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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 crooko bar. In the background,

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Speaker 1: the typ Cooko Borrough That is Tiff, good morning. Well

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Speaker 1: let's start with fucking Captain Spock, who's there in his

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Speaker 1: I wish you could see what I can see everyone,

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Speaker 1: because Patrick looks like he's at the International Space Station.

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Speaker 1: He's got a big round window behind him, and is

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Speaker 1: that I should know? But is that Earth behind you?

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Speaker 1: What is that?

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Speaker 2: No? I think it's a random planet that might be Earth,

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Speaker 2: But now there's too many other satellites and things in

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Speaker 2: the sky behind me to actually be the planet Earth.

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Speaker 2: So I think it's imagined 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, real gorgeous nerde isn't he?

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

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Speaker 1: up and squeeze him till he farts?

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Speaker 2: He's so cute, so much want to do that to

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Speaker 2: anybody there is. There's no circumstance that I want to

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Speaker 2: crushed someone to the point you start screeting stuff. Well,

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Speaker 2: I didn't say screeting. I'm not talking about shitting. I'm

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Speaker 2: not going to squeeze you to your shit, just going

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Speaker 2: to like a little cute.

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Speaker 1: Like harmless shirt out the back. There is it.

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Speaker 2: That's the problem though, right what when it when a

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Speaker 2: fart 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, ah fuck, 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 Harts. How are you very good? Thanks? Happy Friday,

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

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Speaker 1: we are all at at the moment, it's twenty five

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

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

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

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Speaker 2: And can I just say it's not indicative of all

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Speaker 2: the quality recordings that you'll be able to listen to

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Speaker 2: in the back catalog. So this only happens once a fortnight,

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Speaker 2: so listens are really good shows, just you know, do

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Speaker 2: 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 struggle. 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.

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Speaker 2: Anyway, I don't know what you said earlier, but it

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Speaker 2: made me think of my doom scrolling on YouTube this morning,

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Speaker 2: and somehow in my feed it came up with when

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Speaker 2: bullies get their own back? So, you know, when bullies

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Speaker 2: bully people and the underdog gets back, and then it

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Speaker 2: just fight scenes from movies. Basically, oh really, just pick

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Speaker 2: on someone, but then the 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 size, Patrick, and the guy that

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

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

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

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

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

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

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

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

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

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Speaker 1: an eyelid, walk stepped over him, and walked off with

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Speaker 1: his cash.

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Speaker 2: But you know what I took from that thing, just

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Speaker 2: what you just said. Wow, the little guy was your

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Speaker 2: size and the other guy was bigger than me. What

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Speaker 2: impassively bigger and I'm that tiny little weakling the.

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

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

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

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Speaker 1: leave you there.

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

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

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Speaker 1: he like?

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Speaker 2: Four foot taol? How what are you about? Five seven,

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Speaker 2: one hundred and sixty nine centimeters? I don't know five

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Speaker 2: 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 back. Ohright, enough

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

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Speaker 1: know 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?

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Speaker 2: Yeah, you didn't preface it with anything? Should I? I should

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Speaker 2: let you judge whether it's exciting or not?

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Speaker 1: Well, Tiff can judge once atten tive, 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?

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Speaker 2: It is relative though. No. I had an amazing experience

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Speaker 2: last week with Victoria University. I was hosting their very

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Speaker 2: first They're inaugural AI hackathon. So the idea isn't it

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Speaker 2: related to we're coincided with International Disability Day, and what

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Speaker 2: they did was they got their students to come up

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Speaker 2: with ways to use AI to help people with disabilities.

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Speaker 2: So they had a week to plan it and they

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Speaker 2: started it on a Tuesday. We had a session with

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Speaker 2: Microsoft Microsoft brief as I was able to sit in

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Speaker 2: my International Space station right here at home because it

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Speaker 2: was in Sydney, it was in it was in per

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Speaker 2: not Perth. Was it Sydney, Brisbane and Melbourne. And it

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Speaker 2: was amazing. But what was really cool that came out

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Speaker 2: of it was there was a couple of things that

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Speaker 2: I unpacked from it. One was a lot of the

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Speaker 2: students didn't know anybody with a disability, which kind of

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Speaker 2: blew my mind a little bit. They kind of admitted

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Speaker 2: that this was the first time they had really thought

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Speaker 2: about what it was like to be a disabled person.

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Speaker 2: But some of the ideas they came up with were fantastic.

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Speaker 2: You know, one guy came up with an idea for

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Speaker 2: an app so that you could use it to navigate

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Speaker 2: on campus at VU. But what he also did was

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Speaker 2: he said he would integrate, so if you've got a wheelchair,

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Speaker 2: then getting from A to B as the crow flies

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Speaker 2: may not work for you, So you choose the route

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Speaker 2: that you wanted to go to and then you use

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Speaker 2: wheelchair friendly and then it would take you from A

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Speaker 2: 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 2: And another girl came up with a concept and a

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Speaker 2: working model. I couldn't believe she'd done this in a week.

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Speaker 2: A working model of how to teach people and use

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Speaker 2: sign language. So you know, he can use phones now

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Speaker 2: to communicate with people with different languages. Well it also

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Speaker 2: now the concept was to use that for Osland, which

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Speaker 2: is the Australian sign language, and so what it would

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Speaker 2: also do is if you put your hand up to

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Speaker 2: make one of the symbols to copy somebody and it

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Speaker 2: wasn't accurate, it would give you an accuracy rating so

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Speaker 2: that you could improve your use of Osland as well.

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Speaker 2: So it just was amazing that the initiative was put forward.

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Speaker 2: They had some prizes and it was Yeah, I really

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Speaker 2: thoroughly enjoyed it. It was so just enriching to see

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Speaker 2: what young people can come up with and how they

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Speaker 2: 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 younger,

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

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

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

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

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

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Speaker 1: you get that gig?

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Speaker 2: Just throw A friend of mine who works at Victoria University,

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Speaker 2: she knows I do this podcast, and you know I

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Speaker 2: do a bit of this sort of stuff. So I

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Speaker 2: loved it. That's such a fun time. It was really good.

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Speaker 2: And I actually made a new friend too, which was

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Speaker 2: really super exciting. Do you want me to introduce you

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Speaker 2: to my new friend?

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Speaker 1: Oh? Yes, Patrick?

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Speaker 2: Could you?

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Speaker 1: Hey? Patrick?

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Speaker 3: Good morning, what's on your mind?

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

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Speaker 2: was just telling my friends at Craig and Tiff about

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Speaker 2: this new exciting person that I've met you.

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Speaker 3: 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. I

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Speaker 2: mean I was just telling them about the hackathon that

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Speaker 2: I was involved with last week and how exciting it

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Speaker 2: was to work with the Copilot suite of technology.

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Speaker 1: Thrilling.

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Speaker 3: Hackathons are such a great way to dive into new tech.

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Speaker 3: What did you and your team create with the co

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Speaker 3: Pilot suite?

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Speaker 2: Oh, well, some of the students looked at ways to

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Speaker 2: integrate technology to make it easier for people with disabilities,

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Speaker 2: because that was the whole theme.

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Speaker 1: What a meaningful challenge. How did the integration go?

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Speaker 3: Did you end up with something that could make a

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Speaker 3: real difference for people with disabilities?

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Speaker 2: It certainly set the I guess, a benchmark and something

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Speaker 2: to aim for. So obviously a week isn't lot a

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Speaker 2: lot of time, but that was really exciting, and what

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Speaker 2: I really found was fun for me was getting to

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Speaker 2: understand a little bit more about at you and all

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Speaker 2: the resources you can provide me with.

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Speaker 3: Sounds like a really rewarding week. I'm glad you've got

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Speaker 3: to explore the possibilities of co Pilot. There's a lot

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Speaker 3: of potential to tap into. What was the most surprising

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Speaker 3: or eye opening thing you learned about the co Pilot suite?

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Speaker 2: Oh, I think that the conversational quality of being able

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Speaker 2: to talk to an AI that felt so real.

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Speaker 3: It's great to hear that the interaction felt real enough

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Speaker 3: to have a meaningful conversation. Making technology more intuitive and

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Speaker 3: human like has the potential to create so many opportunities

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Speaker 3: for more natural, effective communication.

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Speaker 2: What do you think it's fady to interrupt? I've got

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Speaker 2: to go back to this podcast I'm working on at

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Speaker 2: the moment.

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Speaker 1: But look great the chat.

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Speaker 2: Maybe we can talk again later.

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Speaker 3: 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 4: 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 1: Where 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. Tift 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 copilot. The thing is this is actually already here.

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Speaker 2: So if you have office three, six, five, if you're

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Speaker 2: integrated and you've got Windows on your computer if you're

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Speaker 2: using a PC, if you go down the bottom, there's

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Speaker 2: actually a little copilot icon on everybody's computer. There's a

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Speaker 2: 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 2: Now, it may well very be, it will very well

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Speaker 2: be on your computers. It's called co pilot. So if

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Speaker 2: you're using Windows Office three six five so have you,

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Speaker 2: you know you can get it if you jump into

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Speaker 2: you know, just typing co pilot in your like search bar,

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Speaker 2: what so in Google?

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Speaker 1: Or what search? We? Okay search? Okay? What am I

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Speaker 1: typing co pilot as a hyphenated.

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Speaker 2: Just one word with a new tool.

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Speaker 1: Well, co pilot, Microsoft copilot, your AI companion.

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Speaker 2: Oh see where you go? Now just to click?

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Speaker 1: Okay, So this is great, This is great pod Well,

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Speaker 1: people are going to want to do it as well.

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Speaker 1: Some click and I don't worry everyone, I won't bore

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Speaker 1: you with this for the next ten minutes.

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Speaker 2: I don't know. Maybe he will message.

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Speaker 1: Copilot message Copilot.

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Speaker 2: Yeah, so you can use my or you can type messages. Yeah,

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Speaker 2: it's an interactive.

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Speaker 1: Talk to copilot hengd on oh signing. Oh, they're set

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Speaker 1: to record a message. But what do I say? Do

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

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Speaker 1: I'm excited.

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Speaker 2: Train at first. So what you do is you choose

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Speaker 2: the voice, and then you've chosen the voice, you introduce

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Speaker 2: who you are so that when you talk to it,

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Speaker 2: it knows that it's Craig or Tiff or in this

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Speaker 2: case Patrick. There is a demo version. You only get

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Speaker 2: to use it for five minutes a day if you

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Speaker 2: haven't subscribed to the platform, and I think it's you

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Speaker 2: get the first month free, and I think it's twenty

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Speaker 2: two dollars a month to be able to use Copilot.

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Speaker 2: But the way that Microsoft is using these tools is

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Speaker 2: to use the Copilot technology to streamline what you do.

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Speaker 2: So if you're putting together a PowerPoint presentation and you've

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Speaker 2: got some dot points, you can get co Pilot to

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Speaker 2: make the presentation for you, to brand it, to lay

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Speaker 2: it out for you, and you can refine that you

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Speaker 2: can use a lot of these tools. So Microsoft has

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Speaker 2: a complete range of tools that interact with the AI

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Speaker 2: that they're now starting to use across the entire Microsoft suite.

