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Speaker 1: Hello the Internet, and welcome to season fifteen, episode three

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Speaker 1: of Sir Daily's light Geist for January two thousand eighteen.

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Speaker 1: I just can't say that in the same way. My

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Speaker 1: name is Jack O'Brien a k. Jackson o'brienson. That is

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Speaker 1: courtesy of Christina o d at Chris Loves Life on Twitter.

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Speaker 1: And I am thrilled to be joined as always by

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Speaker 1: my co host, Mr Miles Gray. Yes, it's your boy

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Speaker 1: beats by Gray. I thank you to Kyle Welkes for

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Speaker 1: that A K A A all rhymes. Wow. See I

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Speaker 1: let be a rapper now. And we are thrilled to

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Speaker 1: be joined in our third seat by the creator, writer,

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Speaker 1: and executive producer of a mildly successful TV show called

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Speaker 1: Everybody Loves Raymond and the Netflix show Somebody Feed Phil.

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Speaker 1: Please welcome Bill rosen He Hello boys, Helloo, it's nice

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Speaker 1: to be here in your third seat. Yes, welcome. We're

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Speaker 1: thrilled to have you. What is something that you have

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Speaker 1: searched in the not too distant past that is revealing

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Speaker 1: about who you are as a human being. Somebody told

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Speaker 1: me a rumor about our president, and I googled the

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Speaker 1: name of this president and with the word underage. M h,

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Speaker 1: where did that lead? I leave it to your listeners

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Speaker 1: to find it themselves that the president themselves was under age. Sure,

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Speaker 1: let's go with that, because I don't want any child

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Speaker 1: and impostor opposing as a president. Yeah, he's secretly three

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Speaker 1: children stacked in an oversized suit and yeah, seating foreign policy. Okay,

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Speaker 1: that makes sense. What is I'm not saying anything about

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Speaker 1: its veracity. I'm just saying that I heard a story

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Speaker 1: and that's all. I googled it. That's all. That's all

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Speaker 1: I'm saying about. What's something you think is overrated? Phil?

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Speaker 1: Google searches what's overrated? What's overrated? There's a thing in

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Speaker 1: restaurants you could have, like a nice romantic evening with

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Speaker 1: somebody and server will come over and say we have

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Speaker 1: this and this and this and then you you all

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Speaker 1: sounds beautiful and nice. And then they'll say and choice

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Speaker 1: of protein, right, and the mood is gone. I'm sorry,

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Speaker 1: the music stops playing. What is there a more unromantic

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Speaker 1: term in a restaurant than choice of protein? Routine? Yeah?

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Speaker 1: So stop that. Yeah, what what do you What do

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Speaker 1: you propose is a better way to inform them that

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Speaker 1: they have an option. We have beautiful fresh fish and

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Speaker 1: organic chicken and grass fed beef. Doesn't that all sound

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Speaker 1: better than choice of protein? Why do we what are

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Speaker 1: we working a lab like their grain? Yeah? It sounds

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Speaker 1: like yeah, you're or you're ordering from some sort of

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Speaker 1: space age machine, like I'll have the rice and choice

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Speaker 1: of proteins, right right. What's something you think is underrated film? Well,

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Speaker 1: I think in my travels now I'm finding street food

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Speaker 1: and people think that they might get sick. But I'm

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Speaker 1: telling you if if they were killing people or people

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Speaker 1: were getting sick, they wouldn't last very long. So you

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Speaker 1: don't have to worry that much. And especially if you

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Speaker 1: go to a foreign country and you see a little

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Speaker 1: bit of a line, get on that line. These people

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Speaker 1: know something that you don't know. So the idea of

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Speaker 1: street food, I think is as much as we love it,

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Speaker 1: I think it's still underrated. Yeah, was there what was

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Speaker 1: What's an example of something where you saw a line

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Speaker 1: and you just said, you know what I'm getting in line?

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Speaker 1: I trust, I trust, I trust the crowd. I don't

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Speaker 1: know if you saw the Bangkok Show, but there was

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Speaker 1: a line for soup oh right that you got like

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Speaker 1: in a bag, right, I didn't know that's what it was.

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Speaker 1: Soup in a bag. I thought. I thought it was

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Speaker 1: soup that I could eat right there like like the people.

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Speaker 1: But you're supposed to take it home, so we did.

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Speaker 1: It was amazing. What kind of soup was it? It was.

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Speaker 1: I just heard like I think when you went up there,

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Speaker 1: like you want organ and I think, and you're like, yeah, sure,

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Speaker 1: but do you know what in a soup? God knows

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Speaker 1: what you're eating anyway, got to tell you it's porky

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Speaker 1: and delicious and fresh vegetables. And it was amazing. It

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Speaker 1: was fifty cents. Yeah, I mean crazy, like the food

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Speaker 1: there in Thailand. I mean you can you can go

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Speaker 1: And yeah, I gotta say. I saw that Bangkok episode

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Speaker 1: and I was so hungry and looked at my refrigerator

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Speaker 1: and I was ashamed of myself. I was like, and

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Speaker 1: this is all I have to eat, Like, but you know,

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Speaker 1: we're lucky to live in Los Angeles where you can

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Speaker 1: find a lot of this stuff. Yeah that's true. Yeah,

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Speaker 1: because I mean, yeah, Thai food is great in in

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Speaker 1: l A like we have is awesome. Right, And there's

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Speaker 1: a place called Cowsoi in the valley that makes COWSI,

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Speaker 1: which is one of the best things I've ever eaten

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Speaker 1: in my life. But in Thailand it's a dollar yeah,

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Speaker 1: regular prices for sure. What are you saying, there's a

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Speaker 1: Michelin starred uh street food you can speaking of street food? Yeah,

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Speaker 1: this is uh, this is j fi. She makes a

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Speaker 1: crab omelet that's a football filled with crab, and it

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Speaker 1: is one of the greatest, great is things on planet Earth.

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Speaker 1: It is just I couldn't believe. She stir fries in

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Speaker 1: a walk this crab and she keeps pouring egg over

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Speaker 1: it and the egg forms around this crab until you

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Speaker 1: have a football filled with crab and it's just the greatest.

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Speaker 1: You can't even eat the whole thing yourself. It's twenty

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Speaker 1: five dollars, which is super expensive there right right If

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Speaker 1: the soup you said was crab football, but if you

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Speaker 1: tried to find it in America would be eight and nine.

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Speaker 1: I won't even bother doing it because it's prohibitively expensive.

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Speaker 1: You couldn't possibly make a profit on what this fresh

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Speaker 1: shucked crab, you know, meat. And that's the dish that

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Speaker 1: she was awarded the Michelin starfar that she on her

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Speaker 1: entire If you watch the rest of that sheet, there's

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Speaker 1: a seafood soup. But Tom Tom, Yeah, yeah, I'm telling

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Speaker 1: you all the greatest hits of the ocean are in

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Speaker 1: this soup. He's giant, like lobster tails and giant prawns

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Speaker 1: and beautiful. I mean we I couldn't finish. I couldn't.

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Speaker 1: You saw me. It was with another guy. We couldn't

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Speaker 1: put a dent in this soup, right, and we had

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Speaker 1: more than I had to have. The entire crew couldn't

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Speaker 1: eat this soup. It's amazing. I do think restaurants need

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Speaker 1: to start reaching out to you for for their food

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Speaker 1: descriptions because a football sized omelet. I think we'll build

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Speaker 1: with crab. Yeah, I think that would be the greatest

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Speaker 1: time any man. We feel challenged. Crap football, please, all right,

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Speaker 1: let's get into format. We're trying to take a sample

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Speaker 1: of what people are talking and thinking about right now

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Speaker 1: at this moment, and we like to open up by

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Speaker 1: asking our guest, what is a myth? What's something that

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Speaker 1: people generally believe to be true that is not true.

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Speaker 1: Dorian is supposedly the stinkiest fruit on earth and in Thailand.

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Speaker 1: Since we're on that subject, I can easily go to this.

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Speaker 1: I had it, and I was afraid. I thought it's

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Speaker 1: going to be the limburger, cheese of fruit, the stinkiest socks, smelly.

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Speaker 1: I didn't want to, right, they said you gotta try it,

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Speaker 1: And you know you do things when the cameras on,

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Speaker 1: maybe you wouldn't do so. Maybe I'm a little stupid.

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Speaker 1: But I smelled it and it smelled clean. I don't

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Speaker 1: even understand that was hardly smelled. It was a slightly

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Speaker 1: fruity smell. Now maybe it's my nose. I don't know.

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Speaker 1: Everybody's different. Some people have the cilantro thing right that

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Speaker 1: tastes like soap. I happen not to have that. My

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Speaker 1: son has it, but I don't have it. And I

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Speaker 1: take a bite of this Durian and it's not only fine,

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Speaker 1: it's delicious. It tastes good. It's fruity and nice, creamy, creamy, soft, creamy.

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Speaker 1: But a lot of the fruits in Thailan, like the

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Speaker 1: mangoes there, are infinitely better than the mangoes we get

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Speaker 1: in the United States. The mangoes you get in the

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Speaker 1: United States are kind of fibrous, as delicious as they are,

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Speaker 1: and I am here to say King of Fruits, mangoes.

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Speaker 1: Mango mangoes great, but I don't know if there's better.

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Speaker 1: And so you know, when you eat a mango here,

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Speaker 1: you get the strings in your teeth, right, you gotta

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Speaker 1: pull up. You're gonna better have a floss nearby. There.

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Speaker 1: It's just almost like a mango pudding. Delicious, crazy, delicious. Man,

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Speaker 1: you gotta go. You should go to time and for

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Speaker 1: the fruit alone and the jury. And I'm telling you,

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Speaker 1: I don't know what happened. Maybe people have a bad

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Speaker 1: one and it stinks. Well if it's funny, because like

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Speaker 1: people always use durry and as like a prop on

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Speaker 1: these shows. It's like, oh it stinks like ship, so

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Speaker 1: you shouldn't need it, like it's the grossest thing, and

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Speaker 1: like they try and like you know, I've seen it,

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Speaker 1: Like I'm using prank shows to be like that punishment.

