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<v Speaker 1>This is Gary and Shannon and you're listening to KFI

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<v Speaker 1>A M six forty, the Gary and Shannon Show on

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<v Speaker 1>demand on the iHeartRadio app.

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<v Speaker 2>Luigi Mangioni on Twitter on x This is a master's

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<v Speaker 2>and a bachelor's in computer science from Penn and his

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<v Speaker 2>Twitter profile is just a mostly retweets of other people

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<v Speaker 2>are reposting from other people's posts.

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<v Speaker 1>Look how similar this mis Maryland State senator looks to him.

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<v Speaker 3>Wow, Yeah, that's got to be a family member.

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<v Speaker 4>That those are some strong, strong.

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<v Speaker 3>Italian genes eyebrows.

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<v Speaker 2>And one of the things that he talks about in

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<v Speaker 2>some of these posts I thought was pretty interesting because

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<v Speaker 2>he was talking about, for example, this would have been

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<v Speaker 2>back in April, an ongoing discussion about the population of

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<v Speaker 2>the native population of Japan and what's going wrong with it,

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<v Speaker 2>and that Japanese Japanese politicians and culturalists were concerned that

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<v Speaker 2>the Japanese culture was dying because native Japanese the population

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<v Speaker 2>was falling. And he says the solution to falling birth

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<v Speaker 2>rates isn't immigration, it's cultural. This is Luigi Maanngioni writing,

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<v Speaker 2>and he was talking about the encouragement of natural human

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<v Speaker 2>interaction whether it's sex, physical, fitness, spirituality, et cetera, that

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<v Speaker 2>we needed to in Japan's case, in particular, do away

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<v Speaker 2>with cultural substitutes for the physical human interaction that takes place.

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<v Speaker 2>So do away with the porn shops, do away with

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<v Speaker 2>the sex toys, do away with weird restaurants where guys

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<v Speaker 2>pay girls to dress in anime costumes and dance for them,

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<v Speaker 2>and actually encourage just simple old fashioned matchmaking conversations, relationships.

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<v Speaker 1>That may be why he wanted to study AI. Yeah,

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<v Speaker 1>talked about how he would he would frequently quote or

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<v Speaker 1>retweet or what have you. Jonathan Heite, who was, of

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<v Speaker 1>course the author of all of the books we've referenced

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<v Speaker 1>several times about AI and human connection, the generation was

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<v Speaker 1>the one that generation.

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<v Speaker 2>There is also a question, obviously, if this was targeting

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<v Speaker 2>a guy who is high up in the insurance industry.

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<v Speaker 2>Was that person was Brian Thompson simply a symbol of

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<v Speaker 2>the insurance industry or was it something specific to Brian Thompson,

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<v Speaker 2>something specific to United Healthcare in I mean Trinia, something

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<v Speaker 2>specific to United Healthcare, And a question would be did

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<v Speaker 2>he Luigi Mangioni have some connection to United Healthcare was

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<v Speaker 2>he denied care?

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<v Speaker 4>Was his a family member denied care? Something like that.

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<v Speaker 3>I don't know. If it was personal, I don't think so.

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<v Speaker 2>I mean, on his Twitter profile there are a few

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<v Speaker 2>pictures and one of them is an X ray. It

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<v Speaker 2>appears to be of a back with multiple screws in

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<v Speaker 2>a spinal column repair or whatever, and there's no description

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<v Speaker 2>of it or explanation as to who it is. But

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<v Speaker 2>if that's him, and he's had ongoing back problems and

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<v Speaker 2>may have been a member of United Healthcare insurance, then

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<v Speaker 2>that would that at least gives us, you know, a potential.

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<v Speaker 1>But he comes from money. Though people with money don't

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<v Speaker 1>have insurance problems, do they?

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<v Speaker 4>They don't.

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<v Speaker 2>Well, I mean, they could still have problems, but it

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<v Speaker 2>wouldn't necessarily be as devastating as it would be if

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<v Speaker 2>someone's going to end up in two hundred and fifty

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<v Speaker 2>thousand dollars worth of medical debt.

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<v Speaker 4>So I don't know. Again.

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<v Speaker 2>This Luigi Manngioni is the twenty six year old who

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<v Speaker 2>has been caught in Altuna, Pennsylvania. He has, according to Fox,

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<v Speaker 2>been arrested on gun charges. They said that they did

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<v Speaker 2>find him with a ghost gun capable of firing a

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<v Speaker 2>nine millimeters round, But they have not yet said if

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<v Speaker 2>that is the gun that they think he used for

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<v Speaker 2>the murder or if this is just one he had.

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<v Speaker 2>We do know that dive teams were in Central Park

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<v Speaker 2>today still looking for a murder weapon as of this morning,

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<v Speaker 2>whether or not it means that they have given up

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<v Speaker 2>that search because they believe this is the weapon or not.

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<v Speaker 2>But one of the things that they found, of course,

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<v Speaker 2>was this handwritten manifesto, if you will, a two page

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<v Speaker 2>note that reportedly rails against the healthcare industry. They said

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<v Speaker 2>they also found a mask that would match one that

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<v Speaker 2>they've seen in the pictures that were posted by the NYPD,

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<v Speaker 2>and that this guy didn't put up any sort of

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<v Speaker 2>a fight when they picked him up. So all of

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<v Speaker 2>this they're waiting for the NYPD detect us to show

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<v Speaker 2>up in I'll Tuna, Pennsylvania so that they can sit

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<v Speaker 2>and interview with this guy.

