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00:14:06,405 --> 00:14:09,725
Speaker 1: want this big Catholic ceremony that they had done, and

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Speaker 1: he had actually wanted to be burned in Joshua Tree,

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Speaker 1: and he made Phil promise that Phil would do that

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Speaker 1: if the day ever came. This was literally weeks before

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Speaker 1: the Joshua Tree vacation, which is very strange. That's suspicious.

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Speaker 1: That's suspicious or just convenient that you know he was

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Speaker 1: already in Joshua Tree. Yeah. My thing though, is like

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00:14:32,165 --> 00:14:34,805
Speaker 1: you should put that down in writing if because then

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00:14:34,925 --> 00:14:37,125
Speaker 1: the next thing is about to happen. Let me tell y'all.

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Speaker 1: So Dale calls Phil tells him what happened. Phil is

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Speaker 1: shocked and horrified and really sad because obviously everyone cared

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Speaker 1: about grandm a lot, and he was very talented, a

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00:14:46,525 --> 00:14:49,125
Speaker 1: great person according to the people who loved him and

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Speaker 1: who knew him. So Phil goes and gets both Dale

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00:14:52,605 --> 00:14:56,005
Speaker 1: and Margaret before the police continued to interrogate them, and

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00:14:56,045 --> 00:14:59,085
Speaker 1: they all lay low in la for a bit. But

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00:14:59,245 --> 00:15:02,925
Speaker 1: Phil's promised to Graham is just like eating him alive,

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00:15:03,365 --> 00:15:05,685
Speaker 1: and so he's finally have to do something, So he

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00:15:05,725 --> 00:15:08,325
Speaker 1: calls them mortuary and they tell him that Graham's body

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00:15:08,485 --> 00:15:10,365
Speaker 1: is on the way to lax to be flown to

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Speaker 1: New Orleans for a private ceremony with his pretty wealthy family.

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00:15:15,085 --> 00:15:17,805
Speaker 1: Graham and his family had a very strange relationship. His

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Speaker 1: family had money, and it seems like they just did

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Speaker 1: not get along. Also, they wanted to have a private

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Speaker 1: ceremony without any of Graham's friends, which that's not okay,

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Speaker 1: it's not nice. Exactly. You can't just tell people, no,

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Speaker 1: you can't come to this person's funeral. Like they loved him,

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Speaker 1: they deserve some sort of closure exactly, ceremony, like come on, exactly,

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Speaker 1: I mean. But you know, at the same time, you

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Speaker 1: can imagine how upset the family is that, like, these

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Speaker 1: friends are the ones who I guess encourage the substance

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Speaker 1: abuse or like at the very least tolerated. So it's

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Speaker 1: like you under died on their watch exactly, which is

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Speaker 1: really hard. But at the same time, you know, Phil

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Speaker 1: was not having it. Phil wanted Graham to have his

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Speaker 1: wishes completed basically. So um, Phil convinces Michael, the assistant,

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Speaker 1: to go get Dale's car, which, by the way, Dale's

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Speaker 1: car happened to be an old Cadillac hearse, so like

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Speaker 1: a funeral car. Okay, that's convenient, I know, right, very convenient.

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Speaker 1: And they drive this car to the hangar where Graham's

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00:16:16,045 --> 00:16:21,125
Speaker 1: body was awaiting departure. Mind you. They were very very drunk,

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Speaker 1: and they had these cowboy hats and sparkly jackets and

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00:16:24,405 --> 00:16:28,085
Speaker 1: I'm just like, okay, this it's the seventies blending it. No,

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Speaker 1: clearly not. So they convinced the coroner that plans changed

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00:16:33,765 --> 00:16:37,125
Speaker 1: and Graham's body was actually flying out of Van Nuys

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Speaker 1: Airport and not the airport that they were at Lax

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Speaker 1: and like they the coroner believes them and gives them

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Speaker 1: Graham's body just like that, and also a cop helps them.

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00:16:47,965 --> 00:16:51,925
Speaker 1: A cop is like watching them, and then Phil is like, hey, dude,

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Speaker 1: come help me lay this like put this like stiff

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00:16:54,205 --> 00:16:56,805
Speaker 1: body in. That's exactly what he says, oh jee, And

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00:16:57,085 --> 00:16:59,685
Speaker 1: the cop helps them, and so they start driving away.

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Speaker 1: Michael is driving again. Remember they are both shit faced,

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Speaker 1: and a hangar is a very big opening hank like

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00:17:07,485 --> 00:17:10,565
Speaker 1: you fit a plane through it, right, And this man

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00:17:10,685 --> 00:17:14,925
Speaker 1: Michael like was leaving the hangar and crashes into the

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00:17:15,205 --> 00:17:18,125
Speaker 1: entrance like into one of the side entrances basically because

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00:17:18,125 --> 00:17:20,965
Speaker 1: he's drunk, absolutely, and the cop is watching him go away.

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00:17:21,005 --> 00:17:23,605
Speaker 1: The corner is watching him go away. And Phil was like,

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00:17:23,765 --> 00:17:26,125
Speaker 1: for sure, we're going to get caught right now. And

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00:17:26,165 --> 00:17:29,965
Speaker 1: they didn't, so they drove away hours and hours and hours.

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Speaker 1: By the way it was, the cop was just like

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00:17:31,845 --> 00:17:34,885
Speaker 1: okay bye, literally okay bye. So it was ten pm

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00:17:34,885 --> 00:17:38,045
Speaker 1: when they picked up Graham's body, and it's around two

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00:17:38,045 --> 00:17:41,125
Speaker 1: am when they get to Joshua Chee. So they get

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00:17:41,165 --> 00:17:44,125
Speaker 1: to Cap Rock there in the desert and they open

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00:17:44,485 --> 00:17:47,205
Speaker 1: the coffin and there's Graham and he's naked, and they

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00:17:47,205 --> 00:17:51,445
Speaker 1: stay there goodbyes, and then they light his body and

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Speaker 1: the coffin on fire by the big Rock monument. Thing

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00:17:55,765 --> 00:18:01,165
Speaker 1: that is insane. It is literally crazy. What So they

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00:18:01,325 --> 00:18:03,445
Speaker 1: leave because they see some lights and they think it's

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00:18:03,445 --> 00:18:05,245
Speaker 1: going to be the park rangers, and they just like

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00:18:05,685 --> 00:18:08,685
Speaker 1: speed off basically into the distance. And then as they're

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00:18:08,725 --> 00:18:11,885
Speaker 1: going back to LA, they hit some traffic. Because it's LA,

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00:18:12,125 --> 00:18:15,805
Speaker 1: they're obviously still relatively drunk, and so they rear end someone.

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00:18:16,125 --> 00:18:19,485
Speaker 1: The cops stop them, see all the alcohol literally like

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00:18:19,645 --> 00:18:23,765
Speaker 1: bottles falling out of the hearse and handcuff them. And

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Speaker 1: then apparently this is according to Phil Michael is like

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00:18:28,125 --> 00:18:30,245
Speaker 1: a skinny dude, and so he got out of the

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00:18:30,285 --> 00:18:33,605
Speaker 1: handcuffs okay, started running, like made a run for it.

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Speaker 1: Phil started running, made a run for it, left the

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Speaker 1: Cadillac there, ran all the way back home, escape the cops, Okay, whatever,

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00:18:42,045 --> 00:18:44,725
Speaker 1: and just like, I don't know, we're fugitives, I guess.

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Speaker 1: And obviously at that point people were finding out that

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00:18:47,565 --> 00:18:49,725
Speaker 1: they found this body in Joshua Tree. Eventually it's own

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00:18:49,765 --> 00:18:52,005
Speaker 1: out that it's Grand Parsons. It's like a whole thing.

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Speaker 1: And then, of course, because it's Hollywood, so many rumors

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00:18:55,725 --> 00:18:58,845
Speaker 1: come that it was like a Satanic ritual or that

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00:18:58,885 --> 00:19:01,725
Speaker 1: it was some crazy conspiracy theory, and it's like, no,

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00:19:02,245 --> 00:19:05,885
Speaker 1: they just wanted to give him the funeral that he wanted, which,

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00:19:05,965 --> 00:19:08,245
Speaker 1: by the way, they still they took his they flew

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00:19:08,285 --> 00:19:11,325
Speaker 1: his ashes to New Orleans for the ceremony that his

329
00:19:11,365 --> 00:19:15,565
Speaker 1: family want. Everybody won kind of everybody one exactly, but

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00:19:15,645 --> 00:19:19,845
Speaker 1: Phil and Michael are still except for Graham. Yeah, so

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00:19:19,965 --> 00:19:23,245
Speaker 1: Phil and Michael are still laying low. At this point,

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00:19:23,365 --> 00:19:26,365
Speaker 1: everyone knows that they did it, and so they kind

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00:19:26,365 --> 00:19:31,965
Speaker 1: of just turn themselves in because okay, and they end

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00:19:32,085 --> 00:19:34,925
Speaker 1: up getting away with it. All they have to do

335
00:19:35,045 --> 00:19:38,525
Speaker 1: is pay a thirteen hundred dollars fine for the damaged coffin.

