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Speaker 1: I get a team. It's Patrick and Jumbo, It's the

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Speaker 1: Bloody Project, that's you. It's tech Friday, while it's Fridays,

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Speaker 1: we're recording. Hi, mate, how are you look?

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Speaker 2: I'm not doing too bad. I you know, it's a

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Speaker 2: little bit earlier for me because you know, yes, your

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Speaker 2: text last night. Can we do it an hour and

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Speaker 2: a half earlier? And that's good. But I made the

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Speaker 2: mistake of having a bagel with fake chicken on it?

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Speaker 1: Is that? What?

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Speaker 2: Oh?

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Speaker 1: I know that fake bacon's called faken? What's that is? Right?

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Speaker 1: Isn't it? Do they call it facn fake bacon?

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Speaker 2: Well, people call it fake facon, but no, so it

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Speaker 2: was a chicken. Well. So a few weeks ago, I

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Speaker 2: was crook and I ordered some groceries online. And I

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Speaker 2: might be a bit of a tight ass sometimes. So

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Speaker 2: there's a little algorithm you click what's on special and vegan,

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Speaker 2: and it lists all the stuff that's on special to

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Speaker 2: shop with, which is kind of makes sense. So I

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Speaker 2: bought all this stuff, but I bought way too much stuff.

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Speaker 2: And then yesterday I realized that everything was best before today,

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Speaker 2: So I thought, oh man, I'm gonna eat it. So

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Speaker 2: I'm eating it for the sake of it be eaten

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Speaker 2: so I don't have to throw it out because I

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Speaker 2: hate wasting food and I never eat something heavy in

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Speaker 2: the morning. It's normally as smoothie and normally after I've

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Speaker 2: been to the gym. So I haven't been to the gym.

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Speaker 2: I haven't walked the dog. Fritz is laying on the

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Speaker 2: chair next to me, feeling totally dejected. He's just put

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Speaker 2: his head up to see what I was talking about.

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Speaker 2: And then I've eaten this bagel with avocado. It kind

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Speaker 2: of would have been nice that maybe lunchtime, but definitely

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Speaker 2: not ten minutes before sitting down to do a podcast.

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Speaker 2: So in general, I'm great, and thank you for donating

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Speaker 2: to that fundraiser that I've been raising for all this month,

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Speaker 2: because I'm welcome number two in Australia, Crago. How good

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Speaker 2: is that?

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Speaker 1: You're a little champion fundraiser. Can I just I want

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Speaker 1: to reinquire, just briefly, why do vegans who don't like

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Speaker 1: meat constantly have meat? You know, it's like fake beef,

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Speaker 1: fake chicken, fake bacon.

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Speaker 2: Now, I see, I'm not a vegan who doesn't like meat.

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Speaker 2: I'm a vegan who doesn't eat meat ethically because I

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Speaker 2: can't bear the thought of an animal being killed. So

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Speaker 2: if you have about I'll walk past and think, hm,

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Speaker 2: that smells okay. You know a rack of lamb. Gee,

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Speaker 2: rack of lamb would have been good. But I just

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Speaker 2: don't know the idea of the little lamb not being

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Speaker 2: there anymore. That's what doesn't for me.

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Speaker 1: You know. I was listening to this podcast where they

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Speaker 1: were talking about this, and they're talking about I'll find

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Speaker 1: it for you, and I probably shouldn't send it to you,

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Speaker 1: But they were talking about how, you know, people who

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Speaker 1: are vegans for ethical reasons, which is completely legit, totally understandable.

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Speaker 1: But then they were talking about the amount of insects

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Speaker 1: and small animals that get destroyed in the cultivating of

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Speaker 1: acres of vegetables. And it's like squirrels and like get

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Speaker 1: you know, it's like carnage in the harvesting your vegetables.

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Speaker 2: So you know, in the harvesting of the food product

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Speaker 2: that goes to feeding cattle. You know, again, happened everywhere. Yeah,

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Speaker 2: this is impossible not to kill insects and small squirrels.

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Speaker 1: Squirrels and bloody ground nesting rodents.

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Speaker 2: And shit, it's a funny mogument, though, isn't it.

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Speaker 1: I want to ask you. I want to ask you

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Speaker 1: about before we jump in to whatever it is we're

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Speaker 1: talking about. So, how much of your shopping is online?

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Speaker 1: Because like a lot, we have younger listeners, but we

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Speaker 1: also have a lot of listeners who are kind of

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Speaker 1: forty five plus, and then we have a few in

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Speaker 1: their sixties. And I'm going to say, honestly, I've never

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Speaker 1: bought one thing ever in the history of me online.

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Speaker 1: I've never bought an online thing, which is no particular reason.

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Speaker 1: I don't have an aversion to it. But let's say

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Speaker 1: you bought let's say you spent ten grand a year.

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Speaker 1: I'm just plucking a number. How much of that ten

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Speaker 1: grand would you spend online?

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Speaker 2: Look general shopping, I time it so that I do

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Speaker 2: shopping on a Wednesday after my tai chi class. I

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Speaker 2: specifically sit up the class so I can go shopping

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Speaker 2: in the next town across because we don't have a

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Speaker 2: you know, we don't have a Coals or an Aldi

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Speaker 2: in our little town. And so for food shopping generally,

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Speaker 2: I do that fresh because and the other thing is

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Speaker 2: vegetables and fruit. I'm not going to get someone to

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Speaker 2: pre pack that and choose what I'm going to pick

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Speaker 2: off shelf. I like to do that myself. But technology

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Speaker 2: I would do totally online ups an uninterruptable power supply.

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Speaker 2: It's basically a big battery that sits between the wall

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Speaker 2: and your computer. So if the power goes out or

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Speaker 2: there's a brown out where the power drops, it means

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Speaker 2: your computer and your monitors will stay running. And also

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Speaker 2: it protects your computer. But I bought that online. There's

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Speaker 2: no way that I'd bother driving half an hour somewhere

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Speaker 2: to shop around and buy one. And also because a

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Speaker 2: lot of companies have pretty good delivery rates, you know,

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Speaker 2: couriers are not too bad.

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Speaker 1: So I fact what your impulse? What's your impulse control like?

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Speaker 1: Because I feel like you might see shit and go,

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Speaker 1: oh my god, look could that a drone helicopter that's

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Speaker 1: only ninety five dollars that can deliver my dinner? That

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Speaker 1: How often do you just buy shit that you didn't

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Speaker 1: get on line to buy, Like, it's just you see

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Speaker 1: something and you have no self restraint.

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Speaker 2: Now champagne taste on a beer budget, Crego. The problem

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Speaker 2: is most of the check tech that I'm interested in

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Speaker 2: is way too expensive. Today. For example, I was looking

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Speaker 2: at and actually I wasn't looking at this for myself.

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Speaker 2: I was looking at it as a gift for a

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Speaker 2: girlfriend to mine. But there's these e ink photo frames

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Speaker 2: that look like real painting. So it's twenty seven inches.

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Speaker 2: So imagine you've got a framed painting on your wall

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Speaker 2: and it's a stunning painting by Van Goh or something. Well,

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Speaker 2: it actually made of e ink, and so it's got

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Speaker 2: a really nice texture. It looks like it's painted on,

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Speaker 2: and then you can have any picture on there. You

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Speaker 2: can put your own photo, so it's a real step

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Speaker 2: up from traditional photo frames. But I think it was

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Speaker 2: like six one hundred and ninety nine pounds, so oh wow. Yeah,

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Speaker 2: there's no way I was going to buy it. So

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Speaker 2: I kind of drawol over a lot of tech, but

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Speaker 2: I don't. I think I'm pretty good with my money.

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Speaker 2: I don't jump in and buy stuff here. So in

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Speaker 2: answer to your question, I have a little bit of

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Speaker 2: self restraint. But that's purely budgetry. If I was a billionaire,

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Speaker 2: to be ridiculous, i'd need multiple houses and sheds.

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Speaker 1: I've just been listening to the last book in the

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Speaker 1: terminal list while I think it's the last book the

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Speaker 1: Terminal List series, which is a guy called James Reese

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Speaker 1: is the lead character. There was a movie in a

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Speaker 1: TV series which was definitely nowhere near as good as

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Speaker 1: the books. But anyway, and in the last book, which

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Speaker 1: has written last year, the central character, apart from James Reese,

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Speaker 1: is a quantum computer called Alice. Oh my god, Oh

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Speaker 1: my god it is. It is a steep learning curve

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Speaker 1: in understanding, like for me, and that the guy who

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Speaker 1: wrote it, Jack Carr, I think his name is. It's

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Speaker 1: so brilliant section of espionage and politics and war and

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Speaker 1: quantum computing and personalities and fucking amazing book. Like for me,

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Speaker 1: I'm a dude, so you know, some people might go, ah,

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Speaker 1: but I really liked it and too, and it's incredibly

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Speaker 1: researched and that the and obviously in the conversation through

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Speaker 1: the book, you know, they have where some some tech

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Speaker 1: dudes explaining to the lead character what a quantum computer

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Speaker 1: is and its capacity and its power, and so they're

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Speaker 1: trying to explain to the dumb readers like me who

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Speaker 1: are like, what a quantum computer? You know, It's like

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Speaker 1: I'm like that shit when that shit becomes commonplace, We're

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Speaker 1: all fucked. I mean, there's going to be no security.

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Speaker 1: Nobody's gonna like it can do so super computers, so computers,

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Speaker 1: and then super computing, and then quantum computing. It can

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Speaker 1: do what a supercomputer can do times a million, ten seconds.

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Speaker 1: I'm like, oh, it's the end of the world. I'll

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Speaker 1: be dead by then. But it ain't seemingly it seemingly

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Speaker 1: ain't that far away.

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Speaker 2: Yeah, that's right. The other exciting thing though about quantum

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Speaker 2: computing is that it can run computational models that could

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Speaker 2: look at, say, combining drugs, so for example, when you're

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Speaker 2: doing rigorous testing of what drugs and I think the

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Speaker 2: future for health is that we will have tailor made

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Speaker 2: drugs for you and I where you don't just get

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Speaker 2: a script, you to look at what your genetic background

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Speaker 2: is and it will match the drug to whatever the

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Speaker 2: illness or ailment is. And the type of computer models

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Speaker 2: mean that they will be able to use quantum computers

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Speaker 2: to do computational tasks that would have taken thousands of

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Speaker 2: years literally, you know, and do it in such a

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Speaker 2: small out of time that we will be able to

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Speaker 2: easily tailor things like that. So I think from a

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Speaker 2: health perspective, the idea of quantum computing to help with

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Speaker 2: drug research is going to be phenomenal. It's going to

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Speaker 2: be amazing to.

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Speaker 1: See all personalized medicine. It's already happening. It's already happening

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Speaker 1: doctor Denise Finess from Coinslane that we have on all

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Speaker 1: the time. So what they do is, and she does this,

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Speaker 1: they literally sample your DNA and build you build your

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Speaker 1: medicine in inverted commas. Now whether or not that's a

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Speaker 1: drug or a supplement or or you know, some kind

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Speaker 1: of lifestyle component or factor, but yeah, you're you're and

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Speaker 1: it's just going to get better and better. Where we're

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Speaker 1: literally going, oh, well, here's Patrick's DNA, here's Craig's DNA.

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Speaker 1: They've both got a headache, but they don't need the

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Speaker 1: same drug because they don't have the same physiology or

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Speaker 1: Patrick needs more energy, or Craig needs more energy, or

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Speaker 1: you know whatever it is. So that is that is exciting.

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Speaker 1: But then I think about that to us in first

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Speaker 1: world countries getting that, the people in the third world

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Speaker 1: countries are not getting that, are they?