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<v Speaker 1>So on this other social media site where you put

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<v Speaker 1>your description of yourself basically with emojis, it is a

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<v Speaker 1>computer happy face emoji, right, so you and then it's

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<v Speaker 1>like a bunch of weight stuff, weightlifting books, happy nerd face,

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<v Speaker 1>smiley emoji likes books, weightlifting, computers, and then there's also

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<v Speaker 1>a mushroom next to a brain, wondering if he's into all.

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<v Speaker 4>Of those microdosing.

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<v Speaker 1>Micro dosing situations are macro dosing also a little yin

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<v Speaker 1>yang as well. So I don't like learning about people

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<v Speaker 1>through their emoji descriptions of themselves.

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<v Speaker 3>But here where here is where we are in twenty

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<v Speaker 3>twenty four.

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<v Speaker 2>Yeah, it's very interesting to see this guy's personality play

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<v Speaker 2>out in just sort of this social media breadcrumb trail

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<v Speaker 2>that we have of him. And again, most of what

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<v Speaker 2>we see on his Twitter posts are reposts of other people,

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<v Speaker 2>and not much, if anything about the insurance industry itself,

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<v Speaker 2>other than there's a one reference to a brain injury unit.

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<v Speaker 1>But other than that, his last reposts on Twitter were

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<v Speaker 1>a podcast about smartphones and social media, the impact on

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<v Speaker 1>mental health and brain plasticity with Jonathan Height, and then

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<v Speaker 1>the one right before that was Peter Thiel on many

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<v Speaker 1>great startups being run by people suffering from aspergers and

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<v Speaker 1>how we should view this as an indictment of our society.

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<v Speaker 4>Wow.

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<v Speaker 2>Interesting, we'll find out more about this again. The New

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<v Speaker 2>York City Mayor, the New York Police Commissioner, all came

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<v Speaker 2>out and mentioned this twenty six year old Luigi Mangione,

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<v Speaker 2>arrested in Altoona, Pennsylvania, believed to be the very strong

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<v Speaker 2>suspect in the or a strong person of interest is

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<v Speaker 2>the words that Mayor Eric Adams used in connection with

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<v Speaker 2>that murder of Brian Thompson, the United Healthcare CEO. So

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<v Speaker 2>we get more information, we'll definitely bring it to you

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<v Speaker 2>as we get to we get closer through the day.

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<v Speaker 1>This is going to be a fascinating trial because of

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<v Speaker 1>the anti insurance company sentiment in this country. There's been

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<v Speaker 1>so many people touched by healthcare companies, screwing somebody over

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<v Speaker 1>in their family at some point or knowing somebody.

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<v Speaker 3>I think everyone's been touched.

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<v Speaker 1>By a vicious health care decision, whether it's denying coverage

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<v Speaker 1>or you know what do they call them outstanding conditions?

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<v Speaker 4>What is it? You underground pre existing.

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<v Speaker 1>Pre existing conditions being denied coverage. Because of that, it's

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<v Speaker 1>going to be interesting to get twelve jurors that don't

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<v Speaker 1>have any connection to healthcare, insurance, the industry being on

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<v Speaker 1>the bad side of one of those situations.

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<v Speaker 5>Yeah.

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<v Speaker 2>Again, the twenty six year old who's been picked up

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<v Speaker 2>A guy named Luigi Mangioni in Altoona, Pennsylvania apparently made

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<v Speaker 2>his where they are mad his way there after the

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<v Speaker 2>murder of Brian Thompson. The United Healthcare CEO Police Commissioner

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<v Speaker 2>said they arrested him. He was carrying a weapon consistent

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<v Speaker 2>with the gun used in the killing of Brian Thompson,

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<v Speaker 2>and the NYPD Chief of Detectives said that Luigi Mangioni

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<v Speaker 2>was taken into custody. That Mangioni was taken after police

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<v Speaker 2>got a tip that he had been spotted at the

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<v Speaker 2>McDonald's there in Altoona, PA at twelve thirty. We're going

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<v Speaker 2>to get more into as we gather more and more information,

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<v Speaker 2>We're going to get into some of the details about

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<v Speaker 2>who this guy was, even just through his own words

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<v Speaker 2>and his own posts on social media and thinks he's

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<v Speaker 2>got a relatively healthy I mean, he's twenty six years old.

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<v Speaker 2>Of course he does profile on social media, so we

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<v Speaker 2>can glean some information about him from that.

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<v Speaker 1>Very smart guy went to the best schools. It's obvious

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<v Speaker 1>by it's not crazy smart. It may have taken a

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<v Speaker 1>turn into crazy. He hasn't, at least on Twitter, hasn't

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<v Speaker 1>tweeted things for a while. But you know, I'm talking

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<v Speaker 1>about that that the movie and the book, the John

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<v Speaker 1>Grisham book Runaway Jury, come to mind with John Cusack

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<v Speaker 1>in the theater production of it, of people that are

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<v Speaker 1>so tied emotionally to the tobacco industry or the gun

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<v Speaker 1>lobby or the healthcare industry, Like how how emotional and

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<v Speaker 1>personal those topics are where you get clouded, you know,

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<v Speaker 1>or you or you try to infiltrate the jury. Yeah,

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<v Speaker 1>you want to get on that jury and there's a

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<v Speaker 1>lot of money at stake.