336
00:19:39,245 --> 00:19:43,965
Speaker 1: But basically, according to authorities, Graham's body didn't have any

337
00:19:44,405 --> 00:19:49,205
Speaker 1: financial value really unless someone were to press charges on

338
00:19:49,245 --> 00:19:51,765
Speaker 1: behalf of Graham or whatever, which no one did. The

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00:19:51,805 --> 00:19:54,525
Speaker 1: family was just kind of like let them be whatever,

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00:19:55,245 --> 00:19:58,845
Speaker 1: and so they end up getting away with it, which

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00:19:58,885 --> 00:20:03,325
Speaker 1: I just find bananas. And Phil Kaufman is still working

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00:20:03,365 --> 00:20:08,125
Speaker 1: as a road manager to this day. That's so crazy. Yeah,

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00:20:08,125 --> 00:20:10,285
Speaker 1: the seventies were wild, and reading about this, I was

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00:20:10,325 --> 00:20:13,725
Speaker 1: just like, Okay, I mean, this would never happen in

345
00:20:13,925 --> 00:20:17,405
Speaker 1: this day and age, but that's amazing. I'm sure some

346
00:20:17,525 --> 00:20:21,405
Speaker 1: kukier shit's happened in this day and age. Kukie shit

347
00:20:21,565 --> 00:20:25,725
Speaker 1: be happening still. Also, they were just like down ass buddies,

348
00:20:25,925 --> 00:20:29,685
Speaker 1: Like they really said, no, we're going to steal this

349
00:20:29,765 --> 00:20:33,165
Speaker 1: body because that's what he would have wanted. M I mean,

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00:20:33,725 --> 00:20:35,965
Speaker 1: good for them. I mean I would probably have to

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00:20:35,965 --> 00:20:39,365
Speaker 1: get a little drunk to steal body as well. You know,

352
00:20:39,645 --> 00:20:44,005
Speaker 1: like that's crazy. All right. Well, thank you Unika for

353
00:20:44,085 --> 00:20:48,965
Speaker 1: telling us about Graham Parsons and the crazy body heist,

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00:20:49,525 --> 00:20:57,365
Speaker 1: body burn situation. Well you're back, Welcome back all you

355
00:20:57,405 --> 00:21:00,445
Speaker 1: could have pals. Thank you Unika for sharing the story

356
00:21:00,485 --> 00:21:04,645
Speaker 1: about Graham Parsons. Side note, I'm trying to find this

357
00:21:04,765 --> 00:21:07,885
Speaker 1: to confirm it, but I'm pretty sure that Graham Parsons

358
00:21:08,045 --> 00:21:11,125
Speaker 1: rode horses, just saying and also rode the kind of

359
00:21:11,165 --> 00:21:14,085
Speaker 1: horses that I rode. This is by way of my mom,

360
00:21:14,165 --> 00:21:17,765
Speaker 1: because my mom also like we rode horses together growing up.

361
00:21:18,245 --> 00:21:20,085
Speaker 1: So anyway, I think he rode horses. So I love him,

362
00:21:20,125 --> 00:21:21,525
Speaker 1: and actually makes a lot of sense because he was

363
00:21:21,525 --> 00:21:24,445
Speaker 1: a country boy and there's like a lot of like

364
00:21:24,725 --> 00:21:27,245
Speaker 1: there's like a lot of chat or whether he wrote

365
00:21:27,245 --> 00:21:31,125
Speaker 1: Wild Horses or if it was written about him. But anyway,

366
00:21:32,045 --> 00:21:38,245
Speaker 1: I digress. What an icon. So, you know, speaking of

367
00:21:38,685 --> 00:21:44,045
Speaker 1: famous people and stuff going up bottoms, that is what

368
00:21:44,125 --> 00:21:49,725
Speaker 1: we were speaking of. Good reminder, President Garfield, that's who

369
00:21:49,725 --> 00:21:51,925
Speaker 1: we're going to talk about. You remember him. He was

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00:21:51,965 --> 00:21:55,685
Speaker 1: the twentieth president and literally United States who that I

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00:21:55,725 --> 00:21:58,365
Speaker 1: can't even compute that in my brain. It's okay, I

372
00:21:58,485 --> 00:22:01,965
Speaker 1: kind of forgot about him too well. I remembered him

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00:22:01,965 --> 00:22:05,365
Speaker 1: because it was Garfield. They comic cat and I rebey

374
00:22:05,725 --> 00:22:08,365
Speaker 1: remember that. But yeah, sometimes when you get into those

375
00:22:08,365 --> 00:22:10,565
Speaker 1: metal parts of presidents, you're like, who the fuck are

376
00:22:10,605 --> 00:22:13,765
Speaker 1: these people? Oh my gosh, the thought of Alasagna right now,

377
00:22:14,405 --> 00:22:18,645
Speaker 1: Oh that would be great Okay, Oh that took me

378
00:22:18,685 --> 00:22:21,245
Speaker 1: a second to compute while you just mentioned Lasagna. Okay,

379
00:22:21,445 --> 00:22:26,765
Speaker 1: they're talking about Butts Garfield anyway. All right, President Garfield

380
00:22:27,045 --> 00:22:30,045
Speaker 1: elected in eighteen eighty one. Just as a little reminder,

381
00:22:30,485 --> 00:22:33,325
Speaker 1: he was also shot in the back after two hundred

382
00:22:33,365 --> 00:22:36,405
Speaker 1: days in office at a railroad station. Oh that's why

383
00:22:36,405 --> 00:22:38,925
Speaker 1: I'll go to railroad stations. Why was he shot? Was

384
00:22:38,965 --> 00:22:43,485
Speaker 1: here racist? Oh? He was shot because this guy was like,

385
00:22:44,445 --> 00:22:47,805
Speaker 1: this privileged white man was like, I want this job.

386
00:22:47,925 --> 00:22:49,845
Speaker 1: And he was like, um, a requirements. You have to

387
00:22:49,845 --> 00:22:51,765
Speaker 1: speak French. You're like supposed to be speaking to these

388
00:22:51,805 --> 00:22:53,845
Speaker 1: like French people again in French and you don't know French.

389
00:22:54,165 --> 00:22:57,445
Speaker 1: And he was like, but that's not fair, and so

390
00:22:57,525 --> 00:23:00,365
Speaker 1: then he shot him. That sounds very amazing that he

391
00:23:00,405 --> 00:23:02,525
Speaker 1: didn't get a job. Yeah, that is because he was

392
00:23:02,565 --> 00:23:06,445
Speaker 1: not qualified. But he was yeah, he was like not qualified.

393
00:23:07,125 --> 00:23:09,685
Speaker 1: He got shot in the back, didn't kill him instantly.

394
00:23:10,485 --> 00:23:14,125
Speaker 1: He remained conscious and said, quote, oh my god, what

395
00:23:14,325 --> 00:23:17,285
Speaker 1: is this. So he was like, I know, I would

396
00:23:17,325 --> 00:23:21,125
Speaker 1: also be super inconvenienced if I got shot. Oh, shooters,

397
00:23:21,925 --> 00:23:25,165
Speaker 1: that's what GAF you would say. And oh lord, okay,

398
00:23:25,725 --> 00:23:30,605
Speaker 1: so doctors rushed to his aid side. Note about his doctor.

399
00:23:31,125 --> 00:23:36,325
Speaker 1: His doctor's name was doctor, Doctor, Doctor, Doctor, doctor, Bliss doctor.

400
00:23:37,485 --> 00:23:40,845
Speaker 1: His parents really did not give him a choice. They're like, oh,

401
00:23:40,885 --> 00:23:43,525
Speaker 1: doctors are really good people, so let's honor him. Like

402
00:23:43,565 --> 00:23:45,765
Speaker 1: it was like something weird like that, I'm gonna name

403
00:23:45,845 --> 00:23:55,725
Speaker 1: my kid influencer. Oh, YouTuber YouTube star Watts. Anyway, so

404
00:23:56,165 --> 00:23:58,325
Speaker 1: he survived for a couple of months he was in

405
00:23:58,365 --> 00:24:03,325
Speaker 1: the hospital. Obviously, the doctor's priority was to extract the bullet.

406
00:24:03,365 --> 00:24:05,885
Speaker 1: They were like, we gotta find it. They were ruggling,

407
00:24:06,165 --> 00:24:08,325
Speaker 1: so they call who do they call. They call their buddy,

408
00:24:08,325 --> 00:24:11,965
Speaker 1: Alexander Graham Bell. Oh. Yeah, he was an inventor, as

409
00:24:12,005 --> 00:24:17,005
Speaker 1: we know, and he had this like induction balanced electrical

410
00:24:17,245 --> 00:24:19,685
Speaker 1: device that they were like, oh, maybe that'll help us

411
00:24:19,685 --> 00:24:21,965
Speaker 1: find it. It didn't work, so in his time at

412
00:24:21,965 --> 00:24:25,765
Speaker 1: the hospital he needed help eating. He couldn't He couldn't

413
00:24:25,845 --> 00:24:30,285
Speaker 1: keep anything down. So doctor doctor he was like, we

414
00:24:30,325 --> 00:24:35,165
Speaker 1: need to feed the president through his rectum, which out

415
00:24:35,205 --> 00:24:40,725
Speaker 1: of all orifices. Yeah why. And it was a thing,

416
00:24:41,085 --> 00:24:43,245
Speaker 1: not a very popular thing at the time, but they

417
00:24:43,405 --> 00:24:46,485
Speaker 1: used it for patients like at asylums and people who

418
00:24:46,485 --> 00:24:50,085
Speaker 1: were like on hunger strike. For suffrage. So it was

419
00:24:50,165 --> 00:24:53,085
Speaker 1: kind of like we weren't nice to those people, right,

420
00:24:53,205 --> 00:24:56,365
Speaker 1: So that's not like it's not like a, well, yeah,

421
00:24:56,405 --> 00:24:59,565
Speaker 1: that's like those people who are respected. That sounds like torture.

422
00:24:59,605 --> 00:25:01,645
Speaker 1: And so to say, oh, yeah, let's do that to

423
00:25:01,725 --> 00:25:06,005
Speaker 1: the president, which by the way, already that's unaccepted for

424
00:25:06,285 --> 00:25:09,925
Speaker 1: the victims of you know, suffrage and a silent patients.

