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Speaker 1: I'll good a team. It's Patrick can Jumbo. It's the

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Speaker 1: Bloody You Project where sans Tiff. She's in the thriving

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Speaker 1: metropolis of Brisbane up there on the O in the

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Speaker 1: Sunshine State, I should say, just doing what she does. Hi, mate,

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Speaker 1: how are you?

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Speaker 2: Oh? I'm good. I haven't been to bris Vegas for

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Speaker 2: such a long time. I was just thinking, it's probably

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Speaker 2: been nine years since I've been up that far up

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Speaker 2: and I'm kind of feeling like I need it at

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Speaker 2: the moment because it's three degrees there's a heat wave

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Speaker 2: here in the land at the moment. It was minus

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Speaker 2: two when I got up this morning, so it's positively

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Speaker 2: a heat wave in comparison.

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Speaker 1: Do you know you know that you're old? You and

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Speaker 1: I know that we're old because we talk about the weather.

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Speaker 1: I mean, that's the tally. What old people do? Old people?

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Speaker 1: There's no twenty year olds getting on a podcast going ah,

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Speaker 1: how's the weather? Ah? Fuck? What was it? I was

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Speaker 1: minus too here now.

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Speaker 2: I can tell you if you live in a town

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Speaker 2: where your car's frozen over, people all talk about the

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Speaker 2: weather and those poor kids walking to school and yours.

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Speaker 2: Oh yeah, I couldn't do it.

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Speaker 1: Well, I have many memories growing up in the thriving

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Speaker 1: metropolis of La tro Valley having to go out and

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Speaker 1: the ice the window of the Valiant Regal a number

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Speaker 1: plate LVR four one four, just in case anyone was wondering.

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Speaker 1: And so I was in charge of getting the ice

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Speaker 1: off the window before mum or Dad could drive me

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Speaker 1: to either school or the bus wherever I was gone.

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Speaker 1: And but you've got a hack, yep?

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Speaker 2: Yeah, the night before what you do is you get

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Speaker 2: a beach towel and you put it over the wind screen.

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Speaker 2: So last night, knowing that it was going to be

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Speaker 2: freezing cold this morning, laid the towel out, and then

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Speaker 2: just before I left to go to the gym this morning,

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Speaker 2: I peeled the towel off the wind screen. It was

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Speaker 2: all frozen on one side and the wind screen was

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Speaker 2: perfectly fine.

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Speaker 1: Yeah, that's a good idea.

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Speaker 2: It's a good hack, isn't it.

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Speaker 1: Yeah, it's great. And do you do you have to do?

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Speaker 1: You have to do anything too, because I feel I

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Speaker 1: could see they're going to slide down or blow away.

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Speaker 1: Do you need to tuck it inside the doors?

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Speaker 2: Some people do, and that's what I used to do.

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Speaker 2: But then I realized the only time you have frost

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Speaker 2: is when there's no wind. Anyway, if it's windy, it

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Speaker 2: doesn't get frosty.

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Speaker 1: I did not. Well, I guess that make I've never

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Speaker 1: thought about that, but I guess that makes sense.

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Speaker 2: Yeah. Hey, you know the other thing you're talking about

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Speaker 2: getting up early, going to school, and you got driven

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Speaker 2: to school. But I had to walk to school. You know,

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Speaker 2: when I was young, I walked forty kilometers to school

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Speaker 2: every day. No, when I walked to school. You don't

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Speaker 2: My funnest thing I used to love to do.

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Speaker 1: Is that a word?

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Speaker 2: Funnest? Probably is?

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Speaker 1: It is totally, It's totally a word. Yeah.

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Speaker 2: I used to love walking through the suburbs and when

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Speaker 2: they had that frost on the grass, and as you

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Speaker 2: walk through, it would leave footprints. And what I would

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Speaker 2: do is when I got to a tree, I would

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Speaker 2: hop around the left hand side on one foot and

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Speaker 2: then hop back the other way, And then it looked

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Speaker 2: like for all intensive purposes, when someone walked past that,

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Speaker 2: the footprints diverged and went around the tree, so.

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Speaker 1: They somehow morphed through the tree and kept walking.

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Speaker 2: Yeah. Pretty good.

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Speaker 1: Wow, Yeah, great. I wonder how many people you scared

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Speaker 1: the shit out of.

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Speaker 2: I don't know. I reckon that the X Files people

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Speaker 2: were out there, or at least the Twilight certain people,

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Speaker 2: I don't know, no one probably, but it amused me

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Speaker 2: and it was a way to get the school freezing

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Speaker 2: cold and at least do something.

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Speaker 1: And it's good to you told me just before we

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Speaker 1: hit the go button that you're back in the gym.

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Speaker 1: How long since you've trained consistently in a gym? Was

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Speaker 1: it back in the harpst.

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Speaker 2: Days or oh no, no, no, no, I've been training quite consistently.

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Speaker 2: But I had a really big break last year. For

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Speaker 2: for a year, I got tennis elbow and it took

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Speaker 2: me a long, long, long time to get over it.

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Speaker 2: But I think since I left Harpers, I kind of

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Speaker 2: had a break for maybe two or three years, and

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Speaker 2: then I got back into it and I reckon consistently.

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Speaker 2: I've been back for about maybe seven or eight years

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Speaker 2: and then had last year off. You know, just isn't

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Speaker 2: it funny? It's a psychological game. You know, you've got

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Speaker 2: to talk yourself back into it and when the pain

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Speaker 2: started to go, and you know, at one point the

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Speaker 2: tennis elbow funny, I never played tennis ever played tennis,

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Speaker 2: but I just I couldn't even pick up a bag

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Speaker 2: of groceries with one arm. It was really, really bad.

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Speaker 2: And I did the whole physio. I think I went

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Speaker 2: to about two different physios and got exercises which I'm

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Speaker 2: pretty average at doing. You know, they tell you to

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Speaker 2: do these exercises every day and you don't really do them,

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Speaker 2: which is silly because that's how you recover. Anyway, So

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Speaker 2: I'm back into it. It's been a month and I

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Speaker 2: think I need probably another is it two or three

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Speaker 2: more weeks to kind of establish a routine, although I

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Speaker 2: feel like I'm in a routine now. I didn't want

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Speaker 2: to get out of bit this morning when it was

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Speaker 2: minus two, but I did and I got there. So

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Speaker 2: I reckon, I'm doing okay.

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Speaker 1: And what are you doing? Like, what's you kind of

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Speaker 1: three days a week way to doing like strength stuff?

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Speaker 1: You're doing funtial it's pretty much.