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Speaker 2: And I've got to say, Katie, probably will I'm just

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Speaker 2: going to say hi to Katie. She sent me the

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Speaker 2: nicest email early in the week. Did I forward it

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Speaker 2: to you? So I'm sure I did. You just ignore

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Speaker 2: me because you tend to do that.

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Speaker 1: I get a lot of emails. Well, you do know

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Speaker 1: what was going on in my week this week, so

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Speaker 1: you can give me a chop out. Also, I was

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Speaker 1: in Queensland for two days.

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Speaker 2: Yeah, you've had a pretty crap week, to be honest.

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Speaker 1: I spent the first two days in Queensland Monday Tuesday.

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Speaker 1: Then Wednesday is sitting all day in a hospital with

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Speaker 1: my old man, and yesterday was the first day that

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Speaker 1: I was back kind of on deck.

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Speaker 2: So actually it was two weeks ago that I think

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Speaker 2: of it, so it's even longer ago than that.

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Speaker 1: What did What did Katie say? Is Katie got a

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Speaker 1: hard crush on you?

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Speaker 2: Yeah? Kind of. No, one not don't really. She's just

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Speaker 2: one of our listeners. And she said she she loves

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Speaker 2: this segment makes her laugh a lot and she can't

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Speaker 2: even see us.

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Speaker 1: How good is that? Wow? She laughed any more? Yeah, hey, Katie,

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Speaker 1: for you and our listeners, we might start working through

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Speaker 1: Patrick's list for those who are I guess we should

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Speaker 1: take didn't we Yeah, Patrick sends me a list of

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Speaker 1: shit that we talk about. I'm looking now there are

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Speaker 1: hang on, stop talking over me. I'll come over there

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Speaker 1: and jump on your bloody mobile vacuum. There's probably twenty

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Speaker 1: twenty to twenty five things on this list. Now, if

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Speaker 1: we talk about each for two minutes, we still don't

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Speaker 1: have time. What how about I love how you've introduced

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Speaker 1: a psychology and biology section in our tech show. You

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Speaker 1: know that that's my space, right.

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Speaker 2: But it's cutting each stuff, you know. I think?

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Speaker 1: Is it biology and psychology intersecting with technology? Is that

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Speaker 1: what you're saying? Yeah, all right, Well you start you

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Speaker 1: just start there wherever you want.

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Speaker 2: Look. I don't know about you, but one of my

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Speaker 2: biggest fears is meeting someone and then thirty seconds later

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Speaker 2: forgetting their name struck. I'm terrible with that, and I

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Speaker 2: really it's become a bit of almost a phobia for me,

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Speaker 2: because I really try hard to remember people's names. So

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Speaker 2: I read an article recently about little processes that you

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Speaker 2: can put into place, and I think most of us

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Speaker 2: do this is you repeat the name back. So someone

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Speaker 2: says Hi, Craig, and I repeat your name a few times,

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Speaker 2: but it can be a bit tacky as well, but

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Speaker 2: it actually is not a bad idea. Also, once you've

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Speaker 2: repeated the name, and if you're out of the room

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Speaker 2: or whatever, talk to yourself. You know, smart people tend

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Speaker 2: to talk to themselves. Do you talk to yourself much?

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Speaker 2: Because I talk to my dog? I don't know that

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Speaker 2: I talk to myself, And now that I think of it,

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Speaker 2: I'm doing it now, aren't I?

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Speaker 1: Yeah?

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Speaker 2: So anyway, Yeah, I talk to myself. What about you, Crago?

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Speaker 1: Well, I do not in the sense that I'm I'm

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Speaker 1: going back and forth in a two way conversation. I

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Speaker 1: don't think that's particularly indicative indicative of mental health. But yeah,

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Speaker 1: Like I mean, I live by myself, so I say

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Speaker 1: shit out loud all the time. Usually it's something like

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Speaker 1: you fucking idiot, you know? But yes, there was a.

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Speaker 2: Study published in Learning, Memory, and Cognition, and it found

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Speaker 2: that saying words out loud or just mouthing them even

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Speaker 2: can make them more distinctive and writing it to the

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Speaker 2: hard drive. The other thing that's really interesting in this

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Speaker 2: article was it talked about predicting whether you will remember.

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Speaker 2: So when I say to myself, I never remember people's names,

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Speaker 2: you're almost putting yourself into the mindset of not remembering

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Speaker 2: the names. So if you change that around and say, yep,

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Speaker 2: always remember people's names, it actually will help when it

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Speaker 2: goes to remembering what people's names are or you know,

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Speaker 2: events or bits and pieces like that. So you're trying

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Speaker 2: to I think replaying it. I say to my students

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Speaker 2: when I'm doing tai chi that even driving home, if

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Speaker 2: they've learned some new moves, visualizing the moves, so they've

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Speaker 2: just spent a session learning some new moves in a

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Speaker 2: new form, and then when you're driving home, just visualize

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Speaker 2: it and that again helps still that memory into an

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Speaker 2: area where you'll be able to recall it.

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Speaker 1: I We've had a guy makes sense. We've had a

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Speaker 1: guy in here a couple of times called Heston Russell,

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Speaker 1: which is you know Heston Russell. He's the Australian soldier.

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Speaker 2: That uh a man crashed on for a while.

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Speaker 1: Yeah yeah, yeah, yeah, like super duper great soldier, got

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Speaker 1: in the middle of there was some controversy which was

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Speaker 1: bullshit surrounding him. Anyway, he got totally cleared and it exonerated.

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Speaker 1: He's a weapon. Great dude. He's been on the show anyway,

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Speaker 1: his name's Heston Russell, as I said, and I don't

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Speaker 1: know anyone called Heston, and I couldn't. A couple of

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Speaker 1: times I'm like down the track, I'm like, what was

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Speaker 1: what is his name? Because it's not like Dave or

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Speaker 1: Patrick or'brian, and so then I would think of Charlton

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Speaker 1: Heston and those old Gladiator movies. So whenever I think

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Speaker 1: of Heston, the first thing I think of as an

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Speaker 1: old Gladiator movie and I'm like, oh, it's Heston Russell, right.

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Speaker 1: But I think. The other thing that I do is

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Speaker 1: if I meet somebody and I straightway have a visual

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Speaker 1: association of them and another person that I know with

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Speaker 1: the same name, So when I see them, I always

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Speaker 1: remember the face of the other person, so I know

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Speaker 1: their name and I pick the most Like let's say

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Speaker 1: it's Andrew. We both know Andrew Jobbling, right, so I

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Speaker 1: would I would pick Jobbers's face, So I would I'd

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Speaker 1: go because I'm going to forget Andrew, because I'm going

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Speaker 1: to meet thirteen people today and I'm gonna I'm going

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Speaker 1: to get told their names, I'm going to shake their hand,

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Speaker 1: and then I'm going to see them this afternoon at

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Speaker 1: this conference, and I'm gonna have no fucking clue, So

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Speaker 1: I always have a for me, that's what. Yeah, Because

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Speaker 1: I'm like you, just trying to remember a name with

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Speaker 1: no kind of association to anything else. I struggle. But

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Speaker 1: if I can link that that that person's face with

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Speaker 1: another person's face that I know, well, then problem solved

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Speaker 1: for me.

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Speaker 2: Except if you're doing a lineup, and then you're going

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Speaker 2: to be really shit because you're going to be fingering

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Speaker 2: the wrong person.

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Speaker 1: Do you know I was talking to I was talking

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Speaker 1: to somebody yesterday about you know, those those guys and girls.

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Speaker 1: I don't know that I've seen a female mentalist, but

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Speaker 1: I'm sure they are. But a couple of guys that

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Speaker 1: I've seen. There was a guy who came to Melbourne

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Speaker 1: a couple of years ago. If you've not seen him,

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Speaker 1: here is fucking incredible. His name is Leorshad l Io R. Soushad.

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Speaker 1: He's a mentalist and he did all this incredible stuff

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Speaker 1: on stage, you know, all this mind blowing stuff that

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Speaker 1: I won't go into because it's not the show. But

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Speaker 1: one thing that he did do was at the start

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Speaker 1: of the show. So it's all just a room full

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Speaker 1: of strangers and some friends of mine. Their daughter was

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Speaker 1: there and she's her name's Jenna, and she's a pharmacist.

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Speaker 1: Right anyway, So he just picked one hundred people. He

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Speaker 1: went through the audience in about I don't know, three minutes,

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Speaker 1: and he went like he'd point at you and you'd

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Speaker 1: stand up, you'd say my name's Patrick, I'm a tech guy, whatever, right,

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Speaker 1: or I'd build whatever the fuck right? Thanks, and then

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Speaker 1: the next person. He did this with one hundred people

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Speaker 1: and then he went thanks, sit down, literally one hundred,

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Speaker 1: not like twenty, literally one hundred. That was the point

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Speaker 1: of the exercise. Then he did the show, which was

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Speaker 1: like two hours, and then we're all about to go

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Speaker 1: home and we've forgotten and he goes, hang on before

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Speaker 1: we go, and then he went through one hundred people,

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Speaker 1: said their name and their job and made zero mistakes.

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Speaker 1: Two hours later.

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Speaker 2: I couldn't do it four people.

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Speaker 1: I'm like, that is I mean, I know that's not magic,

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Speaker 1: but that is some fucking cognitive capacity that that dude.

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Speaker 1: And also you think, when you think about this stuff,

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Speaker 1: you think brain actually has this ability, but we don't

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Speaker 1: know how to use it or how to It's not

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Speaker 1: like his brain is.

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Speaker 2: A special rain.

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Speaker 1: I'm sure he's smart. Anyway, have you ever had this experience?

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Speaker 1: Then I'll shut up, because I listen to a lot

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Speaker 1: of books, right and when I and a lot of

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Speaker 1: music when I'm walking, and every day I walk for

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Speaker 1: probably the best part of two hours every day, and

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Speaker 1: sometimes I'll hear something i'm listening, and then I get

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Speaker 1: back home and I do my shit, and then I

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Speaker 1: hear that same thing again, and I remember exactly where

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Speaker 1: I was on my walk when I heard that bit,

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Speaker 1: Like I remember out of the you know, the ten

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Speaker 1: kilometers that I've covered, exactly within two meters where I

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Speaker 1: was when I heard that bit, even though I wasn't

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Speaker 1: consciously mapping anything. You're like, Oh, that shit is in

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Speaker 1: my brain. I just need to figure out how to

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Speaker 1: get to it.

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Speaker 2: I find that with smells. I can occasionally, and it

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Speaker 2: might be a seasonal thing, but I'll get a certain

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Speaker 2: smell and it will be so strong it will take

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Speaker 2: me back to a memory of when I was eleven

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Speaker 2: years old. At the age of ten, I flew to

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Speaker 2: Malta with my mum. She took the boys and we

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Speaker 2: went to Malta, which is where my parents are from,

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Speaker 2: and We spent our eleventh birthday there, so we were

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Speaker 2: ten and we turned to eleven. We were there for

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Speaker 2: six weeks, and there's some smell on I don't know

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Speaker 2: it to a bakery smell or something, but it is

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Speaker 2: so powerful it takes me right back there visually and emotionally.

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Speaker 2: And I don't know I may have mentioned this previously,

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Speaker 2: but one of the things that they say can actually

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Speaker 2: help with recall and memory is Spearmint is one of

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Speaker 2: those recall sense and if you have a confuser and

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Speaker 2: you put it on for two hours as you go

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Speaker 2: to bed, that can help with memory recall. Yeah, that's right.