425
00:25:09,925 --> 00:25:11,565
Speaker 1: But then to say that also to the president, it's

426
00:25:11,565 --> 00:25:13,565
Speaker 1: just like a little bit bananas. To me, doctor doctor,

427
00:25:13,805 --> 00:25:17,645
Speaker 1: what are you doing? So yeah, like I said this

428
00:25:17,725 --> 00:25:20,565
Speaker 1: method or like we were just saying, um, this method

429
00:25:20,645 --> 00:25:24,645
Speaker 1: was very controversial, so like as a defense for it,

430
00:25:25,205 --> 00:25:29,205
Speaker 1: Garfield's physician aka doctor Doctor, a year later creates this

431
00:25:29,285 --> 00:25:32,885
Speaker 1: pamphlet for feeding per rectum, as illustrated in the case

432
00:25:32,925 --> 00:25:36,605
Speaker 1: of President Garfield and others y'all. In the beginning, they

433
00:25:36,605 --> 00:25:39,405
Speaker 1: had to like test to see like what would be

434
00:25:39,485 --> 00:25:43,085
Speaker 1: the best recipe for him, and let me tell you

435
00:25:43,125 --> 00:25:46,565
Speaker 1: what they landed on. No, I don't know if I'm

436
00:25:46,605 --> 00:25:51,885
Speaker 1: going to tell you beef extract, Taylor, Oh, I found

437
00:25:51,885 --> 00:25:56,685
Speaker 1: the recipe done at Taco bell. I feel like that's

438
00:25:55,685 --> 00:26:00,405
Speaker 1: what I'm guys, Stop, guys, stop, guys, stop this is

439
00:26:00,445 --> 00:26:02,285
Speaker 1: not funny, Okay, I'm going to read it to you.

440
00:26:02,885 --> 00:26:08,125
Speaker 1: Infuse a third pound of fresh beef, finely minced, and

441
00:26:08,325 --> 00:26:12,485
Speaker 1: fourteen ounces of cold soft water with a few drops

442
00:26:12,805 --> 00:26:16,245
Speaker 1: four or five of myriadic acid and a little salt

443
00:26:16,725 --> 00:26:20,485
Speaker 1: from ten to eighteen grains of salt, which I think

444
00:26:20,565 --> 00:26:26,005
Speaker 1: is so silly anyway. So after digesting for an hour

445
00:26:26,765 --> 00:26:29,205
Speaker 1: to an hour and a quarter, strain it through a

446
00:26:29,245 --> 00:26:32,445
Speaker 1: sieve and wash the residue with five ounces of cold water,

447
00:26:32,805 --> 00:26:36,165
Speaker 1: pressing it to remove all of the soluble matter. The

448
00:26:36,245 --> 00:26:40,405
Speaker 1: mixed liquid will contain the whole of the soluble constituents

449
00:26:40,445 --> 00:26:42,805
Speaker 1: of the meat, and it may be drank cold or

450
00:26:42,845 --> 00:26:46,845
Speaker 1: slightly warm, even taking off my headphones. I don't think

451
00:26:46,885 --> 00:26:51,845
Speaker 1: we should guess anymore. I think anyway, the line for me, wow,

452
00:26:51,925 --> 00:26:54,565
Speaker 1: do they put it in the butt? So they basically

453
00:26:54,805 --> 00:26:56,645
Speaker 1: actually have a picture that we can put on our

454
00:26:56,685 --> 00:27:00,485
Speaker 1: Instagram they I mean, there's just this tube in there

455
00:27:00,565 --> 00:27:02,725
Speaker 1: and it's kind of like a syringe and they just

456
00:27:02,965 --> 00:27:04,725
Speaker 1: shove it up there and it just like chills in

457
00:27:04,765 --> 00:27:08,165
Speaker 1: the rectum basically, but not possible. You can't. I don't,

458
00:27:08,165 --> 00:27:10,205
Speaker 1: I don't. I'm not a doctor, and my name is

459
00:27:10,205 --> 00:27:12,685
Speaker 1: not doctor. But I don't think that that is how

460
00:27:12,725 --> 00:27:15,245
Speaker 1: your body takes nutrients. I think that's how it disposes

461
00:27:15,565 --> 00:27:21,325
Speaker 1: of nutrients. Shown up doesn't doesn't work spoiler alert. I

462
00:27:21,365 --> 00:27:23,805
Speaker 1: feel like at this point his name doctor is just

463
00:27:23,845 --> 00:27:27,005
Speaker 1: like him ober compensating. You know, it sounds like he's

464
00:27:27,045 --> 00:27:31,445
Speaker 1: more of like a wizard. Yeah, um, he's he's something.

465
00:27:31,965 --> 00:27:36,805
Speaker 1: So President Garfield got two ounces of this beef extract

466
00:27:36,925 --> 00:27:41,285
Speaker 1: every four hours. He also got five teaspoons, which is

467
00:27:41,325 --> 00:27:45,045
Speaker 1: like about a shot a little under a shot of whiskey,

468
00:27:45,445 --> 00:27:48,805
Speaker 1: and sometimes opium. But the opium wasn't for pain. It

469
00:27:48,885 --> 00:27:53,165
Speaker 1: was to help keep the meals inside of him. Okay,

470
00:27:53,365 --> 00:27:55,085
Speaker 1: I don't know how that works either. I mean, I

471
00:27:55,085 --> 00:27:57,965
Speaker 1: feel like he's just like doctor. Doctor's just like making

472
00:27:58,005 --> 00:28:01,005
Speaker 1: stuff up. They fucking killed him, that's what happened, so

473
00:28:01,045 --> 00:28:04,045
Speaker 1: all of this is useless. This I guess he also

474
00:28:04,085 --> 00:28:08,245
Speaker 1: got shot in the back, though he did. But okay,

475
00:28:08,285 --> 00:28:11,365
Speaker 1: so this so the president went from two hundred and

476
00:28:11,445 --> 00:28:14,365
Speaker 1: ten pounds to one hundred and thirty pounds. So this

477
00:28:14,445 --> 00:28:17,525
Speaker 1: was like three months was happening. That's what I wait,

478
00:28:17,605 --> 00:28:20,685
Speaker 1: that's insane. He was a big daddy before and then

479
00:28:20,725 --> 00:28:24,565
Speaker 1: he was a scrawny daddy by the end. Yeah, basically

480
00:28:24,685 --> 00:28:29,525
Speaker 1: at the end of it, he dies obviously, and like

481
00:28:30,045 --> 00:28:32,605
Speaker 1: it's a little confusing of like how he died because

482
00:28:32,605 --> 00:28:34,445
Speaker 1: I don't think these doctors knew what was going on,

483
00:28:34,765 --> 00:28:37,365
Speaker 1: and they didn't even like use a stethoscope to check

484
00:28:37,365 --> 00:28:40,725
Speaker 1: for his heartbeat. He literally the doctor doctor put his

485
00:28:40,765 --> 00:28:46,445
Speaker 1: head to his chest and said it is over. But like, ultimately,

486
00:28:46,965 --> 00:28:51,085
Speaker 1: ultimately what happened was is these doctors were like, germs

487
00:28:51,085 --> 00:28:52,885
Speaker 1: are fake news. We don't have to wash our hands

488
00:28:52,885 --> 00:28:55,885
Speaker 1: when we start fishing through open wounds and bodies. And

489
00:28:55,965 --> 00:28:59,965
Speaker 1: so Garfield also suffered from sepsis, and just like I

490
00:29:00,045 --> 00:29:04,125
Speaker 1: had all kinds of bacterial infections. So really, like today

491
00:29:04,245 --> 00:29:06,285
Speaker 1: people are like, oh, he totally we could have survived

492
00:29:06,325 --> 00:29:09,805
Speaker 1: had had doctor doctor been competent and had Also this

493
00:29:09,845 --> 00:29:11,885
Speaker 1: was a long time ago, so we didn't they didn't

494
00:29:11,925 --> 00:29:14,405
Speaker 1: know what was going on. I guess seen eighties, you know,

495
00:29:14,485 --> 00:29:17,245
Speaker 1: I was talking about how crazy the seventies were, but honestly,

496
00:29:17,925 --> 00:29:22,685
Speaker 1: those eighteen eighties, Yeah, Margaret Fisher, not a doctor, had

497
00:29:23,285 --> 00:29:26,605
Speaker 1: a better idea on how to like take care of

498
00:29:26,645 --> 00:29:30,045
Speaker 1: Graham than doctor doctor did with the president. So yeah,

499
00:29:30,085 --> 00:29:33,405
Speaker 1: well also side note that doesn't help, Like an ice

500
00:29:33,445 --> 00:29:36,365
Speaker 1: cube enema does not help, Like that's just a rumor.

501
00:29:36,405 --> 00:29:39,765
Speaker 1: It does not save an overdose patient. Never mind, they're

502
00:29:39,765 --> 00:29:42,845
Speaker 1: both incompetent. Why did they have ice back then? You know?

503
00:29:43,605 --> 00:29:47,765
Speaker 1: Oh my gosh, Gobby. So but the guy who shot

504
00:29:47,805 --> 00:29:50,045
Speaker 1: him was like, hey, dudes, I didn't kill him. I

505
00:29:50,125 --> 00:29:52,205
Speaker 1: just shot him. The doctors were the one that actually

506
00:29:52,285 --> 00:29:57,845
Speaker 1: killed him. So very controversial. Did he go to jail? Um? Yeah,

507
00:29:57,925 --> 00:30:00,965
Speaker 1: oh okay, well good. I mean he shot the president,

508
00:30:02,245 --> 00:30:03,685
Speaker 1: but I don't know you made you made it sound

509
00:30:03,725 --> 00:30:07,165
Speaker 1: like he got away with it. Just do it just

510
00:30:07,245 --> 00:30:10,765
Speaker 1: because I did it. I was just like a friendly shooting,

511
00:30:10,885 --> 00:30:15,605
Speaker 1: Like I wasn't trying to do that anyway. So that

512
00:30:15,805 --> 00:30:19,285
Speaker 1: is the story of President Garfield and how he died

513
00:30:20,285 --> 00:30:23,845
Speaker 1: by way of doctor. Doctor. Really, Taylor, I feel like

514
00:30:23,925 --> 00:30:26,485
Speaker 1: by you doing a death in your tangent, you're trying

515
00:30:26,485 --> 00:30:29,605
Speaker 1: to steal my shine. You know, I was worried about that,

516
00:30:29,805 --> 00:30:32,365
Speaker 1: not your shine, but just shine. Well that's good because

517
00:30:32,365 --> 00:30:34,765
Speaker 1: that means I don't even have to shine, So thank

518
00:30:34,765 --> 00:30:37,045
Speaker 1: you for taking the pressure off me. I can just

519
00:30:38,125 --> 00:30:40,365
Speaker 1: you know, I'm ready for you to shine right now.

