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Speaker 1: Gay Gruver's Tiffanyanne Kirkpatrick Jones, Bonelo, Craig, Anthony Harper, just

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Speaker 1: rocking up for our fortnight they get together. It really

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Speaker 1: isn't about you. I don't want to disappoint you. We

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Speaker 1: just get together for us, so fuck ya. But look,

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Speaker 1: if you want to join in, no that's not true.

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Speaker 1: It's for you too. But it is a little bit

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Speaker 1: self indulgent this fortnight they get together, but we do

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Speaker 1: it anyway. Hi, Patrick, you know, it just occurred to me.

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Speaker 2: I don't think we've done a shout out for like

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Speaker 2: two years to ask people if there is anything in

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Speaker 2: particular technology wise they would like us to talk about.

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Speaker 2: Maybe you're looking for a new phone and you're just

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Speaker 2: trying to decide whether to go Apple or Android, or

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Speaker 2: maybe I don't know, you know, you want to get

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Speaker 2: a better way of cleaning your teeth.

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Speaker 1: Well, speaking of phones, we'll go to Tiffany An Cook

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Speaker 1: at a moment. But last night on the news they

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Speaker 1: had some bloke on the news who was at the

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Speaker 1: I don't know, some convention you'd know in Las Vegas

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Speaker 1: or wherever, like the groundbreaking Tech for the next next

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Speaker 1: year and the notorious foldable Samsung phone that they've fucked

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Speaker 1: up and ran Bennett thirty five times. They've got a

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Speaker 1: new one, and they reckon, it's amazing because it's the

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Speaker 1: size of a phone. But then it folds out. I

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Speaker 1: don't know why I held up my phone like you

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Speaker 1: two don't know how big a phone is.

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Speaker 2: Sorry, crag see if you can fold them in half.

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Speaker 1: I'll fold you in half. Pack an oragami boy.

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Speaker 2: I think it's the yeah, consumer electronic show that's seldom

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Speaker 2: less last but thank.

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Speaker 1: You, thank you, And then but they reckon it's great.

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Speaker 1: But also they had another thing, a ring that goes

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Speaker 1: on the finger, like the one that Tiff has or

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Speaker 1: I don't know if both of you have. But the

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Speaker 1: new Samsung one that they reckon is amazing and puts

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Speaker 1: all your data into your phone. So I better get

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Speaker 1: one of those or not?

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Speaker 3: Is it all real good at tech?

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Speaker 1: Shut up? I figured out my earphones. Are you proud

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Speaker 1: of me? Look at me?

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Speaker 3: Yeah, I'm so proud of you. You know, you can

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Speaker 3: tuck them around the back of your head so it

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Speaker 3: ain't seen it really, You've got massive lats so they

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Speaker 3: might not reach them my phone.

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Speaker 1: Shut up, Shut up? Tis been yelling at me. Everyone

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Speaker 1: tiss been yelling at me for two years at least

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Speaker 1: to wear headphones. So I've compromised and I've got these

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Speaker 1: little fucking I was going to say something that really

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Speaker 1: would have got me in trouble. These little weird looking

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Speaker 1: things that just hang on my ears. What do you

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Speaker 1: call these tips?

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Speaker 3: Just earbuds? Stick in your ears? Earbuds?

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Speaker 1: How long did it take you to learn how to

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Speaker 1: put them on? A bit? And then then TIV was

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Speaker 1: teaching me how to buy things online last night at

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Speaker 1: the gym. I asked her and she was laughing at

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Speaker 1: how handicapped I am in that space.

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Speaker 3: Patrick, he doesn't know how to buy something online just

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Speaker 3: on Amazon where there's a big button that says buy now.

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Speaker 1: I've never bought anything. I get a bit scared. A

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Speaker 1: quick quiz again?

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Speaker 2: Sure tell me what forward collision avoidance assist is on

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Speaker 2: a car.

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Speaker 1: I assume you're talking not in Southland, when you're in

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Speaker 1: the queue to pay for your stuff. Yep, that would

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Speaker 1: be yes, so that you don't crash into something in

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Speaker 1: front of you.

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Speaker 2: What about what about lane keep assist?

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Speaker 1: Yep, got that on my car as well. You monitor

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Speaker 1: blind spot view monitors. These are all things you're reading

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Speaker 1: off my car, are they? Yeah? Yeah, you've got these.

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Speaker 1: Does it tell you that it can park itself in?

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Speaker 1: That I can get out in the driveway and it

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Speaker 1: puts itself in the garage.

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Speaker 2: You'd have really tight fit there, so maybe it would

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Speaker 2: be helpful exactly.

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Speaker 1: Tiff, good morning, How are you?

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Speaker 3: Good morning? Good thanks, terrific.

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Speaker 1: Welcome to Friday seven thirty five and thriving metropolis. What's

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Speaker 1: the temperature in baland Patrick as we speak?

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Speaker 2: Well, I stoked up the fireplace before I jumped in.

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Speaker 2: Now it's the four point five degrees.

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Speaker 1: It's warm, umped in the fireplace to be stoked it up.

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Speaker 1: Be stoked it up? All right, let's jump into it. So, this,

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Speaker 1: of course is tech central. I try to stay out

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Speaker 1: of the way. Really, it's the Tiff and Patrick show.

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Speaker 1: I'm just did to ask dumb shit that some of you,

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Speaker 1: not all of you, because some of you are brilliant,

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Speaker 1: but some of you are dumb fucks like me when

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Speaker 1: it comes to technology. You know it, I know it.

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Speaker 1: So I'm here to ask the questions on behalf of

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Speaker 1: the dummies. Patrick, where do you want to start? My friend?

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Speaker 2: Four point five billion year old Lego, well not quite

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Speaker 2: four point five billion year old Lego, but Lego that

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Speaker 2: has been made from a four point five billion.

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Speaker 1: Year old media rite has been put together.

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Speaker 2: In conjunction with Lego and the European Space Agency. I

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Speaker 2: know I shouldn't find that super exciting, but I do so.

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Speaker 1: Tiff and I are shocked that you find that exciting. Okay.

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Speaker 2: So the reason is so this media rite was found

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Speaker 2: in Northwest Africa in the year two thousand and so

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Speaker 2: Lego has worked with the A Space Agency to try.

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Speaker 1: To build a Lego block because.

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Speaker 2: They believe that a lot of the materials that make

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Speaker 2: up the Lego block or this metia righte, actually are

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Speaker 2: familiar with materials on the Moon, and they reckon, if

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Speaker 2: you're going to build a base on the Moon, you

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Speaker 2: want to be able to build it on the Moon

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Speaker 2: from materials that are there from the moon dust. So

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Speaker 2: this was a really interesting experiment and it makes sense

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Speaker 2: do it with Lego. You can build everything out of Lego.

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Speaker 3: Hmm.

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Speaker 1: Cool, You're not excited at all, are you? No? I

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Speaker 1: just think building shit on the moon's a stupid idea.

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Speaker 1: The atmosphere can't support life, You can't grow shit there.

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Speaker 1: Let's do a cost benefit analysis on making the Earth

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Speaker 1: better versus trying to fucking you know, habitat is that

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Speaker 1: the word the moon? Like, let's just spend all those

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Speaker 1: trillions on the Earth that we currently live in, and

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Speaker 1: fuck all your plans for living on the moon.

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Speaker 2: But the thing is, so much research went into the

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Speaker 2: first Moon landings and it pushed technology so far forward,

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Speaker 2: and space research does so much to help us here

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Speaker 2: as well.

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Speaker 1: There are molecules that.

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Speaker 2: You can put together now in the International Space Station

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Speaker 2: that you can't put together on Earth because it's zero gravity.

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Speaker 2: So look, I know what you're saying, and it's a

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Speaker 2: bit of a long you know you're drawing back a

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Speaker 2: long bon but I think you need to play the

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Speaker 2: long game and it does help in the long term.

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Speaker 1: Here's what I'm saying. Two billion people who live on

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Speaker 1: less than three dollars a day don't give a fuck

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Speaker 1: about this. They just want clean drinking water, they want safety,

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Speaker 1: they want clothes and warmth. So fuck your moon plans.

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Speaker 1: Let's get all the people on Earth looked after first.

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Speaker 2: I wish I had my AI lie detector. Now, I

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Speaker 2: don't know if you be genuinely believing the shit that's

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Speaker 2: coming out of him.

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Speaker 1: And no, I'm kidding. No, two billion people or more

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Speaker 1: probably two million people, two billion people live in poverty.

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Speaker 1: I'm pretty sure. Not worried about what's fucking happening with

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Speaker 1: the space station.

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Speaker 2: I know inequality is just staggering, But I don't have

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Speaker 2: gold tips to taps either.

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Speaker 1: Relative to a lot of people you do. But it

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Speaker 1: is it interesting thing? Were you a lego kid or not?

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Speaker 1: You would have been more? Yeah, if you were, you

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Speaker 1: ever ever a lego kid?

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Speaker 3: A little bit more a Tonka truck kid?

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Speaker 1: You know? Were you really like we fuck around a bit?

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Speaker 1: Were you really a tomboy? I'm probably going to get

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Speaker 1: in trouble when I say I can't see you ever

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Speaker 1: having played with dolls? Send the emails.

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Speaker 3: Do you have dolls? I remember a Barbie, right, I

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Speaker 3: don't know if it had a head.

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Speaker 1: I had a Steve Austin doll. Fucking If anyone had

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Speaker 1: dolls on this little trio, it was you. You probably

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Speaker 1: had all the dolls.

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Speaker 3: Wait.

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Speaker 2: I had a bionic man doll, Steve, do you remember that?

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Speaker 2: And had a button you could push in the back

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Speaker 2: and it had a bionic arm.

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Speaker 1: Yeah. We used to. We used to flick rulers at

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Speaker 1: school off the edge of the desk, so it sounded

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Speaker 1: like him running. Do you remember that? Yeah? Yeah, And

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Speaker 1: our local milk bar had accessories. I could buy a super.

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Speaker 2: Kid at an inflatable raft and everything.

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Speaker 1: Well, I'm sure they're doing the equivalent of that now.

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Speaker 2: Yeah, that says a lot about me, So tiff Sorry

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Speaker 2: I interrupted you. I just got excited talking about dolls.

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Speaker 2: Did you have dolls?

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Speaker 1: You did?

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Speaker 3: You said I had some dolls I had did have

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Speaker 3: Tonka trucks too?

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Speaker 1: And what that?

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Speaker 3: Match box cars? Used to throw them at my brothers,

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Speaker 3: to piff them at my brother and get in trouble

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Speaker 3: because they're hard. That's not getting shot if you throw

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Speaker 3: all the match box car exone.

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Speaker 1: Wow. So who would have thought young Tiffany and cooking

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Speaker 1: Lon Seston throwing hard objects at her brother beach Nola, Sorry, sorry,

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Speaker 1: Turner's Beach folk, both of you shout out to all

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Speaker 1: our listener in Turner's Beach. It's all gone. Yeah. You

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Speaker 1: tell us about lie detectors.

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Speaker 2: Well, you know it's lie detecting is quite an art

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Speaker 2: and it's hard to do. It's really hard to catch

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Speaker 2: people out in a lie. We think we might be

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Speaker 2: good at it, we might be good at lying, or

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Speaker 2: we might be good at reading a lie, but the

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Speaker 2: reality is that we don't. And even polygraph tests are

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Speaker 2: pretty well crap. So now there's some researchers and a

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Speaker 2: Leisha vonn Schleck and her colleagues have done a series

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Speaker 2: of research. They're from the University of Wartzburg in Germany,

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Speaker 2: and so they ran a number of experiments and they

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Speaker 2: got people to use this AI assistant to be able

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Speaker 2: to try to work out whether people were lying or not.

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Speaker 2: So they got people and they incentivized people to talk

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Speaker 2: about their weekend, and then they incentivize them to lie,

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Speaker 2: and then they tried to catch them man in lies.

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Speaker 2: But what they found was the AI tool was more

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Speaker 2: effective than people at finding out whether people were lying

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Speaker 2: or not.

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Speaker 1: Now do we want to know if people are lying?

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Speaker 2: But you think about how helpful it would be, not

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Speaker 2: just in legal cases and stuff like that, but if

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Speaker 2: you think about it fake news. You know, if you

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Speaker 2: were you know, I don't know, job applications, it would

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Speaker 2: be really interesting.

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Speaker 1: But people selling you stuff. What about when people are

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Speaker 1: selling your shit, the bullshit that comes out of their mouth.

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Speaker 1: That's like a bit true, kind of true, completely untrue.

