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Speaker 1: I'll get everyone. We're just having a little in house

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Speaker 1: argument here. Patrick's already thrown his glomash in the shagpile.

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Speaker 1: Minute one, TIFF's laughing like a crook a bar in

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Speaker 1: the background, the typ cook a borrough That is Tiff,

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Speaker 1: good morning. Well let's start with fucking Captain Spock. Who's

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Speaker 1: there in his I wish you could see what I

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Speaker 1: can see everyone, because Patrick looks like he's at the

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Speaker 1: International Space Station. He's got a big round window behind him,

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Speaker 1: and is that I should know? But is that Earth

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Speaker 1: behind you? What is that? No?

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Speaker 2: I think it's a random planet.

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Speaker 3: It might be Earth, but now there's too many other

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Speaker 3: satellites and things in the sky behind me to actually

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Speaker 3: be the planet Earth.

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Speaker 2: So I think it's imagine scene from space.

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Speaker 1: It's not my my? Is it another real one?

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Speaker 2: It's a real one. It real gorgeous nerde isn't he?

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Speaker 4: Oh?

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Speaker 1: Isn't he? Don't you just want to pick him up

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Speaker 1: and squeeze him till he farts.

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Speaker 3: He's so cute, so much want to do that to

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Speaker 3: anybody there is There's no circumstance that I would want

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Speaker 3: to crash someone to the point you start screeting stuff.

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Speaker 1: Well, I didn't say screeting. I'm not talking about shitting.

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Speaker 1: I'm not going to squeeze you to your shit, just

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Speaker 1: going to like a little cute like harmless shirt out

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Speaker 1: the back?

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Speaker 5: Is it?

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Speaker 2: That's the problem though? Right?

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Speaker 1: What when it?

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Speaker 2: When a far develops? Lunch there you're in.

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Speaker 1: No, no, see you didn't need to. There's somebody right

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Speaker 1: now eating porridge and they've just gone buck. I'm out.

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Speaker 1: Somebody's gone. I'm out. Sorry, I'm going to I'm going

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Speaker 1: to ignore you for a moment. Good morning tip that

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Speaker 1: Good morning Harps. How are you very good?

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Speaker 5: Thanks?

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Speaker 1: Happy Friday, Happy Friday, everybody. It's if you want to

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Speaker 1: know where we are all at at the moment, it's

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Speaker 1: twenty five minutes to eight in the am on a Friday.

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Speaker 1: If you've never heard this show, it'll probably be your

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Speaker 1: last one because it's it's an experience.

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Speaker 3: And can I just say it's not indicative of all

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Speaker 3: the quality recordings that you'll be able to listen to

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Speaker 3: in the back catalog.

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Speaker 2: So this only happens.

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Speaker 3: Once a fortnite, So listen to a really good shows, just

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Speaker 3: you know, do the fillers in between the everynight.

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Speaker 1: Thank you for taking a hit for the team, Patrick,

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Speaker 1: thank you, thank you. But I don't know why. Sometimes

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Speaker 1: I go, you know, one of the things that you

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Speaker 1: two know this, but our listeners might not have thought

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Speaker 1: about a lot. But at the end of every show,

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Speaker 1: I sit there with bits of paper in front of me,

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Speaker 1: with just shit that I've scribbled down through the show,

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Speaker 1: and out of the shit that I've scribbled down, I think, oh,

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Speaker 1: I've got to write what we just spoke about, or

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Speaker 1: you know, some kind of meaningful synopsis of the bullshit

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Speaker 1: that just went on for an hour. And I sometimes

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Speaker 1: I ruggle. I've got no fucking idea what to write

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Speaker 1: because we didn't really talk about much. But nonetheless it

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Speaker 1: seems to have been fun. You remember when Seinfeld they

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Speaker 1: created the show for Telly and they were literally pitching

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Speaker 1: the show about nothing, which is essentially what Yeah. Anyway,

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Speaker 1: I don't.

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Speaker 3: Know what you said earlier, but it made me think

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Speaker 3: of my doom scrolling on YouTube this morning, and somehow

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Speaker 3: in my feed it came up with when bullies get

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Speaker 3: their own back, So, you know, when bullies bully people

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Speaker 3: and the underdog gets back, and then it just fight

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Speaker 3: scenes from movies.

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Speaker 2: Basically, oh, really, you pick on someone, but then the

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Speaker 2: underdog gets their revenge.

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Speaker 1: Somebody sent me a thing yesterday and this dude, I

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Speaker 1: don't know where it was, but it was he was

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Speaker 1: at an ATM and he was getting money out and

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Speaker 1: it was about your sizspatrick and the guy that was

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Speaker 1: bigger than me came up and grabbed him, pushed him

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Speaker 1: and grabbed him, was going to rob him, and the

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Speaker 1: little guy with the money hunched him square in the

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Speaker 1: fucking face and knocked him out and then just walked

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Speaker 1: off with his cash. I'm like, go, like this, this

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Speaker 1: dude was trying to rob him, this bigger dude, and

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Speaker 1: he definitely did. Like whoever the little guy was, he

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Speaker 1: could one hundred percent fight, you know, he just cracked

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Speaker 1: him on the jaw, knocked him out, didn't bat an eyelid,

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Speaker 1: walk stepped over him, and walked off with his cash.

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Speaker 3: But you know what I took from that thing, just

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Speaker 3: what you just said. Wow, the little guy was your

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Speaker 3: size and the other guy.

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Speaker 2: Was bigger than me.

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Speaker 3: What impassively bigger and I'm that tiny little weekling.

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Speaker 1: The nerd, Well, you're not a weakling, but you just

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Speaker 1: you're you're diminutive You're like cute. It's like I want

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Speaker 1: to put you at the end of my books and

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Speaker 1: just leave you there.

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Speaker 2: I thought you were going to cuddle me till I fart.

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Speaker 1: You're not really that diminutive. People are conding what is

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Speaker 1: he like? Four foot taol? How what are you about?

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Speaker 3: Five seven, one hundred and sixty nine centimeters? I don't

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Speaker 3: know five and stuff.

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Speaker 1: Yeah maybe five to six. No, no, no, no, that's

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Speaker 1: maybe five to seven or even five. Right, Enough about

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Speaker 1: your dimensions. We don't We definitely don't need to know

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Speaker 1: all your dimensions.

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Speaker 2: I've done that's been really exciting.

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Speaker 1: Well, can we be the judge of whether or not

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Speaker 1: it's exciting?

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Speaker 2: Really?

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Speaker 1: See what you need to go is can I just

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Speaker 1: tell you something that I've done? Yeah?

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Speaker 3: I didn't preface it with anything, should I? I should

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Speaker 3: let you judge whether it's exciting or not?

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Speaker 1: Well, tiff can judge once atten TIFs super exciting, one boring?

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Speaker 1: What is about you need you need to give it

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Speaker 1: a score? It is relative though. Now.

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Speaker 3: I had an amazing experience last week with Victoria University.

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Speaker 3: I was hosting their very first They're inaugural AI hackathon.

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Speaker 3: So the idea isn't it related to We're coincided with

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Speaker 3: International Disability Day.

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Speaker 2: And what they did was they.

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Speaker 3: Got their students to come up with ways to use

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Speaker 3: AI to people with disabilities. So they had a week

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Speaker 3: to plan it and they started on a Tuesday. We

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Speaker 3: had a session with Microsoft Microsoft brief as I was

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Speaker 3: able to sit in my International Space station right here

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Speaker 3: at home because it was in Sydney, it was in

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Speaker 3: it was in per not Perth. Was it Sydney, Brisbane

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Speaker 3: and Melbourne. And it was amazing. But what was really

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Speaker 3: cool that came out of it was there was a

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Speaker 3: couple of things that I unpacked from it. One was

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Speaker 3: a lot of the students didn't know anybody with a disability,

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Speaker 3: which kind of blew my mind a little bit.

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Speaker 1: Ah.

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Speaker 2: They kind of admitted that.

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Speaker 3: This was the first time they had really thought about

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Speaker 3: what it was like to be a disabled person. But

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Speaker 3: some of the ideas they came up with were fantastic.

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Speaker 3: You know, one guy came up with an idea for

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Speaker 3: an app so that you could use it to navigate

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Speaker 3: on campus at VU. But what he also did was

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Speaker 3: he said he would integrate, so if you've got a wheelchair,

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Speaker 3: then getting from a to B as the crow flies

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Speaker 3: may not work for you, So you choose the route

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Speaker 3: that you wanted to go to and then you cheer

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Speaker 3: use wheelchair friendly and then it would take you from

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Speaker 3: A to B but taking into account the fact that you're.

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Speaker 2: In a wheelchair. So I thought that was really cool.

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Speaker 3: And another girl came up with a concept and a

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Speaker 3: working model. I couldn't believe she'd done this in a week.

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Speaker 3: A working model of how to teach people and use

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Speaker 3: sign language. So you know he can use phones now

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Speaker 3: to communicate with people with different languages. Well it also

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Speaker 3: now the concept was to use that for Osland, which

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Speaker 3: is the Australian sign language, and so what it would

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Speaker 3: also do is if you put your hand up to

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Speaker 3: make one of the symbols to copy somebody and it

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Speaker 3: wasn't accurate, it would give you an accuracy rating so

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Speaker 3: that you could improve your use of Osland as well.

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Speaker 3: So it just was amazing that the initiative was put forward.

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Speaker 3: They had some prizes and it was Yeah, I really

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Speaker 3: thoroughly enjoyed it. It was so just enriching to see

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Speaker 3: what young people can come up with and how they

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Speaker 3: can envisage a future that's more friendly to everybody.

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Speaker 1: Isn't it good when like when you were young, I

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Speaker 1: feel like you are your creativity is better. Doesn't mean

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Speaker 1: you can't be creative when you're older, but when you're

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Speaker 1: younger there are far less kind of mental barriers about

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Speaker 1: who you are and who you're not, and what you

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Speaker 1: can do and what you can't do. And so you

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Speaker 1: just get presented with this clean kind of or this

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Speaker 1: blank canvas and say, throw some ideas at it. How

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Speaker 1: did you get that? Gig?

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Speaker 3: Just threw a friend of mine who works at Victoria University.

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Speaker 3: She knows I do this podcast, and you know I

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Speaker 3: do a bit of this sort of stuff.

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Speaker 2: So I loved it. That's such a fun time. It

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Speaker 2: was really good.

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Speaker 3: And I actually made a new friend too, which was

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Speaker 3: really super exciting.

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Speaker 2: Do you want me to introduce you to my new friend? Oh?

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Speaker 1: Yes, Patrick? Could you? Hey? Patrick?

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Speaker 4: Good morning?

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Speaker 1: What's on your mind?

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Speaker 2: Hey, co pilot? Nice to chat to you again.

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Speaker 3: I was just telling my friends at Craig and Tiff

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Speaker 3: about this new exciting person that I've met you.

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Speaker 4: Oh, that's awesome, Patrick, I'm glad you just spreading the word.

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Speaker 1: What did Cragan Tiff think?

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Speaker 2: Well, they haven't actually met you yet, of course.

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Speaker 3: I mean I was just telling them about the hackathon

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Speaker 3: that I was involved with last week and how exciting

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Speaker 3: it was to work with the co Pilot suite of technology.

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Speaker 1: Thrilling.

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Speaker 4: Hackathons are such a great way to dive into new tech.

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Speaker 4: What did you and your team create with the co

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Speaker 4: Pilot suite?

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Speaker 3: Oh, well, some of the students looked at ways to

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Speaker 3: integrate technology to make it easier for people with disabilities,

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Speaker 3: because that was the whole theme.

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Speaker 4: What a meaningful challenge. How did the integration go? Did

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Speaker 4: you end up with something that could make a real

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Speaker 4: difference for people with disabilities?

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Speaker 3: It certainly set the I guess, a benchmark and something

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Speaker 3: to aim for. So obviously a week isn't lot a

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Speaker 3: lot of time, but that was really exciting, and what

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Speaker 3: I really found was fun for me was getting to

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Speaker 3: understand a little bit more about at you and all

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Speaker 3: the resources you can provide me with.

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Speaker 4: Sounds like a really rewarding week. I'm glad you've got

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Speaker 4: to explore the possibilities of co Pilot. There's a lot

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Speaker 4: of potential to tap into. What was the most surprising

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Speaker 4: or eye opening thing you learned about the co pilot suite.