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Speaker 1: But it's funny because yeah, all right, let's get into

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Speaker 1: the stories of the day. Um just right off the top,

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Speaker 1: we wanted to talk about how on the White House's

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Speaker 1: website they have uh there thank you Mr President video

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Speaker 1: that is just you know, thanking uh the President for

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Speaker 1: a great first year. And I was just curious, Miles,

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Speaker 1: how similar that was to the video that you had

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Speaker 1: produced for thanking the president, because mine was like somewhat similar,

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Speaker 1: but you know, he we hit some of the same points,

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Speaker 1: but I had I guess mine was more detailed. Yeah,

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Speaker 1: I got the Vienna Boys Choir together, uh, and we

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Speaker 1: did a rousing rendition of if you don't always get

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Speaker 1: what you want. By the way, if you google Trump

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Speaker 1: Vienna Boys Choir or get something exactly. But yeah, this

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Speaker 1: video is just it's pure propaganda of just like people

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Speaker 1: who clearly needs something from the president or you know,

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Speaker 1: owe him a favor, or are just puristic of fantasis

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Speaker 1: kind of being like making making up weird things to

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Speaker 1: be thankful for because I'm not I'm not quite sure

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Speaker 1: what what there is that we can be totally thankful for.

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Speaker 1: But yeah, it's a it's a weird it's a weird video.

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Speaker 1: It doesn't have that many views and feels like something

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Speaker 1: clearly like sort of cooked up by Trump to be

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Speaker 1: like show them, show the people that people are thinking me.

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Speaker 1: It's like Trump's YouTube channel where if you go to

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Speaker 1: Trump's YouTube channel or I think he took some of

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Speaker 1: the videos down, but if you went to his YouTube

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Speaker 1: channel like during the campaign when he was like the

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Speaker 1: most talked about man on Uh, he had a bunch

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Speaker 1: of videos that were just you know, had a couple

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Speaker 1: of hundred views, and it was just him like you know,

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Speaker 1: holding court about like some pop culture from like eight

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Speaker 1: years ago. And it was it was just really strange

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Speaker 1: that note like he had all this stuff that nobody

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Speaker 1: really cared about. He's like, they shouldn't have canceled Laguna

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Speaker 1: Beach exactly. It was people still want that we are

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Speaker 1: through the looking glass people, Yes, exactly. Uh, speaking of propaganda,

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Speaker 1: so the fake news will tell you know. Uh, the

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Speaker 1: Sessions was interviewed by Mueller. Apparently we're we're just learning this.

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Speaker 1: Trump says he he doesn't care, He's totally cool with it. Um,

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Speaker 1: but what so what what are the details of this? Man?

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Speaker 1: I mean clearly so, yes, Jeff Sessions last week was

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Speaker 1: interviewed by Robert Mueller for several hours. And you know,

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Speaker 1: I I couldn't think of any reason why you would

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Speaker 1: want to interview interview you know, Jeff Sessions, unless it's

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Speaker 1: like a at the times he was like lying about

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Speaker 1: meeting with Russian ambassadors or uh, you know, maybe committing

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Speaker 1: a little bit of perjury talking about his Russian connections,

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Speaker 1: or that he knew about Jared Kushner and his involvement.

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Speaker 1: And I mean, there's many things, but I don't know,

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Speaker 1: I couldn't. I couldn't tell you why. But clearly, I mean, yes,

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Speaker 1: I have a feeling that Robert Muller was really putting

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Speaker 1: him to the sword to try and get some answers

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Speaker 1: out of him, to begin trying to figure out what

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Speaker 1: is going on in all this Russia ship. But I

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Speaker 1: ask a question, why do we think he's going to

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Speaker 1: tell the truth now? I think now it could be

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Speaker 1: easier to he's not. He hasn't been in front of

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Speaker 1: a grand jury or anything like that. So if he lies,

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Speaker 1: it's there, it's less. I think the stakes are lower

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Speaker 1: versus now lying to like real federal investigators, because now

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Speaker 1: that's that's a completely different bag of worms, because he

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Speaker 1: can put a case together and be like, you actually

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Speaker 1: completely deceived this investigation. So I think there's definitely that

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Speaker 1: degree of uh, you know that that this is a

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Speaker 1: different set of circumstances than before, like whether it's a

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Speaker 1: confirmation hearing where he first lied and things like that,

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Speaker 1: where this is completely different because now he's involved in

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Speaker 1: an investor. It wasn't everything he said up till now

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Speaker 1: was not against the law. Well, I think clearly people

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Speaker 1: are saying that we're making the case, but I think

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Speaker 1: now it's it's sort of it's to the point where

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Speaker 1: we just want to make sure we can get the

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Speaker 1: proper full view of what is happening here and hold

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Speaker 1: everyone responsible from the point of view of this investing.

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Speaker 1: Now it's double secret probation, Yeah, exactly. And who knows

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Speaker 1: already because clearly other people have talked to mother, people

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Speaker 1: have already made plea deals. So I'm sure he has

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Speaker 1: a set of facts that he's operating with and trying

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Speaker 1: to sort of juxtapose that with whatever Sessions is saying.

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Speaker 1: But what was interesting is that, Yeah, the Jeff Sessions

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Speaker 1: actually was really kind of coy about this meeting with

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Speaker 1: the White House. Donald Trump was really like, Hey, what

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Speaker 1: what you talk about? What were the questions? Yeah? What

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Speaker 1: was on the test? Did you ask about this thing?

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Speaker 1: Who did he vote for? Yeah? Exactly, and uh and

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Speaker 1: and apparently Jeff Sessions was holding the cards pretty close

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Speaker 1: to the chest, so which is definitely not what the

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Speaker 1: President wants to hear like, oh, I don't worry about it. Nobody, Yeah, exactly,

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Speaker 1: just giving like non detailed answers exactly. So that's why

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Speaker 1: then you cut to this quote yesterday if they're asking like, hey,

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Speaker 1: are you worried about this meeting? So it's like, oh, no,

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Speaker 1: absolutely not, I'm not. I'm fine. It's great, it's fine.

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Speaker 1: And then and then stop the interviews like Okay, no

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Speaker 1: more questions. Why So clearly, you know Trump is a

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Speaker 1: little shook. But I guess that feeds into now this

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Speaker 1: sort of new attempt from the GOLP to sort of

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Speaker 1: really begin discrediting the FBI, because that's like become like

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Speaker 1: a huge news lineout the last couple of weeks. Whether

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Speaker 1: it's apparently like Trump was leaning on Chris Ray to

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Speaker 1: get rid of the deputy director of the FBI had

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Speaker 1: a McCabe, and it looks just looks like they're trying to,

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Speaker 1: you know, clean house a little bit at FBI because

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Speaker 1: they're like the one law enforcement agency that has the

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Speaker 1: most information, it seems like, or their most vocal about

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Speaker 1: their concerns about what was going on the campaign. Hashtag

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Speaker 1: released the memo, right yeah, and that the memo that

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Speaker 1: so that huge hashtag that got circulated widely, a lot

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Speaker 1: of people respect relating like, oh, it's it's Russian bots

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Speaker 1: that are are you know, are spreading this. I mean,

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Speaker 1: to a certain extent, that may be true. But according

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Speaker 1: to like Twitter's internal analysis, someone on Twitter said, it's

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Speaker 1: it's being you know, retweeted a lot in the States. Um.

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Speaker 1: But yeah, this memo that everyone's talking about is this

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Speaker 1: report that Republicans and the House Intelligence Committee we're saying

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Speaker 1: like they have proof of like widespread surveillance abuse from

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Speaker 1: the FBI, and like that the FBI has this corrupt

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Speaker 1: agenda to undermine the president and you know, to to

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Speaker 1: throw him out of office. And that's makes sense. I

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Speaker 1: guess when you're trying to discredit the people who have

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Speaker 1: all the receipts and evidence that of all you're wrong doing,

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Speaker 1: there's no better way to to try and get no,

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Speaker 1: there's no better defense than to say the offense is lying. Um.

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Speaker 1: So yeah, So they have this memo that's going around

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Speaker 1: and it's just sort of ramping up more and more

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Speaker 1: and more, which looks like the actions of people who

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Speaker 1: may have something to hide, not people who are on

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Speaker 1: the right side of things. It's like roaches complaining that

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Speaker 1: you turn the light up. Yeah, right, and then saying

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Speaker 1: that the light is lying or like, don't believe that

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Speaker 1: this is this is artificial light. We're not roaches either,

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Speaker 1: we are human beings, so we should not kill us. Like,

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Speaker 1: I don't know how far you can go with that.

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Speaker 1: Pretty far, yeah apparently. So now it's just getting kind

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Speaker 1: of dangerous because you have actual senators like Ron Johnson

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Speaker 1: went on Fox News yesterday and is trying to push

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Speaker 1: this like that the FBI has this secret. This is

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Speaker 1: Ron Johnson talking to Brett Bear on Fox last night.

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Speaker 1: We haven't We have an informant that's talking about a

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Speaker 1: group that were holding secret meetings off site. There are

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Speaker 1: there's so much smoke here, there's so much special let's

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Speaker 1: let's stop. There a secret society, a secret meetings offside

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Speaker 1: of the Justice Department. Correct, And you have an informant

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Speaker 1: saying that, yes, is there anything more about that we

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Speaker 1: have to dig into? Wait, where is this? This was?

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Speaker 1: That's Ron Johnson talking to Brett Bear like he's on

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Speaker 1: the hill, like you can clear he's I think he's

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Speaker 1: in the Capitol building. Based on that, like echo um,

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Speaker 1: you know who's Brett Bear. What what Oh, he's like

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Speaker 1: a Fox News anchor. Just discount this as oh well,

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Speaker 1: the comic book said, right, And so what's funny is, yeah,

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Speaker 1: this is him saying, you know, there's a secret society,

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Speaker 1: and they're basing it off these texts that one of

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Speaker 1: the guys in the FBI had said, like the day

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Speaker 1: after the election, that I think could have been read

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Speaker 1: pretty flippantly of sort of like oh god, like I

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Speaker 1: guess I have these like secret secret society meetings now

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Speaker 1: kind of thing. And they've just latched onto this one

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Speaker 1: text to sort of justify this idea of their being

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Speaker 1: this like other dark entity happening. What they don't talk

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Speaker 1: about is that same FBI agent and the beginning of it.