520
00:30:40,725 --> 00:30:46,285
Speaker 1: Go ahead, shine all over us, Gabby. Oh okay, why

521
00:30:46,285 --> 00:30:50,045
Speaker 1: did that sound so gross? I don't know. All right, well,

522
00:30:50,045 --> 00:30:52,125
Speaker 1: I'll go into the seventies again. But it's like a

523
00:30:52,165 --> 00:30:55,045
Speaker 1: totally different vibe. This is like a big different, big

524
00:30:55,085 --> 00:30:59,045
Speaker 1: different vibe energy. But it's like in nineteen seventy nine,

525
00:30:59,045 --> 00:31:01,685
Speaker 1: So this is the end of the seventies and we're

526
00:31:01,685 --> 00:31:09,605
Speaker 1: talking about robots and them people who like. Okay, so

527
00:31:09,845 --> 00:31:14,165
Speaker 1: this is the alleged first ever robotic death that I'm

528
00:31:14,205 --> 00:31:18,205
Speaker 1: going to say. So in nineteen seventy nine, there was

529
00:31:18,245 --> 00:31:22,445
Speaker 1: this twenty five year old dude working at the Ford

530
00:31:23,205 --> 00:31:26,125
Speaker 1: Assembly plant in Flat Rock, Michigan. He was twenty five

531
00:31:26,165 --> 00:31:31,245
Speaker 1: years old, Robert Williams, and he was, you know, working

532
00:31:31,325 --> 00:31:34,965
Speaker 1: the assembly line. And assembly lines they was. They was

533
00:31:35,005 --> 00:31:37,365
Speaker 1: a big thing there happened and you know, mister Ford,

534
00:31:37,405 --> 00:31:39,485
Speaker 1: he innovated that shit a long time ago. But now

535
00:31:39,525 --> 00:31:42,685
Speaker 1: the assembly lines A big part of that. It's having

536
00:31:42,725 --> 00:31:46,205
Speaker 1: you know, these robotic your little robotic helpers and robots

537
00:31:46,205 --> 00:31:49,485
Speaker 1: doing stuff like moving things around. They would have one

538
00:31:49,525 --> 00:31:51,765
Speaker 1: task and do that. And so he was working with

539
00:31:52,525 --> 00:31:57,405
Speaker 1: this one industrial robot that in summary sucked ass Okay,

540
00:31:57,445 --> 00:31:59,885
Speaker 1: it didn't work very well. It was just like kept

541
00:31:59,885 --> 00:32:01,925
Speaker 1: getting stuck. And it was a It was a five

542
00:32:02,445 --> 00:32:06,685
Speaker 1: story tall industrial robot built by the Unit handling division

543
00:32:06,765 --> 00:32:10,605
Speaker 1: of Litton Industries. Very interesting. I know, did he look

544
00:32:10,645 --> 00:32:13,885
Speaker 1: like what did he? Was he like the giant iron Man? Yeah,

545
00:32:13,925 --> 00:32:17,045
Speaker 1: it was giant iron Man and um that you know,

546
00:32:17,045 --> 00:32:20,085
Speaker 1: it was just iron Man would like grab a casting

547
00:32:20,125 --> 00:32:22,405
Speaker 1: of a car and then like move it somewhere else.

548
00:32:22,445 --> 00:32:24,245
Speaker 1: But it didn't look like iron Man exactly. It was

549
00:32:24,325 --> 00:32:26,405
Speaker 1: kind of like a like a low key iron Man

550
00:32:26,445 --> 00:32:28,885
Speaker 1: because it was just basically stacked and had a little

551
00:32:28,965 --> 00:32:31,325
Speaker 1: vehicle that it would put the castings in that was

552
00:32:31,365 --> 00:32:33,405
Speaker 1: about a ton and had a little arms that would

553
00:32:33,405 --> 00:32:35,005
Speaker 1: put in there and like move it somewhere else. It

554
00:32:35,045 --> 00:32:37,445
Speaker 1: was a machine. Then he was a machine, mister machine.

555
00:32:37,485 --> 00:32:40,965
Speaker 1: I find I find some robots kind of hot. But anyway,

556
00:32:41,005 --> 00:32:44,445
Speaker 1: continue this one. This one was a little clunky. You know,

557
00:32:44,845 --> 00:32:46,405
Speaker 1: I don't know if you would have gotten along with

558
00:32:46,445 --> 00:32:50,005
Speaker 1: this one. The seventies and eighties they were they were clunky. Yeah,

559
00:32:50,085 --> 00:32:53,965
Speaker 1: this was a clunker. Um they're a little sleeker now, Okay, yeah,

560
00:32:54,045 --> 00:32:56,405
Speaker 1: go ahead. I mean you can like a thick daddy,

561
00:32:56,445 --> 00:32:58,485
Speaker 1: but do you like a clunky daddy? I don't know.

562
00:32:59,525 --> 00:33:02,365
Speaker 1: Clunky means you have more to grab onto, okay, or

563
00:33:02,485 --> 00:33:05,125
Speaker 1: you just have angles, but then you also like break

564
00:33:05,165 --> 00:33:08,885
Speaker 1: down and probably make terrible sounds, you know. But yeah,

565
00:33:08,885 --> 00:33:11,565
Speaker 1: So there's one article that was saying that, like this robot,

566
00:33:11,645 --> 00:33:14,125
Speaker 1: it just was like working really slow, kept halting and

567
00:33:14,165 --> 00:33:16,645
Speaker 1: like messing up the work. And another said that there

568
00:33:16,725 --> 00:33:20,725
Speaker 1: was also like a reading on the robot that, like

569
00:33:20,925 --> 00:33:24,765
Speaker 1: Robert was like something, something's up, something's going wrong, but regardless,

570
00:33:24,845 --> 00:33:27,285
Speaker 1: didn't seem to be working correctly. So Robert was like,

571
00:33:27,325 --> 00:33:29,645
Speaker 1: I'm just gonna move all this ship myself because you know,

572
00:33:29,645 --> 00:33:31,965
Speaker 1: fuck mister robot, he's not helping at all, and so

573
00:33:32,005 --> 00:33:34,245
Speaker 1: he climbed up onto the third level of the machine.

574
00:33:34,725 --> 00:33:40,125
Speaker 1: But then that's when the robot resumed working conveniently, and

575
00:33:40,365 --> 00:33:43,045
Speaker 1: one of the one ton vehicles that would carry the

576
00:33:43,085 --> 00:33:46,765
Speaker 1: castings from place to place crashed into him, crushed him

577
00:33:46,805 --> 00:33:52,685
Speaker 1: and killed him instantly. Oh out, and he was found

578
00:33:52,885 --> 00:33:55,525
Speaker 1: thirty minutes later when his colleagues were like, oh, what's

579
00:33:55,565 --> 00:33:58,725
Speaker 1: up with Robert? Where do you go? So that sounds

580
00:33:58,765 --> 00:34:01,165
Speaker 1: like a They're like, that would sound like the most

581
00:34:01,725 --> 00:34:05,725
Speaker 1: one of the most painful deaths. Yeah, being crushed. Yes,

582
00:34:06,445 --> 00:34:09,525
Speaker 1: So this death was technically the first human death due

583
00:34:09,525 --> 00:34:13,525
Speaker 1: to a robot, but certainly not the last. Dystopian views

584
00:34:13,565 --> 00:34:17,565
Speaker 1: at this point of robots had already been like circulating.

585
00:34:17,565 --> 00:34:19,405
Speaker 1: There was already a bunch of like, you know, science

586
00:34:19,445 --> 00:34:21,805
Speaker 1: fiction about robots and how they could be evil. And

587
00:34:21,845 --> 00:34:24,205
Speaker 1: I thought it was interesting because, like the word robot

588
00:34:24,485 --> 00:34:28,685
Speaker 1: was first used in the nineteen twenties by this check guy,

589
00:34:28,725 --> 00:34:32,285
Speaker 1: and the word robot actually derives from a check word

590
00:34:32,325 --> 00:34:37,925
Speaker 1: that means forced labor. Oh that's interesting, Yeah, which I

591
00:34:37,965 --> 00:34:40,365
Speaker 1: hope we amend. And don't call them robots because when

592
00:34:40,365 --> 00:34:42,405
Speaker 1: they take over, you know, I don't want to like

593
00:34:42,525 --> 00:34:44,365
Speaker 1: keep referring to them, like they probably don't want to

594
00:34:44,365 --> 00:34:46,365
Speaker 1: be like referred to as like a forced labor or

595
00:34:46,365 --> 00:34:49,925
Speaker 1: that seems rude. Yeah, so no know what that means

596
00:34:49,925 --> 00:34:51,925
Speaker 1: and where it derives. They're like, yeah, they know where

597
00:34:51,925 --> 00:34:55,405
Speaker 1: it derives. They all know check every single robot does.