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Speaker 2: So when you subscribe to it, and it's not dissimilar

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Speaker 2: to the other ones that are out there because Google

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Speaker 2: has its own, Meta has its own as well, and

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Speaker 2: you can start to train it. But look, it's been

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Speaker 2: fun to play around with it, and it's a bit

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Speaker 2: gimmicky too, But for me, what it envisages. What I

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Speaker 2: see is that the future for a lot of people.

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Speaker 2: If you're an older person who's living alone and you

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Speaker 2: integrate this into your daily living, and you know, I

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Speaker 2: helped a friend recently who's got Parkinson's who is struggling

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Speaker 2: to be able to get to sides of the room

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Speaker 2: to turn lights on and off. So now she has

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Speaker 2: a Google Home speaker and she can just get Google

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Speaker 2: to turn the lights on and off. This is really

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Speaker 2: easy tech to set up. It's great to use. But

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Speaker 2: the way I I see it is once you have

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Speaker 2: that integration into something like Copilot, you know it can

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Speaker 2: be lonely being by yourself and having someone just asking

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Speaker 2: how you are could help also help as people you know,

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Speaker 2: get older, if they don't feel well, you can talk

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Speaker 2: to your AI and maybe call a friend or you know,

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Speaker 2: call an ambulance if you got into distress, you know,

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Speaker 2: could you imagine if you suddenly fell and you yelled,

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Speaker 2: you don't have to touch a device, you know, it

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Speaker 2: detects that you've fallen, and it actually is interesting. So

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Speaker 2: I've got a story coming up a little bit later

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Speaker 2: about how AI is going to be used at a

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Speaker 2: public pool in Queensland to detect if swimmers get into

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Speaker 2: trouble and potentially help protect from drowning. So if you

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Speaker 2: had an AI in your home, like co pilot that

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Speaker 2: monitored someone who is elderly or infirm or whatever, or

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Speaker 2: just somebody like me who just loves having a chat

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Speaker 2: to an AI because it's fun. Sense of that companionship

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Speaker 2: 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. There are a number of different ways

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Speaker 2: that they train the AI models and that's the thing.

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Speaker 2: You know, ALI is used in everything that we're doing,

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Speaker 2: you know, so much. It could be something as simple

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Speaker 2: as your new car being able to keep you in

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Speaker 2: your lane, to make effectively to look and look beyond

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Speaker 2: you know, the vehicle to what's coming in front of you,

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Speaker 2: to detect, say a stop sign, does your new car

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Speaker 2: do that crago where you're in an intersection? And so

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Speaker 2: it's using AI and its database, so it knows what

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Speaker 2: a stop sign look like, looks like, but the stop

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Speaker 2: sign might have mud on it, so it looks beyond

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Speaker 2: just that recognition, and it's able to detect what that

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Speaker 2: sign is and then obviously warn you that there's a

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Speaker 2: stop sign coming up, as opposed to say give way.

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Speaker 2: So it is being used in lots of things. And

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Speaker 2: I know it's a hatch phrase. You know, we hear

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Speaker 2: it all the time. AI power it, but and you're

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Speaker 2: using it all the time, So yes, an answer your question.

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Speaker 2: It is like chat GPT and the way that it

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Speaker 2: interacts is just getting smarter all the time. You know,

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Speaker 2: what you should try sometimes is ask chat GPT to

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Speaker 2: give a summary of what it understands about you, because

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Speaker 2: you've done a lot of searching, So just say, can

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Speaker 2: you give me a summary of what you what you

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Speaker 2: know about me? And even maybe can you draw a

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Speaker 2: picture of my world or something and to see what.

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Speaker 1: It comes with. Yeah, do you know what I find interesting?

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Speaker 1: Like 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 being 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 2: I actually I understand that I could kind of think

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Speaker 2: of a little parallel that happened to me this week.

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Speaker 2: I did a mindfulness workshop that was being run by

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Speaker 2: our local health center. I thought I might give it

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Speaker 2: a try and see what because I thought it would

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Speaker 2: be able to help me leverage my tai chi that

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Speaker 2: I do. It gave me a bit of a thought exercise,

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Speaker 2: And what I did with my students was we're learning

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Speaker 2: the beginners of learning a form called the Yang eight.

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Speaker 2: So it's eight designated moves, and learning moves mean remembering

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Speaker 2: what you did to do. There's the repetition and all

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Speaker 2: the rest that goes with it and muscle memory. But

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Speaker 2: what I got them to do this week was I

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Speaker 2: got them instead of actually doing the move to start with,

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Speaker 2: I got them to close their eyes. I talked them

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Speaker 2: through like I normally would, but I got them to

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Speaker 2: visualize it, not to actually physically do it, but to

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Speaker 2: play the moves in their mind, because visualizing is exactly

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Speaker 2: the same. So our mind, even though I'm not physically moving,

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Speaker 2: our minds has this amazing ability to be able to

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Speaker 2: put us into that situation. So if you have an

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Speaker 2: ai give you a compliment, the mechanism in your brain

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Speaker 2: that causes the sensation and the emotions that go with

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Speaker 2: being complemented are exactly the same as if you know

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Speaker 2: myself or TIF gave you a compliment, probably not going

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Speaker 2: to happen. But you know that's why you need AI hurtful. Yeah,

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Speaker 2: you see what I'm saying. It's one of those things

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Speaker 2: where our brain has this amazing ability, is almost full 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?

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Speaker 2: Dogs? Because Tiff and I you know, and I know

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Speaker 2: you're eventually going to get a dog. I'm going to

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Speaker 2: tell you about this because this is so exciting and

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Speaker 2: I want one of these right now, a soundboard to

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Speaker 2: train your dog on so that they can intentionally communicate

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Speaker 2: with you what they want. Go on, okay, So so do.

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Speaker 1: They put their poor on a button and then a

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Speaker 1: speaker goes, I want food? Hurry up?

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Speaker 2: Yes, yep. So they've been training dogs on these soundboards

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Speaker 2: and they're just two word buttons, but they can purse,

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Speaker 2: purposefully communicate. And what they found was they took one

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Speaker 2: hundred and fifty two dogs and they analyzed two hundred

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Speaker 2: and sixty thousand presses of the button, and they found

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Speaker 2: that there was actually meaningful combinations, like they wanted to

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Speaker 2: go outside, they wanted to go potty, so outside and

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Speaker 2: potty they connected the words together, and they associated the

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Speaker 2: two words to say, I need to go outside because

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Speaker 2: I need to go to the toilet where you know,

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Speaker 2: or I wanted food. So what they found the researchers

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Speaker 2: found is that the two word button presses indicated deliberate communication.

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Speaker 2: So and you know this yourself, tif how many times

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Speaker 2: has your dog come up and just poured your leg

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Speaker 2: to try to let you know something? But how do

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Speaker 2: you know what they wanted? So this is and the

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Speaker 2: other thing the researchers felt was actually going beyond just imitation,

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Speaker 2: so they it was significantly differed from you know, the

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Speaker 2: patterns that you know would just be random. So there

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Speaker 2: was a real thought process behind it. And what they're

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Speaker 2: hoping is with future studies they can actually reference past

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Speaker 2: and future events using the soundboards. So what they're thinking

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Speaker 2: is that once the dog knows and it can meaning

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Speaker 2: flea meaningfully communicate, they'll be able to use that. And

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Speaker 2: it's I don't know, I find that amazing that you

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Speaker 2: can get that depth of communication, because for me, a

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Speaker 2: lot of the things that Fritz does. When he wants

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Speaker 2: to communicate is the same thing. So do I you know,

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Speaker 2: does he want to go out? Does he want to

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Speaker 2: have food? Does he want to cuddle? I don't know

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Speaker 2: what his intentions are because he's only got one way

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Speaker 2: to tell me, like a bark or a pat on

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Speaker 2: the leg. But if they could do that, if someone

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Speaker 2: could devise a soundboard that the dog could use to communicate,

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Speaker 2: that would be the best, wouldn't it, Tiff?

437
00:22:34,280 --> 00:22:39,640
Speaker 1: It's yeah, what about if we did it the other

438
00:22:39,680 --> 00:22:43,800
Speaker 1: way and we learned how to speak dog and the

439
00:22:43,880 --> 00:22:50,520
Speaker 1: dog could train us. Yeah, but I just quickly Patrick

440
00:22:50,600 --> 00:22:53,159
Speaker 1: and Tiff and listeners who love dogs. I saw this

441
00:22:53,280 --> 00:22:57,800
Speaker 1: video yesterday and there was a lady who she's got

442
00:22:57,840 --> 00:23:02,879
Speaker 1: a Kelpie. They're the ones that really know the Australian

443
00:23:04,240 --> 00:23:06,520
Speaker 1: you know, the dogs that heard everything that are black

444
00:23:06,560 --> 00:23:12,359
Speaker 1: and white, a Kelpies, Barter Collie's. So this border collie

445
00:23:12,400 --> 00:23:15,120
Speaker 1: that's got an IQ of about one hundred and fifty, right,

446
00:23:15,440 --> 00:23:19,159
Speaker 1: So she's got a border Collie and a husky and

447
00:23:19,200 --> 00:23:23,600
Speaker 1: she's walking them and she's in this like park and

448
00:23:24,040 --> 00:23:26,440
Speaker 1: they're both on lead, and then she lets them go

449
00:23:26,640 --> 00:23:29,200
Speaker 1: and they both just run off right, So the leads

450
00:23:29,200 --> 00:23:33,399
Speaker 1: are trailing and they're both going berserk, and she calls

451
00:23:33,440 --> 00:23:36,679
Speaker 1: the border collie or she calls the dogs back, and

452
00:23:36,720 --> 00:23:39,520
Speaker 1: you see the border collie. It bends down and it

453
00:23:39,560 --> 00:23:41,600
Speaker 1: picks up the end of the lead in its mouth

454
00:23:41,680 --> 00:23:44,000
Speaker 1: and it runs straight back and sits next to her,

455
00:23:44,359 --> 00:23:47,600
Speaker 1: and the husky's fucking all over the shop. The husky

456
00:23:47,720 --> 00:23:50,560
Speaker 1: is like, you can get rooted. I'm not coming back.

457
00:23:50,600 --> 00:23:53,879
Speaker 1: In fact, I'm not even listening. I'm not interested. And

458
00:23:53,920 --> 00:23:56,960
Speaker 1: then the border collie is just sitting there patiently and

459
00:23:57,080 --> 00:24:00,520
Speaker 1: kind of rolls its eyes, not really, but then it

460
00:24:00,640 --> 00:24:03,080
Speaker 1: runs up to the husky, grabs the lead of the

461
00:24:03,160 --> 00:24:08,480
Speaker 1: husky and brings it back to the mum, like such

462
00:24:08,480 --> 00:24:11,160
Speaker 1: a smart dog. And the Husky's like, all right, then

463
00:24:11,680 --> 00:24:14,720
Speaker 1: that's what you need, is a dog that's that's that smart.