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Speaker 2: So I do four days a week of strength and

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Speaker 2: it's a real full body you know, legs, arms, chest,

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Speaker 2: back by tries, all that sort of stuff. And then

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Speaker 2: I recently read an article. I used to do just

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Speaker 2: eight to ten minutes on the rower as my cardio,

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Speaker 2: and I used to do it before I did weights,

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Speaker 2: and then I read an article that said it's actually

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Speaker 2: better if you want to burn fat, it's better to

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Speaker 2: do it after you've done weights, and so I've started

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Speaker 2: doing that. So I've flipped it around a little bit.

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Speaker 2: So once I finished my workout, I do about thirty

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Speaker 2: five forty minutes, and then I do a ten minute

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Speaker 2: you know, eight to ten minute on the rower.

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Speaker 1: That's good. And do you flog yourself or you're just

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Speaker 1: steady state or.

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Speaker 2: Pardon it to public gym?

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Speaker 1: Sorry?

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Speaker 2: Do I go a little hard? Yeah? Now you know

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Speaker 2: what's interesting. I go relatively hard. I don't I mean,

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Speaker 2: I don't kill myself with cardio. But over at the

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Speaker 2: last month, my speed has increased. A little rower has

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Speaker 2: an indicator of how fast you're going, and finding that

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Speaker 2: I'm just I'm getting faster without working too much hard,

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Speaker 2: which it doesn't feel like I'm working harder.

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Speaker 1: Yeah, you know you, I feel like we're pouring the

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Speaker 1: fuck out of everyone listening to the topic at hand.

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Speaker 1: But what's interesting about you is, I don't know how

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Speaker 1: long we've known each other. It's got to be in

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Speaker 1: the ballpark of thirty years. But your body is pretty

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Speaker 1: much the same. Like it doesn't seem to me like, well,

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Speaker 1: you know, like thirty years down the track, most blokes,

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Speaker 1: well now they're like their postures, shit, they're they're a

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Speaker 1: bit they're a bit fatter, they're you know, like that.

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Speaker 1: It's like the guys that I went to school with.

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Speaker 1: Oh yeah, God bless them. It's like, fucking hell, I

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Speaker 1: can't even some of them look good. But there's a

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Speaker 1: few that's like, wow, there's father time has not been

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Speaker 1: good to you, or maybe you've just been haven't looking

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Speaker 1: after you, haven't been looking after you, but you still

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Speaker 1: look kind of the same.

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Speaker 2: Well thanks, mate, that's what it's so to you though,

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Speaker 2: you was just so buffing, you know. That's I don't

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Speaker 2: think your body shapes been pretty consistent.

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Speaker 1: That's because I'm insecure and to like me, Yeah.

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Speaker 2: I'm pretty over the same, I reckon. That's one of

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Speaker 2: the reasons. It's a good motivation. Though.

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Speaker 1: Wow, I don't know if it's a good motivation, but

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Speaker 1: it's a motivation.

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Speaker 2: You know. I shouldn't admit this, but there's a little

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Speaker 2: bit of one upmanship as well, because I have an

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Speaker 2: identical twin brother and it's just hard not to comparee

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Speaker 2: yourself all the time. And because look, you know, when

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Speaker 2: my mom passed away just over ten years ago, I

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Speaker 2: had to go. I went out and bought a black

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Speaker 2: suit because I didn't known a black suit, so I

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Speaker 2: bought a suit. And that's when I started hitting the

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Speaker 2: gym again. So I got back into the gym. And

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Speaker 2: then after about oh maybe six months, I realized I

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Speaker 2: dropped about two sizes and the suit didn't fit me.

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Speaker 2: So I went on my brother's place and I said

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Speaker 2: to my brother, look, you know you were in a

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Speaker 2: kind of high end business. You know you wear suits

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Speaker 2: all the time. Do you want this suit? I've only

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Speaker 2: worn it once and I said it doesn't fit me anymore.

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Speaker 2: It's too big. And he said, well, why don't you

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Speaker 2: hold onto it until you fit into it again. It's like, no,

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Speaker 2: I never want to be able to fit into it.

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Speaker 2: And so, you know, two sizes too big, and I

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Speaker 2: gave it. I don't know if if he wore it

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Speaker 2: or whether it fit him or whatever, but after dropping

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Speaker 2: two sizes, it was just too big. But that's a

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Speaker 2: nice feeling to be able to drop a couple of

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Speaker 2: sizes and then to keep yourself motivated not to fill

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Speaker 2: the suit.

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Speaker 1: Out again, are you guys identical or no?

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Speaker 2: Yeah, we're genetically identical yep, and single organism. We started

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Speaker 2: off as some of us didn't progress much further. But yeah, now.

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Speaker 1: Do you and he have I don't think you do.

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Speaker 1: But you know how some identical twins it's like freakishly

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Speaker 1: intertwined with each other's lives. I know that's not you,

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Speaker 1: but all you know that whole thing where you've seen

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Speaker 1: people on Telly finishing each other's sentences.

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Speaker 2: It was a viral video of those women in Queensland.

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Speaker 2: Did you see that? Wow? That was that was amazing,

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Speaker 2: wasn't it. No, I what I've observed with twins, there

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Speaker 2: are some twins that are almost joined at the hip,

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Speaker 2: and then you've got other twins that are almost mirror

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Speaker 2: images of each other in terms of and I mean

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Speaker 2: biologically mirror images. Like you know, I've got one crooked

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Speaker 2: tooth that's the exact opposite twisted tooth than he has,

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Speaker 2: like mirror image. But but no, we have had some

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Speaker 2: freaky I don't know if I've told you about the

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Speaker 2: freaky things that happened. So my brother's name is Paul

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Speaker 2: and consistently, at least three or four times a year,

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Speaker 2: I'll be on the phone to someone I know relatively well,

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Speaker 2: or it'll be a call center where we start off

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Speaker 2: a conversation and then halfway through the conversation they stopped

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Speaker 2: calling me Patrick and they start calling me Paul. Wow,

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Speaker 2: local politician I've known for about five or six years,

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Speaker 2: and she turned to me one day and called me Paul.

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Speaker 2: And I've known it for years and years and years.

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Speaker 2: So that's a bit freaky. But the freakiest thing I

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Speaker 2: think that ever happened. I don't know if you remember.

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Speaker 2: At one point in harp As, I had what do

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Speaker 2: they call little flavor, say, but the little little beard

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Speaker 2: thing they have under the lip, Remember I had. I

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Speaker 2: wore that for a while.

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Speaker 1: Yeah, yeah, I don't know what.

