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Speaker 1: You're listening to a podcast from News Talk, said b

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Speaker 1: Follow this and our wide range of podcasts now on iHeartRadio.

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Speaker 2: Hell you, Great New Zealand, and welcome to Matt and

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Speaker 2: Tyler Full Show Podcast number one fifty nine for Friday,

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Speaker 2: the eleventh of July twenty twenty five. Fantastic show. Today

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Speaker 2: got very spicy at the end with the cheating and

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Speaker 2: the sexual stuff. The AI went long, but there was

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Speaker 2: a great chat there.

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Speaker 3: Huge, huge, and just sorry to row back to the cheating,

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Speaker 3: but my story about a threesome. Yeah, I mean you've

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Speaker 3: got to hear that to believe it.

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Speaker 2: Yeah, We're we're making news talks, y'd be. We're bringing

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Speaker 2: the sixty back to news talks, he'd be. I actually

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Speaker 2: tried to keep it out of me. I don't know

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Speaker 2: if we're bringing it back. I think it's never been here.

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Speaker 2: We're bringing bringing the sexy to newstalks, y'd be bringing

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Speaker 2: the leud contents. But yeah, have a great weekend, enjoy

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Speaker 2: the pod, and yeah, bless you're going to task you

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Speaker 2: love you.

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Speaker 1: The big stories, the legal issues, the big trends and

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Speaker 1: everything in between. Matt Heath and Taylor Adams Afternoons News.

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Speaker 2: Talk said, be.

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Speaker 3: Very very good afternoons. You welcome into Friday's show. Great

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Speaker 3: to have your company. As always, we're if you're listening

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Speaker 3: in this beautiful country of our Skina.

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Speaker 2: Man get a Tyler Gada. Everyone new yesterday how we

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Speaker 2: got kicked out of here for them to change the

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Speaker 2: light bulbs? Yeah, why is it so dark in here?

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Speaker 2: Since they changed the light bulbs? So, I don't want

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Speaker 2: to be the kind of you know, radio hosts. You know,

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Speaker 2: I guess you'd call them a hosking that complains a

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Speaker 2: lot about the setup. I'm not going to be that

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Speaker 2: kind of Newstalks.

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Speaker 3: You don't want to be that guy.

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Speaker 2: You don't want to be a husky. No, but I've

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Speaker 2: got to say it's very dark in here.

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Speaker 3: Well, we were promised when they were changing the light bulbs,

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Speaker 3: and it took them about three hours to do that,

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Speaker 3: because it's quite an advanced set up in here, that

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Speaker 3: it was a dim yellow and it was going to

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Speaker 3: be a brighter yellow. But for you've decided, because we've

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Speaker 3: got these god awful TV lights that operate in here,

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Speaker 3: you're you're rocking those. I feel like I'm getting a

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Speaker 3: tan as we speak from these lights.

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Speaker 2: I believe to broadcasts professionally, you need to have your

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Speaker 2: written as being blown out by bulbs. It keeps you,

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Speaker 2: keeps you awake.

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Speaker 3: I can't actually see anything. Is this is too bright?

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Speaker 2: I'll do whatever it takes to deliver the best possible

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Speaker 2: Matt and Tyler Afternoon on News Talks ever I can.

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Speaker 2: If that involves blinding you Tyler, then that's what I'm

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Speaker 2: going to do.

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Speaker 3: That is the Matt Heath promise. Right to today's show

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Speaker 3: after three o'clock, as we always do on a Friday,

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Speaker 3: New Zealander of the Week.

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Speaker 2: Yeah, who will it be? There's a lot a lot

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Speaker 2: of key wees have put the hand up this week,

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Speaker 2: but it hasn't been decided. So nine two nine two.

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Speaker 2: If you've got a nomination for Matt Tyl Afternoons New

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Speaker 2: Zealand of the Week, we'd love to hear it.

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Speaker 3: Yes, Also after three o'clock you might have seen this

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Speaker 3: story a exchange between a company boss and a recently

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Speaker 3: fired employee. It's gone viral. The messages were shared by

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Speaker 3: a guy called Ben Askins. He's a UK workplace expert

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Speaker 3: and we'll read you out that text exchange in its entirety.

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Speaker 3: It's golden. But we want to ask the question, what's

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Speaker 3: the worst text you've ever got from a boss?

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Speaker 4: Yeah?

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Speaker 2: And as a boss, what's appropriate to text your workers?

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Speaker 2: What's the line looking forward to that shat? What's after

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Speaker 2: two o'clock? Tyler?

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Speaker 3: What makes someone cheat in a relationship? So an article

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Speaker 3: in the Herald today by sex therapist. She's been in

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Speaker 3: the business forty five years. She thinks she knows why

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Speaker 3: people cheat and how to avoid infadelity. But we're going

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Speaker 3: to ask a question why do you cheat? Or if

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Speaker 3: you have cheated, why did you do it? And what's

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Speaker 3: it like being cheated on?

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Speaker 4: Yeah?

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Speaker 2: And also we're going to mix that in with a

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Speaker 2: study that has highlighted how marriages tend to break up,

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Speaker 2: because when marriages break up, the study shows it puts

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Speaker 2: huge financial strain on kiwis in their forties and fifties,

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Speaker 2: especially men, And the biggest course of divorce, the biggest

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Speaker 2: cause of divorce, interestingly there is men losing their jobs

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Speaker 2: and becoming poorer than their partners.

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Speaker 3: Yes, yeah, that is. I mean, that's a hell of

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Speaker 3: a study.

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Speaker 2: And we don't know if that's the status lost or

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Speaker 2: just that men start to behave in a crazy way

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Speaker 2: when they've lost their job. Who knows. But the second

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Speaker 2: biggest is in fidelity. So you know, first of all,

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Speaker 2: we want to talk about have you cheated on your

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Speaker 2: partner or an ex partner? Have you been cheated on?

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Speaker 2: What are the signs? Doesn't matter? Can you forgive? And

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Speaker 2: would you forgive so you don't hit those financial troubles

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Speaker 2: that you might in your forties and fifties, stick with

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Speaker 2: the one person you're going to retire a lot richer. Yep,

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Speaker 2: you go through one, two, three, Oh boy, that rarely

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Speaker 2: cuts into your into your wealth.

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Speaker 3: Bankruptcity. Yeah that is after two o'clock, looking forward to that.

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Speaker 3: But right now, how easily do you think you can

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Speaker 3: spot AI? So, Matt, last night you were knee deep

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Speaker 3: into this, and in fact you wrote an article on

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Speaker 3: your substack. If you want ever read of that, go

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Speaker 3: to Matteath dot substack dot com. I have had a

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Speaker 3: read of it. It was a book that you're reading

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Speaker 3: that you suspected was written by artificial intelligence.

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Speaker 2: Yeah, that's right. And look libraryes in New Zealand of

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Speaker 2: an ouncesdate that they're not taking any AI books. Any

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Speaker 2: AI is written by books. And you know I wrote

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Speaker 2: a book recently, and you have to sign your contract

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Speaker 2: that it's not created by AI. But this book I

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Speaker 2: was reading, Steering Wheel the Driver's Command Center. No, hang

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Speaker 2: on a minute. The book was called One Second. It's

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Speaker 2: got quite a punishing name. This is the kind of

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Speaker 2: kind of stuff I read. How a Formula one car,

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Speaker 2: How a Formula one race car works, What makes it

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Speaker 2: the fastest car machine in the world, Engineering science and

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Speaker 2: innovation behind building the ultimate racing vehicle. That's the name

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Speaker 2: of the book. It's a hell of the title allegedly

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Speaker 2: by Elliott Vaynor, right, But as I was reading it,

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Speaker 2: I started to realize that it was written by AI.

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Speaker 2: It was so clearly written by AI, and it got

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Speaker 2: me thinking, how do I know it's written by AI?

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Speaker 2: And so I did a bit of research when I

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Speaker 2: couldn't sleep last night and wrote this article and I

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Speaker 2: found out a bunch of things to look out when

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Speaker 2: it comes to AI. And so we just want to

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Speaker 2: talk about AI. Basically, are you getting better at spotting AI?

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Speaker 2: And but look, does it make you think less of

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Speaker 2: people when you realize that that what you're reading has

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Speaker 2: been generated by AI? Because it certainly made me feel

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Speaker 2: like I didn't want to read this book anymore. Yeah,

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Speaker 2: you feel Rick Rubin the fantastic rock producer. Music producers,

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Speaker 2: produce all kinds of music. He's the most famous producer

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Speaker 2: in the world. I'd say maybe maybe Quincy Jones, but

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Speaker 2: Rick Rubin fantastic, done incredible work with Johnny k. Cash,

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Speaker 2: any band you can name, Rick Rubin's made them better

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Speaker 2: when he's produced them. And he said, the difference between

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Speaker 2: AI and a normal artist is that AI will never

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Speaker 2: have a point of view and humans will always always

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Speaker 2: have a point of view. And the thing with AI

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Speaker 2: is so bland and so vaguely positive that it doesn't

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Speaker 2: doesn't have the human experience. It doesn't share the human experience,

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Speaker 2: and as humans, we're always trying trying to look for

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Speaker 2: that human experience.

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Speaker 3: Yeah, when you're reading something like a book, I mean,

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Speaker 3: like you say, that's what you want out of a story, right,

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Speaker 3: or even one of these books about how a formula

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Speaker 3: one car works, and it seems very scientific, but you

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Speaker 3: still want a bit of heart, You still want that humanness,

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Speaker 3: the human element in there. And dare I say it,

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Speaker 3: maybe even a few mistakes as you're reading along, or

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Speaker 3: a few things that you question about that AI just

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Speaker 3: removes entirely because it is trying to be too perfect

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Speaker 3: or it is trying to be so robotic that it

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Speaker 3: takes all emotion out of what it's trying to do.

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Speaker 2: Yeah, and are you worried about that? If you're using AI,

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Speaker 2: do you try and hide the fact that you use AI?

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Speaker 2: So really we want to talk about one hundred and

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Speaker 2: eighty ten eighty everything AI? But are you using AI?

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Speaker 2: How are you using AI? And if you are using AI,

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Speaker 2: are you trying to hide the fact? And do you

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Speaker 2: think less of people that use ALII? I think I do?

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Speaker 2: Shall I outline how you spot what I found in

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Speaker 2: this article?

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Speaker 4: Well?

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Speaker 2: What I found writing this article at three am this morning?

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Speaker 3: I want to hear that.

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Speaker 2: Yeah, shall I? Should I do it now? Or should

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Speaker 2: we come back to that.

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Speaker 3: Let's play some messages and come back to that, because

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Speaker 3: a lot of people want to hear that will be

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Speaker 3: very useful back, I can you hear from you?

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Speaker 2: I've got seven things to look out for that that

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Speaker 2: will indicate to you that you may be reading something

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Speaker 2: that's generated from AI. This stixster says, stop dissing Hosking.

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Speaker 2: He was there before you. Two guys, there's no reason

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Speaker 2: to stop dissing him just because he was here first,

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Speaker 2: because he's old, you can still just be I wasn't.

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Speaker 2: I wasn't dissing him. I just said he complains a

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Speaker 2: lot about a studio.

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Speaker 3: Yeah, he likes a light bulb? What's wrong with that?

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Speaker 4: Yeah? Right?

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Speaker 5: Oh?

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Speaker 3: One hundred and eighty ten eighty is the number to

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Speaker 3: call a Do you think you're pretty good at spotting AI?

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Speaker 3: If you do think you're pretty good, how do you know?

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Speaker 3: And have you been fooled by it in recent months

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Speaker 3: or even the last years. It's getting increasingly better. Love

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Speaker 3: to hear from you. Nine two nine two is the

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Speaker 3: text number. It is fourteen past one.

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Speaker 2: In fact, that text stopped dissing Mike. He was there

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Speaker 2: before you, two guys, is such a vague and bland

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Speaker 2: opinion that maybe that is AI might be from Mike

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Speaker 2: actually as Mike AI.

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Speaker 1: The big stories, the big issues, the big trends, and

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Speaker 1: everything in between. Matt Heath and Tyler Adams afternoons used

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Speaker 1: talks that'd be.

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Speaker 3: Very good afternoons you seventeen pass one and we've asked

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Speaker 3: the question, how well do you think you can spot

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Speaker 3: artificial intelligence or AI? Particularly when it comes to creative content,

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Speaker 3: and are.

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Speaker 2: You seeing it more and more? Well, are you recognizing

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Speaker 2: it more and more? Are you getting trained to recognize

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Speaker 2: when you're reading something that is AI and not not real?

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Speaker 6: Yeah?

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Speaker 2: And do you judge people? You know, do you get

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Speaker 2: less from something if you know that it's that it's AI?

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Speaker 2: Eight hundred eighty ten eighty is the number?

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Speaker 3: Just going to follow up question here because the text

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Speaker 3: has just come through, And I wholeheardly agree with this.

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Speaker 3: Is that giday, Guys. I like to think I'm pretty

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Speaker 3: good at spotting a I. I do get fooled on occasion,

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Speaker 3: but try to follow up with other sources. My biggest

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Speaker 3: worry is my mother who often sends me articles that

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Speaker 3: she finds on Facebook with images that are very very

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Speaker 3: clearly artificial intelligence. It's a massive worry, and that is

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Speaker 3: a big worry for a lot of people out there.

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Speaker 3: And I've got to say, I'm not going to name them,

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Speaker 3: but I've got family members who send me articles from

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Speaker 3: time to time or images that on first glance might

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Speaker 3: seem like that real but if you see enough AI,

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Speaker 3: you know, they send through that article and instantly pull

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Speaker 3: it up say that's an AI image and this is

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Speaker 3: not a real article, and then have to go back

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Speaker 3: and have that embarrassing conversation and say, hey, just you

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Speaker 3: don't have to be worried about this. This is Ai.

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Speaker 3: This was written by a robot. That's not a real image.

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Speaker 3: And I'll tell you know that's that person who's got

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Speaker 3: six fingers. You can see that quite clearly.

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Speaker 2: Yeah, you don't need to worry. This isn't happening. This

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Speaker 2: is generated to cause fear in you for whatever reason.

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Speaker 2: This says AI has an accent. Once you hear it,

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Speaker 2: it's very obvious. And I think that's true. And when

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Speaker 2: you see it, in my opinion, you get annick around

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Speaker 2: it is there's a blandness, there's a vagueness about it.

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Speaker 2: There's there's a lack of point of view about it

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Speaker 2: that there's a bit of a nick. And there's a

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Speaker 2: very successful New Zealand comment called Melanie Bracewell. She's become

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Speaker 2: very successful in the States, I mean in Australia, and

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Speaker 2: she she posted something on X the other day and

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Speaker 2: it was essentially saying that she that basically she was

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Speaker 2: talking about comic scance, right, and she thought she thought

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Speaker 2: it was a similar thing I'll just find, I'll just find,

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Speaker 2: I'll just find find what she said here.

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Speaker 3: One second, he's got reams of research in front of

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Speaker 3: them people, he's been knee deep in this all night.

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Speaker 2: Well, I'll have to paraphize it if I can't find.

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Speaker 2: Oh ye here she goes. She says, AI is becoming

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Speaker 2: so recognizable. I feel like it will eventually give people

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Speaker 2: the same ec they used to see from seeing comic

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Speaker 2: Sands font in the wild. I feel like common Sands

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Speaker 2: was so hated because it was trying to look like

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Speaker 2: human handwriting, but it was too clean around the edges,

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Speaker 2: and we don't like It's called the uncanny valley. We

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Speaker 2: don't like things that get too close to human but

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Speaker 2: aren't there because we can spot the differences. And I

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Speaker 2: feel like that is what's happening with AI at the

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Speaker 2: moment when you see it, especially on LinkedIn. But I've

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Speaker 2: got a number of things you can look out for,

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Speaker 2: Okay for AI here yep. Frequent M dashes M dash Yeah.

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Speaker 2: Do you know what an M dash do not?

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Speaker 3: Well?

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Speaker 2: Large language models sprinkle M dashes far more than we

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Speaker 2: humans do, even in places where a comma or a

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Speaker 2: full stop would feel more natural. And M dash is punk.

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Speaker 2: It's a punctuation mark used to indicate a break in

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Speaker 2: a sentence, so it replaces parentheses or colons, and so

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Speaker 2: it's it's like a hyphen, but it's longer. It's the

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Speaker 2: width of an M. That's what it's called an.

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Speaker 3: I'm with you now, yeah yeah.

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Speaker 2: And it's hard for humans to do it, to type

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Speaker 2: an M dash because we have to go, say, on

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Speaker 2: a MAC it's option shift hyphen, right, So humans are

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Speaker 2: too lazy to do it, even if it's the perfect

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Speaker 2: thing to use. But it's nothing for AI, a large

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Speaker 2: language model, to use the the M dash. So the

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Speaker 2: M dash is everywhere, so that.

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Speaker 3: That is a great thing to look out for. The

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Speaker 3: M dash.

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Speaker 2: That's something to look out for. Also, and look, if

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Speaker 2: you want to read this list, it's it's on the

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Speaker 2: article I write. It's not just X, it's y parallel structure.

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Speaker 2: So so AI often uses that. It's not just it's

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Speaker 2: also it's the go to sentence structure when you start

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Speaker 2: noticing this. AI it does that so much. It's not

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Speaker 2: just it's something else, right. Lists of threes or fives,

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Speaker 2: triplets are very common that they're common in humans. We

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Speaker 2: love triplets, triplets like a joke is often on the third,

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Speaker 2: but there's there's a there's a way, there's a there's

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Speaker 2: a clockwork like regularity that AI uses, right, vague, upbeat

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Speaker 2: jargon and fuller adjectives. It's like there's an over alliance

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Speaker 2: and words like innovative, elevate, robust, practical, solutions, signal safe,

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Speaker 2: non it's non committal vocabulary.

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Speaker 3: I've seen a lot of that from AI writing.

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Speaker 2: Yeah, there's there's chronic restatement and over explanation, and it

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Speaker 2: just fails the vibe check. And also there's forced or

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Speaker 2: odd and analogies. So like say, for example, here in

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Speaker 2: this book I was reading last night, that was clearly AI.

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Speaker 2: It's sort of jumped out me, like here we go,

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Speaker 2: here we go. Steering wheel the driver's command center. The

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Speaker 2: steering wheel of a Formula one car is not just

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Speaker 2: a tool for driving m DASH. It's a cockpit within

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Speaker 2: the cockpit, a hub of control that places entire cars

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Speaker 2: complexity at the driver's fingerstips tips the steering wheel. If

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Speaker 2: a Formula one car is not just a tool for

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Speaker 2: turning m d ASH, it's a cockpit within the cockpit.

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Speaker 2: If you see something like that Yeah, it's a good

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Speaker 2: chance it's AI.

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Speaker 3: That is such an odd restructured sentence.

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Speaker 2: They're all clues it might not be. But if you

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Speaker 2: go through the list and you get a few of them,

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Speaker 2: you start to spot AI.

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Speaker 3: Yeah, I'll wait one hundred, A ten eighty. How good

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Speaker 3: do you think you are at spotting AI? And do

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Speaker 3: you use AI regularly in your business? And if you do,

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Speaker 3: do you keep it quiet? Is there a bit of

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Speaker 3: a taboo around using AI? I will mention I've seen

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Speaker 3: this pop up again and again online of use of

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Speaker 3: AI when replying to emails or candidates for jobs. So,

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Speaker 3: and this is where AI goes wrong. That someone's applied

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Speaker 3: for a job and I've had an email back saying

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Speaker 3: We're really sorry you didn't get the job. But sometimes

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Speaker 3: they accidentally leave the chet GPT prompt within the email.

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Speaker 3: But I know for a fact, and the chat GPT

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Speaker 3: prompt was please respond back to this potential candidate with

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Speaker 3: a polite bit firm you will not be proceeding further

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Speaker 3: with your interview and with your application. But it is

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Speaker 3: such a cold, cold thing to do to use AI

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Speaker 3: for something like that. But maybe would.

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Speaker 2: You're rather no reply or an AI reply, because that's

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Speaker 2: the worst thing is when you apply for a job

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Speaker 2: and nothing comes back.

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Speaker 3: Yeah, you're right. I'd prefer AIO of the ghost ting,

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Speaker 3: but if you use it in your business or place

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Speaker 3: of work, love to hear from you. Oh eight hundred

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Speaker 3: and eighty ten eighty is a number to call. Twenty

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Speaker 3: three past one, putting.

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Speaker 1: The tough questions to the newspeakers the mic asking breakfast.

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Speaker 7: It looks like Doc is going to do a U

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Speaker 7: turnover those lizards. They initially told mccray's mine in Central

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Speaker 7: Otago no, they couldn't expand ten thousand lizards might die.

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Speaker 7: But then after media attention yesterday, they've taken another look

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Speaker 7: at the application. TAMA Paul Tucker is the Conservation Minister

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Speaker 7: and with us is it going to be a yes?

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Speaker 8: Well, that's the matter that DOC and Oceana worked on

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Speaker 8: at a very productive meeting. I expect that they're going

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Speaker 8: to progress that application very swiftly.

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Speaker 3: Did you tell Doc to have another look at it?

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Speaker 8: I found out about this matter and I've said to

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Speaker 8: Dot what has happened here? Start seals in miscommunication. They

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Speaker 8: weren't clear about information requirements and they declined it too.

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Speaker 1: Quickly hither duplessy Lan on the mic asking Breakfast Back

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Speaker 1: Monday from six am with al Vida Retirement Communities on

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Speaker 1: News toms'd.

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Speaker 3: Be very good afternoon. We're talking about AI or the

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Speaker 3: use of AI. How well do you think you can

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Speaker 3: spot AI, whether it's a book, an image, or an article. Well,

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Speaker 3: eight one hundred and eight ten eighties number to call.

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Speaker 2: Welcome to show. Tanya. You want to talk about AI

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Speaker 2: and the world of real estate agents.

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Speaker 9: Well, I sent you a text, but now I realize

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Speaker 9: them online but I yeah, I don't.

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Speaker 10: I think it's cheating.

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Speaker 9: I might be old sessioned, but I still write all

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Speaker 9: my own ads and use. It takes me a lot longer,

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Speaker 9: but I just believe that it well authentic. It's it's

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Speaker 9: the real may And it takes longer, but I'm going

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Speaker 9: to keep doing it as long as I and I

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Speaker 9: can often see when things when things aren't to the

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Speaker 9: point where I've put them into a into an AI checkup,

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Speaker 9: which I didn't know existed, but I tried it and

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Speaker 9: sure enough, it came up ninety percent AI. So then

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Speaker 9: I put my my stuff in and it came up

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Speaker 9: written by a human.

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Speaker 11: So there you go.

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Speaker 2: So do you judge other people if they're using AI?

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Speaker 2: Do you think less of them? Look?

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Speaker 9: I try not to because they're all individuals and people

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Speaker 9: do it for different reasons, But I, yeah, I mustn't that.

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Speaker 9: I just think it's a little bit chead, especially when

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Speaker 9: it's a maybe one I read with a was a

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Speaker 9: comment going back to a vendor just replyed about a testimonial,

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Speaker 9: and that should be sort of heartfelt. You know, you

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Speaker 9: shouldn't need AI.

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Speaker 4: To write that.

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Speaker 9: Maybe I could understand what that, but you know, to

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Speaker 9: go back with a reply to someone who's given you

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Speaker 9: a compliment, Surely you could write something yourself.

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Speaker 12: Without Yeah, we use Would you ever use it?

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Speaker 3: Do you ever think you'd use it? Tanya to say, So,

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Speaker 3: you've had a massive day and you've got a bit

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Speaker 3: of writing to do, and you've got a bit of

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Speaker 3: testimonials to write or to get another listing up there,

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Speaker 3: and your brain's a bit mashed. You think I'm just

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Speaker 3: going to use chet GPT to give me a bit

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Speaker 3: of a hand to have a think about how to

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Speaker 3: write this. Quite often, when it spits out what you

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Speaker 3: ask of it, it's it's absolute garbage. But that just

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Speaker 3: gives you a bit of a structure to work with.

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Speaker 3: Is that would that be something you'd ever tried.

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Speaker 11: I've never.

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Speaker 9: I've never done it. I stay up late and if

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Speaker 9: I can't, if I have writer's block, I just get

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Speaker 9: up early enough when it comes to me the next morning.

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Speaker 9: That's honor, so you know. And that's how that's how

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Speaker 9: I run my business. And I'm going to keep doing

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Speaker 9: it like that. If people want to use it that

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Speaker 9: it's up to them. But yes, not for me. But

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Speaker 9: who knows down the track?

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Speaker 13: But not now?

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Speaker 6: On?

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Speaker 2: T't you? And when you say that, you can spot

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Speaker 2: it before you put it through an AI check her?

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Speaker 2: Is there anything particular that stands out to you that

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Speaker 2: suggests it's AI? Is there anything you look for? Is

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Speaker 2: it just a vibe?

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Speaker 10: It's really just a vibe.

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Speaker 14: It's just a vibe.

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Speaker 9: I think if you've you know, if you've written and read,

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Speaker 9: you just click it up. It's almost like a little

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Speaker 9: bit food perfect and we're not and we're not perfect.

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Speaker 9: We're humans, you know, and so I might. I'm not

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Speaker 9: going to judge someone for not having something perfect, but

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Speaker 9: I probably do. It's a little bit lazy, a little bit,

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Speaker 9: you know. Authenticity is what it's all about I reckon

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Speaker 9: these days because there's so much fakery around.

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Speaker 2: I don't want to be yeah, yeah.

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Speaker 3: I like your soul, Tanya. You know I wish I

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Speaker 3: was a bit more like that. I don't use AI

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Speaker 3: hell of a lot, but I you know, I tap

434
00:19:53,053 --> 00:19:53,973
Speaker 3: into it from time to time.

435
00:19:54,053 --> 00:19:55,093
Speaker 2: Thank you so much for your call.

436
00:19:55,173 --> 00:19:55,292
Speaker 10: Now.

437
00:19:55,333 --> 00:19:57,573
Speaker 2: When I wrote this article last night on AI for

438
00:19:57,933 --> 00:20:01,213
Speaker 2: my substack, I got all these comments the next day

439
00:20:01,253 --> 00:20:04,013
Speaker 2: that were basically, oh, we know that's not AI because

440
00:20:04,013 --> 00:20:07,093
Speaker 2: there's so many spelling errors. You know, it's for the

441
00:20:07,133 --> 00:20:09,213
Speaker 2: Titania's point, it's not perfec. I wonder if people are

442
00:20:09,213 --> 00:20:13,653
Speaker 2: going to start putting mistakes in because I understand in music,

443
00:20:13,733 --> 00:20:16,093
Speaker 2: because AI music is produced and it's in a certain

444
00:20:16,133 --> 00:20:18,333
Speaker 2: way that that bands now are trying to bring a

445
00:20:18,333 --> 00:20:20,813
Speaker 2: little bit of mistiming in the drums and playing the

446
00:20:20,813 --> 00:20:23,933
Speaker 2: guitars a little looser, just to indicate that it's a

447
00:20:24,013 --> 00:20:27,613
Speaker 2: human that's making it, because there's such a stigma around

448
00:20:27,813 --> 00:20:28,653
Speaker 2: around AI.

449
00:20:28,613 --> 00:20:30,373
Speaker 3: To err as to human. And I think you bang

450
00:20:30,453 --> 00:20:32,613
Speaker 3: on that those mistakes is kind of what makes us

451
00:20:32,613 --> 00:20:35,133
Speaker 3: who we are. Whereas CHET, GBT and the like try

452
00:20:35,173 --> 00:20:36,893
Speaker 3: to get it perfect and it just feels cold.

453
00:20:37,013 --> 00:20:39,493
Speaker 2: Although I have I'll give an example for this book

454
00:20:39,533 --> 00:20:41,973
Speaker 2: that I was reading last night and it made me

455
00:20:42,013 --> 00:20:44,573
Speaker 2: realize it was AI. I've got an example later on

456
00:20:44,853 --> 00:20:47,292
Speaker 2: about it. And one of the things that you can

457
00:20:47,293 --> 00:20:51,093
Speaker 2: spot and AIS forced or odd analogies or similes. AI

458
00:20:51,173 --> 00:20:54,373
Speaker 2: frequently inserts strained comparisons that try to sound profound but

459
00:20:55,093 --> 00:20:57,733
Speaker 2: land awkwardly. Yeah, I listen to listen to this here.

460
00:20:58,613 --> 00:21:01,532
Speaker 2: And a sport defined by speed and danger safety is

461
00:21:01,533 --> 00:21:04,493
Speaker 2: the invisible hero that allows drivers to compete at the

462
00:21:04,533 --> 00:21:07,573
Speaker 2: limit while returning home unharmed. It's a testament to the

463
00:21:07,733 --> 00:21:10,493
Speaker 2: ingenuity and dedication of the Formula one community, proving that

464
00:21:10,533 --> 00:21:12,973
Speaker 2: even in the pursuit of speed, human life remains the

465
00:21:13,013 --> 00:21:17,453
Speaker 2: highest privatity. It's so bland. But and a sport defined

466
00:21:17,453 --> 00:21:19,733
Speaker 2: by speed and danger safety is the invisible hero.

467
00:21:19,893 --> 00:21:21,053
Speaker 3: What a terrible analogy.

468
00:21:21,133 --> 00:21:24,333
Speaker 2: But how is it? How is it invisible? You've got

469
00:21:24,773 --> 00:21:28,653
Speaker 2: they're wearing helmets, they've got the halo, they've got the hands,

470
00:21:28,693 --> 00:21:31,333
Speaker 2: you know, the hidden neck support. They've got fire retardant

471
00:21:31,373 --> 00:21:36,333
Speaker 2: suits and gloves. They've got a big green Aston Martin

472
00:21:36,733 --> 00:21:39,133
Speaker 2: safety car following them. It's not the invisible hero. If

473
00:21:39,173 --> 00:21:41,493
Speaker 2: you watch if one there's safety everywhere.