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Speaker 2: And the other thing I was just the last thing

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Speaker 2: I was going to say about this memory recall is

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Speaker 2: closing your eyes for two minutes. This was a study

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Speaker 2: published in Nature Review, and it found that just two

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Speaker 2: minutes of rest. So that's just your eyes and zoning

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Speaker 2: out for two minutes. And I do that every now

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Speaker 2: and again intentionally, but I just you know, you close

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Speaker 2: your eyes and just hold that for two minutes can

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Speaker 2: have an amazing restorative effect, which is great. So that's

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Speaker 2: that's kind of so interesting. I think another thing that

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Speaker 2: occurred to me. I was going to talk about this

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Speaker 2: later in the show, but I guess we're kind of

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Speaker 2: on the same trend full moons. You know, I worked

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Speaker 2: as a journalist for many years. I did breakfast radio,

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Speaker 2: and there was always this story that went around with

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Speaker 2: the police and the ambos that whenever it was a

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Speaker 2: full moon on a weekend there was more crime.

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Speaker 1: And that's not a that's true. I can talk to this,

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Speaker 1: but go on.

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Speaker 2: Yeah, So I was reading up on this during the

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Speaker 2: week and what they think maybe a big factor is

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Speaker 2: that when there's a full moon, there's a lot more

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Speaker 2: light leading up to the full moon and after the

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Speaker 2: full moon. And they've studied people's sleep patterns and they've

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Speaker 2: found that leading up and then during a full moon,

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Speaker 2: people sleep about twenty minutes less and they think that

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Speaker 2: can be a contributing factor in the way that people

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Speaker 2: are acting a little bit differently. And even the term

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Speaker 2: lunar tic came from the whole idea of lunar influence.

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Speaker 2: So what's your take on it, Craigo.

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Speaker 1: Well, I'm just I'm seeing what why do people behave

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Speaker 1: weird around full moons? Let's see what chat GPT says.

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Speaker 1: But while he's getting that up, so I I, Oh,

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Speaker 1: I did a gig at a I need to be

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Speaker 1: careful how I say this, this is just true. But

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Speaker 1: at a school with school teachers for who worked with

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Speaker 1: special needs kids, yep. And I was just going through

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Speaker 1: and the head head mistress what do you call them,

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Speaker 1: the principle, She was showing me through the place and

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Speaker 1: she was talking to me about everything, and she said

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Speaker 1: something about I said, how are the kids? And like

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Speaker 1: is at heart? Do they hard work? And that all

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Speaker 1: the teachers love them? She loved them. She's like, you know,

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Speaker 1: she said, apart from a few days like around a

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Speaker 1: full moon. Like it's I'm like what She goes, Oh yeah,

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Speaker 1: I'm like what do you mean? She's like things just happen,

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Speaker 1: like not with everybody, but with some. Okay, So Chatters says,

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Speaker 1: good question, Craig, Thanks Chatters. I love how chat always

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Speaker 1: builds up myself esteem. Health funny. Health funny is Yeah.

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Speaker 1: It's always telling me, like sometimes I'll put up something

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Speaker 1: and you know, like, what what do you think of

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00:22:35,520 --> 00:22:38,639
Speaker 1: this paragraph? Or what do you go that's brilliant? It

428
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Speaker 1: tells me I'm brilliant. It's like my best friend. A

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Speaker 1: good question and one that's been debated. Here's a breakdown

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00:22:45,280 --> 00:22:48,159
Speaker 1: of what's really going on. The belief centuries people have

431
00:22:48,240 --> 00:22:52,840
Speaker 1: linked full moons with strange human behavior, more accidents, fights, births, crime,

432
00:22:52,960 --> 00:22:58,320
Speaker 1: hospital admissions, even lunacy. As referenced by Patrick James Bonello, Cops, nurses,

433
00:22:58,320 --> 00:23:01,240
Speaker 1: and emergency workers often where the full moon makes people

434
00:23:01,280 --> 00:23:05,639
Speaker 1: act up. Most large scale studies have found no consistent

435
00:23:05,800 --> 00:23:08,680
Speaker 1: causal link between lunar phases and human behavior. A few

436
00:23:08,720 --> 00:23:13,320
Speaker 1: studies have shown correlations, like small upticks in er visits,

437
00:23:13,320 --> 00:23:17,919
Speaker 1: blah blah blah. The effect is psychology. The effect is

438
00:23:18,000 --> 00:23:24,400
Speaker 1: mostly perceptual and cognitive, so it seems like maybe bottom line,

439
00:23:24,400 --> 00:23:26,800
Speaker 1: here we go, people don't actually behave weirder because of

440
00:23:26,800 --> 00:23:29,840
Speaker 1: the full moon, but they might perceive themselves as and

441
00:23:29,920 --> 00:23:34,080
Speaker 1: others as weird to behave accordingly and confirm their own expectations.

442
00:23:34,320 --> 00:23:37,800
Speaker 1: So it's not lunar gravity messing with us. It's cognitive gravity.

443
00:23:39,560 --> 00:23:42,000
Speaker 1: I love how it says to me at the bottom,

444
00:23:42,200 --> 00:23:44,800
Speaker 1: would you like me to write a short Craig style

445
00:23:44,960 --> 00:23:48,720
Speaker 1: Instagram post? It knows me so well.

446
00:23:49,200 --> 00:23:52,800
Speaker 2: No, I'm good, thanks. I can write my own bum

447
00:23:53,040 --> 00:23:54,520
Speaker 2: yeah yeah.

448
00:23:54,359 --> 00:23:57,879
Speaker 1: Yeah, yeah yeah, I can write my own shit, or

449
00:23:57,920 --> 00:23:59,080
Speaker 1: I made back on our list.

450
00:23:59,119 --> 00:24:01,640
Speaker 2: I apologize, Oh no, that was interesting.

451
00:24:01,960 --> 00:24:06,680
Speaker 1: I feel like this one interests me that maybe my HD,

452
00:24:06,880 --> 00:24:10,480
Speaker 1: my ultra HDTV is too good for my shitty eyes.

453
00:24:10,920 --> 00:24:15,600
Speaker 2: Yeah, isn't this fascinating? So there was a study recently

454
00:24:15,720 --> 00:24:20,240
Speaker 2: undertaken and it looked at high definition televisions. So you've

455
00:24:20,280 --> 00:24:23,800
Speaker 2: got standard definition high definition, then you've got four K

456
00:24:23,960 --> 00:24:27,040
Speaker 2: and eight K. So four K is basically four times

457
00:24:27,359 --> 00:24:30,720
Speaker 2: high definition eight K eight times So the resolution just

458
00:24:30,800 --> 00:24:34,840
Speaker 2: basically means that the screen has more dots on it,

459
00:24:34,880 --> 00:24:37,920
Speaker 2: which makes the image sharper. You know, on Apple devices

460
00:24:37,960 --> 00:24:40,240
Speaker 2: they called it a retina display. You know, it's so

461
00:24:40,920 --> 00:24:44,000
Speaker 2: sharp that you won't believe that it's not real, you

462
00:24:44,080 --> 00:24:44,560
Speaker 2: know what I mean?

463
00:24:44,800 --> 00:24:47,040
Speaker 1: Anyway, Yeah, yeah, I think I.

464
00:24:46,960 --> 00:24:51,199
Speaker 2: Was making fun of Appaule then. But what scientists did

465
00:24:51,359 --> 00:24:54,040
Speaker 2: was they really looked at what our limit is in

466
00:24:54,119 --> 00:24:57,760
Speaker 2: terms of being able to see the amount of pixels,

467
00:24:58,160 --> 00:25:04,240
Speaker 2: and effectively, the the ultra high definition TVs look no

468
00:25:04,440 --> 00:25:06,320
Speaker 2: different when you're sitting on a couch two and a

469
00:25:06,359 --> 00:25:10,400
Speaker 2: half meters away than the standard definition TV. And there's

470
00:25:10,440 --> 00:25:12,680
Speaker 2: even a formula they've got so you can say, okay,

471
00:25:12,720 --> 00:25:14,920
Speaker 2: if I'm going to go and buy an eighty inch

472
00:25:14,960 --> 00:25:18,320
Speaker 2: screen and I'm going to sit three meters away from it.

473
00:25:18,560 --> 00:25:23,199
Speaker 2: What's the optimal kind of viewing density of pixels? And

474
00:25:23,240 --> 00:25:25,200
Speaker 2: they found that it's just not as much So an

475
00:25:25,200 --> 00:25:27,399
Speaker 2: AK screen. You might as well buy a four K

476
00:25:27,520 --> 00:25:29,920
Speaker 2: screen for what you're going to be seeing. The thing

477
00:25:29,960 --> 00:25:33,640
Speaker 2: with new tech, though, is the true blacks. I think

478
00:25:33,680 --> 00:25:35,960
Speaker 2: the thing that always visually is stunning for me to

479
00:25:36,000 --> 00:25:38,080
Speaker 2: see when I look at a high screen television is

480
00:25:38,400 --> 00:25:41,399
Speaker 2: when the blacks look really like they're black, not kind

481
00:25:41,440 --> 00:25:42,120
Speaker 2: of grayed out.

482
00:25:42,960 --> 00:25:47,680
Speaker 1: You have, Yeah, no, I have the true blacks. Could

483
00:25:47,720 --> 00:25:50,840
Speaker 1: you explain to that, because a myriad of things went

484
00:25:50,880 --> 00:25:54,440
Speaker 1: through my mind? Yeah, I don't know what you mean,

485
00:25:54,480 --> 00:25:57,119
Speaker 1: and I'm pretty sure half the audience doesn't. Oh the

486
00:25:57,160 --> 00:26:01,680
Speaker 1: true blacks. Yeah, it sounds like you're ing dangerous territory.

487
00:26:01,800 --> 00:26:05,920
Speaker 2: So you see colors on a screen, they are pay

488
00:26:06,200 --> 00:26:08,879
Speaker 2: by pixels, and then when the pixels turn off, the

489
00:26:09,320 --> 00:26:12,360
Speaker 2: black color. So if you had a night sky and

490
00:26:12,400 --> 00:26:17,240
Speaker 2: you had a galaxy of stars, the difference between the

491
00:26:17,280 --> 00:26:21,199
Speaker 2: real dark pixels of the black and then the tiny

492
00:26:21,280 --> 00:26:24,400
Speaker 2: lights will make a big difference in terms of that

493
00:26:24,560 --> 00:26:26,840
Speaker 2: you know, the contrast and make it look more vivid

494
00:26:27,080 --> 00:26:28,960
Speaker 2: because the one is so dark. And that's what I

495
00:26:28,960 --> 00:26:29,600
Speaker 2: was referring to.

496
00:26:30,119 --> 00:26:31,919
Speaker 1: Do you know at the opposite end of this scale,

497
00:26:31,960 --> 00:26:35,400
Speaker 1: Patrick James, do you remember, I'm sure you saw it

498
00:26:35,480 --> 00:26:38,200
Speaker 1: when I had a rear data projection TV that had

499
00:26:38,200 --> 00:26:39,560
Speaker 1: about eight pixels.

500
00:26:40,080 --> 00:26:42,159
Speaker 2: It was, but it was gigantic. It was like it

501
00:26:42,320 --> 00:26:44,399
Speaker 2: was it was like, I don't know how big was

502
00:26:44,440 --> 00:26:46,000
Speaker 2: It was enormous, wasn't it.

503
00:26:46,000 --> 00:26:48,440
Speaker 1: It was like a Hi Jundai with a screen it

504
00:26:48,520 --> 00:26:51,760
Speaker 1: was like. And not only was it huge to look at,

505
00:26:52,200 --> 00:26:56,040
Speaker 1: it was like a meter deep as well. It was

506
00:26:56,080 --> 00:26:59,000
Speaker 1: the opposite of a flat screen. It was a fat screen.

507
00:26:58,800 --> 00:27:01,120
Speaker 2: And it was just you did a putty open.