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Speaker 1: People trying to impress people. Yeah, by the extended warranty

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Speaker 1: all that sort of stuff. So exact, do you know

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Speaker 1: when they sold me my car, we'll come back. Sorry

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Speaker 1: it's not a great car or anything anyone, but just

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Speaker 1: pursuant to this point, they tried to sell me three

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Speaker 1: thousand dollars body protection. I'm like, what so the paint's

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Speaker 1: no good? Oh no, the pain's great. I go, well,

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Speaker 1: why would I need to spend just in the car

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Speaker 1: that was for you? Ah? For me, I need that,

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Speaker 1: then I should have got that. They do the body

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Speaker 1: paint protection thing.

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Speaker 2: They tried to do that with my cake cause I'd

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Speaker 2: never bought a new car until a couple of years ago.

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Speaker 2: It's like, what, yeah, exactly the past. They also try

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Speaker 2: to do the upsell the on the interior. They said,

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Speaker 2: if you go to the next model, you're vinyl at

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Speaker 2: the material seat, we can upgrade that to leather. And

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Speaker 2: I said, I want a dead animal in my gar.

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Speaker 1: Yeah, chart animal flesh in my car. But I think

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Speaker 1: you know what's interesting about lying is that And this

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Speaker 1: sounds obvious and funny, but it is true. Allegedly or

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Speaker 1: am I lying? It's almost impossible to research. It's impossible

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Speaker 1: to get virtually impossible to get really accurate data on

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Speaker 1: lying because nobody wants to be perceived as a liar.

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Speaker 1: So and it's all self kind of reporting research. You know,

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Speaker 1: how often do you lie? Well, of course people are

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Speaker 1: going to lie about how often they lie, because nobody

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Speaker 1: wants to say they're a liar. Yeah.

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Speaker 2: I guess the other thing too, though, is there are

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Speaker 2: different types of lies. You know, there's the oh, yes,

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Speaker 2: there is a Santa clause, or no, you look fantastic.

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Speaker 1: You know what do you say? Well, yeah, there isn't.

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Speaker 1: There isn't. I mean, it's either something's true or it's untrue.

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Speaker 1: But I know what you're saying what we call colloquially

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Speaker 1: white lies. You know, it's like when someone says to you,

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Speaker 1: do you like what I'm wearing? And you think it's

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Speaker 1: fucking horrible? What do you do? You go, No, it's

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Speaker 1: fucking I love you, But that's dog shit. You know

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Speaker 1: what do you say?

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Speaker 3: What about? What about positive affirmations? Remember we've talked about

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Speaker 3: those before, and if they're too far removed from your beliefs,

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Speaker 3: they're actually they have the opposite effect. I wonder if

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Speaker 3: you could throw that on and see where you really

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Speaker 3: sit and how far you can push push your beliefs

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Speaker 3: before you hit.

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Speaker 1: Yeah, I wonder if that's I mean, I think everybody

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Speaker 1: lies because and it might. You know, it's like if

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Speaker 1: somebody like, if you're if you're in danger, tif, I

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Speaker 1: would expect you to lie, like I would expect if

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Speaker 1: not that you'd be in danger, becauld you'd punch anyone

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Speaker 1: in the head. Let's say, I'm home alone, right, I'm

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Speaker 1: in danger, you know, and it's in your interest for

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Speaker 1: someone to think that someone's coming to meet you even

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Speaker 1: though they're not, or that there's someone else in the house,

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Speaker 1: or you know, like there's a time when for your

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Speaker 1: own safety, lying is a good idea, you know, and

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Speaker 1: that it's a really slippery slope and a messy landscape.

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Speaker 1: But Patrick, do you remember a show on TV called

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Speaker 1: Lie to Me?

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Speaker 3: Oh?

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Speaker 2: Yeah, that was fantastic because I reckon, you look like

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Speaker 2: the guy we've spoken about that before, the guy Tim Ruff.

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Speaker 1: Is it Tim Rough? Yeah? Yeah, yeah, you look like him, Tim.

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Speaker 1: Oh thank you.

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Speaker 2: Look up Tim Ruff and then look at Crago. I reckon,

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Speaker 2: they look so similar.

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Speaker 1: I reckon, I don't. But that was based on science

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Speaker 1: from a guy called Paul Eckman, and the character's name

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Speaker 1: was cal Lightman. And that science was all around and

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Speaker 1: you know this around micro expressions and being able to

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Speaker 1: and they mapped all this computer stuff. They mapped like

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Speaker 1: thousands of micro expressions and it was meant it was

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Speaker 1: meant to be the you know, the brain. Through that

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Speaker 1: we could tell what anyone was feeling without whether or

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Speaker 1: not they are lying ronest or sad or happy or

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Speaker 1: resentful or frustrated. And then and then some other people

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Speaker 1: brought out some research and went, yeah, that doesn't work

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Speaker 1: across cultures though, So it's yeah, it's a slippery space Yestive. No,

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Speaker 1: he doesn't look like me, does he?

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Speaker 3: He does? He really does.

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Speaker 1: Yeah, what's his name again, Patrick? I'm going to look

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Speaker 1: him up now, right now, everyone's doing that. You two

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Speaker 1: keep talking, I'm looking them up. I say.

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Speaker 2: You know, the interesting thing, though, Tive, is when does

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Speaker 2: a lie? When does an exaggeration become a lie? Because

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Speaker 2: we exaggerate. So if you're going for a job interview,

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Speaker 2: you can talk about a position that you've held and

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Speaker 2: the type of skills that you you embellish a little bit.

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Speaker 1: I wonder if.

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Speaker 3: There'd be a difference in that between men and women,

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Speaker 3: because men often have no trouble embellishing or stepping into

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Speaker 3: jobs that they maybe not qualified for, whereas women have

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Speaker 3: such imposter syndrome that we kind of do the opposite.

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Speaker 2: Yeah, that's yeah, that's a good point. And you know,

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Speaker 2: whether it's the fisherman going out there saying the fish

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Speaker 2: was this big, it's always a cute one, isn't it.

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Speaker 2: My twin brother is an angler, and I've never understood

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Speaker 2: the interest, and I know that it's the most popular

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Speaker 2: sport in Australia.

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Speaker 1: Did you have fish? You know what?

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Speaker 3: I went fishing once when I had a little trip

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Speaker 3: around Tazzy and didn't catch a single thing. We went

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Speaker 3: fishing and went fishing at the Great Lakes with normal fishing, right,

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Speaker 3: I think you go off for a fly fishing there

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Speaker 3: we're standing there with normal fishing right as well.

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Speaker 2: Fish is at I used to go fishing with my mates,

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Speaker 2: not put a hook on, just put sinker. I just

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Speaker 2: think it's so cruel, the poor fish, like they get

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Speaker 2: a hook And my brother would say to me, that's right,

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Speaker 2: we throw them back in yes, with major trauma to

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Speaker 2: the inside of their mouths. Sorry, sorry to all the

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Speaker 2: anglers out there, because I've probably just alienated sixty seven

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Speaker 2: percent of our listenership.

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Speaker 1: Yeah I did. Yeah, you fish quite a bit and

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Speaker 1: I didn't throw them back. We just ate them. You know.

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Speaker 1: It's the cycle. It's nature, you know. I tell you what,

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Speaker 1: though I'm the closest I've ever got to becoming a vegan.

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Speaker 1: Tiff sent me this video, a reel of this lady

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Speaker 1: who's got a pet cow. Oh my god. Like I

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Speaker 1: grew up in the country around cows, and for me

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Speaker 1: to think this cow was white and fluffy, it looked

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Speaker 1: like a gigantic dog. Tiff send it to Patrick. This

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Speaker 1: is terrible on a video, but the cutest fucking thing

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Speaker 1: I've ever seen. And I felt very guilty eating my

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Speaker 1: mince meat that night, I will tell you, but not

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Speaker 1: too guilty that I couldn't need it.

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Speaker 3: If people want to follow the page, it's Lacey Lacey

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Speaker 3: m Evans, l A. C. I E. M Evans and

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Speaker 3: there's heaps of cow pictures. Patrick, I'll send it to you.

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Speaker 1: I've already looked it up. That's okay. I can see

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Speaker 1: them good lad.

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Speaker 2: I love animals, so you're not gonna You've already got

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Speaker 2: a winner on me.

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Speaker 1: Oh yeah, that's pretty cute.

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Speaker 2: It's a client of mine that has a thing called

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Speaker 2: the Valet's Black Nose sheep that you would never eat

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Speaker 2: lamb again if you ever saw a Valet's Black Nose sheep.

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Speaker 1: They're so cute. I mean they're so cute. I would

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Speaker 1: you would not. By the way, there's got nothing to

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Speaker 1: do with tech. I'm thinking about getting the dog. Oh

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Speaker 1: get a snoutzer or with it? Nah, a proper dog? What?

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Speaker 1: Oh like an actual dog? That's a Palet's black nose sheep.

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Speaker 1: And now tip so old it can you that's terrible

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Speaker 1: for bodka. It's cute, though, I'm very cute. Put it

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Speaker 1: up in the group. If you're not in our group,

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Speaker 1: do you project you project you're going.

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Speaker 3: To miss out on the sheep?

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Speaker 1: You're going to miss.

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Speaker 3: Out on it.

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Speaker 1: So can you put up the video one the video

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Speaker 1: of the cow so people know, like for me to

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Speaker 1: think it's cute, it's fucking cute.

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Speaker 3: I want that you talked about it every single day since.

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Speaker 1: Very cute, It's very cute. My face favorite bit is

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Speaker 1: when she goes and lies on it. She just lies

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Speaker 1: on the back of this cow, and the cow doesn't

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Speaker 1: give a fuck. All right, Patrick, let's talk about tech

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Speaker 1: because we can. Oh.

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Speaker 2: Yeah, that's right, that's what we're here for, right, feel free? Hey,

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Speaker 2: well okay, AI, I know is something we talk about

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Speaker 2: a lot. We talked about the light detectors. A Scottish

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Speaker 2: artist is something done, something quite controversial and this is

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Speaker 2: a fairly I didn't know the guy. His name is

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Speaker 2: is Michael Forbes, but his works. People who own his

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Speaker 2: works include Madonna, Terry Gillham from Monty Python, Ricky Gervais.

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Speaker 1: So he's well known in a lot of circles.

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Speaker 2: Now what he's done is he's taken some of his

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Speaker 2: most impressive work and covered them in black paint, half

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Speaker 2: of them in black paint. So he's destroyed his artwork. Wow,

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Speaker 2: a protest against AI as a protest about you know,

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Speaker 2: the fact that a lot of these AI models have

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Speaker 2: been used by scraping data of artists' works. And he

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Speaker 2: said in a lot of ways. You know, it's a reaction.

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Speaker 2: He's redacted his work. Four of his paintings have been damaged.

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Speaker 2: You know, he's put this black pain on top of them,

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Speaker 2: and he sees he's actually heartbroken.

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Speaker 1: He's deliberately done this. You know.

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Speaker 2: One of the paintings was John Lennon, and you know

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Speaker 2: it featured Taylor Swift, and he's just blacked it all out.

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Speaker 2: And so it's an interesting little protest. You know, you're

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Speaker 2: not agree, you don't agree with Craig.

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Speaker 1: Well, no, it's not agree or disagree. I'm wondering. I

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Speaker 1: think it's well in some ways, I'm like, it's very brave,

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Speaker 1: but I go, is it going to move the needle

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Speaker 1: on anything? Will it create any positive change? And if

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Speaker 1: he's just destroyed his own artwork and there's no positive

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Speaker 1: outcome from that, yeah, and he's depressed and sad. Now

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Speaker 1: I'm like, I don't know. I mean, there's no judgment

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Speaker 1: in there for me, there's just curiosity. I wonder if

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Speaker 1: that will help in any way, if that will change

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Speaker 1: anything for the better. I guess obviously we're talking about

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Speaker 1: it on the other side of the world, so it's

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Speaker 1: brought attention to it and maybe that's a good thing.

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Speaker 1: But I don't know. I think it's an indomitable force. AI.

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Speaker 1: I don't think it's going anywhere.

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Speaker 2: Bit the hardest thing with AI that I try to

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Speaker 2: reconcile myself with is how they've scraped data without people's permission,

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Speaker 2: Because this is another bit of research that's been done,

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Speaker 2: and they're saying that one of the biggest ALI models

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Speaker 2: were actually trained on a lot of Australian kids' photos

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Speaker 2: that were scraped from you know, websites, social media. It

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Speaker 2: might have been like the school swimming carnival and they

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Speaker 2: happen to.

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Speaker 1: Publish a few photos.

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Speaker 2: So this researcher was saying, this is an organization called

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Speaker 2: Human Rights Watch, and what they said is they were

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Speaker 2: really surprised that this data set had a whole lot

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Speaker 2: of information like newborn babies with the mother still there

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Speaker 2: holding the babe and the umbilical cords still connected. I mean,

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Speaker 2: it's kind of pre schoolers playing musical instruments, schools in swimsuits.