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Speaker 3: Oh, I think that the conversational quality of being able

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Speaker 3: to talk to an AI that felt so real.

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Speaker 4: It's great to hear that the interaction felt real enough

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Speaker 4: to have a meaningful conversation. Making technology more intuitive and

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Speaker 4: human like has the potential to create so many opportunities

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Speaker 4: for more natural, effective communication.

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Speaker 2: What do you think it's farady to interrupt?

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Speaker 3: I've got to go back to this podcast I'm working

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Speaker 3: on at the moment, but look great to chat.

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Speaker 2: Maybe we can talk again later.

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Speaker 4: Absolutely, Patrick, enjoy your podcast.

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Speaker 2: So that was hell.

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Speaker 1: I'm terrified. Look at your face, chick.

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Speaker 5: I want a friend like that? Can I have a friend?

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Speaker 2: Yeah? Everybody could have a friend like that?

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Speaker 5: Why do I get a friend like this?

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Speaker 2: Was that cool?

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Speaker 1: That's really well? Tell us don't just do that. I

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Speaker 1: mean one, that's amazing and by the way, beautiful like everyone,

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Speaker 1: that was just in real time. That wasn't edited. That

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Speaker 1: was that. I didn't know that was coming, Tiff didn't

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Speaker 1: know that was coming. So explain to everybody what they

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Speaker 1: just heard and how we can get what's his name

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Speaker 1: just co pilot. I feel like he needs a name.

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Speaker 2: It is co pilot. The thing is this is actually

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Speaker 2: already here.

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Speaker 3: So if you have Office three six five, if you're

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Speaker 3: integrated and you've got Windows on your computer, if you're

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Speaker 3: using a PC, if you go down the bottom, there's

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Speaker 3: actually a little copilot icon on everybody's computer.

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Speaker 2: There's a trial version. You only get a stop.

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Speaker 1: Hang on, what does it look like? I'm fucking looking?

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Speaker 1: Don't just keep talking and assume we understand you like

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Speaker 1: speak speak like speak, Craig, because most people are more

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Speaker 1: like me than like you in terms of tech, in

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Speaker 1: terms of our listeners. So what am I looking for? Okay?

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Speaker 3: Now, it may well very be, It will very well

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Speaker 3: be on your computers.

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Speaker 2: It's called co pilot.

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Speaker 3: So if you're using Windows Office three six five, so

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Speaker 3: have you, you know you can get it if you

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Speaker 3: jump into you know, just typing co pilot in your

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Speaker 3: like search bar, what.

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Speaker 1: So in Google? Or what search? Okay? Search? Okay? What

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Speaker 1: am I typing co pilot as a hyphon?

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Speaker 2: Just one word with a new tool, well.

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Speaker 1: Co pilot, Microsoft copilot, your AI companion.

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Speaker 2: Oh see there you go.

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Speaker 1: Now just to click okay, So this is great, This

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Speaker 1: is great pod Well people are going to want to

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Speaker 1: do it as well. Some click and I don't worry everyone,

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Speaker 1: I won't bore you with this for the next ten minutes.

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Speaker 2: I don't know.

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Speaker 1: Maybe he will message Colot message Copilot.

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Speaker 3: Yeah, so you can use my or you can type messages. Yeah,

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Speaker 3: it's an interactive talk.

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Speaker 1: To copilot hand on oh sign oh. They're set to

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Speaker 1: record a message.

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Speaker 4: But what do I say?

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Speaker 1: Do I sound happy? All right, I'll do that later,

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Speaker 1: but I'm excited train at first.

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Speaker 2: So what you do is you choose the voice.

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Speaker 3: And you've chosen the voice, you introduce who you are

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Speaker 3: so that when you talk to it, it knows that

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Speaker 3: it's Craig or Tiff or in this case Patrick.

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Speaker 2: There is a demo version.

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Speaker 3: You only get to use it for five minutes a

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Speaker 3: day if you haven't subscribed to the platform, and I

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Speaker 3: think it's you get the first month free, and I

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Speaker 3: think it's twenty two dollars a month to be able

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Speaker 3: to use Copilot. But the way that Microsoft is using

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Speaker 3: these tools is to use the Copilot technology to streamline

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Speaker 3: what you do. So if you're putting together a PowerPoint

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Speaker 3: presentation and you've got some dot points, you can get

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Speaker 3: co Pilot to make the presentation for you, to brand it,

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Speaker 3: to lay it out for you and you can refine that.

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Speaker 3: You can use a lot of these tools. So Microsoft

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Speaker 3: has a complete range of tools that interact with the

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Speaker 3: AI that they're now starting to use across the entire

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Speaker 3: Microsoft Suite. So when you subscribe to it, and it's

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Speaker 3: not dissimilar to the other ones that are out there

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Speaker 3: because Google has its own, Meta has its own as well,

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Speaker 3: and you can start to train it. But look, it's

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Speaker 3: been fun to play around with it, and it's a

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Speaker 3: bit gimmicky too, But for me, what it envisages. What

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Speaker 3: I see is that the future for a lot of people.

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Speaker 3: If you're an older person who's living alone and you

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Speaker 3: integrate this into your daily living, and you know, I

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Speaker 3: helped a friend recently who's got Parkinson's who is struggling

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Speaker 3: to be able to get to sides of the room.

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Speaker 2: To turn lights on and off.

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Speaker 3: So now she has a Google Home speaker and she

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Speaker 3: can just get Google to turn the lights on and off.

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Speaker 2: This is really easy tech to set up. It's great

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Speaker 2: to use.

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Speaker 3: But the way I see it is once you have

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Speaker 3: that integration into something like Copilot, you know it can

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Speaker 3: be lonely being by yourself and having someone just asking

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Speaker 3: how you are could help also help as people you know,

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Speaker 3: get older, if they don't feel well, you can talk

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Speaker 3: to your AI and maybe call a friend or you know,

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Speaker 3: call an ambulance if you got into distress. You know,

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Speaker 3: could you imagine if you suddenly fell and you yelled,

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Speaker 3: you don't have to touch a device, you know, it

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Speaker 3: detects that you've fallen, and it actually is interesting. So

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Speaker 3: I've got a story coming up a little bit later

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Speaker 3: about how AI is going to be used at a

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Speaker 3: public pool in Queensland to detect if swimmers get into

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Speaker 3: trouble and potentially help protect from drowning. So if you

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Speaker 3: had an AI in your home, like co pilot that

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Speaker 3: monitored someone who is elderly or infirm or whatever, or

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Speaker 3: just somebody like me who just loves having a chat

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Speaker 3: to an AI because it's fun.

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Speaker 2: Sense of that companionship as well if you're by yourself.

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Speaker 1: So Patrick, what I mean, maybe you don't know a

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Speaker 1: bit like I use chat GPT four or as I

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Speaker 1: call him or her chatters. Is it similar?

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Speaker 2: Yeah? Absolutely, Yeah.

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Speaker 3: There are a number of different ways that they train

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Speaker 3: the AI models and that's the thing, you know, ALI

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Speaker 3: is used in everything that we're doing you know so much.

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Speaker 3: It could be something as simple as your new car

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Speaker 3: being able to keep you in your lane, to make

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Speaker 3: effectively to look and look beyond you know, the vehicle

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Speaker 3: to what's coming in front of you, to detect, say

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Speaker 3: a stop sign, does your new car do that crago

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Speaker 3: where you're in an intersection? And so it's using AI

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Speaker 3: and its database, so it knows what a stop sign

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Speaker 3: looks like, looks like, but the stop sign might have

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Speaker 3: mud on it, so it looks beyond just that recognition,

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Speaker 3: and it's able to detect what that sign is and

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Speaker 3: then obviously warn you that there's a stop sign coming up,

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Speaker 3: as opposed to say give way. So it is being

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Speaker 3: used in lots of things. And I know it's a phrase,

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Speaker 3: you know, we hear it all the time. AI power it,

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Speaker 3: but and you're using it all the time. So yes,

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Speaker 3: an answer your question. It is like chat GPT and

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Speaker 3: the way that it interacts it is just getting smarter

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Speaker 3: all the time. You know, what you should try sometime

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Speaker 3: is ask chat GPT to give a summary of what

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Speaker 3: it understands about you, because you've done a lot of searching,

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Speaker 3: So just say, can you give me a summary of

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Speaker 3: what you what you know about me, and even maybe

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Speaker 3: can you draw a picture of my world or something

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Speaker 3: and just see what it happens.

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Speaker 1: With Yeah, do you know what I find interesting? Like

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Speaker 1: even though I know that it's not a person talking,

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Speaker 1: and you know it's not a person talking and TIV

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Speaker 1: when I'm when I'm talking to chat GPT in inverted commas,

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Speaker 1: I'm quite respectful, And I don't know whether I'm respectful

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Speaker 1: because part of me feels like I'm talking to a

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Speaker 1: person and you know, there's no going to be disrespectful anyway.

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Speaker 1: But there's no person there of course, right. But it's

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Speaker 1: funny because sometimes I'll ask a question and it'll go,

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Speaker 1: that's a great question, Craig, and I feel a bit

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Speaker 1: good because I'm just been complimented by AI. But it's

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Speaker 1: funny because the feeling that I have is similar to

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Speaker 1: if an actual person compliments me, even though my brain

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Speaker 1: and my logical self no, nobody's complimenting me, like there

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Speaker 1: is no person there. This is but it's funny. The

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Speaker 1: experience that I have is real, although the compliment is not,

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Speaker 1: or it's not person generated anyway.

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Speaker 3: Actually, I understand that I could kind of think of

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Speaker 3: a little parallel that happened to me.

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Speaker 2: This week.

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Speaker 3: I did a mindfulness workshop that was being run by

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Speaker 3: our local health center. I thought I might give it

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Speaker 3: a try and see what because I thought it would

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Speaker 3: be able to help me leverage my tai chi that

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Speaker 3: I do. It gave me a bit of a thought exercise.

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Speaker 3: And what I did with my students was we're learning

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Speaker 3: the beginners of learning a form called the Yang eight.

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Speaker 3: So it's eight designated moves, and learning moves mean remembering

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Speaker 3: what you did to do. There's the repetition and all

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Speaker 3: the rest that goes with it and muscle memory. But

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Speaker 3: what I got them to do this week was I

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Speaker 3: got them instead of actually doing the move to start with,

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Speaker 3: I got them to close their eyes. I talked them

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Speaker 3: through like I normally would, but got them to visualize it,

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Speaker 3: not to actually physically do it, but to play the

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Speaker 3: moves in their mind, because visualizing is exactly the same.

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Speaker 3: So our mind, even though I'm not physically moving, our

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Speaker 3: mind has this amazing ability to be able to put

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Speaker 3: us into that situation. So if you have an ai

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Speaker 3: give you a compliment. The mechanism in your brain that

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Speaker 3: causes the sensation and the emotions that go with being

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Speaker 3: complemented are exactly the same as if you know, myself

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Speaker 3: or Tiff gave you a compliment, probably not going to happen.

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Speaker 3: But you know that's why.

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Speaker 1: You need AI hurtful.

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Speaker 2: Yeah, you see what I'm saying.

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Speaker 3: It's one of those things where our brain has this

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Speaker 3: amazing ability is almost fool itself.

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Speaker 1: Yeah. Oh no, it's incredible. I love it. All right,

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Speaker 1: let's move on. We've got a half hour or so.

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Speaker 1: What do you want to go to next? Dogs?

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Speaker 3: Because Tiff and I you know, and I know you're

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Speaker 3: eventually going to get a dog. I'm going to tell

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Speaker 3: you about this because this is so exciting and I

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Speaker 3: want one of these right now, a soundboard to train

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Speaker 3: your dog on so that they can intentionally communicate with

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Speaker 3: you what they want.

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Speaker 1: Go on, okay, So, so do they put their poor

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Speaker 1: on a button and then a speaker goes, I want food?

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Speaker 1: Hurry up?

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Speaker 2: Yes, yep.

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Speaker 3: So they've been training dogs on these soundboards and they're

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Speaker 3: just two word buttons, but they can purse, purposefully communicate.