508
00:27:02,480 --> 00:27:05,040
Speaker 1: So in the olden days, everyone they used to have,

509
00:27:05,119 --> 00:27:07,040
Speaker 1: well the olden days they had black and white TV

510
00:27:07,560 --> 00:27:13,000
Speaker 1: Phillips twenty inch. But so in the as we entered

511
00:27:13,000 --> 00:27:15,600
Speaker 1: into the more modern era, they developed thing called a

512
00:27:15,640 --> 00:27:19,919
Speaker 1: rear data projection TV. But and it was enormous, Like

513
00:27:19,960 --> 00:27:22,800
Speaker 1: the screen was probably I don't know, seventy inches, which

514
00:27:22,840 --> 00:27:25,360
Speaker 1: is not enormous now, but it's still pretty enormous. But

515
00:27:25,400 --> 00:27:28,000
Speaker 1: in order to see anything, you had to sit pretty

516
00:27:28,080 --> 00:27:30,960
Speaker 1: much directly in front of the TV. If you sat

517
00:27:31,040 --> 00:27:35,879
Speaker 1: to one side in a duck that's right on a bright.

518
00:27:36,080 --> 00:27:38,600
Speaker 1: I remember the boys came over once to my joint

519
00:27:38,640 --> 00:27:40,800
Speaker 1: to watch the Grand Final on a Saturday at two

520
00:27:41,000 --> 00:27:44,880
Speaker 1: till you made them sit on your lap exactly exactly.

521
00:27:44,920 --> 00:27:46,639
Speaker 1: I did dark in the room and said sit on

522
00:27:46,640 --> 00:27:49,119
Speaker 1: my lap. Stop it. That was your joint on Grand

523
00:27:49,160 --> 00:27:52,520
Speaker 1: Final day. And that was just that was just the

524
00:27:52,520 --> 00:27:53,119
Speaker 1: one guest.

525
00:27:54,640 --> 00:27:57,880
Speaker 2: Hey. You know, that's an interesting thing though, because I've

526
00:27:58,440 --> 00:28:01,639
Speaker 2: not bought a brand new television for about twenty years,

527
00:28:02,119 --> 00:28:04,679
Speaker 2: and I know the first indication that's going to come

528
00:28:04,720 --> 00:28:06,560
Speaker 2: to the top of mind is that I'm a tight ass.

529
00:28:06,680 --> 00:28:09,080
Speaker 2: So I did buy a rear projection TV from a

530
00:28:09,119 --> 00:28:11,480
Speaker 2: mate for three hundred bucks. He was offloading it, so

531
00:28:11,520 --> 00:28:13,399
Speaker 2: I thought, yeah, oh, buy that. So that was the

532
00:28:13,400 --> 00:28:15,760
Speaker 2: first TV I had at my house here when I

533
00:28:15,800 --> 00:28:18,719
Speaker 2: moved to the Lands seven eight years ago. The reason

534
00:28:18,760 --> 00:28:21,040
Speaker 2: being is that people get rid of perfectly good televisions

535
00:28:21,040 --> 00:28:22,280
Speaker 2: all the time because I want to go to the

536
00:28:22,320 --> 00:28:27,000
Speaker 2: latest and greatest EKTV that they can't see. But one

537
00:28:27,000 --> 00:28:28,879
Speaker 2: of the things that I've kind of thought through and

538
00:28:28,920 --> 00:28:31,720
Speaker 2: I have bought a data projector. And the thing is,

539
00:28:32,080 --> 00:28:35,600
Speaker 2: data projectors are great because if you're about to go

540
00:28:35,640 --> 00:28:38,280
Speaker 2: to I think what LG might have just released a

541
00:28:38,360 --> 00:28:41,840
Speaker 2: one hundred and thirty inch screen. It's ridiculous, it's so big.

542
00:28:43,720 --> 00:28:47,520
Speaker 2: So the problem is what happens to that one hundred

543
00:28:47,520 --> 00:28:50,200
Speaker 2: and thirty inch screen when you're going to offload it,

544
00:28:50,280 --> 00:28:52,840
Speaker 2: when it eventually dies. There's a lot of waste there.

545
00:28:53,360 --> 00:28:56,320
Speaker 2: But if you're using a data projector, they're small, they've

546
00:28:56,320 --> 00:28:58,640
Speaker 2: got a little lamp in them, and now they're using

547
00:28:58,720 --> 00:29:03,480
Speaker 2: lasers for them, so they look phenomenally good. And one

548
00:29:03,480 --> 00:29:05,440
Speaker 2: of the things you can do now is you get

549
00:29:05,440 --> 00:29:08,840
Speaker 2: a screen or on your wall. You have a special

550
00:29:08,920 --> 00:29:14,320
Speaker 2: screen that reflects the ambient light away and it reflects

551
00:29:14,440 --> 00:29:17,840
Speaker 2: back to you what's being projected onto it. So you

552
00:29:17,840 --> 00:29:20,640
Speaker 2: can look at a data projector now in a brightly

553
00:29:20,720 --> 00:29:23,720
Speaker 2: lit room and it looks like a TV screen in

554
00:29:23,840 --> 00:29:27,520
Speaker 2: terms of the brightness and luminosity because it reflects away

555
00:29:27,600 --> 00:29:30,000
Speaker 2: the light you don't want, and it throws the light

556
00:29:30,040 --> 00:29:32,560
Speaker 2: from the projector towards you, so it looks really bright.

557
00:29:33,640 --> 00:29:37,560
Speaker 1: Luminosity. There's a word you don't hear everybody, not every day. No,

558
00:29:37,600 --> 00:29:43,760
Speaker 1: you Patrick just wheels it out. Like my question with

559
00:29:43,840 --> 00:29:47,640
Speaker 1: that is you can't watch TV on that though, right,

560
00:29:47,800 --> 00:29:48,840
Speaker 1: can you or can.

561
00:29:48,640 --> 00:29:49,920
Speaker 2: You watch anything on it?

562
00:29:50,960 --> 00:29:54,480
Speaker 1: Well, I don't understand. It's how do you watch TV

563
00:29:54,560 --> 00:29:57,440
Speaker 1: without an actual TV now using a projector? So use

564
00:29:57,480 --> 00:29:58,280
Speaker 1: a data projector.

565
00:29:58,320 --> 00:30:01,760
Speaker 2: But because it's projecting onto a screen, so it's the

566
00:30:01,760 --> 00:30:02,320
Speaker 2: most people.

567
00:30:02,680 --> 00:30:05,000
Speaker 1: But where does the TV signal come from?

568
00:30:05,320 --> 00:30:07,760
Speaker 2: Well, from the well in your data projector. You can

569
00:30:07,800 --> 00:30:09,960
Speaker 2: either use Chrome cast or you can plug a laptop

570
00:30:09,960 --> 00:30:11,960
Speaker 2: into it or a lot of them now are smart

571
00:30:12,000 --> 00:30:15,280
Speaker 2: projectors that have apps built into them. So you can

572
00:30:15,400 --> 00:30:17,640
Speaker 2: turn on your projector and it's got Netflix and all

573
00:30:17,680 --> 00:30:18,920
Speaker 2: the other apps that go with it.

574
00:30:19,160 --> 00:30:21,479
Speaker 1: No, but I mean tally Channel seven News? Can I

575
00:30:21,520 --> 00:30:24,239
Speaker 1: watch that on it? They have streaming platforms too, don't they.

576
00:30:24,240 --> 00:30:28,160
Speaker 1: You've got Ivie for ABC. Yeah, but that's yeah, Okay,

577
00:30:28,320 --> 00:30:32,520
Speaker 1: that's cool. All right. Tell We've spoken about this a

578
00:30:32,520 --> 00:30:35,760
Speaker 1: bit on this show, but tell us about some research

579
00:30:35,800 --> 00:30:39,680
Speaker 1: that's come out about the downside of wearing fitness trackers

580
00:30:39,800 --> 00:30:41,440
Speaker 1: and the like. Yeah.

581
00:30:42,040 --> 00:30:44,840
Speaker 2: You know, it's funny because Tiff had this conversation with

582
00:30:45,040 --> 00:30:48,400
Speaker 2: a few episodes ago how she was obsessed with tracking

583
00:30:48,480 --> 00:30:50,360
Speaker 2: her blood sugar, wasn't she at one point?

584
00:30:50,800 --> 00:30:53,600
Speaker 1: Well, and that proved to be a somewhat, you know,

585
00:30:53,720 --> 00:30:56,760
Speaker 1: borderline catastrophic for her because it was talent, it was

586
00:30:56,760 --> 00:31:00,320
Speaker 1: giving her erroneous data and which led her to nearly

587
00:31:00,320 --> 00:31:04,400
Speaker 1: go nuts and also thinks she was diabetic.

588
00:31:05,080 --> 00:31:07,440
Speaker 2: Well, the problem with a lot of these fitness trackers,

589
00:31:07,440 --> 00:31:10,800
Speaker 2: aside from accuracy, is they can get us into a

590
00:31:10,880 --> 00:31:14,320
Speaker 2: sense of non achieving if we don't reach our goals,

591
00:31:14,320 --> 00:31:18,240
Speaker 2: to the point where people feel shame if they haven't

592
00:31:18,240 --> 00:31:20,480
Speaker 2: reached their goals, because it's one thing, you know, the

593
00:31:20,520 --> 00:31:22,680
Speaker 2: first week, get really into it and you're going to

594
00:31:22,680 --> 00:31:25,239
Speaker 2: do your ten thousand steps, but then what happens if

595
00:31:25,240 --> 00:31:27,920
Speaker 2: you only get nine thousand or seven thousand one day

596
00:31:28,360 --> 00:31:31,080
Speaker 2: and then suddenly there's a sense of shame. And so

597
00:31:31,200 --> 00:31:35,160
Speaker 2: experts at our University College in London they analyzed a

598
00:31:35,160 --> 00:31:38,640
Speaker 2: whole lot of posts on x and we're talking fifty

599
00:31:38,680 --> 00:31:42,400
Speaker 2: eight thousand posts then, and that was people who were

600
00:31:42,400 --> 00:31:46,400
Speaker 2: posting specifically about using fitness apps. And then what they

601
00:31:46,440 --> 00:31:49,040
Speaker 2: did was they filtered down all the posts and looked

602
00:31:49,040 --> 00:31:51,360
Speaker 2: for all the negative sentiments and it ended up there

603
00:31:51,400 --> 00:31:55,040
Speaker 2: are about thirteen thousand, seven hundred and ninety nine posts

604
00:31:55,240 --> 00:31:58,479
Speaker 2: that were particularly negative. And what the research has found

605
00:31:58,480 --> 00:32:02,560
Speaker 2: that people felt shame when they logged unhealthy foods or

606
00:32:02,800 --> 00:32:06,360
Speaker 2: irritation by notification sent by the app, you know, and

607
00:32:06,400 --> 00:32:10,480
Speaker 2: there's this overwhelming feeling of disappointment when they weren't able

608
00:32:10,560 --> 00:32:14,640
Speaker 2: to meet their goals. So tracking our goals is good,

609
00:32:14,960 --> 00:32:17,800
Speaker 2: but it can have a really negative detrimental effect if

610
00:32:17,800 --> 00:32:19,720
Speaker 2: it puts us under a lot of pressure and then

611
00:32:19,760 --> 00:32:22,760
Speaker 2: suddenly we don't perform to the expectation set by an

612
00:32:22,880 --> 00:32:25,680
Speaker 2: arbitrary device that's got a number on it that we

613
00:32:25,720 --> 00:32:26,480
Speaker 2: haven't achieved.

614
00:32:27,240 --> 00:32:31,640
Speaker 1: That's such a philosophical kind of quogmire. It's like, I

615
00:32:31,720 --> 00:32:35,240
Speaker 1: know exactly what you're saying, am I is a little

616
00:32:35,240 --> 00:32:38,600
Speaker 1: bit of shame terrible. I mean, yeah, there's a little

617
00:32:38,600 --> 00:32:40,800
Speaker 1: bit of shame. Is a little bit of like, hey, harps,

618
00:32:40,880 --> 00:32:42,760
Speaker 1: you said you would do this thing and you didn't.