598
00:34:55,965 --> 00:34:59,125
Speaker 1: Four years after Robert's death, his family ended up suing

599
00:34:59,725 --> 00:35:02,525
Speaker 1: the Ford Company and also the Litton Company because they

600
00:35:02,525 --> 00:35:05,965
Speaker 1: said that they said that the Litton Company was negligent

601
00:35:06,045 --> 00:35:09,445
Speaker 1: and designing manufacturings implying the storage system and and failing

602
00:35:09,485 --> 00:35:14,085
Speaker 1: to warn system operators of foreseeable dangers. Basically, they don't

603
00:35:14,125 --> 00:35:16,485
Speaker 1: go bleep and bloops, so you can't hear them when

604
00:35:16,485 --> 00:35:19,285
Speaker 1: they're starting and not the bleep and bloop you have

605
00:35:19,405 --> 00:35:22,845
Speaker 1: to haven't yea, if like my Google home started talking

606
00:35:22,845 --> 00:35:25,325
Speaker 1: to me in check, like very quickly and loudly, I

607
00:35:25,365 --> 00:35:28,085
Speaker 1: would actually be my hands. I would be terrified anyway, Yeah,

608
00:35:28,125 --> 00:35:30,605
Speaker 1: that would be really scary. But we need the bleep

609
00:35:30,645 --> 00:35:32,445
Speaker 1: and bloop, like I have to say, okay, Google, and

610
00:35:32,485 --> 00:35:36,285
Speaker 1: then she goes, that didn't but that's also yeah, like

611
00:35:36,365 --> 00:35:39,325
Speaker 1: even like trucks that when they're in reverse, they go, baby,

612
00:35:39,485 --> 00:35:41,805
Speaker 1: that's true, that's true. We need thee Yeah, we need

613
00:35:41,805 --> 00:35:45,485
Speaker 1: the bleep boop okay confirmed? So um. I think they

614
00:35:45,485 --> 00:35:47,845
Speaker 1: did add some bleep bloops afterwards, but then the Lynton

615
00:35:47,885 --> 00:35:50,325
Speaker 1: company was like, bro, this is like silly that you like,

616
00:35:51,125 --> 00:35:53,725
Speaker 1: They're like this was human error, that this wasn't the machine,

617
00:35:53,765 --> 00:35:55,765
Speaker 1: like back the fuck off. But then the jury did

618
00:35:55,885 --> 00:35:59,325
Speaker 1: end up awarding the family ten million dollars, but then

619
00:35:59,365 --> 00:36:01,205
Speaker 1: the Lynton company was still like, guys, we didn't do

620
00:36:01,205 --> 00:36:03,405
Speaker 1: anything wrong, shut the fuck up. And then they're like okay, JK,

621
00:36:03,565 --> 00:36:06,005
Speaker 1: now you have to pay fifteen million dollars to the family.

622
00:36:08,045 --> 00:36:11,805
Speaker 1: So I guess like, because they didn't want to admit fault,

623
00:36:11,885 --> 00:36:15,125
Speaker 1: they had to pay five million additional dollars. I love

624
00:36:15,205 --> 00:36:20,845
Speaker 1: when corporations get belief looped. That's a great way to

625
00:36:20,885 --> 00:36:23,405
Speaker 1: put it. So after that, I mean, I'm mostly in

626
00:36:23,485 --> 00:36:28,405
Speaker 1: this talking about like specifically industrial robotic deaths because industrial

627
00:36:28,485 --> 00:36:31,525
Speaker 1: robotics is where people, you know, kind of that was

628
00:36:31,565 --> 00:36:34,485
Speaker 1: where most people were working in proximity to robots at

629
00:36:34,525 --> 00:36:37,365
Speaker 1: the beginning of you know, us using robotics. And so

630
00:36:38,365 --> 00:36:41,965
Speaker 1: in nineteen eighty one there was the second industrial robot death.

631
00:36:42,405 --> 00:36:47,085
Speaker 1: This man Kinji Rata in Japan. Again like the robot

632
00:36:47,085 --> 00:36:48,965
Speaker 1: didn't see him do working, so he jumped over the

633
00:36:49,045 --> 00:36:52,765
Speaker 1: safety barrier to look where the robot was. There's a

634
00:36:52,805 --> 00:36:57,885
Speaker 1: safety barrier, used the safety barrier. It ended up starting

635
00:36:57,925 --> 00:37:01,005
Speaker 1: to work while he was in that area, and he

636
00:37:01,045 --> 00:37:05,285
Speaker 1: was either stabbed or crushed to death. And then his

637
00:37:05,525 --> 00:37:08,285
Speaker 1: colleagues even tried to save him, but they couldn't turn

638
00:37:08,405 --> 00:37:11,445
Speaker 1: the robot off. You gotta have that kill switch. In

639
00:37:11,485 --> 00:37:13,965
Speaker 1: the past thirty years, there's been about thirty three industrial

640
00:37:14,045 --> 00:37:18,125
Speaker 1: robotic deaths, plenty of injuries. There's another woman who had

641
00:37:18,165 --> 00:37:20,845
Speaker 1: a robot drop a trailer part on her head that

642
00:37:20,925 --> 00:37:26,365
Speaker 1: crushed her skull. Another person got caught in a robot's

643
00:37:26,445 --> 00:37:29,445
Speaker 1: little grabby hands and it crushed your death. Like the

644
00:37:29,485 --> 00:37:32,165
Speaker 1: claws stuff so bad. That's so bad. That's so bad.

645
00:37:33,245 --> 00:37:36,085
Speaker 1: That's my least favorite so far. Yeah, the claw just

646
00:37:36,165 --> 00:37:40,485
Speaker 1: mayn't crush and colleagues again try to stop, but they couldn't.

647
00:37:40,525 --> 00:37:44,445
Speaker 1: And then there was at another at this other metal

648
00:37:44,485 --> 00:37:49,485
Speaker 1: manufacturing company, a guy died of either being elexecuted or

649
00:37:49,565 --> 00:37:53,405
Speaker 1: crushed by a robot. And the thing though, with all

650
00:37:53,405 --> 00:37:55,805
Speaker 1: of these deaths is that, yeah, you could be like, oh, no,

651
00:37:55,845 --> 00:37:58,285
Speaker 1: the robots, but most of this most people argue that

652
00:37:58,325 --> 00:38:01,965
Speaker 1: all of these have been you know, due to human error. Yeah,

653
00:38:02,365 --> 00:38:04,525
Speaker 1: or it's kind of like at the beginning, we hadn't

654
00:38:04,565 --> 00:38:07,405
Speaker 1: like designed robots around, like the safety measures. Yet so

655
00:38:07,445 --> 00:38:09,645
Speaker 1: eventually they're like, oh, you know, these robots are they're

656
00:38:09,685 --> 00:38:12,205
Speaker 1: like crushing people. We should like, you know, have these

657
00:38:12,245 --> 00:38:14,965
Speaker 1: safety systems, you know, have the bleeps and the bloops, etc.

658
00:38:15,925 --> 00:38:18,685
Speaker 1: And it's also, you know, these robots their artificial intelligence

659
00:38:18,765 --> 00:38:20,885
Speaker 1: is not such that that they can distinguish like a

660
00:38:20,925 --> 00:38:23,925
Speaker 1: person from like whatever task it was doing. So like

661
00:38:23,965 --> 00:38:26,925
Speaker 1: if you're in the way of the task, you're gonna

662
00:38:26,965 --> 00:38:29,925
Speaker 1: get care. I mean, what couldn't even say it care

663
00:38:29,965 --> 00:38:32,725
Speaker 1: And it's just like doing its thing, you know, they'll

664
00:38:32,725 --> 00:38:35,445
Speaker 1: get in the way if someone doing it. Yeah. Yeah,

665
00:38:36,045 --> 00:38:39,565
Speaker 1: but it is interesting. Even now, the United States Department

666
00:38:39,565 --> 00:38:44,365
Speaker 1: of Labors, Occupational Safety and Health Administrations of OSHA, they

667
00:38:44,405 --> 00:38:49,045
Speaker 1: still have no standards for industrial robotic usage, so there's

668
00:38:49,045 --> 00:38:52,685
Speaker 1: still no like standard across factories or anything of like

669
00:38:52,725 --> 00:38:56,845
Speaker 1: how it works. Yeah, I feel like there should be, right,

670
00:38:56,965 --> 00:39:00,365
Speaker 1: there should be like some guidance from the government as

671
00:39:00,405 --> 00:39:04,885
Speaker 1: to like the standards of your industrial robotics, but there's not.

672
00:39:06,205 --> 00:39:09,365
Speaker 1: Apparently robots are kind of like everybody's afraid of them

673
00:39:09,365 --> 00:39:11,325
Speaker 1: that they're all like it's like a new thing kind

674
00:39:11,325 --> 00:39:14,005
Speaker 1: of relatively speaking, you know, And there's like all of

675
00:39:14,005 --> 00:39:17,445
Speaker 1: these movies that about like robots taking over, so like

676
00:39:18,245 --> 00:39:20,965
Speaker 1: seemingly we should like we have all of the warning

677
00:39:20,965 --> 00:39:26,485
Speaker 1: signs there. Let's yes, Hollywood and it's warning signs. It's logical.

678
00:39:27,165 --> 00:39:32,125
Speaker 1: Do you think is conspiring with the robots? Oh, definitely,

679
00:39:32,605 --> 00:39:36,445
Speaker 1: that would be like the most boring robot dystopian movie.

680
00:39:36,445 --> 00:39:40,165
Speaker 1: It's like the Department of Labor and the industrial robots

681
00:39:40,405 --> 00:39:46,245
Speaker 1: joined forces to kill like pretty few amount of people

682
00:39:46,285 --> 00:39:48,885
Speaker 1: because it's like worldwide, it's like thirty three people have

683
00:39:48,925 --> 00:39:51,485
Speaker 1: died from industrial robots at least it's been what's recorded,

684
00:39:51,645 --> 00:39:54,045
Speaker 1: which is not a lot. Yeah, because they're like in

685
00:39:54,085 --> 00:39:56,245
Speaker 1: the United States, it's like eighty people die every day

686
00:39:56,285 --> 00:39:58,325
Speaker 1: from like car accidents and stuff. So it's like a

687
00:39:58,365 --> 00:40:01,725
Speaker 1: pretty but I mean, now, we do obviously use robotics

688
00:40:01,725 --> 00:40:05,325
Speaker 1: and like artificial intelligence. Our robots like guided by our

689
00:40:05,485 --> 00:40:07,685
Speaker 1: official intelligence in like a variety of ways, and so

690
00:40:07,765 --> 00:40:12,685
Speaker 1: there's like there's been a fair amount of surgical robotic deaths,

691
00:40:12,685 --> 00:40:15,085
Speaker 1: but again that's probably like human error of like the

692
00:40:15,165 --> 00:40:20,405
Speaker 1: doctor's doctor, doctor not you use it the robots correctly.