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Speaker 2: Well, one year I decided I was going to grow

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Speaker 2: a go tea just before Christmas, so maybe six weeks

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Speaker 2: before Christmas, I grew a full go tea. You know,

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Speaker 2: thought I'm gonna look tough, white Craig. Anyway, so a

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Speaker 2: mum and dad I was living. I was obviously not

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Speaker 2: living at home anymore, but I go around to the

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Speaker 2: folks place for Christmas Day, and Mum got to the

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Speaker 2: front door, first opens the door and says, you've been

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Speaker 2: talking to your brother, haven't you? And I said, what,

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Speaker 2: I walk in and my identical twin brother has grown

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Speaker 2: the exact same go tea. Wow, that freaked me out.

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Speaker 2: I shaved it off and was that.

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Speaker 1: The first time both of you had ever done it

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Speaker 1: in your life?

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Speaker 2: Never neither of us had ever grown a go tea.

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Speaker 2: And it wasn't like suddenly people were growing them like.

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Speaker 2: It wasn't like the mullet thing.

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Speaker 1: No.

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Speaker 2: Yeah, yeah, so that was really really strange. That's a

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Speaker 2: few weird things have happened.

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Speaker 1: Yeah, I don't know. There's something in that. There's something

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Speaker 1: in that. And some some twins are a little bit weird,

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Speaker 1: though some are a bit disconcerting. I feel like they're

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Speaker 1: too intertwined.

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Speaker 2: Well, I guess in our case there's the good twin

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Speaker 2: and the evil twin. And I think we know Paul's

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Speaker 2: the good twin, don't we do?

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Speaker 1: We do? But also what else is interesting is that

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Speaker 1: you like boys who likes girls. That that's interesting, like

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Speaker 1: genetically identical, you know, So that.

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Speaker 2: About how don't have to tell you the story about

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Speaker 2: how I added myself.

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Speaker 1: Uh, I think you did. But let's about half of

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Speaker 1: our audience or more who haven't heard it.

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Speaker 2: Well, the ones that are still left because the rest

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Speaker 2: of them have switched off, because this is the shit

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Speaker 2: to start to any podcast. Where's Tiff when you need

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Speaker 2: it now? I did a TV show years ago. There

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Speaker 2: was a TV show on TV show and Channel seven

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Speaker 2: called it was Australia's Most Identical Twins, and it was

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Speaker 2: just a remaker of a British show and they got

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Speaker 2: I think eighty four sets of identical twins from around

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Speaker 2: the country and they got us to do a whole

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Speaker 2: series of tests. So they got us to do coordination tests,

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Speaker 2: so you know those dance you know, video arcades sometimes

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Speaker 2: you had these ending platforms you did that. They did

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Speaker 2: our dental records. That's how I knew that my teeth

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Speaker 2: were the exact mirror image of my brother, which is

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Speaker 2: really common in twins. We did everything from coordination. We

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Speaker 2: did a blind chocolate test, so they put a you know,

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Speaker 2: they got you to taste different chocolates to see which

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Speaker 2: one you liked, and they measured you against your twin brother.

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Speaker 2: But I knew one of the producers of the show,

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Speaker 2: and I knew that there was no chance that Paul

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Speaker 2: and I would be even remotely Australia's most identical twins.

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Speaker 2: We're so different. And so right at the end of

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Speaker 2: this series of you know, activities we have to go through,

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Speaker 2: we had a couch interview separately and they asked us

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Speaker 2: a series of questions. But it was a bit loaded

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Speaker 2: because I knew that I was going to be asked

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Speaker 2: the question, and so the final question was, so, you know,

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Speaker 2: how similar do you really think you are? I said, Oh,

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Speaker 2: we're really similar. We've got all these things in common.

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Speaker 2: And then there's of course, well he's I'm gain he's not.

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Speaker 2: And that was the little clincher, like that little tagline,

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Speaker 2: and they used that.

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Speaker 1: In the como.

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Speaker 2: So so what happened was when it went to air

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Speaker 2: they used that bit and it was great. It just

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Speaker 2: took all the pressure off because all my friends and

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Speaker 2: everybody was watching the show and I conveniently just like, nah,

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Speaker 2: I'm just going to app myself and that.

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Speaker 1: How many of your friends went? Oh? Well, of course,

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Speaker 1: how many people went? Fuck? I didn't see that coming.

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Speaker 2: Oh look it's the close close circle of most of

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Speaker 2: them already knew. Anyway, the closer ish, probably suspected. And

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Speaker 2: then the the you know, the occasional people might have

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Speaker 2: been a bit surprised.

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Speaker 1: I don't know, the old Maltese relatives just you know,

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Speaker 1: raising their eyebrows hysterically.

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Speaker 2: No, but I've got I've got a small little section

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Speaker 2: of mum's family in Malta that refused to meet with

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Speaker 2: me and uncle and his family. So when I went

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Speaker 2: over there, did the whole trip. I tried to make

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Speaker 2: arrangements to meet up. And it's like, I wonder why

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Speaker 2: he never seems to be available and his family never

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Speaker 2: seems to be available. I'm only here for two weeks.

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Speaker 2: And then I took aside one of my other cousins

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Speaker 2: and I said, as has that uncle got a problem

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Speaker 2: you reckon with me being gay? And she said, yeah

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Speaker 2: it is. It's like, oh, okay, that's all right. Then

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Speaker 2: I don't want to see him either. Then hew h.

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Speaker 1: That's one of the things about religion that bothers me. Yeah,

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Speaker 1: that would be for religious reasons. It actually was, Yeah, No,

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Speaker 1: it was for religious anyway. Anyway, all right, let's talk

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Speaker 1: about let's talk about the thing that you're good at,

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Speaker 1: or one of the many Let's talk about let's talk

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Speaker 1: about the ever shifting landscape that is technology.

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Speaker 2: You know, I've got this love hate relationship with AI,

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Speaker 2: and I was reading an article recently see the Great

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Speaker 2: Firewall of China. China has a lot of control over

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Speaker 2: what people see and do, and in terms of the

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Speaker 2: electronic side of I guess a lot of efforts put

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Speaker 2: into sanitizing and controlling what the masses are able to access.