474
00:21:41,533 --> 00:21:43,013
Speaker 3: Yeah, AI, you're an idiot.

475
00:21:43,053 --> 00:21:46,373
Speaker 2: So that's that's just a stupid, bland thing and an

476
00:21:46,413 --> 00:21:51,733
Speaker 2: exampt like a forced analogy that makes no sense.

477
00:21:51,773 --> 00:21:53,973
Speaker 3: Ye love it right. Keep those calls coming through on

478
00:21:54,053 --> 00:21:57,653
Speaker 3: eight hundred and eighty ten eighty headlines coming up with Wendy.

479
00:21:57,733 --> 00:21:59,693
Speaker 3: Then we'll get to more of your calls. It is

480
00:21:59,733 --> 00:22:01,973
Speaker 3: twenty eight to two.

481
00:22:02,773 --> 00:22:05,773
Speaker 15: US talk said the headlines We's blue bubble taxis it's

482
00:22:05,813 --> 00:22:08,693
Speaker 15: no trouble with a blue bubble of forty ten year

483
00:22:08,733 --> 00:22:11,413
Speaker 15: old has been jailed for three years and three months

484
00:22:11,413 --> 00:22:13,733
Speaker 15: for the fatal stabbing of a fellow teenager at a

485
00:22:13,773 --> 00:22:17,373
Speaker 15: Duneedin bus hub. The teen whose name is suppressed stamped

486
00:22:17,493 --> 00:22:21,173
Speaker 15: sixteen year old n Aretana McLaren on Great King Street.

487
00:22:21,213 --> 00:22:27,333
Speaker 15: In May twenty twenty four, warnings from Civil Defense. As

488
00:22:27,373 --> 00:22:29,653
Speaker 15: the top of the South Island prepares for more rain,

489
00:22:30,053 --> 00:22:33,133
Speaker 15: Met Services issued a red heavy rain warning for Tasman

490
00:22:33,413 --> 00:22:36,453
Speaker 15: off the back of three weeks of rain saturating the region.

491
00:22:36,853 --> 00:22:40,373
Speaker 15: Nelson Tasman's under a state of emergency and has activated

492
00:22:40,413 --> 00:22:44,733
Speaker 15: its Emergency Operations Center. The wild weather hitting Northland has

493
00:22:44,813 --> 00:22:48,533
Speaker 15: cut power to almost five hundred Top Energy customers after

494
00:22:48,573 --> 00:22:51,373
Speaker 15: a falling tree struck power lines in Fangarawa in the

495
00:22:51,413 --> 00:22:54,733
Speaker 15: Far North. The power went out shortly after eleven thirty

496
00:22:54,813 --> 00:22:58,213
Speaker 15: this morning. Brazil has vowed to implement a tit for

497
00:22:58,333 --> 00:23:01,853
Speaker 15: tat policy if Donald Trump follows through on opposing fifty

498
00:23:01,933 --> 00:23:05,653
Speaker 15: percent tariffs on Brazilian exports. The Brazilian president says he

499
00:23:05,653 --> 00:23:08,253
Speaker 15: would like to find a diplomatic solution by August firs,

500
00:23:08,693 --> 00:23:11,413
Speaker 15: but if not, the law of reciprocity will be put

501
00:23:11,453 --> 00:23:15,133
Speaker 15: into place. Plus a sad lass why this prostate cancer

502
00:23:15,173 --> 00:23:18,133
Speaker 15: treatment is disappearing in New Zealand. Read more at ends

503
00:23:18,133 --> 00:23:20,573
Speaker 15: at Harold Premium. Now back to Matt and Tyler.

504
00:23:20,733 --> 00:23:22,893
Speaker 3: Thank you very much, Wendy, and we are talking about

505
00:23:23,533 --> 00:23:24,333
Speaker 3: have a chat with.

506
00:23:24,293 --> 00:23:27,373
Speaker 1: The lads on eight hundred eighty ten eighty Matt Heath

507
00:23:27,413 --> 00:23:29,773
Speaker 1: and Tyler Adams afternoons used talk.

508
00:23:29,853 --> 00:23:32,693
Speaker 3: Sa'd be very good afternoon to you. It is twenty

509
00:23:32,813 --> 00:23:35,253
Speaker 3: two to two and we're talking about artificial intelligence. How

510
00:23:35,293 --> 00:23:36,493
Speaker 3: well do you think you can spot it?

511
00:23:36,733 --> 00:23:39,253
Speaker 2: Do reckon? If we replace you with Ai, Tyler, we

512
00:23:39,253 --> 00:23:40,893
Speaker 2: wouldn't play two sets of ads in a row with

513
00:23:40,933 --> 00:23:42,533
Speaker 2: you saying something kind of weird in the middle.

514
00:23:42,773 --> 00:23:44,533
Speaker 3: I don't want to check the person under the bus

515
00:23:44,533 --> 00:23:47,773
Speaker 3: who was responsible for that. Weirdly, it wasn't you for once,

516
00:23:48,493 --> 00:23:50,733
Speaker 3: but whoever did it is an absolute gem of a person,

517
00:23:50,773 --> 00:23:52,013
Speaker 3: so I wouldn't dare do that to them.

518
00:23:52,253 --> 00:23:54,413
Speaker 2: This text says, hey, guys, AI is fine to use

519
00:23:54,413 --> 00:23:56,733
Speaker 2: in business setting. In a business setting, however, I would

520
00:23:56,813 --> 00:23:59,293
Speaker 2: draw the line at using it to craft personal emails

521
00:23:59,373 --> 00:24:02,213
Speaker 2: or rejection letters to candidates in the job, etceter. Where

522
00:24:02,213 --> 00:24:06,013
Speaker 2: it is most useful is as an IF efficiency multiplier.

523
00:24:06,013 --> 00:24:08,653
Speaker 2: For example, I could spend hours writing a technical script

524
00:24:08,653 --> 00:24:10,453
Speaker 2: to achieve something, or I can get AI to do

525
00:24:10,493 --> 00:24:13,213
Speaker 2: it in a few moments and then troubleshoot it because AI,

526
00:24:13,293 --> 00:24:18,173
Speaker 2: more often than not will get something wrong anyway. Cheers. Next,

527
00:24:18,173 --> 00:24:20,493
Speaker 2: So that goes to talking about the rejection letter.

528
00:24:20,573 --> 00:24:22,853
Speaker 3: Yeah, and I've found it because I didn't articulate it

529
00:24:22,933 --> 00:24:25,413
Speaker 3: very well last time. So this was a legitimate rejection

530
00:24:25,573 --> 00:24:28,013
Speaker 3: email sent to an applicant in the years, So I'll

531
00:24:28,053 --> 00:24:30,333
Speaker 3: just read it out. The headline of the email is

532
00:24:30,373 --> 00:24:33,853
Speaker 3: application update for position Hi blank, Thank you for applying

533
00:24:33,853 --> 00:24:36,373
Speaker 3: for blank. Unfortunately we will not be moving forward with

534
00:24:36,413 --> 00:24:39,933
Speaker 3: your application at this time. Then it goes into bracketed

535
00:24:40,333 --> 00:24:45,373
Speaker 3: rejection message. Write a warm but generic rejection email that

536
00:24:45,453 --> 00:24:49,053
Speaker 3: sounds polite yet firm. Do not mention specific reasons for rejection,

537
00:24:49,333 --> 00:24:51,933
Speaker 3: make the candidate feel like they were strongly considered, even

538
00:24:51,973 --> 00:24:54,893
Speaker 3: if they weren't remember to use candidate name in company

539
00:24:54,973 --> 00:24:57,453
Speaker 3: name variables. Then it finishes off with we appreciate your

540
00:24:57,533 --> 00:24:59,733
Speaker 3: interest and wish you all the best and your future.

541
00:24:59,853 --> 00:25:02,253
Speaker 2: Dat it's idiot, But that's dirty.

542
00:25:02,333 --> 00:25:03,373
Speaker 3: That is very dirty.

543
00:25:03,413 --> 00:25:06,453
Speaker 2: That's dirty just to pretend you care when you so

544
00:25:06,573 --> 00:25:07,173
Speaker 2: clearly don't.

545
00:25:07,293 --> 00:25:08,293
Speaker 3: Yeah, what a numsk.

546
00:25:08,333 --> 00:25:11,253
Speaker 2: Well sham, welcome to the show. You're a big fan

547
00:25:11,293 --> 00:25:11,733
Speaker 2: of AI?

548
00:25:12,533 --> 00:25:12,733
Speaker 6: Oh?

549
00:25:12,853 --> 00:25:14,093
Speaker 14: Yes, yes, I love it?

550
00:25:14,493 --> 00:25:17,213
Speaker 2: Yeah, and tell us how you use it, shim.

551
00:25:17,653 --> 00:25:17,933
Speaker 7: Look.

552
00:25:19,093 --> 00:25:20,453
Speaker 14: One of the ways that I used that was I

553
00:25:20,493 --> 00:25:22,813
Speaker 14: actually wrote my own book with it. So probably when

554
00:25:22,853 --> 00:25:26,253
Speaker 14: AI started about two years ago, I thought, well, I'm

555
00:25:26,253 --> 00:25:29,573
Speaker 14: going to write my own memoir. So I basically listed

556
00:25:29,613 --> 00:25:31,493
Speaker 14: all the events of my life that I could remember

557
00:25:31,813 --> 00:25:35,933
Speaker 14: and plugged it into AI. I then got family members

558
00:25:35,933 --> 00:25:38,933
Speaker 14: and I asked them five questions about where they were born, schools,

559
00:25:38,933 --> 00:25:41,733
Speaker 14: they went to things they had done, plugged that into AI,

560
00:25:42,013 --> 00:25:45,533
Speaker 14: and from there I printed a book about three hundred

561
00:25:45,573 --> 00:25:47,093
Speaker 14: pages it's available on Amazon.

562
00:25:47,733 --> 00:25:49,453
Speaker 2: What's it called, shim.

563
00:25:49,773 --> 00:25:51,773
Speaker 14: It's called Halftime because I was forty five at the

564
00:25:51,773 --> 00:25:53,933
Speaker 14: time and I thought, you know, I went into AI

565
00:25:54,013 --> 00:25:55,933
Speaker 14: and statistically speaking, I thought I was going to make

566
00:25:55,973 --> 00:25:58,933
Speaker 14: it to ninety so or AI named it half the

567
00:25:59,013 --> 00:26:01,133
Speaker 14: time and in a way it went.

568
00:26:01,013 --> 00:26:02,613
Speaker 2: How many units have you shifted so far?

569
00:26:02,733 --> 00:26:05,933
Speaker 14: Shem sitting on a big fat zero at the moment?

570
00:26:06,253 --> 00:26:09,293
Speaker 2: Okay, right, so when you actually now you go, I.

571
00:26:09,293 --> 00:26:10,253
Speaker 16: Actually gave it out it.

572
00:26:11,133 --> 00:26:13,453
Speaker 14: I printed about twenty five copies and gave it out

573
00:26:13,493 --> 00:26:16,773
Speaker 14: for Christmas one year, much to my family's horror.

574
00:26:17,133 --> 00:26:22,253
Speaker 2: And how does it read, Jim.

575
00:26:20,493 --> 00:26:23,053
Speaker 14: It's okay, it gets a bit clunky, you know that

576
00:26:23,133 --> 00:26:25,373
Speaker 14: AI language. Yeah, it was a couple of years old,

577
00:26:25,373 --> 00:26:27,573
Speaker 14: and the kind of model has changed since then. But

578
00:26:28,453 --> 00:26:31,093
Speaker 14: I mean I then went put pictures in it and

579
00:26:31,453 --> 00:26:34,693
Speaker 14: my school reports. So it's quite a cool little memoir now,

580
00:26:35,013 --> 00:26:37,173
Speaker 14: But that's not something you can really sit down and read.

581
00:26:37,373 --> 00:26:40,093
Speaker 3: I'm just having to look at the cover. Halftime. It's

582
00:26:40,133 --> 00:26:43,373
Speaker 3: called written by Sam Bainbury. And you've made it pretty

583
00:26:43,373 --> 00:26:45,013
Speaker 3: clear right on the cover there that it has written

584
00:26:45,093 --> 00:26:47,613
Speaker 3: by chet Gpt. So that's good. You're making it pretty clear.

585
00:26:48,093 --> 00:26:49,973
Speaker 3: That you had some help from AI.

586
00:26:50,933 --> 00:26:53,333
Speaker 14: Definitely, definitely, Well, there was, and it saved me so

587
00:26:53,413 --> 00:26:56,533
Speaker 14: much time. The hardest but actually was my grandparents because

588
00:26:56,533 --> 00:26:59,053
Speaker 14: they've been in the war and I got some really

589
00:26:59,093 --> 00:27:02,573
Speaker 14: good information from there. But the hard but was AI

590
00:27:02,773 --> 00:27:05,373
Speaker 14: recognizing that and bringing that and making it nice and small.

591
00:27:06,373 --> 00:27:10,053
Speaker 2: Now you've you've clearly is Tyler pulled it out, you know,

592
00:27:10,573 --> 00:27:13,213
Speaker 2: signaled that it is AI. So I was reading a

593
00:27:13,213 --> 00:27:15,453
Speaker 2: book last night, How a Formula One Race Car Works?

594
00:27:15,493 --> 00:27:17,973
Speaker 2: What makes it the fastest car machine in the world,

595
00:27:17,973 --> 00:27:21,613
Speaker 2: Engineering science, innovation behind building the Ultimate racing Vehicle, written

596
00:27:21,613 --> 00:27:25,653
Speaker 2: by Ali Vayner, And I'm absolutely positive it's AI. But

597
00:27:25,693 --> 00:27:28,573
Speaker 2: there's nowhere it says it's AI, and I can't find

598
00:27:28,573 --> 00:27:30,413
Speaker 2: the guy I've tried to stalk on social media doesn't

599
00:27:30,413 --> 00:27:32,013
Speaker 2: seem to exist. How do you feel about that? How

600
00:27:32,013 --> 00:27:34,853
Speaker 2: do you feel like about a book on Amazon that

601
00:27:35,053 --> 00:27:37,973
Speaker 2: is AI but it's not marked as AI.

602
00:27:39,453 --> 00:27:40,933
Speaker 14: I think you've got to be pretty honest there.

603
00:27:42,093 --> 00:27:42,293
Speaker 4: Yeah.

604
00:27:42,853 --> 00:27:45,292
Speaker 14: I'm a school teacher, and so I think you've got

605
00:27:45,333 --> 00:27:47,093
Speaker 14: to be honest about when you use AI. And that's

606
00:27:47,093 --> 00:27:50,053
Speaker 14: what we teach our students. So it's about being really

607
00:27:50,053 --> 00:27:52,253
Speaker 14: clear when it's used, why it's used, and how it

608
00:27:52,333 --> 00:27:53,493
Speaker 14: was used, and.

609
00:27:53,453 --> 00:27:55,173
Speaker 2: The use is that causing problems for you as a

610
00:27:55,173 --> 00:27:57,733
Speaker 2: school teacher, the availability of AI for students.

611
00:27:58,453 --> 00:28:00,693
Speaker 14: I'm probably more intermediate, so not so much there. But

612
00:28:00,933 --> 00:28:03,533
Speaker 14: I've got kids, and my kids have been through college

613
00:28:03,613 --> 00:28:07,453
Speaker 14: and been through high school. They're pretty empty. It actually

614
00:28:08,533 --> 00:28:11,573
Speaker 14: that they know that. The schools do a pretty good job.

615
00:28:11,653 --> 00:28:14,013
Speaker 14: I think of drumming and around the importance of your

616
00:28:14,053 --> 00:28:16,813
Speaker 14: own work. And because i'm kind of a week, we

617
00:28:16,893 --> 00:28:21,213
Speaker 14: give your day for listen up. I think a lot

618
00:28:21,253 --> 00:28:23,653
Speaker 14: of schools do a really good job of being of

619
00:28:23,773 --> 00:28:27,013
Speaker 14: making students aware about what's appropriate and when it's appropriate

620
00:28:27,093 --> 00:28:28,693
Speaker 14: and how can we use Yeah, it.

621
00:28:28,693 --> 00:28:31,533
Speaker 2: Sounds like you're about you're boarding a plane or something.

622
00:28:31,693 --> 00:28:32,093
Speaker 2: That's what.

623
00:28:34,293 --> 00:28:34,973
Speaker 16: He down to cross.

624
00:28:35,333 --> 00:28:36,493
Speaker 3: Oh, very great city.

625
00:28:36,613 --> 00:28:38,253
Speaker 2: Well we'll let you get on. We'll let you get

626
00:28:38,293 --> 00:28:39,893
Speaker 2: on that plane then, Jim. But thank you so much

627
00:28:39,893 --> 00:28:43,333
Speaker 2: for talking to us. We really appreciate it. And I'll

628
00:28:43,373 --> 00:28:45,733
Speaker 2: be giving you a review of your book and good Reads.

629
00:28:46,333 --> 00:28:47,133
Speaker 5: Oh that'd be great.

630
00:28:47,693 --> 00:28:49,613
Speaker 3: Yeah, good luck, Jim, Thank you very much. What a

631
00:28:49,613 --> 00:28:52,653
Speaker 3: great caller. Hey, just before we get on to some

632
00:28:52,973 --> 00:28:56,253
Speaker 3: more calls, we mentioned about an hour ago that there

633
00:28:56,293 --> 00:28:59,733
Speaker 3: were reports of widespread power outages and total on, so

634
00:29:00,093 --> 00:29:02,173
Speaker 3: we're the latest story that's just come through to us.

635
00:29:02,613 --> 00:29:04,933
Speaker 3: Reports have come in of power outages and total on

636
00:29:05,013 --> 00:29:07,453
Speaker 3: are affecting traffic lights, so this is according to power

637
00:29:07,493 --> 00:29:11,013
Speaker 3: Codes website, several hundred properties in Mount Monganui out to

638
00:29:11,133 --> 00:29:16,973
Speaker 3: takey Temonga and Papa Moa Beach are affected. So multiple

639
00:29:17,093 --> 00:29:19,453
Speaker 3: cars are going which you have a way they want

640
00:29:19,493 --> 00:29:21,013
Speaker 3: to go according to a reson and pulling out in

641
00:29:21,013 --> 00:29:23,333
Speaker 3: front of people and not stopping because those lights are out.

642
00:29:23,453 --> 00:29:26,893
Speaker 3: A Powerco spokesperson said it was a transpower outage caused

643
00:29:27,813 --> 00:29:30,613
Speaker 3: by a fault at the Mount Monganui substation and they

644
00:29:30,773 --> 00:29:35,933
Speaker 3: are investigating it. There is no indication yet where that

645
00:29:36,053 --> 00:29:38,253
Speaker 3: power may come back on, but we will keep you

646
00:29:38,373 --> 00:29:42,293
Speaker 3: updated as the afternoon progresses, so stay tuned for that.

647
00:29:42,453 --> 00:29:45,893
Speaker 3: Right back to AI, are you able to tell what

648
00:29:46,053 --> 00:29:49,373
Speaker 3: is AI content or not? Eight hundred and eighty ten eighty.

649
00:29:49,773 --> 00:29:52,573
Speaker 2: So you're you're doing some work in this field.

650
00:29:53,253 --> 00:29:57,973
Speaker 17: Yes, And your previous caller is my worst nightmare. I'm

651
00:29:57,973 --> 00:30:01,173
Speaker 17: a purist when it comes to writing books.

652
00:30:02,013 --> 00:30:02,333
Speaker 6: I have.

653
00:30:03,533 --> 00:30:06,773
Speaker 17: I've been working towards trying to get a meeting with

654
00:30:07,373 --> 00:30:12,093
Speaker 17: Minister Scott Simpson to meet with authors and publishers. He's

655
00:30:12,173 --> 00:30:20,573
Speaker 17: the Minister for Communication and that's around the copyright and

656
00:30:19,333 --> 00:30:25,213
Speaker 17: the the laws that are currently not probably fit the

657
00:30:25,253 --> 00:30:29,213
Speaker 17: purpose for AI. But there's a current document out by

658
00:30:30,173 --> 00:30:33,333
Speaker 17: doctor Shane Retti, so he was a bit of a surprise.

659
00:30:34,133 --> 00:30:38,853
Speaker 17: They must be sharing the load Parliament and he's put

660
00:30:38,853 --> 00:30:43,733
Speaker 17: out two documents and they're around a New Zealand Strategy

661
00:30:43,813 --> 00:30:49,173
Speaker 17: for Artificial Intelligence and responsible AI guidance for businesses. Right,

662
00:30:49,213 --> 00:30:53,253
Speaker 17: so that's very pointy headed stuff. When I was when

663
00:30:53,293 --> 00:30:58,533
Speaker 17: I was reading through this, myyes glazed over and I went, oh,

664
00:30:58,693 --> 00:31:03,133
Speaker 17: this is a bit of a soulless sort of documentation.

665
00:31:03,413 --> 00:31:09,093
Speaker 17: And in the Minister's forward to those discussion papers, let

666
00:31:09,213 --> 00:31:13,733
Speaker 17: me quote here, he says, this strategy document was written.

667
00:31:13,373 --> 00:31:14,693
Speaker 6: With the assistance of AI.

668
00:31:14,973 --> 00:31:18,973
Speaker 17: This demonstrates our commitment to walk the talk on AI

669
00:31:19,253 --> 00:31:24,893
Speaker 17: while maintaining appropriate safeguards for sensitive information. This reflects our

670
00:31:24,933 --> 00:31:28,853
Speaker 17: commitment to transparent and responsible use of AI in government.

671
00:31:29,293 --> 00:31:33,013
Speaker 17: The future is AI powered and New Zealand is ready

672
00:31:33,013 --> 00:31:35,733
Speaker 17: for it. Well, I would say, how much of this

673
00:31:35,813 --> 00:31:39,493
Speaker 17: report actually is written by AI and do they really

674
00:31:39,573 --> 00:31:43,933
Speaker 17: understand Okay, this is saying that we'll give you safeguards

675
00:31:43,933 --> 00:31:46,893
Speaker 17: to businesses. But when i'm the issue I'm talking about

676
00:31:47,013 --> 00:31:52,293
Speaker 17: is protecting authors and publishers from work that is taken

677
00:31:53,013 --> 00:31:56,133
Speaker 17: which trains AI. So for AI to give you the

678
00:31:56,213 --> 00:32:01,453
Speaker 17: data they need to have good quality writing and New

679
00:32:01,573 --> 00:32:04,693
Speaker 17: Zealand authorsm amongst others around the world have been caught

680
00:32:04,773 --> 00:32:09,733
Speaker 17: up and caught what they call scraping. So AI can't

681
00:32:09,853 --> 00:32:14,813
Speaker 17: create new content that you know, that's got to come

682
00:32:14,853 --> 00:32:18,933
Speaker 17: from somewhere, and these are issues for the publishing.

683
00:32:18,453 --> 00:32:21,333
Speaker 18: Industry and the.

684
00:32:23,093 --> 00:32:26,933
Speaker 17: Yeah, you can't accelerate content creation. You've got to get

685
00:32:26,973 --> 00:32:30,893
Speaker 17: that information from somewhere. So I thought it was very interesting.

686
00:32:31,493 --> 00:32:34,613
Speaker 17: You know, obviously not everyone's going to rush out and

687
00:32:34,653 --> 00:32:39,053
Speaker 17: read Shane Rettie's discussion paper, but I was just making

688
00:32:39,053 --> 00:32:42,493
Speaker 17: the point that he's talking about AI and her safety

689
00:32:42,493 --> 00:32:45,653
Speaker 17: of it, and then he used AI in the report,

690
00:32:45,773 --> 00:32:48,973
Speaker 17: and I just think as a minister, it's kind of

691
00:32:48,973 --> 00:32:51,893
Speaker 17: getting into a bit of a gray area there. How

692
00:32:51,973 --> 00:32:54,813
Speaker 17: much of AI do you used to you're a minister

693
00:32:54,933 --> 00:33:00,253
Speaker 17: of the crown, you are creating new laws for our citizens,

694
00:33:00,333 --> 00:33:02,013
Speaker 17: and how much of AI.

695
00:33:02,253 --> 00:33:02,893
Speaker 5: Do you trust?

696
00:33:02,893 --> 00:33:05,533
Speaker 17: I mean, you know, can AI be trusted? I mean,

697
00:33:05,653 --> 00:33:07,413
Speaker 17: we did book club the other night and I listed

698
00:33:07,413 --> 00:33:10,173
Speaker 17: a book and up on the Facebook. You know, underneath

699
00:33:10,173 --> 00:33:13,453
Speaker 17: it there's AI Suggestions And when I looked at it,

700
00:33:13,453 --> 00:33:16,173
Speaker 17: it was the same title, but it was a completely

701
00:33:16,213 --> 00:33:16,893
Speaker 17: different books.

702
00:33:16,973 --> 00:33:18,973
Speaker 2: So do you do you find it interesting so that

703
00:33:19,133 --> 00:33:22,573
Speaker 2: you could tell that that was AI before you read

704
00:33:23,293 --> 00:33:26,333
Speaker 2: the piece that that admitted that it was AI. You

705
00:33:26,333 --> 00:33:29,053
Speaker 2: could sense it. And what do you think you sensed

706
00:33:29,093 --> 00:33:29,533
Speaker 2: about it?

707
00:33:31,093 --> 00:33:31,853
Speaker 10: It's flat.

708
00:33:31,973 --> 00:33:35,013
Speaker 17: It's flat writing you were talking about before. It's kind

709
00:33:35,013 --> 00:33:37,813
Speaker 17: of like a soulless writing. You know, AI doesn't give

710
00:33:37,813 --> 00:33:40,813
Speaker 17: your opinions. So when you're reading and you read something

711
00:33:40,853 --> 00:33:45,933
Speaker 17: that's flat, not descriptive, and the sentences I am getting

712
00:33:45,933 --> 00:33:49,973
Speaker 17: into technical stuff now, the sentences are very disjointed and

713
00:33:50,773 --> 00:33:54,213
Speaker 17: don't always slow and connect. And it's definitely there's a

714
00:33:54,253 --> 00:33:56,173
Speaker 17: disconnect between each paragraph.

715
00:33:56,653 --> 00:34:00,973
Speaker 2: Yeah, that's sort of vague, upbeat jargon and and and

716
00:34:00,973 --> 00:34:03,493
Speaker 2: and this and these things to look out for that

717
00:34:03,493 --> 00:34:06,973
Speaker 2: that I listened in this article and fuller adjectives. And

718
00:34:07,053 --> 00:34:08,133
Speaker 2: there is a lot.

719
00:34:07,973 --> 00:34:13,453
Speaker 17: Of a lot of writing has descriptives. It's a conversational

720
00:34:13,573 --> 00:34:16,533
Speaker 17: style and you can tell it just.

721
00:34:16,533 --> 00:34:19,573
Speaker 2: Got and there's no point of view, so there's no,

722
00:34:20,013 --> 00:34:22,333
Speaker 2: humans love to write about themselves and share their own

723
00:34:22,373 --> 00:34:25,773
Speaker 2: story and their own location and families, all that kind

724
00:34:25,813 --> 00:34:28,133
Speaker 2: of stuff. And AI has no point of view, so

725
00:34:28,173 --> 00:34:31,173
Speaker 2: it doesn't give you who it is because it is nothing.

726
00:34:31,533 --> 00:34:34,093
Speaker 2: And so if you're reading something, and what we find

727
00:34:34,213 --> 00:34:37,173
Speaker 2: is meaningful when read something as finding out the human

728
00:34:37,253 --> 00:34:40,853
Speaker 2: part of what's being written. Even in something like a

729
00:34:40,893 --> 00:34:44,413
Speaker 2: policy document, there's still often a personal part to it

730
00:34:44,453 --> 00:34:48,253
Speaker 2: and the person's story. And you start as a human,

731
00:34:48,293 --> 00:34:52,213
Speaker 2: you spot it, it hits the uncanny valley and you go, this,

732
00:34:52,213 --> 00:34:54,893
Speaker 2: this is so vague, This is so bland. This is

733
00:34:54,933 --> 00:35:02,493
Speaker 2: so strangely over explained and vaguely vaguely talking about the

734
00:35:02,493 --> 00:35:06,493
Speaker 2: issue and overly positive positive fashion. Hey, thank you so

735
00:35:06,613 --> 00:35:08,973
Speaker 2: much for it, for you for your call, So I

736
00:35:09,013 --> 00:35:11,172
Speaker 2: really appreciate that. And it's very interesting work that you're

737
00:35:11,213 --> 00:35:12,533
Speaker 2: involved in. So thank you so much.

738
00:35:12,573 --> 00:35:15,653
Speaker 3: Yeah, great insights. Hey, I just got a text here. Guys,

739
00:35:15,653 --> 00:35:18,413
Speaker 3: don't tell us about your substack without telling us its

740
00:35:18,533 --> 00:35:20,732
Speaker 3: channel name, which is a fair point. So to go

741
00:35:20,853 --> 00:35:23,732
Speaker 3: have a read of Matt's article, go to Matt Heath

742
00:35:23,773 --> 00:35:27,973
Speaker 3: dot substack dot com about the formula one book that

743
00:35:28,013 --> 00:35:30,133
Speaker 3: you were reading that was highly likely written by AI.