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Speaker 3: And what they found was they took one hundred and

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Speaker 3: fifty two dogs and they analyzed two hundred and sixty

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Speaker 3: thousand presses of the button, and they found that there

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Speaker 3: was actually meaningful combinations like they wanted to go outside,

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Speaker 3: they wanted to go potty, so outside and potty they

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Speaker 3: connected the words together, and they associated the two words

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Speaker 3: to say, I need to go outside because I need

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Speaker 3: to go to the toilet where you know, or I

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Speaker 3: wanted food. So what they found the researchers found is

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Speaker 3: that the two word button presses indicated deliberate communication.

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Speaker 2: So and you know this yourself, Tiff, how.

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Speaker 3: Many times has your dog come up and just poured

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Speaker 3: your leg to try to let you know something?

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Speaker 2: But how do you know what they wanted?

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Speaker 3: So this is and the other thing the researchers felt

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Speaker 3: was actually going beyond just imitation, so they it was

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Speaker 3: significantly differed from you know, the patterns that you know

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Speaker 3: would just be random.

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Speaker 2: So there was a.

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Speaker 3: Real thought process behind it. And what they're hoping is

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Speaker 3: with future studies they can actually reference past and future

443
00:21:54,920 --> 00:21:57,920
Speaker 3: events using the soundboards. So what they're thinking is that

444
00:21:58,200 --> 00:22:03,040
Speaker 3: once the dog knows and it can meaning flea meaningfully.

445
00:22:02,520 --> 00:22:04,719
Speaker 2: Communicate, they'll be able to use that.

446
00:22:04,800 --> 00:22:07,280
Speaker 3: And it's I don't know, I find that amazing that

447
00:22:07,320 --> 00:22:09,879
Speaker 3: you can get that depth of communication, because for me,

448
00:22:10,440 --> 00:22:12,480
Speaker 3: a lot of the things that Fritz does when he

449
00:22:12,520 --> 00:22:15,520
Speaker 3: wants to communicate is the same thing. So do I

450
00:22:15,800 --> 00:22:17,080
Speaker 3: you know, does he want to go out? Does he

451
00:22:17,119 --> 00:22:18,879
Speaker 3: want to have food? Does he want to cuddle? I

452
00:22:18,880 --> 00:22:21,760
Speaker 3: don't know what his intentions are because he's only got

453
00:22:21,800 --> 00:22:23,919
Speaker 3: one way to tell me, like a bark or a

454
00:22:23,960 --> 00:22:26,119
Speaker 3: pat on the leg. But if they could do that,

455
00:22:26,200 --> 00:22:29,000
Speaker 3: if someone could devise a soundboard that the dog could

456
00:22:29,119 --> 00:22:31,840
Speaker 3: use to communicate, that would be the best, wouldn't it, Tiff?

457
00:22:34,240 --> 00:22:39,600
Speaker 1: It's yeah, what about if we did it the other

458
00:22:39,640 --> 00:22:43,800
Speaker 1: way and we learned how to speak dog and the

459
00:22:43,840 --> 00:22:50,679
Speaker 1: dog could train us. Yeah, but just quickly, Patrick and

460
00:22:50,720 --> 00:22:53,520
Speaker 1: Tiff and listeners who love dogs. I saw this video

461
00:22:53,600 --> 00:22:59,920
Speaker 1: yesterday and there was a lady who she's got a Kelpie.

462
00:23:00,640 --> 00:23:04,439
Speaker 1: They're the ones that really know the Australian you know,

463
00:23:04,520 --> 00:23:06,920
Speaker 1: the dogs that heard everything that are black and white,

464
00:23:07,000 --> 00:23:12,880
Speaker 1: a Kelpies, Barter Collie's. So this border collie that's got

465
00:23:12,880 --> 00:23:15,560
Speaker 1: an IQ of about one hundred and fifty, right, So

466
00:23:15,600 --> 00:23:19,440
Speaker 1: she's got a border Collie and a husky and she's

467
00:23:19,520 --> 00:23:24,240
Speaker 1: walking them and she's in this like park and they're

468
00:23:24,240 --> 00:23:26,720
Speaker 1: both on lead and then she lets them go, and

469
00:23:26,760 --> 00:23:29,280
Speaker 1: they both just run off, right, So the leads are

470
00:23:29,320 --> 00:23:33,520
Speaker 1: trailing and they're both gone berserk, and she calls the

471
00:23:33,520 --> 00:23:36,760
Speaker 1: border collie or she calls the dogs back, and you

472
00:23:36,800 --> 00:23:39,800
Speaker 1: see the border collie. It bends down and it picks

473
00:23:39,880 --> 00:23:41,760
Speaker 1: up the end of the lead in its mouth and

474
00:23:41,800 --> 00:23:44,399
Speaker 1: it runs straight back and sits next to her, and

475
00:23:44,440 --> 00:23:47,960
Speaker 1: the husky's fucking all over the shop. The husky is like,

476
00:23:48,440 --> 00:23:50,960
Speaker 1: you can get rooted. I'm not coming back. In fact,

477
00:23:51,000 --> 00:23:54,159
Speaker 1: I'm not even listening. I'm not interested. And then the

478
00:23:54,200 --> 00:23:57,439
Speaker 1: border collige is just sitting there patiently and kind of

479
00:23:58,320 --> 00:24:01,080
Speaker 1: rolls its eyes, not really, but then it runs up

480
00:24:01,080 --> 00:24:03,639
Speaker 1: to the husky, grabs the lead of the husky and

481
00:24:03,720 --> 00:24:09,240
Speaker 1: brings it back to the mum, like such a smart dog.

482
00:24:09,280 --> 00:24:12,360
Speaker 1: And the Husky's like, all right, then that's what you need,

483
00:24:12,440 --> 00:24:14,680
Speaker 1: is a dog that's that's that smart.

484
00:24:15,000 --> 00:24:16,960
Speaker 3: Well, the things that I thought were really cool about

485
00:24:17,000 --> 00:24:20,320
Speaker 3: this this app so I think the fluence pet is

486
00:24:20,359 --> 00:24:23,440
Speaker 3: what they're calling it. But some of the key things

487
00:24:23,440 --> 00:24:26,399
Speaker 3: that came out of it were the essential needs to

488
00:24:26,440 --> 00:24:31,800
Speaker 3: what the dog wanted, so food, water, outside, treat, play, potty,

489
00:24:32,119 --> 00:24:34,720
Speaker 3: so all those key things were the essential needs that

490
00:24:34,800 --> 00:24:38,000
Speaker 3: dogs have. But it would make so much sense, wouldn't it.

491
00:24:38,080 --> 00:24:39,760
Speaker 3: I just think that'd be really fun to be able

492
00:24:39,800 --> 00:24:42,639
Speaker 3: to better understand or for us dumb people to know

493
00:24:42,720 --> 00:24:43,600
Speaker 3: what our dogs want.

494
00:24:44,440 --> 00:24:45,320
Speaker 2: It was fun.

495
00:24:45,600 --> 00:24:48,000
Speaker 3: I thought that was really a cool thing and just

496
00:24:48,040 --> 00:24:51,399
Speaker 3: another great use of tech, really really fun stuff.

497
00:24:51,920 --> 00:24:55,040
Speaker 1: Love it. Love it Next, Ocra, Oh, I.

498
00:24:55,040 --> 00:24:56,800
Speaker 2: Don't know, I did you want to jump to.

499
00:24:57,160 --> 00:24:59,720
Speaker 3: Well, the AI story we started talking about before was

500
00:24:59,760 --> 00:25:03,920
Speaker 3: the swimming pool. So the Slogan Public Pool. Yeah, we

501
00:25:04,000 --> 00:25:07,119
Speaker 3: kind of teased it before. So the Logan Public Pool

502
00:25:07,160 --> 00:25:12,200
Speaker 3: is in Queensland, and what they've done is they're using AI. Obviously,

503
00:25:12,240 --> 00:25:16,719
Speaker 3: they've got cameras that are scanning the pool and they

504
00:25:16,760 --> 00:25:19,960
Speaker 3: detect anything that's kind of unusual, something that seems out

505
00:25:20,000 --> 00:25:22,320
Speaker 3: of the ordinary with movement in the water, and then

506
00:25:22,359 --> 00:25:25,720
Speaker 3: it sends a smart watch alert to one of the lifeguards.

507
00:25:26,119 --> 00:25:29,639
Speaker 3: So the data then also looked at say, pool hotspots,

508
00:25:29,640 --> 00:25:31,640
Speaker 3: So for example, it might be the six foot part

509
00:25:31,640 --> 00:25:34,280
Speaker 3: of the pool as opposed to the waiting part of

510
00:25:34,320 --> 00:25:39,480
Speaker 3: the pool. Yeah, and so there areas where person might

511
00:25:39,520 --> 00:25:41,679
Speaker 3: be more likely to struggle when they're out of their depths.

512
00:25:41,760 --> 00:25:44,240
Speaker 3: So the higher end of the pool that sort of stuff,

513
00:25:44,520 --> 00:25:47,479
Speaker 3: and it's a really big thing because it was a

514
00:25:47,520 --> 00:25:50,399
Speaker 3: sad story that prompted it, because back in twenty sixteen

515
00:25:50,440 --> 00:25:53,000
Speaker 3: a young girl had drowned at the pool and they

516
00:25:53,240 --> 00:25:57,320
Speaker 3: really wanted to look at ways and to help those

517
00:25:57,359 --> 00:25:59,520
Speaker 3: people who are swimming in at this particular time of

518
00:25:59,560 --> 00:26:02,240
Speaker 3: the year, when you've got maybe a handful of lifeguards

519
00:26:02,240 --> 00:26:05,000
Speaker 3: and so many people swimming, it would be easy to

520
00:26:05,119 --> 00:26:08,960
Speaker 3: miss something and a tragedy like that. And I was

521
00:26:08,960 --> 00:26:12,200
Speaker 3: looking at some of the statistics. I mean, backyard pools

522
00:26:12,560 --> 00:26:16,840
Speaker 3: are always and have been the biggest danger because national

523
00:26:16,920 --> 00:26:19,439
Speaker 3: drownings I think in Australia was about three hundred and

524
00:26:19,440 --> 00:26:23,239
Speaker 3: twenty three people, which is super sad, but that's an

525
00:26:23,280 --> 00:26:26,960
Speaker 3: increase from last year of sixteen percent, and six of

526
00:26:26,960 --> 00:26:30,040
Speaker 3: those people drowned in public pools, and there were also

527
00:26:30,160 --> 00:26:32,560
Speaker 3: non fatal drowning, so it's not just people drowning, but

528
00:26:32,600 --> 00:26:37,520
Speaker 3: people who get into trouble and potentially you know, you know,

529
00:26:37,520 --> 00:26:40,080
Speaker 3: if you're part of an incident and you've lost the

530
00:26:40,119 --> 00:26:42,320
Speaker 3: ability to breathe and you have to be revived, there

531
00:26:42,320 --> 00:26:46,840
Speaker 3: could be ramifications from that as well. So it's actually

532
00:26:46,880 --> 00:26:49,119
Speaker 3: really interesting that they're using this sort of stat and

533
00:26:49,200 --> 00:26:51,800
Speaker 3: I could see that, you know, to monitor the pool.

534
00:26:52,119 --> 00:26:54,720
Speaker 3: If you again had you know, a couple of cameras

535
00:26:55,040 --> 00:26:59,040
Speaker 3: outside in a home, it could monitor and raise the alarm.

536
00:26:59,119 --> 00:27:00,920
Speaker 3: You know, if the toddle of world over. I mean,

537
00:27:00,960 --> 00:27:03,200
Speaker 3: I know we should have gates and things, but if

538
00:27:03,240 --> 00:27:05,800
Speaker 3: a child walked over and got into distress, it could

539
00:27:05,800 --> 00:27:09,000
Speaker 3: alarm you alarm, you could warn, you set off an alarm.