464
00:24:15,000 --> 00:24:16,960
Speaker 2: Well, the things that I thought were really cool about

465
00:24:17,000 --> 00:24:20,359
Speaker 2: this this app so I think the fluence pet is

466
00:24:20,400 --> 00:24:23,440
Speaker 2: what they're calling it. But some of the key things

467
00:24:23,480 --> 00:24:26,439
Speaker 2: that came out of it were the essential needs to

468
00:24:26,480 --> 00:24:31,840
Speaker 2: what the dog wanted. So food, water, outside, treat, play, potty,

469
00:24:32,160 --> 00:24:34,760
Speaker 2: So all those key things were the essential needs that

470
00:24:34,840 --> 00:24:38,040
Speaker 2: dogs have. But it would make so much sense, wouldn't it.

471
00:24:38,080 --> 00:24:39,800
Speaker 2: I just think that'd be really fun to be able

472
00:24:39,800 --> 00:24:42,679
Speaker 2: to better understand or for us dumb people to know

473
00:24:42,720 --> 00:24:46,040
Speaker 2: what our dogs want. It was fun. I thought that

474
00:24:46,119 --> 00:24:49,000
Speaker 2: was really a cool thing and just another great use

475
00:24:49,040 --> 00:24:51,439
Speaker 2: of tech, really really fun stuff.

476
00:24:51,960 --> 00:24:53,160
Speaker 1: Love it, Love it.

477
00:24:53,480 --> 00:24:56,399
Speaker 2: Next, Octun Oh, I don't know, did you want to

478
00:24:56,480 --> 00:24:59,160
Speaker 2: jump to or the AI story we started talking about

479
00:24:59,200 --> 00:25:03,920
Speaker 2: before was the swimming pool. So the Logan Public Pool. Yeah,

480
00:25:03,920 --> 00:25:06,840
Speaker 2: we kind of teased it before. So the Logan Public

481
00:25:06,880 --> 00:25:10,160
Speaker 2: Pool is in Queensland, and what they've done is they're

482
00:25:10,280 --> 00:25:14,760
Speaker 2: using AI. Obviously, they've got cameras that are scanning the

483
00:25:14,800 --> 00:25:19,359
Speaker 2: pool and they detect anything that's kind of unusual, something

484
00:25:19,359 --> 00:25:21,680
Speaker 2: that seems out of the ordinary with movement in the water,

485
00:25:22,080 --> 00:25:24,760
Speaker 2: and then it sends a smart watch alert to one

486
00:25:24,800 --> 00:25:28,760
Speaker 2: of the lifeguards. So the data then also looked at say,

487
00:25:28,800 --> 00:25:31,120
Speaker 2: pool hotspots, so for example, it might be the six

488
00:25:31,160 --> 00:25:33,879
Speaker 2: foot part of the pool as opposed to the waiting

489
00:25:34,000 --> 00:25:38,840
Speaker 2: part of the pool. Yeah, and so there areas where

490
00:25:38,920 --> 00:25:41,160
Speaker 2: person might be more likely to struggle when they're out

491
00:25:41,160 --> 00:25:43,480
Speaker 2: of their depth, so the higher end of the pool,

492
00:25:43,480 --> 00:25:45,720
Speaker 2: that sort of stuff. And it's a really big thing

493
00:25:45,800 --> 00:25:49,080
Speaker 2: because it was a sad story that prompted it because

494
00:25:49,119 --> 00:25:51,600
Speaker 2: back in twenty sixteen a young girl had drowned at

495
00:25:51,600 --> 00:25:54,959
Speaker 2: the pool and they really wanted to look at ways

496
00:25:55,040 --> 00:25:58,240
Speaker 2: and you know, to help those people who are swimming

497
00:25:58,280 --> 00:26:00,480
Speaker 2: in at this particular time of the year, when you've

498
00:26:00,480 --> 00:26:03,560
Speaker 2: got maybe a handful of lifeguards and so many people swimming,

499
00:26:03,800 --> 00:26:08,040
Speaker 2: it would be easy to miss something and a tragedy

500
00:26:08,200 --> 00:26:10,960
Speaker 2: like that. And I was looking at some of the statistics.

501
00:26:11,000 --> 00:26:14,080
Speaker 2: I mean, backyard pools are always and have been the

502
00:26:14,119 --> 00:26:18,440
Speaker 2: biggest danger because national drownings I think in Australia was

503
00:26:18,480 --> 00:26:21,679
Speaker 2: about three hundred and twenty three people, which is super sad,

504
00:26:22,680 --> 00:26:25,520
Speaker 2: but that's an increase from last year of sixteen percent,

505
00:26:26,040 --> 00:26:29,520
Speaker 2: and six of those people drowned in public pools, and

506
00:26:29,560 --> 00:26:31,720
Speaker 2: there were also non fatal drowning, so it's not just

507
00:26:31,760 --> 00:26:35,080
Speaker 2: people drowning, but people who get into trouble and potentially

508
00:26:35,320 --> 00:26:38,680
Speaker 2: you know, you know, if you're part of an incident

509
00:26:38,760 --> 00:26:41,480
Speaker 2: and you've lost the ability to breathe and you have

510
00:26:41,520 --> 00:26:44,440
Speaker 2: to be revived, there could be ramifications from that as well.

511
00:26:44,880 --> 00:26:48,520
Speaker 2: So it's actually really interesting that they're using this sort

512
00:26:48,520 --> 00:26:50,879
Speaker 2: of stat and I could see that, you know, to

513
00:26:50,960 --> 00:26:53,840
Speaker 2: monitor the pool. If you again had you know, a

514
00:26:53,880 --> 00:26:58,160
Speaker 2: couple of cameras outside in a home, it could monitor

515
00:26:58,160 --> 00:26:59,800
Speaker 2: and raise the alarm, you know, if a toddle of

516
00:27:00,720 --> 00:27:02,680
Speaker 2: I mean, I know we should have gates and things,

517
00:27:02,720 --> 00:27:05,480
Speaker 2: but if a child walked over and got into distress,

518
00:27:05,520 --> 00:27:08,000
Speaker 2: it could alarm you. Alarm, You could warn you and

519
00:27:08,040 --> 00:27:11,280
Speaker 2: set off an alarm. So there's hope that this sort

520
00:27:11,280 --> 00:27:13,920
Speaker 2: of technology could then be applied on a broader scale,

521
00:27:14,000 --> 00:27:16,680
Speaker 2: because again, once you've got the data set, once you've

522
00:27:16,720 --> 00:27:19,919
Speaker 2: got the information that says that this part of the

523
00:27:19,920 --> 00:27:22,080
Speaker 2: pool is a danger area, or if a person is

524
00:27:22,119 --> 00:27:25,040
Speaker 2: flailing around and moving their arms radically, then that's a

525
00:27:25,080 --> 00:27:28,520
Speaker 2: trigger point to raise the alarm as opposed to someone

526
00:27:28,520 --> 00:27:32,240
Speaker 2: who's just swimming laps or paddling around. And that's where

527
00:27:32,320 --> 00:27:36,200
Speaker 2: you having that data set initially and doing the training

528
00:27:36,240 --> 00:27:38,679
Speaker 2: and then employing that, say in the public pool sector,

529
00:27:39,040 --> 00:27:41,040
Speaker 2: then you can be able to use that same data

530
00:27:41,119 --> 00:27:43,919
Speaker 2: set to apply to a home environment. That's what the

531
00:27:43,960 --> 00:27:47,160
Speaker 2: great thing is about training these sorts of AI models

532
00:27:47,240 --> 00:27:48,560
Speaker 2: or these technology models.

533
00:27:49,280 --> 00:27:52,879
Speaker 1: And I guess the thing too is that AI doesn't

534
00:27:52,920 --> 00:27:56,159
Speaker 1: get tired, it doesn't get distracted, it doesn't need to

535
00:27:56,200 --> 00:27:59,680
Speaker 1: go and get lunch. It doesn't lose focus, it doesn't

536
00:27:59,720 --> 00:28:02,679
Speaker 1: need we that doesn't try and chat up one of

537
00:28:02,680 --> 00:28:05,879
Speaker 1: the girls at the pool or one of the boys.

538
00:28:06,240 --> 00:28:07,840
Speaker 2: I mean, if I was if I was a pool

539
00:28:07,840 --> 00:28:10,440
Speaker 2: guard and you walk past in a MANKINI that would

540
00:28:10,440 --> 00:28:11,159
Speaker 2: be distracting.

541
00:28:11,359 --> 00:28:13,520
Speaker 1: You know, Well that's I mean that probably three people

542
00:28:13,600 --> 00:28:15,800
Speaker 1: would drown just because you were looking at my ass.

543
00:28:15,800 --> 00:28:21,720
Speaker 1: And I mean, we can't have that. Evacuates nobody's looking

544
00:28:21,920 --> 00:28:24,760
Speaker 1: nobody's looking at my ass. But I guess one of

545
00:28:24,800 --> 00:28:27,639
Speaker 1: the things that I think about a bit, and you

546
00:28:27,720 --> 00:28:31,400
Speaker 1: are I've spoken a bit, but is the fact that

547
00:28:31,480 --> 00:28:36,479
Speaker 1: while AI is, you know, growing exponentially, and it's there's

548
00:28:36,560 --> 00:28:41,800
Speaker 1: a myriad of benefits and potential benefits, I guess it

549
00:28:41,840 --> 00:28:45,320
Speaker 1: impacts some people personally, like in the music industry and

550
00:28:45,360 --> 00:28:48,760
Speaker 1: many other industries where I see your headline here is

551
00:28:48,800 --> 00:28:53,760
Speaker 1: that nearly a quarter of over the next four years,

552
00:28:53,800 --> 00:28:55,800
Speaker 1: people are going to be affected, Like people in the

553
00:28:55,880 --> 00:28:58,240
Speaker 1: music industry. Their income is going to come down, and

554
00:28:58,680 --> 00:29:03,440
Speaker 1: some jobs or some als are going to become the

555
00:29:03,440 --> 00:29:07,080
Speaker 1: the I guess the landscape of AI rather than humans.

556
00:29:07,120 --> 00:29:09,520
Speaker 1: But that's happening in a lot of industries and professions.

557
00:29:09,560 --> 00:29:13,400
Speaker 1: But what what's what are the pros and cons of that?

558
00:29:13,520 --> 00:29:17,160
Speaker 2: I guess it's a really good point. And you know,

559
00:29:17,200 --> 00:29:20,360
Speaker 2: it's funny. This topic came up during the hackathon last week,

560
00:29:20,720 --> 00:29:23,280
Speaker 2: and one of the things I think about is when

561
00:29:23,680 --> 00:29:27,600
Speaker 2: we thought about the industrial revolution and when you know,

562
00:29:27,720 --> 00:29:31,360
Speaker 2: when Henry Ford first rolled out the Model T and

563
00:29:31,440 --> 00:29:35,240
Speaker 2: they started factories and production lines and assembly lines. It

564
00:29:35,280 --> 00:29:37,400
Speaker 2: was thought that this could be, you know, the end

565
00:29:37,840 --> 00:29:40,280
Speaker 2: of everything when factories came about.