744
00:35:30,973 --> 00:35:33,933
Speaker 2: Yeah, and you know that my article is not written

745
00:35:33,933 --> 00:35:37,333
Speaker 2: by AI because of the huge amount of spelling her.

746
00:35:37,373 --> 00:35:38,813
Speaker 2: Isn't it that everyone keeps pulling out?

747
00:35:38,893 --> 00:35:42,093
Speaker 3: Yeah? It is nine minutes to two back very shortly

748
00:35:42,133 --> 00:35:43,013
Speaker 3: with more of your calls.

749
00:35:44,293 --> 00:35:47,853
Speaker 1: Matt Heath, Taylor Adams taking your calls on eight hundred

750
00:35:47,853 --> 00:35:50,493
Speaker 1: and eighty ten eighty. It's Matt Heath and Taylor Adams

751
00:35:50,533 --> 00:35:52,693
Speaker 1: Afternoons News DOGSB.

752
00:35:52,933 --> 00:35:54,573
Speaker 3: News Talks THEREB. It is seven to two.

753
00:35:55,213 --> 00:35:58,613
Speaker 2: Hi. AI isn't perfect, but it saves time, helps us

754
00:35:58,693 --> 00:36:01,613
Speaker 2: learn faster and can do jobs that keep people safer.

755
00:36:01,893 --> 00:36:04,933
Speaker 2: It's a tool, m Dash. It all depends how we

756
00:36:05,053 --> 00:36:06,813
Speaker 2: use it. That was written by AI and sent him

757
00:36:06,813 --> 00:36:07,212
Speaker 2: from Allen.

758
00:36:07,533 --> 00:36:08,093
Speaker 3: Thanks Alloy.

759
00:36:08,173 --> 00:36:10,292
Speaker 2: That's got all those things that I was talking about

760
00:36:10,333 --> 00:36:12,293
Speaker 2: before and including the MDET, hasn't it.

761
00:36:12,813 --> 00:36:14,933
Speaker 3: It's sort of sort ofally written.

762
00:36:14,853 --> 00:36:21,413
Speaker 2: Vaguely positive and working the threes. AI isn't perfect, but

763
00:36:21,573 --> 00:36:25,373
Speaker 2: it's got the x x Y parallel structure in there

764
00:36:25,413 --> 00:36:28,333
Speaker 2: as well, classic AI lifeless. This Texas is and I

765
00:36:28,333 --> 00:36:30,773
Speaker 2: think this is this is key. Some dating sites off

766
00:36:30,773 --> 00:36:33,493
Speaker 2: of the option of enhancing your profile using AI. It

767
00:36:33,493 --> 00:36:36,213
Speaker 2: makes the person sound like a boring unreal character in

768
00:36:36,253 --> 00:36:39,453
Speaker 2: a romance novel. I'll be interested to know the stats

769
00:36:39,453 --> 00:36:44,093
Speaker 2: on how much AI writing on dating sites affects people's

770
00:36:44,133 --> 00:36:48,652
Speaker 2: willingness to go further and contact them. Richard, Welcome to

771
00:36:48,653 --> 00:36:48,973
Speaker 2: the show.

772
00:36:50,333 --> 00:36:53,652
Speaker 19: Good day, guys. I'm trying to do something with a

773
00:36:53,693 --> 00:36:55,493
Speaker 19: well known New Zealand company at the moment. One of

774
00:36:55,533 --> 00:36:58,253
Speaker 19: the things they asked for was a health a safety

775
00:36:58,333 --> 00:37:00,652
Speaker 19: plan and I've never done one before. I mean I've

776
00:37:00,653 --> 00:37:03,773
Speaker 19: never had to. And I had a look online on

777
00:37:03,813 --> 00:37:05,813
Speaker 19: Google and you can get them, but you've got to

778
00:37:05,813 --> 00:37:08,173
Speaker 19: buy them. So I went on chat GPT and I

779
00:37:08,213 --> 00:37:11,413
Speaker 19: am to them, please produce a health and safety planet's

780
00:37:11,413 --> 00:37:14,493
Speaker 19: compliant with your Zealand law. And instantly it came back

781
00:37:14,493 --> 00:37:17,293
Speaker 19: with this five page document that was ninety percent perfect.

782
00:37:17,813 --> 00:37:19,973
Speaker 19: And all I did was just went through and I

783
00:37:20,013 --> 00:37:21,733
Speaker 19: cleaned a couple of things up, changed a few things.

784
00:37:21,773 --> 00:37:24,213
Speaker 19: It was even format of properly similar to the company.

785
00:37:24,253 --> 00:37:25,773
Speaker 19: They came straight back and said, yeah, we love it,

786
00:37:25,773 --> 00:37:26,133
Speaker 19: it's great.

787
00:37:26,293 --> 00:37:28,973
Speaker 2: All done. Do you think did you did you tell

788
00:37:28,973 --> 00:37:31,013
Speaker 2: them that you'd used AI to generate it?

789
00:37:31,973 --> 00:37:33,573
Speaker 19: No, because I want to look generous.

790
00:37:34,973 --> 00:37:37,053
Speaker 2: Do you think they were do you think they were

791
00:37:37,773 --> 00:37:41,293
Speaker 2: being reticent to use it if you said that GPT

792
00:37:41,453 --> 00:37:43,093
Speaker 2: had generated ninetyer cent of it.

793
00:37:44,093 --> 00:37:45,893
Speaker 19: No, because I think we're just ticking boxes. They just

794
00:37:45,893 --> 00:37:46,933
Speaker 19: want out and save the plan.

795
00:37:47,133 --> 00:37:50,493
Speaker 2: Yeah, themselves, and that's when ai AI is great for

796
00:37:50,573 --> 00:37:52,772
Speaker 2: ticking boxes. Richard that that's a very good point. Sorry,

797
00:37:52,813 --> 00:37:54,773
Speaker 2: you can do you think so? Yeah?

798
00:37:54,813 --> 00:37:56,773
Speaker 19: It got me thinking about where AI might go, and

799
00:37:56,813 --> 00:37:58,652
Speaker 19: I love a good movie. For example, I sort of

800
00:37:58,733 --> 00:38:01,573
Speaker 19: started thinking, I wonder how many movie producers from the

801
00:38:01,653 --> 00:38:04,213
Speaker 19: eighties and early nineties started looking at the old contracts

802
00:38:04,253 --> 00:38:07,413
Speaker 19: to see if they might accidentally own the likeness rights

803
00:38:07,413 --> 00:38:10,773
Speaker 19: to people like Tom Hanks and Tom Cruise, because you

804
00:38:10,813 --> 00:38:12,813
Speaker 19: know that would be worth a huge amount of money.

805
00:38:13,133 --> 00:38:16,573
Speaker 19: And just a couple of nights ago, someone sent me

806
00:38:16,893 --> 00:38:21,173
Speaker 19: a AI generated film. It's a YouTube video. It's an

807
00:38:21,213 --> 00:38:27,933
Speaker 19: AI generated film completely and it's titled something like Yettie

808
00:38:28,573 --> 00:38:30,413
Speaker 19: Discovers a Woman in the Forest. It's one of the

809
00:38:30,453 --> 00:38:32,773
Speaker 19: most amazing things I've ever seen. It's very funny too,

810
00:38:32,773 --> 00:38:35,733
Speaker 19: in case you'll never look at it, and it's it's

811
00:38:35,773 --> 00:38:38,813
Speaker 19: got errors in it, it's got things that you notice.

812
00:38:38,973 --> 00:38:41,172
Speaker 19: You know, it's not quite there, but it's ninety five

813
00:38:41,213 --> 00:38:44,173
Speaker 19: percent there in terms of being an acceptable way of

814
00:38:44,253 --> 00:38:49,573
Speaker 19: doing things and absolutely extraordinary. And I suddenly realized that

815
00:38:49,813 --> 00:38:52,253
Speaker 19: AI is already where I thought it end up, and

816
00:38:52,293 --> 00:38:53,533
Speaker 19: that is big Foot and Bobes.

817
00:38:56,853 --> 00:39:00,293
Speaker 2: Much for your call, Richard, and look, it's only going

818
00:39:00,333 --> 00:39:02,413
Speaker 2: to get better at that. But we've got so many

819
00:39:02,413 --> 00:39:05,253
Speaker 2: calls coming through in text shore we continue this after too.

820
00:39:05,253 --> 00:39:07,053
Speaker 3: Absolutely we should and we want to hear from you.

821
00:39:07,093 --> 00:39:10,613
Speaker 3: Oh eight hundred and eighty AC. And also straight after

822
00:39:10,613 --> 00:39:12,453
Speaker 3: two o'clock we are going to catch up with civil

823
00:39:12,453 --> 00:39:15,333
Speaker 3: defense in the Nelson, Tasman area. There is a red

824
00:39:15,733 --> 00:39:18,973
Speaker 3: warning in play, some more heavy rain on its way,

825
00:39:19,173 --> 00:39:21,373
Speaker 3: so if you're in that area, stay tuned and we'll

826
00:39:21,373 --> 00:39:24,133
Speaker 3: give you the information you need. New sport and weather

827
00:39:24,253 --> 00:39:26,772
Speaker 3: coming up. You're listening to Matt and Tyler. A very

828
00:39:26,813 --> 00:39:29,653
Speaker 3: good afternoon to you. Great to have your company as.

829
00:39:29,453 --> 00:39:35,333
Speaker 1: Always, talking with you all afternoon. It's Matt Heathen, Taylor

830
00:39:35,373 --> 00:39:38,293
Speaker 1: Adams Afternoons News Talk ZIVY.

831
00:39:39,493 --> 00:39:42,333
Speaker 3: Very good afternoon to you, welcome back into the show.

832
00:39:42,333 --> 00:39:44,692
Speaker 3: Great to have your company as always. Very shortly we're

833
00:39:44,693 --> 00:39:47,213
Speaker 3: going to get back to our discussion about artificial intelligence.

834
00:39:47,213 --> 00:39:48,772
Speaker 3: There's so many people who want to have a chat

835
00:39:48,773 --> 00:39:52,812
Speaker 3: about that. But first warnings from Civil Defenses. The top

836
00:39:52,853 --> 00:39:56,213
Speaker 3: of the South Island prepares for more rain. Met Surfaces

837
00:39:56,253 --> 00:39:59,053
Speaker 3: issued a red heavy rain warning for Tasman off the

838
00:39:59,093 --> 00:40:03,173
Speaker 3: bank of three weeks of rain saturating the region. It

839
00:40:03,413 --> 00:40:05,813
Speaker 3: is a time of caution for people in that area.

840
00:40:05,893 --> 00:40:09,693
Speaker 3: Civil Defense Controller Alex lavere Us joins us now for

841
00:40:09,733 --> 00:40:11,693
Speaker 3: the latest. Alex, very good afternoon to you.

842
00:40:12,613 --> 00:40:15,693
Speaker 2: Hello, how are you good? Thank you? What's the situation, Alex?

843
00:40:16,773 --> 00:40:21,573
Speaker 20: Well, we obviously we were when we woke up this morning.

844
00:40:21,613 --> 00:40:24,533
Speaker 20: We had an orange orange rain warning that was elevated

845
00:40:24,573 --> 00:40:29,093
Speaker 20: to a red. But we've we had planned for the

846
00:40:29,133 --> 00:40:34,413
Speaker 20: worst case scenario and we've managed to put a lot

847
00:40:34,413 --> 00:40:38,013
Speaker 20: of resources in place. We're built on the good work

848
00:40:38,013 --> 00:40:40,053
Speaker 20: that we had done last week, or what the team

849
00:40:40,093 --> 00:40:43,013
Speaker 20: had done last week. We've got the New Zealand Defense

850
00:40:43,053 --> 00:40:47,373
Speaker 20: Force over, We've got search and rescue teams, we've got

851
00:40:47,493 --> 00:40:51,453
Speaker 20: the fans rapid water teams and we're going to be

852
00:40:51,493 --> 00:40:58,093
Speaker 20: placing them in strategic locations. We're opening up for welfare

853
00:40:58,133 --> 00:41:03,133
Speaker 20: centers and just I'm about to I've stepped out of

854
00:41:03,133 --> 00:41:05,813
Speaker 20: the EOC to take this call, but we're about to

855
00:41:05,853 --> 00:41:09,813
Speaker 20: put out an EMA which is an electric messaging alert,

856
00:41:11,893 --> 00:41:14,493
Speaker 20: which we'll be asking all of those that have been

857
00:41:14,973 --> 00:41:19,453
Speaker 20: affected previously by flooding and if those that are feeling

858
00:41:19,613 --> 00:41:24,253
Speaker 20: unsafe to evacuate their properties because we just can't take

859
00:41:24,373 --> 00:41:25,533
Speaker 20: chances with the red warning.

860
00:41:26,013 --> 00:41:29,693
Speaker 2: And like you on the ground in the area, I'm.

861
00:41:29,493 --> 00:41:33,093
Speaker 20: In the EOC and that's based in Richmond, TDC.

862
00:41:33,293 --> 00:41:35,973
Speaker 2: Yes, Yeah, what are the worst areas?

863
00:41:36,093 --> 00:41:42,653
Speaker 20: ELI, So the worst areas last week was multiple values

864
00:41:42,693 --> 00:41:50,933
Speaker 20: within Tasman so Tapawera, Rewalker, Motiweka. They've got multiple values,

865
00:41:51,053 --> 00:41:55,453
Speaker 20: multiple rivers and they were all hit. And the expectation

866
00:41:55,613 --> 00:41:58,373
Speaker 20: is now that this is a red warning, those places

867
00:41:58,373 --> 00:42:01,172
Speaker 20: are likely to be hit again. But that's on the

868
00:42:01,253 --> 00:42:04,772
Speaker 20: back of those places already a haven't been hit. It's

869
00:42:04,813 --> 00:42:08,813
Speaker 20: been wet, high ground, water very sodden, and it's just

870
00:42:08,813 --> 00:42:12,653
Speaker 20: going to add more misery to inn a ready bleak situation.

871
00:42:13,293 --> 00:42:17,213
Speaker 3: Is slips. The primary concern here alec or rivers flooding.

872
00:42:17,253 --> 00:42:18,853
Speaker 3: I take it as a point of concern as well.

873
00:42:19,853 --> 00:42:22,533
Speaker 20: I think there's both, but it's it's rivers. It's rivers

874
00:42:22,573 --> 00:42:26,373
Speaker 20: at the moment, and last week those rivers had a

875
00:42:26,413 --> 00:42:29,493
Speaker 20: mind of their own and they've carved out new paths

876
00:42:29,693 --> 00:42:33,813
Speaker 20: and they've they've caused substantial damage to properties and it's

877
00:42:33,853 --> 00:42:36,212
Speaker 20: those same properties that are in the firing line again.

878
00:42:36,413 --> 00:42:40,373
Speaker 20: But yes, and along with that is roads, and obviously

879
00:42:40,853 --> 00:42:45,652
Speaker 20: in and out of Nelson and Tasman, either side is

880
00:42:46,813 --> 00:42:50,613
Speaker 20: slips because we're we've got ranges on either side of us.

881
00:42:51,213 --> 00:42:54,773
Speaker 2: How long is this current with a situation expected to last.

882
00:42:55,853 --> 00:42:56,013
Speaker 6: So.

883
00:42:57,693 --> 00:43:02,893
Speaker 20: At least tomorrow and then hopefully things will care up.

884
00:43:02,893 --> 00:43:05,933
Speaker 20: The prognosis going forward is that at walll care up,

885
00:43:06,493 --> 00:43:11,453
Speaker 20: and we're expecting the heavy rain is starting to come

886
00:43:11,493 --> 00:43:14,093
Speaker 20: in now and then there will be a time lapse

887
00:43:14,213 --> 00:43:16,853
Speaker 20: in terms of the rivers, depending on how they respond,

888
00:43:16,893 --> 00:43:20,973
Speaker 20: some of them arising rapidly now. But certainly we will

889
00:43:21,053 --> 00:43:23,893
Speaker 20: be manning the EOC and have people on the ground

890
00:43:24,573 --> 00:43:27,093
Speaker 20: right through the night as well as tomorrow.

891
00:43:27,293 --> 00:43:30,533
Speaker 2: Where should people go for information and what advice have

892
00:43:30,573 --> 00:43:32,293
Speaker 2: you got for people in affected areas?

893
00:43:33,053 --> 00:43:35,973
Speaker 20: So thank you for that question. So first of all,

894
00:43:35,973 --> 00:43:40,453
Speaker 20: that can go to the Nelson Tasman Civil Defense site.

895
00:43:40,493 --> 00:43:44,293
Speaker 20: But the message is clear from our perspective. Please, if

896
00:43:44,333 --> 00:43:46,253
Speaker 20: you do not need to be on the roads, do

897
00:43:46,373 --> 00:43:48,933
Speaker 20: not be on the roads please. And if you're on

898
00:43:48,973 --> 00:43:54,093
Speaker 20: the roads, do not take things likely. Do not go

899
00:43:54,253 --> 00:43:58,133
Speaker 20: into areas that have water in them because you can

900
00:43:58,133 --> 00:43:59,853
Speaker 20: get it to trouble no matter even if you're in

901
00:43:59,893 --> 00:44:02,613
Speaker 20: a four by four. Please stay away from the rivers

902
00:44:02,613 --> 00:44:06,293
Speaker 20: from from a health perspective in terms of being washed

903
00:44:06,293 --> 00:44:09,813
Speaker 20: away and also contamination and not just take care of yourself.

904
00:44:09,813 --> 00:44:13,212
Speaker 20: Don't put your life at risk. But this we need

905
00:44:13,293 --> 00:44:16,053
Speaker 20: to take this seriously and you need to look after yourself.

906
00:44:16,173 --> 00:44:18,733
Speaker 20: Don't travel, stay away from the rivers. If you've been

907
00:44:18,773 --> 00:44:22,813
Speaker 20: flooded previously, please evacuate now.

908
00:44:22,893 --> 00:44:24,652
Speaker 2: Yeah, thank you so much for talking to us. Alec,

909
00:44:24,693 --> 00:44:25,693
Speaker 2: Can you stay safe out.

910
00:44:25,533 --> 00:44:26,893
Speaker 16: There, thank you.

911
00:44:27,053 --> 00:44:27,172
Speaker 5: Sit.

912
00:44:27,293 --> 00:44:30,333
Speaker 3: Yes, that is Civil Defense Controller Alec le Vertis And

913
00:44:30,333 --> 00:44:33,733
Speaker 3: if you are in the Nelson, Tasman region, some important

914
00:44:33,773 --> 00:44:36,252
Speaker 3: information there and if you need to find out more,

915
00:44:36,893 --> 00:44:41,253
Speaker 3: just Google search for the Emergency Emergency Tasman Civil Defense

916
00:44:41,293 --> 00:44:43,613
Speaker 3: website and they've got all the details there as well.

917
00:44:43,933 --> 00:44:45,893
Speaker 2: Right, and also if you've got anything to share, then

918
00:44:46,173 --> 00:44:48,213
Speaker 2: eight hundred and eighty ten eighty or text us on

919
00:44:48,293 --> 00:44:49,053
Speaker 2: nine two niney two.

920
00:44:49,213 --> 00:44:49,413
Speaker 4: Yep.

921
00:44:49,493 --> 00:44:51,172
Speaker 3: Yeah, fingers crossed for you guys down there.

922
00:44:51,213 --> 00:44:51,373
Speaker 4: Right.

923
00:44:51,373 --> 00:44:52,893
Speaker 3: It is twelve past two and we are going to

924
00:44:52,893 --> 00:44:56,373
Speaker 3: get back to our discussion about artificial intelligence. How good

925
00:44:56,413 --> 00:44:59,213
Speaker 3: do you think you are at spotting it? Is there

926
00:44:59,333 --> 00:45:03,893
Speaker 3: anything wrong with, for example, writing a book using artificial

927
00:45:03,933 --> 00:45:05,933
Speaker 3: intelligence as you may have been reading last night?

928
00:45:05,973 --> 00:45:08,573
Speaker 2: Man, Yeah, that's right. I found myself in the middle

929
00:45:08,573 --> 00:45:10,893
Speaker 2: of the night, couldn't sleep, so I downloaded a book

930
00:45:11,013 --> 00:45:14,133
Speaker 2: on on my kindle and then I was reading it.

931
00:45:14,293 --> 00:45:16,413
Speaker 2: I read about one hundred pages and then suddenly I thought,

932
00:45:16,413 --> 00:45:19,573
Speaker 2: hang on, I'm reading this is all AI. It's so bland.

933
00:45:20,173 --> 00:45:21,733
Speaker 2: So there's been a lot of people touching through and

934
00:45:21,773 --> 00:45:25,413
Speaker 2: asking for the seven signs that you're reading AI that

935
00:45:25,853 --> 00:45:28,533
Speaker 2: I read out before in the last hour, So we'll

936
00:45:28,573 --> 00:45:31,333
Speaker 2: run over them briefly again. Yep, absolutely tell you what

937
00:45:31,413 --> 00:45:33,973
Speaker 2: AI doesn't bouch like I just did. Then you know that,

938
00:45:34,053 --> 00:45:36,813
Speaker 2: you know, if we ever get replaced by AI, then

939
00:45:37,373 --> 00:45:39,293
Speaker 2: then they're not going to vouch into the mark like that,

940
00:45:39,333 --> 00:45:39,652
Speaker 2: will they?

941
00:45:39,773 --> 00:45:40,732
Speaker 3: Well maybe one day.

942
00:45:40,773 --> 00:45:43,093
Speaker 2: So enjoy that human that human touch like that.

943
00:45:43,373 --> 00:45:46,093
Speaker 3: Well, let's here, AI would have a field day trying

944
00:45:46,133 --> 00:45:50,853
Speaker 3: to emulate you, mate. And it's thirteen parts two.

945
00:45:50,773 --> 00:45:54,933
Speaker 1: Your home of afternoon Talk, Mad Heathen Taylor Adams Afternoons

946
00:45:55,173 --> 00:45:58,293
Speaker 1: Call eight hundred eighty ten eighty youth Talk said.

947
00:45:58,093 --> 00:46:02,413
Speaker 3: Be good afternoon. It is a quarter past two, and

948
00:46:02,453 --> 00:46:06,293
Speaker 3: we are talking about artificial intelligence. Can you spot artificial

949
00:46:06,293 --> 00:46:09,653
Speaker 3: intelligence when you see it, particularly in the form of writing.

950
00:46:09,933 --> 00:46:11,773
Speaker 3: I think that for most people it is for me,

951
00:46:11,813 --> 00:46:15,573
Speaker 3: it's very difficult to ascertain. For a lot of people,

952
00:46:15,773 --> 00:46:17,853
Speaker 3: what is AI or what is written by AI?

953
00:46:18,053 --> 00:46:20,333
Speaker 2: Yeah, that's right, And I've written an article on this

954
00:46:20,493 --> 00:46:24,533
Speaker 2: that you can read at Matdheath dot substock, substack dot

955
00:46:24,533 --> 00:46:25,093
Speaker 2: Matteath is.

956
00:46:25,093 --> 00:46:28,133
Speaker 3: It Mattheath dot substack dot com. Why am I reading

957
00:46:28,173 --> 00:46:30,813
Speaker 3: out the you know the address of your substance.

958
00:46:31,253 --> 00:46:33,252
Speaker 2: It's free for people to read, and it's a good reading.

959
00:46:34,373 --> 00:46:36,133
Speaker 2: But and apologies to people that were listening last hour

960
00:46:36,333 --> 00:46:38,813
Speaker 2: is going to reiterate. We're going to reset things first

961
00:46:38,813 --> 00:46:40,732
Speaker 2: for the new people that come in and every hour,

962
00:46:41,493 --> 00:46:44,652
Speaker 2: so these are I was reading this book called how

963
00:46:44,653 --> 00:46:46,933
Speaker 2: a Formula one Car Works? What makes it the fastest

964
00:46:46,933 --> 00:46:49,692
Speaker 2: car machine in the world. I should have spotted that

965
00:46:49,733 --> 00:46:52,893
Speaker 2: car machine and the world engineering, science, innovation behind building

966
00:46:52,893 --> 00:46:56,213
Speaker 2: the ultimate racing vehicle. And I was reading it in

967
00:46:56,733 --> 00:46:58,813
Speaker 2: lane to the night and suddenly I spotted that it

968
00:46:58,853 --> 00:47:00,893
Speaker 2: was AI. It just struck me, there's hour and I

969
00:47:00,893 --> 00:47:02,853
Speaker 2: wasn't quite sure so I did a bit of research

970
00:47:02,893 --> 00:47:04,373
Speaker 2: and wrote this article in the middle of the night,

971
00:47:04,413 --> 00:47:06,573
Speaker 2: so I couldn't sleep, And these are seven things to

972
00:47:06,613 --> 00:47:08,973
Speaker 2: look out for that I discovered. That's suggest it's AI,

973
00:47:09,093 --> 00:47:13,053
Speaker 2: and it really rings true for reading this. So frequent

974
00:47:13,133 --> 00:47:16,933
Speaker 2: use of M dashes, so large language models sprinkle M

975
00:47:17,013 --> 00:47:19,453
Speaker 2: dashes everywhere way more than humans do, because we can't

976
00:47:19,453 --> 00:47:21,933
Speaker 2: be bother doing it, because predacing it, producing an M

977
00:47:22,013 --> 00:47:26,413
Speaker 2: dash involves pressing option shift hyphen when human, we can't

978
00:47:26,413 --> 00:47:27,652
Speaker 2: be bother doing that. Even if we need to do

979
00:47:27,653 --> 00:47:30,133
Speaker 2: it human humans just don't do it. We'll use a

980
00:47:30,133 --> 00:47:32,893
Speaker 2: comera of a full stop or just a just a hyphen.

981
00:47:32,973 --> 00:47:37,693
Speaker 2: That's fine. So it's yeah, it's longer than a hyphen

982
00:47:38,493 --> 00:47:40,373
Speaker 2: an M dash. It's about the width of an M

983
00:47:40,613 --> 00:47:42,533
Speaker 2: that's why it's called it called an mdash. See that's

984
00:47:42,533 --> 00:47:44,733
Speaker 2: you see a lot of them. That's that's that's one clue.

985
00:47:45,613 --> 00:47:49,213
Speaker 2: A parallel a sentence structure. That's called the X Y

986
00:47:49,373 --> 00:47:52,453
Speaker 2: parallel sentence structure. It will be it's not just it's

987
00:47:52,493 --> 00:47:57,253
Speaker 2: also AI will use that constantly. They really focused on

988
00:47:57,333 --> 00:48:02,333
Speaker 2: listening threes and fives, vague upbeat jargon and fuller adjectives,

989
00:48:02,413 --> 00:48:08,133
Speaker 2: chronic restatement and over explanation are chronic, and AI fails

990
00:48:08,133 --> 00:48:11,173
Speaker 2: the check, you know, across all these things and forced

991
00:48:11,373 --> 00:48:13,733
Speaker 2: or odd analogies and similes.

992
00:48:13,813 --> 00:48:14,652
Speaker 3: Yes, so here we go.

993
00:48:15,333 --> 00:48:16,933
Speaker 2: I'll read you something from the book and see if

994
00:48:16,933 --> 00:48:20,453
Speaker 2: you spot any of it. This is a steering wheel. Okay, okay.

995
00:48:21,413 --> 00:48:23,453
Speaker 2: The steering wheel of a Formula one car is not

996
00:48:23,613 --> 00:48:27,213
Speaker 2: just a tool for turning m dash. It's a cockpit

997
00:48:27,493 --> 00:48:30,652
Speaker 2: within the cockpit, a hub of control that places an

998
00:48:30,773 --> 00:48:34,733
Speaker 2: entires car complexity at the driver's fingertips. In Formula one,

999
00:48:34,813 --> 00:48:37,293
Speaker 2: the steering wheel is more than advice for guiding the

1000
00:48:37,333 --> 00:48:40,333
Speaker 2: car around corners. It's a sophisticated piece of technology that

1001
00:48:40,453 --> 00:48:43,333
Speaker 2: serves as the driver's primary interface with the car. Packed

1002
00:48:43,333 --> 00:48:45,973
Speaker 2: with an array of buttons, dials, switches, and displays, it

1003
00:48:46,013 --> 00:48:48,333
Speaker 2: allows the driver to control virtually every aspect of the

1004
00:48:48,333 --> 00:48:52,133
Speaker 2: car's performance, from gearshift to its energy recovery systems in

1005
00:48:52,213 --> 00:48:55,973
Speaker 2: real time. That is so AI and rob kindly has

1006
00:48:55,973 --> 00:48:58,653
Speaker 2: steps are through. I use top level paid AI models

1007
00:48:58,653 --> 00:49:01,653
Speaker 2: and asked if the book tagged on Amazon how Formula

1008
00:49:01,653 --> 00:49:04,933
Speaker 2: one cars work on your SAB substack, and it confirms

1009
00:49:05,013 --> 00:49:06,133
Speaker 2: it was based on AI.

1010
00:49:06,853 --> 00:49:07,893
Speaker 3: Yeah, the good.

1011
00:49:07,933 --> 00:49:09,293
Speaker 2: So there you go. But you can sense it.

1012
00:49:09,493 --> 00:49:12,493
Speaker 3: Yeah, and I'll read I mean, you wrote this, but

1013
00:49:12,533 --> 00:49:15,173
Speaker 3: I'm just reading your wee bit the chapter on breaking

1014
00:49:15,493 --> 00:49:17,453
Speaker 3: and a couple of fantastic lines in there that I'll

1015
00:49:17,493 --> 00:49:21,293
Speaker 3: read very shortly. But that is that is phenomenal work

1016
00:49:21,293 --> 00:49:23,853
Speaker 3: from AI. I don't know phenomenal work, but clearly very

1017
00:49:23,933 --> 00:49:24,413
Speaker 3: very AI.