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Speaker 2: But for some reason, there seemed to be a higher

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Speaker 2: bias for Australian kids. Now, whatever reason it was, maybe

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Speaker 2: you know, Australians are more likely to post this sort

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Speaker 2: of stuff. I wouldn't have thought so, but there is

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Speaker 2: a real concern that these images, because they've now been

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Speaker 2: scraped and added to this data set if you want.

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Speaker 1: What does that mean? Okay, like most of us don't

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Speaker 1: know what scraped means.

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Speaker 2: So what it means is that these AI algorithms need

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Speaker 2: to learn and they need examples. So they want an

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Speaker 2: example of what a middle aged man wearing glasses wearing

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Speaker 2: a black hoodie looks like.

435
00:21:34,800 --> 00:21:37,200
Speaker 1: As if there's any of them around exactly.

436
00:21:37,640 --> 00:21:40,960
Speaker 2: So they basically go through and what they were doing

437
00:21:41,080 --> 00:21:43,159
Speaker 2: was just searching and scraping all the data from the

438
00:21:43,160 --> 00:21:46,520
Speaker 2: Internet and using that to train the ALI model. The

439
00:21:46,600 --> 00:21:50,000
Speaker 2: problem is this was all kind of done. I think

440
00:21:50,000 --> 00:21:53,520
Speaker 2: it was altruistic initially. I don't think anybody meant it maliciously.

441
00:21:53,600 --> 00:21:56,440
Speaker 2: But what they've done, and whether it's inadvertent or whether

442
00:21:56,480 --> 00:22:01,520
Speaker 2: it's been deliberate, is they have pulled in lots of photographs,

443
00:22:01,560 --> 00:22:05,480
Speaker 2: including photographs of children, real people. And the concern is,

444
00:22:05,680 --> 00:22:09,639
Speaker 2: and this is about this artist, for example, a Scottish artist.

445
00:22:09,720 --> 00:22:12,679
Speaker 2: What he's saying is his style of painting. You know,

446
00:22:12,840 --> 00:22:16,600
Speaker 2: different styles of artwork might be very unique to a

447
00:22:16,640 --> 00:22:20,080
Speaker 2: particular artist. But if you say, I want to create

448
00:22:20,240 --> 00:22:24,400
Speaker 2: a painting in the same style as Craig's Dads, and

449
00:22:24,440 --> 00:22:27,560
Speaker 2: then it will replicate that using AI. Now that's the

450
00:22:27,880 --> 00:22:31,960
Speaker 2: concern is that real children's faces. And it was only

451
00:22:32,000 --> 00:22:34,879
Speaker 2: a few weeks ago that a school in back of

452
00:22:34,880 --> 00:22:38,040
Speaker 2: s Marsh, a grammar school in back of Smash, hit

453
00:22:38,080 --> 00:22:42,560
Speaker 2: the headlines because one of the students was taking photographs

454
00:22:42,600 --> 00:22:46,880
Speaker 2: of girls at the school and using AI to make

455
00:22:47,560 --> 00:22:52,440
Speaker 2: fake naked pictures. And you know, this is what AI

456
00:22:52,560 --> 00:22:55,520
Speaker 2: can be used for. So the reality is that no

457
00:22:55,520 --> 00:22:57,840
Speaker 2: one has given permission for their data to be used.

458
00:22:58,400 --> 00:23:00,280
Speaker 2: It could be one of your books, it could be

459
00:23:00,359 --> 00:23:02,480
Speaker 2: something that Tif's done, it could be something that I've written,

460
00:23:02,560 --> 00:23:05,479
Speaker 2: it could be a graphic design or logo that I've created.

461
00:23:05,480 --> 00:23:05,920
Speaker 1: And that's the.

462
00:23:05,880 --> 00:23:08,240
Speaker 2: Concern is that where does it start and finish in

463
00:23:08,320 --> 00:23:11,600
Speaker 2: terms of the you know, how much of your imagery

464
00:23:11,960 --> 00:23:14,560
Speaker 2: is belongs to you as the artist, as the person

465
00:23:14,560 --> 00:23:16,960
Speaker 2: who's produced it, or the person who's written it, or

466
00:23:16,960 --> 00:23:20,119
Speaker 2: the person who said it. Your voice that's unique to you.

467
00:23:20,119 --> 00:23:23,320
Speaker 2: You have every right to control what you say. And

468
00:23:23,440 --> 00:23:25,720
Speaker 2: it was only a few episodes ago that.

469
00:23:25,880 --> 00:23:29,680
Speaker 1: I as if anyone would use my voice without my permission?

470
00:23:29,880 --> 00:23:32,359
Speaker 1: Is that three episodes ago? Well, I hang on, I

471
00:23:32,480 --> 00:23:36,640
Speaker 1: hang on somebody, Oh somebody did on this bloody call. Yeah,

472
00:23:37,000 --> 00:23:42,080
Speaker 1: look I don't know. I think, I think you know

473
00:23:42,119 --> 00:23:44,560
Speaker 1: we're going to go around in circles with this. There's

474
00:23:44,560 --> 00:23:48,160
Speaker 1: no answer. I mean, there's no there's no answer, there's

475
00:23:48,200 --> 00:23:52,679
Speaker 1: no quick answer anyway, there's no simple answer. And it's

476
00:23:52,880 --> 00:23:58,040
Speaker 1: exponentially increasing, you know. It's it's a fucking tsunami of

477
00:23:58,400 --> 00:24:01,040
Speaker 1: change that's happening in the virtual space.

478
00:24:01,400 --> 00:24:03,720
Speaker 2: Can I give one positive app Let's let's finish off

479
00:24:03,720 --> 00:24:05,359
Speaker 2: on the AI with the positive AI.

480
00:24:05,440 --> 00:24:08,919
Speaker 1: Still can we can we shelve the AI after this? Yep? Okay,

481
00:24:09,000 --> 00:24:09,480
Speaker 1: no problem.

482
00:24:10,240 --> 00:24:12,720
Speaker 2: Now, well there's a new AI model that's that that

483
00:24:12,880 --> 00:24:15,880
Speaker 2: there that's being developed at the moment.

484
00:24:16,119 --> 00:24:18,800
Speaker 1: And you know how you can use text to create

485
00:24:18,840 --> 00:24:19,320
Speaker 1: an image.

486
00:24:19,359 --> 00:24:21,720
Speaker 2: Now you can use text or you'll soon be able

487
00:24:21,720 --> 00:24:23,360
Speaker 2: to use text to create.

488
00:24:23,080 --> 00:24:24,600
Speaker 1: A three D image.

489
00:24:24,800 --> 00:24:26,800
Speaker 2: So say, for example, I wanted to print a three

490
00:24:26,880 --> 00:24:30,120
Speaker 2: D model of my dog, I could sayerate a three

491
00:24:30,200 --> 00:24:33,399
Speaker 2: D Schnautzer wearing a bow tie and it will actually

492
00:24:33,440 --> 00:24:35,960
Speaker 2: create the three D mesh model that you can print

493
00:24:36,000 --> 00:24:37,119
Speaker 2: on your own three D printer.

494
00:24:38,760 --> 00:24:41,200
Speaker 1: Is that kind of cool? Wow? And two of our

495
00:24:41,280 --> 00:24:48,639
Speaker 1: listeners are like that's amazing. Twenty thousand like fuck.

496
00:24:48,560 --> 00:24:52,920
Speaker 2: Next, Okay, the AI we're not going to talk about

497
00:24:52,960 --> 00:24:54,000
Speaker 2: AI ever again.

498
00:24:54,400 --> 00:24:58,159
Speaker 1: Fucking hey, everyone, I can three D print my schnauzer.

499
00:24:58,600 --> 00:25:01,280
Speaker 1: Fucking there's there's the gra of the century. Make that

500
00:25:01,359 --> 00:25:08,159
Speaker 1: into a real tip. Oh, three three D print your schnauzer.

501
00:25:08,480 --> 00:25:13,040
Speaker 1: Know about that. I'm not sure what I meant by that.

502
00:25:13,480 --> 00:25:15,560
Speaker 1: We all know what you meant by that. Do you

503
00:25:15,600 --> 00:25:17,639
Speaker 1: know what? Something I saw on the news, which I

504
00:25:17,680 --> 00:25:21,480
Speaker 1: see is on your list, mate, is what? And look?

505
00:25:21,600 --> 00:25:23,320
Speaker 1: Part of me thinks this is a great part of

506
00:25:23,359 --> 00:25:29,120
Speaker 1: me hates it. I'm conflicted. Speed limiters on cars are

507
00:25:29,200 --> 00:25:34,280
Speaker 1: now mandatory or becoming mandatory in Europe, and I've long

508
00:25:34,400 --> 00:25:39,280
Speaker 1: wondered Patrick about and Tiff and everyone about. You know,

509
00:25:39,359 --> 00:25:42,359
Speaker 1: It's like I have a motorbike that will do nearly

510
00:25:42,440 --> 00:25:45,359
Speaker 1: three hundred kilometers an hour in a country where the

511
00:25:45,440 --> 00:25:50,080
Speaker 1: fastest I can legally go is one hundred and ten. Now,

512
00:25:50,160 --> 00:25:52,199
Speaker 1: I love the fact that you know I've got this

513
00:25:52,440 --> 00:25:56,360
Speaker 1: lead yet, but really it's like, even if it went

514
00:25:56,480 --> 00:25:58,680
Speaker 1: one hundred and forty, you go, well, that's enough, right.

515
00:25:59,600 --> 00:26:03,040
Speaker 1: It is interesting that we produce all of these cars

516
00:26:03,040 --> 00:26:08,119
Speaker 1: and motorbikes that are essentially unusable in most places in

517
00:26:08,160 --> 00:26:10,879
Speaker 1: the world. Can I make one point sure?

518
00:26:11,000 --> 00:26:14,760
Speaker 2: And I live in a rural area and there are

519
00:26:14,920 --> 00:26:18,840
Speaker 2: occasions if I am overtaking where I am going to

520
00:26:18,880 --> 00:26:21,240
Speaker 2: admit this that I have gone over the speed limit

521
00:26:21,320 --> 00:26:24,040
Speaker 2: to overtake a vehicle as safely as I feel I

522
00:26:24,040 --> 00:26:26,760
Speaker 2: could to be able to get past effectively if it's

523
00:26:26,760 --> 00:26:29,639
Speaker 2: a be double or it's a large to get to

524
00:26:29,680 --> 00:26:33,320
Speaker 2: get past them. So look, I've only ever had one

525
00:26:33,320 --> 00:26:37,160
Speaker 2: speeding ticket in my entire driving. Yeah, I have won

526
00:26:37,200 --> 00:26:39,439
Speaker 2: a week, you know what, And you know what the

527
00:26:39,480 --> 00:26:42,399
Speaker 2: irony was. I was driving, I was late, driving to

528
00:26:42,440 --> 00:26:44,600
Speaker 2: a funeral for a person who died in a car accident,

529
00:26:44,640 --> 00:26:45,760
Speaker 2: and I got done speeding.

530
00:26:46,200 --> 00:26:49,040
Speaker 1: Wow, that's that's I don't know if that's a message,

531
00:26:49,040 --> 00:26:51,920
Speaker 1: but but but yeah, I get that everyone's done that.

532
00:26:52,000 --> 00:26:55,960
Speaker 1: But I'm talking about you know, like the new Teslas

533
00:26:55,960 --> 00:26:58,840
Speaker 1: that do not to one hundred and one point nine seconds.

534
00:26:58,920 --> 00:27:04,520
Speaker 1: That where building these like these higher and higher performance vehicles,

535
00:27:04,560 --> 00:27:08,959
Speaker 1: which personally I love, But that's emotion, right, I love them.

536
00:27:09,000 --> 00:27:11,520
Speaker 1: It's fucking I love all that shit. But from a

537
00:27:11,720 --> 00:27:16,720
Speaker 1: responsibility and a strategic and a logical perspective, it makes

538
00:27:16,880 --> 00:27:19,520
Speaker 1: no sense to have vehicles that do three times the

539
00:27:19,600 --> 00:27:21,800
Speaker 1: speed limit. So do we limit the speed.

540
00:27:21,840 --> 00:27:24,280
Speaker 2: Do we limit the ability for the car to accelerate

541
00:27:24,359 --> 00:27:26,680
Speaker 2: so quickly or to be able to go at three

542
00:27:26,800 --> 00:27:28,000
Speaker 2: hundred kilometers an hour?

543
00:27:28,440 --> 00:27:30,840
Speaker 1: Or we could all buy a twenty five year old Mazda.

544
00:27:31,000 --> 00:27:34,800
Speaker 1: You know, we could do that with a head gasket problem.