566
00:29:40,360 --> 00:29:43,880
Speaker 1: Oh yes, and do you remember also, sorry to interrupt,

567
00:29:43,960 --> 00:29:47,680
Speaker 1: but this is quite famous when they started bringing out cars,

568
00:29:48,600 --> 00:29:53,400
Speaker 1: all the all the horse and drawn all the the

569
00:29:53,480 --> 00:29:56,360
Speaker 1: horse drawn carts, and people who you know, they thought

570
00:29:56,360 --> 00:29:57,960
Speaker 1: it was the end of the world and they were

571
00:29:58,080 --> 00:30:02,520
Speaker 1: rallying against these motorized buggies because it meant the end

572
00:30:02,560 --> 00:30:05,800
Speaker 1: of an industry and the end of livelihood for many people.

573
00:30:06,280 --> 00:30:09,120
Speaker 2: And look, it did. It meant that some jobs did go,

574
00:30:09,360 --> 00:30:12,880
Speaker 2: But what it meant was people could be retrained to then,

575
00:30:13,360 --> 00:30:17,640
Speaker 2: you know, work on cars and become mechanics or you know,

576
00:30:17,840 --> 00:30:20,600
Speaker 2: or chauffeurs in your case. You know, there's lots of

577
00:30:20,640 --> 00:30:24,960
Speaker 2: things that you could do. So the industries do evolve

578
00:30:25,040 --> 00:30:29,080
Speaker 2: and move and You know the thing about using a

579
00:30:29,080 --> 00:30:31,680
Speaker 2: lot of this technology is it does make life a

580
00:30:31,680 --> 00:30:35,240
Speaker 2: lot easier and freeze us up to do other things.

581
00:30:35,680 --> 00:30:38,960
Speaker 2: And whether you're using one of those tools at work

582
00:30:39,080 --> 00:30:41,240
Speaker 2: to be able just to check an email that you're

583
00:30:41,280 --> 00:30:45,040
Speaker 2: about to send off for accuracy, for spelling, you know, grammatics,

584
00:30:45,080 --> 00:30:47,480
Speaker 2: that sort of thing. It means that you're getting the

585
00:30:47,480 --> 00:30:50,880
Speaker 2: message across or summarizing something. You know, how many times

586
00:30:51,040 --> 00:30:54,840
Speaker 2: have you received a lot of information at once and think, look,

587
00:30:54,840 --> 00:30:56,720
Speaker 2: I haven't got the time to read all of this.

588
00:30:57,040 --> 00:30:59,240
Speaker 2: And if you throw that into say chat GTP and

589
00:30:59,240 --> 00:31:03,040
Speaker 2: say can you this concisely to just some board points,

590
00:31:03,400 --> 00:31:05,080
Speaker 2: then you've got it and you can. I mean I

591
00:31:05,120 --> 00:31:07,240
Speaker 2: probably should do that before I do the show. I

592
00:31:07,280 --> 00:31:09,680
Speaker 2: should to throw all the notes into chat GPT's they

593
00:31:09,720 --> 00:31:10,920
Speaker 2: just summarize all this crap.

594
00:31:11,240 --> 00:31:13,400
Speaker 1: You know what's funny is the document that you send.

595
00:31:13,520 --> 00:31:16,080
Speaker 1: So this is what happens everyone. Patrick sends me about

596
00:31:16,120 --> 00:31:20,280
Speaker 1: thirteen seconds before we go live. He sends me a

597
00:31:20,440 --> 00:31:23,360
Speaker 1: document and it's got here I'm looking at right now,

598
00:31:23,440 --> 00:31:28,000
Speaker 1: Episode ninety menu. So these are the heading psychology, tech, AI, cars,

599
00:31:28,520 --> 00:31:32,720
Speaker 1: Internet scams, and then Internet Comma And the next one

600
00:31:32,800 --> 00:31:36,640
Speaker 1: is scams. It's a thirty five page document like this,

601
00:31:37,720 --> 00:31:41,600
Speaker 1: thirty five pages of stuff. Hey, before we go to

602
00:31:41,680 --> 00:31:44,680
Speaker 1: your next topic, I wanted to just introduce a quick one.

603
00:31:44,760 --> 00:31:47,600
Speaker 1: I don't know if either of you heard about what

604
00:31:47,800 --> 00:31:52,600
Speaker 1: happened with the VCS is still called VCE Year twelve

605
00:31:52,640 --> 00:31:57,000
Speaker 1: exams in Victoria, where a whole lot of a whole

606
00:31:57,040 --> 00:32:01,080
Speaker 1: lot of actual exam questions got leaked to early. So

607
00:32:01,160 --> 00:32:05,440
Speaker 1: something like sixty five different subjects, which is fucking most

608
00:32:05,480 --> 00:32:10,480
Speaker 1: of them, right, So students were getting sent actual exam

609
00:32:10,720 --> 00:32:15,640
Speaker 1: questions and requie but so they knew ahead of time

610
00:32:15,720 --> 00:32:17,680
Speaker 1: what was going to be in some of the exams,

611
00:32:18,160 --> 00:32:22,040
Speaker 1: which obviously was nobody's intention that that. By the way,

612
00:32:22,080 --> 00:32:24,440
Speaker 1: how happy would you be if you're a year twelve student?

613
00:32:24,480 --> 00:32:27,280
Speaker 1: But I guess that's one of the downsides. Mate.

614
00:32:27,680 --> 00:32:30,480
Speaker 2: Well, I think there were test exams, weren't they, But

615
00:32:30,560 --> 00:32:33,960
Speaker 2: the test exam questions were actual exam questions. There was

616
00:32:33,960 --> 00:32:34,840
Speaker 2: a major stuff up.

617
00:32:35,000 --> 00:32:38,720
Speaker 1: Yeah, yeah, so somebody I wonder if that's somebody or

618
00:32:38,760 --> 00:32:40,000
Speaker 1: something or get in trouble.

619
00:32:40,400 --> 00:32:43,800
Speaker 2: Look, you know, it's funny. I'm digressing a lot here,

620
00:32:43,880 --> 00:32:46,920
Speaker 2: but I really am not a big fan of the

621
00:32:46,960 --> 00:32:50,800
Speaker 2: whole idea of exams and how much weight is put

622
00:32:50,880 --> 00:32:52,960
Speaker 2: on them. You know, there were a lot of allowances

623
00:32:53,000 --> 00:32:56,000
Speaker 2: made during COVID, and I think that what came out

624
00:32:56,000 --> 00:32:57,800
Speaker 2: of that is why are we putting all these people

625
00:32:57,840 --> 00:33:01,520
Speaker 2: under so much pressure and making just one lot of

626
00:33:01,560 --> 00:33:04,920
Speaker 2: exams so crucial to the rest of their life? You know,

627
00:33:05,000 --> 00:33:07,320
Speaker 2: don't you think that it's kind of skewed the wrong way.

628
00:33:07,360 --> 00:33:10,200
Speaker 2: I mean, think exams are important to test people's knowledge

629
00:33:10,440 --> 00:33:14,440
Speaker 2: and maybe have exams worth twenty percent, But you know,

630
00:33:14,640 --> 00:33:17,640
Speaker 2: surely all the work you do throughout the year has

631
00:33:17,680 --> 00:33:20,480
Speaker 2: got to count for something. I mean, what about what

632
00:33:20,520 --> 00:33:23,120
Speaker 2: you're doing right now. I think all the research that

633
00:33:23,160 --> 00:33:25,160
Speaker 2: you've done, you've spoken to people, you've I mean, I

634
00:33:25,200 --> 00:33:27,400
Speaker 2: can't even begin to imagine how much work you've had

635
00:33:27,400 --> 00:33:30,000
Speaker 2: to do. But reality of it is, if it all

636
00:33:30,040 --> 00:33:33,600
Speaker 2: then had to hinge, you know, on a two hour exam,

637
00:33:34,040 --> 00:33:36,600
Speaker 2: I mean, would that that would just be crazy stuff?

638
00:33:37,600 --> 00:33:40,920
Speaker 1: Well it kind of does, it kind of does. Yeah, yeah, yeah,

639
00:33:40,920 --> 00:33:44,480
Speaker 1: because what you do is like five years depends Like

640
00:33:44,560 --> 00:33:48,000
Speaker 1: I'm slow, but five years of research and out of

641
00:33:48,040 --> 00:33:52,040
Speaker 1: that essentially will come three or four or five papers

642
00:33:52,080 --> 00:33:54,560
Speaker 1: that you will submit to be published that will get

643
00:33:54,560 --> 00:33:58,560
Speaker 1: published or not. So, but I know what you're saying,

644
00:33:58,680 --> 00:34:04,320
Speaker 1: and I think the down look, I'm I'm I can

645
00:34:04,360 --> 00:34:06,760
Speaker 1: see the pros and cons of exams. The pros, Yeah,

646
00:34:06,760 --> 00:34:09,000
Speaker 1: but it's pressure, it says, yeah, but life's pressure like

647
00:34:09,120 --> 00:34:12,440
Speaker 1: work is pressure. Like I was having this chat yesterday

648
00:34:12,480 --> 00:34:17,480
Speaker 1: with doctor Jody Richardson about how how much how much

649
00:34:17,520 --> 00:34:21,240
Speaker 1: do we protect and look after and you know, kind

650
00:34:21,280 --> 00:34:23,960
Speaker 1: of take care of our kids and how much do

651
00:34:24,000 --> 00:34:25,799
Speaker 1: we let them go for a run and fall over

652
00:34:25,880 --> 00:34:28,640
Speaker 1: and scrape their knees and not pick them up because

653
00:34:28,640 --> 00:34:31,680
Speaker 1: the truth is that, you know, once they hit adulthood,

654
00:34:31,719 --> 00:34:34,799
Speaker 1: nobody's running around to protect their feelings or emotions or

655
00:34:34,800 --> 00:34:40,360
Speaker 1: their mindset or their So it's like, I think kids

656
00:34:40,360 --> 00:34:43,280
Speaker 1: dealing with hard things is good because life is hard,

657
00:34:43,400 --> 00:34:46,360
Speaker 1: or life is hard at times, and life is pressure

658
00:34:46,440 --> 00:34:49,200
Speaker 1: and stress and life is all of that. But I

659
00:34:49,280 --> 00:34:52,120
Speaker 1: know what you're saying, because there are actually some people

660
00:34:53,560 --> 00:34:56,400
Speaker 1: who are really good at exams naturally, and other people

661
00:34:56,400 --> 00:35:00,640
Speaker 1: who are really intelligent but just happen to crumble at time.

662
00:35:00,960 --> 00:35:04,399
Speaker 1: So it's it's I don't know what the ideal model is, mate,

663
00:35:04,440 --> 00:35:06,000
Speaker 1: but you do make a good point.

664
00:35:06,400 --> 00:35:08,680
Speaker 2: Yeah, yeah, I kind of I mean I wasn't. I

665
00:35:08,680 --> 00:35:11,320
Speaker 2: mean I did okay with exams. I felt that I

666
00:35:11,360 --> 00:35:13,760
Speaker 2: was actually not too bad with them. But I certainly

667
00:35:13,800 --> 00:35:16,839
Speaker 2: had friends who, as you say, would crumble and just

668
00:35:16,960 --> 00:35:20,600
Speaker 2: become a mess when put under that sort of sort

669
00:35:20,600 --> 00:35:25,760
Speaker 2: of pressure, which wasn't indicative of their knowledge and skills anyway.