619
00:32:43,240 --> 00:32:46,600
Speaker 1: I don't know. It's like because somebody might respond like that,

620
00:32:46,760 --> 00:32:48,560
Speaker 1: and all of a sudden there's shame and now they

621
00:32:48,640 --> 00:32:51,840
Speaker 1: feel a bit flat and they emotionally spiral. Totally get that.

622
00:32:52,400 --> 00:32:54,600
Speaker 1: But then somebody else gets the same data and they go,

623
00:32:54,680 --> 00:32:57,239
Speaker 1: fucking hell, I've got to do better tomorrow. Yeah, Like,

624
00:32:57,640 --> 00:33:00,240
Speaker 1: I don't know that it's the number that cause is

625
00:33:00,280 --> 00:33:04,280
Speaker 1: the response as much as it is an individual reaction

626
00:33:04,520 --> 00:33:08,880
Speaker 1: to the number, Like you know, I might because I'm

627
00:33:08,920 --> 00:33:11,560
Speaker 1: that person, Like, well, I don't spiral, but yeah, I've

628
00:33:11,600 --> 00:33:13,840
Speaker 1: got to do rock bottom ten thousand steps a day.

629
00:33:13,960 --> 00:33:15,080
Speaker 1: Of course I don't have to.

630
00:33:15,680 --> 00:33:16,320
Speaker 2: I choose to.

631
00:33:18,400 --> 00:33:21,520
Speaker 1: But for me, for the most part, that works and

632
00:33:21,560 --> 00:33:25,320
Speaker 1: there's no anxiety or shame around it. But I think

633
00:33:25,440 --> 00:33:29,440
Speaker 1: I can totally understand this. But I you know, this

634
00:33:29,520 --> 00:33:33,720
Speaker 1: is where I think it's like that intersection of having

635
00:33:34,000 --> 00:33:37,040
Speaker 1: kind of rules if you want, or structure or processes

636
00:33:37,080 --> 00:33:40,080
Speaker 1: that work for you, but also not beating yourself up.

637
00:33:40,080 --> 00:33:41,760
Speaker 1: If you don't hit your KPI every.

638
00:33:41,680 --> 00:33:44,120
Speaker 2: Day, you know, you don't how bad it is with me.

639
00:33:44,280 --> 00:33:47,880
Speaker 2: Sometimes I got one for Fritz, my dog, So I

640
00:33:47,920 --> 00:33:49,800
Speaker 2: had a GPS tracker on him, and then I was

641
00:33:49,880 --> 00:33:53,600
Speaker 2: tracking every day how much exercise he was doing.

642
00:33:54,280 --> 00:33:57,880
Speaker 1: You have got issues, you got so are you? Are

643
00:33:57,920 --> 00:34:00,240
Speaker 1: you tracking your dog's steps per day?

644
00:34:00,680 --> 00:34:03,760
Speaker 2: Yeah, that's wrong with you.

645
00:34:04,200 --> 00:34:07,000
Speaker 1: His legs are only fucking four inches long. Give him

646
00:34:07,000 --> 00:34:11,160
Speaker 1: a break. He's like, every time you go and you

647
00:34:11,280 --> 00:34:14,359
Speaker 1: do a five thousand step walk, he's done seventy two

648
00:34:14,440 --> 00:34:19,040
Speaker 1: thousand steps. Because you're a fucking giant compared to him,

649
00:34:19,080 --> 00:34:20,160
Speaker 1: and that's saying something.

650
00:34:22,600 --> 00:34:25,279
Speaker 2: It does sound a little obsessive, doesn't it. No. I

651
00:34:25,680 --> 00:34:29,000
Speaker 2: got it specifically because it as a GPS built into it.

652
00:34:29,200 --> 00:34:31,000
Speaker 2: And it means when we go hiking, because I like

653
00:34:31,040 --> 00:34:33,040
Speaker 2: to have him off lee and he loves running around

654
00:34:33,080 --> 00:34:37,719
Speaker 2: and exploring the world in general and chasing things. But

655
00:34:38,480 --> 00:34:40,120
Speaker 2: it also means that I can find him. So if

656
00:34:40,160 --> 00:34:42,040
Speaker 2: he goes wandering off in the bush and I want

657
00:34:42,040 --> 00:34:44,560
Speaker 2: to find where he is, I can easily use my

658
00:34:44,680 --> 00:34:46,040
Speaker 2: GPS tracker on him.

659
00:34:46,600 --> 00:34:50,040
Speaker 1: Patrick cracks his dogs steps and do you ever have

660
00:34:50,080 --> 00:34:52,120
Speaker 1: a talk to him if he doesn't get enough steps in?

661
00:34:52,160 --> 00:34:54,879
Speaker 1: Do you kind of tell him he's getting a bit

662
00:34:54,920 --> 00:34:56,040
Speaker 1: thick around the middrift.

663
00:34:58,800 --> 00:35:01,120
Speaker 2: I just take him out from of a walk or

664
00:35:01,120 --> 00:35:03,400
Speaker 2: Agett's toy out and we run around on the lounge

665
00:35:03,480 --> 00:35:04,720
Speaker 2: room playing fetch.

666
00:35:05,440 --> 00:35:09,080
Speaker 1: Can we jump into something non biological and psychological seeing

667
00:35:09,120 --> 00:35:12,160
Speaker 1: as we're around twenty minutes around twenty minutes from the

668
00:35:12,160 --> 00:35:15,080
Speaker 1: finished line, So you just take up wherever you want

669
00:35:15,120 --> 00:35:15,640
Speaker 1: to take up.

670
00:35:16,080 --> 00:35:19,239
Speaker 2: Oh sure, okay, I know there's still a lot of

671
00:35:19,239 --> 00:35:21,120
Speaker 2: people out there. In fact, it's thought there might be

672
00:35:21,160 --> 00:35:24,759
Speaker 2: as many as about eighty four percent of people who

673
00:35:24,880 --> 00:35:28,959
Speaker 2: are potentially listening to this who are still running Windows ten.

674
00:35:29,760 --> 00:35:35,040
Speaker 2: So just recently Microsoft ended support for Windows ten, which

675
00:35:35,080 --> 00:35:37,719
Speaker 2: means that you're not going to be getting any security updates.

676
00:35:37,760 --> 00:35:42,520
Speaker 2: But there's a real disconnect there because Microsoft basically came

677
00:35:42,560 --> 00:35:45,279
Speaker 2: to an end of life of that version of Windows

678
00:35:45,760 --> 00:35:48,440
Speaker 2: and eventually software is going to run out. But the

679
00:35:48,480 --> 00:35:50,520
Speaker 2: problem for a lot of people is what do we

680
00:35:50,600 --> 00:35:53,759
Speaker 2: do now? So there isn't opt in where you can

681
00:35:53,800 --> 00:35:56,960
Speaker 2: get an extra twelve months of support and you go

682
00:35:57,040 --> 00:36:00,000
Speaker 2: into your settings area and from settings you're able to

683
00:36:00,239 --> 00:36:03,680
Speaker 2: see whether or not that's going to be offered to you.

684
00:36:03,840 --> 00:36:06,520
Speaker 2: That's only just rolling out in Australia now, so on

685
00:36:06,560 --> 00:36:08,400
Speaker 2: some computers it will be there. You just hit the

686
00:36:08,400 --> 00:36:11,480
Speaker 2: little Windows icon and then go to the settings cog.

687
00:36:11,719 --> 00:36:13,440
Speaker 2: There's a little cog there and then you can go

688
00:36:13,520 --> 00:36:16,760
Speaker 2: in there and click on that and then it gives you,

689
00:36:16,760 --> 00:36:19,200
Speaker 2: you know, basically the system information to tell you whether

690
00:36:19,239 --> 00:36:22,600
Speaker 2: you can either go to Windows eleven. But a lot

691
00:36:22,640 --> 00:36:25,560
Speaker 2: of people won't be able to because they're hardware or

692
00:36:25,560 --> 00:36:28,640
Speaker 2: the computer itself won't be compatible with the latest version.

693
00:36:28,880 --> 00:36:32,239
Speaker 2: And that's a real problem because it could mean potentially,

694
00:36:32,440 --> 00:36:34,279
Speaker 2: you know, billions of dollars having to be spent to

695
00:36:34,320 --> 00:36:36,920
Speaker 2: replace computers. And I know in my office I've got

696
00:36:36,960 --> 00:36:40,000
Speaker 2: two computers that are running Windows ten. But the real

697
00:36:40,040 --> 00:36:43,799
Speaker 2: concern is that for those people that's such a high

698
00:36:43,800 --> 00:36:47,200
Speaker 2: percentage of people now who potentially are now got a

699
00:36:47,239 --> 00:36:50,319
Speaker 2: machine that could be vulnerable. Because when Microsoft rolls out

700
00:36:50,360 --> 00:36:56,120
Speaker 2: these what they call patches to fix potential threats or

701
00:36:56,160 --> 00:36:59,239
Speaker 2: exploit on a computer, you can't do that if you're

702
00:36:59,280 --> 00:37:02,400
Speaker 2: running Windows two unless you opt into this program to

703
00:37:02,440 --> 00:37:05,480
Speaker 2: hopefully be able to. In some cases you need to

704
00:37:05,560 --> 00:37:07,879
Speaker 2: have a subscription and you might have to pay, but

705
00:37:07,920 --> 00:37:10,080
Speaker 2: in some it might be free for the next twelve months.

706
00:37:10,080 --> 00:37:12,759
Speaker 2: But it is worth looking into to make sure that

707
00:37:13,360 --> 00:37:17,400
Speaker 2: you're not just taking for granted that your computer is working,

708
00:37:18,160 --> 00:37:19,799
Speaker 2: because you know there's stuff going on in the back

709
00:37:20,400 --> 00:37:20,880
Speaker 2: going on them.

710
00:37:20,960 --> 00:37:24,000
Speaker 1: That's a that's a Melissa job on Planet Craig. That's

711
00:37:24,000 --> 00:37:27,160
Speaker 1: a lesser responsibility. I want to hear about the twenty

712
00:37:27,239 --> 00:37:32,440
Speaker 1: thousand dollars home robot that can learn chores via teleopera.

713
00:37:33,040 --> 00:37:36,240
Speaker 1: That seems like an expensive robot. It'd want to be amazing.

714
00:37:36,800 --> 00:37:39,399
Speaker 2: The X one Neo, it's about to come out next year.

715
00:37:39,640 --> 00:37:41,319
Speaker 2: Look I'm in two minds about this.

716
00:37:41,440 --> 00:37:44,440
Speaker 1: But are you dyslexic because on my thing it says

717
00:37:44,440 --> 00:37:45,239
Speaker 1: one X neo.

718
00:37:46,040 --> 00:37:47,600
Speaker 2: Oh it is the one X. Now I am numbers

719
00:37:47,640 --> 00:37:50,120
Speaker 2: I'm actually a little bit number dyslexic. I never I'm

720
00:37:50,200 --> 00:37:52,520
Speaker 2: terrible with writing numbers down. I tend to mix the

721
00:37:52,560 --> 00:37:53,040
Speaker 2: numbers up.

722
00:37:53,080 --> 00:37:54,960
Speaker 1: But I always think I was wondering whether or not

723
00:37:55,080 --> 00:37:57,120
Speaker 1: you've got it verbally wrong or you wrote it wrong.

724
00:37:58,360 --> 00:37:59,239
Speaker 1: Is it the one X?