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Speaker 1: One of the texts they have was also him saying

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Speaker 1: that he wasn't quite sure there was any there there

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Speaker 1: with the Russia collusion thing. So it's funny they're like,

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Speaker 1: rather than using this text like they could hang up like,

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Speaker 1: well this FBI guy never thought there was anything, they're

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Speaker 1: being like this FBI guys started a secret society to

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Speaker 1: try and undermine the president. Um And now then you

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Speaker 1: cut too lou Dobbs. Who's my goodness, he has fallen

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Speaker 1: quite far. Uh, Trump are very way back talk on

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Speaker 1: a regular basis. Yeah, So this is what Lou Dobbs

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Speaker 1: was pushing in his opening monologue of his show UH

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Speaker 1: last night, uh for on Fox Business Channel. Good evening, everybody.

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Speaker 1: It may be time to declare war outright against the

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Speaker 1: deep state and clear out the rot in the upper

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Speaker 1: levels of the FBI and the Justice Department. Yes, I

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Speaker 1: said the rot. The FBI and the d o J

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Speaker 1: have broken the public trust by destroying evidence, defying oversight,

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Speaker 1: and actively trying to bring down the Trump presidency. Tonight,

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Speaker 1: there are new concerns that anti Trump FBI officials formed

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Speaker 1: even a secret society at the FBI to subvert the

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Speaker 1: president after his election. So this is it's like a

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Speaker 1: comic book. Yeah, of course. It just sounds like like

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Speaker 1: second rate propaganda, you know, and not even good. Yeah,

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Speaker 1: it's not even good. It should learn more from the

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Speaker 1: Russians on how to do it. Yeah, exactly. It's just

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Speaker 1: it's just really it's just dangerous. You know that this

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Speaker 1: is like if you are living in this Fox News

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Speaker 1: bubble that now they're trying to force feed this idea

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Speaker 1: that the FBI is actually the enemy of the state. Well,

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Speaker 1: the only dangerous thing is that a third of the

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Speaker 1: country believes this. Yeah. Right, it's that concerted kind of

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Speaker 1: messaging machine of Fox News. And then when you go

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Speaker 1: to a Drudge Report, Uh it is. It has been

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Speaker 1: nothing but memo gate ever since, you know, a few

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Speaker 1: days ago. Now, Secret Society is one of the top

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Speaker 1: headlines on Drudge Report. Whistleblower Colin's Secret Society held off

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Speaker 1: site meeting. Uh. This is based on a single text

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Speaker 1: message that you know, we don't know what the context

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Speaker 1: is at all. Um. There's also five months of miss

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Speaker 1: missing messages between two FBI agents I think to Department

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Speaker 1: of I mean, at this point, they're just really grasping

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Speaker 1: at anything to try and to try and give any

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Speaker 1: kind of veracity to right, That's how guilty people act. Yeah, exactly, everybody.

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Speaker 1: You know, it's like a rat in a corner. You're

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Speaker 1: gonna bite you. Yeah, and they're running out of teeth basically,

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Speaker 1: So we'll see how crazy it gets. But yeah, clearly

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Speaker 1: the FBI, you know, time and time again, they have

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Speaker 1: been the ones to uncover the lies or whenever someone

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Speaker 1: has not been honest about their interactions with foreign governments

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Speaker 1: and things. They ever been one step in like, actually,

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Speaker 1: we we heard, like we we've we've we've surveilled the

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Speaker 1: phone conversations, and we know that for a fact that

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Speaker 1: that's a lie. So that's their tactic is like if

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Speaker 1: you describit the FBI, then maybe they can get the

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Speaker 1: whole thing to go away. And so the release the memo,

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Speaker 1: Uh what memo are they talking? This is the one

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Speaker 1: that the House Intel Committee is saying, like, hey, we've

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Speaker 1: got we've we've been looking at some stuff and we've

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Speaker 1: put together this report, this memo, this memorandum of you know,

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Speaker 1: outlining all the abuses of power that we you know,

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Speaker 1: that the FBI is doing and overreaching with surveillance. And

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Speaker 1: they're like, oh, when you see this, it's gonna blow

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Speaker 1: your mind. So the memo is produced by them, Yeah right,

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Speaker 1: it's produced by these people who want to claim, who

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Speaker 1: want to discredit the FBI's investigation. So they're just saying, oh, man,

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Speaker 1: if only you could see this thing we wrote over here,

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Speaker 1: and then then they're trying to create like this groundswell

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Speaker 1: of people being like you should release it. Then release

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Speaker 1: the memo but I assumed when I saw that hashtag

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Speaker 1: that it was some sort of primary document like that.

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Speaker 1: They were like, wait, way, do you see this memo

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Speaker 1: that we uncovered? But it's way do you see this

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Speaker 1: memo that we wait, yeah, wait until you see this

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Speaker 1: book report. I did exactly. Okay, why don't why don't

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Speaker 1: you just tell us what you want to tell us? Um,

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Speaker 1: But anyways, it's all grand theater. Yeah, so it's pretty

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Speaker 1: bad theater. Yeah. Is there any indication that this is

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Speaker 1: going to affect the Muller investigation. We'll see. I mean,

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Speaker 1: I who's who's to know anymore, because there's constantly been

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Speaker 1: this threat of Mueller being fired, and who knows if

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Speaker 1: it's going to happen. If it doesn't, I think Sara

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Speaker 1: Hugaby Sanders yesterday said, oh I can't you know, we

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Speaker 1: don't we're not thinking about firing mother because the left

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Speaker 1: wing media would go crazy, Like yeah, that's why, because

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Speaker 1: you're you're worried more about the optics from the media

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Speaker 1: than you are left the actual investigations. So uh, well

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Speaker 1: she's she's at the left. Yeah, And once again, this

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Speaker 1: is a great uh indication of you know what I've

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Speaker 1: been talking about the past couple of days about how

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Speaker 1: they they talk about there being a left wing bias

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Speaker 1: in the media, they actually have a whole corporation that

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Speaker 1: is just regurgitating their talking points like straight from like

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Speaker 1: what they want them to say. It's going out directly

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Speaker 1: to the audience. And the left doesn't have that. The

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Speaker 1: left has people who are trying to tell both sides

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Speaker 1: of the story. Uh. And I think it's a huge disadvantage.

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Speaker 1: I think that's how these crazy sort of conspiracy theories

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Speaker 1: suddenly take off, is that there, you know, the left

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Speaker 1: has totally It's like watching the Democrats try and negotiate

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Speaker 1: with the GOP, and you know, the GOP says, oh, well,

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Speaker 1: you guys have a left wing bias. And so that's

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Speaker 1: the beginning of the argument that like the media is

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Speaker 1: just starts from well, we do have left wing bias.

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Speaker 1: But the thing you have to understand is rather than

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Speaker 1: you know, having their own Fox News. Uh So, and

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Speaker 1: by left wing they mean human right exactly or yeah,

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Speaker 1: or you know, uh, fact oriented. Right. All right, we're

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Speaker 1: gonna take a quick break and we'll be right back,

423
00:22:40,080 --> 00:22:43,600
Speaker 1: and we're back. Uh And we wanted to talk about

424
00:22:43,600 --> 00:22:48,760
Speaker 1: how Facebook is saving the news by fixing their algorithm.

425
00:22:48,760 --> 00:22:52,840
Speaker 1: So our writer Sam Rabman talked about how, uh you know,

426
00:22:52,880 --> 00:22:56,520
Speaker 1: Mark Zuckerberg and Facebook talked a good game like Mark,

427
00:22:56,600 --> 00:23:00,240
Speaker 1: Mark Zuckerberg was at a conference with the editor in

428
00:23:00,359 --> 00:23:04,000
Speaker 1: chief of the San Francisco Chronicle, I think just a

429
00:23:04,080 --> 00:23:06,760
Speaker 1: year ago. I was talking about how, you know, they

430
00:23:06,800 --> 00:23:10,120
Speaker 1: were huge fans of what the newspapers, like, all the

431
00:23:10,200 --> 00:23:13,680
Speaker 1: journalistic work that the newspapers of America were doing, and

432
00:23:13,840 --> 00:23:18,040
Speaker 1: they were, you know, trying to find ways to fix

433
00:23:18,440 --> 00:23:22,480
Speaker 1: the problem that was identified during the uh two thousand

434
00:23:22,560 --> 00:23:27,639
Speaker 1: sixteen election. And they appear to have just punted on

435
00:23:27,760 --> 00:23:31,880
Speaker 1: that whole thing, because their solution now appears to be,

436
00:23:32,560 --> 00:23:39,720
Speaker 1: uh to let users rate their news sources essentially, Um,

437
00:23:39,760 --> 00:23:45,880
Speaker 1: It's it's basically like letting the users themselves, whose judgment

438
00:23:46,119 --> 00:23:48,920
Speaker 1: was the problem in the first place, use their judgment

439
00:23:49,080 --> 00:23:54,840
Speaker 1: to fix the problem. Right. So their tactic, against their

440
00:23:54,920 --> 00:23:58,600
Speaker 1: tactic to combat fake news is to ask people, how

441
00:23:58,640 --> 00:24:01,159
Speaker 1: do you rate this news source and this one right,

442
00:24:01,320 --> 00:24:04,959
Speaker 1: and then from their figure out if you know what

443
00:24:04,960 --> 00:24:08,200
Speaker 1: what what is dubious or what is less trustworthy? I

444
00:24:08,240 --> 00:24:11,240
Speaker 1: actually think that makes it worse, it does, That's what

445
00:24:11,320 --> 00:24:13,919
Speaker 1: we think. Yeah, it's it definitely seems like the worst

446
00:24:13,960 --> 00:24:16,760
Speaker 1: way to figure out, like how to figure out what

447
00:24:16,880 --> 00:24:18,960
Speaker 1: is fake news, because their whole thing is they want

448
00:24:18,960 --> 00:24:20,840
Speaker 1: to do that to avoid you know, right in the

449
00:24:20,960 --> 00:24:23,919
Speaker 1: utopia or in our bubble, maybe we would say, well,

450
00:24:23,960 --> 00:24:26,639
Speaker 1: of course, these other websites their garbage, they're just pushing

451
00:24:26,720 --> 00:24:29,440
Speaker 1: like fake facts, they're propaganda to keep like there are