545
00:27:36,720 --> 00:27:40,400
Speaker 1: Now look, I mean yeah, I mean at the same time,

546
00:27:40,600 --> 00:27:42,439
Speaker 1: I love them, but I think if we didn't have

547
00:27:42,560 --> 00:27:45,119
Speaker 1: cars that went in But also I think if we

548
00:27:45,119 --> 00:27:48,159
Speaker 1: didn't have cigarettes that were we no kill people, but

549
00:27:48,240 --> 00:27:51,639
Speaker 1: anyone can buy them anywhere. You know. It's like, this

550
00:27:51,800 --> 00:27:53,760
Speaker 1: is just our society, isn't it. We've got a whole

551
00:27:53,760 --> 00:27:56,440
Speaker 1: lot of stuff that's legal that can kill you. Albeit

552
00:27:56,800 --> 00:27:59,240
Speaker 1: speeding is not legal, but we give you a car

553
00:27:59,280 --> 00:28:03,040
Speaker 1: that gives you the capability of speeding in a million ways. Yeah,

554
00:28:03,080 --> 00:28:03,480
Speaker 1: for sure.

555
00:28:04,160 --> 00:28:07,960
Speaker 2: Interestingly, this this legislation that's going through in Europe actually

556
00:28:08,000 --> 00:28:10,560
Speaker 2: has kicked off already. It's just come into law. It's

557
00:28:10,600 --> 00:28:15,800
Speaker 2: called ISA Intelligent Speed Assistance. It's now mandatory, so any

558
00:28:15,840 --> 00:28:20,080
Speaker 2: new car that's now sold in the EU will either

559
00:28:20,160 --> 00:28:23,200
Speaker 2: warn drivers when they're driving over the speed limit or

560
00:28:23,240 --> 00:28:26,719
Speaker 2: actively prevent the vehicle. So I like the warning idea,

561
00:28:27,080 --> 00:28:27,639
Speaker 2: mind does that?

562
00:28:28,240 --> 00:28:31,960
Speaker 1: Yes? Mine literally has on my window. It'll have the

563
00:28:32,040 --> 00:28:35,800
Speaker 1: speed on the left, like sorry, the speed limit whatever

564
00:28:35,880 --> 00:28:38,360
Speaker 1: area you're in, on the literally the fucking windscreen.

565
00:28:38,440 --> 00:28:40,360
Speaker 2: Wait a minute, you mean a head's up display.

566
00:28:40,560 --> 00:28:44,120
Speaker 1: Yeah, that's what I mean. Well, that's why I've got you.

567
00:28:44,680 --> 00:28:47,080
Speaker 1: And then next to that will be the speed I'm going.

568
00:28:47,120 --> 00:28:49,000
Speaker 1: And if I'm in a hundred zone and I'm doing

569
00:28:49,000 --> 00:28:52,120
Speaker 1: one hundred and two, shit starts fucking making noise. I'm like,

570
00:28:52,160 --> 00:28:55,760
Speaker 1: this is annoying. I better slow down. Two k's tiff.

571
00:28:55,800 --> 00:28:58,040
Speaker 2: Didn't he take the piss out of me last episode

572
00:28:58,080 --> 00:29:01,120
Speaker 2: about using the word hud and now he's actively using

573
00:29:01,120 --> 00:29:01,920
Speaker 2: a heart every day?

574
00:29:02,680 --> 00:29:07,719
Speaker 1: What heads up display doesn't think? We don't have to

575
00:29:07,800 --> 00:29:12,560
Speaker 1: use your bloody tech, bloody acronyms. Why it's easy to

576
00:29:12,560 --> 00:29:17,000
Speaker 1: stay hard? Tell me about an EV that charges in

577
00:29:17,240 --> 00:29:21,600
Speaker 1: under five minutes. That seems unlikely, that seems impossible.

578
00:29:22,200 --> 00:29:25,680
Speaker 2: Well that this is This is the Niyebolt. I think

579
00:29:25,680 --> 00:29:30,080
Speaker 2: it's called the Niebolt EV. And yeah, they've I reckon

580
00:29:30,120 --> 00:29:32,640
Speaker 2: that they've been able to get it up and charged.

581
00:29:32,720 --> 00:29:33,640
Speaker 1: I think it's eighty.

582
00:29:33,440 --> 00:29:37,720
Speaker 2: Percent within five within five minutes. That's pretty amazing. So

583
00:29:37,760 --> 00:29:41,120
Speaker 2: from ten percent up to eighty percent. And look The

584
00:29:41,280 --> 00:29:46,200
Speaker 2: problem we've got is standard lithium ion batteries don't allow

585
00:29:46,400 --> 00:29:47,200
Speaker 2: a charge.

586
00:29:47,840 --> 00:29:49,360
Speaker 1: It has to be charged at a certain rate.

587
00:29:49,440 --> 00:29:52,200
Speaker 2: You can't just force a whole lot of electricity into

588
00:29:52,240 --> 00:29:55,080
Speaker 2: it and it charges faster. So the way the batteries

589
00:29:55,120 --> 00:29:58,560
Speaker 2: built is one of the reasons that they're they're really

590
00:29:58,600 --> 00:30:01,360
Speaker 2: looking at how to speed up the charge process. So

591
00:30:01,840 --> 00:30:04,680
Speaker 2: that because the concern with lithium batteries, and of course

592
00:30:04,680 --> 00:30:06,920
Speaker 2: we've seen a lot of this recent times is fires

593
00:30:07,240 --> 00:30:09,760
Speaker 2: because they can catch fire, and this concerns about, you know,

594
00:30:09,840 --> 00:30:13,760
Speaker 2: how combustable they are. But it's a startup company. It's

595
00:30:14,240 --> 00:30:17,240
Speaker 2: teamed up with Cambridge University in the UK, and they're

596
00:30:17,280 --> 00:30:19,880
Speaker 2: saying that they can speed up that by changing the

597
00:30:19,960 --> 00:30:23,200
Speaker 2: chemistry of a lithium ion battery, it will allow more

598
00:30:23,240 --> 00:30:26,960
Speaker 2: electricity into the battery to be retained at a much, much,

599
00:30:27,120 --> 00:30:30,520
Speaker 2: much faster rate. So that would be a massive boon

600
00:30:30,560 --> 00:30:32,520
Speaker 2: to I think a lot of people. If you knew

601
00:30:32,520 --> 00:30:35,120
Speaker 2: that you could charge up your vehicle so quickly, I

602
00:30:35,200 --> 00:30:38,000
Speaker 2: reckon that would be a real, you know, really incentive

603
00:30:38,000 --> 00:30:39,440
Speaker 2: for a lot of people who are maybe a little

604
00:30:39,440 --> 00:30:40,360
Speaker 2: bit resistant.

605
00:30:40,040 --> 00:30:43,680
Speaker 1: To get Definitely that would be a game changer. Because

606
00:30:43,760 --> 00:30:46,840
Speaker 1: right now it's for a lot of people. It's very impractical.

607
00:30:47,040 --> 00:30:53,760
Speaker 1: So the car that they adapted to do this was

608
00:30:53,800 --> 00:30:54,280
Speaker 1: a Lotus.

609
00:30:54,280 --> 00:30:58,520
Speaker 2: The lease that's looking car. Isn't that as a gorgeous.

610
00:30:58,080 --> 00:31:01,959
Speaker 1: Cars Lotus make, which is a British car. Everyone. They

611
00:31:02,000 --> 00:31:06,040
Speaker 1: make beautiful cars. They've traditionally, or I should say historically,

612
00:31:06,120 --> 00:31:10,120
Speaker 1: been quite unreliable, but they are amazing looking cars. But

613
00:31:10,200 --> 00:31:13,840
Speaker 1: I'm pretty sure they're not built in England anymore. Shout

614
00:31:13,880 --> 00:31:17,280
Speaker 1: out to our English listeners. Not everything else is good though,

615
00:31:17,520 --> 00:31:20,440
Speaker 1: just maybe the old the old, old school Lotuses were

616
00:31:20,480 --> 00:31:21,160
Speaker 1: a bit dodgy.

617
00:31:23,320 --> 00:31:25,160
Speaker 2: So you're going to say something before I.

618
00:31:25,040 --> 00:31:28,080
Speaker 1: Was just gonna I'm interested in the night curfew feature.

619
00:31:28,280 --> 00:31:31,440
Speaker 1: That's new software that Tesla are bringing out. What is

620
00:31:31,480 --> 00:31:31,960
Speaker 1: that about?

621
00:31:33,320 --> 00:31:37,160
Speaker 2: That's a good question, you tell me, Just.

622
00:31:36,560 --> 00:31:39,280
Speaker 1: Just take this bit out. What's on your list? Motherfucker?

623
00:31:42,640 --> 00:31:45,960
Speaker 1: You can leave all this into fucking no fuck him.

624
00:31:46,120 --> 00:31:48,040
Speaker 1: He sends his little list and then he doesn't know

625
00:31:48,280 --> 00:31:50,360
Speaker 1: do you know what? Though in his defense, he did

626
00:31:50,400 --> 00:31:53,240
Speaker 1: send the list on Tuesday. It was such a why

627
00:31:53,240 --> 00:31:56,640
Speaker 1: were you so organized this week? Because I had can

628
00:31:56,680 --> 00:32:00,000
Speaker 1: I sure? Okay, this is how I prepare for the show.

629
00:32:00,160 --> 00:32:03,520
Speaker 2: I read throughout the two weeks, and when I find

630
00:32:03,560 --> 00:32:06,360
Speaker 2: an article that potentially could assist, I email it to

631
00:32:06,400 --> 00:32:09,240
Speaker 2: myself and I have a folder. I had a kit

632
00:32:09,320 --> 00:32:12,000
Speaker 2: of work experience, and I got him to collate it

633
00:32:12,000 --> 00:32:12,520
Speaker 2: all for me.

634
00:32:13,520 --> 00:32:18,080
Speaker 1: Ah so the kids, well, yeah, can you see if

635
00:32:18,120 --> 00:32:20,800
Speaker 1: the kids available for the podcast? Because you're doing shit?

636
00:32:21,640 --> 00:32:24,000
Speaker 2: Hey Christian, thanks for putting hard work.

637
00:32:24,040 --> 00:32:27,560
Speaker 1: It's hey Christian, are you available in two weeks mate?

638
00:32:27,600 --> 00:32:33,000
Speaker 1: Because the bloke that's currently doing it not that good. No.

639
00:32:33,120 --> 00:32:35,400
Speaker 2: I do a live radio spot on a little community

640
00:32:35,480 --> 00:32:37,960
Speaker 2: radio station as well, but I didn't get to that part.

641
00:32:38,520 --> 00:32:41,440
Speaker 2: So I looked at it, thought, that's an interesting story.

642
00:32:41,520 --> 00:32:41,680
Speaker 1: You know.

643
00:32:42,880 --> 00:32:45,920
Speaker 2: New update that is calls got a night curfew mode

644
00:32:46,240 --> 00:32:49,160
Speaker 2: and it's great for parents right when they want to

645
00:32:49,200 --> 00:32:51,520
Speaker 2: make sure that their kids don't drive the car at night.

646
00:32:51,800 --> 00:32:55,640
Speaker 2: So basically you can make sure that the can't being

647
00:32:55,840 --> 00:32:57,880
Speaker 2: used by someone who shouldn't be using it.

648
00:32:59,640 --> 00:33:03,280
Speaker 1: You just got busted. You just got busted time. Well,

649
00:33:03,320 --> 00:33:06,160
Speaker 1: how about instead of me asking you questions about the

650
00:33:06,200 --> 00:33:09,520
Speaker 1: notes that you've sent me and throwing you under the

651
00:33:09,560 --> 00:33:12,360
Speaker 1: bus because you're ill prepared, how about you just steer

652
00:33:12,360 --> 00:33:12,800
Speaker 1: the ship.

653
00:33:14,040 --> 00:33:15,920
Speaker 2: Why would you want me to steer the ship because

654
00:33:15,920 --> 00:33:17,800
Speaker 2: I'll be talking about shit you're not interested in.

655
00:33:18,160 --> 00:33:22,280
Speaker 1: That's not interested in all of it. I want to

656
00:33:22,320 --> 00:33:25,880
Speaker 1: know about a six legged robot guide dog because.