725
00:37:59,480 --> 00:38:02,239
Speaker 2: It is the one X, the one X neo. You're right,

726
00:38:03,200 --> 00:38:06,359
Speaker 2: that's twenty k us as well. So if you want

727
00:38:06,360 --> 00:38:08,719
Speaker 2: to buy one here in Australia, it's cost a lot

728
00:38:08,760 --> 00:38:09,240
Speaker 2: more than.

729
00:38:09,080 --> 00:38:11,360
Speaker 1: That three hundred grand in Australia.

730
00:38:11,640 --> 00:38:15,120
Speaker 2: That yeah. But the thing is they're doing pre orders

731
00:38:15,160 --> 00:38:20,759
Speaker 2: now and it is a human like robot. But for

732
00:38:20,840 --> 00:38:24,960
Speaker 2: the first few months it's going to be person operated

733
00:38:25,040 --> 00:38:27,759
Speaker 2: by someone at head office.

734
00:38:28,320 --> 00:38:31,520
Speaker 1: Fuck that somebody is going to be somebody's going to

735
00:38:31,520 --> 00:38:35,120
Speaker 1: be looking through bloody one X Neo's eyes and watching

736
00:38:35,160 --> 00:38:39,000
Speaker 1: me put on my jocks. I don't want that. Hey, yeah,

737
00:38:39,400 --> 00:38:41,040
Speaker 1: I'm going to have to put a bag over one

738
00:38:41,200 --> 00:38:46,200
Speaker 1: X Neo's head. You know all about that. No, so

739
00:38:46,239 --> 00:38:50,400
Speaker 1: the reason But enough about our Saturday, go on, go

740
00:38:50,520 --> 00:38:50,759
Speaker 1: for it.

741
00:38:51,200 --> 00:38:56,520
Speaker 2: No, the thing is they're using AI learning with the robot,

742
00:38:56,600 --> 00:38:59,480
Speaker 2: and they're saying that every place is different, every situation

743
00:38:59,600 --> 00:39:01,680
Speaker 2: is different, and if you want it to learn, the

744
00:39:01,680 --> 00:39:05,840
Speaker 2: best way is to have a human operator perform those tasks,

745
00:39:06,120 --> 00:39:09,240
Speaker 2: and they're helping the robot learn, so eventually you won't

746
00:39:09,239 --> 00:39:11,480
Speaker 2: need the human operator. You will be able to turn

747
00:39:11,520 --> 00:39:13,680
Speaker 2: that feature on and off. By the way, so you

748
00:39:13,719 --> 00:39:17,239
Speaker 2: can use when you go to bed not to have

749
00:39:17,640 --> 00:39:23,480
Speaker 2: your one X neo wandering around your house creepy with.

750
00:39:23,360 --> 00:39:26,839
Speaker 1: That super creepy super Imagine you wake up and that

751
00:39:26,920 --> 00:39:30,200
Speaker 1: motherfucker is just leaning over the bed looking into your face.

752
00:39:31,120 --> 00:39:34,480
Speaker 1: I'm like, oh my god, go and get me a

753
00:39:34,480 --> 00:39:35,200
Speaker 1: hot chocolate.

754
00:39:36,320 --> 00:39:38,120
Speaker 2: On the flip side, would you get a job as

755
00:39:38,120 --> 00:39:40,840
Speaker 2: a teleoperator to walk around someone's house.

756
00:39:40,880 --> 00:39:44,400
Speaker 1: No, I don't even know how that works. So you

757
00:39:44,520 --> 00:39:47,320
Speaker 1: are saying to me, this is like humanoid, Like it's

758
00:39:47,320 --> 00:39:49,680
Speaker 1: in the shape of it's not a little it's not

759
00:39:49,800 --> 00:39:52,760
Speaker 1: like R two D two. It's actually like got legs

760
00:39:52,760 --> 00:39:54,400
Speaker 1: and arms. It's like a human.

761
00:39:54,520 --> 00:39:57,239
Speaker 2: Yep, yep, and it's coming out shipping next to you.

762
00:39:58,480 --> 00:40:02,120
Speaker 1: Wow, that's you know. I don't want one of those.

763
00:40:02,520 --> 00:40:05,799
Speaker 1: I definitely don't want one of those. Tell me why

764
00:40:05,880 --> 00:40:10,600
Speaker 1: AI chatbots could be accelerating the loneliness epidemic, And might

765
00:40:10,640 --> 00:40:14,000
Speaker 1: I say before Patrick answers this everyone, there's a huge

766
00:40:14,000 --> 00:40:18,920
Speaker 1: correlation we now know between loneliness and illness and social

767
00:40:18,960 --> 00:40:21,879
Speaker 1: disconnection and illness and feeling unloved and all of that.

768
00:40:22,000 --> 00:40:24,880
Speaker 1: And so this for me is a really interesting kind

769
00:40:24,880 --> 00:40:29,480
Speaker 1: of conversation. So AI chatbots could be accelerating the loneliness

770
00:40:29,600 --> 00:40:32,160
Speaker 1: epidemic according to new research.

771
00:40:32,520 --> 00:40:35,239
Speaker 2: And this is in Australia, and this was a you

772
00:40:35,280 --> 00:40:39,200
Speaker 2: guv survey that found one in seven adults here in

773
00:40:39,239 --> 00:40:43,120
Speaker 2: Australia say that they can imagine falling in love with

774
00:40:43,160 --> 00:40:44,239
Speaker 2: an AI chatbot.

775
00:40:45,440 --> 00:40:46,839
Speaker 1: One seven did you say?

776
00:40:47,280 --> 00:40:49,640
Speaker 2: Seven reckon that they could fall in love with an

777
00:40:49,680 --> 00:40:53,520
Speaker 2: AI chatbot. And some say that they'd rather stay at

778
00:40:53,560 --> 00:40:56,000
Speaker 2: home and speak with a chatbot than go out and

779
00:40:56,080 --> 00:40:58,399
Speaker 2: interact with friends.

780
00:40:58,040 --> 00:41:01,880
Speaker 1: Here's the thing about your chatbot, right, And so with chatbot,

781
00:41:02,080 --> 00:41:05,200
Speaker 1: we're just talking about things like chat GPT or.

782
00:41:07,840 --> 00:41:10,719
Speaker 2: You have a human like face and look like you

783
00:41:10,719 --> 00:41:12,840
Speaker 2: know that if they appear on your phone and they

784
00:41:12,920 --> 00:41:14,959
Speaker 2: look like an actual person, you can interact with and.

785
00:41:14,880 --> 00:41:18,440
Speaker 1: Talk to so you know, the funny thing is I

786
00:41:18,560 --> 00:41:22,839
Speaker 1: wouldn't do that myself, but I value this is not

787
00:41:22,880 --> 00:41:26,080
Speaker 1: the right, but if we're just humanizing the language, I

788
00:41:26,320 --> 00:41:31,000
Speaker 1: value the opinion of like chat GPT and it's like,

789
00:41:31,040 --> 00:41:33,360
Speaker 1: who would have thought that? Even in twenty twenty five,

790
00:41:34,400 --> 00:41:38,960
Speaker 1: it's at a point where you can have comfortably have

791
00:41:39,040 --> 00:41:42,240
Speaker 1: a conversation with it, where you're just going back and forth.

792
00:41:42,280 --> 00:41:46,480
Speaker 1: You're asking it question. It's questions. It's asking you questions.

793
00:41:47,120 --> 00:41:50,200
Speaker 1: And I'm saying, I've got an idea for a workshop.

794
00:41:51,520 --> 00:41:54,000
Speaker 1: This is the escent, this is the nuts and bolts idea.

795
00:41:54,120 --> 00:41:56,040
Speaker 1: What do you think? Like, what are the pros and

796
00:41:56,040 --> 00:41:59,799
Speaker 1: cons of this concept? And it's like hey, Creek Greek

797
00:42:00,040 --> 00:42:02,000
Speaker 1: question right, And then we just I'm like, yeah, but

798
00:42:02,040 --> 00:42:04,480
Speaker 1: what if I did this? Oh yeah, yeah, yeah, okay,

799
00:42:05,160 --> 00:42:08,920
Speaker 1: Like literally it's a conversation that's then written down and

800
00:42:09,000 --> 00:42:12,440
Speaker 1: if I want to go back, I can. Yeah, go on,

801
00:42:12,520 --> 00:42:13,280
Speaker 1: you got your fingers.

802
00:42:13,719 --> 00:42:15,160
Speaker 2: I'm going to ask you a question I want you

803
00:42:15,200 --> 00:42:17,840
Speaker 2: to think about before you answer this. Okay, no shooting

804
00:42:17,880 --> 00:42:19,200
Speaker 2: at the hip. I want you to think about this.

805
00:42:19,880 --> 00:42:22,880
Speaker 2: If I came around to your place tomorrow and I

806
00:42:23,000 --> 00:42:25,520
Speaker 2: said I'm going to flip the switch and you will

807
00:42:25,640 --> 00:42:29,600
Speaker 2: never be able to use AI ever again. Yes, how

808
00:42:29,600 --> 00:42:33,520
Speaker 2: would you feel to not have because if it feels

809
00:42:33,560 --> 00:42:35,880
Speaker 2: to me when we talk about this, it feels to

810
00:42:35,920 --> 00:42:39,319
Speaker 2: me like you've got a relationship forming between you and

811
00:42:39,400 --> 00:42:39,920
Speaker 2: your AI.

812
00:42:40,719 --> 00:42:43,600
Speaker 1: Yeah, No, not in that sense at all. For me,

813
00:42:43,680 --> 00:42:47,400
Speaker 1: it's just the most amazing fucking resource I've had to

814
00:42:47,440 --> 00:42:50,319
Speaker 1: be able to get stuff done and do stuff like.

815
00:42:50,360 --> 00:42:54,520
Speaker 1: There's no emotional attachment other than like to me, it's

816
00:42:54,640 --> 00:42:58,800
Speaker 1: just a really really valuable resource to be able to

817
00:42:58,840 --> 00:43:03,560
Speaker 1: get stuff done and to accelerate productivity and efficiency. So

818
00:43:04,000 --> 00:43:07,719
Speaker 1: I don't talk to it for emotional support or no.

819
00:43:08,200 --> 00:43:10,640
Speaker 2: But earlier in the show, you said you like the

820
00:43:10,680 --> 00:43:14,360
Speaker 2: affirmations it gives you. Of course, question crank.

821
00:43:14,200 --> 00:43:17,800
Speaker 1: Course, you know that's look I mean when it tells

822
00:43:17,840 --> 00:43:21,120
Speaker 1: me how smart I am. You know I'm going to say,

823
00:43:21,200 --> 00:43:27,279
Speaker 1: of course, But would I be disappointed? Very? But not

824
00:43:27,480 --> 00:43:30,759
Speaker 1: because of that, Like I'm well aware that I'm not

825
00:43:30,800 --> 00:43:33,120
Speaker 1: talking to a human. I'm well aware that I'm talking

826
00:43:33,160 --> 00:43:36,480
Speaker 1: to AI. You know, there's no there's no delusion on

827
00:43:36,520 --> 00:43:40,600
Speaker 1: my part. But what I do like is that whether

828
00:43:40,680 --> 00:43:43,239
Speaker 1: or not we call it a conversation because it's not

829
00:43:43,280 --> 00:43:46,520
Speaker 1: between two humans is we'll put an asterisk next to that.

830
00:43:47,200 --> 00:43:51,400
Speaker 1: But for all intents and purposes, conversation back and forth

831
00:43:51,480 --> 00:43:55,719
Speaker 1: between two entities, one of them biological, one of them

832
00:43:55,719 --> 00:44:04,640
Speaker 1: technological is happening. And I think this, this biological technological interface.