452
00:24:29,440 --> 00:24:31,840
Speaker 1: people in line, or just to keep that sort of

453
00:24:32,240 --> 00:24:35,360
Speaker 1: uh narrative going in people's minds. That we would say, oh,

454
00:24:35,400 --> 00:24:37,000
Speaker 1: that's all trash and we have to get rid of

455
00:24:37,040 --> 00:24:39,200
Speaker 1: it because clearly it's not backed by fact or it's

456
00:24:39,440 --> 00:24:43,200
Speaker 1: based on conspiracy theories. And then the people on their

457
00:24:43,240 --> 00:24:45,639
Speaker 1: conservative end of the spectrum will say Facebook is being

458
00:24:45,640 --> 00:24:49,679
Speaker 1: biased towards this without sort of really actually looking objectively

459
00:24:49,680 --> 00:24:52,239
Speaker 1: at the news that they're ingesting. So to avoid that,

460
00:24:52,240 --> 00:24:54,240
Speaker 1: they're like, well, we don't want that, so we'll let

461
00:24:54,280 --> 00:24:56,919
Speaker 1: everybody decide and everybody can shime in and guess what

462
00:24:56,960 --> 00:24:59,760
Speaker 1: they're gonna pick the news they like, right, and that's

463
00:24:59,760 --> 00:25:02,560
Speaker 1: that's that's right. That's the problem I think that we were,

464
00:25:02,960 --> 00:25:04,760
Speaker 1: I mean could be. I mean, we don't really know

465
00:25:04,800 --> 00:25:07,280
Speaker 1: how this is going to be implemented, but literally pick

466
00:25:07,400 --> 00:25:11,760
Speaker 1: the facts you like they I mean that that has

467
00:25:11,800 --> 00:25:14,320
Speaker 1: been the problem is that it is a self selecting

468
00:25:14,840 --> 00:25:19,400
Speaker 1: you know, news finding mechanism is Facebook, and I mean

469
00:25:19,480 --> 00:25:23,480
Speaker 1: they so zero oversight. Now, well there, I think there.

470
00:25:23,520 --> 00:25:27,080
Speaker 1: It's not clear exactly how they're going to implement it,

471
00:25:27,119 --> 00:25:29,560
Speaker 1: but I mean the statements that they've put out. So

472
00:25:29,640 --> 00:25:32,920
Speaker 1: this is Mark Zuckerberry explaining that rationale. We could try

473
00:25:32,960 --> 00:25:35,600
Speaker 1: to make that decision ourselves what is fake and what

474
00:25:35,800 --> 00:25:40,200
Speaker 1: is trustworthy source, but that's not something we're comfortable with.

475
00:25:40,600 --> 00:25:44,360
Speaker 1: We considered asking outside experts, which would take the decision

476
00:25:44,359 --> 00:25:46,760
Speaker 1: out of our hands, but would likely not solve the

477
00:25:46,840 --> 00:25:51,679
Speaker 1: objectivity problem. We decided that having the community determine which

478
00:25:51,720 --> 00:25:55,760
Speaker 1: sources are broadly trusted would be most objective. Now, keep

479
00:25:55,760 --> 00:25:58,760
Speaker 1: in mind, like Facebook is well aware they have put

480
00:25:59,400 --> 00:26:04,480
Speaker 1: tons of uh newspapers out of business in the past decade,

481
00:26:04,560 --> 00:26:07,840
Speaker 1: Like there are so many out of work fact checkers

482
00:26:07,880 --> 00:26:10,480
Speaker 1: that like that that would seem to be the obvious

483
00:26:10,560 --> 00:26:15,600
Speaker 1: solution is just get people who for a living check facts,

484
00:26:15,840 --> 00:26:20,159
Speaker 1: who like fact check stories, and you know, create a

485
00:26:20,240 --> 00:26:27,160
Speaker 1: huge I mean there's been presumably conservative and liberal newspapers

486
00:26:27,160 --> 00:26:30,160
Speaker 1: that have been put out of business so why not

487
00:26:30,560 --> 00:26:34,320
Speaker 1: use the huge I mean there's also Wikipedia, which is

488
00:26:34,520 --> 00:26:37,080
Speaker 1: a bunch of people doing this stuff for free, right,

489
00:26:37,400 --> 00:26:43,320
Speaker 1: So why why can't a three fifty billion dollar company, uh,

490
00:26:43,440 --> 00:26:46,520
Speaker 1: you know find a way to you know, put a

491
00:26:46,680 --> 00:26:51,160
Speaker 1: fact finding group together that was just like a division

492
00:26:51,200 --> 00:26:54,880
Speaker 1: of their company. Um, but going to create a new

493
00:26:54,920 --> 00:26:57,480
Speaker 1: group of people instead of the old right or all left.

494
00:26:57,520 --> 00:27:01,400
Speaker 1: There's gonna be old universe, right, right, you choose what

495
00:27:01,480 --> 00:27:04,320
Speaker 1: you want to believe, ye, right, it's yeah, it's like

496
00:27:04,359 --> 00:27:07,880
Speaker 1: a form of virtual reality. Actually, yeah, yeah. I think

497
00:27:07,920 --> 00:27:12,280
Speaker 1: the big leap that they have yet to make is

498
00:27:12,520 --> 00:27:15,000
Speaker 1: that they don't want to admit that they're at all

499
00:27:15,080 --> 00:27:19,520
Speaker 1: responsible for the veracity of the information that appears on

500
00:27:19,560 --> 00:27:22,800
Speaker 1: their website. They're still claiming we're not a media company,

501
00:27:22,960 --> 00:27:27,720
Speaker 1: We're just a platform, and you know, this gives them,

502
00:27:27,720 --> 00:27:32,080
Speaker 1: you know, deniability. This allows them to say that, I'm sorry,

503
00:27:32,119 --> 00:27:35,760
Speaker 1: they're not just a platform. They're broadcasting, right, yeah, and

504
00:27:35,800 --> 00:27:39,600
Speaker 1: they're they're everybody has held to standards. CBS would not

505
00:27:39,640 --> 00:27:43,160
Speaker 1: be allowed to just spout whatever they want, and if

506
00:27:43,200 --> 00:27:48,160
Speaker 1: you want to believe it, they're standards. Human beings have standards, right,

507
00:27:48,440 --> 00:27:51,840
Speaker 1: They're not that's Bologna. It's not the telephone where it's

508
00:27:51,840 --> 00:27:54,000
Speaker 1: a private line where you and I can talk about

509
00:27:54,040 --> 00:27:57,080
Speaker 1: whatever we want to talk about. This is broadcasting to people,

510
00:27:57,119 --> 00:27:59,480
Speaker 1: to children, for God's sakes, and that's I think that's

511
00:27:59,480 --> 00:28:01,560
Speaker 1: like the next evolution. And how we're trying to view

512
00:28:01,600 --> 00:28:04,639
Speaker 1: these platforms is like, wait, you actually have a responsibility now,

513
00:28:04,680 --> 00:28:06,760
Speaker 1: Like I know you're calling it social media, but now

514
00:28:06,800 --> 00:28:08,440
Speaker 1: we have to really look at this is like, are

515
00:28:08,480 --> 00:28:11,400
Speaker 1: you spreading information on a massive scale that we can

516
00:28:11,480 --> 00:28:13,480
Speaker 1: say that you maybe need to fall in line with

517
00:28:13,560 --> 00:28:15,919
Speaker 1: things that like the f SEC or something would have

518
00:28:16,000 --> 00:28:18,800
Speaker 1: oversight over but we're yet to kind of I guess

519
00:28:18,840 --> 00:28:21,600
Speaker 1: it's one thing at a time. And this is Facebook's

520
00:28:21,600 --> 00:28:23,880
Speaker 1: way of saying like, hey, look, we're trying our best

521
00:28:23,920 --> 00:28:27,640
Speaker 1: to avoid being a news source. But who who knows

522
00:28:27,760 --> 00:28:31,520
Speaker 1: there is a law against yelling fire in a crowded

523
00:28:31,600 --> 00:28:36,840
Speaker 1: theater that the same law should apply to Facebook. I did.

524
00:28:36,320 --> 00:28:39,120
Speaker 1: I think that definitely the equivalent of that has been

525
00:28:39,160 --> 00:28:44,280
Speaker 1: happening for sure. Yeah. I mean over in Western European countries,

526
00:28:44,520 --> 00:28:47,800
Speaker 1: they are further ahead legally on this. They're starting to

527
00:28:48,040 --> 00:28:52,560
Speaker 1: regard Facebook and other social media networks as you know,

528
00:28:53,080 --> 00:28:57,280
Speaker 1: like broadcasters rather than like phone companies, which in America,

529
00:28:57,400 --> 00:29:00,760
Speaker 1: I think because of how powerful corporate rations are and

530
00:29:01,040 --> 00:29:04,920
Speaker 1: the way our system works, they're just still being viewed

531
00:29:05,120 --> 00:29:08,840
Speaker 1: like phone companies essentially, like they're just providing a platform

532
00:29:08,960 --> 00:29:12,080
Speaker 1: for people to communicate on. But phone companies didn't put

533
00:29:12,360 --> 00:29:16,600
Speaker 1: like the entire news industry out of business like that's uh,

534
00:29:16,960 --> 00:29:21,680
Speaker 1: people are clearly coming to you four facts. So um,

535
00:29:22,600 --> 00:29:25,840
Speaker 1: I don't know. I would have a lot harder of

536
00:29:25,920 --> 00:29:29,680
Speaker 1: a time, you know, holding them accountable if they weren't,

537
00:29:30,080 --> 00:29:34,200
Speaker 1: you know, making billion dollars. And they also make it

538
00:29:34,240 --> 00:29:37,600
Speaker 1: look like news right, we should also be illegal, right,

539
00:29:38,520 --> 00:29:40,560
Speaker 1: And I mean they say the reality is there is

540
00:29:40,600 --> 00:29:42,840
Speaker 1: too much content for us to check, and we imagine

541
00:29:42,840 --> 00:29:45,520
Speaker 1: there's plenty more material in need of fact checking that

542
00:29:45,640 --> 00:29:49,240
Speaker 1: we aren't seeing. And you know, that's just giving up

543
00:29:49,240 --> 00:29:51,440
Speaker 1: before you've tried, so the genies out of the bottle.