657
00:33:25,880 --> 00:33:30,680
Speaker 2: Was this yeah, yeah, yeah, okay, Well, so when we

658
00:33:30,720 --> 00:33:33,320
Speaker 2: think of all those robot dogs, and Boston Dynamics has

659
00:33:33,400 --> 00:33:36,200
Speaker 2: been very popular in the past with the development that

660
00:33:36,200 --> 00:33:38,480
Speaker 2: they've done. We've all seen the Boston Dynamics dogs, you know,

661
00:33:38,600 --> 00:33:40,640
Speaker 2: or the robots that get pushed over, they get back

662
00:33:40,720 --> 00:33:43,600
Speaker 2: up again. But I thought this was interesting because when

663
00:33:43,680 --> 00:33:47,200
Speaker 2: we think about, you know the fact that what could

664
00:33:47,200 --> 00:33:50,960
Speaker 2: you use those dogs for? You know, and now they're saying, well,

665
00:33:51,280 --> 00:33:53,440
Speaker 2: why not use them to help people who are visually

666
00:33:53,440 --> 00:33:56,400
Speaker 2: impaired or vision impaired, I should say, to be able

667
00:33:56,440 --> 00:33:59,000
Speaker 2: to help them, and that way don't have to worry

668
00:33:59,040 --> 00:34:01,000
Speaker 2: about cleaning up after dog. I mean, it's got a

669
00:34:01,000 --> 00:34:02,840
Speaker 2: lot of benefits there. You just charge it up then

670
00:34:02,840 --> 00:34:06,080
Speaker 2: have defeated. No, but I height of Fritz, by the way,

671
00:34:06,160 --> 00:34:08,279
Speaker 2: my dog. I would never replace him with an electronic dog.

672
00:34:08,320 --> 00:34:12,400
Speaker 2: By the way, No, I wouldn't. We were about to

673
00:34:12,400 --> 00:34:15,799
Speaker 2: get a dog, aren't you. Didn't you say, yeah, so yeah,

674
00:34:15,840 --> 00:34:16,560
Speaker 2: you couldn't.

675
00:34:16,239 --> 00:34:16,840
Speaker 1: Possibly do that.

676
00:34:16,960 --> 00:34:20,360
Speaker 2: You'll you'll realize once you've got one. Anyway, So this

677
00:34:21,040 --> 00:34:25,080
Speaker 2: guide dog robot it also the benefit of this is

678
00:34:25,160 --> 00:34:27,200
Speaker 2: would have a whole lot of tech built into it

679
00:34:27,239 --> 00:34:30,120
Speaker 2: to be able to It's been developed in Shang Hih University,

680
00:34:30,440 --> 00:34:33,120
Speaker 2: and it would have things like light ar and radar

681
00:34:33,480 --> 00:34:37,640
Speaker 2: and sensors built into it to be able to accurately,

682
00:34:38,120 --> 00:34:41,640
Speaker 2: you know, see and navigate around the world and assist people.

683
00:34:42,640 --> 00:34:45,880
Speaker 2: And I guess you know, I think seeing eye dogs

684
00:34:45,880 --> 00:34:49,320
Speaker 2: are amazing, but you know they only have a certain

685
00:34:49,400 --> 00:34:50,760
Speaker 2: lifespan and you've got.

686
00:34:50,600 --> 00:34:51,120
Speaker 1: To train them.

687
00:34:51,200 --> 00:34:53,120
Speaker 2: A whole lot of effort and energy goes into it.

688
00:34:53,320 --> 00:34:56,880
Speaker 2: So yeah, this could be really beneficial. You're not interested

689
00:34:56,920 --> 00:34:57,359
Speaker 2: at all with.

690
00:34:57,360 --> 00:35:01,360
Speaker 1: Rose and cons pros. I'm with you, like they're probably

691
00:35:01,400 --> 00:35:06,239
Speaker 1: going to be in some ways more effective and more

692
00:35:06,360 --> 00:35:11,600
Speaker 1: features and functions. But you need the love. You need

693
00:35:11,600 --> 00:35:14,080
Speaker 1: the emotion you need to get. You need to line

694
00:35:14,160 --> 00:35:17,120
Speaker 1: the floor with your guide dog and snuggle. You need

695
00:35:17,200 --> 00:35:20,480
Speaker 1: your guide dogs sleeping on your bed and licking your

696
00:35:20,560 --> 00:35:22,320
Speaker 1: face and farting.

697
00:35:22,680 --> 00:35:25,160
Speaker 2: The problem is in countries, say, for exact, China is

698
00:35:25,200 --> 00:35:28,120
Speaker 2: a really good example. They're just a not enough guide dogs.

699
00:35:27,920 --> 00:35:29,120
Speaker 1: That's true.

700
00:35:28,640 --> 00:35:31,640
Speaker 2: That's the other problem so there's this long learning curve

701
00:35:31,640 --> 00:35:33,879
Speaker 2: where you've got to teach them, you've got to breathe them.

702
00:35:34,200 --> 00:35:37,320
Speaker 2: And so there are people who are missing out because

703
00:35:37,600 --> 00:35:40,160
Speaker 2: they're just as a more need than there are supply

704
00:35:40,719 --> 00:35:42,279
Speaker 2: and so much work that goes into it. But if

705
00:35:42,320 --> 00:35:44,680
Speaker 2: you can build them, train them, it means you just

706
00:35:44,760 --> 00:35:47,520
Speaker 2: download it straight into the dog or the electronic dog,

707
00:35:47,560 --> 00:35:49,520
Speaker 2: the six legged dog, and off your go and running.

708
00:35:50,080 --> 00:35:56,560
Speaker 1: I see that Japan has finally discovered Viagrapatrick, So the

709
00:35:56,680 --> 00:36:03,279
Speaker 1: story above, the one you just read, discovered viagra. Yeah, No,

710
00:36:03,480 --> 00:36:06,880
Speaker 1: Japanese government finally says farewell to floppy discs.

711
00:36:06,920 --> 00:36:11,560
Speaker 2: Really, I thought this was interesting because I know the

712
00:36:11,640 --> 00:36:13,920
Speaker 2: Japanese government was still using floppies.

713
00:36:15,120 --> 00:36:19,279
Speaker 1: Yes, they've got an electronic viagra, so they're getting rid

714
00:36:19,320 --> 00:36:20,799
Speaker 1: of the flopping discs.

715
00:36:21,200 --> 00:36:22,960
Speaker 2: It's like descended to primary s.

716
00:36:23,120 --> 00:36:26,600
Speaker 1: Its fucking year eight all over again. Isn't it Friday morning?

717
00:36:26,640 --> 00:36:32,600
Speaker 1: It's early yes, so at it's why were the China,

718
00:36:32,880 --> 00:36:36,040
Speaker 1: sorry Japanese government using floppy discs anyway?

719
00:36:36,719 --> 00:36:39,920
Speaker 2: Well, they just had computer systems that hadn't been updated.

720
00:36:40,000 --> 00:36:43,160
Speaker 2: They were still using a lot of this old tech

721
00:36:43,320 --> 00:36:46,960
Speaker 2: because it was working. You know, why fix what's not broken.

722
00:36:47,000 --> 00:36:50,200
Speaker 2: I mean, it sounds absolutely absurd. I think the last

723
00:36:50,239 --> 00:36:52,839
Speaker 2: floppy disks would have been used what twenty five years

724
00:36:52,840 --> 00:36:55,880
Speaker 2: ago or something. I didn't know in terms of traditionally,

725
00:36:56,239 --> 00:37:02,160
Speaker 2: but so it was. Japan has a pretty rigid culture.

726
00:37:02,200 --> 00:37:05,160
Speaker 2: I mean, I really want to go to Japan. It's

727
00:37:05,200 --> 00:37:08,080
Speaker 2: one of those bucket list countries that I'd love to

728
00:37:08,120 --> 00:37:12,760
Speaker 2: go to. And so, yeah, there was some single environment

729
00:37:12,880 --> 00:37:17,600
Speaker 2: systems where floppy disks were working and people were still

730
00:37:17,680 --> 00:37:19,920
Speaker 2: using them in the government departments.

731
00:37:20,400 --> 00:37:22,840
Speaker 1: I mean, if I feel like I interrupted you, Sorry, Patrick,

732
00:37:23,160 --> 00:37:26,680
Speaker 1: were you going to say something? And in opening the

733
00:37:26,719 --> 00:37:30,520
Speaker 1: door for Tiff, I then interrupted Patrick, Sorry, Patrick, go on, Tiff.

734
00:37:30,960 --> 00:37:33,520
Speaker 3: No, that was way back in the guide dog conversation,

735
00:37:33,800 --> 00:37:36,040
Speaker 3: and all I was going to say is I missed

736
00:37:36,280 --> 00:37:38,799
Speaker 3: hearing the whole reason they have six legs, because soon

737
00:37:38,800 --> 00:37:41,279
Speaker 3: as the conversation started, I went and googled how to

738
00:37:41,280 --> 00:37:44,239
Speaker 3: blind people pick up after their dog? So I missed

739
00:37:44,280 --> 00:37:45,440
Speaker 3: the whole conversation.

740
00:37:46,400 --> 00:37:50,920
Speaker 1: How do they Yeah, I'm interested. That's interesting. Well, I'm

741
00:37:50,920 --> 00:37:55,400
Speaker 1: pretty sure that they are trained not to kind of

742
00:37:55,560 --> 00:37:59,520
Speaker 1: pooh in a certain spot and on command. I'm sure

743
00:37:59,520 --> 00:38:01,759
Speaker 1: that there's a about one hundred people listening now who

744
00:38:01,880 --> 00:38:05,080
Speaker 1: absolutely know the answer to this. What's the I'll tell

745
00:38:05,080 --> 00:38:08,960
Speaker 1: you what this is not technology is it's it's pretty good,

746
00:38:09,080 --> 00:38:12,600
Speaker 1: all right? Why while tists finding that out? You just

747
00:38:12,719 --> 00:38:15,719
Speaker 1: keep plowing on, Patrick, I'll try. I don't know, I

748
00:38:15,719 --> 00:38:17,040
Speaker 1: don't know where to go from here. Now.

749
00:38:18,000 --> 00:38:21,960
Speaker 2: The we kind of mock the Japanese government for using

750
00:38:21,960 --> 00:38:24,120
Speaker 2: floppy disks, but you know, a lot of the medical

751
00:38:24,160 --> 00:38:25,720
Speaker 2: profession still uses fax machines.

752
00:38:25,719 --> 00:38:27,800
Speaker 1: When was the last time I used a fax machine, Craigo,

753
00:38:29,160 --> 00:38:32,279
Speaker 1: Not for well personally, not for a long time. But yeah,

754
00:38:32,320 --> 00:38:34,680
Speaker 1: I know some even in some medical centers now, they

755
00:38:34,800 --> 00:38:38,000
Speaker 1: still they still use faxes and stuff. Because the other

756
00:38:38,080 --> 00:38:39,759
Speaker 1: day I was at the dock and I saw a

757
00:38:39,800 --> 00:38:42,440
Speaker 1: fax machine and a facts coming through. I'm like, am

758
00:38:42,480 --> 00:38:44,320
Speaker 1: I in back to the future? Yeah?

759
00:38:44,760 --> 00:38:48,080
Speaker 2: You know, the interesting thing is fax machines and facts

760
00:38:48,160 --> 00:38:53,560
Speaker 2: transmissions can't be intercepted. If I wanted to safely send

761
00:38:53,640 --> 00:38:58,160
Speaker 2: you a message, email is a bit precarious, So I

762
00:38:58,160 --> 00:38:59,799
Speaker 2: guess if I said the facts, it might be a

763
00:38:59,800 --> 00:39:01,680
Speaker 2: say way to send a message to you. Hadn't thought

764
00:39:01,680 --> 00:39:04,400
Speaker 2: about that, because have I admitted this story to you?

765
00:39:04,480 --> 00:39:06,520
Speaker 2: When I was working at a newsroom in Geelong. As

766
00:39:06,520 --> 00:39:09,360
Speaker 2: a young journalist, I used to get it was a

767
00:39:09,400 --> 00:39:11,200
Speaker 2: guy by the name of Stuart MacArthur who was a

768
00:39:11,239 --> 00:39:15,080
Speaker 2: local politician, and he used to on a Friday send

769
00:39:15,120 --> 00:39:18,200
Speaker 2: a media release that was like twelve to twenty pages

770
00:39:18,640 --> 00:39:22,680
Speaker 2: and wow, his faxes had rolls of fax paper and

771
00:39:22,719 --> 00:39:26,440
Speaker 2: when the role ran out, that was it. So at

772
00:39:26,480 --> 00:39:28,880
Speaker 2: the start of the weekend, we'd get it on Monday

773
00:39:28,920 --> 00:39:31,239
Speaker 2: morning because we hadn't done news at the weekend, and

774
00:39:31,280 --> 00:39:33,279
Speaker 2: suddenly all the facts had run out because it'd sent

775
00:39:33,320 --> 00:39:35,360
Speaker 2: this twenty page fax the night before.

776
00:39:36,200 --> 00:39:37,680
Speaker 1: Really annoying, and this happened.

777
00:39:37,400 --> 00:39:40,400
Speaker 2: Quite regularly so and we didn't need all that information

778
00:39:40,480 --> 00:39:41,640
Speaker 2: because it wasn't print.