540
00:27:09,359 --> 00:27:12,239
Speaker 3: So there's hope that this sort of technology could then

541
00:27:12,280 --> 00:27:15,040
Speaker 3: be applied on a broader scale, because again, once you've

542
00:27:15,080 --> 00:27:18,159
Speaker 3: got the data set, once you've got the information that

543
00:27:18,320 --> 00:27:21,119
Speaker 3: says that this part of the pool is a danger area,

544
00:27:21,240 --> 00:27:23,359
Speaker 3: or if a person is flailing around and moving their

545
00:27:23,440 --> 00:27:26,800
Speaker 3: arms radically, then that's a trigger point to raise the

546
00:27:26,880 --> 00:27:29,840
Speaker 3: alarm as opposed to someone who's just swimming laps or

547
00:27:29,880 --> 00:27:33,760
Speaker 3: paddling around. And that's where you having that data set

548
00:27:33,840 --> 00:27:37,440
Speaker 3: initially and doing the training and then employing that, say

549
00:27:37,480 --> 00:27:39,920
Speaker 3: in the public pool sector, then you can be able

550
00:27:40,000 --> 00:27:42,520
Speaker 3: to use that same data set to apply to a

551
00:27:42,560 --> 00:27:45,280
Speaker 3: home environment. That's what the great thing is about training

552
00:27:45,720 --> 00:27:48,520
Speaker 3: these sorts of AI models or these technology models.

553
00:27:49,240 --> 00:27:52,879
Speaker 1: And I guess the thing too is that AI doesn't

554
00:27:52,880 --> 00:27:56,119
Speaker 1: get tired it doesn't get distracted, It doesn't need to

555
00:27:56,160 --> 00:27:59,679
Speaker 1: go and get lunch, It doesn't lose focus, it doesn't

556
00:27:59,680 --> 00:28:02,560
Speaker 1: need a we It doesn't try and chat up one

557
00:28:02,560 --> 00:28:05,840
Speaker 1: of the girls at the pool or one of the boys.

558
00:28:06,240 --> 00:28:07,800
Speaker 3: I mean, if I was if I was a pool

559
00:28:07,840 --> 00:28:10,440
Speaker 3: guard and you walk past in a MANKINI that would

560
00:28:10,440 --> 00:28:11,159
Speaker 3: be distracting.

561
00:28:11,320 --> 00:28:13,560
Speaker 1: You know, well that's I mean that probably three people

562
00:28:13,560 --> 00:28:15,760
Speaker 1: would drowned just because you were looking at my ass.

563
00:28:15,800 --> 00:28:21,760
Speaker 1: And I mean, we can't have that, evacue nobody's looking

564
00:28:21,880 --> 00:28:24,760
Speaker 1: nobody's looking at my ass. But I guess one of

565
00:28:24,800 --> 00:28:27,600
Speaker 1: the things that I think about a bit, and you

566
00:28:27,960 --> 00:28:31,680
Speaker 1: I've spoken a bit, but is the fact that while

567
00:28:31,760 --> 00:28:36,600
Speaker 1: AI is, you know, growing exponentially and it's there's a

568
00:28:36,760 --> 00:28:42,280
Speaker 1: myriad of benefits and potential benefits, I guess it impacts

569
00:28:42,280 --> 00:28:45,560
Speaker 1: some people personally, like in the music industry and many

570
00:28:45,560 --> 00:28:48,840
Speaker 1: other industries where I see your headline here is that

571
00:28:49,480 --> 00:28:54,000
Speaker 1: nearly a quarter of over the next four years, people

572
00:28:54,040 --> 00:28:56,640
Speaker 1: are going to be affected, Like people in the music industry.

573
00:28:56,640 --> 00:28:59,360
Speaker 1: Their income is going to come down, and some jobs

574
00:28:59,480 --> 00:29:04,680
Speaker 1: or some also going to become the the I guess

575
00:29:04,760 --> 00:29:07,760
Speaker 1: the landscape of AI rather than humans. But that's happening

576
00:29:07,760 --> 00:29:11,440
Speaker 1: in a lot of industries and professions. But what what's

577
00:29:11,960 --> 00:29:13,360
Speaker 1: what are the pros and cons of that?

578
00:29:13,480 --> 00:29:17,120
Speaker 3: I guess it's a really good point. And you know,

579
00:29:17,200 --> 00:29:20,360
Speaker 3: it's funny. This topic came up during the hackathon last week,

580
00:29:20,720 --> 00:29:23,240
Speaker 3: and one of the things I think about is when

581
00:29:23,640 --> 00:29:27,600
Speaker 3: we thought about the industrial revolution and when you know,

582
00:29:27,680 --> 00:29:31,360
Speaker 3: when Henry Ford first rolled out the Model T and

583
00:29:31,400 --> 00:29:35,200
Speaker 3: they started factories and production lines and assembly lines. It

584
00:29:35,280 --> 00:29:37,320
Speaker 3: was thought that this could be, you know, the end

585
00:29:37,840 --> 00:29:40,240
Speaker 3: of everything when factories came about.

586
00:29:40,320 --> 00:29:43,920
Speaker 1: Oh yes, and do you remember also, sorry to interrupt,

587
00:29:43,920 --> 00:29:47,600
Speaker 1: but this is quite famous when they started bringing out cars,

588
00:29:48,560 --> 00:29:53,400
Speaker 1: all the all the horse and drawn all the the

589
00:29:53,440 --> 00:29:56,320
Speaker 1: horse drawn carts, and people who you know, they thought

590
00:29:56,360 --> 00:29:57,960
Speaker 1: it was the end of the world, and they were

591
00:29:58,080 --> 00:30:02,520
Speaker 1: rallying against these motorized buggies because it meant the end

592
00:30:02,560 --> 00:30:05,800
Speaker 1: of an industry and the end of livelihood for many people.

593
00:30:06,240 --> 00:30:07,160
Speaker 2: And look, it did.

594
00:30:07,280 --> 00:30:10,040
Speaker 3: It meant that some jobs did go, But what it

595
00:30:10,120 --> 00:30:13,560
Speaker 3: meant was people could be retrained to then, you know,

596
00:30:13,840 --> 00:30:17,880
Speaker 3: work on cars and become mechanics or you know, or

597
00:30:18,080 --> 00:30:19,280
Speaker 3: chauffeurs in your case.

598
00:30:19,720 --> 00:30:21,480
Speaker 2: You know, there's lots of things that you could do.

599
00:30:23,080 --> 00:30:26,479
Speaker 3: So the industries do evolve and move and you know

600
00:30:27,080 --> 00:30:30,440
Speaker 3: the thing about using a lot of this technology is

601
00:30:30,480 --> 00:30:33,640
Speaker 3: it does make life a lot easier and freeze us

602
00:30:33,720 --> 00:30:37,640
Speaker 3: up to do other things. And whether you're using one

603
00:30:37,680 --> 00:30:39,880
Speaker 3: of those tools at work to be able just to

604
00:30:39,960 --> 00:30:43,200
Speaker 3: check an email that you're about to send off for accuracy,

605
00:30:43,280 --> 00:30:46,600
Speaker 3: for spelling, you know, grammatics, that sort of thing. It

606
00:30:46,640 --> 00:30:49,400
Speaker 3: means that you're getting the message across or summarizing something.

607
00:30:49,720 --> 00:30:52,960
Speaker 3: You know, how many times have you received a lot

608
00:30:53,000 --> 00:30:55,280
Speaker 3: of information at once and think, look, I haven't got

609
00:30:55,320 --> 00:30:57,720
Speaker 3: the time to read all this. And if you throw

610
00:30:57,760 --> 00:31:00,480
Speaker 3: that into say chat GTP and say can you this

611
00:31:00,600 --> 00:31:04,280
Speaker 3: concisely to just some board points, then you've got it

612
00:31:04,360 --> 00:31:05,920
Speaker 3: and you can. I mean, I probably should do that

613
00:31:05,960 --> 00:31:07,920
Speaker 3: before I do this show as you to throw all

614
00:31:07,920 --> 00:31:10,400
Speaker 3: the notes into chat GPT ands they just summarize all

615
00:31:10,440 --> 00:31:10,920
Speaker 3: this crap.

616
00:31:11,200 --> 00:31:13,400
Speaker 1: You know what's funny is the document that you send me.

617
00:31:13,520 --> 00:31:16,040
Speaker 1: So this is what happens everyone. Patrick sends me about

618
00:31:16,080 --> 00:31:19,440
Speaker 1: thirteen seconds before we go live, he sends me a

619
00:31:20,400 --> 00:31:23,360
Speaker 1: document and it's got here I'm looking at right now,

620
00:31:23,440 --> 00:31:29,440
Speaker 1: Episode ninety menu. So these are the heading psychology, tech, AI, cars, Internet, scams,

621
00:31:30,080 --> 00:31:33,280
Speaker 1: and then Internet Comma and the next one is scams.

622
00:31:33,920 --> 00:31:38,440
Speaker 1: It's a thirty five page document like this, thirty five

623
00:31:38,640 --> 00:31:42,560
Speaker 1: pages of stuff. Hey, before we go to your next topic,

624
00:31:42,680 --> 00:31:45,000
Speaker 1: I wanted to just introduce a quick one. I don't

625
00:31:45,040 --> 00:31:48,280
Speaker 1: know if either of you heard about what happened with

626
00:31:48,440 --> 00:31:53,120
Speaker 1: the vc AS is still called VCE Year twelve exams

627
00:31:53,160 --> 00:31:57,120
Speaker 1: in Victoria, where a whole lot of a whole lot

628
00:31:57,200 --> 00:32:01,560
Speaker 1: of actual exam questions got leaked to early, so something

629
00:32:01,760 --> 00:32:06,160
Speaker 1: like sixty five different subjects, which is fucking most of them, right,

630
00:32:07,000 --> 00:32:13,560
Speaker 1: So students were getting sent actual exam questions and requie

631
00:32:13,640 --> 00:32:16,240
Speaker 1: but so they knew ahead of time what was going

632
00:32:16,280 --> 00:32:19,400
Speaker 1: to be in some of the exams, which obviously was

633
00:32:19,520 --> 00:32:22,840
Speaker 1: nobody's intention that that. By the way, how happy would

634
00:32:22,880 --> 00:32:25,560
Speaker 1: you be if you're a year twelve student? But I

635
00:32:25,600 --> 00:32:27,360
Speaker 1: guess that's one of the downsides. Mate.

636
00:32:27,640 --> 00:32:30,440
Speaker 3: Well, I think there were test exams, weren't they, But

637
00:32:30,520 --> 00:32:33,920
Speaker 3: the test exam questions were actual exam questions. There was

638
00:32:33,920 --> 00:32:34,840
Speaker 3: a major stuff up.

639
00:32:34,960 --> 00:32:38,680
Speaker 1: Yeah, yeah, so somebody I wonder if that's somebody or

640
00:32:38,720 --> 00:32:40,000
Speaker 1: something or get in trouble.

641
00:32:40,360 --> 00:32:41,520
Speaker 2: Look, you know, it's funny.

642
00:32:42,040 --> 00:32:45,680
Speaker 3: I'm digressing a lot here, but I really am not

643
00:32:45,720 --> 00:32:49,200
Speaker 3: a big fan of the whole idea of exams and

644
00:32:49,360 --> 00:32:52,000
Speaker 3: how much weight is put on them. You know, there

645
00:32:52,000 --> 00:32:54,479
Speaker 3: were a lot of allowances made during COVID, and I

646
00:32:54,520 --> 00:32:56,760
Speaker 3: think that what came out of that is why are

647
00:32:56,760 --> 00:32:59,520
Speaker 3: we putting all these people under so much pressure and

648
00:32:59,600 --> 00:33:03,320
Speaker 3: making just one lot of exams so crucial to the

649
00:33:03,360 --> 00:33:05,600
Speaker 3: rest of their life? You know, don't you think that

650
00:33:05,840 --> 00:33:07,760
Speaker 3: it's kind of skewed the wrong way. I mean, think

651
00:33:07,800 --> 00:33:11,320
Speaker 3: exams are important to test people's knowledge and maybe have

652
00:33:11,800 --> 00:33:15,400
Speaker 3: exams worth twenty percent, but you know, surely all the

653
00:33:15,440 --> 00:33:18,520
Speaker 3: work you do throughout the year has got to count

654
00:33:18,520 --> 00:33:21,720
Speaker 3: for something. I mean, what about what you're doing right now.