544
00:29:51,480 --> 00:29:54,160
Speaker 1: There's nothing we could do right, right, not even trying,

545
00:29:54,600 --> 00:29:57,520
Speaker 1: you know. Yeah, this survey just falls so short. I mean,

546
00:29:57,520 --> 00:30:00,720
Speaker 1: the first question, do you recognize the following websites? Yes? No? Second,

547
00:30:00,720 --> 00:30:02,360
Speaker 1: it's only two questions. Second question, how much do you

548
00:30:02,360 --> 00:30:05,240
Speaker 1: trust each of these domains entirely? A lot, somewhat barely

549
00:30:05,280 --> 00:30:08,800
Speaker 1: not at all Fox News, And it's like that we're

550
00:30:08,840 --> 00:30:11,760
Speaker 1: talking before, It's like you already live in this echo

551
00:30:11,840 --> 00:30:15,240
Speaker 1: chamber on Facebook. So if you're conservative, it's unlikely you're

552
00:30:15,240 --> 00:30:18,240
Speaker 1: going to encounter money liberal news stories because the algorithm

553
00:30:18,320 --> 00:30:20,800
Speaker 1: will keep you like like, oh, we're just gonna serve

554
00:30:20,800 --> 00:30:23,560
Speaker 1: you things we know you're receptive to. So is that

555
00:30:23,640 --> 00:30:27,400
Speaker 1: survey gonna just then be people existing in their echo chamber,

556
00:30:27,840 --> 00:30:31,200
Speaker 1: like you know, giving their survey on this thing, or

557
00:30:31,240 --> 00:30:33,680
Speaker 1: is it every news source and they're they're appealing to

558
00:30:33,920 --> 00:30:36,440
Speaker 1: the broad spectrum of Facebook users. I mean, like, I

559
00:30:36,480 --> 00:30:39,360
Speaker 1: guess there's not enough information right now to know how

560
00:30:39,400 --> 00:30:41,520
Speaker 1: exactly they're gonna apply out right, if it was just

561
00:30:41,680 --> 00:30:45,080
Speaker 1: a at random survey that you know, they were just

562
00:30:45,200 --> 00:30:49,440
Speaker 1: asking a random sampling of the population on Facebook, how

563
00:30:49,480 --> 00:30:52,719
Speaker 1: do you rate this news source, then you might get

564
00:30:52,840 --> 00:30:58,320
Speaker 1: some sort of analysis or a useful flagging of incorrect

565
00:30:58,400 --> 00:31:03,480
Speaker 1: news sources. But would entail Facebook giving people a bad

566
00:31:03,600 --> 00:31:08,960
Speaker 1: user experience. Nobody likes taking, you know, random sampled surveys, right,

567
00:31:09,040 --> 00:31:10,920
Speaker 1: So I mean, unless I can win a free iPod

568
00:31:11,680 --> 00:31:14,600
Speaker 1: that I would. Yeah, that is miles main thing He's

569
00:31:14,600 --> 00:31:16,800
Speaker 1: still stuck on the iPod. I'm telling you've won an

570
00:31:16,840 --> 00:31:19,239
Speaker 1: iPod touch the web browser told me that, and I

571
00:31:19,280 --> 00:31:21,760
Speaker 1: haven't got my iPod, And I'm just I'm just telling you.

572
00:31:22,480 --> 00:31:27,320
Speaker 1: Underneath this all is the problem that people don't ever

573
00:31:27,440 --> 00:31:31,240
Speaker 1: want to admit they're wrong. Yes, why we are where

574
00:31:31,280 --> 00:31:33,360
Speaker 1: we are, right, That's why I like, I don't think

575
00:31:33,480 --> 00:31:37,560
Speaker 1: that the objectivity problem isn't with the objectivity of this study.

576
00:31:37,600 --> 00:31:40,760
Speaker 1: It's with the objectivity of the people they're asking. You know,

577
00:31:40,840 --> 00:31:43,400
Speaker 1: I don't think I don't to be handcuffed by the

578
00:31:43,440 --> 00:31:45,480
Speaker 1: idea that like, well, then they're gonna a lot of

579
00:31:45,520 --> 00:31:48,960
Speaker 1: conservative users are gonna say we're being biased towards them,

580
00:31:49,000 --> 00:31:51,480
Speaker 1: that that isn't that isn't a good enough excuse to

581
00:31:51,680 --> 00:31:55,680
Speaker 1: not really higher like like real journalistic fact checkers to

582
00:31:55,720 --> 00:31:58,440
Speaker 1: do their job, because you can tell that they're they're

583
00:31:58,440 --> 00:32:00,480
Speaker 1: more in the business of being objective than maybe someone

584
00:32:00,520 --> 00:32:03,880
Speaker 1: who reads Drudge every day or who reads Daily Coasts

585
00:32:03,920 --> 00:32:06,480
Speaker 1: every day or something like that. Well, but even if

586
00:32:06,480 --> 00:32:08,840
Speaker 1: you're even if you're very religious, you know, and I

587
00:32:08,840 --> 00:32:11,960
Speaker 1: have great respect for people who have deep beliefs, how

588
00:32:12,080 --> 00:32:15,800
Speaker 1: much proof do they need that this particular human being

589
00:32:15,920 --> 00:32:19,840
Speaker 1: is not your pro Christian values. How much proof? What

590
00:32:19,880 --> 00:32:22,840
Speaker 1: are you waiting for? Again? And they still they are

591
00:32:22,960 --> 00:32:26,960
Speaker 1: not gonna, can't admit you're wrong. No, that's really what's underneath.

592
00:32:27,040 --> 00:32:29,080
Speaker 1: It's a human thing. That's like when my dad bought

593
00:32:29,080 --> 00:32:31,840
Speaker 1: one of those weird exercise machines from the nineties that

594
00:32:32,000 --> 00:32:34,000
Speaker 1: just looked like a weird like it wasn't a bike,

595
00:32:34,080 --> 00:32:35,720
Speaker 1: but it was like this thing where you would just

596
00:32:36,200 --> 00:32:38,200
Speaker 1: pull the thing and it's told you would give you

597
00:32:38,200 --> 00:32:40,120
Speaker 1: six pack ads. You did it for the longest time,

598
00:32:40,320 --> 00:32:43,600
Speaker 1: and I was like, I don't know this, some snake oils.

599
00:32:44,160 --> 00:32:46,880
Speaker 1: You just bought it, but for a long time. No work,

600
00:32:46,960 --> 00:32:48,600
Speaker 1: it's working. It's working the same kind of thing. You

601
00:32:48,640 --> 00:32:50,240
Speaker 1: don't want. You never want to admit that you've been duped.

602
00:32:50,760 --> 00:32:52,840
Speaker 1: And then sometimes you you go, you go great lengths

603
00:32:52,880 --> 00:32:54,920
Speaker 1: to be like, I'll show you how right I am,

604
00:32:54,960 --> 00:32:57,840
Speaker 1: because I will not I'll keep using this everybody. We

605
00:32:57,920 --> 00:33:02,320
Speaker 1: now have that bike in the white hut. Yeah. They've yeah,

606
00:33:02,320 --> 00:33:06,640
Speaker 1: they they hooked people's brains up to like brain scanners, uh,

607
00:33:06,760 --> 00:33:10,680
Speaker 1: while having political discussions, and you know, they've found that

608
00:33:10,760 --> 00:33:13,400
Speaker 1: the thing you're looking for is not to find the truth.

609
00:33:13,480 --> 00:33:17,800
Speaker 1: The thing you're looking for is a victory essentially. Um.

610
00:33:17,800 --> 00:33:21,840
Speaker 1: It's so that's really wow, that's right, let's say it right,

611
00:33:23,280 --> 00:33:28,160
Speaker 1: um and further sad news. So we we also wanted

612
00:33:28,200 --> 00:33:32,760
Speaker 1: to talk about this Google selfie app, um, where they

613
00:33:33,360 --> 00:33:36,080
Speaker 1: like take you, you take your own picture and then

614
00:33:36,120 --> 00:33:38,320
Speaker 1: they find a match for you. I just did it,

615
00:33:38,520 --> 00:33:40,840
Speaker 1: uh And who did it say? You were? St? Paul

616
00:33:41,040 --> 00:33:57,400
Speaker 1: oh Nice, Mr Bean Yea van r. Yeah. I just

617
00:33:57,400 --> 00:33:59,479
Speaker 1: wanted to bring it up because I disagreed with who

618
00:33:59,560 --> 00:34:04,040
Speaker 1: they found. They said I looked like a nineteenth century uh,

619
00:34:04,520 --> 00:34:09,160
Speaker 1: Italian woman. I think none, um, and I just disagree.

620
00:34:09,360 --> 00:34:11,360
Speaker 1: A lot of it's based on your hairline I found.

621
00:34:11,880 --> 00:34:14,920
Speaker 1: They said mine was I looked like Martin Luther King

622
00:34:15,320 --> 00:34:17,160
Speaker 1: and I looked. I think all it said was it

623
00:34:17,239 --> 00:34:20,640
Speaker 1: was going off my mustache. But I don't know. But

624
00:34:20,719 --> 00:34:22,200
Speaker 1: again that's why we're like trying to find it. Like

625
00:34:22,239 --> 00:34:25,279
Speaker 1: all these things are great in theory, right, exactly like

626
00:34:25,360 --> 00:34:32,719
Speaker 1: the iPhone itself exactly. Um. But our writer Jam McNabb

627
00:34:33,239 --> 00:34:37,200
Speaker 1: from Canada is able to look at our culture from

628
00:34:37,239 --> 00:34:41,360
Speaker 1: from a slight distance and point out that this is

629
00:34:41,400 --> 00:34:46,000
Speaker 1: basically a black mirror episode sort of waiting to happen, right, Yeah,

630
00:34:46,000 --> 00:34:49,160
Speaker 1: who knows, probably millions of people maybe like they probably

631
00:34:49,200 --> 00:34:51,120
Speaker 1: I think it's safe to assume maybe millions of people

632
00:34:51,120 --> 00:34:53,879
Speaker 1: have used this app, if not hundreds of thousands, where

633
00:34:53,920 --> 00:34:57,920
Speaker 1: you're just possibly contributing to their like machine learning ability

634
00:34:57,960 --> 00:35:01,080
Speaker 1: to begin like face matching technology. So you give it.