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Speaker 2: So you can't use Facebook, you can't use Google, you

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Speaker 2: can't use Instagram or anything when you're in China. There

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Speaker 2: are the equivalent Chinese equivalents of those, but this really

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Speaker 2: struck home to me that China literally shut down. They're

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Speaker 2: using AI their entire nationwide access for students, for people

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Speaker 2: to be able to access AI during exams. So they

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Speaker 2: have this one period of the year where they had

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Speaker 2: these major exams, like the I guess the VCE exams

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Speaker 2: that we have, they turned off a whole lot of

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Speaker 2: AI features right around the entire country just so that

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Speaker 2: I think it's thirteen million students were doing exams, and

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Speaker 2: so they switched all these features off so that they

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Speaker 2: couldn't cheat during exams during the exam work. That seems unfair, well,

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Speaker 2: It's kind of amazing, isn't it that you can flick

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Speaker 2: a switch. That's the disconcerting thing. But you know, on

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Speaker 2: the other side of that, it's now there was a

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Speaker 2: really interesting article on the ABC in the last week

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Speaker 2: or so that was talking about the anniversary of Tianamin

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Speaker 2: Square and how now the kind of communications directorate in

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Speaker 2: China is sanitizing the history and trying to erase that

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Speaker 2: from all of their records. So if you go to

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Speaker 2: the deep, if you go on to you know, one

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Speaker 2: of the like the AI search tools of Chinese AI

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Speaker 2: search tools, and you type in you know, what happened

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Speaker 2: during the Tianamin Square massacre, it won't give you any information.

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Speaker 2: It doesn't exist as far as far as concerned. So,

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Speaker 2: you know, it's nice to live in a country where

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Speaker 2: we have free media and ability to be able to

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Speaker 2: talk about whatever we're talking about. But when you can

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Speaker 2: erase history and effectively, that's exactly what the Chinese government's doing.

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Speaker 2: So whether you're turning off AI or using AI to

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Speaker 2: sanitize what is historically, you know, information is just disappearing.

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Speaker 2: So there will be a lot of young people in China.

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Speaker 2: Three was it twenty six years ago, tianam in Square

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Speaker 2: or so something like that. So there'll be all these

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Speaker 2: young people who have no knowledge of what happened in Square.

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Speaker 2: That's that's kind of frightening too, isn't it.

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Speaker 1: Well? I mean yeah, one, yes, and then two you

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Speaker 1: think about how different versions of that play out all

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Speaker 1: around the world in different ways. I mean North Korea

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Speaker 1: and you know, even I don't know even in western countries.

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Speaker 1: I think, how much do we actually know about what

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Speaker 1: gets withheld that we don't know about? Or I was

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Speaker 1: talking to somebody recently on the podcast and we were

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Speaker 1: talking about how the the shifting landscape of technology but

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Speaker 1: also AI that we're talking about right now, but also

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Speaker 1: in terms of education, and it's it's so interesting because

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Speaker 1: I don't know how it's going to play out, especially

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Speaker 1: in say a secondary school kind of setting, an undergraduate

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Speaker 1: university setting, where you can kind of get AI to

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Speaker 1: do the work, you know, to do your to do

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Speaker 1: your projects, to do your literature reviews, to do you know,

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Speaker 1: all of the things that you can. You know, it's

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Speaker 1: different with, for example, what I'm doing because I'm running

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Speaker 1: my own research and getting my own data and it's

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Speaker 1: all brand new. But for people who say I've got

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Speaker 1: to do a two thousand report, two thousand word report

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Speaker 1: on Tianaman Square, and you live in Australia, you can

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Speaker 1: just go, you know, write me, give me an overview,

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Speaker 1: use references and quotes, and then it gets produced and

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Speaker 1: you're like, yeah, now write it like a year eleven

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Speaker 1: student who's pretty smart. Well they're not an academic.

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Speaker 2: Yeah. Well. The other interesting thing is that CEOs now

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Speaker 2: in Silicon Valley. What they're doing is there's a new

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Speaker 2: startup that's using AI to effectively clone the CEO. So

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Speaker 2: what they're doing is they're putting all of their public appearances,

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Speaker 2: anything they've ever written, all of their emails, that anything

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Speaker 2: they can ga gather electronically about the principles, the way

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Speaker 2: that they speak, all the different thought processes that have

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Speaker 2: been saved, and they're uploading it and then the CEOs

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Speaker 2: who are all megabusy. What they're doing is if junior

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Speaker 2: staffers want to speak to the CEO, they're speaking to

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Speaker 2: the CEOs AI instead. So if you're if you're if

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Speaker 2: you've pulled up every assignment you've ever written for the

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Speaker 2: last two years, and you send every email you've ever sent,

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Speaker 2: you don't have to make it sound like a year

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Speaker 2: eleven student. Just make it sound like you, because it'd

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Speaker 2: be able to get to the point where it effectively

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Speaker 2: mimics the way and your voice, you know, your authentic voice,

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Speaker 2: and that's pretty interesting too. So I don't know that

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Speaker 2: i'd like to talk to the AI version of you.

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Speaker 2: I think i'd prefer to a spect to the real

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Speaker 2: version of you. But but there's a possibility, you know,

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Speaker 2: you might not. We might be out at a cafe

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Speaker 2: having coffee while we're recording a podcast with our AI equivalents.

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Speaker 1: I was looking for something, but I'll just recall it

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Speaker 1: as best I can. But yeah, not Yeah, last weekend,

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Speaker 1: so on on Saturday, i'd done a little post for Instagram,

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Speaker 1: and I jumped into AI and I said into chat

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Speaker 1: GPT specifically, and I said, what's a good time to

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Speaker 1: post tonight on Instagram? And it said, you know, for

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Speaker 1: maximum engagement. It said, ah, so tonight in tonight in Melbourne,

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Speaker 1: And then it put in brackets Friday night and it

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Speaker 1: was Saturday night, and it said this is a good

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Speaker 1: time da da da da, And I said, I said, yeah,

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Speaker 1: but it's Saturday night. Have you been drinking right, yeah,

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Speaker 1: and it went ah, it said, nah, Craig, no booze here.

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Speaker 1: Just my prefrontal cortex went offline for a moment. I said, yeah,

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Speaker 1: this is what AI said, No booze here, Craig laughing

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Speaker 1: face emoji. My prefrontal cortex went offline for a moment.

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Speaker 1: Sorry about that. And I mean, like, for me, that's

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Speaker 1: the perfect AI because it's cheeky and it's sarcastic, and

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Speaker 1: it's I'm like, oh my god, it talks like me.

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Speaker 1: It's a smarter version of me.

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Speaker 2: Yeah. I interesting you mentioned chat JPT because you know,

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Speaker 2: at the moment there's a court case in the US

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Speaker 2: where a lot of the media outlets New York Times

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Speaker 2: et cetera, et cetera, and now trying to sue open

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Speaker 2: AI and they're trying to basically force them and they

433
00:22:39,280 --> 00:22:42,680
Speaker 2: have by court and it's being appealed, but they have

434
00:22:42,800 --> 00:22:48,680
Speaker 2: to keep every single chat GPT dialogue interaction. So every

435
00:22:48,680 --> 00:22:52,440
Speaker 2: including the things you delete, are now going to be saved.