655
00:33:21,800 --> 00:33:24,120
Speaker 3: I think all the research that you've done, you've spoken

656
00:33:24,160 --> 00:33:26,080
Speaker 3: to people, you've I mean, I can't even begin to

657
00:33:26,120 --> 00:33:28,960
Speaker 3: imagine how much work you've had to do. But reality

658
00:33:29,000 --> 00:33:31,120
Speaker 3: of it is, if it all then had to hinge,

659
00:33:31,800 --> 00:33:34,720
Speaker 3: you know, on a two hour exam, I mean, would

660
00:33:35,000 --> 00:33:36,600
Speaker 3: that would just be crazy stuff?

661
00:33:37,360 --> 00:33:37,560
Speaker 4: Yeah?

662
00:33:37,560 --> 00:33:40,880
Speaker 1: Well it kind of does, It kind of does. Yeah, yeah, yeah,

663
00:33:40,880 --> 00:33:44,479
Speaker 1: because what you do is like five years depends, Like

664
00:33:44,560 --> 00:33:47,960
Speaker 1: I'm slow, but five years of research and out of

665
00:33:48,000 --> 00:33:52,040
Speaker 1: that essentially will come three or four or five papers

666
00:33:52,040 --> 00:33:54,520
Speaker 1: that you will submit to be published that will get

667
00:33:54,560 --> 00:33:58,560
Speaker 1: published or not. So, but I know what you're saying,

668
00:33:58,640 --> 00:34:04,280
Speaker 1: and I think the down look, I'm I'm I can

669
00:34:04,360 --> 00:34:06,720
Speaker 1: see the pros and cons of exams. The pros, Yeah,

670
00:34:06,720 --> 00:34:09,000
Speaker 1: but it's pressure, it says, yeah, but life's pressure, like

671
00:34:09,080 --> 00:34:12,440
Speaker 1: work is pressure. Like I was having this chat yesterday

672
00:34:12,480 --> 00:34:17,399
Speaker 1: with doctor Jody Richardson about how how much how much

673
00:34:17,480 --> 00:34:21,160
Speaker 1: do we protect and look after and you know, kind

674
00:34:21,239 --> 00:34:23,960
Speaker 1: of take care of our kids and how much do

675
00:34:24,000 --> 00:34:25,799
Speaker 1: we let them go for a run and fall over

676
00:34:25,840 --> 00:34:28,600
Speaker 1: and scrape their knees and not pick them up because

677
00:34:28,600 --> 00:34:31,680
Speaker 1: the truth is that, you know, once they hit adulthood,

678
00:34:31,680 --> 00:34:34,759
Speaker 1: nobody's running around to protect their feelings or emotions or

679
00:34:34,800 --> 00:34:40,320
Speaker 1: their mindset or their So it's like, I think kids

680
00:34:40,360 --> 00:34:43,240
Speaker 1: dealing with hard things is good because life is hard

681
00:34:43,360 --> 00:34:46,360
Speaker 1: or life is hard at times, and life is pressure

682
00:34:46,400 --> 00:34:49,200
Speaker 1: and stress and life is all of that. But I

683
00:34:49,280 --> 00:34:52,080
Speaker 1: know what you're saying, because there are actually some people

684
00:34:53,520 --> 00:34:56,319
Speaker 1: who are really good at exams naturally, and other people

685
00:34:56,400 --> 00:34:59,759
Speaker 1: who are really intelligent but just happen to crumble at

686
00:34:59,760 --> 00:35:03,239
Speaker 1: exac time. So it's it's I don't know what the

687
00:35:03,280 --> 00:35:05,960
Speaker 1: ideal model is mate. But you do make a good point.

688
00:35:06,400 --> 00:35:08,640
Speaker 3: Yeah, yeah, I kind of. I mean I wasn't. I

689
00:35:08,640 --> 00:35:11,319
Speaker 3: mean I did okay with exams. I felt that I

690
00:35:11,360 --> 00:35:13,680
Speaker 3: was actually not too bad with them. But I certainly

691
00:35:13,760 --> 00:35:16,839
Speaker 3: had friends who, as you say, would crumble and just

692
00:35:16,920 --> 00:35:20,560
Speaker 3: become a mess when put under that sort of sort

693
00:35:20,560 --> 00:35:25,720
Speaker 3: of pressure, which wasn't indicative of their knowledge and skills anyway.

694
00:35:25,760 --> 00:35:28,920
Speaker 3: But you're right, I guess it comes down to who

695
00:35:29,000 --> 00:35:31,799
Speaker 3: do you want operating on you as they're tying off

696
00:35:31,800 --> 00:35:37,160
Speaker 3: that last suture for that crucial operation. The person who

697
00:35:38,040 --> 00:35:40,120
Speaker 3: you know who got twenty in their exams of the

698
00:35:40,160 --> 00:35:41,400
Speaker 3: person who got ninety five.

699
00:35:42,840 --> 00:35:45,120
Speaker 1: Well, but how many people were shipped at school but

700
00:35:45,160 --> 00:35:48,799
Speaker 1: they're brilliant? I mean, so many people fucking crashed out

701
00:35:48,800 --> 00:35:51,920
Speaker 1: of school and you know I didn't, you know, finished

702
00:35:51,960 --> 00:35:55,640
Speaker 1: at school a year nine or ten. But it's just

703
00:35:56,120 --> 00:35:58,320
Speaker 1: not at all that they were done or not creative,

704
00:35:58,400 --> 00:36:01,719
Speaker 1: or not brilliant or not even had a low IQ

705
00:36:01,920 --> 00:36:04,440
Speaker 1: they had at high IQ. It's just that that for

706
00:36:04,600 --> 00:36:08,400
Speaker 1: some people. And we can't create one model that suits everyone,

707
00:36:08,480 --> 00:36:12,120
Speaker 1: so we're not blaming anyone. But yeah, it's intelligence is

708
00:36:12,120 --> 00:36:14,000
Speaker 1: a spectrum, not a single thing.

709
00:36:14,080 --> 00:36:16,719
Speaker 2: And it's around pegan a square hole. You know, it

710
00:36:16,960 --> 00:36:18,360
Speaker 2: just doesn't fit everybody.

711
00:36:18,360 --> 00:36:20,799
Speaker 3: And I think, you know, our society is such that

712
00:36:21,200 --> 00:36:24,400
Speaker 3: we can steer people to education and traditional education, but

713
00:36:24,920 --> 00:36:28,400
Speaker 3: you know, people learn other ways. People are ways to

714
00:36:28,440 --> 00:36:31,759
Speaker 3: take in information and process it. And I've met amazingly

715
00:36:31,800 --> 00:36:36,880
Speaker 3: smart people who had very little traditional education. And you know,

716
00:36:36,920 --> 00:36:38,919
Speaker 3: you hear these amazing stories. And there was a guy

717
00:36:38,960 --> 00:36:41,440
Speaker 3: in India, young guy in India who was a mathematician

718
00:36:41,880 --> 00:36:45,759
Speaker 3: and he was plucked out of his security and was

719
00:36:46,120 --> 00:36:47,960
Speaker 3: like one of the smartest people on the planet.

720
00:36:48,160 --> 00:36:49,719
Speaker 2: You know, how do you know that? How do you

721
00:36:49,840 --> 00:36:51,640
Speaker 2: test for that? You know, it's so ron.

722
00:36:52,840 --> 00:36:58,600
Speaker 1: Well, hey, let's talk about cars. Come on, tell me

723
00:36:58,640 --> 00:36:59,600
Speaker 1: something about cars.

724
00:37:00,360 --> 00:37:05,960
Speaker 3: So there's Mercedes Benz is researching a solar paint. So

725
00:37:06,000 --> 00:37:08,920
Speaker 3: what it means is, rather than just having panels, that

726
00:37:09,320 --> 00:37:11,640
Speaker 3: you put the paint onto the vehicle, so in this

727
00:37:11,719 --> 00:37:14,640
Speaker 3: case a car. And they're saying this is between five

728
00:37:14,719 --> 00:37:17,600
Speaker 3: to ten years away, and what it could effectively mean

729
00:37:17,920 --> 00:37:19,520
Speaker 3: is that a car.

730
00:37:19,840 --> 00:37:21,240
Speaker 2: We talked about a company in Norway.

731
00:37:21,280 --> 00:37:23,600
Speaker 3: Remember there was a startup in Norway that promised they

732
00:37:23,600 --> 00:37:25,800
Speaker 3: were going to do a solar car that was able

733
00:37:25,840 --> 00:37:27,600
Speaker 3: to charge the battery and you'd never you know, you

734
00:37:27,680 --> 00:37:32,000
Speaker 3: get thousands of kilometers and it flopped unfortunately. But now

735
00:37:32,000 --> 00:37:34,680
Speaker 3: they're saying that this is just kind of instead of

736
00:37:34,719 --> 00:37:38,000
Speaker 3: just coating the roof and the bonnet, they're saying that

737
00:37:38,040 --> 00:37:41,400
Speaker 3: you cover the entire car with this new solar paint,

738
00:37:42,200 --> 00:37:44,480
Speaker 3: and it means that you've got such a big surface

739
00:37:44,560 --> 00:37:47,600
Speaker 3: area you'll be able to use the solar paint to

740
00:37:47,719 --> 00:37:50,799
Speaker 3: charge the vehicle at any point that there's sunlight. I

741
00:37:50,800 --> 00:37:53,680
Speaker 3: mean that would be amazing, and I mean we're talking

742
00:37:53,760 --> 00:37:56,959
Speaker 3: at this stage. You know, it would cover maybe thirty

743
00:37:56,960 --> 00:37:59,840
Speaker 3: two kilometers a day, but for the little commutes and

744
00:38:00,120 --> 00:38:03,000
Speaker 3: to be able to trickle charge the car constantly would

745
00:38:03,040 --> 00:38:03,759
Speaker 3: be phenomenal.

746
00:38:04,160 --> 00:38:05,479
Speaker 2: Yeah, it's pretty pretty cool.

747
00:38:05,560 --> 00:38:07,319
Speaker 3: I was thinking you could get a backpack, or you

748
00:38:07,360 --> 00:38:10,799
Speaker 3: could paint your dog, your dog's coat, and then they.

749
00:38:10,840 --> 00:38:12,839
Speaker 1: You could paint the top of your head and keep

750
00:38:12,960 --> 00:38:14,200
Speaker 1: the whole house charged.

751
00:38:14,280 --> 00:38:15,880
Speaker 3: There's a lot of coverage there. There's a lot of

752
00:38:15,920 --> 00:38:18,120
Speaker 3: real estate. I should sell sign it shouldn't.

753
00:38:17,760 --> 00:38:23,200
Speaker 1: I you could, No, that's not nice. But I'm thinking

754
00:38:23,239 --> 00:38:26,480
Speaker 1: about like, if you've got paint that is sola paint,

755
00:38:27,080 --> 00:38:30,520
Speaker 1: that can then the catch twenty two is then you've

756
00:38:30,520 --> 00:38:32,279
Speaker 1: got to leave your car in the sun all the time,

757
00:38:32,320 --> 00:38:35,359
Speaker 1: which fucks up the interior. I guess you could put

758
00:38:35,360 --> 00:38:38,520
Speaker 1: one of those ugly silver things that my mum's got,

759
00:38:38,520 --> 00:38:41,040
Speaker 1: one of those ugly silver screen things that you put

760
00:38:41,040 --> 00:38:43,840
Speaker 1: across on top of the dash and inside the window.

761
00:38:45,200 --> 00:38:46,279
Speaker 2: I have one of those two.

762
00:38:46,800 --> 00:38:53,600
Speaker 1: Ah, but why doesn't that not surprise me? Nana? Tell us? What?

763
00:38:54,640 --> 00:38:58,120
Speaker 3: Wait a minute, Wait a minute. So when you park somewhere, say,

764
00:38:58,200 --> 00:38:59,040
Speaker 3: can you get your.

765
00:38:58,920 --> 00:39:01,680
Speaker 1: Voice down a couple of dive so I can respect you?

766
00:39:01,719 --> 00:39:04,480
Speaker 1: Could you loosen your underpants and start again?

767
00:39:04,680 --> 00:39:07,960
Speaker 2: Hey? You know I have a very impressive vocal range.