635
00:35:01,160 --> 00:35:03,960
Speaker 1: You give this machine enough reps with enough faces, it

636
00:35:04,040 --> 00:35:07,279
Speaker 1: can start really zeroing in on a more sophisticated way

637
00:35:07,320 --> 00:35:11,279
Speaker 1: to begin matching. Yeah. And the one thing was like

638
00:35:11,400 --> 00:35:13,440
Speaker 1: in the beginning, people were concerned of like are they

639
00:35:13,480 --> 00:35:15,920
Speaker 1: storing you know this these pictures? Am I going into

640
00:35:15,920 --> 00:35:17,680
Speaker 1: a database and Googles? Like no, no, no, no, Like

641
00:35:17,719 --> 00:35:20,360
Speaker 1: you're the face that that picture is only used for

642
00:35:20,440 --> 00:35:23,040
Speaker 1: the time it takes for us to you know, analyze

643
00:35:23,040 --> 00:35:25,800
Speaker 1: it against our database of of all these paintings to see,

644
00:35:26,120 --> 00:35:29,000
Speaker 1: you know, who you look like and if they decide

645
00:35:29,000 --> 00:35:31,520
Speaker 1: to turn on us, right, And so that's I guess

646
00:35:31,560 --> 00:35:33,759
Speaker 1: that's the thing. So like in Chicago and Illinois, or

647
00:35:33,800 --> 00:35:36,680
Speaker 1: in Illinois and in Texas, they have very strict like

648
00:35:36,719 --> 00:35:39,839
Speaker 1: biometric laws of sort of like you cannot give up

649
00:35:39,840 --> 00:35:43,719
Speaker 1: like your fingerprints or ice scans or face scans unwittingly, uh,

650
00:35:43,760 --> 00:35:46,000
Speaker 1: and without knowing like how it's going to be used.

651
00:35:46,040 --> 00:35:48,000
Speaker 1: So if you're in those states, you can't actually use

652
00:35:48,040 --> 00:35:50,479
Speaker 1: these apps. Um, but wait a minute. You just brought

653
00:35:50,520 --> 00:35:52,640
Speaker 1: up a very good point. I unlocked my phone with

654
00:35:52,680 --> 00:35:56,239
Speaker 1: my fingerprint, so that means they have my fingerprint. Well,

655
00:35:56,280 --> 00:35:58,960
Speaker 1: Apple will say, well, that information isn't being sent to us,

656
00:35:59,000 --> 00:36:01,640
Speaker 1: that stored locally in your phone. But if somebody should

657
00:36:01,640 --> 00:36:05,680
Speaker 1: suddenly take charge of Apple that wants to use my fingerprint,

658
00:36:06,080 --> 00:36:08,720
Speaker 1: there is a way to probably access that information remotely.

659
00:36:08,719 --> 00:36:11,279
Speaker 1: For it has to be it's in there, they are

660
00:36:11,280 --> 00:36:13,440
Speaker 1: reading it. It's going to the cloud and unlocking my

661
00:36:13,480 --> 00:36:17,239
Speaker 1: phone exactly. Well, and it's crazy too because they already say,

662
00:36:17,239 --> 00:36:20,319
Speaker 1: I think what was this? This show is scaring the

663
00:36:20,320 --> 00:36:23,640
Speaker 1: hell out of me. Yeah, I mean not to mention

664
00:36:23,760 --> 00:36:26,719
Speaker 1: my iPhone I have to look into to unlock now,

665
00:36:26,760 --> 00:36:28,279
Speaker 1: so now they have your face, So they now have

666
00:36:28,360 --> 00:36:32,279
Speaker 1: my face. But there's a report already that Georgetown Laws

667
00:36:32,320 --> 00:36:34,719
Speaker 1: Center on Privacy and Technology found that more than a

668
00:36:34,760 --> 00:36:38,400
Speaker 1: hundred seventeen million American adults are captured in a virtual

669
00:36:38,520 --> 00:36:42,480
Speaker 1: perpetual lineup, which means law enforcement offices across the US

670
00:36:42,560 --> 00:36:45,520
Speaker 1: can scan their photos and use unregulated software to track

671
00:36:45,600 --> 00:36:48,400
Speaker 1: law abiding citizens in government data sets. So there's a

672
00:36:48,480 --> 00:36:51,200
Speaker 1: very good chance they're saying nearly over half of Americans

673
00:36:51,239 --> 00:36:54,560
Speaker 1: faces are already in a database anyway, So and then

674
00:36:54,600 --> 00:36:57,560
Speaker 1: our phones are recording us whenever they want to be. Yeah,

675
00:36:57,560 --> 00:37:00,640
Speaker 1: I think exactly. And if you have an Amazon Echo,

676
00:37:00,960 --> 00:37:03,880
Speaker 1: who knows you're already broadcasting to the CIA or whatever however,

677
00:37:03,920 --> 00:37:08,040
Speaker 1: whatever conspiracy you want the voice voice recognition, Yeah, it

678
00:37:08,120 --> 00:37:10,279
Speaker 1: really well, Thank god, we can trust the people in

679
00:37:10,360 --> 00:37:15,879
Speaker 1: charge not to change the law for their benefits, Mark

680
00:37:15,960 --> 00:37:19,319
Speaker 1: zucker But Mark Zuckerberg will figure it out for us. Yeah,

681
00:37:19,360 --> 00:37:21,960
Speaker 1: my phone makes me tell it my deepest, darkest secrets

682
00:37:22,000 --> 00:37:24,640
Speaker 1: before to un Yeah, but it has to be a

683
00:37:24,680 --> 00:37:29,520
Speaker 1: new one every time. Uh yeah, And I mean China

684
00:37:29,800 --> 00:37:33,960
Speaker 1: is basically at the forefront of facial recognition technology because

685
00:37:34,120 --> 00:37:39,959
Speaker 1: they have you know, because they're government is the Chinese government. There,

686
00:37:40,360 --> 00:37:44,719
Speaker 1: they have every citizens face in a database. Uh. And

687
00:37:44,920 --> 00:37:49,200
Speaker 1: they've been able to use that enormous database of faces too,

688
00:37:49,840 --> 00:37:53,880
Speaker 1: you know. Just it's basically feeding data into an algorithm.

689
00:37:53,920 --> 00:37:58,160
Speaker 1: So there algorithm is way way more data than errors does.

690
00:37:58,840 --> 00:38:02,320
Speaker 1: So this is potentially Google finding a way to catch

691
00:38:02,400 --> 00:38:04,319
Speaker 1: up and the A C L you also mentioned like

692
00:38:04,360 --> 00:38:08,240
Speaker 1: in the US, they're like major police departments here already

693
00:38:08,239 --> 00:38:11,480
Speaker 1: have this real time face recognition technology that you know,

694
00:38:11,560 --> 00:38:14,960
Speaker 1: enables surveillance cameras to like scan faces of pedestrians walking

695
00:38:15,000 --> 00:38:17,880
Speaker 1: down the street. And you know in Maryland apparently police

696
00:38:17,880 --> 00:38:20,000
Speaker 1: have been using the software to I D people and

697
00:38:20,040 --> 00:38:23,319
Speaker 1: protest photos and match them to people with warrants. So

698
00:38:23,480 --> 00:38:27,120
Speaker 1: there's your black mirror. So I mean, I guess just

699
00:38:27,160 --> 00:38:28,560
Speaker 1: have fun with the Google app. I mean, I think

700
00:38:28,600 --> 00:38:32,480
Speaker 1: the jig is up. I think, yeah, you already have

701
00:38:32,480 --> 00:38:35,520
Speaker 1: a Facebook, so you've already given up your face. I

702
00:38:35,560 --> 00:38:37,880
Speaker 1: was a little creeped up by how much I looked

703
00:38:37,920 --> 00:38:41,560
Speaker 1: like one of the like paintings. Yeah. Yeah, it was

704
00:38:41,600 --> 00:38:44,360
Speaker 1: like everything was off. Like it was like like my

705
00:38:44,600 --> 00:38:47,719
Speaker 1: the coloring wasn't close or anything. But then like just

706
00:38:47,840 --> 00:38:50,120
Speaker 1: like in the face place, it was like my face

707
00:38:50,200 --> 00:38:56,960
Speaker 1: had been just pasted on big ryers, chubby body. You know,

708
00:38:57,040 --> 00:38:59,040
Speaker 1: if you make a different face. I've played with this

709
00:38:59,120 --> 00:39:01,840
Speaker 1: a little bit. If you make a different face, an expression,

710
00:39:02,000 --> 00:39:05,759
Speaker 1: or even the angle, you'll get something completely different, completely different. Yeah,

711
00:39:05,800 --> 00:39:08,120
Speaker 1: we'll have to do the face app challenge, you see,

712
00:39:08,160 --> 00:39:12,120
Speaker 1: if I can. I also wonder how racist. It is

713
00:39:12,239 --> 00:39:15,000
Speaker 1: because my Korean wife did it and it was just

714
00:39:15,040 --> 00:39:18,239
Speaker 1: like a random Korean woman. I was just like, it's

715
00:39:18,320 --> 00:39:21,399
Speaker 1: not anything like. Yeah, there was also reading for people

716
00:39:21,440 --> 00:39:23,520
Speaker 1: of color. There's not that many people like there aren't

717
00:39:23,520 --> 00:39:26,520
Speaker 1: that many art pieces based on classical art or portraits

718
00:39:26,520 --> 00:39:28,640
Speaker 1: of like people of color. That's why I feel like, well,

719
00:39:28,680 --> 00:39:32,719
Speaker 1: they couldn't just use paint on anybody. Yeah right, yeah,

720
00:39:32,760 --> 00:39:34,400
Speaker 1: so that's why I'm like, maybe that's why I was

721
00:39:34,440 --> 00:39:36,279
Speaker 1: Martin Luther King because it was like one of eight

722
00:39:36,560 --> 00:39:40,600
Speaker 1: things African American in there. And yeah, the other ones

723
00:39:40,640 --> 00:39:42,120
Speaker 1: there wore a couple of spot on ones. I think

724
00:39:42,120 --> 00:39:44,160
Speaker 1: one was like a like a weird guy in opium

725
00:39:44,200 --> 00:39:47,960
Speaker 1: ting like whatever. It's still working on it. Yeah, it's

726
00:39:47,960 --> 00:39:50,400
Speaker 1: it's again, it's not perfect. All right, we're gonna take

727
00:39:50,440 --> 00:40:01,440
Speaker 1: a quick break. We will be right back and we're back.