670
00:35:25,760 --> 00:35:29,000
Speaker 2: But you're right, I guess it comes down to who

671
00:35:29,000 --> 00:35:31,799
Speaker 2: do you want operating on you as they're tying off

672
00:35:31,840 --> 00:35:37,200
Speaker 2: that last suture for that crucial operation. The person who

673
00:35:38,080 --> 00:35:40,160
Speaker 2: you know, who got twenty in their exams of the

674
00:35:40,160 --> 00:35:41,399
Speaker 2: person who got ninety five.

675
00:35:42,880 --> 00:35:45,160
Speaker 1: Well, but how many people were shipped at school but

676
00:35:45,200 --> 00:35:48,839
Speaker 1: they're brilliant? I mean, so many people fucking crashed out

677
00:35:48,840 --> 00:35:51,960
Speaker 1: of school and you know I didn't, you know, finished

678
00:35:51,960 --> 00:35:55,640
Speaker 1: at school a year nine or ten. But it's just

679
00:35:56,120 --> 00:35:58,360
Speaker 1: not at all that they were done or not creative,

680
00:35:58,440 --> 00:36:01,719
Speaker 1: or not brilliant or not even had a low IQ.

681
00:36:01,920 --> 00:36:04,000
Speaker 1: They had a hot high IQ. It's just that that

682
00:36:04,400 --> 00:36:07,640
Speaker 1: for some people. And we can't create one model that

683
00:36:07,680 --> 00:36:10,480
Speaker 1: suits everyone, so we're not blaming anyone. But yeah, it's

684
00:36:11,120 --> 00:36:14,000
Speaker 1: intelligence is a spectrum, not a single thing.

685
00:36:14,120 --> 00:36:16,759
Speaker 2: And it's around pegan a square hole. You know, it

686
00:36:16,960 --> 00:36:19,560
Speaker 2: just doesn't fit everybody. And I think, you know, our

687
00:36:19,600 --> 00:36:22,160
Speaker 2: society is such that, you know, we can steer people

688
00:36:22,200 --> 00:36:25,640
Speaker 2: to education and traditional education, but you know, people learn

689
00:36:25,800 --> 00:36:29,480
Speaker 2: other ways. People are ways to take in information and

690
00:36:29,560 --> 00:36:33,080
Speaker 2: process it. And I've met amazingly smart people who had

691
00:36:33,960 --> 00:36:37,399
Speaker 2: very little traditional education. And you know, you hear these

692
00:36:37,440 --> 00:36:39,919
Speaker 2: amazing stories. And there was a guy in India, young

693
00:36:39,920 --> 00:36:42,640
Speaker 2: guy in India who was a mathematician and he was

694
00:36:42,800 --> 00:36:46,520
Speaker 2: plucked out of his security and was like one of

695
00:36:46,560 --> 00:36:48,719
Speaker 2: the smartest people on the planet. You know, how do

696
00:36:48,800 --> 00:36:51,120
Speaker 2: you know that? How do you test for that? You know,

697
00:36:51,120 --> 00:36:51,760
Speaker 2: it's so round.

698
00:36:52,880 --> 00:36:58,640
Speaker 1: Well, hey, let's talk about cars. Come on, tell me

699
00:36:58,680 --> 00:36:59,640
Speaker 1: something about cars.

700
00:37:00,400 --> 00:37:06,000
Speaker 2: So there's Mercedes Benz is researching a solar paint. So

701
00:37:06,040 --> 00:37:08,920
Speaker 2: what it means is, rather than just having panels, that

702
00:37:09,320 --> 00:37:11,680
Speaker 2: you put the paint onto the vehicle, so in this

703
00:37:11,760 --> 00:37:14,680
Speaker 2: case a car. And they're saying this is between five

704
00:37:14,719 --> 00:37:17,640
Speaker 2: to ten years away, and what it could effectively mean

705
00:37:17,960 --> 00:37:21,280
Speaker 2: is that a car. We talked about a company in Norway.

706
00:37:21,320 --> 00:37:23,560
Speaker 2: Remember there was a startup in Norway that promised they

707
00:37:23,600 --> 00:37:25,800
Speaker 2: were going to do a solar car that was able

708
00:37:25,840 --> 00:37:27,640
Speaker 2: to charge the battery and you'd never you know, you

709
00:37:27,719 --> 00:37:32,000
Speaker 2: get thousands of kilometers and it flopped unfortunately. But now

710
00:37:32,040 --> 00:37:34,279
Speaker 2: they're saying that this is so it just kind of

711
00:37:34,280 --> 00:37:37,600
Speaker 2: instead of just coating the roof and the bonnet, they're

712
00:37:37,640 --> 00:37:40,399
Speaker 2: saying that you cover the entire car with this new

713
00:37:40,440 --> 00:37:43,880
Speaker 2: solar paint, and it means that you've got such a

714
00:37:43,880 --> 00:37:47,160
Speaker 2: big surface area you'll be able to use the solar

715
00:37:47,200 --> 00:37:50,600
Speaker 2: paint to charge the vehicle at any point that there's sunlight.

716
00:37:50,760 --> 00:37:53,359
Speaker 2: I mean that would be amazing, and I mean we're

717
00:37:53,360 --> 00:37:56,680
Speaker 2: talking at this stage. You know, it would cover maybe

718
00:37:56,719 --> 00:37:59,640
Speaker 2: thirty two kilometers a day, but for the little commutes

719
00:38:00,160 --> 00:38:03,040
Speaker 2: to be able to trickle charge the car constantly would

720
00:38:03,080 --> 00:38:06,080
Speaker 2: be phenomenal. Yeah, it's pretty pretty cool. I was thinking

721
00:38:06,080 --> 00:38:08,959
Speaker 2: you could get a backpack, or you could paint your dog,

722
00:38:09,239 --> 00:38:10,839
Speaker 2: your dog's coat, and then they.

723
00:38:10,840 --> 00:38:12,879
Speaker 1: You could paint the top of your head and keep

724
00:38:12,960 --> 00:38:14,200
Speaker 1: the whole house charged.

725
00:38:14,320 --> 00:38:15,880
Speaker 2: There's a lot of coverage there. There's a lot of

726
00:38:15,920 --> 00:38:20,160
Speaker 2: real estate. I should sell sign it shouldn't I we could, No,

727
00:38:20,400 --> 00:38:21,320
Speaker 2: that's not nice.

728
00:38:21,640 --> 00:38:25,279
Speaker 1: But I'm thinking about like, if you've got paint that

729
00:38:25,400 --> 00:38:30,200
Speaker 1: is sola paint that can then the catch twenty two

730
00:38:30,239 --> 00:38:31,560
Speaker 1: is then you've got to leave your car in the

731
00:38:31,600 --> 00:38:34,640
Speaker 1: sun all the time, which fucks up the interior. I

732
00:38:34,680 --> 00:38:37,040
Speaker 1: guess you could put one of those ugly silver things

733
00:38:37,080 --> 00:38:40,280
Speaker 1: that my mum's got, one of those ugly silver screen

734
00:38:40,360 --> 00:38:42,799
Speaker 1: things that you put across on top of the dash

735
00:38:42,840 --> 00:38:43,840
Speaker 1: and inside the window.

736
00:38:45,200 --> 00:38:50,920
Speaker 2: I have one of those two. Ah, but why.

737
00:38:50,760 --> 00:38:53,640
Speaker 1: Doesn't that not surprise me? Nana? Tell us? What?

738
00:38:54,680 --> 00:38:58,400
Speaker 2: Wait a minute, Wait a minute. So when you park somewhere, say.

739
00:38:58,200 --> 00:39:00,360
Speaker 1: Can you get your voice down a couple of otive

740
00:39:00,440 --> 00:39:03,680
Speaker 1: so I can respect you? Could you loosen your underpants

741
00:39:03,719 --> 00:39:04,480
Speaker 1: and start again?

742
00:39:04,719 --> 00:39:08,000
Speaker 2: Hey, you know I have a very impressive physical range.

743
00:39:08,000 --> 00:39:12,839
Speaker 2: I'd like to tell you I can sing bo and bass. No, no,

744
00:39:13,080 --> 00:39:17,719
Speaker 2: what I When you go out into like you go

745
00:39:17,800 --> 00:39:20,640
Speaker 2: to park at Bunnings, there's no shade. Right, you're out

746
00:39:20,680 --> 00:39:22,920
Speaker 2: of Bunnings, You've just spent an hour and a half

747
00:39:23,040 --> 00:39:25,320
Speaker 2: doing all your shopping. You get back into the car

748
00:39:25,719 --> 00:39:29,080
Speaker 2: and your ass sticks to the seat. Your hands are

749
00:39:29,120 --> 00:39:31,800
Speaker 2: glued to the steering wheel because they've just been seared

750
00:39:32,640 --> 00:39:34,920
Speaker 2: because it's a hot day. How do you protect yourself

751
00:39:34,960 --> 00:39:35,640
Speaker 2: from that crago?

752
00:39:36,120 --> 00:39:39,399
Speaker 1: Can I just say you've never been You've never been

753
00:39:39,440 --> 00:39:42,680
Speaker 1: to Bunnings. You're talking about spotlight or something.

754
00:39:42,520 --> 00:39:44,560
Speaker 2: Going to Bunnies today, you dick.

755
00:39:46,320 --> 00:39:48,440
Speaker 1: What to get a sausage and then head home.

756
00:39:48,680 --> 00:39:51,600
Speaker 2: I'm getting some sausage. What I'm Vegan. I'm not going

757
00:39:51,680 --> 00:39:54,120
Speaker 2: to get a sausage from Vegan ones.

758
00:39:54,400 --> 00:39:58,280
Speaker 1: Hey, tell us what Ford Australia are doing in twenty

759
00:39:58,360 --> 00:39:58,959
Speaker 1: twenty five.

760
00:39:59,120 --> 00:40:02,840
Speaker 2: No, seriously, I'm defending every motorist in Australia has the

761
00:40:02,880 --> 00:40:03,719
Speaker 2: silver foil thing.

762
00:40:04,000 --> 00:40:06,759
Speaker 1: Look, I get the practicality, I get it all right.

763
00:40:06,800 --> 00:40:08,879
Speaker 1: I'll give you a pass. I shall probably get one

764
00:40:08,960 --> 00:40:10,520
Speaker 1: that I won't thing tip.

765
00:40:11,000 --> 00:40:11,759
Speaker 2: Have you ever had one?

766
00:40:12,360 --> 00:40:12,480
Speaker 1: No?

767
00:40:12,880 --> 00:40:16,600
Speaker 2: I really? Is it just me? I'm the nana here.

768
00:40:17,920 --> 00:40:21,239
Speaker 1: It's probably I could guarantee more than half hour listeners agoing. No,

769
00:40:21,360 --> 00:40:23,719
Speaker 1: that's actually a good idea. Patrick's right, Craig, you're a

770
00:40:23,760 --> 00:40:27,839
Speaker 1: fucking idiot, protectsper Craig, you're a dickhead. I think you're

771
00:40:27,840 --> 00:40:30,799
Speaker 1: funny and not funny at all, and stop cutting Patrick off.