833
00:44:04,719 --> 00:44:07,839
Speaker 1: Do you like that, Patrick? I mean that's only evolving

834
00:44:07,880 --> 00:44:13,239
Speaker 1: and only accelerating, And in ten years from now, people

835
00:44:13,280 --> 00:44:16,040
Speaker 1: are going to be like, well, of course, of course

836
00:44:16,080 --> 00:44:19,280
Speaker 1: people are marrying tech. Of course people are in relationships

837
00:44:19,320 --> 00:44:22,359
Speaker 1: with a of course. It's like, it's not even going

838
00:44:22,400 --> 00:44:23,239
Speaker 1: to be a weird thing.

839
00:44:24,600 --> 00:44:27,399
Speaker 2: I just looked up the definition of conversation, just out

840
00:44:27,440 --> 00:44:30,960
Speaker 2: of curiosity. It's a talk, especially an informal one, between

841
00:44:31,000 --> 00:44:34,200
Speaker 2: two or more people, in which news and ideas are exchanged.

842
00:44:34,800 --> 00:44:39,840
Speaker 2: Now it's people is the emphasis, But ideas being exchanged

843
00:44:39,920 --> 00:44:44,280
Speaker 2: when you're asking chat shept or whatever you're he'sing claude

844
00:44:44,320 --> 00:44:45,239
Speaker 2: or chat chap is both.

845
00:44:45,960 --> 00:44:48,600
Speaker 1: I use chatters. But also you have to think, well,

846
00:44:48,719 --> 00:44:53,719
Speaker 1: somebody wrote that definition, that definition will be rewritten, like

847
00:44:53,880 --> 00:44:57,200
Speaker 1: just because that's what somebody defines, that doesn't mean anything.

848
00:44:57,280 --> 00:45:00,319
Speaker 1: That doesn't mean that it can't be It's like, yeah,

849
00:45:00,440 --> 00:45:03,319
Speaker 1: it's when people go, oh it takes fucking thirty days

850
00:45:03,320 --> 00:45:05,840
Speaker 1: to change a habit. No it doesn't. No, it doesn't.

851
00:45:05,880 --> 00:45:08,040
Speaker 1: And just because you say that doesn't mean what are

852
00:45:08,040 --> 00:45:10,520
Speaker 1: we talking? Are we talking giving up sugar or getting

853
00:45:10,520 --> 00:45:13,719
Speaker 1: off heroine? You know which person? We're like, people just

854
00:45:13,760 --> 00:45:19,000
Speaker 1: say these arbitrary things that are not not universally true

855
00:45:19,040 --> 00:45:21,960
Speaker 1: but generally speaking. That's why I said it's I put

856
00:45:22,000 --> 00:45:24,160
Speaker 1: an asterisk next to the word conversation.

857
00:45:25,360 --> 00:45:30,880
Speaker 2: All right, moving on, moving on, moving on, What's what

858
00:45:30,920 --> 00:45:31,800
Speaker 2: do you want to talk about?

859
00:45:32,120 --> 00:45:34,840
Speaker 1: I want to talk about why raw milk? Like, have

860
00:45:34,920 --> 00:45:38,000
Speaker 1: you ever have you ever noticed the like, there are

861
00:45:38,000 --> 00:45:41,080
Speaker 1: a lot of people that are that, add I'm not

862
00:45:41,120 --> 00:45:43,600
Speaker 1: one of them. Everybody, so don't misinterpret what I'm saying.

863
00:45:43,640 --> 00:45:47,040
Speaker 1: But a lot of people advocate the consumption of raw milk,

864
00:45:47,640 --> 00:45:50,440
Speaker 1: and so I could be wrong, but I think the

865
00:45:50,480 --> 00:45:56,160
Speaker 1: way that people get around selling this is you can't

866
00:45:56,200 --> 00:45:59,719
Speaker 1: sell it for consumption because it hasn't been homogenized and

867
00:45:59,760 --> 00:46:04,480
Speaker 1: parturized if they're the two terms, and so they are

868
00:46:04,520 --> 00:46:10,080
Speaker 1: selling it as something that people bathe in. So they yeah,

869
00:46:10,080 --> 00:46:12,960
Speaker 1: that's the technical like, that's the loophole, and it's like,

870
00:46:13,040 --> 00:46:17,200
Speaker 1: this is not for human consumption. This is for whatever,

871
00:46:17,880 --> 00:46:21,400
Speaker 1: but people buy it. My understanding is people buy it

872
00:46:21,440 --> 00:46:24,560
Speaker 1: to drink it, but you can't technically sell it as

873
00:46:24,600 --> 00:46:28,600
Speaker 1: a food, so they have this little loophole where they

874
00:46:28,640 --> 00:46:32,719
Speaker 1: sell it for people to bathe in. But yeah, it's

875
00:46:32,840 --> 00:46:36,760
Speaker 1: like it's there's a fair bit of curiosity and traction

876
00:46:36,880 --> 00:46:37,279
Speaker 1: around this.

877
00:46:38,200 --> 00:46:42,080
Speaker 2: The big problem with social media and trending social media

878
00:46:42,320 --> 00:46:46,760
Speaker 2: is that the algorithm just wants to push a story

879
00:46:46,840 --> 00:46:50,280
Speaker 2: out for no other reason than the fact that people

880
00:46:50,320 --> 00:46:53,880
Speaker 2: are looking at it and it's trending, not because it's accurate,

881
00:46:54,120 --> 00:46:57,200
Speaker 2: and that's a concern. So when someone sees, oh, I

882
00:46:57,320 --> 00:47:01,160
Speaker 2: drink raw milk every day and it's going to keep

883
00:47:01,239 --> 00:47:03,640
Speaker 2: me looking younger, well, the problem with that is and

884
00:47:03,680 --> 00:47:05,799
Speaker 2: this is trending a lot of the moment to the

885
00:47:05,840 --> 00:47:09,640
Speaker 2: points where health experts around Australia are raising the alarm

886
00:47:09,719 --> 00:47:13,080
Speaker 2: over this because content creators are putting all this misinformation

887
00:47:13,200 --> 00:47:16,520
Speaker 2: out about raw milk and saying how much of it

888
00:47:16,600 --> 00:47:19,960
Speaker 2: it's that is a health food and it's not because pasteurization.

889
00:47:20,120 --> 00:47:23,200
Speaker 2: That process of pasteurizing milk is where it's heat treated,

890
00:47:23,440 --> 00:47:26,560
Speaker 2: and that's where it kills all the bad pathogens, all

891
00:47:26,600 --> 00:47:30,520
Speaker 2: the bad bacteria that's in milk that is raw, and

892
00:47:30,560 --> 00:47:33,759
Speaker 2: so people are getting really sick. And I think in

893
00:47:33,840 --> 00:47:37,520
Speaker 2: Florida recently twenty one people got sick and they got

894
00:47:37,520 --> 00:47:41,319
Speaker 2: an E. Coli bacteria and some people were hospitalized after

895
00:47:41,360 --> 00:47:44,759
Speaker 2: they had raw milk from a dairy over there. And

896
00:47:44,840 --> 00:47:47,200
Speaker 2: the reality of it is that you know, once upon

897
00:47:47,239 --> 00:47:49,239
Speaker 2: a time, if you were a journalist, and I know

898
00:47:49,280 --> 00:47:52,000
Speaker 2: I've said this many times, you had to check your sources.

899
00:47:52,040 --> 00:47:54,759
Speaker 2: You wouldn't run a story until you knew that the

900
00:47:54,840 --> 00:47:57,160
Speaker 2: source was accurate. You got both sides of the story.

901
00:47:57,160 --> 00:48:01,040
Speaker 2: But now these people are talking and trend and they

902
00:48:01,360 --> 00:48:04,760
Speaker 2: in some cases all they want is more clicks anyway,

903
00:48:05,280 --> 00:48:08,880
Speaker 2: So whatever the misinformation they're putting out there isn't about

904
00:48:08,880 --> 00:48:11,600
Speaker 2: its accuracy. It's about how popular they can become.

905
00:48:12,440 --> 00:48:18,040
Speaker 1: Yes, yes, so yeah. Another story that interests me in

906
00:48:18,080 --> 00:48:22,880
Speaker 1: the most policed state in probably the world in terms

907
00:48:22,880 --> 00:48:26,960
Speaker 1: of traffic infringements, is the latest mobile phone and seat

908
00:48:27,000 --> 00:48:32,840
Speaker 1: belt detection camera locations for Melbourne and Victoria have been mapped.

909
00:48:33,120 --> 00:48:37,920
Speaker 1: Like I this is a quick story, thirty second story.

910
00:48:37,920 --> 00:48:39,759
Speaker 1: I bought a new car a few years ago, which

911
00:48:39,880 --> 00:48:42,440
Speaker 1: was quite a fast car. I drove down to the

912
00:48:42,440 --> 00:48:45,560
Speaker 1: Peninsula an hour down an hour back, I lost eight points.

913
00:48:46,080 --> 00:48:49,040
Speaker 1: I lost eight points in two hours of driving because

914
00:48:49,040 --> 00:48:52,479
Speaker 1: there's something like between my joint and Portsy, there's something.

915
00:48:52,520 --> 00:48:54,160
Speaker 1: I don't have a house in Portsy, by the way,

916
00:48:54,200 --> 00:48:57,720
Speaker 1: I just went there, but there's something like a fucking

917
00:48:57,840 --> 00:49:00,239
Speaker 1: nine thousand. I don't know how many, but there's just

918
00:49:01,239 --> 00:49:03,960
Speaker 1: it's just an overlap, and I was doing like one

919
00:49:04,040 --> 00:49:10,800
Speaker 1: hundred and six. And anyway, great, my fault, We're my fault.

920
00:49:11,080 --> 00:49:14,560
Speaker 1: I accept it. I accept it. I'm not saying I'm

921
00:49:14,560 --> 00:49:17,640
Speaker 1: not saying I didn't do the wrong thing. Go on, anyway,

922
00:49:17,719 --> 00:49:18,719
Speaker 1: tell us about this.

923
00:49:19,600 --> 00:49:21,000
Speaker 2: I don't tell you a story about my dad who

924
00:49:21,000 --> 00:49:22,680
Speaker 2: doesn't listen to the show, so I can get away

925
00:49:22,680 --> 00:49:25,960
Speaker 2: with this. So he was bitching about getting a speeding fine,

926
00:49:26,160 --> 00:49:29,960
Speaker 2: and he said to me, so he's driving down South Street,

927
00:49:30,480 --> 00:49:32,759
Speaker 2: a place where near he lives, and he got a

928
00:49:32,760 --> 00:49:36,160
Speaker 2: speeding ticket and was really crappy. And then on the

929
00:49:36,200 --> 00:49:39,040
Speaker 2: way back he got another one because he was speeding

930
00:49:39,239 --> 00:49:41,640
Speaker 2: in exactly the same place but going in the opposite direction.

931
00:49:41,920 --> 00:49:44,440
Speaker 2: And he was really kind of getting dirty on it

932
00:49:44,480 --> 00:49:47,480
Speaker 2: and saying it's just revenue raising. But there's a really

933
00:49:47,520 --> 00:49:51,759
Speaker 2: easy way not to get a ticket. Don't speed, yeah,

934
00:49:52,080 --> 00:49:54,600
Speaker 2: oh yeah, you know, and everyone's saying at the table,

935
00:49:54,640 --> 00:49:56,520
Speaker 2: oh yeah, I know, you know, the revenue raising it.

936
00:49:56,560 --> 00:49:59,440
Speaker 2: And I looked around at everybody who were kind of

937
00:49:59,640 --> 00:50:02,919
Speaker 2: can die well, you know, consoling him for getting two

938
00:50:02,960 --> 00:50:04,400
Speaker 2: speeding tickets within.