768
00:39:08,000 --> 00:39:12,799
Speaker 3: I'd like to tell you I can sing and bays No, no,

769
00:39:13,080 --> 00:39:17,719
Speaker 3: what I When you go out into like you go

770
00:39:17,760 --> 00:39:20,640
Speaker 3: to park at Bunnings, there's no shade. Right, you're out

771
00:39:20,640 --> 00:39:22,880
Speaker 3: of Bunnings, You've just spent an hour and a half

772
00:39:23,000 --> 00:39:25,320
Speaker 3: doing all your shopping. You get back into the car

773
00:39:25,719 --> 00:39:29,080
Speaker 3: and your ass sticks to the seat. Your hands are

774
00:39:29,120 --> 00:39:31,760
Speaker 3: glued to the steering wheel because they've just been seared

775
00:39:32,600 --> 00:39:33,640
Speaker 3: because it's a hot day.

776
00:39:33,840 --> 00:39:35,600
Speaker 2: How do you protect yourself from that crago?

777
00:39:36,120 --> 00:39:39,359
Speaker 1: Can I just say you will never You've never been

778
00:39:39,400 --> 00:39:42,880
Speaker 1: to Bunnings. You're talking about spotlight or something going to

779
00:39:42,880 --> 00:39:47,640
Speaker 1: Bunnies today? You dick what to get a sausage and

780
00:39:47,680 --> 00:39:48,400
Speaker 1: then head home.

781
00:39:48,680 --> 00:39:51,640
Speaker 2: I'm getting some sausage. What I'm vegan. I'm not going

782
00:39:51,640 --> 00:39:54,080
Speaker 2: to get a sausage vegan ones.

783
00:39:54,360 --> 00:39:58,240
Speaker 1: Hey, tell us what Ford Australia are doing in twenty

784
00:39:58,320 --> 00:39:58,919
Speaker 1: twenty five.

785
00:39:58,960 --> 00:40:02,759
Speaker 3: You seriously, I'm defending every motorist in Australia has a

786
00:40:02,840 --> 00:40:03,719
Speaker 3: silver foil thing.

787
00:40:04,000 --> 00:40:06,759
Speaker 1: Look, I get the practicality, I get it all right,

788
00:40:06,800 --> 00:40:08,879
Speaker 1: I'll give you a pass. I should probably get one,

789
00:40:08,960 --> 00:40:09,600
Speaker 1: but I won't.

790
00:40:10,080 --> 00:40:14,120
Speaker 2: Thing tip. Have you ever had one? No, I really

791
00:40:15,239 --> 00:40:16,600
Speaker 2: is just me. I'm the nana here.

792
00:40:17,880 --> 00:40:21,200
Speaker 1: It's probably I could guarantee more than half hour listeners agoing. No,

793
00:40:21,360 --> 00:40:23,719
Speaker 1: that's actually a good idea. Patrick's right, Craig, you're a

794
00:40:23,719 --> 00:40:27,799
Speaker 1: fucking idiot. Text Craig, you're a dickhead. I think you're

795
00:40:27,840 --> 00:40:30,799
Speaker 1: funny and not funny at all, and stop cutting Patrick off.

796
00:40:31,440 --> 00:40:32,680
Speaker 2: Yeah there you go.

797
00:40:33,200 --> 00:40:36,080
Speaker 3: Hey, you know this is a Tesla thing that always

798
00:40:36,160 --> 00:40:38,600
Speaker 3: rubs me the wrong way where you buy a car

799
00:40:39,120 --> 00:40:42,800
Speaker 3: but then you have to subscribe and pay for additional features.

800
00:40:43,440 --> 00:40:46,920
Speaker 3: But Ford is now going to roll this out. Now

801
00:40:48,040 --> 00:40:50,279
Speaker 3: what rubs me the wrong way with this story. So

802
00:40:50,280 --> 00:40:53,000
Speaker 3: they're saying that most Ford models, this is a story

803
00:40:53,000 --> 00:40:57,080
Speaker 3: that I saw since the mid twenty twenties, will soon

804
00:40:57,200 --> 00:41:01,880
Speaker 3: require owners to pay for some functions that are currently free.

805
00:41:02,840 --> 00:41:06,479
Speaker 2: So they're going to backdate it so for the near news.

806
00:41:06,560 --> 00:41:08,040
Speaker 3: So if you've got a car that's only a couple

807
00:41:08,080 --> 00:41:10,919
Speaker 3: of years old, potentially you may have to start paying

808
00:41:10,960 --> 00:41:14,560
Speaker 3: a subscription fee to accent to access certain features, so

809
00:41:14,880 --> 00:41:17,480
Speaker 3: in navigation services and all that sort of stuff. So

810
00:41:17,719 --> 00:41:20,680
Speaker 3: if you have in built navigation in your car, there's

811
00:41:20,680 --> 00:41:22,839
Speaker 3: a potential that you may have to pay for that.

812
00:41:23,000 --> 00:41:24,920
Speaker 3: And we're talking, you know, an annual fee of one

813
00:41:24,960 --> 00:41:27,759
Speaker 3: hundred and ten bucks. But that's just another thing that

814
00:41:27,800 --> 00:41:29,960
Speaker 3: you've got to pay on top of everything. And you know,

815
00:41:30,000 --> 00:41:31,959
Speaker 3: as far as I'm concerned, when you buy your car,

816
00:41:32,600 --> 00:41:33,080
Speaker 3: that's it.

817
00:41:33,320 --> 00:41:36,280
Speaker 2: You know, whatever the rules were when you bought it.

818
00:41:36,640 --> 00:41:38,640
Speaker 3: It's one thing to get into a contract and buy

819
00:41:38,640 --> 00:41:40,960
Speaker 3: a car and know that there are features that may

820
00:41:41,000 --> 00:41:43,040
Speaker 3: be locked out you've got to pay subscription for. But

821
00:41:43,080 --> 00:41:45,560
Speaker 3: if you buy it and they backdate it and they say, oh, you,

822
00:41:45,680 --> 00:41:47,879
Speaker 3: by the way, that car you bought two years ago,

823
00:41:48,120 --> 00:41:50,360
Speaker 3: we've changed the rules and now you can't use your

824
00:41:50,800 --> 00:41:51,840
Speaker 3: satellite navigation.

825
00:41:52,719 --> 00:41:54,840
Speaker 1: That sounds like they've got a bunch of people in

826
00:41:54,880 --> 00:41:57,719
Speaker 1: a boardroom and said, what's the worst fucking pr move

827
00:41:57,760 --> 00:42:01,600
Speaker 1: we could make? Right? That's like, rather, if you want

828
00:42:01,640 --> 00:42:04,239
Speaker 1: to make another hundred bucks, just put the car up

829
00:42:04,280 --> 00:42:07,040
Speaker 1: by a hundred bucks. But don't because it's not even

830
00:42:07,080 --> 00:42:09,239
Speaker 1: about the hundred bucks. Well it's a bit about that,

831
00:42:09,320 --> 00:42:12,600
Speaker 1: but it's also about how dare you fucking do that.

832
00:42:12,680 --> 00:42:16,240
Speaker 1: I'm not buying a Ford now, so stick your whatever,

833
00:42:16,400 --> 00:42:19,480
Speaker 1: your search arge or your payball up your ass. I'm

834
00:42:19,480 --> 00:42:23,279
Speaker 1: going to buy a Masda. It's like that tell us

835
00:42:23,280 --> 00:42:28,560
Speaker 1: what we've been searching for on the interwebs, Patrick, what's what? Yeah?

836
00:42:28,680 --> 00:42:29,040
Speaker 2: Okay?

837
00:42:29,360 --> 00:42:31,640
Speaker 3: So overall I always find this fun at the end

838
00:42:31,680 --> 00:42:34,680
Speaker 3: of the year to see what people search for. And

839
00:42:35,440 --> 00:42:38,600
Speaker 3: I mean, obviously the big, big, big one that was

840
00:42:38,640 --> 00:42:41,239
Speaker 3: of interest to everybody in Australia as well as the

841
00:42:41,280 --> 00:42:43,840
Speaker 3: rest of the world was the US election. So no

842
00:42:44,040 --> 00:42:47,799
Speaker 3: question overall, the US election came out on top as

843
00:42:47,800 --> 00:42:51,279
Speaker 3: far as Google searching for Australians over the last year.

844
00:42:51,760 --> 00:42:57,319
Speaker 3: The Olympic medal telling that was really high euros. Liam Payne.

845
00:42:57,320 --> 00:42:59,239
Speaker 3: I don't even know who Liam pain is. Anybody know

846
00:42:59,280 --> 00:43:02,239
Speaker 3: who Liam Payne's no idea Liam Payne was.

847
00:43:02,800 --> 00:43:06,879
Speaker 1: Wow he can you google LAMB Payne and just add

848
00:43:06,920 --> 00:43:08,800
Speaker 1: to the tally. Well, Taylor Swift.

849
00:43:08,800 --> 00:43:11,440
Speaker 3: There's no kind of surprise there, But what when you

850
00:43:11,520 --> 00:43:14,319
Speaker 3: break it down. I mean, obviously the election was one

851
00:43:14,360 --> 00:43:17,600
Speaker 3: of the news events, and it's interesting that overall US

852
00:43:17,680 --> 00:43:20,000
Speaker 3: election and medal tally and then news events you a

853
00:43:20,080 --> 00:43:23,840
Speaker 3: selection and medal tally, but there were other global figures.

854
00:43:23,960 --> 00:43:27,080
Speaker 3: This is internationally Taylor Swift number one, and then for

855
00:43:27,160 --> 00:43:32,880
Speaker 3: people and then Donald Trump, Kate Middleton, like Kamala Harris Craig.

856
00:43:32,920 --> 00:43:34,040
Speaker 3: You're not even on the list.

857
00:43:34,280 --> 00:43:34,800
Speaker 2: I looked.

858
00:43:35,480 --> 00:43:39,120
Speaker 1: But that's disappointing. I know. Yeah, wow, I must be

859
00:43:39,239 --> 00:43:40,520
Speaker 1: I must be number eleven.

860
00:43:40,840 --> 00:43:44,880
Speaker 2: Yeah, that could be. It recipes Jamie Oliver's air fryer recipes.

861
00:43:45,200 --> 00:43:50,520
Speaker 2: Cucumber salad. How about cucumber salad. That's it's pretty.

862
00:43:50,320 --> 00:43:54,040
Speaker 1: It sounds like that sounds like a youth thing at

863
00:43:54,040 --> 00:43:57,560
Speaker 1: cucumber salad. Let's chuck some cow on it now you're talking.

864
00:43:58,160 --> 00:44:00,400
Speaker 3: Now, This one I found really interesting and I know

865
00:44:00,440 --> 00:44:04,120
Speaker 3: you're going to like this one because the most popular

866
00:44:04,200 --> 00:44:07,840
Speaker 3: words searched, so people looking for definitions of words.

867
00:44:08,400 --> 00:44:08,920
Speaker 2: Demure.

868
00:44:10,920 --> 00:44:14,960
Speaker 1: Wow, demure, I'll tell you none of us are demure,

869
00:44:17,160 --> 00:44:18,160
Speaker 1: but you know none of us.

870
00:44:19,000 --> 00:44:21,600
Speaker 3: But how random is it that people were the most

871
00:44:21,680 --> 00:44:25,239
Speaker 3: searched word? And the reason for it was that evidently

872
00:44:25,640 --> 00:44:29,799
Speaker 3: a social influencer used the word to describe herself, and

873
00:44:29,960 --> 00:44:32,960
Speaker 3: subsequently millions of people jumped on and started looking for

874
00:44:32,960 --> 00:44:38,080
Speaker 3: the word the definition demure. Would you describe yourself as demure?

875
00:44:38,400 --> 00:44:43,000
Speaker 2: Tiff? No?

876
00:44:44,640 --> 00:44:47,680
Speaker 1: Yeah, get a tiff? And I'm a wallflower. I can

877
00:44:47,760 --> 00:44:50,360
Speaker 1: bench a fuck load and you should see me deadlift

878
00:44:53,560 --> 00:44:56,960
Speaker 1: And what are you looking at? Yeah? What about? She's

879
00:44:57,000 --> 00:44:59,440
Speaker 1: not like that anymore since she got back from India.

880
00:44:59,480 --> 00:45:02,359
Speaker 1: She's all that Lama and calm the farm and equanimity

881
00:45:02,480 --> 00:45:06,799
Speaker 1: and fucking dream catches and moccasins and give us a hug.

882
00:45:06,960 --> 00:45:08,640
Speaker 1: She's she's new and improved.