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<v Speaker 3>You want to deliver justice. Yeah, there is.

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<v Speaker 4>All right, let's quickly jump into swamp.

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<v Speaker 3>Watch.

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<v Speaker 5>Swamp is horrible.

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<v Speaker 3>The government doesn't work.

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<v Speaker 2>Man, make it like a reality TV show, Bad Noos.

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<v Speaker 3>Always a pleasure to be anywhere from.

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<v Speaker 4>Washington, d C.

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<v Speaker 3>Hey, Joe.

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<v Speaker 2>A town hall too clearly built on a swamp and

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<v Speaker 2>in so many ways still a swamp.

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<v Speaker 4>I have to watch make he.

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<v Speaker 3>Said, drained the swamp.

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<v Speaker 4>I said, oh, that's so he keep.

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<v Speaker 3>You know the thing.

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<v Speaker 2>Some interesting things took place in the Big Meet the

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<v Speaker 2>Press interview to President elect Trump gave yesterday. Kristin Walker

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<v Speaker 2>asked him about a lot of things. It was really

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<v Speaker 2>the lengthiest interview we've seen with Trump in a while.

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<v Speaker 2>It didn't generate as many headlines as I thought it would,

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<v Speaker 2>But among other things, he did say that he wants

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<v Speaker 2>to figure out something to do about the Dreamers. He says,

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<v Speaker 2>people have been brought to here at a very young age.

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<v Speaker 2>Many of these are middle aged people now they don't

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<v Speaker 2>even speak the language of their native country. Yes, we're

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<v Speaker 2>going to do something about that, and he says I

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<v Speaker 2>will work with Democrats on a plan and if we

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<v Speaker 2>can come up with it, but Democrats have made it

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<v Speaker 2>very difficult to do anything. But Republicans are very open

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<v Speaker 2>to the Dreamers. That even as he's talked about a

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<v Speaker 2>record number of deportations that he has said that he

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<v Speaker 2>wants to achieve early on, he also says he wants

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<v Speaker 2>to end birthright citizenship and says that any American citizen

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<v Speaker 2>that has family here illegally could be deported. Which it's

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<v Speaker 2>just this is again, this is bluster, this is his

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<v Speaker 2>he's throwing meat to the base. What we've seen him promise.

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<v Speaker 2>We saw this eight years ago, the things he promised

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<v Speaker 2>he would do versus what he actually came up with

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<v Speaker 2>and was able to do were very very different.

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<v Speaker 1>Laura Trump is stepping down as co chair of the RNC,

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<v Speaker 1>and it's gotten some people to whisper about is this

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<v Speaker 1>going to be.

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<v Speaker 3>The replacement for Marco Rubio?

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<v Speaker 4>Oh, that she would be appointed to the Senate.

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<v Speaker 3>Yeah, that is possible.

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<v Speaker 4>Of course.

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<v Speaker 2>Marco Rubio has been nominated to be that come the

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<v Speaker 2>next Secretary of State, and surprisingly he's gotten a lot

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<v Speaker 2>of support from Democrats. There were a lot of people

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<v Speaker 2>who came out and spoke very highly of Marco Rubio,

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<v Speaker 2>which I did not expect. I didn't know that he

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<v Speaker 2>had that reputation in the Senate necessarily.

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<v Speaker 1>Oh yeah, that's why I thought he was such a

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<v Speaker 1>good pick.

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<v Speaker 3>Yeah.

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<v Speaker 1>So she told the Associated Press that it was a

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<v Speaker 1>role she would seriously consider.

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<v Speaker 3>This was an interview published yesterday.

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<v Speaker 2>But again that goes to Ronda Santis, the one who

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<v Speaker 2>gets to make the final call on that, because the

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<v Speaker 2>governor of Florida.

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<v Speaker 1>But she has the backing of Elon Musk as well,

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<v Speaker 1>so there's money behind the push.

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<v Speaker 2>One of my favorite dramatic things that has gone on

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<v Speaker 2>in all of Congress is former House Speaker Kevin McCarthy

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<v Speaker 2>and his rivalry I guess you could say with now

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<v Speaker 2>former Congressman Matt Gates. In an interview yesterday, Kevin McCarthy

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<v Speaker 2>said no one thought that Matt gates nomination would pass,

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<v Speaker 2>and he said, I blame Matt Gates for lying to

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<v Speaker 2>the President about his ethics report. People know that that's

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<v Speaker 2>why he made the motion against me. People know these

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<v Speaker 2>guys absolutely hate each other. And I'll watch every moment

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<v Speaker 2>I can about these guys going after each other because

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<v Speaker 2>Kevin McCarthy is convinced Matt Gates is the dirt bag

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<v Speaker 2>that people suspect him of being. What that's what Kevin

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<v Speaker 2>McCarthy feels about Matt Gates, And Matt Gates says, well,

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<v Speaker 2>Kevin McCarthy's not Republican enough, or he's a Republican name

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<v Speaker 2>only or whatever.

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<v Speaker 4>That's the reason that he's not.

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<v Speaker 2>In Congress anymore. But Kevin McCarthy said he's not capable

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<v Speaker 2>of staying in Congress. He needed an excuse to resign

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<v Speaker 2>because the ethics report would have been done in a

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<v Speaker 2>couple of days.

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<v Speaker 4>And that's unfortunate.