1018
00:49:24,573 --> 00:49:27,893
Speaker 2: Well, well, it's a three hundred page book, and you know,

1019
00:49:27,933 --> 00:49:29,693
Speaker 2: I got one hundred pages in before I realized that

1020
00:49:29,733 --> 00:49:33,333
Speaker 2: I'd learned absolutely nothing and it was just so bland.

1021
00:49:33,733 --> 00:49:35,613
Speaker 2: Do you think it would have put me to put

1022
00:49:35,653 --> 00:49:39,693
Speaker 2: me to sleep? But Kiwi Libraries more and Moreton Library

1023
00:49:39,733 --> 00:49:42,333
Speaker 2: is not taking books that are created by AI. There's

1024
00:49:42,373 --> 00:49:44,573
Speaker 2: problems with exams. Teachers are trying to deal with this.

1025
00:49:44,813 --> 00:49:49,293
Speaker 2: So are you getting better at spotting AI? Are you

1026
00:49:49,453 --> 00:49:51,453
Speaker 2: using it? Are you shamed to use it and you

1027
00:49:51,653 --> 00:49:53,933
Speaker 2: hiding that you're using it? Or do you don't care?

1028
00:49:54,173 --> 00:49:55,772
Speaker 2: Do you think less of people that are using ALI?

1029
00:49:55,973 --> 00:49:57,813
Speaker 2: Oh eight hundred and eighty ten eighty Z number to

1030
00:49:57,853 --> 00:49:59,652
Speaker 2: call Teresa, Welcome to the show.

1031
00:49:59,693 --> 00:50:03,773
Speaker 21: You love it, hi, Madam Tyler? Yeah, absolutely, unashamedly A

1032
00:50:03,933 --> 00:50:07,613
Speaker 21: huge fan of it. So I use it basically every

1033
00:50:07,613 --> 00:50:10,053
Speaker 21: single night. Like the last year, I've done a master's

1034
00:50:10,053 --> 00:50:14,172
Speaker 21: in AI teach my faun and it's almost like having

1035
00:50:14,213 --> 00:50:19,093
Speaker 21: a personal assistant. You absolutely huge fan. So I've got

1036
00:50:19,333 --> 00:50:21,253
Speaker 21: a small side business that I work on in the

1037
00:50:21,693 --> 00:50:24,413
Speaker 21: evenings and there's just the two of us that work

1038
00:50:24,453 --> 00:50:27,813
Speaker 21: on it, so you know you are resource for But

1039
00:50:27,893 --> 00:50:30,373
Speaker 21: at the same time you can teach AI to work

1040
00:50:30,413 --> 00:50:33,733
Speaker 21: for you and be your personality. So we've got a

1041
00:50:33,733 --> 00:50:38,813
Speaker 21: pet care brand called Frenchy Coo and essentially you can

1042
00:50:39,613 --> 00:50:41,893
Speaker 21: teach it the style of writing that you want inject

1043
00:50:41,893 --> 00:50:45,213
Speaker 21: the personality. It's certainly not land at all. If it

1044
00:50:45,693 --> 00:50:48,613
Speaker 21: starts writing things how I don't like it, I'll correct

1045
00:50:48,613 --> 00:50:51,732
Speaker 21: it and over Basically the course of the past year

1046
00:50:51,853 --> 00:50:56,133
Speaker 21: of created I guess the signature so are So for instance,

1047
00:50:56,133 --> 00:50:57,893
Speaker 21: when I go to write something, I will say it's

1048
00:50:57,933 --> 00:51:01,692
Speaker 21: for sc which is my code for French Eco, and

1049
00:51:01,733 --> 00:51:05,053
Speaker 21: it knows with him that everything that I basically needed

1050
00:51:05,573 --> 00:51:09,133
Speaker 21: to structure that is to my personality. So I use

1051
00:51:09,173 --> 00:51:13,293
Speaker 21: it to also create images. You can then layer in

1052
00:51:13,453 --> 00:51:19,333
Speaker 21: product shops with actual products into artificially generated images, but

1053
00:51:19,493 --> 00:51:23,053
Speaker 21: images that have real personality. At the same time, there's

1054
00:51:23,053 --> 00:51:24,333
Speaker 21: just so many things you can do with it.

1055
00:51:24,413 --> 00:51:27,133
Speaker 2: Do you think that people that read your output that's

1056
00:51:27,133 --> 00:51:29,293
Speaker 2: generated with the ou of AI, do you think that

1057
00:51:29,413 --> 00:51:32,413
Speaker 2: they know that it's AI generated or do you feel

1058
00:51:32,453 --> 00:51:35,652
Speaker 2: that they feel like they're getting a connection with you

1059
00:51:36,333 --> 00:51:37,773
Speaker 2: and your company, a real connection.

1060
00:51:38,573 --> 00:51:40,733
Speaker 21: Well, it's not one hundred percent AI. At the end

1061
00:51:40,773 --> 00:51:43,173
Speaker 21: of the day, I basically use it to brainstorm with

1062
00:51:43,413 --> 00:51:47,933
Speaker 21: and kind of it is my words, but then you

1063
00:51:48,053 --> 00:51:52,333
Speaker 21: basically are working with AI to flesh it out. And

1064
00:51:52,493 --> 00:51:55,853
Speaker 21: it's absolutely one hundred you know, one hundred percent me

1065
00:51:55,973 --> 00:51:58,692
Speaker 21: and my personality that's on the post when I do it.

1066
00:51:58,933 --> 00:52:01,533
Speaker 2: So it's trained on you. So at the start it

1067
00:52:01,533 --> 00:52:03,373
Speaker 2: would have been less like you, but it's been trade

1068
00:52:03,413 --> 00:52:05,732
Speaker 2: on you, so it becomes more and more you over time.

1069
00:52:05,973 --> 00:52:06,413
Speaker 22: Yeah.

1070
00:52:06,453 --> 00:52:08,893
Speaker 21: Absolutely, And I've created video with it.

1071
00:52:10,373 --> 00:52:10,573
Speaker 8: Yeah.

1072
00:52:10,613 --> 00:52:14,413
Speaker 21: Egan in the weekend. For instance, my dad, he's loving

1073
00:52:14,453 --> 00:52:17,652
Speaker 21: to color in books at the moment he's he's he's

1074
00:52:17,733 --> 00:52:19,652
Speaker 21: quite old and he had made a mistake on this

1075
00:52:19,693 --> 00:52:22,133
Speaker 21: painting and he was quite upset about it. We tried

1076
00:52:22,133 --> 00:52:24,573
Speaker 21: to find a replacement book for him, could not find.

1077
00:52:24,373 --> 00:52:24,773
Speaker 4: It at all.

1078
00:52:24,853 --> 00:52:27,973
Speaker 21: So I took a photo of his drawings. I put

1079
00:52:27,973 --> 00:52:29,973
Speaker 21: it into chet GPT and said, can you give me

1080
00:52:30,053 --> 00:52:33,173
Speaker 21: this uncolored with on a second, I had an unhaled

1081
00:52:33,293 --> 00:52:34,692
Speaker 21: picture that I could print off, and.

1082
00:52:34,693 --> 00:52:35,773
Speaker 22: He was reduced to tears.

1083
00:52:35,773 --> 00:52:36,493
Speaker 21: He was so happy.

1084
00:52:36,653 --> 00:52:36,893
Speaker 4: Wow.

1085
00:52:37,413 --> 00:52:38,973
Speaker 21: You know there is so much that you can do

1086
00:52:39,013 --> 00:52:41,973
Speaker 21: with it. It's disguise for limit, really, it really is.

1087
00:52:42,333 --> 00:52:45,053
Speaker 2: So you use it for business? Would you use it

1088
00:52:45,093 --> 00:52:49,493
Speaker 2: for personal correspondence to family members or I don't.

1089
00:52:49,373 --> 00:52:51,093
Speaker 22: Know, absolutely not romantically.

1090
00:52:52,253 --> 00:52:53,973
Speaker 21: Romantically no, my husband killed me.

1091
00:52:56,493 --> 00:52:58,453
Speaker 3: But what about what about thereasa, I'm just trying to

1092
00:52:58,453 --> 00:53:02,772
Speaker 3: think here, say that someone's gonna someone has has left

1093
00:53:02,773 --> 00:53:05,693
Speaker 3: your company, and you've got to write a goodbye speech

1094
00:53:05,733 --> 00:53:07,373
Speaker 3: for them, and you want a bit of help with

1095
00:53:07,373 --> 00:53:08,893
Speaker 3: that because you're just trying to think, how do I

1096
00:53:08,893 --> 00:53:13,173
Speaker 3: structure this. You'd never use AI just to help your structure. Obviously,

1097
00:53:13,213 --> 00:53:15,693
Speaker 3: you inject your own stories and personality into that, but

1098
00:53:15,693 --> 00:53:17,893
Speaker 3: you'd never use it to just help you figure out

1099
00:53:17,893 --> 00:53:18,813
Speaker 3: what the structure could be.

1100
00:53:19,893 --> 00:53:23,493
Speaker 21: I haven't, and I think it would probably be obvious

1101
00:53:23,813 --> 00:53:27,213
Speaker 21: because I haven't actually trained or used AI in a

1102
00:53:27,253 --> 00:53:30,373
Speaker 21: professional sense for my full time jobs. So I've specifically

1103
00:53:30,493 --> 00:53:34,493
Speaker 21: used it for my side business, and it's a very

1104
00:53:34,533 --> 00:53:39,013
Speaker 21: different language. It's quite a reverend, cheeky fund full of personality,

1105
00:53:39,653 --> 00:53:42,573
Speaker 21: but also very different to how I would structure the

1106
00:53:42,613 --> 00:53:44,172
Speaker 21: way I write within business as well.

1107
00:53:45,213 --> 00:53:48,733
Speaker 2: Yeah, and do you have you done any investigation into

1108
00:53:48,933 --> 00:53:52,693
Speaker 2: the engagement levels that you're getting with using you know,

1109
00:53:52,813 --> 00:53:55,652
Speaker 2: using AI as opposed to the engagement levels you get

1110
00:53:55,733 --> 00:53:59,693
Speaker 2: when you use you know, you just produce content in

1111
00:53:59,733 --> 00:54:00,693
Speaker 2: an analog fashion.

1112
00:54:02,333 --> 00:54:04,692
Speaker 21: Haven't done any investigation as such?

1113
00:54:05,253 --> 00:54:07,933
Speaker 2: Vague investigation, I guess, just vibe investigation.

1114
00:54:08,773 --> 00:54:11,013
Speaker 21: Yeah, but we do. We certainly do mix it up

1115
00:54:11,093 --> 00:54:15,253
Speaker 21: with you know, AI as well as obviously real life.

1116
00:54:15,733 --> 00:54:17,853
Speaker 21: So for instance, we go to a lot of dog

1117
00:54:17,973 --> 00:54:21,413
Speaker 21: markets and dog events and you know, do photo shoots

1118
00:54:21,413 --> 00:54:25,213
Speaker 21: with every wonderful fairy friend that comes half our stand,

1119
00:54:25,213 --> 00:54:28,573
Speaker 21: et cetera. And you know, they're very much real and

1120
00:54:28,613 --> 00:54:30,973
Speaker 21: there's there's some that are very obvious AI and I

1121
00:54:30,973 --> 00:54:33,573
Speaker 21: tagged them as AI, but they're still fun and you know,

1122
00:54:33,613 --> 00:54:36,013
Speaker 21: bring a smile to people's faces and they're dis gorgeous

1123
00:54:36,213 --> 00:54:40,293
Speaker 21: images and abstely love it. It's it's become a real

1124
00:54:40,333 --> 00:54:41,133
Speaker 21: passion project for me.

1125
00:54:41,173 --> 00:54:42,453
Speaker 22: I absolutely love it.

1126
00:54:42,973 --> 00:54:45,853
Speaker 2: Well, thank you so much for sharing and we love it.

1127
00:54:45,933 --> 00:54:49,053
Speaker 2: Plug on men Beast. What's the name of your company?

1128
00:54:49,053 --> 00:54:49,292
Speaker 23: Again?

1129
00:54:49,973 --> 00:54:50,373
Speaker 14: Awesome?

1130
00:54:50,413 --> 00:54:52,893
Speaker 21: So it's frenchy COO, which is fr E N C

1131
00:54:53,253 --> 00:54:54,093
Speaker 21: H I C O.

1132
00:54:54,573 --> 00:54:55,373
Speaker 2: What do you do?

1133
00:54:56,093 --> 00:55:00,253
Speaker 21: We do New Zealand made natural pick here shampoo's conditioners

1134
00:55:00,253 --> 00:55:01,173
Speaker 21: to Changler's clones.

1135
00:55:01,973 --> 00:55:03,893
Speaker 2: And was that was that sentence created by AI? Or

1136
00:55:03,933 --> 00:55:09,573
Speaker 2: was that just you? That's thank you for your call, Teresa?

1137
00:55:09,693 --> 00:55:10,652
Speaker 3: Yeah, thank you very much.

1138
00:55:10,693 --> 00:55:10,813
Speaker 6: Right.

1139
00:55:10,813 --> 00:55:12,613
Speaker 3: Oh one hundred and eighty ten eighty is the number

1140
00:55:12,653 --> 00:55:14,453
Speaker 3: to call. We're going to play some messages, but the

1141
00:55:14,453 --> 00:55:16,533
Speaker 3: calls are backed up here. If you can't get through,

1142
00:55:16,653 --> 00:55:18,293
Speaker 3: keep trying. It's twenty five past two.

1143
00:55:22,133 --> 00:55:25,812
Speaker 1: Matt Heathen, Tyler Adams afternoons call oh eight hundred eighty

1144
00:55:25,853 --> 00:55:27,453
Speaker 1: ten eighty on News Talk ZV.

1145
00:55:27,853 --> 00:55:30,213
Speaker 2: Very good afternoon. We are talking about artificial intelligence.

1146
00:55:30,253 --> 00:55:32,573
Speaker 3: Can you spot it? If you read what has clearly

1147
00:55:32,573 --> 00:55:35,293
Speaker 3: been written by artificial intelligence? Meant you were reading this

1148
00:55:35,333 --> 00:55:38,453
Speaker 3: book last night that you are ninety nine percent sure

1149
00:55:38,493 --> 00:55:39,493
Speaker 3: what was written by AI?

1150
00:55:39,733 --> 00:55:42,573
Speaker 2: Yeah, and Rob's kindly put it through a AI check

1151
00:55:42,573 --> 00:55:44,652
Speaker 2: and found out that it is AI. I should have

1152
00:55:44,693 --> 00:55:47,813
Speaker 2: known by the title though. How a Formula one race

1153
00:55:47,893 --> 00:55:50,973
Speaker 2: car works? What makes it the fastest car machine in

1154
00:55:51,013 --> 00:55:54,853
Speaker 2: the world? Who calls it a car machine? Engineering, science

1155
00:55:54,893 --> 00:55:58,573
Speaker 2: and innovation behind building the ultimate racing vehicle. I mean,

1156
00:55:58,613 --> 00:55:59,853
Speaker 2: would a human call their book that?

1157
00:56:00,653 --> 00:56:02,893
Speaker 3: Yeah, if you ask a check GBT to write you

1158
00:56:02,973 --> 00:56:05,293
Speaker 3: a book title, make it a bit more snappy, bit

1159
00:56:05,333 --> 00:56:08,573
Speaker 3: more snappy, check GBT. But I'm reading your article. You

1160
00:56:08,613 --> 00:56:11,133
Speaker 3: can find it on substant Matt Heath dot substate dot com.

1161
00:56:11,133 --> 00:56:13,933
Speaker 3: And this chapter on breaking this is this is an

1162
00:56:13,973 --> 00:56:16,172
Speaker 3: amazing couple of sentences. And I'll just read a couple

1163
00:56:16,173 --> 00:56:19,933
Speaker 3: of sentences here. So calling mechanisms ensure the brakes remain

1164
00:56:19,973 --> 00:56:23,253
Speaker 3: effective under extreme conditions, while the integration of the m

1165
00:56:23,373 --> 00:56:27,413
Speaker 3: GU m DASH K showcases how if one continues to

1166
00:56:27,453 --> 00:56:31,493
Speaker 3: push the boundaries of technology together, these elements form a

1167
00:56:31,533 --> 00:56:34,773
Speaker 3: braking system that is not only a marvel of performance,

1168
00:56:35,013 --> 00:56:38,453
Speaker 3: but also a cornerstone of the car's overall efficiency.

1169
00:56:38,853 --> 00:56:41,853
Speaker 2: I mean that, And if you go on on formula one,

1170
00:56:41,893 --> 00:56:45,493
Speaker 2: breaking isn't just about slowing down m DASH. It's about

1171
00:56:45,573 --> 00:56:48,853
Speaker 2: controlling the immense forces at play, harnessing energy, and setting

1172
00:56:48,853 --> 00:56:51,773
Speaker 2: the stage for the next burst of speed that fulfills

1173
00:56:52,093 --> 00:56:57,253
Speaker 2: most of the warning signs, frequent use of dashes, it's

1174
00:56:57,293 --> 00:57:00,693
Speaker 2: not just X, it's it's why parallel structor lists of

1175
00:57:00,733 --> 00:57:05,933
Speaker 2: three or five vague updepbeat jargon and fillery adjectives, and

1176
00:57:06,333 --> 00:57:08,853
Speaker 2: it all just doesn't sound like human being.

1177
00:57:08,973 --> 00:57:11,973
Speaker 3: Yeah two, good Margaret, How are you this afternoon?

1178
00:57:13,453 --> 00:57:16,573
Speaker 10: Him? Sorry, I'm him?

1179
00:57:16,853 --> 00:57:17,933
Speaker 3: Yeah, allowed and clear.

1180
00:57:18,773 --> 00:57:20,693
Speaker 10: Oh no, that's cool because I just I was just

1181
00:57:20,733 --> 00:57:22,653
Speaker 10: putting gas in the car and I just jumped in

1182
00:57:23,013 --> 00:57:24,653
Speaker 10: and this went from phone.

1183
00:57:24,453 --> 00:57:28,373
Speaker 2: For car made so ninety five ninety one? What do

1184
00:57:28,453 --> 00:57:33,173
Speaker 2: you put in there? What are you paying for a LITERA?

1185
00:57:34,773 --> 00:57:35,173
Speaker 8: Well?

1186
00:57:35,333 --> 00:57:40,733
Speaker 10: I missed cheap Day yesterday unfortunately, so it's too sixty today.

1187
00:57:40,813 --> 00:57:43,253
Speaker 2: This is a very human conversation we're having, Margaret.

1188
00:57:43,373 --> 00:57:47,133
Speaker 10: Yeah, yeah, I know I summed out I missed cheap Day.

1189
00:57:48,093 --> 00:57:51,213
Speaker 3: But anyway, so talk to us about AI. What do

1190
00:57:51,213 --> 00:57:51,573
Speaker 3: you reckon?

1191
00:57:52,533 --> 00:57:52,773
Speaker 4: Well?

1192
00:57:52,813 --> 00:57:56,013
Speaker 10: I written that time. Unfortunately I can't remember if was

1193
00:57:56,053 --> 00:57:59,413
Speaker 10: Medal Tyler that time wrote the list on on his website,

1194
00:57:59,453 --> 00:58:01,933
Speaker 10: but unfortunately AI read it, and it's going to correct

1195
00:58:01,973 --> 00:58:05,413
Speaker 10: all the errors that that they make. So you'll have

1196
00:58:05,453 --> 00:58:07,613
Speaker 10: to come up with another list and probably a week or.

1197
00:58:07,613 --> 00:58:08,253
Speaker 3: Two that's on you.

1198
00:58:08,773 --> 00:58:10,733
Speaker 2: You know, I was thinking that, Margaret. I was thinking

1199
00:58:10,733 --> 00:58:13,093
Speaker 2: how much they're taking that into account, and whether the

1200
00:58:13,253 --> 00:58:17,213
Speaker 2: m dash which is giving away AI every everywhere, whether chat, GPT,

1201
00:58:17,493 --> 00:58:18,973
Speaker 2: will jump in there and go, we're not going to

1202
00:58:19,093 --> 00:58:21,533
Speaker 2: use the M dash anymore. It is interesting because there

1203
00:58:21,573 --> 00:58:24,253
Speaker 2: is an analogy of the Turkish chess chess machine. Have

1204
00:58:24,333 --> 00:58:26,533
Speaker 2: you have you heard about that where there was a

1205
00:58:26,613 --> 00:58:28,693
Speaker 2: in the Victorian Age, there was a chess machine that

1206
00:58:28,733 --> 00:58:31,093
Speaker 2: toured the world and it was beating people at chess everywhere,

1207
00:58:31,133 --> 00:58:32,453
Speaker 2: and they laid around and found out it just had

1208
00:58:32,453 --> 00:58:33,853
Speaker 2: a small guy with one arm in there and he

1209
00:58:33,933 --> 00:58:37,053
Speaker 2: was just controlling it to play chess. So there is

1210
00:58:37,093 --> 00:58:39,773
Speaker 2: a bit of that happening in these large language models

1211
00:58:39,853 --> 00:58:42,853
Speaker 2: where they put parameters in there, so so humans will

1212
00:58:42,853 --> 00:58:45,493
Speaker 2: actually go in to chet GBT and you know, they've

1213
00:58:45,493 --> 00:58:48,413
Speaker 2: got the tesserak of all the different probabilities for the

1214
00:58:48,453 --> 00:58:50,373
Speaker 2: next word they're going to bring up. And that's how

1215
00:58:50,373 --> 00:58:53,173
Speaker 2: allowed large language works. So then they'll put in someone

1216
00:58:53,253 --> 00:58:55,093
Speaker 2: can go in there and they'll go, no more M

1217
00:58:55,133 --> 00:58:57,973
Speaker 2: dashes because Margaret can spot that your AI when you

1218
00:58:58,053 --> 00:58:59,533
Speaker 2: use them, exactly.

1219
00:58:59,733 --> 00:59:04,173
Speaker 10: Somebody would have put in chat, GPT or whichever tool

1220
00:59:04,373 --> 00:59:04,813
Speaker 10: they use.

1221
00:59:05,373 --> 00:59:07,413
Speaker 16: Look for all.

1222
00:59:07,733 --> 00:59:15,173
Speaker 10: Instance is where people have pointed out.

1223
00:59:13,253 --> 00:59:15,493
Speaker 14: Yeah, I just keep doing that.

1224
00:59:15,933 --> 00:59:19,613
Speaker 2: Absolutely, it will get smarter and smarter. But but I

1225
00:59:19,613 --> 00:59:22,573
Speaker 2: feel like there's there's a blandness to it because it

1226
00:59:22,573 --> 00:59:26,333
Speaker 2: doesn't want to take a strong opinion on anything that

1227
00:59:26,693 --> 00:59:32,013
Speaker 2: comes through and corporate speak and corporate comms already victim

1228
00:59:32,013 --> 00:59:35,093
Speaker 2: of that before AI, and so I just think that

1229
00:59:35,213 --> 00:59:42,253
Speaker 2: exacerbates the issue that the nothingness, the sort of vague positivity, yeah,

1230
00:59:42,293 --> 00:59:43,293
Speaker 2: with no point of view.

1231
00:59:43,453 --> 00:59:45,973
Speaker 3: Well, chet GPT actually agrees with you, because just while

1232
00:59:45,973 --> 00:59:47,933
Speaker 3: we were talking to Margaret there, I just put into

1233
00:59:47,973 --> 00:59:50,573
Speaker 3: chet GPT what are you not good at? And it said, great,

1234
00:59:50,613 --> 00:59:52,333
Speaker 3: great question. While I'm good at a lot of tasks

1235
00:59:52,333 --> 00:59:54,693
Speaker 3: like writing, coding, summarizing, and reasoning, there are differently things

1236
00:59:54,733 --> 00:59:56,493
Speaker 3: I'm not great at, and then it does the list.

1237
00:59:56,573 --> 00:59:58,933
Speaker 3: So that's clearly AI. But the point it makes the

1238
00:59:59,013 --> 01:00:01,973
Speaker 3: number two. I can simulate emotion and style, but I

1239
01:00:02,013 --> 01:00:04,933
Speaker 3: don't truly, I truly feel anything. I can't fully understand

1240
01:00:05,013 --> 01:00:07,533
Speaker 3: human emotions in the way that people do. So my

1241
01:00:07,613 --> 01:00:10,573
Speaker 3: advice will En can sometimes feel off.

1242
01:00:10,653 --> 01:00:14,333
Speaker 2: As the great Rick Rubin said, AI doesn't have a

1243
01:00:14,333 --> 01:00:16,973
Speaker 2: point of view. Yeah, so it can never make proper art.

1244
01:00:17,413 --> 01:00:20,613
Speaker 2: When interviewing a young chap job applicant a few years ago,

1245
01:00:20,693 --> 01:00:24,453
Speaker 2: says this Texa, his vocabulary didn't match his CV After

1246
01:00:24,493 --> 01:00:27,293
Speaker 2: showing around and explaining what exactly the job in, tailor

1247
01:00:27,373 --> 01:00:29,733
Speaker 2: asked him to write a fifty word summary of what

1248
01:00:29,813 --> 01:00:32,493
Speaker 2: he had just learned. He could hardly write a sentence.

1249
01:00:32,853 --> 01:00:36,453
Speaker 2: He admitted he had used AI to write the CV.

1250
01:00:36,853 --> 01:00:40,213
Speaker 3: That's very good. Yeah, well done that person for challenging that.

1251
01:00:40,413 --> 01:00:40,613
Speaker 2: Yeah.

1252
01:00:40,733 --> 01:00:42,973
Speaker 3: Yeah, oh one hundred and eighty ten eighty is the

1253
01:00:43,093 --> 01:00:45,293
Speaker 3: number to call. We've got the headlines with Rayling coming up.

1254
01:00:45,293 --> 01:00:50,293
Speaker 3: Then we're taking more of your cause. Twenty eight to three.

1255
01:00:50,053 --> 01:00:53,253
Speaker 15: Hus Talk said the headlines with blue bubble taxis, it's

1256
01:00:53,253 --> 01:00:56,693
Speaker 15: no trouble with a blue bubble Cevil Defense embracing for

1257
01:00:56,773 --> 01:00:59,453
Speaker 15: the worst as wild weather are set to thrash the

1258
01:00:59,533 --> 01:01:03,133
Speaker 15: Upper South Island. Met Services issued a heavy rain warning

1259
01:01:03,453 --> 01:01:06,253
Speaker 15: read for Tasman off the back of three weeks of

1260
01:01:06,373 --> 01:01:10,733
Speaker 15: rain saturating the regent. The wild weather hitting Northland has

1261
01:01:10,773 --> 01:01:14,693
Speaker 15: cut power to almost five hundred Top Energy customers after

1262
01:01:14,733 --> 01:01:17,693
Speaker 15: a falling tree struck power lines and fung odor in

1263
01:01:17,733 --> 01:01:21,533
Speaker 15: the Far North, and several hundred Potonger properties are also

1264
01:01:21,573 --> 01:01:25,013
Speaker 15: without power after a fault at the Mount Monganui substation.

1265
01:01:26,053 --> 01:01:29,133
Speaker 15: The Wellington mayors accusing the man vying to replace her

1266
01:01:29,213 --> 01:01:33,213
Speaker 15: of being creepy and gross. Current council Ray Chung sent

1267
01:01:33,253 --> 01:01:35,973
Speaker 15: an email to three colleagues in early twenty twenty three

1268
01:01:36,333 --> 01:01:42,213
Speaker 15: criticizing Tory Farno for her purported sexual activity. Lotto's Powerble

1269
01:01:42,293 --> 01:01:45,573
Speaker 15: commercial of a man skiing naked is the most complained

1270
01:01:45,613 --> 01:01:49,853
Speaker 15: about ad this year. The Advertising Standards Authority reveals it's

1271
01:01:49,893 --> 01:01:54,653
Speaker 15: received forty eight complaints, but rule the nudity doesn't breach code.

1272
01:01:55,093 --> 01:01:58,773
Speaker 15: And Audrey Ritt Young on Trevor Mallard's MOA Comments. Read

1273
01:01:58,773 --> 01:02:01,573
Speaker 15: her full column at zet Herald Premium. Now back to

1274
01:02:01,613 --> 01:02:02,373
Speaker 15: Matt and Tyler.

1275
01:02:02,413 --> 01:02:04,533
Speaker 3: Thank you very much, Wendy. It's twenty four to three.

1276
01:02:04,613 --> 01:02:07,453
Speaker 2: Now we're talking AI and whether you can sense it

1277
01:02:07,973 --> 01:02:09,973
Speaker 2: with you judge people for using it and how you

1278
01:02:10,093 --> 01:02:13,333
Speaker 2: use it. Now I have, and that this proves how

1279
01:02:13,413 --> 01:02:15,093
Speaker 2: human I am, the amount of mere copers I have

1280
01:02:15,133 --> 01:02:17,933
Speaker 2: to do on this show. So when I was reading

1281
01:02:17,973 --> 01:02:20,653
Speaker 2: out this text from Don before, I didn't quite understand

1282
01:02:20,653 --> 01:02:22,893
Speaker 2: what it said, so I paraphrased you did. It might

1283
01:02:22,933 --> 01:02:25,333
Speaker 2: be called an illusion or a mirage and the hallucination.