436
00:22:52,840 --> 00:22:55,320
Speaker 2: Now who can access them as up to speculation whether

437
00:22:55,359 --> 00:22:58,199
Speaker 2: it's just the courts. The reason being is that a

438
00:22:58,240 --> 00:23:02,080
Speaker 2: lot of these media are outlets behind paywalls. So if

439
00:23:02,119 --> 00:23:04,120
Speaker 2: you want to access their articles, you've got to pay,

440
00:23:04,640 --> 00:23:09,280
Speaker 2: but chat GPT can go behind the paywalls somehow. What's

441
00:23:09,320 --> 00:23:12,080
Speaker 2: happening is a lot of these media outlets stories are

442
00:23:12,119 --> 00:23:15,760
Speaker 2: still ending up as accessible to chat GPT, and so

443
00:23:15,800 --> 00:23:19,120
Speaker 2: people are now finding that they can bypass the paywalls

444
00:23:19,320 --> 00:23:22,199
Speaker 2: and access the articles, and of course the media outlets

445
00:23:22,240 --> 00:23:25,440
Speaker 2: are not happy about that, understandably. But what it's done

446
00:23:25,520 --> 00:23:29,000
Speaker 2: is it's basically said as from now, everything that's being

447
00:23:29,040 --> 00:23:32,080
Speaker 2: done has to be stored and has to be kept

448
00:23:32,200 --> 00:23:35,840
Speaker 2: indefinitely until otherwise whether they can launch an appeal. But

449
00:23:36,000 --> 00:23:38,560
Speaker 2: everything that you're doing, it's all going to be saved

450
00:23:38,600 --> 00:23:41,600
Speaker 2: and stored indefinitely at the moment, including stuff you do.

451
00:23:41,880 --> 00:23:46,480
Speaker 1: I mean to me, that's almost incomprehensible, Like, I mean,

452
00:23:46,560 --> 00:23:50,200
Speaker 1: what is that even? What does that even mean? Speaking

453
00:23:50,280 --> 00:23:55,280
Speaker 1: of speaking of nothing to do with that, but China anyway,

454
00:23:55,400 --> 00:23:57,800
Speaker 1: And Tech, did you get that thing I sent you

455
00:23:57,880 --> 00:24:01,480
Speaker 1: through the week? Oh about the battery?

456
00:24:02,240 --> 00:24:03,800
Speaker 2: No? I didn't. I don't think I've got anything from you.

457
00:24:04,160 --> 00:24:06,240
Speaker 2: Tell me about it. Come on, Oh really.

458
00:24:06,640 --> 00:24:10,040
Speaker 1: So I'll tell you what. Just jump on your phone

459
00:24:10,119 --> 00:24:12,080
Speaker 1: right now, I'll talk to our listeners.

460
00:24:12,160 --> 00:24:13,959
Speaker 2: Okay, I did get a new phone this week, by

461
00:24:14,000 --> 00:24:15,560
Speaker 2: the way, so have a.

462
00:24:15,560 --> 00:24:18,159
Speaker 1: Quick look at what I sent you through the week,

463
00:24:19,280 --> 00:24:22,720
Speaker 1: and because I thought that would fascinate you. And so

464
00:24:22,840 --> 00:24:26,400
Speaker 1: what it is, everybody, you have a quick read. It's

465
00:24:26,440 --> 00:24:30,520
Speaker 1: a battery, like a small battery the size of your thumbnail.

466
00:24:30,680 --> 00:24:34,679
Speaker 1: I guess it's a nuclear battery that lasts for one

467
00:24:34,760 --> 00:24:35,520
Speaker 1: hundred years.

468
00:24:35,880 --> 00:24:37,680
Speaker 2: Didn't I send that to you?

469
00:24:37,720 --> 00:24:39,720
Speaker 1: No? I sent that to you this week. No.

470
00:24:40,119 --> 00:24:42,240
Speaker 2: I realized why. I wasn't able to open it because

471
00:24:42,280 --> 00:24:45,160
Speaker 2: I actually haven't installed Instagram on my phone yet because

472
00:24:45,160 --> 00:24:47,400
Speaker 2: it's a new phone. And so I took the link.

473
00:24:47,440 --> 00:24:49,159
Speaker 2: It was like, oh man, now I've got to install

474
00:24:49,200 --> 00:24:50,320
Speaker 2: it and I'm going to log in, and I thought,

475
00:24:50,440 --> 00:24:54,600
Speaker 2: I can't be bothered. But how I know the nuclear battery. Yeah,

476
00:24:54,600 --> 00:24:57,879
Speaker 2: it's the last one hundred years. It's pretty epic, isn't it.

477
00:24:58,080 --> 00:24:59,240
Speaker 2: I think it was a topic that I was going

478
00:24:59,280 --> 00:25:01,600
Speaker 2: to chat about it point. But that's phenomenal, isn't it.

479
00:25:01,680 --> 00:25:07,520
Speaker 2: The fact that they can I mean, the applications are amazing. Pacemakers.

480
00:25:07,800 --> 00:25:09,440
Speaker 2: It means that people will be able to be fitted

481
00:25:09,480 --> 00:25:11,679
Speaker 2: out with the pacemaker and they never have to replace

482
00:25:11,720 --> 00:25:15,000
Speaker 2: the battery. So for medical application, even at the outset.

483
00:25:15,560 --> 00:25:17,320
Speaker 2: You know, watches you never you have to worry about

484
00:25:17,320 --> 00:25:18,879
Speaker 2: your watch ever happened to, you know, because I've got

485
00:25:18,880 --> 00:25:21,159
Speaker 2: a stack of really nice watches. I went through a

486
00:25:21,200 --> 00:25:24,840
Speaker 2: phase of just buying watches that were analog, you know,

487
00:25:24,920 --> 00:25:28,280
Speaker 2: without the need for having any technology aside from the

488
00:25:28,280 --> 00:25:30,919
Speaker 2: fact they do need batteries. And the problem now I've

489
00:25:30,960 --> 00:25:34,119
Speaker 2: got about I don't know eighteen watches that need batteries,

490
00:25:35,240 --> 00:25:37,800
Speaker 2: and so I did this sit in there collecting dust

491
00:25:37,880 --> 00:25:40,359
Speaker 2: because I can't be bothered getting a battery the last

492
00:25:40,520 --> 00:25:42,320
Speaker 2: twelve months or six months.

493
00:25:42,680 --> 00:25:45,560
Speaker 1: And I think you spoke last time about some of

494
00:25:45,600 --> 00:25:48,040
Speaker 1: the car batteries now that can be charged to like

495
00:25:48,119 --> 00:25:50,760
Speaker 1: eighty percent within four or five minutes or something.