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<v Speaker 2>But it is a better Congress today where they're going

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<v Speaker 2>forward without Matt Gates.

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<v Speaker 3>Sorry, I've gone down a hole, which one.

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<v Speaker 1>And you know how I get because I am now

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<v Speaker 1>literally wearing a tin hat every day. But I when

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<v Speaker 1>we were talking about the suspect in New York, this

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<v Speaker 1>Luigi Mangi, Luigi Mangione, great name. This is the guy

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<v Speaker 1>who took out the United Healthcare CEO in As soon

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<v Speaker 1>as I read that, he was interested in his Valedictorian

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<v Speaker 1>speed or what have you, and he went to Penn

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<v Speaker 1>to study artificial intelligence. And then what you read about

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<v Speaker 1>him talking about the dangers of artificial intelligence and lack

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<v Speaker 1>of human to human contact and things in Jonathan Hate's

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<v Speaker 1>book and all that. I was reminded by something that

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<v Speaker 1>came out after the CEO was shot and killed in

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<v Speaker 1>Midtown last week, and it was that the United Healthcare

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<v Speaker 1>had gotten a lot of press that had kind of

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<v Speaker 1>snowballed in the past year over them using AI to

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<v Speaker 1>deny claims like that's been the major knock on this guy,

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<v Speaker 1>and it's gotten a bunch of attention in the past year.

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<v Speaker 4>Well, that's not down a hole. That's just connecting dots.

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<v Speaker 3>Right or same kind of same kind of thing.

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<v Speaker 4>You have to be in the hole to connect those dots.

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<v Speaker 3>You know what I mean?

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<v Speaker 1>Yeah, what if he's just like this super smart warrior

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<v Speaker 1>against AI and wants more human connection and wanted to

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<v Speaker 1>make a statement about.

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<v Speaker 2>It, and that a I is now planting this evidence again.

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<v Speaker 2>It's like this show if we killed people if because

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<v Speaker 2>we not that we would, but we vouched for this

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<v Speaker 2>all the time. We feel very strongly.

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<v Speaker 1>About the lack of human to human interaction and what

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<v Speaker 1>it does to society.

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<v Speaker 4>Uh, you went to peanuts?

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<v Speaker 3>Is that the peanuts?

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<v Speaker 4>The peanut?

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<v Speaker 6>Hey, this has been in Kansas City and Shannon, how

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<v Speaker 6>dare you? That is a Kansas City institution? I got

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<v Speaker 6>the delicious wings that said I have definitely gotten food

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<v Speaker 6>poisoning there before.

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<v Speaker 1>Yeah, let's just say one of the people went back

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<v Speaker 1>to the hotel and went straight to the bathroom.

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<v Speaker 4>That's part of the allure, is it not.

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<v Speaker 2>I mean, there's a if there's a possibility that you

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<v Speaker 2>run the risk of you know, urgent.

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<v Speaker 1>It's the same reason we have a bacon wrapped hot

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<v Speaker 1>dog outside the stadium on our way home, because you

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<v Speaker 1>roll the dice, You roll the dice.

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<v Speaker 4>Guys. It's important to point out this is not a

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<v Speaker 4>gay song.

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<v Speaker 3>It is not it.

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<v Speaker 1>It was kind of adopted by the gay community.

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<v Speaker 3>Could one argue.

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<v Speaker 2>That's one way to put it. But and I guess

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<v Speaker 2>it's because maybe there was a lot of Mirando hookups

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<v Speaker 2>at YMCA's around the world, and was there.

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<v Speaker 3>I thought it was a place for children.

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<v Speaker 4>The YMCA is it not?

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<v Speaker 6>Well?

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<v Speaker 4>I mean, I know it's young Man's.

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<v Speaker 2>Christian Association, but I don't I don't know if it

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<v Speaker 2>meant like young child no Ah. I always assumed it

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<v Speaker 2>was a you know, an adult thing, just young adults.

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<v Speaker 3>Interesting kids. Oh.

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<v Speaker 1>Victor Willis, the writer of the song, has clarified that

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<v Speaker 1>the song is not gay themed. He says in a

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<v Speaker 1>Facebook post, there's nothing gay about it.

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<v Speaker 3>He says he's thrilled.

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<v Speaker 1>Donald Trump likes the song because he's made since Trump

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<v Speaker 1>started using it.

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<v Speaker 2>Which is funny because that's not the usual reaction anytime

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<v Speaker 2>a musician or a band finds out that one politician

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<v Speaker 2>or the other is using their their song as a

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<v Speaker 2>walk up song or part of their parties of the rallies,

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<v Speaker 2>they want to pull it. I don't support the even

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<v Speaker 2>though they're making money on it. I'm glad that Victor

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<v Speaker 2>at least understands where the money is coming from.

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<v Speaker 1>The way this is written is funny. He says, his

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<v Speaker 1>wife will start suing people who insist the song is

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<v Speaker 1>gay themed. They say, whoever his wife is, she's pretty

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<v Speaker 1>pretty busy according to him, carrying out their efforts. He

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<v Speaker 1>says my wife more often in his posts than the

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<v Speaker 1>word fiance is used in that famous Seinfeld episode.