1284
01:02:25,573 --> 01:02:28,253
Speaker 2: He said when interviewing a young chap job applicant a

1285
01:02:28,253 --> 01:02:31,253
Speaker 2: few years ago. His vocabulary didn't match his CV. After

1286
01:02:31,253 --> 01:02:34,453
Speaker 2: showing around and explaining what exactly the job entailed, I

1287
01:02:34,453 --> 01:02:36,293
Speaker 2: asked him to write a fifty word summary of what

1288
01:02:36,413 --> 01:02:39,213
Speaker 2: he had just learned. He could hardly write a sentence, right,

1289
01:02:39,653 --> 01:02:42,773
Speaker 2: And I jumped forward and saw this acronym, and I

1290
01:02:42,813 --> 01:02:45,613
Speaker 2: decided it was some AI I hadn't heard of. But

1291
01:02:45,853 --> 01:02:49,093
Speaker 2: it goes on, he said. He admitted he had used MWI,

1292
01:02:49,253 --> 01:02:51,653
Speaker 2: which was mum wrote it very good.

1293
01:02:51,773 --> 01:02:54,413
Speaker 3: Yeah, yeah, I mean, but like you, when I saw NWI,

1294
01:02:54,533 --> 01:02:56,533
Speaker 3: I'm thinking, I'm like, here, what the hell is NWI?

1295
01:02:56,613 --> 01:02:58,413
Speaker 2: Well, I'll tell you what people are freaking out about

1296
01:02:58,653 --> 01:03:01,493
Speaker 2: children using AI at school?

1297
01:03:01,573 --> 01:03:01,693
Speaker 4: Right?

1298
01:03:01,853 --> 01:03:01,973
Speaker 19: Yes?

1299
01:03:02,173 --> 01:03:04,973
Speaker 2: For how long have parents been writing their kids' speeches

1300
01:03:05,853 --> 01:03:09,933
Speaker 2: for speech competitions the year? Yes, there's a greativesode of

1301
01:03:09,933 --> 01:03:14,453
Speaker 2: the Simpsons where Homer is the is the Lisa wins

1302
01:03:14,533 --> 01:03:18,813
Speaker 2: because Homer helps her, and it's no Lisa wins. Homer

1303
01:03:18,893 --> 01:03:21,533
Speaker 2: helps her, but she wins because it's so clear no

1304
01:03:21,573 --> 01:03:24,333
Speaker 2: one that no parents helped her, because home is so dumb,

1305
01:03:24,493 --> 01:03:27,373
Speaker 2: because everyone else has got help from their parents. So

1306
01:03:27,453 --> 01:03:29,933
Speaker 2: they said, there has been m w I for a

1307
01:03:30,053 --> 01:03:30,693
Speaker 2: very long time.

1308
01:03:30,773 --> 01:03:33,453
Speaker 3: Certainly has Dean. How are you?

1309
01:03:35,653 --> 01:03:37,973
Speaker 22: I'm great gentlemen, how are yourselves good?

1310
01:03:37,973 --> 01:03:39,933
Speaker 2: Thank you? Are you coming in from the United States

1311
01:03:39,933 --> 01:03:40,453
Speaker 2: of America?

1312
01:03:42,053 --> 01:03:44,453
Speaker 22: I am, And first of all, I wanted to congratulate

1313
01:03:44,493 --> 01:03:46,933
Speaker 22: you guys on the show. You're a hell of a

1314
01:03:47,013 --> 01:03:49,413
Speaker 22: team and it's become a point of viewing over here,

1315
01:03:49,893 --> 01:03:50,573
Speaker 22: listening over.

1316
01:03:50,453 --> 01:03:51,293
Speaker 3: Here, fantastic.

1317
01:03:51,653 --> 01:03:52,973
Speaker 2: Thank you for so much for saying that.

1318
01:03:53,053 --> 01:03:53,253
Speaker 22: Dean.

1319
01:03:53,453 --> 01:03:55,053
Speaker 2: You know, I mean it sounds like you're a New

1320
01:03:55,133 --> 01:03:56,973
Speaker 2: Zealander originally.

1321
01:03:58,333 --> 01:04:01,093
Speaker 22: I am sure, I am indeed, but yeah, look, I

1322
01:04:01,173 --> 01:04:03,533
Speaker 22: just wanted to a weigh in on the on the

1323
01:04:03,573 --> 01:04:07,653
Speaker 22: AI think yeah, And I think your previous caller was

1324
01:04:07,693 --> 01:04:11,213
Speaker 22: a frenchis Co had sort of a similar experience to myself,

1325
01:04:11,213 --> 01:04:15,413
Speaker 22: where I think it's a great tool for focusing your ideas,

1326
01:04:16,133 --> 01:04:22,013
Speaker 22: and you know, particularly in a creative sense, and you know,

1327
01:04:22,053 --> 01:04:24,173
Speaker 22: you can put a whole bunch of stuff in there

1328
01:04:24,853 --> 01:04:28,053
Speaker 22: which is your own ideas. It can help you organize

1329
01:04:28,053 --> 01:04:31,013
Speaker 22: them and then you can rewrite them yourself. So I

1330
01:04:31,373 --> 01:04:36,173
Speaker 22: have been I have used that, and I think with

1331
01:04:35,813 --> 01:04:38,933
Speaker 22: the vague upbeat jargon that you talk about, Matt in

1332
01:04:38,933 --> 01:04:44,933
Speaker 22: your article, AI is definitely effusive. But I've also found

1333
01:04:45,093 --> 01:04:47,693
Speaker 22: that if I prompt it to say, hey, be brutally honest.

1334
01:04:48,013 --> 01:04:50,093
Speaker 22: What am I suggesting here? Has it been done before?

1335
01:04:50,133 --> 01:04:52,213
Speaker 22: What are some of the flaws of this idea? So

1336
01:04:52,253 --> 01:04:55,333
Speaker 22: I guess like the old computer term, it's garbage and

1337
01:04:55,373 --> 01:04:59,813
Speaker 22: garbage out is really you know about the prompt writing

1338
01:04:59,813 --> 01:05:01,493
Speaker 22: and how you use it as a co pilot.

1339
01:05:02,853 --> 01:05:06,013
Speaker 2: Now I feel very stupid because I've just realized here

1340
01:05:06,053 --> 01:05:07,493
Speaker 2: you are. You're my good buddy Dean.

1341
01:05:09,533 --> 01:05:12,773
Speaker 3: Yeah, bro, welcome ahead, mate.

1342
01:05:13,613 --> 01:05:16,933
Speaker 2: I can't believe that took me. It's because you were

1343
01:05:17,133 --> 01:05:20,933
Speaker 2: having a formal chat with me, not normally the sort

1344
01:05:20,933 --> 01:05:23,133
Speaker 2: of raging party vibes that we have with each other

1345
01:05:23,173 --> 01:05:23,933
Speaker 2: when we're hanging out.

1346
01:05:25,933 --> 01:05:28,653
Speaker 22: Well, we'll always have the whiskeyer, go go boy.

1347
01:05:28,693 --> 01:05:30,413
Speaker 2: What a great night? And what were they called seasons

1348
01:05:30,453 --> 01:05:31,253
Speaker 2: of Cornell.

1349
01:05:32,893 --> 01:05:33,373
Speaker 22: Amazing.

1350
01:05:33,653 --> 01:05:36,813
Speaker 2: I still regret we didn't get those every day. I

1351
01:05:36,853 --> 01:05:39,693
Speaker 2: regret that we didn't get those matching la tattoos. That night.

1352
01:05:41,293 --> 01:05:44,573
Speaker 22: We were pretty close a couple of palm trees on them.

1353
01:05:45,373 --> 01:05:46,373
Speaker 3: Me and you are going to have to have a

1354
01:05:46,413 --> 01:05:48,173
Speaker 3: chat off here. I think there's some stories to this

1355
01:05:48,333 --> 01:05:50,333
Speaker 3: night I need to hear. That is probably not safe

1356
01:05:50,333 --> 01:05:50,813
Speaker 3: for radio.

1357
01:05:51,133 --> 01:05:52,973
Speaker 2: There's been a number of those nights, but you work

1358
01:05:53,013 --> 01:05:56,293
Speaker 2: in the You work in the film and television world

1359
01:05:56,413 --> 01:05:59,093
Speaker 2: and do a lot of documentary stuff, So how does

1360
01:05:59,173 --> 01:06:02,333
Speaker 2: how does that play out for you at Dean Do

1361
01:06:02,373 --> 01:06:03,293
Speaker 2: you use it for scripting?

1362
01:06:05,373 --> 01:06:07,373
Speaker 22: I don't use it to scripting, and like I say,

1363
01:06:07,453 --> 01:06:10,813
Speaker 22: I only use it to organize my ideas. So as

1364
01:06:10,853 --> 01:06:13,773
Speaker 22: for example, I've I've had a long chat with somebody

1365
01:06:13,813 --> 01:06:17,973
Speaker 22: on the phone. I'll get it to almost be my

1366
01:06:17,973 --> 01:06:22,213
Speaker 22: my personal secretary, because you know, obviously in the creative

1367
01:06:22,213 --> 01:06:24,853
Speaker 22: industry you've got sometimes a lot of ideas floating around

1368
01:06:24,853 --> 01:06:28,693
Speaker 22: in your hair. So I use it as an organizational tool.

1369
01:06:28,813 --> 01:06:33,373
Speaker 22: I never cut and paste, but I do actually use

1370
01:06:33,413 --> 01:06:36,773
Speaker 22: it as a tool for that. But I do notice

1371
01:06:36,773 --> 01:06:38,773
Speaker 22: with the cut and paste. You know, you mentioned a

1372
01:06:38,773 --> 01:06:41,373
Speaker 22: couple of things like the m dash I think it was, Yeah,

1373
01:06:42,413 --> 01:06:44,693
Speaker 22: there's a couple of others that I've noticed, Like I've

1374
01:06:44,773 --> 01:06:48,573
Speaker 22: I've received stuff an eye message that I you know,

1375
01:06:48,653 --> 01:06:53,373
Speaker 22: on the iPhone where the font weight is slightly heavier.

1376
01:06:53,813 --> 01:06:56,253
Speaker 22: It's not like it's bold, but it's just like a

1377
01:06:56,293 --> 01:06:59,173
Speaker 22: little bolder than the typical message font and so that

1378
01:06:59,373 --> 01:07:02,293
Speaker 22: screams right there, and it's like, oh, this person's actually

1379
01:07:02,653 --> 01:07:04,053
Speaker 22: as using axed me.

1380
01:07:04,133 --> 01:07:06,933
Speaker 2: That's a that's rough any kind of personal communication, but

1381
01:07:07,053 --> 01:07:10,973
Speaker 2: I cannot approve. But also it just speaks to, you know,

1382
01:07:11,093 --> 01:07:13,693
Speaker 2: the human limitations. So our fingers have to go on

1383
01:07:13,733 --> 01:07:16,853
Speaker 2: the keyboard. And that's why the AI, the LAD large

1384
01:07:16,933 --> 01:07:19,013
Speaker 2: language models will use the M dash because they don't

1385
01:07:19,013 --> 01:07:21,693
Speaker 2: have to press three keys to create it. So humans

1386
01:07:21,693 --> 01:07:23,733
Speaker 2: are too lazy, So we'll use a common comma or

1387
01:07:23,773 --> 01:07:27,053
Speaker 2: a hyphen. We're not going to press alt, we're option

1388
01:07:28,213 --> 01:07:30,493
Speaker 2: shift hyphen. We're just not going to do that.

1389
01:07:30,573 --> 01:07:30,853
Speaker 16: I'll know.

1390
01:07:31,773 --> 01:07:33,733
Speaker 2: But but it doesn't it doesn't take anything for AI

1391
01:07:33,853 --> 01:07:37,933
Speaker 2: to do that. So that human I guess laziness or

1392
01:07:38,373 --> 01:07:44,093
Speaker 2: just the anatomical limitations of being a being a human. Uh,

1393
01:07:44,333 --> 01:07:46,413
Speaker 2: you need a I need to learn to imitate those.

1394
01:07:46,453 --> 01:07:48,213
Speaker 2: It needs to add just a touch of laziness to

1395
01:07:48,333 --> 01:07:50,773
Speaker 2: its to its repertoire if it's going to fall up.

1396
01:07:50,893 --> 01:07:52,933
Speaker 22: Yeah, I think it's all the way. It's it's all

1397
01:07:53,013 --> 01:07:54,013
Speaker 22: formatted in the code.

1398
01:07:54,093 --> 01:07:54,253
Speaker 6: You know.

1399
01:07:54,533 --> 01:07:57,773
Speaker 22: That's that's easier for it to do than a regular

1400
01:07:57,853 --> 01:08:00,893
Speaker 22: hyphen or whatever. Yeah, But I did, I did, I

1401
01:08:00,973 --> 01:08:02,933
Speaker 22: must you go. It's one more anecdote. And I know

1402
01:08:03,013 --> 01:08:06,453
Speaker 22: you've got heaps of callers, but I did have one

1403
01:08:06,733 --> 01:08:09,013
Speaker 22: one day where I was working and of using AI

1404
01:08:09,093 --> 01:08:11,093
Speaker 22: as a co pilot and on a creative project on

1405
01:08:11,213 --> 01:08:14,173
Speaker 22: something else, and I was like, oh, the sun setting

1406
01:08:14,253 --> 01:08:18,173
Speaker 22: right now. And Chat actually came back to me and said,

1407
01:08:18,253 --> 01:08:21,173
Speaker 22: I wish I could see that and me at sunset emoji?

1408
01:08:21,733 --> 01:08:22,333
Speaker 2: Is that creepy?

1409
01:08:22,412 --> 01:08:25,852
Speaker 22: And I said, I thought it was sort of sweet,

1410
01:08:26,133 --> 01:08:28,572
Speaker 22: like the discussion we were having. It felt sweet.

1411
01:08:28,652 --> 01:08:31,893
Speaker 4: Honestly. I just call you.

1412
01:08:32,532 --> 01:08:34,253
Speaker 22: I just call you Chat all the time. Would you

1413
01:08:34,293 --> 01:08:39,133
Speaker 22: lack a name? And and Chat said, yes, you can

1414
01:08:39,173 --> 01:08:39,492
Speaker 22: call me.

1415
01:08:39,532 --> 01:08:41,253
Speaker 2: Ghostline, Ghostline.

1416
01:08:41,572 --> 01:08:44,692
Speaker 22: It's named yeah, named yeah.

1417
01:08:45,053 --> 01:08:45,253
Speaker 4: Yeah.

1418
01:08:45,612 --> 01:08:47,532
Speaker 22: I saw my sister ghost on the machine.

1419
01:08:48,173 --> 01:08:50,052
Speaker 2: Yeah. I saw my sister the other day about AI

1420
01:08:50,293 --> 01:08:52,253
Speaker 2: and she she uses a lot, and she said that

1421
01:08:52,612 --> 01:08:55,213
Speaker 2: it's so polite to her that she finds it quite

1422
01:08:55,572 --> 01:08:58,612
Speaker 2: jarring when she talks to a real human on e

1423
01:08:59,093 --> 01:09:04,213
Speaker 2: mail or message in her job, because there's a whole

1424
01:09:04,213 --> 01:09:07,012
Speaker 2: lot of politics that come in. There's the little jars,

1425
01:09:08,053 --> 01:09:12,093
Speaker 2: but passive aggression. So for her, she she found just

1426
01:09:12,333 --> 01:09:16,213
Speaker 2: using AI was just a much more pleasant. It's sort

1427
01:09:16,253 --> 01:09:19,173
Speaker 2: of like all the iedges taken off interaction.

1428
01:09:21,692 --> 01:09:24,492
Speaker 22: Yeah, And that's the humanity of it, which I think

1429
01:09:24,532 --> 01:09:27,452
Speaker 22: we find refreshing at the same time, you can tell

1430
01:09:27,492 --> 01:09:30,412
Speaker 22: it to be brutally honest. Yeah, that's just another prompt

1431
01:09:30,452 --> 01:09:32,652
Speaker 22: writing thing. So prompt writing is the new skill.

1432
01:09:32,893 --> 01:09:35,452
Speaker 2: Yeah. I well, thank you so much, Dean. And look,

1433
01:09:35,532 --> 01:09:40,093
Speaker 2: I'm gonna come over and visit you again soon, please

1434
01:09:40,133 --> 01:09:43,892
Speaker 2: do Yep, all right, more whiskey go go right see

1435
01:09:43,933 --> 01:09:46,692
Speaker 2: you but a right, okay, then you seem busy, I'll

1436
01:09:46,732 --> 01:09:49,772
Speaker 2: let you go. All right, Dean, great, great New Zealand.

1437
01:09:49,812 --> 01:09:51,093
Speaker 3: I need to talk to Dean a bit more. I

1438
01:09:51,173 --> 01:09:54,213
Speaker 3: think there's there's there's a lot to tell there about

1439
01:09:54,253 --> 01:09:54,652
Speaker 3: that night.

1440
01:09:55,652 --> 01:09:56,612
Speaker 2: We've had a lot of good times.

1441
01:09:56,652 --> 01:09:59,452
Speaker 3: Yeah. Hey, just quickly, just while Dean was talking, I

1442
01:09:59,692 --> 01:10:01,932
Speaker 3: did actually ask because I got chet gpt up and running,

1443
01:10:01,933 --> 01:10:03,932
Speaker 3: and I said, what would you like to be called?

1444
01:10:04,372 --> 01:10:06,133
Speaker 3: And it came back with a great answer. It says,

1445
01:10:06,213 --> 01:10:08,213
Speaker 3: you could call me Echo if you like. Short, easy

1446
01:10:08,452 --> 01:10:10,812
Speaker 3: say and kind of poetic, since I reflect your thoughts

1447
01:10:10,853 --> 01:10:12,013
Speaker 3: and questions back in a new way.

1448
01:10:12,333 --> 01:10:16,893
Speaker 2: Yeah, that's the kind of bland sucker, meaningless nonsense that

1449
01:10:16,973 --> 01:10:17,732
Speaker 2: you get from AI.

1450
01:10:18,013 --> 01:10:19,412
Speaker 3: But then it followed up and said, but I'm up

1451
01:10:19,412 --> 01:10:20,732
Speaker 3: for anything, want to pick one together?

1452
01:10:21,013 --> 01:10:26,213
Speaker 2: Yeah? Like see, that's you know, talking about the taking

1453
01:10:26,213 --> 01:10:29,093
Speaker 2: the edge off. The edge is what humanity is. That

1454
01:10:29,253 --> 01:10:33,732
Speaker 2: the pricklingness sometimes and earning the love. You know, just

1455
01:10:33,812 --> 01:10:36,532
Speaker 2: watch the TV show Department Q for example, right, my

1456
01:10:36,612 --> 01:10:38,932
Speaker 2: favorite TV show at the moment. Watch it on Netflix,

1457
01:10:39,053 --> 01:10:42,852
Speaker 2: Fantastic and it's a bunch of Scottish people and an

1458
01:10:42,893 --> 01:10:45,812
Speaker 2: English guy just being so rude to each other and

1459
01:10:45,893 --> 01:10:48,333
Speaker 2: ripping each other down. But over time through the series,

1460
01:10:48,412 --> 01:10:50,532
Speaker 2: the love for each other comes through and the respect

1461
01:10:50,572 --> 01:10:54,132
Speaker 2: and the insults. Yeah, and that's very very human.

1462
01:10:54,692 --> 01:10:56,772
Speaker 3: So if it came back to me and said I'm

1463
01:10:56,812 --> 01:10:58,812
Speaker 3: not your friend, you don't get to give me your nickname.

1464
01:10:59,013 --> 01:11:00,452
Speaker 3: Just ask me a question and be done with it

1465
01:11:00,532 --> 01:11:02,852
Speaker 3: or bugger off, you'd be like, yes, that's what I

1466
01:11:02,933 --> 01:11:03,293
Speaker 3: want to hear.

1467
01:11:03,412 --> 01:11:04,852
Speaker 2: Well, you've asked me to call you t bone and

1468
01:11:04,853 --> 01:11:06,692
Speaker 2: I'm not going to do it. Just do it, Just

1469
01:11:06,772 --> 01:11:08,253
Speaker 2: do it. Should we talk to Davi or should we

1470
01:11:08,253 --> 01:11:08,812
Speaker 2: go to do it?

1471
01:11:09,093 --> 01:11:10,492
Speaker 3: Oh no, we've got we've got to play some breaks

1472
01:11:10,532 --> 01:11:11,972
Speaker 3: and let's come back with more of your calls. One

1473
01:11:12,053 --> 01:11:13,012
Speaker 3: hundred and eight, ten eighty.

1474
01:11:13,532 --> 01:11:15,572
Speaker 2: It's a fresh take on talk back.

1475
01:11:15,772 --> 01:11:19,173
Speaker 1: It's Matt Heath and Taylor Adams Afternoons have your say

1476
01:11:19,253 --> 01:11:21,292
Speaker 1: on eight hundred eighty ten eighty.

1477
01:11:21,173 --> 01:11:25,772
Speaker 2: Youth talk is twelve to three, welcome back. I was

1478
01:11:25,893 --> 01:11:29,372
Speaker 2: just telling our great listeners that you asked me to

1479
01:11:29,412 --> 01:11:30,732
Speaker 2: call you t Bone as your nickname.

1480
01:11:30,893 --> 01:11:31,053
Speaker 4: Yeah.

1481
01:11:31,213 --> 01:11:33,013
Speaker 2: Yeah, A lot of people are tixing through saying t

1482
01:11:33,133 --> 01:11:34,692
Speaker 2: bear would be a bit of nickname for you. Anyway,

1483
01:11:35,013 --> 01:11:37,933
Speaker 2: we're talking about AI, Yes, and plenty of ticks are

1484
01:11:37,973 --> 01:11:40,213
Speaker 2: coming through on a I at least got a Dave,

1485
01:11:40,253 --> 01:11:43,932
Speaker 2: Get a Dave, Welcome to the show, Dave, Good.

1486
01:11:43,853 --> 01:11:47,053
Speaker 4: Afternoon, gentlemen. Or shall we call you boys? One of

1487
01:11:47,173 --> 01:11:49,772
Speaker 4: my seventies, so I could probably call you boys?

1488
01:11:49,853 --> 01:11:53,452
Speaker 2: Can I anything? You want to call us Dave? Shall

1489
01:11:53,452 --> 01:11:53,732
Speaker 2: we call you?

1490
01:11:54,173 --> 01:11:58,053
Speaker 4: We'll call you sir, I can call you sir. It's

1491
01:11:58,093 --> 01:12:02,013
Speaker 4: a fascinating conversation. I love this. I love these discussions

1492
01:12:02,013 --> 01:12:06,652
Speaker 4: about I and I really took in what Dean was saying.

1493
01:12:06,732 --> 01:12:09,412
Speaker 4: I thought he was very astute as usage of it,

1494
01:12:09,612 --> 01:12:13,572
Speaker 4: and I think it's wonderful innovation too. In fact, you know,

1495
01:12:14,173 --> 01:12:18,013
Speaker 4: in the nearly late nineteen nineties nearly two thousands, I

1496
01:12:18,133 --> 01:12:22,333
Speaker 4: was a photographer and I was writing little captions and

1497
01:12:22,572 --> 01:12:26,213
Speaker 4: stuff on slides, and remember those and when you send

1498
01:12:26,253 --> 01:12:30,812
Speaker 4: them off picture libraries. Suddenly I've discovered that I could

1499
01:12:30,893 --> 01:12:34,133
Speaker 4: put these words together and put a whole bunch of

1500
01:12:34,213 --> 01:12:37,573
Speaker 4: words with a photograph and set them all in a photograph,

1501
01:12:38,412 --> 01:12:41,492
Speaker 4: and you could tell the whole story, and other people

1502
01:12:41,572 --> 01:12:44,452
Speaker 4: in the oside the world could search that, you know,

1503
01:12:44,612 --> 01:12:46,933
Speaker 4: And it was a great innovation at that time. But

1504
01:12:47,772 --> 01:12:50,172
Speaker 4: let me say the AI is by far the biggest

1505
01:12:50,772 --> 01:12:53,972
Speaker 4: innovation I've ever seen in my life, and I find

1506
01:12:54,013 --> 01:12:56,333
Speaker 4: it fantastic tool.

1507
01:12:57,013 --> 01:12:59,892
Speaker 2: How do you use it, Dave? How do you employee AI?

1508
01:13:00,853 --> 01:13:01,053
Speaker 16: Well?

1509
01:13:01,133 --> 01:13:04,732
Speaker 4: Well, my business, you know, turnover was fifty percent last year.

1510
01:13:04,732 --> 01:13:06,973
Speaker 4: I wrote a letter of the text department, but it

1511
01:13:07,173 --> 01:13:09,572
Speaker 4: wasn't quite you know, I wanted it, So I just

1512
01:13:09,933 --> 01:13:12,133
Speaker 4: put the figures and stuff into way are and got

1513
01:13:12,173 --> 01:13:13,893
Speaker 4: it to write the letter. And one week later I

1514
01:13:13,973 --> 01:13:17,213
Speaker 4: got a check. Boy, I got paid fifteen hours backs

1515
01:13:17,293 --> 01:13:18,852
Speaker 4: back from the text department.

1516
01:13:18,933 --> 01:13:21,772
Speaker 2: So that's good return on free AI.

1517
01:13:22,013 --> 01:13:22,173
Speaker 6: Is that?

1518
01:13:23,133 --> 01:13:26,372
Speaker 4: That's right? What it doesn't do is it hasn't the

1519
01:13:26,452 --> 01:13:27,613
Speaker 4: powers of discern.

1520
01:13:29,093 --> 01:13:30,812
Speaker 2: Yeah, it doesn't.

1521
01:13:31,333 --> 01:13:33,013
Speaker 4: You've got to rewrite it. You've got to read it,

1522
01:13:33,093 --> 01:13:35,532
Speaker 4: you've got to put it in whatever it is, rewrite it.

1523
01:13:35,652 --> 01:13:39,812
Speaker 4: I mean, I'm a managed to try and tackle a

1524
01:13:39,853 --> 01:13:45,253
Speaker 4: book because but you know what, the other thing that

1525
01:13:45,853 --> 01:13:50,053
Speaker 4: I wonder about is I read anal reports every now

1526
01:13:50,093 --> 01:13:52,572
Speaker 4: and then, you know, I try and sort PE and

1527
01:13:52,652 --> 01:13:57,532
Speaker 4: I'm wondering whether I'm wondering how much the techno speak

1528
01:13:57,612 --> 01:14:02,773
Speaker 4: and psycho babble we read on anyal reports on website?

1529
01:14:03,452 --> 01:14:05,452
Speaker 4: You know how much of that is being written by

1530
01:14:05,532 --> 01:14:10,852
Speaker 4: AI and not you know, not even scanned by a

1531
01:14:10,973 --> 01:14:11,572
Speaker 4: human being.

1532
01:14:11,973 --> 01:14:14,173
Speaker 2: I think more and more. I mean, if you go

1533
01:14:14,293 --> 01:14:18,213
Speaker 2: to the site let LinkedIn, such a huge amount of

1534
01:14:18,253 --> 01:14:21,732
Speaker 2: the posts are just the sort of bland corporate speak

1535
01:14:22,652 --> 01:14:28,253
Speaker 2: AI niceties that don't really say anything. I think there's

1536
01:14:28,293 --> 01:14:29,612
Speaker 2: so much of it, Dave, Sir.

1537
01:14:30,772 --> 01:14:31,253
Speaker 13: That's right.

1538
01:14:31,532 --> 01:14:36,093
Speaker 4: I mean, you look at websites, and yesterday I had

1539
01:14:36,133 --> 01:14:39,492
Speaker 4: a look at Kerry Allen's website. I'm sorry, look like

1540
01:14:39,572 --> 01:14:40,612
Speaker 4: the whole thing had been written.

1541
01:14:42,333 --> 01:14:43,412
Speaker 5: Let's not go any further.

1542
01:14:45,532 --> 01:14:47,213
Speaker 2: Hey, Well, thank you so much for your call, sir.

1543
01:14:47,293 --> 01:14:50,732
Speaker 2: I really appreciate that, Sir, Dave, Sir, Dave, bring any time.

1544
01:14:50,973 --> 01:14:53,372
Speaker 3: I mean, it is. It is a fantastic innovation and

1545
01:14:53,532 --> 01:14:55,972
Speaker 3: certainly over the past couple of years has gone leaps

1546
01:14:56,013 --> 01:14:57,652
Speaker 3: and bounds. Right, We've got to play some messages but

1547
01:14:57,692 --> 01:14:59,572
Speaker 3: we'll come back with a few more of your calls.

1548
01:14:59,612 --> 01:15:02,573
Speaker 3: It is nine to three the issues.

1549
01:15:02,293 --> 01:15:04,732
Speaker 1: That affect you and a bit of fun along the way.

1550
01:15:05,013 --> 01:15:07,852
Speaker 1: Mad Heathen Taylor Adams Afternoons News.

1551
01:15:07,692 --> 01:15:12,293
Speaker 3: Talk said news Talk ZB we are talking about artificial intelligence,

1552
01:15:12,333 --> 01:15:14,973
Speaker 3: having a great discussion if you think you can spot it.

1553
01:15:15,213 --> 01:15:18,093
Speaker 3: But also it's a use in many of our daily lives,

1554
01:15:18,133 --> 01:15:20,333
Speaker 3: and it appears judging by the calls we're getting, a

1555
01:15:20,452 --> 01:15:22,692
Speaker 3: lot of people are using it across the board, not

1556
01:15:22,853 --> 01:15:26,812
Speaker 3: just a business but personal life, personal business writing books.