728
00:40:01,960 --> 00:40:04,799
Speaker 1: Um so, yeah, as we mentioned yesterday, there was a

729
00:40:04,840 --> 00:40:09,200
Speaker 1: school shooting in Kentucky. Um it was in Marshall, Kentucky. Yeah,

730
00:40:09,239 --> 00:40:11,400
Speaker 1: I was a student. He killed I think two people

731
00:40:11,440 --> 00:40:14,560
Speaker 1: and injured another seventeen and has been taken into custoday.

732
00:40:14,560 --> 00:40:17,759
Speaker 1: A fifteen year old. Um, let me guess somebody got

733
00:40:17,760 --> 00:40:20,520
Speaker 1: a gun that shouldn't have one. Guess I'm not sure

734
00:40:20,560 --> 00:40:22,319
Speaker 1: how he got the body. I was a handgun, so

735
00:40:22,400 --> 00:40:24,319
Speaker 1: I mean imagine if he had gone in with like

736
00:40:24,360 --> 00:40:26,280
Speaker 1: one of these assault rifles, it could have been probably

737
00:40:26,280 --> 00:40:28,520
Speaker 1: a lot worse. If you read about so this is

738
00:40:28,560 --> 00:40:32,560
Speaker 1: eleven shootings just this month, this first month. We're not

739
00:40:32,600 --> 00:40:35,400
Speaker 1: even out of the month yet. If you read about

740
00:40:35,440 --> 00:40:38,040
Speaker 1: a place where some of this stuff is going on

741
00:40:38,239 --> 00:40:40,879
Speaker 1: that we're reading about every day, from the top of

742
00:40:40,920 --> 00:40:46,080
Speaker 1: our government to these things, would you come here, You

743
00:40:46,080 --> 00:40:48,800
Speaker 1: would say, this place is it should hole you wouldn't

744
00:40:48,800 --> 00:40:50,680
Speaker 1: want to live in. And it's true too. I mean

745
00:40:50,680 --> 00:40:53,080
Speaker 1: you can tell because even like you know, even tourism

746
00:40:53,160 --> 00:40:55,759
Speaker 1: is is on the decline in this country too. So

747
00:40:56,320 --> 00:40:59,560
Speaker 1: but let's not admit that anything's wrong. No, no, no, no,

748
00:40:59,600 --> 00:41:03,319
Speaker 1: it's the FBI's line that's the problem. So yeah, I

749
00:41:03,320 --> 00:41:06,560
Speaker 1: grew up being told like, just be thankfully you live

750
00:41:06,600 --> 00:41:09,120
Speaker 1: in our country. Compared to other countries, this place is

751
00:41:09,160 --> 00:41:13,480
Speaker 1: like the best, the best it's ever been anywhere. And yeah,

752
00:41:13,560 --> 00:41:17,160
Speaker 1: I'm starting to only just starting, by the way, that's

753
00:41:17,200 --> 00:41:20,640
Speaker 1: true too, both things are. But one of the things

754
00:41:20,680 --> 00:41:22,719
Speaker 1: that we always thought made it the best is that

755
00:41:22,800 --> 00:41:26,280
Speaker 1: we could criticize it and try to make it even

756
00:41:26,360 --> 00:41:30,839
Speaker 1: better towards a more perfect union. Right, what are we doing?

757
00:41:31,360 --> 00:41:37,239
Speaker 1: Not that? In more upbeat news? Uh, Larry Naser, I

758
00:41:37,280 --> 00:41:39,360
Speaker 1: guess this is kind of a beat that Larry Naser,

759
00:41:39,880 --> 00:41:44,239
Speaker 1: the doctor in the Michigan sexual abuse case with the

760
00:41:44,320 --> 00:41:48,440
Speaker 1: U S gymnastics team and just Michigan State, and he

761
00:41:48,800 --> 00:41:50,840
Speaker 1: was the team doctor for a number of different people,

762
00:41:50,960 --> 00:41:57,799
Speaker 1: was sentenced. They after testimony from a number over a

763
00:41:57,840 --> 00:42:01,040
Speaker 1: hundred women, I believe, Yeah, I don't know it was testimony,

764
00:42:01,080 --> 00:42:04,640
Speaker 1: but they were victims statements. Victims statements from over a

765
00:42:04,719 --> 00:42:07,880
Speaker 1: hundred women. Uh. And he was sentenced to girls by

766
00:42:07,920 --> 00:42:11,680
Speaker 1: the way. Yeah, and he was sentenced to anywhere from

767
00:42:11,719 --> 00:42:15,000
Speaker 1: I think forty to a hundred and seventy five years. Yeah.

768
00:42:15,600 --> 00:42:17,239
Speaker 1: How about the people who knew about it and let

769
00:42:17,280 --> 00:42:20,200
Speaker 1: him keep going. Yeah them, Yeah exactly, because they were

770
00:42:20,239 --> 00:42:23,799
Speaker 1: still a lot of people were complicit in that. Yep. Yeah, yeah,

771
00:42:23,800 --> 00:42:25,520
Speaker 1: we'll see if yeah, it'll turn on them. But yeah,

772
00:42:25,560 --> 00:42:28,920
Speaker 1: that that whole sentencing was it was pretty dramatic, like

773
00:42:29,000 --> 00:42:32,400
Speaker 1: the judge was really was going to Like normally they

774
00:42:32,400 --> 00:42:34,640
Speaker 1: would post the letter so people can see what the

775
00:42:34,640 --> 00:42:37,640
Speaker 1: defendant wrote. In there like sort of their letter in

776
00:42:37,760 --> 00:42:40,359
Speaker 1: the sentencing phase, and she was like, I'm not even

777
00:42:40,360 --> 00:42:43,399
Speaker 1: going to post this because it's just garbage, and I'm

778
00:42:43,400 --> 00:42:45,600
Speaker 1: not going to re victimize these women and and and

779
00:42:45,719 --> 00:42:48,160
Speaker 1: girls for what you did. Because he was saying stuff like,

780
00:42:48,400 --> 00:42:51,399
Speaker 1: you know, the judges being unfair and hysterical, and at

781
00:42:51,400 --> 00:42:54,640
Speaker 1: the end he was like, I was manipulated into, uh

782
00:42:55,000 --> 00:42:58,399
Speaker 1: admitting that these that the acts I committee weren't because

783
00:42:58,400 --> 00:43:00,880
Speaker 1: of a medical in nature, they were actual or whatever,

784
00:43:01,200 --> 00:43:02,960
Speaker 1: and like you could hear the gallery just laugh that,

785
00:43:03,080 --> 00:43:05,520
Speaker 1: like he could somehow be saying that he was manipulated.

786
00:43:05,760 --> 00:43:08,480
Speaker 1: He also used the phrase, hell, hath no fury like

787
00:43:08,520 --> 00:43:15,239
Speaker 1: a woman's scorn, which is just unbelievable. Yeah, is it

788
00:43:15,360 --> 00:43:19,759
Speaker 1: that unbelievable. He's a monster, so we can't be surprised

789
00:43:19,800 --> 00:43:22,759
Speaker 1: when the monsters also a lot. That's very true. I

790
00:43:22,760 --> 00:43:24,920
Speaker 1: guess it's just still very shocking, you know, Like, is

791
00:43:24,960 --> 00:43:28,479
Speaker 1: that because you're already confronted with the horrible, horrible ship

792
00:43:28,560 --> 00:43:31,480
Speaker 1: that he's done to all these these people, and then

793
00:43:31,520 --> 00:43:33,319
Speaker 1: on top of it to have that added level of

794
00:43:33,560 --> 00:43:35,920
Speaker 1: you know, denial delusion. But I guess you know that

795
00:43:36,520 --> 00:43:38,319
Speaker 1: as we've seen people do whatever in the name of

796
00:43:38,320 --> 00:43:44,080
Speaker 1: self preservation. Yeah, but shout out to Judge Rosemary Akulina,

797
00:43:44,920 --> 00:43:48,440
Speaker 1: who you know, I feel like, didn't let this just

798
00:43:48,600 --> 00:43:51,359
Speaker 1: kind of get swept under the road and kind of

799
00:43:51,400 --> 00:43:55,160
Speaker 1: made made sure that this was a spectacle and that

800
00:43:55,400 --> 00:43:58,719
Speaker 1: you know, we were asking the questions like how did

801
00:43:58,760 --> 00:44:02,120
Speaker 1: people let this happen? Uh? But those people have to

802
00:44:02,160 --> 00:44:05,800
Speaker 1: be held accountable to otherwise it will keep happening. Especially

803
00:44:05,840 --> 00:44:07,560
Speaker 1: when you read about like the Cooly Ranch and like

804
00:44:07,719 --> 00:44:10,839
Speaker 1: how like where it's situated and like with horrible cell

805
00:44:10,960 --> 00:44:14,160
Speaker 1: service and there's like nothing near it. It's just sounds

806
00:44:14,360 --> 00:44:18,240
Speaker 1: just awful. I remember when I was a kid watching

807
00:44:18,520 --> 00:44:22,680
Speaker 1: a like movie of the Week about Nadia Comanich and

808
00:44:23,200 --> 00:44:27,080
Speaker 1: like she was being essentially abused by Bella Caroli, like

809
00:44:27,160 --> 00:44:31,080
Speaker 1: he was just so brutal to her. And then yeah,

810
00:44:31,200 --> 00:44:33,399
Speaker 1: that guy ended up like being the coach of all

811
00:44:33,400 --> 00:44:35,920
Speaker 1: these other of all these other young women, and we

812
00:44:35,960 --> 00:44:40,080
Speaker 1: were like, ah, tough love he does, he does good work. Well.