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<v Speaker 2>The key paragraph in this says the financial benefits have

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<v Speaker 2>been great as well, as YMCA is estimated to grow

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<v Speaker 2>several million dollars since the President Alex continued use of

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<v Speaker 2>the song. Therefore, I'm glad I allowed the President Alex

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<v Speaker 2>continued use of YMC, and I thank him for choosing

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<v Speaker 2>my song. I've never quite wrapped my head around licensing

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<v Speaker 2>for music played in an event like that. I kind

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<v Speaker 2>of get it when it comes to light music licensing

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<v Speaker 2>and radio stations, and we've always dealt with those contracts

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<v Speaker 2>because of our experience just in the industry. But for

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<v Speaker 2>a live event like that, do.

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<v Speaker 1>You think that we're going to get in trouble from

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<v Speaker 1>the trumpets of Jesus people?

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<v Speaker 3>I doubt it, but I mean, I mean, we'd play

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<v Speaker 3>it enough.

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<v Speaker 4>It would be the songwriter.

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<v Speaker 2>That and I don't know who the songwriter is on

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<v Speaker 2>trumpets of Jesus. But this is just one version of

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<v Speaker 2>it by the Imperials, and I think there have been

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<v Speaker 2>several different versions of it recorded by different groups.

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<v Speaker 3>Oh really, let's see, okay.

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<v Speaker 1>Russ Taff wrote the song Trumpet of Jesus, and.

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<v Speaker 4>It's a singular trumpet by the way.

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<v Speaker 3>Right, Russtaff the Jesus trumpet?

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<v Speaker 2>Yeah, I mean that's it sounds like he's saying the

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<v Speaker 2>trump bets.

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<v Speaker 4>But it is just a singular trumpet. He's only got one.

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<v Speaker 1>Russ Staff, Russ taxcuse me, seventy one years old, grew

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<v Speaker 1>up in Farmersville, California.

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<v Speaker 3>I bet we could get him on.

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<v Speaker 2>Not a problem. We start the new year with Russ

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<v Speaker 2>Taft interviews.

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<v Speaker 3>What would you ask him?

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<v Speaker 2>How many songs he's written? Was this a one off?

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<v Speaker 2>This is a pretty popular song. It'd be crazy if

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<v Speaker 2>it was just a one off. I want to go

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<v Speaker 2>through some of the lyrics though of YMCA, because I

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<v Speaker 2>feel like there is something to be said about why

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<v Speaker 2>people believe that it is a song. When it comes

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<v Speaker 2>to a song that would be embraced perhaps by the

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<v Speaker 2>gay A gay song. Go ahead, Well, I didn't say

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<v Speaker 2>gay song, but there's something wrong with that. Let's start

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<v Speaker 2>out with the first two words of the song, young man,

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<v Speaker 2>I rest my case.

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<v Speaker 4>That's the end of it.

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<v Speaker 2>No, And then the second line starts off there's with

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<v Speaker 2>the two words young man, and then.

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<v Speaker 4>The third line starts with young man.

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<v Speaker 3>Well it it's all about mentorship.

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<v Speaker 2>Yes, young man, there's a place you can go, I said,

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<v Speaker 2>young man, when you're short on your dough.

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<v Speaker 4>You could find many ways to have a good time.

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<v Speaker 2>Now, none of it has to be gay, but if

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<v Speaker 2>you're looking for something to symbolize it, and you're looking

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<v Speaker 2>for an anthem or a theme, this would fit everything.

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<v Speaker 2>It's fun to stay at the YMCA, and they have

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<v Speaker 2>everything for young men to enjoy.

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<v Speaker 4>You can hang out with all the boys.

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<v Speaker 3>Lots of boys like to do.

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<v Speaker 1>They like to hang out with other boys. And then

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<v Speaker 1>this is where it kind of is like a life helper. Right.

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<v Speaker 1>You can get yourself clean, you can have a good male,

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<v Speaker 1>you can do whatever you feel.

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<v Speaker 4>You can have a good what meal meal?

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<v Speaker 3>Yeah?

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<v Speaker 4>Like, oh, I thought it sounded like you said you

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<v Speaker 4>can have a good male.

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<v Speaker 3>No meal again me hot dog or whatever it is. Boys.

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<v Speaker 1>It's all about young men who are kind of bouncing

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<v Speaker 1>around in life.

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<v Speaker 3>They might need friends.

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<v Speaker 1>They might need a nice warm place to say, a

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<v Speaker 1>nice warm male and meal.

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<v Speaker 2>It's again, it sounds like you said mail, but you

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<v Speaker 2>said a nice warm meal.

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<v Speaker 3>Meal right, right?

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<v Speaker 1>Okay, a nice warm male can really meal really set you?

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<v Speaker 3>Right? You know what I mean? Like, don't you feel

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<v Speaker 3>better after you have a hot meal?

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<v Speaker 2>Yes, you're You're still saying that there's a little I

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<v Speaker 2>have to be careful.

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<v Speaker 3>How I answer a love song about community.

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<v Speaker 2>I didn't realize the YMCA was started in Switzerland. I

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<v Speaker 2>thought that was an America thing, all right. It's currently

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<v Speaker 2>based in Switzerland. I guess it started in London and

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<v Speaker 2>it was founded in eighteen forty four as the Young

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<v Speaker 2>Men's Christian Association. The aim was to put Christian values

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<v Speaker 2>into practic just by developing a healthy body, mind and spirit.

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<v Speaker 4>So it's almost a.

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<v Speaker 2>You could argue opposite of what a lot of people

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<v Speaker 2>have thought have thought that it was.