693
00:40:21,085 --> 00:40:23,485
Speaker 1: And then also you know, we have drones which have

694
00:40:23,645 --> 00:40:27,725
Speaker 1: killed thousands of people, so we who we got more robotics.

695
00:40:28,485 --> 00:40:30,845
Speaker 1: But then also speaking of cars, those self driving cars

696
00:40:30,965 --> 00:40:34,965
Speaker 1: is kind of like the new industry that people really

697
00:40:34,965 --> 00:40:40,645
Speaker 1: concerned about obviously because it's it doesn't work all the time,

698
00:40:40,805 --> 00:40:43,205
Speaker 1: you know. Um, And the first self driving car death

699
00:40:43,285 --> 00:40:48,245
Speaker 1: was a Tesla thank you, Elon Musk and basically the guy. Again,

700
00:40:48,285 --> 00:40:50,045
Speaker 1: it would seem like human error because it was the

701
00:40:50,085 --> 00:40:53,085
Speaker 1: Tesla driver just had the car an autopilot and then

702
00:40:53,205 --> 00:40:55,965
Speaker 1: just was driving really fast and then the car just

703
00:40:56,085 --> 00:41:00,565
Speaker 1: drove straight under the trailer of a truck and they're

704
00:41:00,645 --> 00:41:02,245
Speaker 1: seeming and it's sort of you know, ripped off the

705
00:41:02,285 --> 00:41:05,405
Speaker 1: top of the car off, and the driver of the car,

706
00:41:05,805 --> 00:41:09,885
Speaker 1: the Tesla died because like usually the autopilot system, they're like, yeah,

707
00:41:09,925 --> 00:41:11,685
Speaker 1: you know, you should probably still keep your hands on

708
00:41:11,725 --> 00:41:14,605
Speaker 1: the wheel just in case, but he hadn't. He apparently

709
00:41:14,645 --> 00:41:17,045
Speaker 1: had been on autopilot for thirty seven minutes, hadn't even

710
00:41:17,085 --> 00:41:21,165
Speaker 1: touched the wheel. And then also they discovered that he

711
00:41:21,285 --> 00:41:23,965
Speaker 1: might have been distracted because he was watching a Harry

712
00:41:24,005 --> 00:41:30,725
Speaker 1: Potter movie. Oh my gosh, unintended consequences of technology, Harry

713
00:41:30,765 --> 00:41:34,485
Speaker 1: Potter killing people. If I get an if I get

714
00:41:34,525 --> 00:41:38,245
Speaker 1: a self automated car, I want to be able to

715
00:41:38,285 --> 00:41:41,245
Speaker 1: watch my Harry Potter movie in peace and know that

716
00:41:41,285 --> 00:41:44,165
Speaker 1: the car is okay. So honestly, elon, musk, what are

717
00:41:44,205 --> 00:41:46,245
Speaker 1: you doing? What are you doing? If we can't even

718
00:41:46,325 --> 00:41:53,125
Speaker 1: do that, We're not exactly till then I'll be texting

719
00:41:53,125 --> 00:41:55,965
Speaker 1: and driving kidding, But like you know that people aren't

720
00:41:56,005 --> 00:41:59,925
Speaker 1: smart enough to like be responsible, Like people aren't responsible

721
00:41:59,925 --> 00:42:01,685
Speaker 1: and driving, you know what I mean, Like I don't

722
00:42:01,685 --> 00:42:04,725
Speaker 1: trust people. I don't trust the general public. So the robots,

723
00:42:04,805 --> 00:42:10,645
Speaker 1: these robot deaths, you know, are pretty like relatively unexciting.

724
00:42:11,325 --> 00:42:15,405
Speaker 1: But there is now like a growing, growing field of

725
00:42:15,725 --> 00:42:20,045
Speaker 1: existential risk literature that's talking about how you know that

726
00:42:20,085 --> 00:42:23,645
Speaker 1: does kind of the more scary types of artificial intelligence.

727
00:42:24,205 --> 00:42:26,325
Speaker 1: And you know, there's like this big argument where it's like, oh,

728
00:42:26,445 --> 00:42:29,285
Speaker 1: you know, if we keep developing, we don't need to

729
00:42:29,365 --> 00:42:31,725
Speaker 1: like worry about you know, don't worry about it's gonna

730
00:42:31,765 --> 00:42:34,605
Speaker 1: be like great for humanity whatever. But then there's a

731
00:42:34,605 --> 00:42:38,125
Speaker 1: bunch of other people, specifically like billionaire bad boy Elon

732
00:42:38,205 --> 00:42:41,565
Speaker 1: Musk and newly single Bill Gates, that are like, AI

733
00:42:41,725 --> 00:42:44,805
Speaker 1: is the greatest existential threat threat to the human race.

734
00:42:45,565 --> 00:42:49,805
Speaker 1: So as we go forward with artificial intel, we'll see,

735
00:42:50,085 --> 00:42:52,845
Speaker 1: we're gonna see what's gonna happen. But I think Taylor's

736
00:42:52,885 --> 00:42:56,565
Speaker 1: gonna tell us a little bit more about how it

737
00:42:56,605 --> 00:43:00,485
Speaker 1: could be bad. It could be bad, could be bad.

738
00:43:00,725 --> 00:43:06,565
Speaker 1: All right, we will be right back, fuck back. Rogue

739
00:43:06,685 --> 00:43:10,645
Speaker 1: robots were scared. Everybody's everybody's afraid they're going to take over.

740
00:43:10,765 --> 00:43:15,165
Speaker 1: While scared, but also it's kind of hot. Nika's turned on.

741
00:43:15,325 --> 00:43:18,765
Speaker 1: I'm scared, but Nika's turned on. Okay, So really it's

742
00:43:18,805 --> 00:43:21,205
Speaker 1: not the robots that we should be as afraid of.

743
00:43:21,525 --> 00:43:26,405
Speaker 1: It's artificial intelligence. It is the AI that is the

744
00:43:26,525 --> 00:43:30,725
Speaker 1: scary thing, because the robots are just metal without the AI.

745
00:43:31,285 --> 00:43:34,965
Speaker 1: Then you put the AI in there and suddenly it's

746
00:43:35,045 --> 00:43:42,925
Speaker 1: got a bran. So AI is just come. It's coming

747
00:43:42,965 --> 00:43:46,685
Speaker 1: from humans teaching aka writing code for computers to do

748
00:43:46,805 --> 00:43:50,365
Speaker 1: tasks to think and to think for us. Okay, so

749
00:43:50,445 --> 00:43:54,445
Speaker 1: it's gotten so advanced at this point, right that AI

750
00:43:54,805 --> 00:43:58,405
Speaker 1: now has the ability to learn on its own by

751
00:43:58,405 --> 00:44:02,245
Speaker 1: gathering data and information or whatever that is collected through

752
00:44:02,325 --> 00:44:05,165
Speaker 1: the trials and errors of what it was originally taught

753
00:44:05,165 --> 00:44:08,845
Speaker 1: to do. And so basically they've created these things called

754
00:44:08,925 --> 00:44:14,085
Speaker 1: neural nets which work very similar to like the brain synapsis,

755
00:44:14,725 --> 00:44:18,365
Speaker 1: And what do we know about the brain not very much.

756
00:44:18,605 --> 00:44:22,445
Speaker 1: So guess what else? We don't know much about how

757
00:44:22,565 --> 00:44:29,565
Speaker 1: the robots work. Yeah, that's really bad. That's like really bad. Yeah,

758
00:44:29,605 --> 00:44:32,205
Speaker 1: if you don't get it the way the AI works, Nope, Yeah,

759
00:44:32,245 --> 00:44:35,045
Speaker 1: it's scary. We don't really know exactly how it's gathering,

760
00:44:35,125 --> 00:44:37,805
Speaker 1: what it's like how it's learning. We just know that

761
00:44:37,885 --> 00:44:44,325
Speaker 1: it's basically coding itself. Where are these ais? They're everywhere?

762
00:44:44,645 --> 00:44:48,325
Speaker 1: No no, no stop. So um, Gabby sent me this

763
00:44:48,365 --> 00:44:52,125
Speaker 1: really great podcast by our buddy Josh Clark. My first

764
00:44:52,205 --> 00:44:55,885
Speaker 1: job outside of college was a TV show with Josh Clark.

765
00:44:56,045 --> 00:44:59,285
Speaker 1: So he has this podcast called End of the World

766
00:44:59,445 --> 00:45:02,325
Speaker 1: where he talks about all of this. So I'm going

767
00:45:02,365 --> 00:45:04,125
Speaker 1: to kind of like break it down for you. You

768
00:45:04,125 --> 00:45:08,205
Speaker 1: should listen to the podcast. It's salont so basically, so right,

769
00:45:08,285 --> 00:45:11,405
Speaker 1: AIS code, it's coding itself, it's learning. We don't really

770
00:45:11,445 --> 00:45:13,365
Speaker 1: know how it works, just like we don't know how

771
00:45:13,445 --> 00:45:15,605
Speaker 1: our brain works. We talked about this the other day, like,

772
00:45:15,605 --> 00:45:18,565
Speaker 1: could you imagine if, like, you know, you get conked

773
00:45:18,605 --> 00:45:19,645
Speaker 1: on the head and all of a sudden, you know,

774
00:45:19,685 --> 00:45:21,965
Speaker 1: Spanish or whatever, Like, could you imagine if we could

775
00:45:22,005 --> 00:45:26,285
Speaker 1: access our whole brains? Like we can't because we're dumb.