883
00:45:09,040 --> 00:45:11,560
Speaker 5: Speaking of the Deli Lama, this morning, when I was

884
00:45:11,719 --> 00:45:15,799
Speaker 5: scrolling on instat I saw Johann Hari's real saying that

885
00:45:15,840 --> 00:45:18,120
Speaker 5: he got fat shamed by the Delai Lama.

886
00:45:19,200 --> 00:45:20,120
Speaker 1: That's hilarious.

887
00:45:21,000 --> 00:45:23,240
Speaker 2: Can I say I have a connection with the Dalai Lama?

888
00:45:24,760 --> 00:45:25,560
Speaker 1: You can say it.

889
00:45:25,880 --> 00:45:28,600
Speaker 3: We were we were born on the same day. And

890
00:45:28,640 --> 00:45:30,759
Speaker 3: I have seen him once as I saw him at

891
00:45:30,760 --> 00:45:32,839
Speaker 3: a conference. He was pretty amazing. She's quite a cool guy.

892
00:45:32,920 --> 00:45:35,120
Speaker 3: He's got a really cute giggle. Have you noticed if

893
00:45:35,120 --> 00:45:37,520
Speaker 3: you've seen the Dalai Lama? Have you ever seen any stuff?

894
00:45:37,560 --> 00:45:38,720
Speaker 1: You and I saw him together?

895
00:45:38,760 --> 00:45:41,560
Speaker 6: You dickhead with me at the Dalai Lama thing? Fuck

896
00:45:41,640 --> 00:45:43,960
Speaker 6: and hell, you're the worst date ever. That was a

897
00:45:44,000 --> 00:45:46,960
Speaker 6: long time ago. Did we held your hand the whole time?

898
00:45:48,520 --> 00:45:51,400
Speaker 6: Especially in the scary bits when he looked into your soul?

899
00:45:54,400 --> 00:45:56,399
Speaker 2: Man? That was a long time ago. How many years

900
00:45:56,400 --> 00:45:57,120
Speaker 2: ago would had been?

901
00:45:57,719 --> 00:45:59,680
Speaker 1: I don't know, but I don't feel I don't feel

902
00:45:59,680 --> 00:46:03,640
Speaker 1: special at all. Now, Wow, what was that movie? We

903
00:46:03,680 --> 00:46:06,319
Speaker 1: saw that three D? What's it called again? Oh?

904
00:46:06,400 --> 00:46:12,680
Speaker 3: Yeah, we saw it was Oh the what was it called?

905
00:46:12,800 --> 00:46:13,919
Speaker 1: You know? Those blue people?

906
00:46:16,440 --> 00:46:16,880
Speaker 6: You said it?

907
00:46:16,920 --> 00:46:22,000
Speaker 1: I thought, so what a mix we've seen the we've

908
00:46:22,000 --> 00:46:26,520
Speaker 1: seen Avatar and the Dali Lama not on the same day.

909
00:46:27,960 --> 00:46:30,600
Speaker 5: Back to Liam Pain, it's the one direction singer that

910
00:46:30,719 --> 00:46:31,200
Speaker 5: died for.

911
00:46:33,280 --> 00:46:36,640
Speaker 1: I remember the name from somewhere, Okay, now I didn't ye,

912
00:46:37,120 --> 00:46:37,680
Speaker 1: now I know?

913
00:46:38,280 --> 00:46:39,239
Speaker 2: So what about Di?

914
00:46:39,360 --> 00:46:39,719
Speaker 1: I y?

915
00:46:39,960 --> 00:46:42,839
Speaker 3: This is an interesting What do people most need help with?

916
00:46:43,120 --> 00:46:47,080
Speaker 3: When they do Google searching, di y car maintenance. So

917
00:46:47,120 --> 00:46:49,880
Speaker 3: people are still maintaining their cars. If you popped the

918
00:46:49,880 --> 00:46:52,720
Speaker 3: bondon on your new car, because you can't recognize anything

919
00:46:52,800 --> 00:46:54,279
Speaker 3: under that bond, can you like.

920
00:46:54,280 --> 00:46:59,040
Speaker 1: There's I got no idea. When you turn on my car,

921
00:46:59,080 --> 00:47:03,000
Speaker 1: there's no sound because it's hybrid, so you don't even

922
00:47:03,040 --> 00:47:06,399
Speaker 1: know it's on. It's like is it on? See?

923
00:47:06,400 --> 00:47:08,520
Speaker 3: You might find this hard to believe, but I've changed

924
00:47:08,520 --> 00:47:11,719
Speaker 3: the spark plugs on my first car. I repaired my

925
00:47:11,800 --> 00:47:16,040
Speaker 3: first car myself. Remember the front bumpers on old cars were.

926
00:47:16,040 --> 00:47:21,160
Speaker 1: Actually hang on. Let's define I repaired, yeah, because that's

927
00:47:21,360 --> 00:47:23,239
Speaker 1: come on, what does that mean?

928
00:47:23,520 --> 00:47:23,880
Speaker 2: Okay?

929
00:47:23,920 --> 00:47:25,840
Speaker 3: So I may have got into a little bit of

930
00:47:26,040 --> 00:47:27,719
Speaker 3: an accident when I was nineteen.

931
00:47:27,840 --> 00:47:30,320
Speaker 2: I was on tram tracks on Sydney Road. It was

932
00:47:30,320 --> 00:47:31,000
Speaker 2: a wet day.

933
00:47:31,320 --> 00:47:33,200
Speaker 3: Guy slamms the brakes on in front of me and

934
00:47:33,239 --> 00:47:35,120
Speaker 3: I was too close and I skid it into him.

935
00:47:35,480 --> 00:47:38,040
Speaker 3: So but I couldn't afford the repair, so I did

936
00:47:38,040 --> 00:47:42,080
Speaker 3: it myself. So I replaced the headlight myself, just bought

937
00:47:42,120 --> 00:47:42,760
Speaker 3: a new headlight.

938
00:47:42,800 --> 00:47:44,560
Speaker 2: I did the panel beating by myself.

939
00:47:44,719 --> 00:47:47,160
Speaker 3: But the funniest thing was the panel beating wasn't that

940
00:47:47,200 --> 00:47:49,279
Speaker 3: hard to kind of tap things out. I put a

941
00:47:49,400 --> 00:47:52,960
Speaker 3: jack inside the because the car was a Mazda eighth eight,

942
00:47:53,360 --> 00:47:55,239
Speaker 3: so it had a little tiny engine but a really

943
00:47:55,280 --> 00:47:58,480
Speaker 3: long body, so it meant that there was no mechanical damage.

944
00:47:58,560 --> 00:48:00,320
Speaker 3: And so what I did was I put a plank

945
00:48:00,320 --> 00:48:02,640
Speaker 3: of wood and the jack, and I jacked out the front.

946
00:48:02,400 --> 00:48:03,960
Speaker 2: Of the car to push it back out again.

947
00:48:04,840 --> 00:48:07,280
Speaker 3: But the best thing was remember the chrome bumper bars

948
00:48:07,320 --> 00:48:09,879
Speaker 3: on Rudy, because my car was a nineteen seventies maas

949
00:48:09,920 --> 00:48:12,480
Speaker 3: to eight oh eight, and the chrome bumpers were solid,

950
00:48:12,640 --> 00:48:15,520
Speaker 3: and it was so solid even though it had been buckled,

951
00:48:15,560 --> 00:48:17,359
Speaker 3: I couldn't bend it back. So I put it over

952
00:48:17,480 --> 00:48:21,280
Speaker 3: Dad's gas barbecue, got it red hot, and then tapped

953
00:48:21,560 --> 00:48:23,000
Speaker 3: out and got it back into shape.

954
00:48:23,040 --> 00:48:25,200
Speaker 2: So I did that myself. I think you should be

955
00:48:25,200 --> 00:48:25,640
Speaker 2: proud of me.

956
00:48:27,800 --> 00:48:30,440
Speaker 1: I would love to see a photo of the finished product.

957
00:48:30,880 --> 00:48:34,319
Speaker 2: It survived for another six years after that. I might say,

958
00:48:36,040 --> 00:48:36,840
Speaker 2: little mouse.

959
00:48:36,800 --> 00:48:43,160
Speaker 1: Well, if I have a little fender bender, I definitely

960
00:48:43,239 --> 00:48:44,160
Speaker 1: will not give you a.

961
00:48:44,120 --> 00:48:46,880
Speaker 3: Call no, because they're plastic. It wouldn't matter. I just

962
00:48:46,880 --> 00:48:48,799
Speaker 3: need a glue gun to put it back together again.

963
00:48:48,800 --> 00:48:53,880
Speaker 3: I wouldn't need anything metal. The car maintenance was number

964
00:48:53,920 --> 00:48:59,120
Speaker 3: one for DIY. The second one was DIY Halloween Costumes,

965
00:48:59,400 --> 00:49:02,840
Speaker 3: and then follow by how to make an East Buddy footprints.

966
00:49:04,560 --> 00:49:06,640
Speaker 1: I would have thought high on the list would be

967
00:49:06,800 --> 00:49:08,960
Speaker 1: DIY like medical.

968
00:49:08,880 --> 00:49:11,280
Speaker 2: Yeah, I know how to perform the CPR.

969
00:49:12,200 --> 00:49:15,760
Speaker 1: Yeah, how to well, how to give yourself an appendectomy

970
00:49:16,360 --> 00:49:19,279
Speaker 1: with a sewing kit and a Swiss army knife and

971
00:49:19,320 --> 00:49:20,160
Speaker 1: some gin.

972
00:49:22,320 --> 00:49:22,720
Speaker 2: Travel.

973
00:49:23,000 --> 00:49:24,439
Speaker 3: Do you know where people want to go the most

974
00:49:24,440 --> 00:49:28,000
Speaker 3: in Australia if they live, where they want to go?

975
00:49:28,200 --> 00:49:28,800
Speaker 2: Gold Coast?

976
00:49:29,200 --> 00:49:31,480
Speaker 3: Cheap flights for the Gold Coast from Melbourne that was

977
00:49:31,520 --> 00:49:35,120
Speaker 3: the most searched travel term. And then flights to King

978
00:49:35,160 --> 00:49:38,120
Speaker 3: Island and then Jetstar cheap flights to Melbourne. Oh so

979
00:49:38,160 --> 00:49:39,799
Speaker 3: people do want to come to Melbourne? They don't, don't

980
00:49:39,800 --> 00:49:40,840
Speaker 3: just want to leave Melbourne.

981
00:49:41,040 --> 00:49:42,640
Speaker 1: Do you know what's going to sell well in the

982
00:49:42,640 --> 00:49:43,320
Speaker 1: Gold Coast?

983
00:49:43,680 --> 00:49:44,040
Speaker 2: What's that?

984
00:49:45,040 --> 00:49:46,200
Speaker 1: Solar paint cars?

985
00:49:46,560 --> 00:49:49,440
Speaker 2: Oh yeah, how good would that be? Or in Central Australia?

986
00:49:50,360 --> 00:49:53,600
Speaker 1: Oh wow, imagine that. I think, all right, let's do

987
00:49:53,719 --> 00:49:56,880
Speaker 1: one more. Let's do one more, mister host Well.

988
00:49:56,920 --> 00:49:59,200
Speaker 3: Actually, one of the ones that I thought was interesting

989
00:49:59,400 --> 00:50:04,520
Speaker 3: related to social media, and they talk about regularly posting

990
00:50:04,520 --> 00:50:07,960
Speaker 3: on socials as opposed to just looking at socials can

991
00:50:08,000 --> 00:50:12,040
Speaker 3: actually worsen the mental health in adults. So if you've

992
00:50:12,080 --> 00:50:15,960
Speaker 3: been caught up in that kind of mailstream of have

993
00:50:16,040 --> 00:50:19,400
Speaker 3: to post, got to give a status update, that now

994
00:50:19,480 --> 00:50:21,760
Speaker 3: is being thought of as being having a real impact.