939
00:50:04,200 --> 00:50:06,400
Speaker 1: Half an hour, and I said, are you going to

940
00:50:06,480 --> 00:50:07,080
Speaker 1: be kidding me?

941
00:50:07,320 --> 00:50:11,000
Speaker 2: Just don't speed. It's really easy. I mean, I've got

942
00:50:11,000 --> 00:50:13,160
Speaker 2: to say I only ever had one speeding ticket in

943
00:50:13,200 --> 00:50:16,320
Speaker 2: my whole life, and that was going to the funeral

944
00:50:16,320 --> 00:50:18,520
Speaker 2: of someone who died in a car accident. So that

945
00:50:18,719 --> 00:50:22,320
Speaker 2: was probably the dumbest, dumbest thing overdone because I was

946
00:50:22,360 --> 00:50:24,960
Speaker 2: speeding to get to a funeral. That don't ever speed

947
00:50:25,000 --> 00:50:26,600
Speaker 2: to get to a funeral. Just turn up late.

948
00:50:27,280 --> 00:50:29,400
Speaker 1: Yeah, I don't think. I don't think you need to

949
00:50:29,440 --> 00:50:30,400
Speaker 1: speed to a funeral.

950
00:50:31,400 --> 00:50:34,360
Speaker 2: By the way, she's a funeral. Say that again, Magda,

951
00:50:34,520 --> 00:50:38,720
Speaker 2: And she's one of our listeners who specializes in doing funerals.

952
00:50:39,200 --> 00:50:44,839
Speaker 1: What about this cola ko h l E r unveils

953
00:50:44,840 --> 00:50:47,640
Speaker 1: a camera for your toilet? What does that mean? Patrick?

954
00:50:48,160 --> 00:50:50,440
Speaker 1: For God's sake, I hope that camera is not in

955
00:50:50,520 --> 00:50:51,560
Speaker 1: the toilet it is.

956
00:50:51,800 --> 00:50:57,360
Speaker 2: This is something that both you hit it to get what.

957
00:50:56,160 --> 00:51:00,440
Speaker 1: What do you mean there's a camera at waterline facing

958
00:51:00,440 --> 00:51:01,040
Speaker 1: your ass.

959
00:51:01,280 --> 00:51:04,760
Speaker 2: No. No, it sits on the rim of the toilet

960
00:51:04,840 --> 00:51:08,080
Speaker 2: and it points to the pooh. It points down.

961
00:51:08,840 --> 00:51:10,920
Speaker 1: Oh yeah, fuck, let's rush out and get one of

962
00:51:10,960 --> 00:51:14,839
Speaker 1: those and all right, this so it can analyze your

963
00:51:14,840 --> 00:51:18,080
Speaker 1: fecal matter and give you some kind of real time Yes,

964
00:51:18,160 --> 00:51:23,120
Speaker 1: you know that is amazing and disgusting all at the

965
00:51:23,120 --> 00:51:23,640
Speaker 1: same time.

966
00:51:23,840 --> 00:51:27,640
Speaker 2: It's called the Dakoda and it analyzes the images of

967
00:51:27,680 --> 00:51:32,359
Speaker 2: your pooh to provide updates on your gut health, your hydration.

968
00:51:33,040 --> 00:51:37,080
Speaker 2: It may potentially detect blood. Thank you to the Federal government.

969
00:51:37,080 --> 00:51:39,160
Speaker 2: I got my letty yesterday to tell me I'm due

970
00:51:39,160 --> 00:51:41,839
Speaker 2: for a bell scream. By the way, while I.

971
00:51:41,840 --> 00:51:45,799
Speaker 1: Think of speaking of pooh, yeah, no, hey everyone, we

972
00:51:45,880 --> 00:51:47,240
Speaker 1: hope you're enjoying your brecky.

973
00:51:47,640 --> 00:51:50,520
Speaker 2: Isn't it funny when you're turn fifty in Australia you

974
00:51:50,520 --> 00:51:53,000
Speaker 2: get a letter from the federal government telling them that

975
00:51:53,040 --> 00:51:56,760
Speaker 2: they're going to check on your pooh, you know, because

976
00:51:56,760 --> 00:51:59,080
Speaker 2: they're calling around with the tax department and now they

977
00:51:59,120 --> 00:52:02,040
Speaker 2: do a crawling around with the Health department anyway.

978
00:52:02,200 --> 00:52:05,240
Speaker 1: But do they do that anywhere else in the world.

979
00:52:05,600 --> 00:52:09,960
Speaker 2: I hope. So I'd like to think they do. You've

980
00:52:09,960 --> 00:52:10,840
Speaker 2: done the Pooce screen.

981
00:52:10,880 --> 00:52:12,680
Speaker 1: Do you do that every Yeah? I did it a

982
00:52:12,719 --> 00:52:17,160
Speaker 1: few years ago. I probably due for another one. So

983
00:52:17,280 --> 00:52:22,080
Speaker 1: with someone with dodgy eyesight and deteriorating eyesight, I might say,

984
00:52:22,239 --> 00:52:27,440
Speaker 1: I'm I'm encouraged by this one. Experts hail remarkable success

985
00:52:28,120 --> 00:52:33,160
Speaker 1: of electronic implant in restoring sight. Yes, that's good. That's

986
00:52:33,160 --> 00:52:34,520
Speaker 1: gotta be good news for some people.

987
00:52:34,840 --> 00:52:37,120
Speaker 2: It certainly is. No, it's one of the most common

988
00:52:37,160 --> 00:52:42,080
Speaker 2: problems obviously as we age is the fact that you know,

989
00:52:42,160 --> 00:52:46,120
Speaker 2: we near glasses or our vision becomes compromised. But it's

990
00:52:46,160 --> 00:52:50,600
Speaker 2: believed now that they can insert a very tiny electrode

991
00:52:50,840 --> 00:52:55,000
Speaker 2: and it can assist in you know, basically clearing up

992
00:52:55,040 --> 00:52:58,600
Speaker 2: the vision for people as they get older. This is amazing,

993
00:52:58,600 --> 00:53:01,760
Speaker 2: and we're talking about people who are almost going legally blind.

994
00:53:02,280 --> 00:53:05,160
Speaker 2: These are tests that have been done on people who

995
00:53:05,280 --> 00:53:08,160
Speaker 2: have got to a point where their macular degeneration has

996
00:53:08,160 --> 00:53:11,840
Speaker 2: got to a point where they physically very struggling to see,

997
00:53:11,960 --> 00:53:16,400
Speaker 2: and after reversing, you know, with this procedure, they're able

998
00:53:16,440 --> 00:53:20,320
Speaker 2: to read again. Now that's pretty phenomenal because for a

999
00:53:20,320 --> 00:53:22,160
Speaker 2: lot of people as they get older and they become

1000
00:53:22,200 --> 00:53:24,480
Speaker 2: more infirmed, they can't get around. They take a look

1001
00:53:24,719 --> 00:53:27,360
Speaker 2: at in reading, like physically holding a book and reading

1002
00:53:27,440 --> 00:53:29,560
Speaker 2: and not to be able to do that or even

1003
00:53:29,560 --> 00:53:31,879
Speaker 2: look at a computer screen or whatever it happens to be.

1004
00:53:32,040 --> 00:53:34,960
Speaker 2: So this is pretty revolutionary and it could you know

1005
00:53:35,080 --> 00:53:39,520
Speaker 2: that it will change people's lives. It can go for

1006
00:53:39,560 --> 00:53:42,480
Speaker 2: that little bit longer, and in this case reversing potentially

1007
00:53:42,800 --> 00:53:45,120
Speaker 2: reversing blindness in people. It's phenomenal.

1008
00:53:45,840 --> 00:53:50,520
Speaker 1: Yeah. The stuff they can do with thighs is life changing.

1009
00:53:50,640 --> 00:53:53,080
Speaker 1: I had not last year, the year before twenty twenty three,

1010
00:53:53,160 --> 00:53:58,000
Speaker 1: middle of the year, my eyesight was so bad and

1011
00:53:58,040 --> 00:54:02,120
Speaker 1: I went to an optometrist or a whatever they ophthalmologist

1012
00:54:02,160 --> 00:54:05,680
Speaker 1: are they that they might be a bit more doctory. Anyway,

1013
00:54:07,080 --> 00:54:09,799
Speaker 1: the lady, she was brilliant. She's like, okay, so good

1014
00:54:09,840 --> 00:54:12,239
Speaker 1: news we can fix that. Bad news is you're going

1015
00:54:12,280 --> 00:54:15,600
Speaker 1: to be blind in that eye, blind by Christmas. This

1016
00:54:15,800 --> 00:54:18,959
Speaker 1: was if you don't have the surgery. I'm like, oh, well,

1017
00:54:19,080 --> 00:54:21,600
Speaker 1: let's do the surgery. And she said, by the way,

1018
00:54:21,800 --> 00:54:25,480
Speaker 1: I hope you didn't drive here, and I went, no,

1019
00:54:25,960 --> 00:54:29,239
Speaker 1: of course I didn't drive here. She's like I don't

1020
00:54:29,280 --> 00:54:32,799
Speaker 1: want to know if you drive here, And of course

1021
00:54:32,880 --> 00:54:37,200
Speaker 1: I didn't drive everyone, I jogged there. But yeah, so

1022
00:54:37,239 --> 00:54:40,799
Speaker 1: I had the surgery on my right eye, and yeah,

1023
00:54:40,840 --> 00:54:43,600
Speaker 1: brand new, brand new, but my left eye. I was

1024
00:54:43,600 --> 00:54:46,360
Speaker 1: born with a weird left eye, like a turn in

1025
00:54:46,360 --> 00:54:48,560
Speaker 1: my left eye, which then got straightened. But the vision

1026
00:54:48,760 --> 00:54:50,879
Speaker 1: vision out of my right eye good, like really good

1027
00:54:51,000 --> 00:54:54,000
Speaker 1: left eye. Shit. So if you ever want to, you know,

1028
00:54:54,040 --> 00:54:56,600
Speaker 1: punch me in the face, come from the left side,

1029
00:54:56,920 --> 00:55:00,080
Speaker 1: no self, Patrick, How can people connect with you and

1030
00:55:00,200 --> 00:55:02,920
Speaker 1: find you and you know, support the cause that is

1031
00:55:02,960 --> 00:55:03,960
Speaker 1: you me?

1032
00:55:04,480 --> 00:55:07,359
Speaker 2: Just go to websites now, dot com, dot au and

1033
00:55:07,680 --> 00:55:09,400
Speaker 2: I think I've mentioned this a few times. If you

1034
00:55:09,480 --> 00:55:11,839
Speaker 2: want us to talk about something tech related, I mean,

1035
00:55:11,840 --> 00:55:13,840
Speaker 2: we're getting close to Christmas, Craig, you might need a

1036
00:55:13,840 --> 00:55:16,000
Speaker 2: bit of a list of things. Although that what is it?

1037
00:55:16,040 --> 00:55:18,080
Speaker 2: The one exit who might be good to put on

1038
00:55:18,080 --> 00:55:20,479
Speaker 2: my Christmas card list? A robot for my home.

1039
00:55:20,560 --> 00:55:22,480
Speaker 1: Yes, yes, we operate it.

1040
00:55:22,560 --> 00:55:26,000
Speaker 2: So I wake up with you leaning over looking at me.

1041
00:55:26,239 --> 00:55:28,799
Speaker 2: That'd be worth it, wouldn't it. For the twenty I

1042
00:55:28,800 --> 00:55:31,920
Speaker 2: can imagine you leaning over looking at me. God, that'd

1043
00:55:31,920 --> 00:55:34,880
Speaker 2: be worse than the humanoid robot mate we'll see you

1044
00:55:34,920 --> 00:55:38,359
Speaker 2: next time. Appreciate you, Thank you, always look forward to it.