779
00:39:41,760 --> 00:39:43,720
Speaker 1: It was just a thirty second news story.

780
00:39:44,040 --> 00:39:47,320
Speaker 2: So then one Friday I sent him back a fax,

781
00:39:47,360 --> 00:39:49,400
Speaker 2: but what I wrote on it was I had a

782
00:39:49,400 --> 00:39:52,000
Speaker 2: long sheet and I wrote, do not send long facts,

783
00:39:52,160 --> 00:39:53,759
Speaker 2: and then I taped it to the other end.

784
00:39:54,000 --> 00:39:55,320
Speaker 1: So it was a continuous loop.

785
00:39:56,920 --> 00:39:59,360
Speaker 2: So when he got into his office on the Monday morning,

786
00:39:59,360 --> 00:40:00,080
Speaker 2: it just had the same.

787
00:40:00,000 --> 00:40:03,200
Speaker 1: A message on loop for his entire effects role. Sorry

788
00:40:03,200 --> 00:40:05,680
Speaker 1: my god, did you ever get busted for that? No

789
00:40:05,800 --> 00:40:10,160
Speaker 1: that he didn't send long factors anymore. It worked. That's hilarious. Hey,

790
00:40:10,400 --> 00:40:15,000
Speaker 1: I don't know why this is a weird departure, but

791
00:40:15,200 --> 00:40:17,160
Speaker 1: in the middle of the night last night, I woke

792
00:40:17,280 --> 00:40:21,160
Speaker 1: up and my mind was busy and I couldn't sleep,

793
00:40:21,160 --> 00:40:23,480
Speaker 1: so I turned on the TV. Now, neither of you

794
00:40:23,840 --> 00:40:26,040
Speaker 1: will know, probably, and neither do you need to know.

795
00:40:26,400 --> 00:40:30,440
Speaker 1: A guy called Joe Walsh who's a musician and he

796
00:40:30,680 --> 00:40:33,720
Speaker 1: was part of the Eagles. Anyway, I love Joe Walsh,

797
00:40:33,719 --> 00:40:36,080
Speaker 1: and I love the Eagles because I'm super old. But

798
00:40:36,160 --> 00:40:38,279
Speaker 1: I was listening to him talking about his music and

799
00:40:38,320 --> 00:40:42,240
Speaker 1: he wrote a song patrick In I think it got

800
00:40:42,280 --> 00:40:47,719
Speaker 1: published in twenty twelve called analog Man, and here are

801
00:40:47,760 --> 00:40:50,440
Speaker 1: some of the lyrics. Welcome to cyberspace. I'm lost in

802
00:40:50,480 --> 00:40:55,239
Speaker 1: the fog. Everything's digital. I'm still analog. When something goes wrong,

803
00:40:55,280 --> 00:40:57,320
Speaker 1: I don't have a clue. Some ten year old smart

804
00:40:57,320 --> 00:40:59,640
Speaker 1: ass tests has show me what to do. Sign on

805
00:40:59,719 --> 00:41:01,520
Speaker 1: with high speed. You don't have to wait to sit

806
00:41:01,560 --> 00:41:04,920
Speaker 1: there for days and vegetate. I access my email, read

807
00:41:04,960 --> 00:41:08,720
Speaker 1: all my spam. I'm an analog man. The world's living

808
00:41:08,719 --> 00:41:11,000
Speaker 1: in a digital dream. It's not really there. It's all

809
00:41:11,000 --> 00:41:13,520
Speaker 1: on the screen. Makes me forget who I am. I'm

810
00:41:13,560 --> 00:41:16,239
Speaker 1: an analog man. Yeah, I'm an analog man in a

811
00:41:16,280 --> 00:41:18,600
Speaker 1: digital world. I'm going to get me an analog girl

812
00:41:18,680 --> 00:41:21,439
Speaker 1: who loves me for what I am. I'm an analog man.

813
00:41:21,880 --> 00:41:26,600
Speaker 1: That could have been written for me. That's fucking Joe Walsh,

814
00:41:26,880 --> 00:41:30,799
Speaker 1: just fucking groundbreaking twelve years ago. That was That's good.

815
00:41:31,040 --> 00:41:33,000
Speaker 1: I actually like it. They're great lyrics.

816
00:41:33,280 --> 00:41:36,880
Speaker 2: Hey, you mentioned that your new car has a bone

817
00:41:37,000 --> 00:41:40,359
Speaker 2: sterilizer in it.

818
00:41:39,760 --> 00:41:44,720
Speaker 1: So it's got a what looks like a glove box,

819
00:41:44,760 --> 00:41:47,640
Speaker 1: but it's skinny, so same in the same spot, but

820
00:41:47,719 --> 00:41:53,400
Speaker 1: above the actual glove box. And I guess it's about

821
00:41:53,800 --> 00:41:57,879
Speaker 1: as like a couple of inches deep, so not much

822
00:41:58,000 --> 00:42:00,840
Speaker 1: like five centimeters, And you open the door or the

823
00:42:01,680 --> 00:42:04,120
Speaker 1: whatever to that, and you put your phone in, you

824
00:42:04,160 --> 00:42:09,000
Speaker 1: press a button and then it it sterilizes, I guess

825
00:42:09,160 --> 00:42:11,719
Speaker 1: is the word. It sterilizes your phone. It kills all

826
00:42:11,760 --> 00:42:15,319
Speaker 1: the germs that are on your phone, which I don't

827
00:42:15,360 --> 00:42:18,240
Speaker 1: think anyone in the world was asking for that feature.

828
00:42:18,480 --> 00:42:23,480
Speaker 2: But nonetheless, I beg to differ really different because the

829
00:42:23,520 --> 00:42:25,360
Speaker 2: next little story that I did want to chat about

830
00:42:25,560 --> 00:42:30,600
Speaker 2: is how your phone your watch and particularly the you know,

831
00:42:30,920 --> 00:42:33,920
Speaker 2: the clip or the not the clip the band on

832
00:42:33,960 --> 00:42:37,160
Speaker 2: your watch is a breeding ground for E. Coli and

833
00:42:37,320 --> 00:42:41,480
Speaker 2: staff bacteria, and how none of us really. I mean,

834
00:42:41,520 --> 00:42:43,360
Speaker 2: I've got to say I never cleaned the band of

835
00:42:43,480 --> 00:42:45,200
Speaker 2: my watch, and it's cloth, which is one.

836
00:42:45,120 --> 00:42:45,880
Speaker 1: Of the worst things.

837
00:42:46,680 --> 00:42:50,520
Speaker 2: So they say that were we should be cleaning our

838
00:42:50,640 --> 00:42:52,880
Speaker 2: device is a lot more than we do. You know,

839
00:42:52,920 --> 00:42:55,160
Speaker 2: you're pressing it up against your face. You know you've

840
00:42:55,200 --> 00:42:58,799
Speaker 2: got on your body, and it makes more sense when

841
00:42:58,840 --> 00:43:00,520
Speaker 2: you start to think of it. In those two you

842
00:43:00,560 --> 00:43:02,200
Speaker 2: can sterilizing kits.

843
00:43:02,760 --> 00:43:02,920
Speaker 1: You know.

844
00:43:02,960 --> 00:43:04,719
Speaker 2: The funny thing is every time I look at your

845
00:43:04,800 --> 00:43:08,560
Speaker 2: window and I say zoom window, it looks like you're

846
00:43:08,640 --> 00:43:10,239
Speaker 2: sitting in a sterilizing kit.

847
00:43:11,440 --> 00:43:14,160
Speaker 1: That's deliberate because it's glowing. Oh yeah, yeah, yeah, because

848
00:43:14,200 --> 00:43:17,759
Speaker 1: it's got your car. Yeah, that's true. Well, speaking of

849
00:43:17,800 --> 00:43:22,120
Speaker 1: things that are on your person, you've seen this bracelet

850
00:43:22,160 --> 00:43:24,279
Speaker 1: that's been on my wrist for twenty nine years and

851
00:43:24,320 --> 00:43:30,799
Speaker 1: never come off. It's very politically incorrectly called back in

852
00:43:30,800 --> 00:43:34,560
Speaker 1: the day a slave bracelet. Oh right, and then and

853
00:43:34,640 --> 00:43:36,680
Speaker 1: they put it on, they screw it on, and it's

854
00:43:36,719 --> 00:43:41,120
Speaker 1: got the initials, my initials and my mate who passed away, Maddie.

855
00:43:41,880 --> 00:43:44,160
Speaker 1: But that's been on my wrist for twenty nine years

856
00:43:44,200 --> 00:43:47,160
Speaker 1: and never come off once ever. And I wonder when

857
00:43:47,200 --> 00:43:52,120
Speaker 1: on in nine ninety five, I wonder, I wonder what's

858
00:43:52,160 --> 00:43:54,520
Speaker 1: living under that? Wow?

859
00:43:55,000 --> 00:43:58,440
Speaker 2: No, the interesting thing is copper is actually bacteria resistant.

860
00:43:58,640 --> 00:44:04,799
Speaker 2: Some metals gold to thank you's the original bracelets, cop up,

861
00:44:05,040 --> 00:44:07,800
Speaker 2: There were copper bracelets. They were used as a medium

862
00:44:07,800 --> 00:44:11,320
Speaker 2: of exchange in West Africa as part of the slave trade.

863
00:44:11,400 --> 00:44:18,000
Speaker 1: What has that got to do with my gold bracelet? Oh? Okay, okay, okay, okay. Well,

864
00:44:18,040 --> 00:44:21,200
Speaker 1: I think the reason that they called it anyway is

865
00:44:21,239 --> 00:44:23,520
Speaker 1: because it gets when you buy it, they put it

866
00:44:23,560 --> 00:44:25,960
Speaker 1: on with a screwdriver. It gets screwed on. There's no

867
00:44:26,120 --> 00:44:30,640
Speaker 1: clip or clasp. You can't take it off unless you

868
00:44:30,640 --> 00:44:33,680
Speaker 1: you know, you have those tools. But anyway, nobody needed

869
00:44:33,760 --> 00:44:36,000
Speaker 1: to know that. Oh. I think it's a lovely thing

870
00:44:36,040 --> 00:44:39,200
Speaker 1: that you're still wearing that. I think, Well, it makes

871
00:44:39,239 --> 00:44:40,439
Speaker 1: me think about him every day.

872
00:44:40,840 --> 00:44:43,200
Speaker 2: Yeah, when you go to an airport, what happens when you.

873
00:44:43,160 --> 00:44:45,560
Speaker 1: Get Yeah, that's interesting. A few times they've asked me

874
00:44:45,600 --> 00:44:47,439
Speaker 1: to take it off and I say I can't take

875
00:44:47,440 --> 00:44:51,359
Speaker 1: it off, and they're like, then I sometimes have to

876
00:44:51,400 --> 00:44:54,640
Speaker 1: like put my arm through the thing before the rest

877
00:44:54,640 --> 00:44:59,319
Speaker 1: of my body or one place they taped it like

878
00:44:59,360 --> 00:45:03,760
Speaker 1: they put like gaffer tape around my wrist and said,

879
00:45:03,840 --> 00:45:06,320
Speaker 1: I'm like, okay, sure.

880
00:45:06,200 --> 00:45:10,080
Speaker 3: That's not gonna Well, I don't know what a knife

881
00:45:10,080 --> 00:45:11,760
Speaker 3: to your body and stroll on through?

882
00:45:12,120 --> 00:45:15,360
Speaker 1: Yeah, no, no, no, because I don't know. I actually

883
00:45:15,400 --> 00:45:19,560
Speaker 1: don't understand what they think A round bracelet I might

884
00:45:20,600 --> 00:45:23,319
Speaker 1: what I might do with that on a plane, or

885
00:45:23,360 --> 00:45:25,600
Speaker 1: maybe it's I don't know, maybe yeah, I don't know.

886
00:45:25,960 --> 00:45:28,600
Speaker 1: Who knows, Patrick save us.

887
00:45:29,960 --> 00:45:32,160
Speaker 2: So I thought this is kind of not a tech

888
00:45:32,239 --> 00:45:34,640
Speaker 2: related story so much, but I thought it would be

889
00:45:34,640 --> 00:45:39,120
Speaker 2: one that would interest you because you're a big advocate

890
00:45:39,160 --> 00:45:41,880
Speaker 2: of exercise, and I know tiff is as well. Not

891
00:45:41,920 --> 00:45:46,239
Speaker 2: only is it thought that exercise mitigates cognitive decline, but

892
00:45:46,320 --> 00:45:50,920
Speaker 2: it also is linked to your gut microbia as well,

893
00:45:51,680 --> 00:45:55,400
Speaker 2: so it can actually assist in lots of different ways. So,

894
00:45:56,200 --> 00:45:59,800
Speaker 2: I mean, we know that cognitive decline is something that.