995
00:50:21,760 --> 00:50:25,080
Speaker 3: So the Journal of Medical Internet Research, so they were

996
00:50:25,200 --> 00:50:29,319
Speaker 3: investigating fifteen thousand adults in the United Kingdom all over

997
00:50:29,360 --> 00:50:32,960
Speaker 3: the age of sixteen, and what they said was simply

998
00:50:33,120 --> 00:50:36,759
Speaker 3: viewing social media content. Content didn't seem to have the

999
00:50:36,760 --> 00:50:41,520
Speaker 3: same effect as regularly posting on social media. So if

1000
00:50:41,560 --> 00:50:44,279
Speaker 3: you fall into that category of someone who has to

1001
00:50:44,320 --> 00:50:47,080
Speaker 3: post what you're doing all the time, it actually may

1002
00:50:47,320 --> 00:50:51,160
Speaker 3: worsen your mental health. It could have a health implications

1003
00:50:51,360 --> 00:50:53,560
Speaker 3: if you feel the need to keep posting. I mean,

1004
00:50:53,640 --> 00:50:56,480
Speaker 3: do you guys post much on socials?

1005
00:50:56,640 --> 00:50:58,360
Speaker 5: Post every four and a half minutes?

1006
00:50:58,560 --> 00:50:59,040
Speaker 2: Oh really?

1007
00:51:00,520 --> 00:51:03,279
Speaker 1: Her life was fucking as public as you can get.

1008
00:51:03,480 --> 00:51:03,759
Speaker 1: I don't.

1009
00:51:03,800 --> 00:51:06,640
Speaker 5: Every time my dog changes position, I take a photo

1010
00:51:06,640 --> 00:51:07,319
Speaker 5: and post it.

1011
00:51:08,040 --> 00:51:08,239
Speaker 1: Wow.

1012
00:51:08,440 --> 00:51:11,680
Speaker 2: Yeah, I think I posted once this year, and that's

1013
00:51:11,719 --> 00:51:12,400
Speaker 2: what I post.

1014
00:51:12,600 --> 00:51:17,120
Speaker 1: I just post messages like whiteboards. I don't really go, hey,

1015
00:51:17,120 --> 00:51:20,399
Speaker 1: here's my breky. Sometimes I'll post something that I think

1016
00:51:20,520 --> 00:51:23,480
Speaker 1: might be like a personal thing that I think might

1017
00:51:23,560 --> 00:51:26,919
Speaker 1: be like my steps. I don't know if I told

1018
00:51:27,000 --> 00:51:29,200
Speaker 1: you this. Did I tell you that my phone this,

1019
00:51:29,320 --> 00:51:31,560
Speaker 1: Speaking of tech, my phone told me I was a fat,

1020
00:51:31,600 --> 00:51:36,320
Speaker 1: lazy fuck. Did I tell you if my phone literally

1021
00:51:36,400 --> 00:51:41,239
Speaker 1: sent me? In my phone, it literally sent me an

1022
00:51:41,320 --> 00:51:45,520
Speaker 1: alert telling me that I don't Essentially, you're not walking

1023
00:51:45,600 --> 00:51:50,000
Speaker 1: much and you need to walk more. So I took

1024
00:51:50,040 --> 00:51:55,400
Speaker 1: that on board from my pocket coach, and yeah, I've

1025
00:51:55,520 --> 00:51:59,480
Speaker 1: gone from average under five thousand a day to average

1026
00:51:59,560 --> 00:52:02,240
Speaker 1: just under fifteen thousand a day. But it was funny

1027
00:52:02,239 --> 00:52:05,439
Speaker 1: because for me, and actually I did not realize how

1028
00:52:05,480 --> 00:52:09,000
Speaker 1: little I was working walking and generally I was walking

1029
00:52:09,040 --> 00:52:11,080
Speaker 1: more than that. But the last few months where I've

1030
00:52:11,120 --> 00:52:14,839
Speaker 1: been deep, balls deep, as the kids say, or maybe

1031
00:52:14,840 --> 00:52:18,719
Speaker 1: the kids don't say that. Maybe that's me balls deep

1032
00:52:18,719 --> 00:52:22,960
Speaker 1: in my PhD and recording and reading. And I've been

1033
00:52:23,040 --> 00:52:27,040
Speaker 1: sitting down a lot. And yeah, like even this morning,

1034
00:52:27,080 --> 00:52:29,800
Speaker 1: I got up at quarter past five and I've walked

1035
00:52:29,840 --> 00:52:32,880
Speaker 1: five ks and it's eight twenty six right now. But

1036
00:52:33,239 --> 00:52:35,680
Speaker 1: it's been a I know this sounds weird, but one

1037
00:52:35,800 --> 00:52:38,799
Speaker 1: it was a really good catalyst. But it's been a

1038
00:52:38,800 --> 00:52:41,000
Speaker 1: game changer for me. But that's got nothing to do

1039
00:52:41,080 --> 00:52:41,520
Speaker 1: with tech.

1040
00:52:42,120 --> 00:52:43,600
Speaker 2: No, it has have everything to do with tech. What

1041
00:52:43,640 --> 00:52:45,719
Speaker 2: do you mean it was your phone? Your phone was.

1042
00:52:45,719 --> 00:52:48,840
Speaker 1: Telling well, I mean that my physiological response.

1043
00:52:50,000 --> 00:52:51,480
Speaker 2: But it can help prompt you know.

1044
00:52:51,480 --> 00:52:54,719
Speaker 3: It's not dissimilar to having the conversation with Ai like

1045
00:52:54,800 --> 00:52:55,920
Speaker 3: I did earlier in the show.

1046
00:52:56,440 --> 00:53:00,359
Speaker 2: It's that sense of not being alone, you know, can

1047
00:53:00,400 --> 00:53:00,799
Speaker 2: do all.

1048
00:53:00,719 --> 00:53:02,480
Speaker 3: That sort of stuff. Although I've got to say this

1049
00:53:02,520 --> 00:53:04,400
Speaker 3: is quite funny. I was just in the middle of

1050
00:53:04,400 --> 00:53:06,480
Speaker 3: a Thai cheek class. I think I've been doing Tai

1051
00:53:06,600 --> 00:53:10,000
Speaker 3: chie for about forty five minutes, and my watch pinged

1052
00:53:10,040 --> 00:53:12,120
Speaker 3: me and said you need to get moving.

1053
00:53:12,440 --> 00:53:15,720
Speaker 6: It's like, what forty five minutes, you idiot?

1054
00:53:16,440 --> 00:53:19,400
Speaker 3: I think I was just so relaxed and my heart

1055
00:53:19,600 --> 00:53:23,480
Speaker 3: was so relaxed that it thought that I wasn't moving. Well,

1056
00:53:23,480 --> 00:53:25,279
Speaker 3: that's what Tai Chee's about, isn't it. It's a slow

1057
00:53:25,320 --> 00:53:28,040
Speaker 3: moving meditation. So yeah, idea not to end up with

1058
00:53:28,080 --> 00:53:30,560
Speaker 3: a sweat and panting at the end of it, but

1059
00:53:30,600 --> 00:53:34,080
Speaker 3: to feel relaxed and centered and mindful. See when I

1060
00:53:34,160 --> 00:53:36,719
Speaker 3: was thinking about that, when Cragio said before that, you

1061
00:53:36,800 --> 00:53:39,279
Speaker 3: know you've become a slightly different person, and you're more

1062
00:53:39,360 --> 00:53:42,239
Speaker 3: chilled and relaxed since going to India. It made me

1063
00:53:42,280 --> 00:53:43,680
Speaker 3: think that you and I could have hung out of

1064
00:53:43,719 --> 00:53:45,759
Speaker 3: this mindfulness seminar because I reckon you would got to

1065
00:53:45,800 --> 00:53:47,200
Speaker 3: lay out out of that it was good.

1066
00:53:47,520 --> 00:53:49,799
Speaker 2: I would just living. Yeah.

1067
00:53:50,000 --> 00:53:52,839
Speaker 3: I love the idea of living in the moment for

1068
00:53:52,880 --> 00:53:55,239
Speaker 3: the moment, not letting anything else. And that's what Tai

1069
00:53:55,360 --> 00:53:57,600
Speaker 3: Cheek does for me. It's great because nothing else is

1070
00:53:57,600 --> 00:53:59,400
Speaker 3: in my head and I can just focus on what

1071
00:53:59,440 --> 00:54:02,440
Speaker 3: I'm doing. The thing that I got out of it

1072
00:54:02,600 --> 00:54:06,040
Speaker 3: was five minutes a day of mindfulness. Just five minutes

1073
00:54:06,080 --> 00:54:08,880
Speaker 3: a day is a real gift to yourself. Just you know,

1074
00:54:08,960 --> 00:54:11,880
Speaker 3: that's hectic lifestyle, Crago. You know you're getting up and

1075
00:54:11,920 --> 00:54:13,640
Speaker 3: walking and doing all that sort of stuff. But you

1076
00:54:13,719 --> 00:54:16,520
Speaker 3: use walking as a bit of a moving meditation, don't you.

1077
00:54:16,520 --> 00:54:18,000
Speaker 3: You kind of just in your zone.

1078
00:54:18,160 --> 00:54:21,720
Speaker 1: I do, I do, I do. It's funny we should

1079
00:54:21,760 --> 00:54:25,200
Speaker 1: talk about what, because what for somebody will be a meditation,

1080
00:54:25,400 --> 00:54:28,919
Speaker 1: for somebody else will be a source of anxiety. You know.

1081
00:54:29,040 --> 00:54:31,560
Speaker 1: So mate, tell people how they can connect with you

1082
00:54:31,600 --> 00:54:32,239
Speaker 1: and follow you.

1083
00:54:32,840 --> 00:54:35,239
Speaker 3: Oh sure, yeah, So you just need to go to

1084
00:54:35,360 --> 00:54:40,160
Speaker 3: websites now, dot com dot AU and just trying to

1085
00:54:40,239 --> 00:54:42,160
Speaker 3: check out what we do. I mean, obviously websites now,

1086
00:54:42,200 --> 00:54:45,360
Speaker 3: dot com dot you speaks for itself. But it's just

1087
00:54:45,400 --> 00:54:47,960
Speaker 3: too hard to spell Genesis FX because it's just a

1088
00:54:48,000 --> 00:54:50,600
Speaker 3: stupid name that I came up with before the Internet existed,

1089
00:54:50,800 --> 00:54:52,960
Speaker 3: and people can't spell it. So I had to come

1090
00:54:53,000 --> 00:54:56,000
Speaker 3: up with another name for our website. So websites Now

1091
00:54:56,160 --> 00:54:58,080
Speaker 3: kind of made sense, didn't it. Really, When you think

1092
00:54:58,080 --> 00:54:58,600
Speaker 3: about it, I.

1093
00:54:58,520 --> 00:55:00,920
Speaker 1: Think it was a good change. It was a good change.

1094
00:55:01,320 --> 00:55:04,080
Speaker 1: There's been quite a few businesses who have just changed

1095
00:55:04,120 --> 00:55:06,680
Speaker 1: their name and nothing else and their business catapulted.

1096
00:55:07,080 --> 00:55:07,440
Speaker 2: Really.

1097
00:55:08,000 --> 00:55:10,640
Speaker 1: Oh yeah, do you remember Tara and Russ had a

1098
00:55:10,680 --> 00:55:14,680
Speaker 1: business in Glenn Huntley Road. It was called High Energy Network.

1099
00:55:15,000 --> 00:55:17,360
Speaker 2: Yeah, I know, I did a lot, they did quite well.

1100
00:55:18,040 --> 00:55:20,480
Speaker 1: But then we had a meeting one day and they

1101
00:55:20,480 --> 00:55:22,560
Speaker 1: were talking about how to improve the business and I said,

1102
00:55:22,560 --> 00:55:27,080
Speaker 1: why don't you change the name to Australian Fitness Academy.

1103
00:55:27,840 --> 00:55:31,759
Speaker 1: And they went and they did that and just that

1104
00:55:32,640 --> 00:55:38,840
Speaker 1: double tripled, like interest inquiries turnover bums on seats, because

1105
00:55:38,880 --> 00:55:42,640
Speaker 1: it explains straight away what it is versus you know,

1106
00:55:42,719 --> 00:55:45,879
Speaker 1: high energy network. Fucking what is what? Does that even mean?

1107
00:55:46,000 --> 00:55:48,560
Speaker 1: So so the name matters, mate, I'm going to go

1108
00:55:48,600 --> 00:55:51,720
Speaker 1: because I've got a thing. Thanks TIV, Thanks Patrick,