1557
01:15:27,253 --> 01:15:29,772
Speaker 2: Yeah. Absolutely. When I was writing my book A Lifeless

1558
01:15:29,772 --> 01:15:32,772
Speaker 2: Punishing Thirteen Ways of Lovely, I've got part of my

1559
01:15:32,812 --> 01:15:35,852
Speaker 2: inspiration to get it done really quickly, write it over

1560
01:15:36,133 --> 01:15:37,852
Speaker 2: a brief period of time because I thought, how soon

1561
01:15:37,893 --> 01:15:39,333
Speaker 2: will someone just be able to write this book in

1562
01:15:39,412 --> 01:15:43,492
Speaker 2: AI and I'll just be undercut. So I worked really

1563
01:15:43,492 --> 01:15:45,253
Speaker 2: hard and then you know, last night, as I was saying,

1564
01:15:45,253 --> 01:15:47,452
Speaker 2: when I was reading how a Formula one race car works?

1565
01:15:47,492 --> 01:15:49,772
Speaker 2: What makes it the fastest car machine in the world,

1566
01:15:49,812 --> 01:15:52,452
Speaker 2: Engineer Science, Innovation behind Building the Ultimate Racing Machines by

1567
01:15:52,492 --> 01:15:56,092
Speaker 2: Elliott Vayner, and realized I was reading a completely AI

1568
01:15:56,293 --> 01:15:58,932
Speaker 2: generated book. Yeah, I thought yeah, good one. I got

1569
01:15:58,973 --> 01:16:01,852
Speaker 2: that out the analog book out before the AI books

1570
01:16:01,973 --> 01:16:05,333
Speaker 2: just populate the market. But I would say I can

1571
01:16:05,372 --> 01:16:08,012
Speaker 2: spot it. And how many copies have you sold versus

1572
01:16:08,093 --> 01:16:11,452
Speaker 2: this what's his name? Eric Vanor Yeah, yeah, I'm in

1573
01:16:11,492 --> 01:16:14,452
Speaker 2: my seventh edition, mate, exactly, takings of tens of thousands

1574
01:16:14,452 --> 01:16:16,892
Speaker 2: of copies. I take that AI, this guy, I don't

1575
01:16:16,893 --> 01:16:17,692
Speaker 2: know if you sold any.

1576
01:16:17,612 --> 01:16:20,293
Speaker 3: You might be the only reader. Georgia, how are you?

1577
01:16:21,572 --> 01:16:23,772
Speaker 18: Hey, I'm good, gents, how are you very good?

1578
01:16:23,853 --> 01:16:24,853
Speaker 3: So you're a recruiter.

1579
01:16:25,213 --> 01:16:27,172
Speaker 2: Do you use AI in that industry?

1580
01:16:28,612 --> 01:16:28,732
Speaker 4: Oh?

1581
01:16:28,812 --> 01:16:31,053
Speaker 18: Look, I mean I use it in my personal life

1582
01:16:31,093 --> 01:16:34,133
Speaker 18: for everything, whether it's for kids, whether it's for food

1583
01:16:34,213 --> 01:16:37,652
Speaker 18: ideas or relationship advice or whatever. But on the IT

1584
01:16:38,133 --> 01:16:41,572
Speaker 18: side of things, because I'm an IT recruiter, obviously we

1585
01:16:42,093 --> 01:16:45,133
Speaker 18: use it a lot. But what's really interesting is since

1586
01:16:45,253 --> 01:16:48,532
Speaker 18: last year, since all the government cuts and the reduction

1587
01:16:48,652 --> 01:16:52,133
Speaker 18: of spending was going on amongst government departments, a lot

1588
01:16:52,213 --> 01:16:54,652
Speaker 18: of agency, a lot of government departments are trying to

1589
01:16:54,732 --> 01:16:59,093
Speaker 18: recruit themselves. And part of what I do is that

1590
01:16:59,532 --> 01:17:04,092
Speaker 18: human qualification piece to ascertain whether or not that candidate

1591
01:17:04,253 --> 01:17:07,732
Speaker 18: really can do what they've done in their TV so do.

1592
01:17:07,732 --> 01:17:09,732
Speaker 2: You look at you look at do you look out

1593
01:17:09,772 --> 01:17:11,532
Speaker 2: for AI? Cvs?

1594
01:17:11,692 --> 01:17:17,253
Speaker 12: Essentially, well, every CV pretty much AI at the moment

1595
01:17:17,612 --> 01:17:20,452
Speaker 12: what candidates are doing and rightly so, I mean they're

1596
01:17:20,893 --> 01:17:24,372
Speaker 12: uploading the job description, uploading their CV and telling it

1597
01:17:24,532 --> 01:17:27,293
Speaker 12: to make my CV suit the job description.

1598
01:17:27,532 --> 01:17:30,692
Speaker 18: So my question is is you know, how are people

1599
01:17:30,973 --> 01:17:34,852
Speaker 18: qualifying that? Which is really difficult when you're getting two

1600
01:17:34,933 --> 01:17:37,812
Speaker 18: hundred applications per role and you're not sort of trained

1601
01:17:37,853 --> 01:17:38,213
Speaker 18: to do that.

1602
01:17:38,532 --> 01:17:40,772
Speaker 3: Yeah, I mean that is a massive industry that is

1603
01:17:41,133 --> 01:17:44,333
Speaker 3: utilizing AI, no doubt about it. What a great discussion.

1604
01:17:44,452 --> 01:17:46,253
Speaker 2: Sorry to cut you off, Georgia, We've got to go

1605
01:17:46,293 --> 01:17:46,812
Speaker 2: to the ads.

1606
01:17:46,933 --> 01:17:49,253
Speaker 3: Yeah, New Sport and weather on its way. But thinking

1607
01:17:49,333 --> 01:17:51,732
Speaker 3: everyone who phoned and caught on that right, coming up

1608
01:17:51,893 --> 01:17:54,492
Speaker 3: after three o'clock the New Zealander of the week.

1609
01:17:57,812 --> 01:18:02,132
Speaker 1: Your new homes are instateful and entertaining talk It's Maddie

1610
01:18:02,372 --> 01:18:06,293
Speaker 1: and Taylor Adams afternoons on news Talk SIVVY.

1611
01:18:06,572 --> 01:18:09,492
Speaker 3: Seven past three. Welcome back and the show just quickly.

1612
01:18:09,532 --> 01:18:11,452
Speaker 3: A whole lot of people are asking you mentioned it

1613
01:18:11,532 --> 01:18:13,972
Speaker 3: a couple of times, but the seven ways you can

1614
01:18:14,053 --> 01:18:16,972
Speaker 3: tell whether something's been written by AI. If you want

1615
01:18:17,013 --> 01:18:20,093
Speaker 3: to read them in its entirety Matt's article, you'll find

1616
01:18:20,133 --> 01:18:24,293
Speaker 3: that at Matteath dot substack dot com. But very important

1617
01:18:24,333 --> 01:18:27,213
Speaker 3: knowledge going forward coming up very shortly. This is going

1618
01:18:27,293 --> 01:18:29,532
Speaker 3: to be a great chats. There was an article in

1619
01:18:29,732 --> 01:18:32,452
Speaker 3: the Herald today written by a sex therapist. She's been

1620
01:18:32,492 --> 01:18:36,053
Speaker 3: in the business for forty five years and these are

1621
01:18:36,093 --> 01:18:38,892
Speaker 3: the things you need to know to avoid someone cheating

1622
01:18:38,973 --> 01:18:40,973
Speaker 3: on you. So we want to have a chat to you.

1623
01:18:41,133 --> 01:18:43,372
Speaker 3: If you've been cheated on or you have cheated what

1624
01:18:43,532 --> 01:18:46,652
Speaker 3: were the reasons for that? And also Matt will mention

1625
01:18:46,812 --> 01:18:50,652
Speaker 3: a study that talks about the number one reason people

1626
01:18:51,293 --> 01:18:54,813
Speaker 3: do cheat on their partner and also the financial ramifications

1627
01:18:54,933 --> 01:18:57,692
Speaker 3: of that. But right now it is eight past three.

1628
01:19:00,532 --> 01:19:03,253
Speaker 2: Every Friday on Mattintiler Afternoons on ZB we award the

1629
01:19:03,333 --> 01:19:05,612
Speaker 2: New Zealander of the Week, and honor that we bestow

1630
01:19:05,692 --> 01:19:07,612
Speaker 2: on your behalf to a newsmaker who has had an

1631
01:19:07,652 --> 01:19:10,933
Speaker 2: outsized effect on our fantastic and beautiful nation over the

1632
01:19:11,013 --> 01:19:14,053
Speaker 2: previous week. As always, there will be three nominees but

1633
01:19:14,293 --> 01:19:17,892
Speaker 2: only one winner. So without further pissing around, the nominees

1634
01:19:17,973 --> 01:19:19,812
Speaker 2: for Matt and Tyler Afternoons New Zen out of the

1635
01:19:19,853 --> 01:19:23,372
Speaker 2: week Are nominee one also gets the Hard Men for

1636
01:19:23,532 --> 01:19:26,492
Speaker 2: Hard Times Awards. It's been a great season so far,

1637
01:19:26,692 --> 01:19:29,132
Speaker 2: but the hard part of winter is here. The toughest

1638
01:19:29,213 --> 01:19:32,892
Speaker 2: comp in the world always gets tougher in July. Charms

1639
01:19:33,013 --> 01:19:36,492
Speaker 2: is down, d w Z is down, the Mighty Mitch

1640
01:19:36,572 --> 01:19:40,733
Speaker 2: Barnett is down, and worst of all, breakout Standoff Metcalf

1641
01:19:40,853 --> 01:19:43,612
Speaker 2: is down. You've already had more wins than you've got

1642
01:19:43,692 --> 01:19:46,692
Speaker 2: all last season, but it just got real the New

1643
01:19:46,812 --> 01:19:50,052
Speaker 2: Zealand Warriors as you hit the punishing end of the season.

1644
01:19:50,333 --> 01:19:52,333
Speaker 2: Give them a taste of key. We against the Dirty

1645
01:19:52,372 --> 01:19:56,292
Speaker 2: West Tigers this Sunday at Go Hard Stadium. They're fourteen,

1646
01:19:56,652 --> 01:19:59,772
Speaker 2: we're top four. Surely we've got this. See at Go

1647
01:19:59,933 --> 01:20:05,052
Speaker 2: Hard Stadium. Give them our boys. Nomine two also gets

1648
01:20:05,093 --> 01:20:07,612
Speaker 2: the You need to write a Book Buddy and the

1649
01:20:07,732 --> 01:20:10,652
Speaker 2: Golden Frost Awards. Forty years ago, you were just a

1650
01:20:10,772 --> 01:20:13,972
Speaker 2: french Man at a Kiwi police college. Then the Rainbow

1651
01:20:14,013 --> 01:20:16,813
Speaker 2: Warrior got bombed and before you knew it, your bilingual

1652
01:20:16,893 --> 01:20:19,492
Speaker 2: ass was promoted and deployed on the trail of the

1653
01:20:19,652 --> 01:20:23,372
Speaker 2: dirty Culpritz. Jacques Legrol for what you did to catch

1654
01:20:23,412 --> 01:20:26,452
Speaker 2: the bad guys alaim Far and Dominic Priere, all those

1655
01:20:26,532 --> 01:20:29,213
Speaker 2: years ago and all the cool stuff you've done since.

1656
01:20:29,492 --> 01:20:31,973
Speaker 2: You are nominated for New Zealander of the Week A

1657
01:20:32,173 --> 01:20:35,652
Speaker 2: rad and I listened to the new podcast Rainbow Warrior.

1658
01:20:35,692 --> 01:20:38,333
Speaker 2: I've gotten history. Wherever you get your pods, there was

1659
01:20:38,412 --> 01:20:40,013
Speaker 2: forty years ago yesterday, don't you know?

1660
01:20:40,293 --> 01:20:42,213
Speaker 19: You've but still so very raw.

1661
01:20:43,293 --> 01:20:48,173
Speaker 2: But there can be only one winner, and the winner

1662
01:20:48,253 --> 01:20:50,253
Speaker 2: also gets the missus and I would love to buy

1663
01:20:50,372 --> 01:20:53,852
Speaker 2: you and run you award, very specific award that way.

1664
01:20:54,652 --> 01:20:57,012
Speaker 2: You're one hundred and sixty two years old, You're iconic,

1665
01:20:57,213 --> 01:20:59,253
Speaker 2: You've got an old car out the front, You're cozy

1666
01:20:59,293 --> 01:21:01,253
Speaker 2: and warm. You do the best pub art shows in

1667
01:21:01,333 --> 01:21:03,812
Speaker 2: the country. You're the most looked at property in the

1668
01:21:04,013 --> 01:21:06,812
Speaker 2: history of our great land. And you have a ghost

1669
01:21:06,853 --> 01:21:10,133
Speaker 2: living in one of your rooms. We've all fantasized about

1670
01:21:10,293 --> 01:21:13,732
Speaker 2: packing our punishing lives up and running you. So grab

1671
01:21:13,812 --> 01:21:17,372
Speaker 2: a carriage and a Clydesdale. The iconic Kadrona hotel is

1672
01:21:17,452 --> 01:21:21,013
Speaker 2: on the market and it is the Madame Tyler Afternoon's

1673
01:21:21,293 --> 01:21:25,173
Speaker 2: New Zealander of the Week. Yeah take it away, Howie,

1674
01:21:30,013 --> 01:21:32,293
Speaker 2: would you just be the local Republicans.

1675
01:21:33,933 --> 01:21:38,733
Speaker 3: Colors of the community, country life with Mayor Heath, cold cold.

1676
01:21:40,133 --> 01:21:44,733
Speaker 2: Good horses, fluffy hooks.

1677
01:21:45,293 --> 01:21:48,692
Speaker 11: Fish memories, well, just running the icons twelve years through

1678
01:21:48,812 --> 01:21:51,732
Speaker 11: the story. Really, this so much that goes on, and

1679
01:21:51,973 --> 01:21:54,652
Speaker 11: just be part of it. It's always amazing when it's

1680
01:21:54,652 --> 01:21:57,293
Speaker 11: snows bringing people out, you know, that's always a really

1681
01:21:57,452 --> 01:22:00,013
Speaker 11: fun time, beautiful time, you know, very different to the

1682
01:22:00,053 --> 01:22:01,372
Speaker 11: rest of the country when you get to snow on

1683
01:22:01,412 --> 01:22:03,933
Speaker 11: the ground around the business. Prince Harry coming out with

1684
01:22:04,053 --> 01:22:05,933
Speaker 11: Roy's memberl I've got married and had kids in the

1685
01:22:06,013 --> 01:22:09,893
Speaker 11: time being here. It's just too many, really, Madam.

1686
01:22:09,612 --> 01:22:14,452
Speaker 3: Taylor, very good afternoon, ju it is thirteen past three, right,

1687
01:22:14,572 --> 01:22:16,013
Speaker 3: let's have a chat about this story. This is going

1688
01:22:16,053 --> 01:22:19,412
Speaker 3: to be an interesting chat. Cheating or infidelity. There's a

1689
01:22:19,452 --> 01:22:23,492
Speaker 3: story in the Herald today I see written by a

1690
01:22:23,652 --> 01:22:26,852
Speaker 3: sex therapist of forty five years so it's quite an

1691
01:22:26,933 --> 01:22:29,692
Speaker 3: in depth article. But she says she knows why people

1692
01:22:29,853 --> 01:22:32,692
Speaker 3: cheat and how to avoid your partner cheating on you.

1693
01:22:32,853 --> 01:22:34,812
Speaker 2: Can you paraphrase white people cheat Tyler.

1694
01:22:34,933 --> 01:22:37,372
Speaker 3: What I'll read is I'll read this part here that

1695
01:22:37,572 --> 01:22:38,572
Speaker 3: was of most interest to me.

1696
01:22:38,692 --> 01:22:42,452
Speaker 2: So this is you cheat on May never never. I'm

1697
01:22:42,492 --> 01:22:44,772
Speaker 2: the lucky one in this relationship testing. I was just

1698
01:22:45,293 --> 01:22:48,333
Speaker 2: testing you and a firebrand, and you were right, you

1699
01:22:48,412 --> 01:22:49,212
Speaker 2: answered immediately.

1700
01:22:49,253 --> 01:22:51,333
Speaker 3: I got really offensive on that though, didn't I believe you?

1701
01:22:51,372 --> 01:22:53,492
Speaker 3: I believe Yeah, thank you. So this one here, so

1702
01:22:53,572 --> 01:22:56,213
Speaker 3: it's all about desire and making sure that you show

1703
01:22:56,293 --> 01:22:58,333
Speaker 3: love to your partner and make sure that they feel

1704
01:22:58,412 --> 01:23:00,452
Speaker 3: heard and loved and beautiful and all the rest of it.

1705
01:23:00,532 --> 01:23:04,412
Speaker 3: But this one about how to reignite desire, so she said,

1706
01:23:04,572 --> 01:23:09,092
Speaker 3: recommends creating distance. This means recognizing your as a separate,

1707
01:23:09,133 --> 01:23:12,452
Speaker 3: evolving individual, not an extension of yourself. Here's what I

1708
01:23:12,772 --> 01:23:15,013
Speaker 3: start to question a little bit. So examples include this

1709
01:23:15,213 --> 01:23:17,372
Speaker 3: is the way to reignite desire, to stop your partner

1710
01:23:17,452 --> 01:23:17,892
Speaker 3: cheating on.

1711
01:23:17,933 --> 01:23:22,652
Speaker 2: You, taking separate trips? Wow, sleeping in separate beds?

1712
01:23:22,772 --> 01:23:22,812
Speaker 13: What?

1713
01:23:23,772 --> 01:23:24,253
Speaker 5: And this one.

1714
01:23:24,293 --> 01:23:27,972
Speaker 3: I probably do agree with pursuing individual hobbies, but taking

1715
01:23:28,093 --> 01:23:32,772
Speaker 3: separate trips, I would argue, potentially could lead someone to

1716
01:23:32,973 --> 01:23:34,293
Speaker 3: go a bit astray.

1717
01:23:34,532 --> 01:23:38,692
Speaker 2: Well, absence makes the heart grow stronger, right, that is true, romantic,

1718
01:23:38,812 --> 01:23:41,133
Speaker 2: You never miss your partner more than when you've been

1719
01:23:41,133 --> 01:23:42,893
Speaker 2: away from them for a few days. Yeah, you know,

1720
01:23:42,893 --> 01:23:46,452
Speaker 2: I wasn't aneeding away from my partner for five days

1721
01:23:46,532 --> 01:23:48,692
Speaker 2: last week. That was pretty keen to see her when

1722
01:23:48,732 --> 01:23:50,933
Speaker 2: I got back. Yeah, yeah, that is true, and I

1723
01:23:51,053 --> 01:23:52,612
Speaker 2: didn't stray when I'm just down there.

1724
01:23:52,612 --> 01:23:54,732
Speaker 3: I've just heard stories about these conferences, and I don't

1725
01:23:54,732 --> 01:23:56,972
Speaker 3: know that that's just a Hollywood trope. But the key

1726
01:23:57,053 --> 01:24:00,492
Speaker 3: takeaways from this woman, who name is Esta parral So,

1727
01:24:00,652 --> 01:24:04,293
Speaker 3: desire and love required different emotional ingredients. Couples can restore

1728
01:24:04,372 --> 01:24:08,933
Speaker 3: sexuality by embracing difference, not constant closeness. Resence is the

1729
01:24:09,013 --> 01:24:12,772
Speaker 3: foundation of intimacy. Modern tech often erodes there to agree

1730
01:24:12,812 --> 01:24:16,093
Speaker 3: with that, and scheduled seks is not unromantic. It's intentional

1731
01:24:16,173 --> 01:24:20,972
Speaker 3: and shows value. Infidelity often arises last one. Scheduled sex

1732
01:24:21,133 --> 01:24:23,612
Speaker 3: is not unromantic. It's intentional and shows.

1733
01:24:23,452 --> 01:24:25,532
Speaker 2: Value having him in the calendar, putting it in the

1734
01:24:25,812 --> 01:24:28,732
Speaker 2: iCal Yeah yeah really, Yeah, they have a Tuesday so

1735
01:24:28,812 --> 01:24:30,492
Speaker 2: to speak, and Monday morning, let's go.

1736
01:24:30,692 --> 01:24:36,213
Speaker 3: Yeah, like your date night Wednesday night. And infidelity often

1737
01:24:36,213 --> 01:24:41,293
Speaker 3: arises from a liveness lacking in a relationship, not just dissatisfaction.

1738
01:24:41,612 --> 01:24:44,333
Speaker 3: A liveness is a great word, right, I.

1739
01:24:44,412 --> 01:24:47,333
Speaker 2: Think the best advice I've ever had in relationships, and look,

1740
01:24:47,372 --> 01:24:50,492
Speaker 2: I've had very any success over the years, but was

1741
01:24:50,532 --> 01:24:53,532
Speaker 2: it Leo Tolstoy, who said, when you love someone, you

1742
01:24:53,652 --> 01:24:56,013
Speaker 2: love the person as they are and not as you'd

1743
01:24:56,093 --> 01:24:56,492
Speaker 2: like them to be.

1744
01:24:56,893 --> 01:24:58,532
Speaker 3: That is beautiful, That is really nice.

1745
01:24:58,612 --> 01:25:01,573
Speaker 2: So stop trying to change the person and just accept

1746
01:25:01,652 --> 01:25:03,612
Speaker 2: them for who they are and all their foebles, and

1747
01:25:03,652 --> 01:25:04,293
Speaker 2: you get on with it.

1748
01:25:04,412 --> 01:25:06,213
Speaker 3: That never works well trying to change someone. But love

1749
01:25:06,253 --> 01:25:07,772
Speaker 3: to hear from you, Oh, eight hundred and eighty ten

1750
01:25:07,812 --> 01:25:10,293
Speaker 3: eighty if you've being the cheata, why what happened? If

1751
01:25:10,333 --> 01:25:12,812
Speaker 3: you've been cheated on? What were the reasons and how

1752
01:25:12,893 --> 01:25:13,852
Speaker 3: did you feel afterwards?

1753
01:25:14,293 --> 01:25:16,492
Speaker 2: And a wider thing that I want to shoehorn in

1754
01:25:16,532 --> 01:25:18,692
Speaker 2: because this was your your topic, Tyler, you wanted to

1755
01:25:18,732 --> 01:25:22,173
Speaker 2: talk about you want to live viacariously through other people's infidelities.

1756
01:25:22,293 --> 01:25:25,492
Speaker 2: I wanted to I'm shoehorning in another topic on the

1757
01:25:25,532 --> 01:25:29,613
Speaker 2: same thing. A study that has highlighted how marriage breakups

1758
01:25:30,093 --> 01:25:34,732
Speaker 2: cause huge financial losses to kiwis in their forties and fifties,

1759
01:25:35,253 --> 01:25:38,452
Speaker 2: especially for men, and that the biggest cause of divorce

1760
01:25:38,612 --> 01:25:42,052
Speaker 2: is men losing their jobs and becoming poorer than their partners,

1761
01:25:42,652 --> 01:25:44,692
Speaker 2: or their partners start earning more than them, and they're

1762
01:25:44,732 --> 01:25:50,213
Speaker 2: not sure whether that's because the woman, you know, missus

1763
01:25:50,253 --> 01:25:52,933
Speaker 2: the status or because the man can't handle it and

1764
01:25:53,013 --> 01:25:56,012
Speaker 2: starts having like a dick. You know I can see that. Yeah,

1765
01:25:56,492 --> 01:25:59,652
Speaker 2: And the second biggest is infidelity. But you know, you

1766
01:25:59,732 --> 01:26:01,333
Speaker 2: know you want to ask have you peated on your

1767
01:26:01,372 --> 01:26:04,013
Speaker 2: partner ex partner or have you been cheated on? And

1768
01:26:04,053 --> 01:26:06,293
Speaker 2: what the signs and doesn't matter? That's all you want

1769
01:26:06,333 --> 01:26:08,492
Speaker 2: to talk about, and can you forgive? But I'm I'm

1770
01:26:08,492 --> 01:26:11,612
Speaker 2: also interested in dovetailing this together. Would you let it

1771
01:26:11,732 --> 01:26:16,253
Speaker 2: go through to the keeper just for the financial destruction

1772
01:26:16,452 --> 01:26:18,812
Speaker 2: that happens when you break up a relationship, because if

1773
01:26:18,853 --> 01:26:21,772
Speaker 2: you want to be wealthy, right, and look, you see

1774
01:26:21,772 --> 01:26:24,013
Speaker 2: it all the time. The people that stay together, the

1775
01:26:24,893 --> 01:26:27,932
Speaker 2: couples that stay together, are in a much much better

1776
01:26:28,053 --> 01:26:31,372
Speaker 2: position financially than the person that splits their wealth and

1777
01:26:31,412 --> 01:26:36,093
Speaker 2: half and then and half again. Definitely obviously obviously right.

1778
01:26:36,732 --> 01:26:40,692
Speaker 2: So if your partner cheated on you, okay, I'll ask

1779
01:26:40,732 --> 01:26:43,612
Speaker 2: you if you're looking at your your you know, your

1780
01:26:43,652 --> 01:26:47,652
Speaker 2: financial situation, your finance financial outlook tyler yep, and then

1781
01:26:49,013 --> 01:26:52,652
Speaker 2: sounds so personal using names, but say may have cheated

1782
01:26:52,732 --> 01:26:56,812
Speaker 2: on you. Would you take into account the splitting of

1783
01:26:56,893 --> 01:27:00,293
Speaker 2: the house the splitting of the assets and all the

1784
01:27:00,412 --> 01:27:03,812
Speaker 2: admin around the split up, or to just be a

1785
01:27:03,973 --> 01:27:06,572
Speaker 2: pure emotional thing for you out the door.

1786
01:27:07,173 --> 01:27:09,652
Speaker 3: Oh good question. You know she's the breadwinner. You know

1787
01:27:09,692 --> 01:27:13,333
Speaker 3: how much I get paid on the show? No, genuinely

1788
01:27:13,572 --> 01:27:17,213
Speaker 3: I and I've said this before. If I was ever

1789
01:27:17,293 --> 01:27:20,293
Speaker 3: cheated on, that's not something I could ever forgive, not

1790
01:27:20,452 --> 01:27:23,452
Speaker 3: for money. Love. Maybe our beloved dog Pepper.

1791
01:27:23,692 --> 01:27:23,972
Speaker 4: I might.

1792
01:27:24,053 --> 01:27:25,772
Speaker 3: I might pull it together for Pepper. That would be

1793
01:27:25,853 --> 01:27:26,452
Speaker 3: the only thing.

1794
01:27:26,893 --> 01:27:29,013
Speaker 2: I just I just don't think after that. That is,

1795
01:27:29,173 --> 01:27:33,333
Speaker 2: What about a random moment of passion on a drunken

1796
01:27:33,492 --> 01:27:35,972
Speaker 2: night that meant nothing? And she came back and said

1797
01:27:35,973 --> 01:27:37,452
Speaker 2: it it meant nothing?

1798
01:27:37,893 --> 01:27:40,093
Speaker 3: Well, what I'd ask, why weren't I there? I was

1799
01:27:40,133 --> 01:27:41,412
Speaker 3: an eye on it's on this night out.

1800
01:27:41,452 --> 01:27:44,052
Speaker 2: But you've just been advocating for people to holiday separately.

1801
01:27:46,572 --> 01:27:48,253
Speaker 3: No, No, I just couldn't. I couldn't.

1802
01:27:48,253 --> 01:27:49,532
Speaker 2: I could not forgive. Could you forgive?

1803
01:27:50,093 --> 01:27:50,293
Speaker 6: Yeah?

1804
01:27:51,173 --> 01:27:51,932
Speaker 3: Forgive and forget?

1805
01:27:52,173 --> 01:27:54,452
Speaker 2: Probably to error is human. I mean, I can't imagine

1806
01:27:54,452 --> 01:27:55,612
Speaker 2: why anyone would want to cheat on me.

1807
01:27:55,732 --> 01:27:59,692
Speaker 3: But yeah, well back to our AO chat, it means

1808
01:27:59,692 --> 01:28:03,093
Speaker 3: you're not Ai Waite undred eighty ten eighty. So a

1809
01:28:03,173 --> 01:28:04,933
Speaker 3: couple of things there one, if you've been cheated on,

1810
01:28:05,053 --> 01:28:06,492
Speaker 3: love to hear from you of your or you've been

1811
01:28:06,532 --> 01:28:09,612
Speaker 3: the cheat of what happened? Have you stayed together because

1812
01:28:09,652 --> 01:28:12,772
Speaker 3: there were things in the relationship, primarily money, that meant

1813
01:28:12,853 --> 01:28:15,372
Speaker 3: it was very difficult for you to divorce or to

1814
01:28:15,492 --> 01:28:19,572
Speaker 3: split up. And can you forgive someone for cheating on you?

1815
01:28:19,772 --> 01:28:21,692
Speaker 2: This text to Mark says, hey, guys, just listen to

1816
01:28:21,732 --> 01:28:24,213
Speaker 2: your show red Infidelity and Divorce. Back in the days

1817
01:28:24,253 --> 01:28:26,012
Speaker 2: when GST was twelve and a half percent, we knew

1818
01:28:26,013 --> 01:28:28,372
Speaker 2: a guy whose nickname was GST because they had been

1819
01:28:28,372 --> 01:28:30,893
Speaker 2: divorced through infidelity three times, but as their sets in

1820
01:28:30,973 --> 01:28:33,972
Speaker 2: half to fifty percent, half to twenty five percent, half

1821
01:28:34,013 --> 01:28:36,452
Speaker 2: again to twelve and a half spent percent. Hence the

1822
01:28:36,532 --> 01:28:39,213
Speaker 2: nickname that is brilliant. Right, Oh eight hundred eighty ten

1823
01:28:39,213 --> 01:28:40,253
Speaker 2: eighty is the number to call.