813
00:44:40,160 --> 00:44:42,680
Speaker 1: I think the spotlight will be making its way towards

814
00:44:42,760 --> 00:44:45,799
Speaker 1: these other people pretty soon, or hopefully we can hold

815
00:44:45,800 --> 00:44:52,200
Speaker 1: these people accountable to. Amazon continues to devour stores, toys

816
00:44:52,280 --> 00:44:56,720
Speaker 1: rs is closing. I think they declared bankruptcy a couple

817
00:44:57,280 --> 00:45:00,480
Speaker 1: months back, and now they're closing a hundred and eight stores,

818
00:45:01,080 --> 00:45:03,759
Speaker 1: like a fifth of their stores, because yeah, I mean,

819
00:45:04,040 --> 00:45:06,200
Speaker 1: young people don't like to go into they don't like

820
00:45:06,239 --> 00:45:08,680
Speaker 1: to go out in public anymore, so we buy everything online.

821
00:45:09,120 --> 00:45:11,120
Speaker 1: And I think, yeah, they've they've noticed, especially for toy

822
00:45:11,200 --> 00:45:14,000
Speaker 1: purchases like Amazon, a lot of people just it's easier

823
00:45:14,000 --> 00:45:16,040
Speaker 1: to order it because kids already know what they want

824
00:45:16,040 --> 00:45:18,160
Speaker 1: and you can just order it. And yet the Amazon

825
00:45:18,440 --> 00:45:22,640
Speaker 1: physical store is crowded. Yeah, exactly. I don't understand that

826
00:45:22,680 --> 00:45:24,799
Speaker 1: Amazon Go store, which is funny. Someone on Twitter showed

827
00:45:24,880 --> 00:45:26,480
Speaker 1: us that we were talking about that earlier, about you

828
00:45:26,520 --> 00:45:28,040
Speaker 1: just go in, fill up your bag and walk out

829
00:45:28,040 --> 00:45:31,279
Speaker 1: and your charged or whatever, and now I was saying, like,

830
00:45:31,400 --> 00:45:33,880
Speaker 1: that's probably gonna be the easiest place to shop, and

831
00:45:33,920 --> 00:45:35,719
Speaker 1: then it turns out like one of the journalists who

832
00:45:35,800 --> 00:45:38,160
Speaker 1: went and gone in there, she went in, filled up

833
00:45:38,160 --> 00:45:41,080
Speaker 1: her bag and walked out, assuming that everything would be charged,

834
00:45:41,160 --> 00:45:43,000
Speaker 1: and like they forgot to charge her for a couple

835
00:45:43,000 --> 00:45:46,040
Speaker 1: of things and it wasn't quite perfect. So someone inevitably

836
00:45:46,280 --> 00:45:50,000
Speaker 1: kind of shoplifted. But we're going to continue paying attention

837
00:45:50,000 --> 00:45:53,000
Speaker 1: to this and give you the hack for how to

838
00:45:53,080 --> 00:45:57,000
Speaker 1: steal from Amazon, which is I think I think a

839
00:45:57,840 --> 00:46:03,120
Speaker 1: just thing to do, probably take Amazon down. I just

840
00:46:03,160 --> 00:46:07,759
Speaker 1: thought of the Giraffe said, you know, they're supposed to

841
00:46:07,760 --> 00:46:13,000
Speaker 1: be monopoly laws that protect against Amazon doing what Amazon

842
00:46:13,640 --> 00:46:18,880
Speaker 1: is doing. But you know, because of the benevolent feelings

843
00:46:18,960 --> 00:46:24,000
Speaker 1: towards tech companies among the public, and because of you know,

844
00:46:24,080 --> 00:46:29,000
Speaker 1: some pretty favorable interpretations of current laws, and you know

845
00:46:29,080 --> 00:46:33,800
Speaker 1: how they apply to these giant tech companies like Amazon

846
00:46:34,040 --> 00:46:39,000
Speaker 1: and Google and Facebook. Uh, you know, they they've been protected.

847
00:46:39,040 --> 00:46:43,840
Speaker 1: But presumably that's going to start changing in the in

848
00:46:43,880 --> 00:46:46,359
Speaker 1: the next few years. I mean it needs to. We

849
00:46:46,400 --> 00:46:51,000
Speaker 1: shall see. UM. And finally, we wanted to talk about

850
00:46:51,040 --> 00:46:56,680
Speaker 1: Mattress Firm, which is a company I think I had

851
00:46:56,719 --> 00:47:00,320
Speaker 1: seen a few of these signs here and there. Stress

852
00:47:00,320 --> 00:47:05,720
Speaker 1: Firm is a mattress store that, as our engineer UH

853
00:47:05,800 --> 00:47:09,320
Speaker 1: and producer Nick Stump called it or described it, it's

854
00:47:09,800 --> 00:47:14,520
Speaker 1: you see them everywhere and you've never seen anybody inside one. Um,

855
00:47:14,760 --> 00:47:18,040
Speaker 1: which I know of a number of stores like that,

856
00:47:18,280 --> 00:47:21,279
Speaker 1: but I wasn't at least overly familiar with Mattress Firm.

857
00:47:21,320 --> 00:47:25,799
Speaker 1: But apparently there was a big conspiracy theory launched on

858
00:47:25,920 --> 00:47:30,919
Speaker 1: Reddit uh earlier this week, saying that they must be

859
00:47:31,040 --> 00:47:35,400
Speaker 1: like a a mafia shell company or something, because it

860
00:47:35,520 --> 00:47:40,359
Speaker 1: was just they were omnipresent and yet nobody had ever,

861
00:47:41,120 --> 00:47:45,440
Speaker 1: uh you know, spent money in one before. And the

862
00:47:45,480 --> 00:47:49,320
Speaker 1: concentration of those kinds of stores like Heavy is weird

863
00:47:49,480 --> 00:47:51,920
Speaker 1: because it's not like you buy mattresses its frequently you

864
00:47:51,960 --> 00:47:54,480
Speaker 1: do like paper towels or something. It's like a one

865
00:47:54,520 --> 00:47:57,160
Speaker 1: in every you would like eight to ten year purchase.

866
00:47:57,400 --> 00:48:00,560
Speaker 1: Oh miles, No, what you have to every couple of

867
00:48:00,560 --> 00:48:05,480
Speaker 1: months you got to burn through? Yeah, uh, but you know,

868
00:48:05,520 --> 00:48:10,680
Speaker 1: the company responded with a charming meme. It was like,

869
00:48:10,880 --> 00:48:14,200
Speaker 1: you know, uh, Nick young face type thing like what

870
00:48:14,800 --> 00:48:19,760
Speaker 1: who us? And everybody laughed it off. But Nick pointed

871
00:48:19,760 --> 00:48:24,600
Speaker 1: out that the retailer is owned by South African retailer Steinhoff,

872
00:48:25,080 --> 00:48:27,960
Speaker 1: and they came under scrutiny at the end of two

873
00:48:27,960 --> 00:48:33,680
Speaker 1: thousand seventeen after it stopped publishing postponed publishing its full

874
00:48:33,760 --> 00:48:39,359
Speaker 1: year accounts UH citing accounting irregularities. So they do have

875
00:48:39,440 --> 00:48:44,200
Speaker 1: some shady things happening. Uh. Their stock price has started tanking,

876
00:48:44,280 --> 00:48:47,840
Speaker 1: so it's not just like Reddit was pulling this out

877
00:48:47,840 --> 00:48:51,960
Speaker 1: of nowhere. They do seem to be a somewhat shady company. Um,

878
00:48:52,000 --> 00:48:56,520
Speaker 1: but I'm curious to hear from our listeners about any

879
00:48:56,560 --> 00:48:59,920
Speaker 1: examples of this. But what are some companies or you know,

880
00:49:00,080 --> 00:49:03,759
Speaker 1: businesses that you feel like must be mafia shell companies

881
00:49:04,040 --> 00:49:10,799
Speaker 1: because uh government? Um? All right, well this has been

882
00:49:11,000 --> 00:49:14,759
Speaker 1: a blast, Phil, Thank you so much. I thank you

883
00:49:14,920 --> 00:49:18,120
Speaker 1: a lot of fun. I never get political where this

884
00:49:18,239 --> 00:49:21,040
Speaker 1: is a you know, I thought my show was was

885
00:49:21,600 --> 00:49:25,840
Speaker 1: just a humanistic show, and now suddenly it's a political

886
00:49:25,880 --> 00:49:30,239
Speaker 1: statement to embrace other people in other cultures. Really, Yeah,

887
00:49:30,360 --> 00:49:33,120
Speaker 1: that's just showing your liberal bias. That's it. Yeah, you're

888
00:49:33,200 --> 00:49:36,319
Speaker 1: human biace. So what does this show again? And where

889
00:49:36,360 --> 00:49:39,440
Speaker 1: can people find it? Somebody feed Phil on Netflix. I've

890
00:49:39,480 --> 00:49:43,000
Speaker 1: heard of this Netflix. Yeah, Miles, where can people find? You?

891
00:49:43,160 --> 00:49:46,080
Speaker 1: Find me on Twitter and Instagram at Miles of great Phil.

892
00:49:46,120 --> 00:49:48,760
Speaker 1: Do you have a social media Phil Rosenthal on Twitter,

893
00:49:48,800 --> 00:49:52,759
Speaker 1: Phil dot Rosenthal on Instagram something, Phil Rosenhaal on the

894
00:49:53,400 --> 00:49:57,120
Speaker 1: All right, and you can find me at Jack Undersquore

895
00:49:57,160 --> 00:50:00,920
Speaker 1: O'Brien on Twitter. You can find us at the Daily

896
00:50:01,000 --> 00:50:04,680
Speaker 1: Zeitgeist on Instagram. We're at Daily Zeitgeist on Twitter, we

897
00:50:04,760 --> 00:50:07,400
Speaker 1: have Facebook fan page just search daily like guys, and

898
00:50:07,600 --> 00:50:10,160
Speaker 1: we have a website daily zeys dot com where you

899
00:50:10,200 --> 00:50:14,000
Speaker 1: can find our episodes and footnote footnote for each episode

900
00:50:14,000 --> 00:50:17,240
Speaker 1: where we link off to the sources for the stuff

901
00:50:17,400 --> 00:50:21,400
Speaker 1: we're talking about. Uh, that's gonna do it for today's show.

902
00:50:22,120 --> 00:50:24,920
Speaker 1: We'll be back tomorrow because it is a daily podcast,

903
00:50:25,000 --> 00:50:25,600
Speaker 1: Talk you guys on