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<v Speaker 3>And so did they.

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<v Speaker 1>Dress up as a Native American and the police officer

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<v Speaker 1>and the construction worker and the bit to show that

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<v Speaker 1>it's for everybody.

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<v Speaker 4>I don't know why the costumes came about.

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<v Speaker 1>Because like if I'm a firefighter, policeman or a Native American,

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<v Speaker 1>I probably am not in need of staying with all

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<v Speaker 1>the boys, right, Like I've I've I have a career,

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<v Speaker 1>I have a pension. I mean, I don't know about

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<v Speaker 1>the Native American, but he has a community, yes, reservation.

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<v Speaker 1>But the cop and the firefighter, they've got pensions. They

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<v Speaker 1>they don't need a warm mail. They've got one at home.

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<v Speaker 1>Probably meal, meal, is what you're saying.

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<v Speaker 4>Right.

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<v Speaker 2>Do you know anybody who has a great memory. My

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<v Speaker 2>dad did great memory.

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<v Speaker 1>Yes, could remember you know who played third base for

434
00:23:12.000 --> 00:23:15.400
<v Speaker 1>the Cubs in nineteen sixty four, right, you know right away?

435
00:23:16.440 --> 00:23:19.519
<v Speaker 4>Ray Bell. By the way, I'm just kidding. I just

436
00:23:19.559 --> 00:23:20.200
<v Speaker 4>made that name up.

437
00:23:20.200 --> 00:23:26.079
<v Speaker 2>It sounded like an early fifties anyway. Like super recognizers

438
00:23:26.799 --> 00:23:32.279
<v Speaker 2>don't just have great memories. They specifically remember people's faces

439
00:23:32.920 --> 00:23:38.599
<v Speaker 2>that they can identify unfamiliar faces at just a brief glimpse.

440
00:23:38.960 --> 00:23:43.440
<v Speaker 2>And they say that by hoping, by studying these super recognizers,

441
00:23:44.000 --> 00:23:47.400
<v Speaker 2>that researchers would understand how we in general recognize that

442
00:23:47.440 --> 00:23:49.359
<v Speaker 2>a face belongs to someone we know, which is of

443
00:23:49.400 --> 00:23:53.319
<v Speaker 2>course important when it comes to being a social species

444
00:23:53.440 --> 00:23:55.839
<v Speaker 2>like we are, or hope to continue to be.

445
00:23:56.880 --> 00:23:59.200
<v Speaker 1>In that case, On the other end, are the two

446
00:23:59.200 --> 00:24:02.359
<v Speaker 1>to three percent of people with face blindness who have

447
00:24:02.400 --> 00:24:06.200
<v Speaker 1>trouble recognizing faces that should be familiar to them, including

448
00:24:06.240 --> 00:24:08.200
<v Speaker 1>those of loved ones or even their own.

449
00:24:08.400 --> 00:24:14.319
<v Speaker 2>Developmental prosopagnosia is what that's called the face blindness. I've

450
00:24:14.680 --> 00:24:18.240
<v Speaker 2>I've heard also of people with this, but I've never

451
00:24:18.519 --> 00:24:21.599
<v Speaker 2>recognized I mean, I don't recognize them. No, I don't

452
00:24:22.160 --> 00:24:25.079
<v Speaker 2>know anybody specifically that has face blindness.

453
00:24:25.279 --> 00:24:28.640
<v Speaker 1>Sometimes I think that if you're not paying attention to

454
00:24:28.720 --> 00:24:32.400
<v Speaker 1>who you're talking to or meeting, they can go in

455
00:24:32.480 --> 00:24:34.200
<v Speaker 1>one eye and out the other, so to speak.

456
00:24:35.039 --> 00:24:38.400
<v Speaker 2>Well, we were just we have we were just talking

457
00:24:38.440 --> 00:24:43.079
<v Speaker 2>the other day about somebody we both know and have

458
00:24:43.240 --> 00:24:47.000
<v Speaker 2>met multiple times. But put that person into a room,

459
00:24:47.640 --> 00:24:51.279
<v Speaker 2>they fade into the Wallpa, man, just nothing there.

460
00:24:51.400 --> 00:24:54.640
<v Speaker 4>And it's not I don't know if it's you know

461
00:24:54.680 --> 00:24:57.200
<v Speaker 4>that they're distinguishing or out.

462
00:24:56.960 --> 00:25:03.400
<v Speaker 1>Of there's nothing distinguishing about them, Yes, like your forehead distinguishes, right, Yes,

463
00:25:04.039 --> 00:25:07.640
<v Speaker 1>I'm just kidding. Yeah, that was a stupid shot, but

464
00:25:07.799 --> 00:25:08.680
<v Speaker 1>mildly amusing.

465
00:25:08.759 --> 00:25:14.039
<v Speaker 3>And your ankles, my ankles, they're probably a little fat

466
00:25:14.079 --> 00:25:15.160
<v Speaker 3>actually after the plane.

467
00:25:15.400 --> 00:25:19.839
<v Speaker 2>I but that's not your face, that's true, But there

468
00:25:19.839 --> 00:25:22.720
<v Speaker 2>were there's no distinguishing characteristics or something. There's no scar

469
00:25:22.880 --> 00:25:25.039
<v Speaker 2>under the eye or a snaggle tooth or something like

470
00:25:25.039 --> 00:25:26.319
<v Speaker 2>that that would make that person.