895
00:45:59,719 --> 00:46:02,040
Speaker 1: A lot of people worry about as they get older.

896
00:46:02,360 --> 00:46:04,799
Speaker 2: And you know, remember we had those brain games that

897
00:46:04,840 --> 00:46:08,239
Speaker 2: you'd play on in Well, they realized that none of

898
00:46:08,239 --> 00:46:11,680
Speaker 2: that it does help a little bit, but physical exercise

899
00:46:12,000 --> 00:46:16,120
Speaker 2: getting out their circulation include, you know, is the best

900
00:46:16,239 --> 00:46:19,719
Speaker 2: possible thing you can do for your brain health, is

901
00:46:19,760 --> 00:46:23,080
Speaker 2: your overall body health and exercise, but it also can

902
00:46:23,160 --> 00:46:25,759
Speaker 2: be linked to gut microbia and I thought, wow, that's

903
00:46:25,920 --> 00:46:29,920
Speaker 2: kind of interesting. So and people go through difficult times

904
00:46:30,200 --> 00:46:33,880
Speaker 2: and I've got friends who have real problems with with

905
00:46:34,560 --> 00:46:36,879
Speaker 2: digestion and all that sort of stuff. So it may

906
00:46:36,920 --> 00:46:41,520
Speaker 2: be that simple exercise rather than a sedentary lifestyle.

907
00:46:41,680 --> 00:46:45,440
Speaker 1: Yeah, I prove that Union of South Australia last year,

908
00:46:45,480 --> 00:46:48,080
Speaker 1: I think it was in December twenty twenty three published

909
00:46:48,640 --> 00:46:53,319
Speaker 1: a whole raft of research and findings around this, and

910
00:46:53,920 --> 00:47:02,320
Speaker 1: you're exactly right. And they compared an exercise intervention against

911
00:47:03,000 --> 00:47:08,880
Speaker 1: medical interventions, so anti axiotics and antidepressants. And this is

912
00:47:08,920 --> 00:47:13,600
Speaker 1: not a direction or a recommendation anyone. Consult your consult

913
00:47:13,640 --> 00:47:18,680
Speaker 1: your person or persons. But yeah, the exercise exercise alone

914
00:47:18,719 --> 00:47:21,640
Speaker 1: created much better results. So I think it was like

915
00:47:21,680 --> 00:47:24,680
Speaker 1: one hundred and fifty percent, which seems a very convenient number,

916
00:47:24,719 --> 00:47:28,920
Speaker 1: but yeah, much better results versus just medication. So just

917
00:47:29,040 --> 00:47:33,640
Speaker 1: exercise versus just medication from that perspective. But even like

918
00:47:33,680 --> 00:47:35,560
Speaker 1: we've had a lady on who was I don't know

919
00:47:35,600 --> 00:47:39,600
Speaker 1: if she's any more Professor Ingrid, the head of psychiatry

920
00:47:40,040 --> 00:47:43,960
Speaker 1: at Royal Adelaide Hospital, who's obviously a doctor and a

921
00:47:43,960 --> 00:47:48,560
Speaker 1: psychiatrist and someone who exercises a lot, and for her,

922
00:47:49,560 --> 00:47:53,480
Speaker 1: medication even for people with mental health issues is the

923
00:47:53,560 --> 00:48:00,000
Speaker 1: last option like drugs that she tries, lifestyle, food, exercise, life,

924
00:48:00,080 --> 00:48:03,799
Speaker 1: like trying to get people to change their own biochemistry

925
00:48:05,040 --> 00:48:08,120
Speaker 1: and what's happening in their brain on a biochemical level

926
00:48:08,800 --> 00:48:13,520
Speaker 1: with healthy interventions, because you know, any drug that you take,

927
00:48:14,600 --> 00:48:17,120
Speaker 1: even if it works on one level, there are going

928
00:48:17,200 --> 00:48:20,480
Speaker 1: to be there are going to be side effects somewhere between,

929
00:48:20,640 --> 00:48:24,000
Speaker 1: you know, very minor and very major, so you know

930
00:48:24,040 --> 00:48:26,399
Speaker 1: that's going to be factored in as well. But yeah,

931
00:48:26,440 --> 00:48:29,200
Speaker 1: that's no revelation to me. But you're exactly right, mate,

932
00:48:29,239 --> 00:48:32,799
Speaker 1: and it's great and I wish everybody. The problem is

933
00:48:32,840 --> 00:48:36,440
Speaker 1: in inverted commas is that exercise is hard and popping

934
00:48:36,480 --> 00:48:37,480
Speaker 1: a pill is easy.

935
00:48:38,120 --> 00:48:40,680
Speaker 2: And another bit of research I read recently was that

936
00:48:41,080 --> 00:48:44,480
Speaker 2: just walking forty minutes three times a week if you

937
00:48:44,520 --> 00:48:47,120
Speaker 2: can't manage anything else, just doing a vigorous walk.

938
00:48:47,400 --> 00:48:49,439
Speaker 1: But now you know more about this than I would.

939
00:48:49,480 --> 00:48:53,600
Speaker 2: The hippocampus is directly the part of the brain that

940
00:48:54,080 --> 00:48:57,719
Speaker 2: is engaged when we do more exercise. And now they're

941
00:48:57,719 --> 00:49:01,120
Speaker 2: linking the hipper campus with what's happening in gut health.

942
00:49:01,480 --> 00:49:02,880
Speaker 1: So that's the correlation.

943
00:49:03,160 --> 00:49:06,440
Speaker 2: And what they're trying connection to is what's going on

944
00:49:06,520 --> 00:49:09,480
Speaker 2: with you, hippocampus. Can you explain more about that? I mean,

945
00:49:09,480 --> 00:49:10,560
Speaker 2: what does actually happen?

946
00:49:11,360 --> 00:49:16,200
Speaker 1: I mean, so, like the gut is interestingly called the

947
00:49:16,239 --> 00:49:19,320
Speaker 1: second brain now and the heart is called the third

948
00:49:19,360 --> 00:49:22,200
Speaker 1: brain now a lot of people you know, but everything's

949
00:49:22,400 --> 00:49:25,880
Speaker 1: interacting all the time. Like the gut microbiome that you

950
00:49:25,920 --> 00:49:30,520
Speaker 1: were talking about is affected by everything from the food

951
00:49:30,600 --> 00:49:33,400
Speaker 1: you eat to the sleep that you have to. Like if,

952
00:49:33,480 --> 00:49:36,200
Speaker 1: for example, you think a negative thought now which I've

953
00:49:36,200 --> 00:49:40,200
Speaker 1: said this many times, but that produces cortisol and cortisole

954
00:49:40,239 --> 00:49:44,560
Speaker 1: affects your gut microbiome. Like everything be the thought's mind

955
00:49:44,600 --> 00:49:46,600
Speaker 1: as we call the mind, which is really a construct

956
00:49:46,719 --> 00:49:49,880
Speaker 1: or the physical brain itself, or the nervous system, or

957
00:49:49,920 --> 00:49:53,600
Speaker 1: the gut or the heart. You know, everything's intertwined all

958
00:49:53,640 --> 00:49:56,200
Speaker 1: the time, mate, and that's the that's the beauty of

959
00:49:56,200 --> 00:50:00,799
Speaker 1: this stuff. Is trying to like, research is important. I

960
00:50:00,840 --> 00:50:04,320
Speaker 1: wrote a post about this the other day, and there's

961
00:50:05,560 --> 00:50:08,160
Speaker 1: there's this term that gets thrown around a lot, and

962
00:50:08,200 --> 00:50:11,440
Speaker 1: the term is evidence based, right, So people come out

963
00:50:11,480 --> 00:50:13,920
Speaker 1: all the time and go, oh, this is an evidence

964
00:50:13,960 --> 00:50:20,480
Speaker 1: based protocol, or this is evidence based formula, or but often,

965
00:50:20,920 --> 00:50:24,440
Speaker 1: like I would say, more often than not, it's either

966
00:50:24,800 --> 00:50:29,279
Speaker 1: completely untrue or misleading. And so and I know I've

967
00:50:29,320 --> 00:50:34,359
Speaker 1: diverged a little bit, but you know, learning to you know,

968
00:50:34,520 --> 00:50:37,120
Speaker 1: pay it. Even with what we talk about, which is

969
00:50:37,320 --> 00:50:40,719
Speaker 1: very informal and very casual, but even if we say

970
00:50:40,800 --> 00:50:44,200
Speaker 1: something on here that might resonate with people, I always

971
00:50:44,239 --> 00:50:47,040
Speaker 1: say to people, don't do something because we spoke about it.

972
00:50:47,120 --> 00:50:50,879
Speaker 1: Do something. Maybe go and open the door and do

973
00:50:50,960 --> 00:50:53,880
Speaker 1: some research for yourself and talk to somebody smarter than

974
00:50:53,960 --> 00:50:58,400
Speaker 1: us three dickheads, and then maybe after that then try

975
00:50:58,480 --> 00:51:01,360
Speaker 1: something or or oh, you know, if you want to

976
00:51:01,360 --> 00:51:04,480
Speaker 1: do something really basic like an equals one, you're the

977
00:51:04,600 --> 00:51:09,400
Speaker 1: researcher and the researched, just try something like Patrick said,

978
00:51:10,160 --> 00:51:12,719
Speaker 1: you know, walking every day for thirty minutes, or and

979
00:51:12,840 --> 00:51:15,000
Speaker 1: just see how you feel. You know, it's you don't

980
00:51:15,040 --> 00:51:17,640
Speaker 1: need to go to a doctor for that. Or you know,

981
00:51:17,800 --> 00:51:19,719
Speaker 1: instead of having three meals a day, have the same

982
00:51:19,719 --> 00:51:22,239
Speaker 1: amount of calories, but in five meals and maybe your

983
00:51:22,360 --> 00:51:27,319
Speaker 1: digestive issues will be, you know, diminished somewhat. Like I think,

984
00:51:27,360 --> 00:51:29,319
Speaker 1: it's a little bit of trial and error with all

985
00:51:29,320 --> 00:51:29,920
Speaker 1: of this stuff.

986
00:51:31,080 --> 00:51:33,560
Speaker 2: I have a little kind of mental policy that I do.

987
00:51:33,680 --> 00:51:36,480
Speaker 2: I always take stairs when I can. Well, probably not

988
00:51:36,520 --> 00:51:39,520
Speaker 2: in a thirty story building, but for the most part,

989
00:51:39,560 --> 00:51:41,799
Speaker 2: if I can take the stairs, I always will. But

990
00:51:41,840 --> 00:51:44,680
Speaker 2: then someone said to me, actually a tai Chi master

991
00:51:44,800 --> 00:51:47,880
Speaker 2: who I met in China, who couldn't speak English, and

992
00:51:47,920 --> 00:51:53,080
Speaker 2: we translated our entire day of conversations through phones. But

993
00:51:53,160 --> 00:51:56,680
Speaker 2: he said to me, don't go downstairs because of the impact.

994
00:51:56,840 --> 00:52:00,319
Speaker 2: So upstairs good downstairs, use an escalator or lift.

995
00:52:00,680 --> 00:52:03,640
Speaker 1: So there you go. That's pretty true. I mean, we've

996
00:52:03,640 --> 00:52:06,000
Speaker 1: got to go downstairs. But it's like you're way better

997
00:52:06,040 --> 00:52:08,520
Speaker 1: off running up a hill for a range of reasons

998
00:52:08,560 --> 00:52:11,359
Speaker 1: than running down a hill. Yep, yep, exactly. So that's

999
00:52:11,360 --> 00:52:12,040
Speaker 1: what he said to me.

1000
00:52:12,160 --> 00:52:14,320
Speaker 2: And the other thing that I tell my Tishi students

1001
00:52:14,600 --> 00:52:16,240
Speaker 2: is when you're brushing your teeth.

1002
00:52:16,400 --> 00:52:17,959
Speaker 1: I don't know I've said this before, but when you're.

1003
00:52:17,840 --> 00:52:20,920
Speaker 2: Brushing your teeth your two minute roughly routine, stand on

1004
00:52:20,920 --> 00:52:24,280
Speaker 2: one foot as you're brushing your tooth, teeth, then change

1005
00:52:24,280 --> 00:52:27,160
Speaker 2: to the other foot halfway through, and it's just such

1006
00:52:27,160 --> 00:52:30,160
Speaker 2: a simple thing that's not going to impact in any way,

1007
00:52:30,239 --> 00:52:33,439
Speaker 2: doesn't take any extra efforts, but the.

1008
00:52:33,360 --> 00:52:34,399
Speaker 1: Ability for you.