496
00:25:50,880 --> 00:25:54,280
Speaker 2: Well, it's almost well now the car making because this

497
00:25:54,320 --> 00:25:55,960
Speaker 2: has always been a bit of a holy grail for

498
00:25:56,040 --> 00:25:58,720
Speaker 2: the ev car market to be able to have that

499
00:25:59,000 --> 00:26:01,879
Speaker 2: time parody between filling up with fuel. So if you

500
00:26:01,880 --> 00:26:04,040
Speaker 2: fill up your tank get five hundred kilometers, you fill

501
00:26:04,119 --> 00:26:05,880
Speaker 2: up your battery for five minutes and you get five

502
00:26:05,920 --> 00:26:08,600
Speaker 2: hundred kilometers. And now they believe the achieved that, so

503
00:26:08,680 --> 00:26:11,359
Speaker 2: that's not far off. I've got to tell you though,

504
00:26:11,520 --> 00:26:14,320
Speaker 2: one of the most interesting little articles I've read recently

505
00:26:14,840 --> 00:26:18,000
Speaker 2: was Nicola Tesla. One of the things that Nicola Tesla

506
00:26:18,240 --> 00:26:22,639
Speaker 2: Tesla theorized was being able to send electricity without the

507
00:26:22,720 --> 00:26:26,400
Speaker 2: need for wires. So this is like a concept that's

508
00:26:26,400 --> 00:26:29,920
Speaker 2: one twenty four years old. Now they're saying they may

509
00:26:29,960 --> 00:26:32,760
Speaker 2: be able to beam power. They're looking at a whole

510
00:26:32,760 --> 00:26:36,400
Speaker 2: lot of different things, lasers, microwaves, But what it could

511
00:26:36,400 --> 00:26:39,640
Speaker 2: effectively mean is that you would never need to have

512
00:26:39,680 --> 00:26:42,840
Speaker 2: wires and cables to turn on any devices you could have.

513
00:26:43,160 --> 00:26:45,479
Speaker 2: You know, you could run your whatever it is, you know,

514
00:26:45,520 --> 00:26:48,520
Speaker 2: your car, your electric car, wouldn't need anything, just be

515
00:26:48,520 --> 00:26:50,240
Speaker 2: beamed from space. How good would that be?

516
00:26:50,920 --> 00:26:54,640
Speaker 1: Yeah? I don't know that's good because I think, fucking

517
00:26:54,880 --> 00:26:57,520
Speaker 1: where is that beam coming from? And what if I'm

518
00:26:57,560 --> 00:27:01,480
Speaker 1: in between where it's coming from and the charging or

519
00:27:02,200 --> 00:27:05,920
Speaker 1: I don't know, all of those unseen beams and microwaves

520
00:27:05,920 --> 00:27:08,760
Speaker 1: of and energy fields and scares me a little bit.

521
00:27:09,040 --> 00:27:10,760
Speaker 2: Now, I know we kind of got off the topic

522
00:27:10,760 --> 00:27:12,800
Speaker 2: of AI, but I've got to ask you a quick question,

523
00:27:13,040 --> 00:27:16,119
Speaker 2: and particularly with the ability for AI now to mimic

524
00:27:16,640 --> 00:27:18,920
Speaker 2: in the hands of those people who would do bad

525
00:27:18,960 --> 00:27:21,840
Speaker 2: things with it, obviously a lot of the scams now

526
00:27:21,880 --> 00:27:25,119
Speaker 2: are deep fake scams where people are called by a

527
00:27:25,119 --> 00:27:28,919
Speaker 2: relative of saying they need money. Now, do you have

528
00:27:29,000 --> 00:27:35,960
Speaker 2: a safe word with Tiff or Melissa? Just quietly?

529
00:27:36,560 --> 00:27:40,440
Speaker 1: Are we talking about No? I do not see.

530
00:27:41,080 --> 00:27:43,480
Speaker 2: You might need to think about a safe word. Okay,

531
00:27:43,560 --> 00:27:47,040
Speaker 2: So what it is is if suddenly Melissa gets a

532
00:27:47,040 --> 00:27:49,920
Speaker 2: phone call from you or an email saying I've lost

533
00:27:50,000 --> 00:27:53,360
Speaker 2: my credit card, I've just about to buy a Tesla,

534
00:27:53,720 --> 00:27:58,119
Speaker 2: can you urgently transfer this money to my new bank account?

535
00:27:58,359 --> 00:28:03,879
Speaker 2: Those lines right, well, have a safe word? Is it

536
00:28:03,960 --> 00:28:06,520
Speaker 2: really you, Craig? What's the safe word?

537
00:28:07,080 --> 00:28:07,879
Speaker 1: Yeah?

538
00:28:07,680 --> 00:28:09,919
Speaker 2: Yeah, I'll be the safe word now if you're right?

539
00:28:11,080 --> 00:28:14,920
Speaker 1: Yeah, ad or just ask them something that only they

540
00:28:14,960 --> 00:28:15,280
Speaker 1: could not.

541
00:28:16,119 --> 00:28:20,160
Speaker 2: But this is but say, security experts are really actively

542
00:28:20,280 --> 00:28:23,719
Speaker 2: encouraging this that they're saying that you should have with

543
00:28:23,800 --> 00:28:26,720
Speaker 2: your close personal loved ones, people who have access to

544
00:28:26,760 --> 00:28:30,480
Speaker 2: your life. Think about creating a safe word so that

545
00:28:30,560 --> 00:28:34,600
Speaker 2: if there is that kind of weird request, you know,

546
00:28:34,680 --> 00:28:36,920
Speaker 2: and with you know, the Hey Mum scam, the Hey

547
00:28:36,960 --> 00:28:39,400
Speaker 2: Dad scam where you get a text I've lost my phone.

548
00:28:40,240 --> 00:28:43,000
Speaker 2: You know, I've got a borrow to someone else's phone

549
00:28:43,040 --> 00:28:45,080
Speaker 2: to send you a message, I desperately need some money,

550
00:28:45,200 --> 00:28:47,280
Speaker 2: and it might only be two hundred bucks, and the

551
00:28:47,320 --> 00:28:49,920
Speaker 2: person might be traveling, or they might be somewhere where

552
00:28:49,920 --> 00:28:52,880
Speaker 2: you can't contact them. But having that safe word is

553
00:28:52,920 --> 00:28:55,360
Speaker 2: a really simple way. You know, it might be the

554
00:28:55,480 --> 00:28:58,000
Speaker 2: name of your first dog or something, but it's not

555
00:28:58,040 --> 00:28:59,680
Speaker 2: a bad idea when you think about it.