471
00:25:26.799 --> 00:25:30.440
<v Speaker 1>I didn't which one which one? So you're saying, I

472
00:25:30.440 --> 00:25:31.559
<v Speaker 1>have multiple snaggles?

473
00:25:31.559 --> 00:25:35.480
<v Speaker 4>What are you pointing to? No.

474
00:25:37.839 --> 00:25:41.480
<v Speaker 5>Super recognizers they first came out, I'm uninteresting came up

475
00:25:41.519 --> 00:25:44.720
<v Speaker 5>within two thousand and nine said that the excel at

476
00:25:44.759 --> 00:25:48.079
<v Speaker 5>identifying matching faces, even those that would be unfamiliar with

477
00:25:48.119 --> 00:25:51.599
<v Speaker 5>them strangers up until the experiment, whether they're flipped upside down,

478
00:25:51.640 --> 00:25:54.400
<v Speaker 5>they have low resolution, sometimes the pictures are presented at

479
00:25:54.400 --> 00:25:55.200
<v Speaker 5>a different angle.

480
00:25:55.519 --> 00:25:59.079
<v Speaker 2>They have the unique ability to derive a three dimensional

481
00:25:59.119 --> 00:26:03.799
<v Speaker 2>representation of a face, even if they only see a

482
00:26:03.839 --> 00:26:06.200
<v Speaker 2>picture of it a two D image of a person.

483
00:26:06.359 --> 00:26:09.759
<v Speaker 1>If they're like this with photographic memory and other things,

484
00:26:09.799 --> 00:26:10.920
<v Speaker 1>not just faces.

485
00:26:10.640 --> 00:26:12.759
<v Speaker 2>Well, and they're saying no, they're saying that this is

486
00:26:12.799 --> 00:26:17.400
<v Speaker 2>specific to faces because of how important recognizing someone has

487
00:26:17.440 --> 00:26:20.839
<v Speaker 2>been over the course of our evolution as human beings.

488
00:26:21.319 --> 00:26:25.799
<v Speaker 2>They said that when presented with even random pictures of

489
00:26:25.839 --> 00:26:30.599
<v Speaker 2>everyday life, a beach, scene of traffic or something, super

490
00:26:30.640 --> 00:26:36.279
<v Speaker 2>recognizers spend more time looking at faces, which is interesting

491
00:26:36.279 --> 00:26:39.880
<v Speaker 2>because We've done stories before about people with autism spectrum

492
00:26:39.880 --> 00:26:43.920
<v Speaker 2>disorders who do the exact opposite. They spend time looking

493
00:26:44.000 --> 00:26:47.920
<v Speaker 2>at the vehicles or the things, or the plants or

494
00:26:47.960 --> 00:26:51.640
<v Speaker 2>the birds. They do not look at the faces. They

495
00:26:51.680 --> 00:26:55.880
<v Speaker 2>said that eyes of super recognizers are immediately drawn to

496
00:26:55.960 --> 00:26:58.880
<v Speaker 2>the face, regardless of where that face is in the picture,

497
00:26:59.359 --> 00:27:02.799
<v Speaker 2>and mostly near the optimal place for identification, which is

498
00:27:03.079 --> 00:27:07.680
<v Speaker 2>just below your eyes. Not the forehead, mind you, but

499
00:27:07.920 --> 00:27:10.279
<v Speaker 2>just below the eyes. I was wondering about this and

500
00:27:10.319 --> 00:27:13.519
<v Speaker 2>I just found my answer. And I'm not trying to

501
00:27:13.519 --> 00:27:16.400
<v Speaker 2>say that people are racist, but sometimes you look at

502
00:27:16.400 --> 00:27:19.400
<v Speaker 2>someone who looks a certain.

503
00:27:19.079 --> 00:27:21.839
<v Speaker 3>Way and they you.

504
00:27:21.799 --> 00:27:23.720
<v Speaker 1>Don't really look at them, because you just kind of

505
00:27:23.759 --> 00:27:25.599
<v Speaker 1>glance at them, and then you think you know exactly

506
00:27:25.640 --> 00:27:28.160
<v Speaker 1>what their face looks like. For example, it doesn't have

507
00:27:28.240 --> 00:27:31.519
<v Speaker 1>to just be black or Asian or what have you.

508
00:27:33.240 --> 00:27:35.160
<v Speaker 3>It could be it could be blonde white girl.

509
00:27:35.200 --> 00:27:36.680
<v Speaker 1>Because this happened to be this weekend, one of the

510
00:27:36.720 --> 00:27:38.599
<v Speaker 1>security guys for the chargers says, I could have sworn

511
00:27:38.680 --> 00:27:40.680
<v Speaker 1>I saw you at the lobby bar, so I want

512
00:27:40.720 --> 00:27:42.279
<v Speaker 1>to go say hi, and it wasn't you.

513
00:27:43.000 --> 00:27:44.279
<v Speaker 3>But she has blonde.