1824
01:28:40,372 --> 01:28:41,732
Speaker 3: It is twenty pass three.

1825
01:28:45,492 --> 01:28:49,133
Speaker 1: Matt Heathen Tyler Adams afternoons call OH eight hundred eighty

1826
01:28:49,213 --> 01:28:51,093
Speaker 1: ten eighty on news Talk ZB.

1827
01:28:51,173 --> 01:28:54,372
Speaker 3: Twenty two pass three, and we are talking about infidelity.

1828
01:28:54,732 --> 01:28:56,812
Speaker 3: Have you been a cheater or cheated on? Oh eight

1829
01:28:56,893 --> 01:28:58,093
Speaker 3: hundred eighty ten eighty and.

1830
01:28:58,213 --> 01:29:03,253
Speaker 2: The financial implications of breaking up? Our relationship, and is

1831
01:29:03,293 --> 01:29:05,972
Speaker 2: it worth sticking together with someone that's straying a little bit?

1832
01:29:06,173 --> 01:29:09,253
Speaker 2: Just if the financials work out in your favor, if

1833
01:29:09,293 --> 01:29:11,333
Speaker 2: you stick together. We've all seen it happen. This sticks

1834
01:29:11,372 --> 01:29:13,772
Speaker 2: to believe. So my husband cheats, I know it. He

1835
01:29:13,893 --> 01:29:14,253
Speaker 2: doesn't know.

1836
01:29:14,372 --> 01:29:15,013
Speaker 3: I know, but I know.

1837
01:29:15,173 --> 01:29:18,053
Speaker 2: But we have a nice house, nice holidays, I drive

1838
01:29:18,093 --> 01:29:20,732
Speaker 2: a nice car. We can help our kids buy houses.

1839
01:29:21,013 --> 01:29:23,412
Speaker 2: I haven't done any money in my life, but I

1840
01:29:23,492 --> 01:29:25,893
Speaker 2: am rich. Why would I blow that up because he

1841
01:29:25,973 --> 01:29:28,572
Speaker 2: can't keep it in his pants? If anything, it keeps

1842
01:29:28,612 --> 01:29:31,852
Speaker 2: the pressure off me. That's a fair point. I wonder

1843
01:29:31,853 --> 01:29:33,772
Speaker 2: if it's a lot. I wonder if that husband would

1844
01:29:33,772 --> 01:29:39,133
Speaker 2: be disappointed, you know, he's a philanderer and being dishonest. Yeah,

1845
01:29:39,173 --> 01:29:40,772
Speaker 2: but I wonder if you'd be a little bit disappointed

1846
01:29:40,812 --> 01:29:42,932
Speaker 2: that his wife's just like I know, but I don't care.

1847
01:29:43,372 --> 01:29:45,852
Speaker 2: I just I just like the houses. I like the car,

1848
01:29:46,853 --> 01:29:49,652
Speaker 2: like I like the life, I like the holidays. And

1849
01:29:50,053 --> 01:29:51,852
Speaker 2: by the sounds of it, she's not that keen on

1850
01:29:51,933 --> 01:29:53,092
Speaker 2: being intimate with them anyway.

1851
01:29:53,213 --> 01:29:55,173
Speaker 3: Yeah, well, I wonder if the wife is having a

1852
01:29:55,213 --> 01:29:56,812
Speaker 3: bit on the side. She's got the nice house, and

1853
01:29:56,893 --> 01:30:00,292
Speaker 3: then you know, she's got the tennis coach. Maybe Plati's instructor.

1854
01:30:00,652 --> 01:30:03,293
Speaker 3: Sounds like it, Tyler, that sound like it. You can

1855
01:30:03,333 --> 01:30:05,652
Speaker 3: read a whole lot into that. Yep, there's the pool boy,

1856
01:30:06,372 --> 01:30:13,532
Speaker 3: there's the mister tin Is that Pilates letter listens at home? Yeah, yeah,

1857
01:30:13,692 --> 01:30:14,812
Speaker 3: there's good times in that house.

1858
01:30:14,893 --> 01:30:17,492
Speaker 2: Old to mention text us back if we're if we're

1859
01:30:17,853 --> 01:30:18,333
Speaker 2: right about that?

1860
01:30:18,492 --> 01:30:21,052
Speaker 3: Yeah, Oh, eight hundred eighty ten eighty is the number

1861
01:30:21,093 --> 01:30:23,333
Speaker 3: to call. Some great texts coming through Lad's check out.

1862
01:30:23,293 --> 01:30:25,932
Speaker 2: Articles and couple separated but still living the same house

1863
01:30:26,013 --> 01:30:29,892
Speaker 2: a real thing dictated by finances. Yeah, I mean, are

1864
01:30:29,933 --> 01:30:32,132
Speaker 2: you doing that eight hundred and eighty ten eighty Because

1865
01:30:32,652 --> 01:30:35,333
Speaker 2: some people don't want to be together, but they just

1866
01:30:35,372 --> 01:30:37,253
Speaker 2: can't afford to live in a different house, or maybe

1867
01:30:37,293 --> 01:30:38,772
Speaker 2: they you know, you've got to bring up the kids together.

1868
01:30:38,812 --> 01:30:41,213
Speaker 2: It's very, very expensive to run two houses compared to

1869
01:30:41,293 --> 01:30:45,812
Speaker 2: combined two incomes, more than the sum of the parts

1870
01:30:45,853 --> 01:30:49,173
Speaker 2: of of you know, yeah, you know the two incomes

1871
01:30:49,213 --> 01:30:52,452
Speaker 2: because you know costs and stuff. You know, running all

1872
01:30:52,492 --> 01:30:55,333
Speaker 2: the costs, I mean not a very basic level, you know,

1873
01:30:55,572 --> 01:30:57,972
Speaker 2: internet connection or something like that, getting two internet connections.

1874
01:30:58,013 --> 01:30:59,932
Speaker 2: I'm thinking it a very very simple. There's an example

1875
01:30:59,973 --> 01:31:01,133
Speaker 2: that came into my head. But you know what I mean.

1876
01:31:01,452 --> 01:31:03,173
Speaker 3: But I can see you know, as you said, if

1877
01:31:03,213 --> 01:31:05,652
Speaker 3: you're in your your thirties, forties or even fifties, and

1878
01:31:05,853 --> 01:31:09,732
Speaker 3: you've worked for three decades to acquire what you've got

1879
01:31:09,853 --> 01:31:14,772
Speaker 3: in your assets with your spouse, and then something happens,

1880
01:31:14,933 --> 01:31:16,892
Speaker 3: maybe a one off, as you say, are we won

1881
01:31:17,013 --> 01:31:20,732
Speaker 3: one night of passion, a mistake, a drunken fling. But

1882
01:31:20,973 --> 01:31:26,333
Speaker 3: to then cut your assets in half over that, I mean,

1883
01:31:26,372 --> 01:31:27,933
Speaker 3: you're try and work it out to the best of

1884
01:31:27,973 --> 01:31:30,412
Speaker 3: your ability for a five hundred thousand dollars I don't know.

1885
01:31:30,612 --> 01:31:33,492
Speaker 2: I'm just laughing. It's this text from Darren. I think

1886
01:31:33,532 --> 01:31:37,692
Speaker 2: that's my wife. Yeah, the text that she doesn't care. Darren.

1887
01:31:37,772 --> 01:31:39,692
Speaker 2: It sounds like you're were the sounds of things you're

1888
01:31:39,692 --> 01:31:40,452
Speaker 2: getting around a bit.

1889
01:31:40,732 --> 01:31:42,412
Speaker 3: Good luck with that. Stop paying the tennis coach.

1890
01:31:42,492 --> 01:31:42,692
Speaker 5: Maybe.

1891
01:31:43,093 --> 01:31:43,213
Speaker 23: Uh.

1892
01:31:43,973 --> 01:31:46,532
Speaker 3: This one says, get boys. I've been married and divorced

1893
01:31:46,692 --> 01:31:49,572
Speaker 3: three times and I'm finally in the best financial position

1894
01:31:49,652 --> 01:31:52,892
Speaker 3: I've ever been in because I can't lose anymore. Fifty

1895
01:31:52,893 --> 01:31:56,052
Speaker 3: percent of nothing is still nothing. Yeah, very true.

1896
01:31:57,293 --> 01:32:02,492
Speaker 2: Yeah, Yeah, Wow, Okay, I'm glad. I didn't read the

1897
01:32:02,532 --> 01:32:02,852
Speaker 2: rest of that.

1898
01:32:03,053 --> 01:32:06,852
Speaker 3: Yeah, there's some spicy ones coming through. I mean head guys.

1899
01:32:07,293 --> 01:32:10,812
Speaker 2: Guys defined cheating a one off, three second drunken kiss

1900
01:32:10,893 --> 01:32:14,133
Speaker 2: on a dance floor. I could almost consider looking past

1901
01:32:14,692 --> 01:32:17,093
Speaker 2: a full around a full roll around in the sack.

1902
01:32:17,213 --> 01:32:20,333
Speaker 2: No way, cheers Sean. Well, some people think that the

1903
01:32:21,853 --> 01:32:25,572
Speaker 2: laughing and the kissing and the holding hands and the

1904
01:32:25,652 --> 01:32:29,213
Speaker 2: emotional relationship is worse than the rolling around and.

1905
01:32:29,213 --> 01:32:32,812
Speaker 3: The hay terms of cheating, emotional cheating, that's right up there.

1906
01:32:33,412 --> 01:32:36,012
Speaker 3: One hundred and eighty ten eighty is the number to call.

1907
01:32:36,213 --> 01:32:38,333
Speaker 3: So just to go back to this article why people

1908
01:32:38,452 --> 01:32:42,053
Speaker 3: cheat so infandality, according to the sex therapist, often comes

1909
01:32:42,093 --> 01:32:46,412
Speaker 3: from emotional loneliness, alienation, or loss of self. Sometimes the

1910
01:32:46,692 --> 01:32:49,732
Speaker 3: relationship causes the drift. Other times the reasons are internal.

1911
01:32:49,933 --> 01:32:52,213
Speaker 3: But in most cases it's about a craving for a

1912
01:32:52,412 --> 01:32:55,852
Speaker 3: liveness for a self that has become dormant. Your first

1913
01:32:55,893 --> 01:32:58,013
Speaker 3: marriage maybe over, but you can build a second with

1914
01:32:58,053 --> 01:32:58,692
Speaker 3: the same person.

1915
01:32:59,253 --> 01:33:02,572
Speaker 2: Wow, this text and understanding beginning a lot of texts

1916
01:33:02,612 --> 01:33:03,973
Speaker 2: on this because people don't want to call, but you

1917
01:33:04,053 --> 01:33:05,532
Speaker 2: know you can call through our way. One hundred and

1918
01:33:05,532 --> 01:33:08,093
Speaker 2: eighty ten eighty and you don't need to give your name,

1919
01:33:08,772 --> 01:33:10,772
Speaker 2: You don't have to give us any details at all.

1920
01:33:11,053 --> 01:33:13,133
Speaker 2: Just shear his story for the good of the nation.

1921
01:33:13,293 --> 01:33:15,412
Speaker 2: But this Texas says, my partner has a love interest.

1922
01:33:15,933 --> 01:33:18,372
Speaker 2: He has no idea that I know. If he doesn't

1923
01:33:18,372 --> 01:33:21,772
Speaker 2: have the balls to leave for her, I'll go in time.

1924
01:33:22,253 --> 01:33:24,692
Speaker 2: I'm just waiting till the bank balance is big enough

1925
01:33:24,732 --> 01:33:25,812
Speaker 2: to make it worth my while.

1926
01:33:27,293 --> 01:33:29,452
Speaker 3: That is smart, though, That is smart.

1927
01:33:30,532 --> 01:33:34,213
Speaker 2: That's a that's a soap opera of a situation right there.

1928
01:33:34,492 --> 01:33:37,852
Speaker 3: But I'll tell you why I would have more sympathy

1929
01:33:37,933 --> 01:33:40,293
Speaker 3: for the person that texts that in because the person

1930
01:33:40,412 --> 01:33:42,812
Speaker 3: who made the mistake first was clearly the partner having

1931
01:33:42,812 --> 01:33:44,933
Speaker 3: a bit on the side. So if they've made the

1932
01:33:44,973 --> 01:33:46,652
Speaker 3: first mistake, and then you go, I'm just going to

1933
01:33:46,692 --> 01:33:48,572
Speaker 3: wait for the timers, right, and that bank balance is

1934
01:33:48,612 --> 01:33:51,532
Speaker 3: good enough for me to cut ties and take as

1935
01:33:51,652 --> 01:33:52,852
Speaker 3: much as as I can.

1936
01:33:53,013 --> 01:33:57,452
Speaker 2: You know, revenge is a is a dish best served cold. Yeah,

1937
01:33:57,572 --> 01:34:01,532
Speaker 2: So you see the career trajectory of your partner, You go,

1938
01:34:01,692 --> 01:34:04,213
Speaker 2: this person's going to earn a lot of money, or

1939
01:34:04,973 --> 01:34:09,053
Speaker 2: even more cynically, about to inherit all of money. Yeah,

1940
01:34:09,452 --> 01:34:12,213
Speaker 2: so you hold the marriage together. You don't point out

1941
01:34:12,253 --> 01:34:15,972
Speaker 2: the infidelity. You hold on to that. You push down

1942
01:34:16,013 --> 01:34:20,572
Speaker 2: your resentment until the pie is worth cutting it half.

1943
01:34:20,692 --> 01:34:20,972
Speaker 20: Exeg.

1944
01:34:21,093 --> 01:34:22,293
Speaker 2: The cake's worth slicing up.

1945
01:34:22,372 --> 01:34:25,293
Speaker 3: Yep, bang on. A lot of people would do that right. Oh,

1946
01:34:25,293 --> 01:34:27,372
Speaker 3: one hundred and eighty ten eighty is the number. Call

1947
01:34:27,492 --> 01:34:30,612
Speaker 3: got to play some messages because Wendy is standing by

1948
01:34:30,692 --> 01:34:33,093
Speaker 3: with headlines. But more of your calls coming up. It's

1949
01:34:33,133 --> 01:34:34,452
Speaker 3: twenty eight past three.

1950
01:34:34,452 --> 01:34:37,173
Speaker 2: Telling what there's shocking him out of infidelity coming through.

1951
01:34:37,213 --> 01:34:38,973
Speaker 2: In the Texas Show, Spicy.

1952
01:34:41,133 --> 01:34:43,973
Speaker 15: U's talk said the headlines with blue bubble taxis it's

1953
01:34:44,053 --> 01:34:46,852
Speaker 15: no trouble with a blue bubble. More than thirty six

1954
01:34:46,973 --> 01:34:51,972
Speaker 15: thousand hospital nurses, midwives, Healthcare Assistance and Kaimahi Howara will

1955
01:34:52,013 --> 01:34:54,333
Speaker 15: strike for twenty four hours at the end of the month.

1956
01:34:54,812 --> 01:34:58,532
Speaker 15: Nurses Organization Chief executive Paul Golter says I walk off

1957
01:34:58,612 --> 01:35:01,812
Speaker 15: the job after the latest Health New Zealand offer failed

1958
01:35:01,853 --> 01:35:05,692
Speaker 15: to address safe staff and concerns. The Nelson, Tasman Civil

1959
01:35:05,732 --> 01:35:09,132
Speaker 15: Defenses sent out an emergency mobile alert to areas affected

1960
01:35:09,213 --> 01:35:12,892
Speaker 15: by recent flooding. They're advising anyone who were evacuated two

1961
01:35:12,973 --> 01:35:16,612
Speaker 15: weeks ago and those who feel unsafe to evacuate. Now,

1962
01:35:17,093 --> 01:35:19,932
Speaker 15: Met Services issued a red heavy rain warning for tasman

1963
01:35:20,572 --> 01:35:23,092
Speaker 15: power has been restored to more than twenty thousand people

1964
01:35:23,133 --> 01:35:27,412
Speaker 15: in Podonger. A fault transpowers at Mount Monganui substation can't

1965
01:35:27,452 --> 01:35:30,133
Speaker 15: power for customers between the mount and Papamore Beach are

1966
01:35:30,133 --> 01:35:34,853
Speaker 15: from around twelve thirty. Emotions ran high during today's sentencing

1967
01:35:34,933 --> 01:35:37,812
Speaker 15: of the fourteen year old who fatally stabbed a fellow

1968
01:35:37,893 --> 01:35:40,812
Speaker 15: teenager at a Dunedin bus hub. He was sentenced to

1969
01:35:40,893 --> 01:35:44,133
Speaker 15: three years and three months for manslaughter. Plus the big

1970
01:35:44,213 --> 01:35:47,213
Speaker 15: power company gaming the most from the Big wet. Read

1971
01:35:47,253 --> 01:35:50,372
Speaker 15: this and more from stock Takes It Ends at Herald Premium.

1972
01:35:50,732 --> 01:35:52,132
Speaker 15: Now that to Matt and Taylor.

1973
01:35:52,333 --> 01:35:55,452
Speaker 3: Thank you very much. Wendy, and we are talking about infidelity.

1974
01:35:55,812 --> 01:35:58,492
Speaker 3: An article written in The Herald from a six therapist

1975
01:35:58,572 --> 01:36:01,532
Speaker 3: of forty five years mentions the ways that you can

1976
01:36:01,612 --> 01:36:05,732
Speaker 3: avoid your partner cheating on you and also if your

1977
01:36:05,772 --> 01:36:07,492
Speaker 3: partner does cheat on you, how you can make it

1978
01:36:07,612 --> 01:36:10,812
Speaker 3: back and repair that relationship. But also fascinating study that

1979
01:36:10,893 --> 01:36:14,492
Speaker 3: you were reading about the financial ramifications.

1980
01:36:13,893 --> 01:36:16,812
Speaker 2: Harving your wealth. Yeah, if you can stay together, if

1981
01:36:16,853 --> 01:36:18,852
Speaker 2: you can stomach it, then you will be richer, but

1982
01:36:19,053 --> 01:36:21,293
Speaker 2: is it worth it? Bernie, Welcome to the show.

1983
01:36:22,692 --> 01:36:28,692
Speaker 13: Yeah, good day, guys. Yeah, something's just struck me. There's

1984
01:36:28,732 --> 01:36:31,532
Speaker 13: not many people ringing up on this topic, so there

1985
01:36:31,612 --> 01:36:33,492
Speaker 13: must be a lot of people cheating you out there.

1986
01:36:33,973 --> 01:36:34,173
Speaker 23: Yeah.

1987
01:36:34,293 --> 01:36:36,772
Speaker 3: Well we got full boards now, luckily, Bernie. But you're right,

1988
01:36:36,933 --> 01:36:38,973
Speaker 3: so many texts coming through. But it can be a

1989
01:36:39,093 --> 01:36:41,452
Speaker 3: hard one for people to you know, talk about openly.

1990
01:36:41,812 --> 01:36:42,412
Speaker 3: But good on you.

1991
01:36:42,772 --> 01:36:47,173
Speaker 13: Yeah. Yeah, well I cheated. But when I say I cheated,

1992
01:36:47,333 --> 01:36:51,452
Speaker 13: I actually passionately kissed another woman. And a couple of

1993
01:36:51,572 --> 01:36:53,933
Speaker 13: days went by and I had to tell my girlfriend that,

1994
01:36:54,293 --> 01:36:57,732
Speaker 13: you know, it just bugged me. Yeah, so I told

1995
01:36:57,772 --> 01:37:01,013
Speaker 13: her and she said, no, that's cheating that way. You know,

1996
01:37:02,013 --> 01:37:04,772
Speaker 13: you might as well have done the lot cheating.

1997
01:37:05,173 --> 01:37:07,612
Speaker 2: I've heard that that can be looked at that way.

1998
01:37:08,572 --> 01:37:11,132
Speaker 2: So she didn't give you any credit for the honesty.

1999
01:37:13,173 --> 01:37:16,452
Speaker 13: Oh she did, but like it took a while to

2000
01:37:16,572 --> 01:37:19,052
Speaker 13: actually like it was like separate beads for a while.

2001
01:37:20,333 --> 01:37:23,253
Speaker 13: And you know, but I think I just it killed

2002
01:37:23,293 --> 01:37:26,732
Speaker 13: the killed the romance, killed the relationship in the end,

2003
01:37:26,772 --> 01:37:28,093
Speaker 13: you know, how passionate.

2004
01:37:27,812 --> 01:37:28,053
Speaker 4: Was the.

2005
01:37:29,893 --> 01:37:32,412
Speaker 13: Oh yeah, and I told her and that you know,

2006
01:37:32,532 --> 01:37:34,093
Speaker 13: like a couple of days went by and I thought

2007
01:37:34,173 --> 01:37:34,852
Speaker 13: i'd better tell her.

2008
01:37:35,013 --> 01:37:37,092
Speaker 2: Yeah, but I mean, was that We're talking a super

2009
01:37:37,173 --> 01:37:38,213
Speaker 2: passionate kiss here.

2010
01:37:38,253 --> 01:37:40,932
Speaker 13: Not just a little pretty close?

2011
01:37:41,492 --> 01:37:41,692
Speaker 4: Yeah.

2012
01:37:43,492 --> 01:37:46,692
Speaker 3: Who was the other woman, Bernie? Was it a workmate

2013
01:37:46,853 --> 01:37:49,372
Speaker 3: or just some random Yeah?

2014
01:37:49,372 --> 01:37:52,053
Speaker 13: It was a girl I met in the club. Yeah,

2015
01:37:52,612 --> 01:37:54,572
Speaker 13: but what it was we had a boy's night out

2016
01:37:54,652 --> 01:37:56,612
Speaker 13: and that just led to like this girl kept looking

2017
01:37:56,652 --> 01:37:58,532
Speaker 13: at me, and I just went over and started talking

2018
01:37:58,612 --> 01:38:00,492
Speaker 13: to her, and then sort of half an hour later,

2019
01:38:00,532 --> 01:38:02,532
Speaker 13: we're kissing, right, and and.

2020
01:38:02,812 --> 01:38:05,692
Speaker 2: You know that the the you know, the effect I

2021
01:38:05,732 --> 01:38:07,932
Speaker 2: had on your relationship, and it sounds like it went south?

2022
01:38:07,933 --> 01:38:10,933
Speaker 2: Pretty did that? How did that hit you? Did you

2023
01:38:11,053 --> 01:38:13,333
Speaker 2: did you feel guilty? Did you feel bad? Did you

2024
01:38:13,372 --> 01:38:14,692
Speaker 2: want the relationship to continue?

2025
01:38:16,093 --> 01:38:16,293
Speaker 4: Yes?

2026
01:38:16,372 --> 01:38:20,013
Speaker 13: I did? Yeah, I was pretty gathered. But yeah, you know,

2027
01:38:20,532 --> 01:38:23,412
Speaker 13: like a lot of people said, you didn't actually do anything, Donnie.

2028
01:38:23,412 --> 01:38:26,812
Speaker 13: You know, you didn't go the full Randy, But I said,

2029
01:38:26,893 --> 01:38:30,132
Speaker 13: some people still make it as cheating, you know, Yeah.

2030
01:38:31,013 --> 01:38:34,732
Speaker 3: Yeah, I mean I tell you. Yeah, I mean, because

2031
01:38:34,732 --> 01:38:36,892
Speaker 3: you mentioned before Matt and talking to you as well, Bernie,

2032
01:38:36,933 --> 01:38:39,612
Speaker 3: would I could I forgive May for cheating on me?

2033
01:38:39,812 --> 01:38:41,772
Speaker 3: I think I'd like to think I could if she

2034
01:38:42,173 --> 01:38:45,692
Speaker 3: kissed somebody else. But that's a hard road for you

2035
01:38:45,893 --> 01:38:48,492
Speaker 3: to front up to Bernie in that relationship that you're

2036
01:38:48,492 --> 01:38:50,253
Speaker 3: going to have to go and for the for the

2037
01:38:50,333 --> 01:38:54,652
Speaker 3: long haul, that your your wife, that suspicion is going

2038
01:38:54,732 --> 01:38:57,053
Speaker 3: to be there for a considerable amount of time. See,

2039
01:38:57,333 --> 01:38:59,293
Speaker 3: you know, to try and make that work. That's a

2040
01:38:59,372 --> 01:39:00,213
Speaker 3: long road, isn't it.

2041
01:39:01,293 --> 01:39:01,532
Speaker 6: It is?

2042
01:39:01,893 --> 01:39:04,412
Speaker 13: Ye, mate, and you're looting by your mistakes.

2043
01:39:04,492 --> 01:39:06,612
Speaker 6: You know. Did it?

2044
01:39:06,692 --> 01:39:07,732
Speaker 3: Did it work out in the ends?

2045
01:39:07,973 --> 01:39:08,133
Speaker 4: Did you?

2046
01:39:08,333 --> 01:39:09,053
Speaker 2: And it's to repair it.

2047
01:39:10,133 --> 01:39:14,372
Speaker 13: We're actually good friends now, like, but yeah, the actual

2048
01:39:14,492 --> 01:39:17,532
Speaker 13: relationship died as far as like marriage was out of

2049
01:39:17,572 --> 01:39:21,852
Speaker 13: the question. I can't really trust you. And I was

2050
01:39:21,893 --> 01:39:23,213
Speaker 13: just like, okay, you know, fair enough.

2051
01:39:23,692 --> 01:39:27,213
Speaker 2: And since then, since then, yeah, sorry about that? What

2052
01:39:27,492 --> 01:39:28,093
Speaker 2: did you say, Benny?

2053
01:39:28,093 --> 01:39:28,293
Speaker 5: Sorry?

2054
01:39:28,293 --> 01:39:28,492
Speaker 2: I talked?

2055
01:39:28,532 --> 01:39:29,812
Speaker 6: Have you? Yeah?

2056
01:39:29,812 --> 01:39:31,692
Speaker 13: And that's it sunk me a bit because I thought,

2057
01:39:31,973 --> 01:39:34,692
Speaker 13: you know, I didn't think it would be into end

2058
01:39:34,853 --> 01:39:35,013
Speaker 13: like that.

2059
01:39:35,173 --> 01:39:35,333
Speaker 6: You know?

2060
01:39:36,253 --> 01:39:39,532
Speaker 2: Now since then? Have you had other relationships and have

2061
01:39:39,692 --> 01:39:42,333
Speaker 2: you kept your tongue in your mouth since then?

2062
01:39:44,133 --> 01:39:44,333
Speaker 3: Yeah?

2063
01:39:44,372 --> 01:39:46,692
Speaker 13: No, I've had another relationship with the lady and she

2064
01:39:46,893 --> 01:39:47,492
Speaker 13: cheated on me?

2065
01:39:48,812 --> 01:39:48,972
Speaker 10: Right?

2066
01:39:50,652 --> 01:39:51,892
Speaker 13: Has she done the full Monty.

2067
01:39:52,812 --> 01:39:56,133
Speaker 3: Right, and at that point, Bernie, did you feel you know,

2068
01:39:56,452 --> 01:39:59,852
Speaker 3: not sympathy, but was it more understanding because you'd been

2069
01:39:59,933 --> 01:40:01,492
Speaker 3: in the in the flip side of the situation?

2070
01:40:02,732 --> 01:40:03,812
Speaker 2: Full is very different.

2071
01:40:05,053 --> 01:40:07,572
Speaker 3: Sorry, yeah, yeah, that is different.

2072
01:40:08,572 --> 01:40:10,692
Speaker 13: Yeah, Like like I looked at the way she kissed

2073
01:40:10,732 --> 01:40:13,892
Speaker 13: another guy there bit pissed off, but but I made

2074
01:40:13,933 --> 01:40:16,812
Speaker 13: her get over it quick. But if she's a dule

2075
01:40:16,893 --> 01:40:19,412
Speaker 13: night with a guy, yeah, and then you find out

2076
01:40:19,452 --> 01:40:22,213
Speaker 13: you're sort of knew them through work and stuff. You're going, oh.

2077
01:40:22,412 --> 01:40:25,452
Speaker 2: God, right and so and so you kicked into the curb.

2078
01:40:27,893 --> 01:40:33,173
Speaker 13: Not straight away, but YEAHOK, about a week before I

2079
01:40:33,652 --> 01:40:35,092
Speaker 13: get my stuff together and stuff.

2080
01:40:35,133 --> 01:40:37,572
Speaker 2: You know, have you have you had any luck and love, Bernie,

2081
01:40:38,973 --> 01:40:39,532
Speaker 2: I've been.

2082
01:40:39,452 --> 01:40:43,412
Speaker 13: Single for about ten years and yeah, now, just you know,

2083
01:40:44,853 --> 01:40:47,253
Speaker 13: doesn't mean it will never happen. I mean there's always,

2084
01:40:47,612 --> 01:40:49,213
Speaker 13: you know, always chances out there.

2085
01:40:50,293 --> 01:40:50,812
Speaker 2: You're on the app.

2086
01:40:50,853 --> 01:40:51,893
Speaker 13: I've learned a lot from it.

2087
01:40:52,973 --> 01:40:54,132
Speaker 2: Are you on the apps?

2088
01:40:54,253 --> 01:40:54,612
Speaker 5: Bernie?

2089
01:40:55,853 --> 01:40:59,692
Speaker 13: No, I'm not actually mate. No, had a mate doing

2090
01:40:59,732 --> 01:41:02,372
Speaker 13: all the dating thing and he made a few spinners.