556
00:29:00,520 --> 00:29:03,320
Speaker 1: One hundred percent. And could you imagine how easy Ron

557
00:29:03,360 --> 00:29:05,880
Speaker 1: and Mary would beat a scam fuck? And how yeah,

558
00:29:06,160 --> 00:29:09,680
Speaker 1: like with like you could make an AI of my

559
00:29:09,880 --> 00:29:13,440
Speaker 1: voice and go hey mom. Fortunately, the saving grace is

560
00:29:13,520 --> 00:29:16,600
Speaker 1: she doesn't know how to transfer anything. She's got to

561
00:29:16,640 --> 00:29:21,800
Speaker 1: pull twenty bucks out of her purse. You're getting nothing. Yeah, yeah, yeah.

562
00:29:21,680 --> 00:29:23,840
Speaker 2: And you could I know what you're safe word could

563
00:29:23,880 --> 00:29:27,280
Speaker 2: be with your parents? We wish you girl.

564
00:29:28,400 --> 00:29:33,280
Speaker 1: Yeah. I've been told they were shadow girl and my

565
00:29:33,400 --> 00:29:35,400
Speaker 1: name was going to be Lisa if I was a girl.

566
00:29:35,920 --> 00:29:36,720
Speaker 1: So there's that.

567
00:29:37,040 --> 00:29:39,880
Speaker 2: Really. Oh, there you go, Lisa, there is your safe word.

568
00:29:40,480 --> 00:29:42,840
Speaker 1: Yeah yeah, hey, Now I don't know if I told

569
00:29:42,880 --> 00:29:46,200
Speaker 1: you this, and listeners, I apologize because I've shared this before.

570
00:29:46,280 --> 00:29:50,960
Speaker 1: But so one of my papers that I'm writing for

571
00:29:51,040 --> 00:29:55,120
Speaker 1: my PhD is what's called a systematic literature review. So

572
00:29:55,120 --> 00:29:59,440
Speaker 1: it's two years of work, it's about thirty thousand words.

573
00:29:59,480 --> 00:30:03,480
Speaker 1: It's blah blah. Anyway. The other day, so Melissa, for

574
00:30:03,680 --> 00:30:06,240
Speaker 1: our new listeners, you might not know who Melissa is.

575
00:30:06,280 --> 00:30:09,640
Speaker 1: She runs my life. She's the boss of me. She's

576
00:30:09,680 --> 00:30:14,240
Speaker 1: my business partner. Anyway, So Melissa took my paper, which

577
00:30:14,280 --> 00:30:17,239
Speaker 1: is now finished, well, the first draft is finished, so

578
00:30:17,640 --> 00:30:20,640
Speaker 1: it's a complete paper. Still got to be tweaked, but

579
00:30:21,320 --> 00:30:26,920
Speaker 1: she took that paper. She loaded it into AI. Now,

580
00:30:27,000 --> 00:30:31,040
Speaker 1: just reminding you that the paper is all about how

581
00:30:31,080 --> 00:30:34,080
Speaker 1: accurate we are at understanding how other people see us.

582
00:30:34,120 --> 00:30:38,360
Speaker 1: So you know, what Patrick thinks of me, that's perception

583
00:30:39,040 --> 00:30:42,840
Speaker 1: versus what I think he thinks of me. That's metaperception.

584
00:30:43,000 --> 00:30:46,920
Speaker 1: Right now, meta accuracy is how close those two things are,

585
00:30:47,480 --> 00:30:49,920
Speaker 1: how he actually sees me and how I think he does. Anyway,

586
00:30:50,200 --> 00:30:53,560
Speaker 1: it sounds complex, but it's a really interesting thing when

587
00:30:53,600 --> 00:30:55,640
Speaker 1: you do a deep dive. So she put my whole

588
00:30:55,680 --> 00:30:59,200
Speaker 1: paper in there and it gets turned into a two

589
00:30:59,360 --> 00:31:03,520
Speaker 1: person podcast between a lady and a guy in three

590
00:31:03,560 --> 00:31:07,880
Speaker 1: minutes Patrick, it comes up with the twenty five minute podcast,

591
00:31:08,880 --> 00:31:14,120
Speaker 1: which is indistinguishable. Like I've played it to people and

592
00:31:14,160 --> 00:31:17,120
Speaker 1: they didn't know They're like, oh, what this is great?

593
00:31:17,240 --> 00:31:20,400
Speaker 1: What was this on? And how good are their voices

594
00:31:20,440 --> 00:31:23,040
Speaker 1: and how interesting is this? And I let them listen

595
00:31:23,080 --> 00:31:24,840
Speaker 1: to a few minutes and I go, that's not real.

596
00:31:24,880 --> 00:31:30,520
Speaker 1: They're not real people. And the whole conversation, all of

597
00:31:30,560 --> 00:31:33,720
Speaker 1: the conversation is drawn out of my paper, nothing else

598
00:31:33,760 --> 00:31:38,480
Speaker 1: on the internet. And so because my research some of

599
00:31:38,520 --> 00:31:40,960
Speaker 1: it's a bit complex, right, So I can just send

600
00:31:41,000 --> 00:31:43,400
Speaker 1: people this thing and go here, listen to this, and

601
00:31:43,440 --> 00:31:46,320
Speaker 1: this will give you six years of work in twenty

602
00:31:46,320 --> 00:31:47,000
Speaker 1: five minutes.

603
00:31:47,240 --> 00:31:48,800
Speaker 2: I kind of feel you need to send it to me.

604
00:31:48,960 --> 00:31:49,560
Speaker 2: I would love you.

605
00:31:50,160 --> 00:31:52,920
Speaker 1: I will send it to you. You'll I mean not

606
00:31:52,960 --> 00:31:55,560
Speaker 1: only will you because you're all over tech and AI,

607
00:31:55,680 --> 00:31:58,959
Speaker 1: but like, I really think you'll like the actual I

608
00:31:59,040 --> 00:32:03,160
Speaker 1: love the conversation because they explained it in They explained

609
00:32:03,160 --> 00:32:05,560
Speaker 1: it in a way which is very user friendly. And

610
00:32:05,560 --> 00:32:07,760
Speaker 1: not only you user friendly, but you go, oh god,

611
00:32:07,800 --> 00:32:11,320
Speaker 1: this is actually an important thing that I understand. Understanding

612
00:32:11,320 --> 00:32:15,240
Speaker 1: how other people see me actually matters because it improves

613
00:32:15,240 --> 00:32:20,080
Speaker 1: a whole lot of interpersonal and sociological outcomes, you know,

614
00:32:20,240 --> 00:32:23,280
Speaker 1: at work and away from work, and you know, problem

615
00:32:23,400 --> 00:32:26,640
Speaker 1: solving and conflict resolution and leadership and all of these

616
00:32:26,680 --> 00:32:30,000
Speaker 1: things where, oh, if I have an insight into someone

617
00:32:30,040 --> 00:32:31,960
Speaker 1: else's mind, that's a good thing.