514
00:27:44.000 --> 00:27:46.839
<v Speaker 1>Hair, so like it happens with everybody you know where

515
00:27:47.799 --> 00:27:51.519
<v Speaker 1>you don't really You look at somebody and you think

516
00:27:51.559 --> 00:27:54.039
<v Speaker 1>you take in all the information, but then you put

517
00:27:54.079 --> 00:27:57.000
<v Speaker 1>them in a lineup and you don't. That's why lineups

518
00:27:57.000 --> 00:27:58.920
<v Speaker 1>and six packs or whatever you want to call them

519
00:27:59.000 --> 00:28:04.200
<v Speaker 1>are so traditional, not reliable sources of evidence because people

520
00:28:04.279 --> 00:28:06.960
<v Speaker 1>don't really spend that time getting all the minutia of

521
00:28:07.000 --> 00:28:08.039
<v Speaker 1>someone's face well.

522
00:28:07.920 --> 00:28:10.839
<v Speaker 2>And that's that kind of a criminal lineup. Like that,

523
00:28:10.960 --> 00:28:12.759
<v Speaker 2>you're dealing most of the time with people who aren't

524
00:28:12.759 --> 00:28:15.720
<v Speaker 2>professionals there. They don't know what to look for, what

525
00:28:15.759 --> 00:28:18.599
<v Speaker 2>they recognize or anything. If you have a job where

526
00:28:18.640 --> 00:28:22.519
<v Speaker 2>it's important for you to recognize faces, think of somebody

527
00:28:22.839 --> 00:28:26.200
<v Speaker 2>like I don't know, a TSA agent or a security

528
00:28:26.240 --> 00:28:28.559
<v Speaker 2>guard at some place where you have to keep an

529
00:28:28.559 --> 00:28:32.119
<v Speaker 2>eye on people's faces. You can you can train your

530
00:28:32.200 --> 00:28:35.440
<v Speaker 2>way into some of those characteristics of super recognizers, but

531
00:28:35.440 --> 00:28:36.599
<v Speaker 2>you're still far behind.

532
00:28:36.839 --> 00:28:37.200
<v Speaker 4>They said.

533
00:28:37.200 --> 00:28:42.000
<v Speaker 2>In many cases, forensic examiners, as an example, are very methodical,

534
00:28:42.119 --> 00:28:45.079
<v Speaker 2>very systematic when they look at faces to determine identity,

535
00:28:45.119 --> 00:28:47.720
<v Speaker 2>and they said they take longer to make decisions, sometimes

536
00:28:47.799 --> 00:28:52.119
<v Speaker 2>up as many as long as thirty seconds, but they

537
00:28:52.160 --> 00:28:54.720
<v Speaker 2>do pick up on some facial clues that most people

538
00:28:54.759 --> 00:28:57.640
<v Speaker 2>wouldn't be paying attention to. But when you do talk

539
00:28:57.680 --> 00:29:01.960
<v Speaker 2>about those super recognizers, they have quicker, greater accuracy, and

540
00:29:02.000 --> 00:29:05.319
<v Speaker 2>sometimes it takes them two seconds to make a decision

541
00:29:05.440 --> 00:29:08.319
<v Speaker 2>about somebody as opposed to thirty seconds for someone who's

542
00:29:08.319 --> 00:29:09.240
<v Speaker 2>professionally trained.

543
00:29:10.799 --> 00:29:13.880
<v Speaker 3>Just so we're clear, I do believe you have an

544
00:29:13.920 --> 00:29:14.839
<v Speaker 3>average forehead.

545
00:29:14.960 --> 00:29:15.640
<v Speaker 4>That's not true.

546
00:29:15.920 --> 00:29:16.519
<v Speaker 3>It is true.

547
00:29:16.799 --> 00:29:18.920
<v Speaker 2>I have an average five head. It's a big forehead,

548
00:29:18.920 --> 00:29:19.960
<v Speaker 2>it's an average five head.

549
00:29:20.000 --> 00:29:21.279
<v Speaker 3>It's not true.

550
00:29:22.119 --> 00:29:24.039
<v Speaker 2>Oh, I'm going to play this for you because this

551
00:29:24.119 --> 00:29:25.599
<v Speaker 2>guy was very complimentary of you.

552
00:29:27.160 --> 00:29:29.559
<v Speaker 7>Every day I listened to you both, and you guys

553
00:29:29.720 --> 00:29:33.640
<v Speaker 7>like the best best duel ever. And I'm just laughing

554
00:29:33.680 --> 00:29:38.680
<v Speaker 7>my butt off, just like out loud, like they are stupid,

555
00:29:39.240 --> 00:29:41.960
<v Speaker 7>like it just laughing, And that's like, you guys are

556
00:29:42.000 --> 00:29:45.000
<v Speaker 7>my type with people like it's it's just so funny

557
00:29:45.039 --> 00:29:47.160
<v Speaker 7>the way you guys work together. And for the record,

558
00:29:47.480 --> 00:29:49.880
<v Speaker 7>Shannon was saying that you would get a nice hot nail.

559
00:29:50.599 --> 00:29:52.359
<v Speaker 4>She said it like four or five times. I was like,

560
00:29:52.359 --> 00:29:59.079
<v Speaker 4>wait a minute, that's yeah, I did. Yeah. Weird, very weird.

561
00:30:00.119 --> 00:30:02.480
<v Speaker 4>You've been listening to the Gary and Shannon Show.

562
00:30:02.559 --> 00:30:04.960
<v Speaker 2>You can always hear us live on KFI AM six

563
00:30:05.039 --> 00:30:08.160
<v Speaker 2>forty nine am to one pm every Monday through Friday,

564
00:30:08.279 --> 00:30:11.039
<v Speaker 2>and anytime on demand on the iHeartRadio ap