772
00:40:31,480 --> 00:40:34,680
Speaker 2: Yeah there you go. Hey, you know this is a

773
00:40:34,719 --> 00:40:37,640
Speaker 2: Tesla thing that always rubs me the wrong way where

774
00:40:37,680 --> 00:40:40,759
Speaker 2: you buy a car but then you have to subscribe

775
00:40:40,960 --> 00:40:45,439
Speaker 2: and pay for additional features. But Ford is now going

776
00:40:45,440 --> 00:40:49,439
Speaker 2: to roll this out. Now what rubs me the wrong way?

777
00:40:49,440 --> 00:40:52,280
Speaker 2: With this story. So they're saying that most Ford models,

778
00:40:52,320 --> 00:40:55,200
Speaker 2: this is a story that I saw since the mid

779
00:40:55,280 --> 00:40:59,200
Speaker 2: twenty twenties, will soon require owners to pay for some

780
00:40:59,520 --> 00:41:03,920
Speaker 2: functions that are currently free. So they're going to backdate

781
00:41:03,960 --> 00:41:07,120
Speaker 2: it so for the near news. So if you've got

782
00:41:07,160 --> 00:41:09,359
Speaker 2: a car that's only a couple of years old, potentially

783
00:41:09,719 --> 00:41:11,960
Speaker 2: you may have to start paying a subscription fee to

784
00:41:12,000 --> 00:41:16,600
Speaker 2: accent to access certain features in navigation services and all

785
00:41:16,600 --> 00:41:18,799
Speaker 2: that sort of stuff. So if you have in built

786
00:41:18,880 --> 00:41:21,919
Speaker 2: navigation in your car, there's a potential that you may

787
00:41:21,960 --> 00:41:24,080
Speaker 2: have to pay for that, and we're talking, you know,

788
00:41:24,120 --> 00:41:26,359
Speaker 2: an annual fee of one hundred and ten bucks. But

789
00:41:26,480 --> 00:41:28,680
Speaker 2: that's just another thing that you've got to pay on

790
00:41:28,760 --> 00:41:30,920
Speaker 2: top of everything. And you know, as far as I'm concerned,

791
00:41:30,960 --> 00:41:34,200
Speaker 2: when you buy your car, that's it. You know, whatever

792
00:41:34,600 --> 00:41:37,200
Speaker 2: the rules were when you bought it. It's one thing

793
00:41:37,239 --> 00:41:39,400
Speaker 2: to get into a contract and buy a car and

794
00:41:39,520 --> 00:41:41,560
Speaker 2: know that there are features that may be locked out

795
00:41:41,600 --> 00:41:43,680
Speaker 2: you've got to pay subscription for. But if you buy

796
00:41:43,719 --> 00:41:45,640
Speaker 2: it and they backdate it and they say, oh, you,

797
00:41:45,680 --> 00:41:47,879
Speaker 2: by the way, that car you bought two years ago,

798
00:41:48,120 --> 00:41:50,760
Speaker 2: we've changed the rules and now you can't use your

799
00:41:50,840 --> 00:41:51,840
Speaker 2: satellite navigation.

800
00:41:52,719 --> 00:41:54,880
Speaker 1: That sounds like they've got a bunch of people in

801
00:41:54,880 --> 00:41:57,719
Speaker 1: a boardroom and said, what's the worst fucking pr move

802
00:41:57,800 --> 00:42:01,640
Speaker 1: we could make? Right? That's like, rather, if you want

803
00:42:01,640 --> 00:42:04,279
Speaker 1: to make another hundred bucks, just put the car up

804
00:42:04,280 --> 00:42:07,080
Speaker 1: by one hundred bucks. But don't because it's not even

805
00:42:07,120 --> 00:42:09,239
Speaker 1: about the hundred bucks. Well it's a bit about that,

806
00:42:09,320 --> 00:42:12,640
Speaker 1: but it's also about how dare you fucking do that.

807
00:42:12,719 --> 00:42:16,240
Speaker 1: I'm not buying a Ford now, so stick your whatever,

808
00:42:16,440 --> 00:42:19,480
Speaker 1: your search arge or your payball up your ass. I'm

809
00:42:19,520 --> 00:42:23,279
Speaker 1: going to buy a Masda. It's like that tell us

810
00:42:23,320 --> 00:42:28,600
Speaker 1: what we've been searching for on the interwebs, Patrick, what's yeah?

811
00:42:28,719 --> 00:42:31,480
Speaker 2: Okay? So overall I always find this fun at the

812
00:42:31,560 --> 00:42:34,080
Speaker 2: end of the year to see what people search for.

813
00:42:34,520 --> 00:42:38,440
Speaker 2: And I mean, obviously the big, big, big one that

814
00:42:38,560 --> 00:42:41,160
Speaker 2: was of interest to everybody in Australia as well as

815
00:42:41,200 --> 00:42:43,440
Speaker 2: the rest of the world was the US election. So

816
00:42:43,800 --> 00:42:47,160
Speaker 2: no question overall, the US election came out on top

817
00:42:47,640 --> 00:42:51,279
Speaker 2: as far as Google searching for Australians over the last year.

818
00:42:51,760 --> 00:42:57,319
Speaker 2: The Olympic medal telling that was really high euros. Liam Paine,

819
00:42:57,360 --> 00:42:59,279
Speaker 2: I don't even know who Liam pain is. Anybody know

820
00:42:59,320 --> 00:43:00,640
Speaker 2: who Liam Payne. It's no idea.

821
00:43:01,600 --> 00:43:05,880
Speaker 1: Liam Payne wasn't wow he can you google LAMB Payne

822
00:43:06,200 --> 00:43:08,800
Speaker 1: and just add to the tally, well, Taylor Swift.

823
00:43:08,840 --> 00:43:11,480
Speaker 2: There's no kind of surprise there. But what when you

824
00:43:11,560 --> 00:43:14,359
Speaker 2: break it down. I mean, obviously the election was one

825
00:43:14,360 --> 00:43:17,640
Speaker 2: of the news events, and it's interesting that overall US

826
00:43:17,719 --> 00:43:20,120
Speaker 2: election and medal tally and then news events U S

827
00:43:20,160 --> 00:43:23,840
Speaker 2: election and medal tally, but there were other global figures.

828
00:43:23,960 --> 00:43:27,080
Speaker 2: This is internationally Taylor Swift number one, and then for

829
00:43:27,200 --> 00:43:32,920
Speaker 2: people and then Donald Trump, Kate Middleton, like Kamala Harris Craig.

830
00:43:32,960 --> 00:43:34,840
Speaker 2: You're not even on the list. I looked.

831
00:43:35,760 --> 00:43:39,719
Speaker 1: That's disappointing. Yeah, wow, I must be I must be

832
00:43:39,800 --> 00:43:40,520
Speaker 1: number eleven.

833
00:43:40,840 --> 00:43:44,920
Speaker 2: Yeah, that could be. It recipes Jamie Oliver's air fryer recipes.

834
00:43:45,200 --> 00:43:48,120
Speaker 2: Cucumber salad. How about cucumber salad.

835
00:43:48,880 --> 00:43:51,840
Speaker 1: That's it's pretty it sounds like that sounds like a

836
00:43:51,880 --> 00:43:56,480
Speaker 1: you thing at cucumber salad. Let's chuck some cow on it.

837
00:43:56,719 --> 00:43:57,600
Speaker 1: Now you're talking.

838
00:43:58,200 --> 00:44:00,640
Speaker 2: Now, This one I found really interesting. I know you're

839
00:44:00,640 --> 00:44:05,520
Speaker 2: going to like this one because the most popular words searched,

840
00:44:05,560 --> 00:44:12,000
Speaker 2: so people looking for definitions of words demure. Wow, demure.

841
00:44:13,239 --> 00:44:17,400
Speaker 1: I'll tell you none of us are demure, but you

842
00:44:17,440 --> 00:44:18,200
Speaker 1: know none of us.

843
00:44:19,000 --> 00:44:21,560
Speaker 2: But how random is it that people were the most

844
00:44:21,680 --> 00:44:25,279
Speaker 2: searched word? And the reason for it was that evidently

845
00:44:25,640 --> 00:44:29,839
Speaker 2: a social influencer used the word to describe herself, and

846
00:44:30,000 --> 00:44:32,960
Speaker 2: subsequently millions of people jumped on and started looking for

847
00:44:33,000 --> 00:44:38,680
Speaker 2: the word the definition demure. Would you describe yourself as demure? Tiff?

848
00:44:44,680 --> 00:44:47,680
Speaker 1: Yeah, get a tiff and I'm a wallflower. I can

849
00:44:47,800 --> 00:44:50,360
Speaker 1: bench a fuck load and you should see me deadlift

850
00:44:53,600 --> 00:44:54,719
Speaker 1: And what are you looking at?

851
00:44:55,000 --> 00:44:56,680
Speaker 2: Yeah? What about?

852
00:44:56,800 --> 00:44:59,480
Speaker 1: You're not like that anymore? Since she got back from India,

853
00:44:59,480 --> 00:45:02,399
Speaker 1: she's all a lama and calm the farm and equanimity

854
00:45:02,480 --> 00:45:06,839
Speaker 1: and fucking dreamcatchers and moccasins and give us a hug.

855
00:45:07,000 --> 00:45:10,520
Speaker 1: She's she's new and improved. Speaking of the Delai Lama.

856
00:45:10,600 --> 00:45:13,520
Speaker 4: This morning, when I was scrolling on instat I saw

857
00:45:13,640 --> 00:45:17,160
Speaker 4: Johann Hari's real saying that he got fat shamed by

858
00:45:17,200 --> 00:45:18,120
Speaker 4: the Delai Lama.

859
00:45:19,200 --> 00:45:20,120
Speaker 1: That's hilarious.

860
00:45:20,520 --> 00:45:23,280
Speaker 2: Can I say I have a connection with the Dalai Lama?

861
00:45:24,800 --> 00:45:27,319
Speaker 2: You can say it were we were born on the

862
00:45:27,320 --> 00:45:30,319
Speaker 2: same day and I have seen him once as I

863
00:45:30,320 --> 00:45:32,200
Speaker 2: saw him at a conference he was pretty amazing. She's

864
00:45:32,239 --> 00:45:34,360
Speaker 2: quite a cool guy. He's got a really cute giggle.

865
00:45:34,440 --> 00:45:36,120
Speaker 2: Have you noticed that? If you've seen the Dalai Lama,

866
00:45:36,160 --> 00:45:37,839
Speaker 2: if you ever seen any stuff.

867
00:45:37,560 --> 00:45:38,760
Speaker 1: You and I saw him together.

868
00:45:38,800 --> 00:45:41,160
Speaker 2: You dickhead with me at the Dalai Lama thing?

869
00:45:41,360 --> 00:45:43,279
Speaker 1: Fuck and hell, you're the worst date ever.

870
00:45:43,600 --> 00:45:44,719
Speaker 2: That was a long time ago.