776
00:45:28,005 --> 00:45:31,405
Speaker 1: That's what we're dumb. We're dumb. Let's think about it

777
00:45:31,445 --> 00:45:34,525
Speaker 1: as far as like simpler technology, so like not like Sirie,

778
00:45:34,645 --> 00:45:38,165
Speaker 1: not Alexa. Technology is coded to do the thing that

779
00:45:38,205 --> 00:45:42,005
Speaker 1: it's originally purpose is so like what Gabby was talking

780
00:45:42,045 --> 00:45:45,005
Speaker 1: about earlier, like the robots do, and it's one task, right,

781
00:45:45,885 --> 00:45:50,565
Speaker 1: So with AI involved, it's learning and coming up with

782
00:45:50,605 --> 00:45:54,925
Speaker 1: more efficient ways to do its task and all that's

783
00:45:54,925 --> 00:45:56,765
Speaker 1: all that it cares about because that's what it's doing.

784
00:45:56,765 --> 00:45:59,885
Speaker 1: It's not it has no mind for for humans or

785
00:45:59,885 --> 00:46:02,805
Speaker 1: anything else. It's just like, how can I be the

786
00:46:02,845 --> 00:46:07,845
Speaker 1: best task? Girl? Like? It wants to be efficient. It

787
00:46:07,885 --> 00:46:11,245
Speaker 1: wants to be so so um it could in theory

788
00:46:11,285 --> 00:46:13,645
Speaker 1: create the bots that would like make it more efficient

789
00:46:13,685 --> 00:46:16,565
Speaker 1: to do something and like destroy what's ever in its

790
00:46:16,605 --> 00:46:20,085
Speaker 1: way in order to achieve its task, because like Gabby

791
00:46:20,245 --> 00:46:24,365
Speaker 1: was saying earlier, it doesn't have like a care for

792
00:46:24,365 --> 00:46:26,885
Speaker 1: for humans, Like it's all it cares about. It is

793
00:46:26,925 --> 00:46:30,285
Speaker 1: its task. And so that is one potential way in

794
00:46:30,405 --> 00:46:33,965
Speaker 1: theory that robots could just it's doing its thing and

795
00:46:34,045 --> 00:46:36,485
Speaker 1: destroying whatever's in its past. What kind of robots though,

796
00:46:36,485 --> 00:46:38,725
Speaker 1: because this is my question, like if I know, if

797
00:46:38,765 --> 00:46:41,405
Speaker 1: I know that a robot is going rogue and is

798
00:46:41,485 --> 00:46:43,485
Speaker 1: you know, trying to figure out its task to the

799
00:46:43,525 --> 00:46:48,245
Speaker 1: point that it's eliminating people in in what metal casing

800
00:46:48,365 --> 00:46:50,605
Speaker 1: is this AI in Because if you tell me, oh,

801
00:46:50,645 --> 00:46:53,645
Speaker 1: it's Spotify, what Spotify gonna do? Drama Bank account, Like

802
00:46:53,845 --> 00:46:55,725
Speaker 1: I don't like, you know, it's she's in the air,

803
00:46:55,805 --> 00:46:58,205
Speaker 1: like she this isn't real now if you told me, like, oh,

804
00:46:58,245 --> 00:47:00,605
Speaker 1: it's you know, the robodog. So the New York Police

805
00:47:00,645 --> 00:47:03,605
Speaker 1: is not using. That's a different that's a different situation

806
00:47:03,725 --> 00:47:07,685
Speaker 1: because there's a metal casing in this scenario, Like it's

807
00:47:07,725 --> 00:47:10,005
Speaker 1: it could be creating cars, Like it could be creating

808
00:47:10,045 --> 00:47:12,365
Speaker 1: cars right like and then it's just like going out

809
00:47:12,365 --> 00:47:14,805
Speaker 1: to like grab the materials. But in its way, it's

810
00:47:14,845 --> 00:47:17,125
Speaker 1: like you know, say, it's like dragging the materials and

811
00:47:17,165 --> 00:47:19,885
Speaker 1: like knocking people on the head. I don't know, Okay, okay, gotcha, No,

812
00:47:19,925 --> 00:47:22,685
Speaker 1: that that makes sense. Yeah. And it's also I mean

813
00:47:22,725 --> 00:47:25,165
Speaker 1: the example that they use like that was developed by

814
00:47:25,165 --> 00:47:28,485
Speaker 1: this guy Nick Bostrom, who's like the dude who writes

815
00:47:28,485 --> 00:47:32,525
Speaker 1: all about like the risks that AI could pose. He like,

816
00:47:32,605 --> 00:47:34,605
Speaker 1: you know, does this, Yeah, this model of like a

817
00:47:34,645 --> 00:47:36,845
Speaker 1: paper clip factory, and it's just kind of like, oh,

818
00:47:36,885 --> 00:47:39,085
Speaker 1: if it's like one thing is like make paper clips,

819
00:47:39,085 --> 00:47:41,405
Speaker 1: it could yeah source the world and also eventually start

820
00:47:41,485 --> 00:47:45,085
Speaker 1: like in order to be the most efficient paper clip maker,

821
00:47:45,205 --> 00:47:47,965
Speaker 1: it could like even start sourcing like molecules off of

822
00:47:48,005 --> 00:47:51,285
Speaker 1: like human bodies and stuff and like you know, making

823
00:47:51,885 --> 00:47:54,005
Speaker 1: you know, it's like fixing our economy and doing all

824
00:47:54,045 --> 00:47:57,245
Speaker 1: these other things too. Um, And it's like, yeah, that

825
00:47:57,365 --> 00:48:00,805
Speaker 1: is also like a problem of like if AI is

826
00:48:00,805 --> 00:48:02,965
Speaker 1: like on a server, it could just like live on

827
00:48:03,005 --> 00:48:05,485
Speaker 1: the internet and then you just like couldn't like unplug it,

828
00:48:05,885 --> 00:48:09,085
Speaker 1: you know, Yeah, there must be AI already on on

829
00:48:09,125 --> 00:48:13,445
Speaker 1: a server, right, Oh yeah, it's all over servers. It's great.

830
00:48:13,725 --> 00:48:18,765
Speaker 1: Oh great, I mean goody Google is to some degree, right,

831
00:48:18,765 --> 00:48:20,645
Speaker 1: you know, I know, you know, my mom when I

832
00:48:20,685 --> 00:48:24,045
Speaker 1: first got the Google Home my mom literally like got

833
00:48:24,125 --> 00:48:26,885
Speaker 1: mad at me about getting it and like didn't speak

834
00:48:26,885 --> 00:48:29,445
Speaker 1: to me and also unplugged it multiple times. Well, but

835
00:48:29,525 --> 00:48:32,885
Speaker 1: it's also like Google dot com, right, the more you search,

836
00:48:33,005 --> 00:48:35,685
Speaker 1: like it's learning about your search habits, it's learning about

837
00:48:35,765 --> 00:48:39,285
Speaker 1: it's learning about you in particular, and what makes you know,

838
00:48:40,285 --> 00:48:45,565
Speaker 1: they're making themselves more efficient for you. But it starts

839
00:48:45,565 --> 00:48:48,165
Speaker 1: to get a little bit scary because we like because

840
00:48:48,205 --> 00:48:49,685
Speaker 1: at that point, yeah, like what if they like take

841
00:48:49,685 --> 00:48:54,045
Speaker 1: over and like I don't know whatever. So the problem

842
00:48:54,125 --> 00:48:56,445
Speaker 1: is is that we need the robots or the AI

843
00:48:56,925 --> 00:49:01,285
Speaker 1: to care about people and like bid and fitting the

844
00:49:01,325 --> 00:49:04,085
Speaker 1: world or the and the people that live in the world. Right,

845
00:49:04,405 --> 00:49:08,325
Speaker 1: people don't even do that roll, right, but like we

846
00:49:08,365 --> 00:49:10,765
Speaker 1: have but we have to or all all they are

847
00:49:10,805 --> 00:49:12,405
Speaker 1: going to care. All the AI is going to care

848
00:49:12,445 --> 00:49:15,885
Speaker 1: about is itself, So we need to teach it how

849
00:49:15,925 --> 00:49:19,685
Speaker 1: to care about humans. I just really complicated, right, because

850
00:49:20,245 --> 00:49:22,885
Speaker 1: because then that could be misinterpreted and the bots could

851
00:49:22,925 --> 00:49:26,005
Speaker 1: just you know, so it's like created robots, right, the AI,

852
00:49:26,405 --> 00:49:28,725
Speaker 1: and they're like just trying to keep us safe, and

853
00:49:29,245 --> 00:49:33,125
Speaker 1: perhaps keeping humans safe isn't isn't in the best interest

854
00:49:33,125 --> 00:49:35,765
Speaker 1: of a human, right, so they could they could have

855
00:49:35,805 --> 00:49:38,045
Speaker 1: like you know, in panic room or something, so they

856
00:49:38,085 --> 00:49:39,725
Speaker 1: just put them in like a padded room or something,

857
00:49:39,765 --> 00:49:42,885
Speaker 1: and that's not happiness, but they're safe, right, So then

858
00:49:42,925 --> 00:49:45,005
Speaker 1: it's like, okay, well then we need to program it

859
00:49:45,165 --> 00:49:47,725
Speaker 1: to be like for the greater good and make people happy.

860
00:49:48,245 --> 00:49:51,885
Speaker 1: But like what makes one person happy might not make

861
00:49:51,885 --> 00:49:55,085
Speaker 1: the other person happy. So like, you know, whatever would

862
00:49:55,125 --> 00:49:58,405
Speaker 1: make Trump happy and in Trump's world is not exactly

863
00:49:58,445 --> 00:50:00,565
Speaker 1: going to be the same thing that I would say

864
00:50:00,765 --> 00:50:02,965
Speaker 1: would make me happy. And the same with the three

865
00:50:03,005 --> 00:50:06,845
Speaker 1: of us, because we are three very different people. So

866
00:50:06,965 --> 00:50:10,565
Speaker 1: it's just kind of like spirals, right, and and we

867
00:50:10,605 --> 00:50:14,045
Speaker 1: already don't really know how it's learning, so it's just

868
00:50:14,245 --> 00:50:17,805
Speaker 1: kind of scary. There's like all of these we just

869
00:50:18,045 --> 00:50:20,445
Speaker 1: we have to learn how to code it ourselves because

870
00:50:20,485 --> 00:50:22,845
Speaker 1: it came from us. But now it's just like learning

871
00:50:22,845 --> 00:50:25,525
Speaker 1: and it's going rogue. Right, I'm going to start googling

872
00:50:25,805 --> 00:50:28,645
Speaker 1: robot erotica so that way the robots know that, like

873
00:50:28,965 --> 00:50:33,605
Speaker 1: I'm on their side, you know, like we just start googling.