1009
00:52:34,480 --> 00:52:37,319
Speaker 2: And I'm trying to think of the term that our

1010
00:52:37,400 --> 00:52:41,920
Speaker 2: brain uses to predict a fall. I can't think of

1011
00:52:42,480 --> 00:52:47,600
Speaker 2: the terminology, but it's the same. It's the same instinct

1012
00:52:47,680 --> 00:52:50,440
Speaker 2: that cats us to land on all four feet. And

1013
00:52:50,600 --> 00:52:53,600
Speaker 2: you can improve that, so our ability to be able

1014
00:52:53,640 --> 00:52:56,560
Speaker 2: to improve and work on our brain to get better

1015
00:52:56,600 --> 00:52:57,960
Speaker 2: at balance core stability.

1016
00:52:58,280 --> 00:52:59,800
Speaker 1: That also includes predicting.

1017
00:53:00,239 --> 00:53:02,440
Speaker 2: You know, when you have that sensation almost that you

1018
00:53:02,480 --> 00:53:04,839
Speaker 2: can get stumble and then you do what you need

1019
00:53:04,920 --> 00:53:06,399
Speaker 2: to stop yourself falling over.

1020
00:53:06,719 --> 00:53:07,640
Speaker 1: You can improve that.

1021
00:53:08,680 --> 00:53:10,680
Speaker 2: Part of the way to improve that is literally just

1022
00:53:10,680 --> 00:53:13,080
Speaker 2: to stand on one foot. So if you just stand

1023
00:53:13,120 --> 00:53:15,560
Speaker 2: on your foot every day while you're brushing your teeth

1024
00:53:15,640 --> 00:53:18,719
Speaker 2: or twice a day, and then alternate that, then it's

1025
00:53:18,719 --> 00:53:20,680
Speaker 2: something so so simple to do.

1026
00:53:20,680 --> 00:53:22,520
Speaker 1: You know what, I love that. I love that you

1027
00:53:22,560 --> 00:53:26,320
Speaker 1: say that because I'm weird. Right since I was a kid,

1028
00:53:26,400 --> 00:53:29,520
Speaker 1: I've always done weird shit. I've always gone like I

1029
00:53:29,520 --> 00:53:31,719
Speaker 1: remember being seven and going wonder how long I can

1030
00:53:31,760 --> 00:53:34,160
Speaker 1: hold my breath. I've spoken about this before. I wonder

1031
00:53:34,200 --> 00:53:36,359
Speaker 1: how long I can do it underwater. I wonder how

1032
00:53:36,440 --> 00:53:37,959
Speaker 1: you know, I used to just do all this shit,

1033
00:53:38,040 --> 00:53:41,319
Speaker 1: but even now, like I have this rule and here

1034
00:53:41,400 --> 00:53:44,600
Speaker 1: at home where I only walk up the stairs two

1035
00:53:44,600 --> 00:53:47,160
Speaker 1: at a time, yeah, me too. I do that as well.

1036
00:53:47,200 --> 00:53:49,319
Speaker 1: I only walk two at a time, and even if

1037
00:53:49,360 --> 00:53:51,480
Speaker 1: I'm carrying my dinner and shit up only two at

1038
00:53:51,520 --> 00:53:54,120
Speaker 1: a time. Another thing that I do not all the time,

1039
00:53:54,160 --> 00:53:57,439
Speaker 1: if I'm being honest, but probably half the time. When

1040
00:53:57,440 --> 00:54:00,080
Speaker 1: I put my shoes and socks on, I stand on

1041
00:54:00,040 --> 00:54:03,160
Speaker 1: one foot I put on. So my rule is I've

1042
00:54:03,200 --> 00:54:05,719
Speaker 1: got to stand up. I don't sit down to put

1043
00:54:05,760 --> 00:54:07,360
Speaker 1: my shoes and socks on. I've got to stand up.

1044
00:54:07,360 --> 00:54:09,440
Speaker 1: I've got to lift one foot obviously off the ground,

1045
00:54:09,440 --> 00:54:11,560
Speaker 1: put the sock on, put the shoe on, and then

1046
00:54:11,600 --> 00:54:14,120
Speaker 1: do it on the other one. And it's like I

1047
00:54:14,239 --> 00:54:16,279
Speaker 1: used to. I started doing that a couple of years ago,

1048
00:54:16,320 --> 00:54:19,359
Speaker 1: and I'd fucking overbalance and fall, not fall, But you know,

1049
00:54:19,920 --> 00:54:23,160
Speaker 1: and now pretty much I never like it. And my

1050
00:54:23,360 --> 00:54:26,600
Speaker 1: stability and balance is way better than it was three

1051
00:54:26,680 --> 00:54:30,640
Speaker 1: years ago. And I'm sixty, so but that's just doing those,

1052
00:54:30,920 --> 00:54:33,560
Speaker 1: and there's things like you know, it's like sometimes if

1053
00:54:33,600 --> 00:54:35,960
Speaker 1: I'm writing notes on the whiteboard, i'd write with my

1054
00:54:36,160 --> 00:54:39,799
Speaker 1: right hand and I'm left handed. And then if I

1055
00:54:39,960 --> 00:54:41,920
Speaker 1: do that, like sometimes I'll go, I'm only going to

1056
00:54:41,960 --> 00:54:44,680
Speaker 1: write on not when I'm doing my messages for social media,

1057
00:54:44,680 --> 00:54:46,880
Speaker 1: but just when I'm thinking out loud, which I do

1058
00:54:46,920 --> 00:54:49,560
Speaker 1: on the whiteboard a lot, I just use my right hand.

1059
00:54:50,080 --> 00:54:52,759
Speaker 1: And it's amazing how quickly you improve in a matter

1060
00:54:52,840 --> 00:54:56,560
Speaker 1: of days at doing something that's completely atypical for you.

1061
00:54:57,480 --> 00:54:59,080
Speaker 1: Brain plasticity, it's amazing.

1062
00:54:59,320 --> 00:55:03,200
Speaker 2: I think appropriate exception is the ability to predict a

1063
00:55:03,280 --> 00:55:06,240
Speaker 2: fall or to right yourself. But I kind of probably

1064
00:55:06,239 --> 00:55:07,640
Speaker 2: should have looked it up before I said it, but

1065
00:55:07,680 --> 00:55:09,720
Speaker 2: it occurred to me while we're talking, while you were talking.

1066
00:55:09,760 --> 00:55:13,279
Speaker 2: But you can rewrite your brain to be able to

1067
00:55:13,320 --> 00:55:15,440
Speaker 2: work in different ways, whether as you said, you know,

1068
00:55:15,800 --> 00:55:17,200
Speaker 2: I think it's great that you do the left hand

1069
00:55:17,320 --> 00:55:19,880
Speaker 2: right hand thing, but I was also quietly thinking, oh

1070
00:55:19,960 --> 00:55:22,319
Speaker 2: my god, we could be the same person because I

1071
00:55:22,360 --> 00:55:25,400
Speaker 2: stand on one foot to put my shoe on as well,

1072
00:55:25,680 --> 00:55:29,520
Speaker 2: and always tie my shoelacers one foot.

1073
00:55:30,200 --> 00:55:34,399
Speaker 1: We definitely should be married. So you get right out

1074
00:55:34,400 --> 00:55:39,000
Speaker 1: of the way, Tiff. Yeah, but it's that you know.

1075
00:55:39,080 --> 00:55:41,480
Speaker 1: So there's a guy called Norman Deutsge without trying to

1076
00:55:41,480 --> 00:55:44,080
Speaker 1: sound like a geek, he wrote a book years ago

1077
00:55:44,160 --> 00:55:46,759
Speaker 1: called The Brain That Changes Itself, and he was he

1078
00:55:46,880 --> 00:55:49,280
Speaker 1: was one of the pioneers in this space. He opened

1079
00:55:49,280 --> 00:55:54,439
Speaker 1: the door because forever, until about forty years ago, we

1080
00:55:54,600 --> 00:55:58,440
Speaker 1: thought that the brain you talked about the hippocampus before,

1081
00:55:58,480 --> 00:56:02,719
Speaker 1: which is pretty much like the epicenter of learning and memory, right,

1082
00:56:03,600 --> 00:56:05,840
Speaker 1: and we'd go, ah, well, if you hip a campus

1083
00:56:05,880 --> 00:56:09,000
Speaker 1: as fuck, you won't remember anything anymore. But that's not

1084
00:56:09,080 --> 00:56:12,360
Speaker 1: true because the brain can adopt other roles and you

1085
00:56:12,400 --> 00:56:15,879
Speaker 1: can retrain the brain as you said, which is neural plasticity.

1086
00:56:15,960 --> 00:56:19,440
Speaker 1: But yeah, we used to go, well, this bit of

1087
00:56:19,440 --> 00:56:21,080
Speaker 1: the brain does that, and if that bit of the

1088
00:56:21,080 --> 00:56:25,480
Speaker 1: brain's broken, then you don't have that ability anymore. But yeah,

1089
00:56:25,480 --> 00:56:29,120
Speaker 1: we now know that that's not necessary. It will probably

1090
00:56:29,160 --> 00:56:32,680
Speaker 1: be impaired, but other brains can take over some of

1091
00:56:32,719 --> 00:56:34,960
Speaker 1: the function for one of a better term.

1092
00:56:35,160 --> 00:56:39,400
Speaker 2: It's interesting because there's now there's a lot of focus

1093
00:56:39,680 --> 00:56:43,879
Speaker 2: on teenagers using the internet and being addicted to their

1094
00:56:43,920 --> 00:56:49,120
Speaker 2: phones because of what is happening to their brains. During adolescence.

1095
00:56:49,160 --> 00:56:51,239
Speaker 2: We know there's a great lot of formation going on

1096
00:56:51,280 --> 00:56:55,600
Speaker 2: in a teenage brain. So there's a real concern that

1097
00:56:55,680 --> 00:56:59,360
Speaker 2: the use of technology and internet could be impacting the

1098
00:56:59,400 --> 00:57:02,279
Speaker 2: way you know, juvenile development.

1099
00:57:01,840 --> 00:57:06,480
Speaker 1: And particularly adolescent development is progressing. And I think the.

1100
00:57:06,480 --> 00:57:10,920
Speaker 2: Jury's still out, but you know, playing games, using the internet,

1101
00:57:11,160 --> 00:57:15,279
Speaker 2: scrolling away, all those things that we see teenagers doing.

1102
00:57:15,440 --> 00:57:19,560
Speaker 2: Everybody does, but saying it could actually be more damaging

1103
00:57:19,640 --> 00:57:22,280
Speaker 2: in an adolescent brain for the pure fact that that's

1104
00:57:22,320 --> 00:57:25,960
Speaker 2: where their brain is doing the most real developmental changes.

1105
00:57:26,360 --> 00:57:29,240
Speaker 1: Well, you know, sixty years ago, what was going to

1106
00:57:29,280 --> 00:57:32,720
Speaker 1: derail the world, what was going to destroy adolescence and

1107
00:57:32,760 --> 00:57:38,280
Speaker 1: the future, was Elvis Presley's hips. So you know, hey, mate,

1108
00:57:38,360 --> 00:57:40,440
Speaker 1: how can people find you and connect with you?

1109
00:57:41,000 --> 00:57:45,200
Speaker 2: Well, websites noow, dot com, dot au or tichi at home.

1110
00:57:45,520 --> 00:57:47,720
Speaker 2: You know, we still haven't done our virtual tai Chi

1111
00:57:47,840 --> 00:57:49,520
Speaker 2: class crago. I'm going to take you up on that

1112
00:57:49,560 --> 00:57:52,360
Speaker 2: one again. Can we please do a zoom call.

1113
00:57:52,480 --> 00:57:54,280
Speaker 1: You know what we should do. We should wait till

1114
00:57:54,640 --> 00:57:57,920
Speaker 1: you've got your bloody special studio built. Yeah, okay, you

1115
00:57:57,960 --> 00:57:59,880
Speaker 1: can come up. I'll come up for the weekend. We'll

1116
00:58:00,040 --> 00:58:03,880
Speaker 1: boon on the couch and Tiff can make us dinner.

1117
00:58:07,320 --> 00:58:10,240
Speaker 1: That was a joke. Everyone. If I don't even be there,

1118
00:58:10,280 --> 00:58:12,240
Speaker 1: we wouldn't let her come. It's a man's weekend.

1119
00:58:12,560 --> 00:58:15,240
Speaker 3: I've already got the first DIBs on. We've already talked

1120
00:58:15,280 --> 00:58:18,000
Speaker 3: about yesterday. So you're you're second in line.

1121
00:58:17,760 --> 00:58:20,560
Speaker 2: Bro, We've already organized a date.

1122
00:58:21,800 --> 00:58:23,000
Speaker 3: Wife has come first.

1123
00:58:24,080 --> 00:58:29,800
Speaker 1: Really yeah, okay, thanks everyone, Thanks Thanks Steve.

1124
00:58:30,400 --> 00:58:30,800
Speaker 3: Thanks