871
00:45:44,920 --> 00:45:49,080
Speaker 1: Did we held your hand the whole time? Especially in

872
00:45:49,120 --> 00:45:51,400
Speaker 1: the scary bits when he looked into your soul?

873
00:45:54,440 --> 00:45:54,640
Speaker 3: Man?

874
00:45:54,920 --> 00:45:56,760
Speaker 2: That was a long time ago. How many years agould

875
00:45:56,760 --> 00:45:57,120
Speaker 2: that had been?

876
00:45:57,719 --> 00:45:59,680
Speaker 1: I don't know, but I don't feel I don't feel

877
00:45:59,680 --> 00:46:03,640
Speaker 1: special at all. Now, Wow, what was that movie? We

878
00:46:03,719 --> 00:46:06,319
Speaker 1: saw that three D? What's it called again? Oh?

879
00:46:06,400 --> 00:46:11,920
Speaker 2: Yeah, we saw it was Oh the what was it?

880
00:46:11,880 --> 00:46:14,000
Speaker 1: It's called you know those Blue People?

881
00:46:16,480 --> 00:46:16,840
Speaker 2: You said it?

882
00:46:16,920 --> 00:46:18,200
Speaker 4: I thought, why?

883
00:46:19,800 --> 00:46:22,680
Speaker 1: So what a mix? We've seen the We've seen Avatar

884
00:46:22,840 --> 00:46:28,480
Speaker 1: and the Dali Lama, not in the same day, battally

885
00:46:28,520 --> 00:46:28,919
Speaker 1: in pain.

886
00:46:29,040 --> 00:46:30,680
Speaker 3: It's the one direction singer that.

887
00:46:30,719 --> 00:46:31,239
Speaker 1: Died for.

888
00:46:33,320 --> 00:46:36,640
Speaker 2: I remember the name from somewhere, Okay, now I didn't ye,

889
00:46:37,160 --> 00:46:40,080
Speaker 2: now I know? So what about di I Y? This

890
00:46:40,160 --> 00:46:42,879
Speaker 2: is an interesting what do people most need help with

891
00:46:43,160 --> 00:46:47,120
Speaker 2: when they do Google searching? Di Y car maintenance so

892
00:46:47,160 --> 00:46:49,920
Speaker 2: people are still maintaining their cars. If you popped the

893
00:46:49,920 --> 00:46:52,760
Speaker 2: bond on your new car, because you can't recognize anything

894
00:46:52,800 --> 00:46:54,720
Speaker 2: under that bond, can you like, there's.

895
00:46:56,200 --> 00:46:59,040
Speaker 1: I got no idea. When you turn on my car,

896
00:46:59,120 --> 00:47:03,000
Speaker 1: there's no sound because it's hybrid, so you don't even

897
00:47:03,040 --> 00:47:04,840
Speaker 1: know it's on. It's like is it on?

898
00:47:06,200 --> 00:47:08,120
Speaker 2: See? You might find this hard to believe, but I've

899
00:47:08,200 --> 00:47:11,600
Speaker 2: changed the spark plugs on my first car. I repaired

900
00:47:11,640 --> 00:47:15,360
Speaker 2: my first car myself. Remember the front bumpers on old

901
00:47:15,440 --> 00:47:16,200
Speaker 2: cars were.

902
00:47:16,040 --> 00:47:21,200
Speaker 1: Actually hang on. Let's define I repaired, yeah, because that's

903
00:47:21,400 --> 00:47:23,279
Speaker 1: come on, what does that mean?

904
00:47:23,560 --> 00:47:25,759
Speaker 2: Okay? So I may have got into a little bit

905
00:47:25,800 --> 00:47:28,200
Speaker 2: of an accident when I was nineteen. I was on

906
00:47:28,360 --> 00:47:31,040
Speaker 2: tram tracks on Sydney Road. It was a wet day.

907
00:47:31,360 --> 00:47:33,239
Speaker 2: Guy slams the brakes on in front of me and

908
00:47:33,280 --> 00:47:35,160
Speaker 2: I was too close and I skid it into him.

909
00:47:35,480 --> 00:47:38,040
Speaker 2: So but I couldn't afford the repair, so I did

910
00:47:38,080 --> 00:47:42,120
Speaker 2: it myself. So I replaced the headlight myself, just bought

911
00:47:42,160 --> 00:47:44,600
Speaker 2: a new headlight. I did the panel beating by myself.

912
00:47:44,760 --> 00:47:47,160
Speaker 2: But the funniest thing was the panel beating wasn't that

913
00:47:47,200 --> 00:47:49,319
Speaker 2: hard to kind of tap things out? I put a

914
00:47:49,440 --> 00:47:52,560
Speaker 2: jack inside the because the car was a Mazda eight

915
00:47:52,600 --> 00:47:54,880
Speaker 2: oh eight, so it had a little tiny engine but

916
00:47:54,920 --> 00:47:57,040
Speaker 2: a really long body, so it meant that there was

917
00:47:57,080 --> 00:47:59,680
Speaker 2: no mechanical damage. And so what I did was I

918
00:47:59,680 --> 00:48:01,560
Speaker 2: put a plank of wood and the jack, and I

919
00:48:01,640 --> 00:48:03,279
Speaker 2: jacked out the front of the car to push it

920
00:48:03,320 --> 00:48:06,200
Speaker 2: back out again. But the best thing was remember the

921
00:48:06,280 --> 00:48:08,759
Speaker 2: chrome bumper bars on Rudy, because my car was a

922
00:48:08,800 --> 00:48:11,040
Speaker 2: nineteen to seventies mas to eight oh eight, and the

923
00:48:11,120 --> 00:48:14,480
Speaker 2: chrome bumpers were solid, and it was so solid even

924
00:48:14,520 --> 00:48:16,719
Speaker 2: though it had been buckled, I couldn't bend it back.

925
00:48:16,719 --> 00:48:19,400
Speaker 2: So I put it over Dad's gas barbecue, got it

926
00:48:19,480 --> 00:48:22,480
Speaker 2: red hot, and then tapped out and got it back

927
00:48:22,480 --> 00:48:24,880
Speaker 2: into shape. So I did that myself. I think you

928
00:48:24,880 --> 00:48:25,680
Speaker 2: should be proud of me.

929
00:48:27,840 --> 00:48:30,440
Speaker 1: I would love to see a photo of the finished product.

930
00:48:30,920 --> 00:48:34,359
Speaker 2: It survived for another six years after that. I might say,

931
00:48:36,040 --> 00:48:36,920
Speaker 2: little mouse.

932
00:48:36,840 --> 00:48:43,240
Speaker 1: Well, if I have a little fender bender, I definitely

933
00:48:43,239 --> 00:48:45,480
Speaker 1: will not give you a call no, because they're plastic.

934
00:48:45,560 --> 00:48:47,799
Speaker 2: It wouldn't matter. I just need a glue gun to

935
00:48:47,800 --> 00:48:50,160
Speaker 2: put it back together again. I wouldn't need anything metal.

936
00:48:52,200 --> 00:48:55,880
Speaker 2: So car maintenance was number one for DIY. The second

937
00:48:55,880 --> 00:49:00,680
Speaker 2: one was DIY Halloween Costumes and then followed by how

938
00:49:00,719 --> 00:49:03,440
Speaker 2: to make an Easter Buddy footprints.

939
00:49:04,600 --> 00:49:07,440
Speaker 1: I would have thought high on the list would be DIY.

940
00:49:07,719 --> 00:49:12,439
Speaker 1: Like medical, Yeah, I know, how to perform the CPR. Yeah,

941
00:49:12,520 --> 00:49:16,480
Speaker 1: how to well, how to give yourself an appendectomy with

942
00:49:16,640 --> 00:49:20,000
Speaker 1: a sewing kit and a Swiss Army knife and some gin.

943
00:49:22,360 --> 00:49:24,200
Speaker 2: Travel. Do you know where people want to go the

944
00:49:24,239 --> 00:49:27,799
Speaker 2: most in Australia if they live in where they want

945
00:49:27,800 --> 00:49:30,640
Speaker 2: to go? Gold Coast? Cheap flights to the Gold Coast

946
00:49:30,680 --> 00:49:34,080
Speaker 2: from Melbourne that was the most searched travel term, and

947
00:49:34,120 --> 00:49:37,400
Speaker 2: then flights to King Island and then Jetstar cheap flights

948
00:49:37,440 --> 00:49:39,320
Speaker 2: to Melbourne. Oh so people do want to come to Melbourne?

949
00:49:39,320 --> 00:49:40,840
Speaker 2: They don't do they't just want to leave Melbourne.

950
00:49:41,080 --> 00:49:42,640
Speaker 1: Do you know what's going to sell well in the

951
00:49:42,680 --> 00:49:43,360
Speaker 1: Gold Coast?

952
00:49:43,719 --> 00:49:44,040
Speaker 2: What's that?

953
00:49:45,040 --> 00:49:46,240
Speaker 1: Solar paint cars?

954
00:49:46,560 --> 00:49:49,440
Speaker 2: Oh yeah, how good would that be? Or in Central Australia?

955
00:49:50,360 --> 00:49:53,640
Speaker 1: Oh wow, imagine that? I think, all right, let's do

956
00:49:53,719 --> 00:49:56,839
Speaker 1: one more. Let's do one more, mister host Well.

957
00:49:56,920 --> 00:49:59,200
Speaker 2: Actually, one of the ones that I thought was interesting

958
00:49:59,440 --> 00:50:04,520
Speaker 2: related to social media, and they talk about regularly posting

959
00:50:04,560 --> 00:50:08,000
Speaker 2: on socials as opposed to just looking at socials can

960
00:50:08,040 --> 00:50:12,040
Speaker 2: actually worsen the mental health in adults. So if you've

961
00:50:12,080 --> 00:50:16,000
Speaker 2: been caught up in that kind of mailstream of have

962
00:50:16,080 --> 00:50:19,400
Speaker 2: to post, got to give a status update, that now

963
00:50:19,480 --> 00:50:21,760
Speaker 2: is being thought of as being having a real impact.

964
00:50:21,800 --> 00:50:25,120
Speaker 2: So the Journal of Medical Internet Research, so they were

965
00:50:25,200 --> 00:50:29,359
Speaker 2: investigating fifteen thousand adults in the United Kingdom all over

966
00:50:29,400 --> 00:50:33,000
Speaker 2: the age of sixteen, and what they said was simply

967
00:50:33,160 --> 00:50:36,759
Speaker 2: viewing social media content. Content didn't seem to have the

968
00:50:36,800 --> 00:50:41,520
Speaker 2: same effect as regularly posting on social media. So if

969
00:50:41,560 --> 00:50:44,279
Speaker 2: you fall into that category of someone who has to

970
00:50:44,320 --> 00:50:47,080
Speaker 2: post what you're doing all the time, it actually may

971
00:50:47,320 --> 00:50:51,200
Speaker 2: worsen your mental health. It could have a health implications

972
00:50:51,400 --> 00:50:53,560
Speaker 2: if you feel the need to keep posting. I mean,

973
00:50:53,680 --> 00:50:56,480
Speaker 2: do you guys post much on socials?