874
00:50:33,645 --> 00:50:40,845
Speaker 1: I love robots, are cool, I love AI. Um So,

875
00:50:40,845 --> 00:50:43,525
Speaker 1: so right, the we have to have like a fail safe.

876
00:50:43,605 --> 00:50:46,325
Speaker 1: You have to have a kill switch because otherwise who

877
00:50:46,325 --> 00:50:49,005
Speaker 1: knows what's what's going to happen. But then like the

878
00:50:49,045 --> 00:50:51,645
Speaker 1: AI can learn so much, you know at that point

879
00:50:51,685 --> 00:50:56,045
Speaker 1: that the AI could have some level of awareness of death,

880
00:50:56,605 --> 00:50:59,205
Speaker 1: and then it doesn't want that because its goal is

881
00:50:59,205 --> 00:51:01,805
Speaker 1: to continue on with what it was learning to do,

882
00:51:01,965 --> 00:51:06,565
Speaker 1: and so it can in theory make it difficult to

883
00:51:07,365 --> 00:51:13,925
Speaker 1: have that kill switch. So basically, all philosophers, scientists, physicists,

884
00:51:13,965 --> 00:51:17,165
Speaker 1: and all the people that are smarter than us, some

885
00:51:17,285 --> 00:51:19,245
Speaker 1: that are dumber than us as well. I don't know

886
00:51:19,365 --> 00:51:22,685
Speaker 1: who goes, I don't know, but everybody is pretty much

887
00:51:22,765 --> 00:51:25,925
Speaker 1: sure the AI is going to learn too much and

888
00:51:25,965 --> 00:51:28,445
Speaker 1: take over. It's just kind of a matter of when

889
00:51:29,605 --> 00:51:32,725
Speaker 1: because it is just slowly it exists and it is

890
00:51:32,765 --> 00:51:37,205
Speaker 1: slowly collecting data like it has been collecting data for

891
00:51:39,365 --> 00:51:41,285
Speaker 1: the thing that I feel like, I mean that's the

892
00:51:41,285 --> 00:51:45,685
Speaker 1: problem with like all human like our hubrists or whatever.

893
00:51:45,725 --> 00:51:49,165
Speaker 1: It's just like, we could just stop developing this, but

894
00:51:49,245 --> 00:51:52,125
Speaker 1: it's like, also it's so now intertwined with like money

895
00:51:52,165 --> 00:51:54,645
Speaker 1: and shit, so it's like why would people stop making

896
00:51:54,685 --> 00:51:57,125
Speaker 1: this so that because it's yearly making things more efficient.

897
00:51:57,605 --> 00:52:00,565
Speaker 1: And then it's also just like we'll develop something that's

898
00:52:00,565 --> 00:52:03,205
Speaker 1: so smart that it's like it's so much smarter than

899
00:52:03,205 --> 00:52:05,965
Speaker 1: that than we are that and then that smart intelligence

900
00:52:05,965 --> 00:52:08,965
Speaker 1: will be able to make an even bigger intelligence, and

901
00:52:09,045 --> 00:52:12,805
Speaker 1: like it, we will be so dumb compared to artificial intelligence,

902
00:52:12,805 --> 00:52:15,205
Speaker 1: so we can't even like theorize what would happen because

903
00:52:15,245 --> 00:52:18,565
Speaker 1: we just like our little brains can't do it. Yeah,

904
00:52:18,605 --> 00:52:23,125
Speaker 1: so exactly even scarier we rely on technology, like we

905
00:52:23,245 --> 00:52:26,205
Speaker 1: rely on it so much, and all it takes is

906
00:52:26,285 --> 00:52:29,245
Speaker 1: someone to piss off because this nearly happened a couple

907
00:52:29,245 --> 00:52:31,805
Speaker 1: of years ago. It all it takes is someone to

908
00:52:31,805 --> 00:52:35,965
Speaker 1: piss off. Kim Jong un or putin and they take

909
00:52:36,005 --> 00:52:38,925
Speaker 1: over a power grid, because that was an actual threat

910
00:52:38,965 --> 00:52:42,245
Speaker 1: when mister Cheetah was in office. All that would take

911
00:52:42,365 --> 00:52:45,125
Speaker 1: was for them to shut off the power or whatever.

912
00:52:45,205 --> 00:52:48,645
Speaker 1: Because then because the other threat of technology is like

913
00:52:48,725 --> 00:52:51,445
Speaker 1: people who have like a vendetta against some other culture

914
00:52:51,525 --> 00:52:54,805
Speaker 1: or whatever, and then they destroy it for us. And

915
00:52:54,845 --> 00:52:57,845
Speaker 1: then or they take over the AI and they tell

916
00:52:57,885 --> 00:53:00,085
Speaker 1: it what to do. And then so it takes like

917
00:53:00,125 --> 00:53:02,405
Speaker 1: one bad person, right, one bad seed to tell the

918
00:53:02,445 --> 00:53:05,165
Speaker 1: AI and train it or code it to do something bad.

919
00:53:05,525 --> 00:53:08,245
Speaker 1: You're super screwed. And then when it takes over so

920
00:53:08,245 --> 00:53:10,565
Speaker 1: so and then so okay, say they just shut off

921
00:53:10,565 --> 00:53:11,965
Speaker 1: the power and shut off the power grid, like we

922
00:53:12,005 --> 00:53:15,325
Speaker 1: lose all power whatever. How long would you be able

923
00:53:15,325 --> 00:53:18,485
Speaker 1: to live without technology? Because that's like no power. You

924
00:53:18,525 --> 00:53:22,965
Speaker 1: don't you don't have TV, phone, internet, heat, air conditioning, refridgerators.

925
00:53:23,285 --> 00:53:28,245
Speaker 1: We'd be so so doomed. So I don't know, y'all.

926
00:53:28,285 --> 00:53:32,325
Speaker 1: Now that I have a garden not a rag, I

927
00:53:32,365 --> 00:53:35,045
Speaker 1: was just about to say, gets your veggie gardens ready?

928
00:53:35,565 --> 00:53:41,245
Speaker 1: I know, wow, Taylor. Yeah, we're the way that you

929
00:53:41,445 --> 00:53:45,245
Speaker 1: just prepared me for therapy. Right now, I just want

930
00:53:45,245 --> 00:53:48,365
Speaker 1: to take a long nap and never wake up. Kidding, kiding, kiddingkdding,

931
00:53:48,365 --> 00:53:51,045
Speaker 1: get kid, I'm not kidding, because we're probably not going

932
00:53:51,085 --> 00:53:52,965
Speaker 1: to live long enough to see this happen. So yes,

933
00:53:53,005 --> 00:53:56,405
Speaker 1: well they say in thirty and fifty years it could happen. Well,

934
00:53:56,605 --> 00:53:59,685
Speaker 1: never mind, but then they'll be another pandemic then, so

935
00:53:59,725 --> 00:54:01,885
Speaker 1: we'll have lots of things to deal with. Yeah, so

936
00:54:01,925 --> 00:54:08,765
Speaker 1: we're scared. Well, you know, I gotta give myself a garden.

937
00:54:08,805 --> 00:54:10,525
Speaker 1: All I got is a bunch of bamboo, which I

938
00:54:10,525 --> 00:54:13,525
Speaker 1: don't think it's gonna last me very long. You can

939
00:54:13,565 --> 00:54:18,005
Speaker 1: turn the bamboo into like paper products, you can and

940
00:54:18,045 --> 00:54:21,925
Speaker 1: then demand people then like steal food from people. Bamboo

941
00:54:22,005 --> 00:54:24,245
Speaker 1: sticks were actually used as a form of torture as well,

942
00:54:24,245 --> 00:54:27,045
Speaker 1: which we can about another episode. Yeah, and also still

943
00:54:27,165 --> 00:54:30,765
Speaker 1: used as corporal punishment in like some countries you get

944
00:54:30,805 --> 00:54:34,245
Speaker 1: like a hundred lashes with the bamboo. I don't like that,

945
00:54:35,485 --> 00:54:38,085
Speaker 1: but and the robot does it. You can use bamboo

946
00:54:38,165 --> 00:54:42,205
Speaker 1: for something good. So so everybody, get your start your gardens,

947
00:54:42,685 --> 00:54:47,605
Speaker 1: get your get some canned goods because we're we're in trouble. Okay, Taylor,

948
00:54:47,645 --> 00:54:53,485
Speaker 1: you're thirty years like, just start stockpiling some cans, No, immediately,

949
00:54:53,565 --> 00:54:56,885
Speaker 1: just slowly get some cans, but also beans are good.

950
00:54:57,325 --> 00:54:59,605
Speaker 1: All right, Well, I'm going to wrap this up. Thank

951
00:54:59,645 --> 00:55:02,045
Speaker 1: you for listening. If you made it all the way through,

952
00:55:03,045 --> 00:55:20,165
Speaker 1: good for you, argall. Cadaver Gals is a production of

953
00:55:20,205 --> 00:55:24,285
Speaker 1: School of Humans in iHeartRadio. It is hosted, produced, mixed, researched,

954
00:55:24,325 --> 00:55:27,525
Speaker 1: et cetera by Gabby Watts, Nika dark Day, and Taylor Church.

955
00:55:27,925 --> 00:55:31,245
Speaker 1: You can follow us on Instagram and Twitter at cadaver Gals.

956
00:55:31,525 --> 00:55:33,285
Speaker 1: Say some nice things about us, please,