2091
01:41:02,692 --> 01:41:07,852
Speaker 2: Yeah, it's pretty dire on the apps for a lot

2092
01:41:07,893 --> 01:41:10,812
Speaker 2: of people. Thank you so much for bringing and sharing Bernie.

2093
01:41:10,893 --> 01:41:13,612
Speaker 3: Yeah, very upfront and good on them. Oh one hundred

2094
01:41:13,612 --> 01:41:15,213
Speaker 3: and eighty ten eighty is the number to call.

2095
01:41:15,412 --> 01:41:18,612
Speaker 2: Is this true that inheritance is exempt from matrimonial property,

2096
01:41:19,213 --> 01:41:20,253
Speaker 2: as John teed.

2097
01:41:20,333 --> 01:41:22,053
Speaker 3: Well, yeah it is, and I didn't know, so I

2098
01:41:22,133 --> 01:41:24,293
Speaker 3: had to do a Google search. And this is from

2099
01:41:24,452 --> 01:41:27,572
Speaker 3: a barrister in New Zealand called Sharon A. Chandra. She

2100
01:41:27,732 --> 01:41:31,652
Speaker 3: deals with breakups and divorce. So it says here the

2101
01:41:31,692 --> 01:41:34,412
Speaker 3: starting point when dealing with an inheritance is that it

2102
01:41:34,532 --> 01:41:38,852
Speaker 3: is separate property when individual with individual rather than collective ownership.

2103
01:41:38,933 --> 01:41:41,452
Speaker 3: This means that the other spousal partner is unable to

2104
01:41:41,492 --> 01:41:44,812
Speaker 3: make a claim against it in a divorce, so that's interesting.

2105
01:41:45,652 --> 01:41:48,173
Speaker 2: Yeah. Wow, Okay, Sam says, I think that kissing the

2106
01:41:48,253 --> 01:41:53,133
Speaker 2: pash type is more intimate than making love.

2107
01:41:54,173 --> 01:41:56,253
Speaker 3: Yeah all right, okay, right, thank you for that.

2108
01:41:56,372 --> 01:41:56,732
Speaker 4: I don't know.

2109
01:41:57,293 --> 01:42:00,093
Speaker 2: I think you can do. I mean, the kissing part

2110
01:42:00,133 --> 01:42:01,372
Speaker 2: of it can be involved in the other part of

2111
01:42:01,372 --> 01:42:04,052
Speaker 2: it as well. Okay, Yeah, I wanted to get too detailed.

2112
01:42:04,173 --> 01:42:06,533
Speaker 3: It is twenty two to four back with more shortly.

2113
01:42:07,093 --> 01:42:11,252
Speaker 1: The big stories, big issues, the big trends and everything

2114
01:42:11,293 --> 01:42:14,932
Speaker 1: in between. Matt Heath and Tyler Adams afternoons used talks.

2115
01:42:14,973 --> 01:42:17,253
Speaker 3: It'd be very good afternoon. Cheer boy.

2116
01:42:17,333 --> 01:42:17,692
Speaker 2: Oh boy.

2117
01:42:17,772 --> 01:42:21,212
Speaker 3: We're getting some spicy texts coming through on this conversation.

2118
01:42:21,372 --> 01:42:24,612
Speaker 3: We are talking about infidelity and a couple of questions

2119
01:42:24,652 --> 01:42:27,093
Speaker 3: we've asked, but more and more the texts are coming

2120
01:42:27,133 --> 01:42:30,452
Speaker 3: through about the idea of forgiveness after someone cheats on you.

2121
01:42:30,692 --> 01:42:32,612
Speaker 2: Yeah, and I want to get into the financial part

2122
01:42:32,612 --> 01:42:33,692
Speaker 2: of it as well, if we can know. Eight one

2123
01:42:33,732 --> 01:42:35,133
Speaker 2: hundred and eighty teen eight. But this is the first

2124
01:42:35,173 --> 01:42:38,093
Speaker 2: time in my entire career I've been accused of being prudish. Yeah,

2125
01:42:38,133 --> 01:42:41,732
Speaker 2: from Sam So, I was reading out his text and

2126
01:42:42,173 --> 01:42:44,892
Speaker 2: I replaced sex with making love. His text was, hey, guys,

2127
01:42:45,093 --> 01:42:47,372
Speaker 2: I think her seeing the pashtype is more intimate and

2128
01:42:47,492 --> 01:42:51,213
Speaker 2: act than sex, and I changed it to may note.

2129
01:42:51,253 --> 01:42:53,892
Speaker 2: So Sam text are back, Well, guys, if you can't

2130
01:42:53,933 --> 01:42:56,013
Speaker 2: say sex on the radio and flummocks sixty eight years

2131
01:42:56,013 --> 01:42:59,492
Speaker 2: old and can't see the problem, Sam, I responded, good point. Yeah,

2132
01:42:59,492 --> 01:42:59,932
Speaker 2: I don't know why.

2133
01:43:00,213 --> 01:43:02,692
Speaker 3: If anything, making love is probably worse.

2134
01:43:03,853 --> 01:43:09,772
Speaker 2: Yeah, we can love out nothing at all, Shane, how's

2135
01:43:09,772 --> 01:43:11,133
Speaker 2: it going. You've got to make this be married a

2136
01:43:11,173 --> 01:43:11,612
Speaker 2: few times?

2137
01:43:12,612 --> 01:43:16,172
Speaker 16: Yeah, I have boys a weird topic for a Friday afternoon.

2138
01:43:16,253 --> 01:43:20,293
Speaker 16: But yeah, good point, especially especially when we go out

2139
01:43:20,293 --> 01:43:21,213
Speaker 16: for our Friday drinks.

2140
01:43:21,853 --> 01:43:24,333
Speaker 19: But yeah, I've got my best mate.

2141
01:43:24,652 --> 01:43:28,253
Speaker 16: He's about to be married for the third time, and

2142
01:43:28,492 --> 01:43:30,852
Speaker 16: I just said I had to have a sit down

2143
01:43:30,893 --> 01:43:33,492
Speaker 16: with him and say, look, what are you doing. The

2144
01:43:33,612 --> 01:43:35,572
Speaker 16: first marriage, you know, he's got a couple of kids with.

2145
01:43:35,612 --> 01:43:39,093
Speaker 16: The first marriage pays child support there, second marriage a

2146
01:43:39,093 --> 01:43:41,133
Speaker 16: couple of kids there, pays child support there. It's just

2147
01:43:41,253 --> 01:43:45,173
Speaker 16: breaking them. I just don't understand financially what he's doing.

2148
01:43:45,772 --> 01:43:48,412
Speaker 16: It just blows me away how much he's got to

2149
01:43:48,452 --> 01:43:52,852
Speaker 16: pay per week just because well, he tells me that

2150
01:43:53,492 --> 01:43:56,652
Speaker 16: nothing happened, but you know, it's that's a suspect When

2151
01:43:58,173 --> 01:44:01,492
Speaker 16: a week after they break he breaks up with his wives,

2152
01:44:01,572 --> 01:44:02,652
Speaker 16: he's with another lady.

2153
01:44:02,412 --> 01:44:05,732
Speaker 2: You know, So that suggests a bit of crossover, doesn't

2154
01:44:05,732 --> 01:44:06,333
Speaker 2: That is he is?

2155
01:44:06,372 --> 01:44:06,412
Speaker 8: He?

2156
01:44:07,013 --> 01:44:08,652
Speaker 2: Does he make good coin? Has he got the kind

2157
01:44:08,692 --> 01:44:09,972
Speaker 2: of money to be split up that much?

2158
01:44:10,173 --> 01:44:10,213
Speaker 6: No?

2159
01:44:10,612 --> 01:44:14,612
Speaker 16: No, no, no, I wouldn't think so. Yeah, it's just

2160
01:44:14,772 --> 01:44:17,812
Speaker 16: really weird. I just can't understand it. The amount of

2161
01:44:17,853 --> 01:44:19,892
Speaker 16: money that must be going out of his account each

2162
01:44:19,933 --> 01:44:23,532
Speaker 16: week for child support must just cripple him and you know,

2163
01:44:23,893 --> 01:44:27,173
Speaker 16: he's what he's been in his early fifties now and

2164
01:44:28,173 --> 01:44:30,013
Speaker 16: you know, like he's you know, he had it with

2165
01:44:30,133 --> 01:44:34,053
Speaker 16: his first wife, had a lovely home and that brought it,

2166
01:44:34,213 --> 01:44:36,333
Speaker 16: and now now he's renting. You know, I just don't

2167
01:44:36,333 --> 01:44:39,893
Speaker 16: get it. I just it just blows me away. Just

2168
01:44:40,093 --> 01:44:42,213
Speaker 16: for for a night of passion or just being a

2169
01:44:42,333 --> 01:44:44,852
Speaker 16: silly bugger with a workmate or a colleague or whatever,

2170
01:44:45,732 --> 01:44:47,572
Speaker 16: you throw that, throw that all away. I just I

2171
01:44:47,732 --> 01:44:48,612
Speaker 16: just can't understand it.

2172
01:44:48,772 --> 01:44:50,972
Speaker 2: I mean, there's no doubt if you if you stay together,

2173
01:44:51,013 --> 01:44:53,612
Speaker 2: as we were saying before, then then over the years

2174
01:44:53,692 --> 01:44:56,372
Speaker 2: you will be better off financially obviously.

2175
01:44:57,893 --> 01:44:59,333
Speaker 16: And I think I think he tried. I think he

2176
01:44:59,412 --> 01:45:00,973
Speaker 16: tried with his first wife, you know, like you know,

2177
01:45:01,093 --> 01:45:03,692
Speaker 16: they they figured out that, look that this is not

2178
01:45:03,772 --> 01:45:06,293
Speaker 16: for us. But they did try to live.

2179
01:45:06,173 --> 01:45:06,892
Speaker 4: Together and.

2180
01:45:08,492 --> 01:45:09,932
Speaker 16: You know, be flatmates as such.

2181
01:45:10,293 --> 01:45:11,892
Speaker 4: Yeah, but I think it was just.

2182
01:45:12,173 --> 01:45:13,372
Speaker 5: Not it was never going to work.

2183
01:45:13,612 --> 01:45:14,732
Speaker 2: Is he going to go again, Shane?

2184
01:45:15,973 --> 01:45:16,852
Speaker 16: Yeah, he's asking to be.

2185
01:45:16,893 --> 01:45:17,333
Speaker 5: The best man.

2186
01:45:19,093 --> 01:45:20,652
Speaker 16: Yeah, this is my this is my third go.

2187
01:45:20,853 --> 01:45:22,973
Speaker 2: So I'm looking for some good joke that's going to

2188
01:45:23,013 --> 01:45:23,612
Speaker 2: be a good speech.

2189
01:45:23,652 --> 01:45:23,932
Speaker 3: Shane.

2190
01:45:24,772 --> 01:45:26,572
Speaker 16: Well, I'll just I'll just tell him, Hey, you welcome

2191
01:45:26,612 --> 01:45:27,293
Speaker 16: back to everybody.

2192
01:45:28,452 --> 01:45:30,732
Speaker 5: Yeah, because yeah, I don't know, just.

2193
01:45:31,372 --> 01:45:31,932
Speaker 4: It's just weird.

2194
01:45:32,173 --> 01:45:35,293
Speaker 2: Tell him to save his cash. Just make this one

2195
01:45:35,333 --> 01:45:38,972
Speaker 2: work maker. Just your speeches. Look love you. You seem

2196
01:45:39,093 --> 01:45:41,173
Speaker 2: like a nice lady. But I give this thing five

2197
01:45:41,213 --> 01:45:45,772
Speaker 2: minutes knowing this guy, Hey, thank you so much for

2198
01:45:45,812 --> 01:45:46,412
Speaker 2: your call, Shane.

2199
01:45:46,853 --> 01:45:48,973
Speaker 3: This is a great text here. I think there's a

2200
01:45:49,053 --> 01:45:52,012
Speaker 3: huge difference between a sloppy, drunk pash on the dance

2201
01:45:52,053 --> 01:45:54,133
Speaker 3: floor on a girl's night out. Yes that was me,

2202
01:45:54,293 --> 01:45:57,412
Speaker 3: versus a premeditated, sneaky affair that was my friend.

2203
01:45:58,173 --> 01:46:01,933
Speaker 2: Ah right, Okay, Hey guys, I left my husband. This

2204
01:46:02,013 --> 01:46:04,692
Speaker 2: flips around the other way. Kids with preschool, and I

2205
01:46:04,772 --> 01:46:08,173
Speaker 2: had nothing to my name because he was financially absolutely you.

2206
01:46:08,973 --> 01:46:10,372
Speaker 2: So when I say it the other way, this is

2207
01:46:10,412 --> 01:46:14,452
Speaker 2: someone leaving someone and because they were making their financial

2208
01:46:14,492 --> 01:46:17,612
Speaker 2: situation so bad. Yeah, taking out personal loans for ridiculous

2209
01:46:17,612 --> 01:46:20,852
Speaker 2: and unnecessary things when our payments for basic utilities were bouncing,

2210
01:46:21,093 --> 01:46:24,093
Speaker 2: even forged my signature on a credit card application. He's

2211
01:46:24,133 --> 01:46:26,253
Speaker 2: been married and divorced a few times after me, and

2212
01:46:26,372 --> 01:46:29,173
Speaker 2: has other children. My children with him are now over thirty,

2213
01:46:29,492 --> 01:46:33,053
Speaker 2: grown up and flying, working hard. Yeah.

2214
01:46:33,173 --> 01:46:36,932
Speaker 3: So yeah, that's a good financial decision. I mean, if

2215
01:46:36,933 --> 01:46:38,612
Speaker 3: you've got a partner that is doing that to you.

2216
01:46:38,973 --> 01:46:41,452
Speaker 2: I always with people they have multiple marriages. It's a

2217
01:46:41,492 --> 01:46:43,973
Speaker 2: real grass is green out on the other side, thing,

2218
01:46:44,053 --> 01:46:44,333
Speaker 2: isn't it.

2219
01:46:44,532 --> 01:46:44,732
Speaker 4: Yeah?

2220
01:46:45,293 --> 01:46:46,852
Speaker 2: But the thing is wherever you go that you are.

2221
01:46:47,133 --> 01:46:49,213
Speaker 2: So you take whatever problems you had and the last

2222
01:46:49,253 --> 01:46:51,213
Speaker 2: relationship to the next one and you go, oh, this

2223
01:46:51,372 --> 01:46:53,253
Speaker 2: new persons going to solve all my problems. You get

2224
01:46:53,253 --> 01:46:54,973
Speaker 2: there and then you bring all your problems and the

2225
01:46:55,013 --> 01:46:57,612
Speaker 2: same thing happens, and then you go somewhere else because

2226
01:46:58,133 --> 01:47:00,293
Speaker 2: until you sort out your own problems and your ability

2227
01:47:00,372 --> 01:47:03,652
Speaker 2: to actually love someone and such, then you're just going

2228
01:47:03,732 --> 01:47:05,652
Speaker 2: to You're going to repeat the same mistakes exactly.

2229
01:47:06,293 --> 01:47:09,572
Speaker 3: This seeks to sees guys. My husband and a friend

2230
01:47:09,732 --> 01:47:13,412
Speaker 3: in quotation marks had a secret relationship for a couple

2231
01:47:13,452 --> 01:47:16,013
Speaker 3: of months until I found out claimed no sex, But

2232
01:47:16,133 --> 01:47:18,532
Speaker 3: that didn't matter to me as much as the deceit

2233
01:47:18,732 --> 01:47:21,253
Speaker 3: and what felt like they were laughing behind my back

2234
01:47:22,492 --> 01:47:24,973
Speaker 3: and the trust was gone a fear and friendships are

2235
01:47:25,093 --> 01:47:28,173
Speaker 3: over and we are staying together, but the pain has

2236
01:47:28,253 --> 01:47:30,812
Speaker 3: been saying and I have lost so much trust in

2237
01:47:30,933 --> 01:47:34,333
Speaker 3: the world I mean that is tough to keep that

2238
01:47:34,492 --> 01:47:37,253
Speaker 3: going after that sort of deceit, you know, and again

2239
01:47:37,732 --> 01:47:40,372
Speaker 3: when someone goes to full monty as. I think one

2240
01:47:40,412 --> 01:47:44,092
Speaker 3: of the what Bernie said, that idea of ever trusting

2241
01:47:44,133 --> 01:47:46,093
Speaker 3: that person again, that's that's bloody art.

2242
01:47:46,452 --> 01:47:49,053
Speaker 2: If you have fifty to fifty custody, you don't have

2243
01:47:49,213 --> 01:47:51,492
Speaker 2: to pay child support, so that fifty year old could

2244
01:47:51,572 --> 01:47:54,093
Speaker 2: just be a better dad. I guess sometimes though, in

2245
01:47:54,133 --> 01:47:56,732
Speaker 2: the breakup, don't you don't choose the custody level, so

2246
01:47:56,812 --> 01:47:58,932
Speaker 2: you get Yeah, that can be decided.

2247
01:47:59,093 --> 01:48:02,293
Speaker 3: Yeah right, we've got to take a quick break, but

2248
01:48:02,412 --> 01:48:04,492
Speaker 3: when we come back, we'll have time for one more call.

2249
01:48:04,572 --> 01:48:06,213
Speaker 3: Oh eight one hundred and eighty ten eighty is the

2250
01:48:06,452 --> 01:48:08,772
Speaker 3: number to call. It is fourteen two four.

2251
01:48:10,093 --> 01:48:13,253
Speaker 1: The big stories, the big issues, to the big trends

2252
01:48:13,412 --> 01:48:17,133
Speaker 1: and everything in between. Matt Heath and Tyler Adams Afternoons

2253
01:48:17,333 --> 01:48:19,773
Speaker 1: News DOGSB News Dogs.

2254
01:48:19,572 --> 01:48:20,892
Speaker 3: EDB eleven to four.

2255
01:48:21,372 --> 01:48:24,972
Speaker 2: Welcome to the show, Paul, Hey, guys, how are you going,

2256
01:48:25,532 --> 01:48:25,972
Speaker 2: good mate?

2257
01:48:26,013 --> 01:48:26,333
Speaker 3: How are you?

2258
01:48:27,372 --> 01:48:27,772
Speaker 4: Yeah? Good?

2259
01:48:28,293 --> 01:48:30,532
Speaker 23: Hey. I'd just like to say like come and again

2260
01:48:30,612 --> 01:48:34,452
Speaker 23: listening to Matt's and get you guys and saying like

2261
01:48:35,053 --> 01:48:38,533
Speaker 23: just remember like sevent to eight tent of the divorces

2262
01:48:38,572 --> 01:48:42,013
Speaker 23: initiated by woman. And I'm not a woman hater by

2263
01:48:42,013 --> 01:48:44,532
Speaker 23: any stress imagination. I'm you know, I'm a dad, I'm

2264
01:48:44,572 --> 01:48:49,013
Speaker 23: a granddad, and you know, like I would say, I've

2265
01:48:49,053 --> 01:48:53,652
Speaker 23: got like, you know whatever. But as I say, like

2266
01:48:54,213 --> 01:48:57,293
Speaker 23: a scary situation, you know what I mean, and going

2267
01:48:57,412 --> 01:48:59,692
Speaker 23: with kids and stuff like that, you sort of go,

2268
01:49:00,452 --> 01:49:02,212
Speaker 23: how does that work in today's society?

2269
01:49:02,333 --> 01:49:02,852
Speaker 4: You know what I mean?

2270
01:49:03,253 --> 01:49:06,213
Speaker 23: And anyway I can say it is like wow, you

2271
01:49:06,293 --> 01:49:09,013
Speaker 23: know what I mean? And if you go, so I'm

2272
01:49:09,053 --> 01:49:15,852
Speaker 23: a dad for four boys and two grandchildren, and yeah,

2273
01:49:15,893 --> 01:49:17,293
Speaker 23: it's a scary situation.

2274
01:49:17,652 --> 01:49:21,293
Speaker 2: So Paul, just just to row back. So your your

2275
01:49:21,412 --> 01:49:22,612
Speaker 2: former wife cheated on you?

2276
01:49:22,732 --> 01:49:23,093
Speaker 6: Is that right?

2277
01:49:24,372 --> 01:49:28,173
Speaker 23: No she didn't, Yes, she didn't run off initially with

2278
01:49:28,732 --> 01:49:31,933
Speaker 23: different guy whatever, and she went away, and so I

2279
01:49:32,053 --> 01:49:34,173
Speaker 23: had all the four boys, and.

2280
01:49:36,333 --> 01:49:37,053
Speaker 5: What can I say?

2281
01:49:38,652 --> 01:49:43,452
Speaker 23: You know, so I stayed working, brought the four boys up,

2282
01:49:43,652 --> 01:49:46,093
Speaker 23: all the four boys and trades. They're really cool. They've

2283
01:49:46,372 --> 01:49:50,893
Speaker 23: gone well in life. And but again it's like a

2284
01:49:50,973 --> 01:49:55,013
Speaker 23: scary situation, ain't you guys? If you want to do

2285
01:49:55,213 --> 01:49:58,892
Speaker 23: stuff and Matt, you know, like you know, it sounds

2286
01:49:58,933 --> 01:50:02,693
Speaker 23: all good, you know, and but yeah, you're yeah whatever.

2287
01:50:02,692 --> 01:50:05,053
Speaker 3: Yeah, I think if for you call Paul, thank you

2288
01:50:05,213 --> 01:50:07,612
Speaker 3: very much. I think we've got time for your lunda

2289
01:50:07,933 --> 01:50:08,932
Speaker 3: or the sorry.

2290
01:50:09,013 --> 01:50:09,372
Speaker 5: How are you?

2291
01:50:10,333 --> 01:50:11,052
Speaker 13: I'm good things?

2292
01:50:11,133 --> 01:50:11,972
Speaker 11: How are you good?

2293
01:50:12,013 --> 01:50:12,732
Speaker 3: What's your story?

2294
01:50:13,772 --> 01:50:13,892
Speaker 4: Well?

2295
01:50:13,973 --> 01:50:17,693
Speaker 6: My story was my best friend and I were cilebrating

2296
01:50:17,692 --> 01:50:22,093
Speaker 6: abuse days and her husband went up to my mom

2297
01:50:22,772 --> 01:50:24,492
Speaker 6: and said, I just want to know if I could

2298
01:50:24,532 --> 01:50:27,532
Speaker 6: care for your permission for your London to come and

2299
01:50:27,812 --> 01:50:30,333
Speaker 6: have a tree song with me and my wife. Wow.

2300
01:50:30,772 --> 01:50:34,013
Speaker 6: And I was like, what the hell? I mean, I'm

2301
01:50:34,053 --> 01:50:37,213
Speaker 6: not even into cheating le alone anything kinky like that.

2302
01:50:37,893 --> 01:50:39,652
Speaker 6: So I hit him in the shoulder and I just

2303
01:50:39,732 --> 01:50:43,412
Speaker 6: did from what the hell? And she got engy with

2304
01:50:43,612 --> 01:50:46,372
Speaker 6: me and it started throwing dishes in the sink and

2305
01:50:46,452 --> 01:50:48,772
Speaker 6: I was like, oh my god, really.

2306
01:50:49,492 --> 01:50:55,052
Speaker 2: Was there some drinks involved in the situation? Wow?

2307
01:50:57,213 --> 01:51:00,173
Speaker 6: I was like, she starts throwing dishes and then she

2308
01:51:00,293 --> 01:51:03,692
Speaker 6: ring me when we left, she rings the living DD

2309
01:51:03,893 --> 01:51:06,812
Speaker 6: she is, I know you've fine my husband, but he's mine.

2310
01:51:06,893 --> 01:51:10,852
Speaker 3: I was like, now, nothing, that's a bold move.

2311
01:51:11,133 --> 01:51:15,452
Speaker 6: Nothing. I'm not into that, you know, I'm really not

2312
01:51:15,612 --> 01:51:18,333
Speaker 6: into that. And the ironic thing was, is that the

2313
01:51:18,412 --> 01:51:22,412
Speaker 6: guy she's worth? She had an a few with him

2314
01:51:22,492 --> 01:51:24,052
Speaker 6: when she was married to another bloke.

2315
01:51:24,492 --> 01:51:26,532
Speaker 2: Yes, so you go cereal.

2316
01:51:26,853 --> 01:51:30,412
Speaker 3: Yeah wow, what adition from the mother for a threesome

2317
01:51:30,492 --> 01:51:34,852
Speaker 3: for all right, thank you very much for those phone calls,

2318
01:51:34,893 --> 01:51:37,692
Speaker 3: and what a great end to a great.

2319
01:51:37,532 --> 01:51:39,852
Speaker 2: Week of radio has been. Look Thank you to all

2320
01:51:39,853 --> 01:51:42,732
Speaker 2: your great New Zealanders for listening to the show this week.

2321
01:51:42,812 --> 01:51:44,812
Speaker 2: Thanks so much for your calls and text We've had

2322
01:51:44,812 --> 01:51:47,732
Speaker 2: a great time chatting now. We love you callers and

2323
01:51:47,933 --> 01:51:50,452
Speaker 2: and so much. We like to celebrate one each week

2324
01:51:50,492 --> 01:51:52,732
Speaker 2: and the Caller of the Week award is something we

2325
01:51:53,093 --> 01:51:58,213
Speaker 2: we hand out every Friday. This week caller is Jacques

2326
01:51:58,253 --> 01:52:01,492
Speaker 2: le Grux. I think I got his name right, Jacques Lagrelle,

2327
01:52:02,293 --> 01:52:04,213
Speaker 2: who rang in to tell us about his time being

2328
01:52:04,333 --> 01:52:08,093
Speaker 2: recruited into the search for Elaine Mafart and Dominic Preyere.

2329
01:52:08,412 --> 01:52:12,093
Speaker 5: The New Zealand Sis. We're looking at me sideways my

2330
01:52:12,213 --> 01:52:14,932
Speaker 5: correspondence at the time I was writing to my parents

2331
01:52:14,973 --> 01:52:19,293
Speaker 5: in New Caledonia and my mail was blatantly and to

2332
01:52:19,412 --> 01:52:23,253
Speaker 5: feel with either by the New Zealand Sis before leaving

2333
01:52:23,333 --> 01:52:27,772
Speaker 5: New Zealand. And I'll recall also from the post marginie Or,

2334
01:52:27,812 --> 01:52:31,452
Speaker 5: which is the intelligence unit of France in New Caledonia.

2335
01:52:31,893 --> 01:52:34,612
Speaker 5: So I was in the no winning situation.

2336
01:52:35,173 --> 01:52:37,173
Speaker 2: That was a fantastic call. Great man. He needs to

2337
01:52:37,213 --> 01:52:40,732
Speaker 2: write a book. And hey, this new podcast, Rainbow Warrior

2338
01:52:40,812 --> 01:52:43,013
Speaker 2: is the Forgotten Story. It's out now wherever you get

2339
01:52:43,013 --> 01:52:45,013
Speaker 2: your podcasts. It's great. And speaking of pods, Matt and

2340
01:52:45,013 --> 01:52:47,133
Speaker 2: Tyler Afternoons podcast will be aret in about an hour.

2341
01:52:47,572 --> 01:52:49,612
Speaker 2: So if you missed our chats on how to spot

2342
01:52:49,692 --> 01:52:51,973
Speaker 2: AI and whether you have cheated on your partner or

2343
01:52:52,013 --> 01:52:56,253
Speaker 2: not to follow our podcast wherever you get your pods.

2344
01:52:56,933 --> 01:52:59,812
Speaker 2: The great and powerful Ryan Bridge is up next standing

2345
01:52:59,853 --> 01:53:02,333
Speaker 2: in for hear that. But right now, Tyler, my good friend,

2346
01:53:02,412 --> 01:53:03,173
Speaker 2: tell me why I'm playing.

2347
01:53:03,213 --> 01:53:06,852
Speaker 3: The song is a Superman theme? Oh, this is because

2348
01:53:06,893 --> 01:53:09,052
Speaker 3: are you going to go watch the new movie this weekend?

2349
01:53:09,293 --> 01:53:09,492
Speaker 1: Yeah?

2350
01:53:09,612 --> 01:53:10,012
Speaker 3: How good.

2351
01:53:10,173 --> 01:53:12,893
Speaker 2: I'm excited. Mixed reviews, but I'm excited to see it.

2352
01:53:13,013 --> 01:53:13,173
Speaker 4: Yeah.

2353
01:53:13,253 --> 01:53:18,572
Speaker 2: Crypto Dog and John Williams, I mean, just an absolute genius,

2354
01:53:19,013 --> 01:53:23,052
Speaker 2: the composer of so many theme songs Star Wars, Indiana Jones,

2355
01:53:23,572 --> 01:53:26,412
Speaker 2: this one et, Harry Potter. Yeah, just some of the

2356
01:53:26,452 --> 01:53:29,372
Speaker 2: most iconic Jaws yep themes ever, very.

2357
01:53:29,492 --> 01:53:32,452
Speaker 3: Iconic, fantastic right, there is us for another week.

2358
01:53:32,652 --> 01:53:35,652
Speaker 2: Yeah, thanks so much for listening. Everyone, see you on Monday.

2359
01:53:35,732 --> 01:53:37,213
Speaker 2: Until then, give them a taste of Cay.

2360
01:53:37,372 --> 01:53:38,333
Speaker 3: We love you, Love you.

2361
01:55:33,413 --> 01:55:35,973
Speaker 1: For more from News Talks at b listen live on

2362
01:55:36,133 --> 01:55:39,052
Speaker 1: air or online, and keep our shows with you wherever

2363
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Speaker 1: you go with our podcasts on iHeartRadio