974
00:50:56,640 --> 00:50:58,360
Speaker 4: Post every four and a half minutes?

975
00:50:58,600 --> 00:51:02,920
Speaker 1: Oh really? Her life was fucking as public as you

976
00:51:02,960 --> 00:51:03,759
Speaker 1: can get. I don't.

977
00:51:03,840 --> 00:51:06,640
Speaker 4: Every time my dog changes position, I take a photo

978
00:51:06,680 --> 00:51:07,319
Speaker 4: and post it.

979
00:51:08,080 --> 00:51:11,520
Speaker 2: Wow. Yeah, I think I posted once this year, and

980
00:51:11,520 --> 00:51:12,440
Speaker 2: that's post.

981
00:51:12,600 --> 00:51:17,120
Speaker 1: I just post messages like whiteboards. I don't really go, hey,

982
00:51:17,160 --> 00:51:20,439
Speaker 1: here's my breky. Sometimes I'll post something that I think

983
00:51:20,560 --> 00:51:23,520
Speaker 1: might be like a personal thing that I think might

984
00:51:23,560 --> 00:51:27,000
Speaker 1: be like my steps. I don't know if I told

985
00:51:27,000 --> 00:51:29,239
Speaker 1: you this. Did I tell you that my phone this,

986
00:51:29,320 --> 00:51:31,560
Speaker 1: Speaking of tech, my phone told me I was a fat,

987
00:51:31,640 --> 00:51:36,320
Speaker 1: lazy fuck. Did I tell you if my phone literally

988
00:51:36,400 --> 00:51:41,279
Speaker 1: sent me? In my phone, it literally sent me an

989
00:51:41,320 --> 00:51:45,520
Speaker 1: alert telling me that I don't. Essentially, you're not walking

990
00:51:45,640 --> 00:51:50,000
Speaker 1: much and you need to walk more. So I took

991
00:51:50,040 --> 00:51:55,440
Speaker 1: that on board from my pocket coach, and yeah, I've

992
00:51:55,560 --> 00:51:59,480
Speaker 1: gone from average under five thousand a day to average

993
00:51:59,560 --> 00:52:02,240
Speaker 1: just under fifteen thousand a day. But it was funny

994
00:52:02,280 --> 00:52:05,439
Speaker 1: because for me, and actually I did not realize how

995
00:52:05,520 --> 00:52:09,000
Speaker 1: little I was working walking and generally I was walking

996
00:52:09,080 --> 00:52:11,120
Speaker 1: more than that. But the last few months where I've

997
00:52:11,120 --> 00:52:14,879
Speaker 1: been deep, balls deep as the kids say, or maybe

998
00:52:14,880 --> 00:52:18,759
Speaker 1: the kids don't say that. Maybe that's me balls deep

999
00:52:18,760 --> 00:52:23,000
Speaker 1: in my PhD and recording and reading. And I've been

1000
00:52:23,040 --> 00:52:27,040
Speaker 1: sitting down a lot. And yeah, like even this morning,

1001
00:52:27,080 --> 00:52:29,800
Speaker 1: I got up at quarter past five and I've walked

1002
00:52:29,840 --> 00:52:32,920
Speaker 1: five ks and it's eight twenty six right now. But

1003
00:52:33,239 --> 00:52:35,719
Speaker 1: it's been a I know this sounds weird, but one

1004
00:52:35,840 --> 00:52:38,799
Speaker 1: it was a really good catalyst. But it's been a

1005
00:52:38,840 --> 00:52:41,040
Speaker 1: game changer for me. But that's got nothing to do

1006
00:52:41,080 --> 00:52:41,520
Speaker 1: with tech.

1007
00:52:42,120 --> 00:52:43,640
Speaker 2: No, it has have everything to do with tech. What

1008
00:52:43,640 --> 00:52:45,799
Speaker 2: do you mean it was your phone? Your phone was.

1009
00:52:45,760 --> 00:52:48,840
Speaker 1: Telling well, I mean that my physiological response.

1010
00:52:50,040 --> 00:52:52,360
Speaker 2: But it can help prompt you know. It's not dissimilar

1011
00:52:52,400 --> 00:52:55,440
Speaker 2: to having the conversation with Ai like I did earlier

1012
00:52:55,480 --> 00:52:58,840
Speaker 2: in the show. It's that sense of not being alone,

1013
00:52:59,040 --> 00:53:01,680
Speaker 2: you know, can do all that sort of stuff. Although

1014
00:53:01,680 --> 00:53:03,440
Speaker 2: I've got to say this is quite funny. I was

1015
00:53:03,520 --> 00:53:05,560
Speaker 2: just in the middle of a Thai cheek class. I

1016
00:53:05,560 --> 00:53:08,160
Speaker 2: think I've been doing Tai chie for about forty five minutes,

1017
00:53:08,440 --> 00:53:11,520
Speaker 2: and my watch pinged me and said you need to

1018
00:53:11,560 --> 00:53:16,360
Speaker 2: get moving. It's like, what forty five minutes? You idiot?

1019
00:53:16,440 --> 00:53:19,560
Speaker 2: I think I was just so relaxed and my heart

1020
00:53:19,640 --> 00:53:23,520
Speaker 2: was so relaxed that it thought that I wasn't moving. Well,

1021
00:53:23,520 --> 00:53:26,160
Speaker 2: that's what Tai Chee's about, isn't It's a slow moving meditation.

1022
00:53:26,360 --> 00:53:28,759
Speaker 2: So yeah, idea not to end up with a sweat

1023
00:53:28,800 --> 00:53:31,040
Speaker 2: and panting at the end of it, but to feel

1024
00:53:31,080 --> 00:53:34,239
Speaker 2: relaxed and centered and mindful. See if when I was

1025
00:53:34,239 --> 00:53:36,960
Speaker 2: thinking about that, when Crago said before that, you know,

1026
00:53:37,000 --> 00:53:39,640
Speaker 2: you've become a slightly different person and you're more chilled

1027
00:53:39,680 --> 00:53:42,480
Speaker 2: and relaxed since going to India. It made me think

1028
00:53:42,480 --> 00:53:43,839
Speaker 2: that you and I could have hung out of this

1029
00:53:43,920 --> 00:53:45,960
Speaker 2: mindfulness seminar because I reckon you would got to lay

1030
00:53:46,000 --> 00:53:49,839
Speaker 2: out out of that it was good. I would just living. Yeah.

1031
00:53:50,000 --> 00:53:52,839
Speaker 2: I love the idea of living in the moment for

1032
00:53:52,920 --> 00:53:55,239
Speaker 2: the moment, not letting anything else. And that's what Tai

1033
00:53:55,360 --> 00:53:57,640
Speaker 2: Cheek does for me. It's great because nothing else is

1034
00:53:57,640 --> 00:53:59,440
Speaker 2: in my head and I can just focus on what

1035
00:53:59,480 --> 00:54:02,480
Speaker 2: I'm doing. The thing that I got out of it

1036
00:54:02,640 --> 00:54:06,080
Speaker 2: was five minutes a day of mindfulness. Just five minutes

1037
00:54:06,080 --> 00:54:08,920
Speaker 2: a day is a real gift to yourself. Just you know,

1038
00:54:08,960 --> 00:54:11,920
Speaker 2: that's hectic lifestyle, Crago. You know you're getting up and

1039
00:54:11,960 --> 00:54:13,640
Speaker 2: walking and doing all that sort of stuff. But you

1040
00:54:13,760 --> 00:54:16,560
Speaker 2: use walking as a bit of a moving meditation, don't you.

1041
00:54:16,560 --> 00:54:18,080
Speaker 2: You kind of just in your zone.

1042
00:54:18,200 --> 00:54:21,760
Speaker 1: I do, I do, I do. It's funny we should

1043
00:54:21,800 --> 00:54:24,640
Speaker 1: talk about what because what for somebody will be a

1044
00:54:24,680 --> 00:54:27,719
Speaker 1: meditation for somebody else will be a source of anxiety.

1045
00:54:28,640 --> 00:54:31,320
Speaker 1: You know. So mate, tell people how they can connect

1046
00:54:31,320 --> 00:54:32,200
Speaker 1: with you and follow you.

1047
00:54:32,840 --> 00:54:35,239
Speaker 2: Oh sure, yeah, so you just need to go to

1048
00:54:35,400 --> 00:54:40,200
Speaker 2: websites now, dot com, dot au and just trying to

1049
00:54:40,280 --> 00:54:42,200
Speaker 2: check out what we do. I mean, obviously, websites now,

1050
00:54:42,200 --> 00:54:45,400
Speaker 2: dot com, dot you speaks for itself. But it's just

1051
00:54:45,440 --> 00:54:47,960
Speaker 2: too hard to spell Genesis FX because it's just a

1052
00:54:48,000 --> 00:54:50,640
Speaker 2: stupid name that I came up with before the Internet existed,

1053
00:54:50,840 --> 00:54:53,000
Speaker 2: and people can't spell it. So I had to come

1054
00:54:53,040 --> 00:54:56,040
Speaker 2: up with another name for our website. So websites Now

1055
00:54:56,160 --> 00:54:58,080
Speaker 2: kind of made sense, didn't it. Really? When you think

1056
00:54:58,120 --> 00:54:58,959
Speaker 2: about it, I think it.

1057
00:54:58,880 --> 00:55:01,560
Speaker 1: Was a good change. It was a good change. There's

1058
00:55:01,600 --> 00:55:04,279
Speaker 1: been quite a few businesses who have just changed their

1059
00:55:04,360 --> 00:55:06,680
Speaker 1: name and nothing else and their business catapulted.

1060
00:55:07,080 --> 00:55:07,480
Speaker 2: Really.

1061
00:55:08,040 --> 00:55:10,680
Speaker 1: Oh yeah, do you remember Tara and Russ had a

1062
00:55:10,719 --> 00:55:14,720
Speaker 1: business in glen Huntley Road. It was called High Energy Network.

1063
00:55:15,040 --> 00:55:17,400
Speaker 2: Yeah, I know, I did a lot, they did quite well.

1064
00:55:18,040 --> 00:55:20,520
Speaker 1: But then we had a meeting one day and they

1065
00:55:20,520 --> 00:55:22,600
Speaker 1: were talking about how to improve the business and I said,

1066
00:55:22,600 --> 00:55:27,080
Speaker 1: why don't you change the name to Australian Fitness Academy

1067
00:55:27,880 --> 00:55:31,600
Speaker 1: And they went ah, and they did that and just

1068
00:55:31,719 --> 00:55:37,920
Speaker 1: that double tripled, like interest inquiries, turnover bums on seats,

1069
00:55:38,520 --> 00:55:42,640
Speaker 1: because it explains straight away what it is versus you know,

1070
00:55:42,760 --> 00:55:45,919
Speaker 1: high Energy network. Fucking what is what does that even mean?

1071
00:55:46,040 --> 00:55:48,600
Speaker 1: So so the name matters mate, I'll got to go

1072
00:55:48,640 --> 00:55:52,399
Speaker 1: because we've got a thing. Thanks TIV, Thanks Patrick, thank

1073
00:55:52,440 --> 00:55:52,840
Speaker 1: you