618
00:32:32,840 --> 00:32:35,280
Speaker 2: Oh, without doubt. I mean, when you think about the

619
00:32:35,800 --> 00:32:38,480
Speaker 2: interactions we have on a daily basis. I had the

620
00:32:38,560 --> 00:32:40,880
Speaker 2: loveliest thing said to me recently. I was at the

621
00:32:40,920 --> 00:32:45,040
Speaker 2: local pharmacy and I it was a really busy day

622
00:32:45,160 --> 00:32:47,240
Speaker 2: and you could see that the staff were all a

623
00:32:47,240 --> 00:32:49,640
Speaker 2: little bit stressed. And then I finally got to the

624
00:32:49,680 --> 00:32:51,680
Speaker 2: counter and I chatted to the lady at the counter

625
00:32:51,720 --> 00:32:55,080
Speaker 2: and she said, it's so nice seeing you. You're always

626
00:32:55,280 --> 00:32:58,000
Speaker 2: so friendly. You've made my day. It's been such a

627
00:32:58,040 --> 00:33:01,120
Speaker 2: tough day. And I just said hello and smiled and chatted.

628
00:33:01,720 --> 00:33:04,520
Speaker 2: But it was It made me feel so good. And

629
00:33:04,560 --> 00:33:07,760
Speaker 2: it makes you realize that, you know, it doesn't take

630
00:33:07,920 --> 00:33:12,120
Speaker 2: much to change someone's whole day just because you say

631
00:33:12,160 --> 00:33:14,080
Speaker 2: a nice thing to someone, or you know, a nice

632
00:33:14,120 --> 00:33:15,000
Speaker 2: gesture or whatever.

633
00:33:15,120 --> 00:33:15,280
Speaker 1: You know.

634
00:33:15,760 --> 00:33:18,040
Speaker 2: And I guess that's the metaperception thing too, and in

635
00:33:18,080 --> 00:33:21,880
Speaker 2: some ways working you know, knowing or trying to trigger

636
00:33:21,960 --> 00:33:24,920
Speaker 2: that or you know, when you're interacting with somebody.

637
00:33:25,120 --> 00:33:30,160
Speaker 1: Yeah, I love that. So the first personal development book

638
00:33:30,280 --> 00:33:32,120
Speaker 1: I ever read. You would have heard of the book.

639
00:33:32,680 --> 00:33:35,720
Speaker 1: It's called How to Win Friends and Info. Yeah, very

640
00:33:35,840 --> 00:33:39,360
Speaker 1: very famous. So a guy called Dale Carnegie wrote that

641
00:33:39,400 --> 00:33:43,640
Speaker 1: book in nine thirty six, and you think about that,

642
00:33:43,720 --> 00:33:46,040
Speaker 1: nearly one hundred there were books before that, but that

643
00:33:46,200 --> 00:33:48,800
Speaker 1: was one of the groundbreaking what we would call self

644
00:33:48,840 --> 00:33:53,240
Speaker 1: help type of books. Are you looking that up to

645
00:33:53,640 --> 00:33:54,760
Speaker 1: make sure? I know?

646
00:33:55,160 --> 00:33:58,280
Speaker 2: No, I'm very familiar with it. You've mentioned quite a

647
00:33:58,280 --> 00:33:59,080
Speaker 2: few times. Yeah.

648
00:33:59,760 --> 00:34:02,320
Speaker 1: But anyway, there's this thing in the book where he says,

649
00:34:03,680 --> 00:34:09,919
Speaker 1: try to find something to compliment people on. That's genuine. Yeah,

650
00:34:09,960 --> 00:34:12,680
Speaker 1: And I thought that's such a He's not just saying,

651
00:34:13,440 --> 00:34:17,279
Speaker 1: you know, talk crap. It's like even people who were difficult,

652
00:34:18,040 --> 00:34:20,120
Speaker 1: and he talks about this. I might fuck this up

653
00:34:20,160 --> 00:34:22,040
Speaker 1: a little bit. But one of his jobs was he

654
00:34:22,160 --> 00:34:25,719
Speaker 1: was basically a boy Friday when he was young, and

655
00:34:25,760 --> 00:34:27,520
Speaker 1: he used to have to deliver the mail. He has

656
00:34:27,640 --> 00:34:29,680
Speaker 1: to go to the post office, and he was basically

657
00:34:29,719 --> 00:34:33,360
Speaker 1: a in his younger days, a shitkicker, like an office shitkicker.

658
00:34:33,440 --> 00:34:34,760
Speaker 1: And he used to have to go to the post

659
00:34:34,760 --> 00:34:37,719
Speaker 1: office and wait in the queue with all the parcels

660
00:34:37,800 --> 00:34:40,480
Speaker 1: and all the post and all the things. And there

661
00:34:40,520 --> 00:34:43,200
Speaker 1: was a lady at the post office that let's just say,

662
00:34:43,280 --> 00:34:46,680
Speaker 1: she was somewhat surly, and she probably didn't love her

663
00:34:46,760 --> 00:34:50,200
Speaker 1: job or her life, and she was just, you know,

664
00:34:50,640 --> 00:34:54,720
Speaker 1: just didn't seem like a happy human. And he spent

665
00:34:54,880 --> 00:34:56,719
Speaker 1: like a week trying to figure out how he could

666
00:34:56,800 --> 00:35:00,799
Speaker 1: break through that. And he remembered the you know, I

667
00:35:00,800 --> 00:35:03,840
Speaker 1: don't know something he got told, but the one not

668
00:35:03,960 --> 00:35:06,560
Speaker 1: one thing, but he noticed that she had really beautiful eyes,

669
00:35:07,000 --> 00:35:09,600
Speaker 1: which probably these days might sound creepy, but one hundred

670
00:35:09,640 --> 00:35:12,440
Speaker 1: years ago, who knows. And one that he got up

671
00:35:12,480 --> 00:35:14,320
Speaker 1: and she knew him and he knew her, but that

672
00:35:14,520 --> 00:35:17,040
Speaker 1: was just very business, and he said, has anyone ever

673
00:35:17,080 --> 00:35:20,160
Speaker 1: told you you have like just really beautiful eyes? And

674
00:35:20,200 --> 00:35:23,719
Speaker 1: that was it. That was the turning point because he

675
00:35:23,840 --> 00:35:27,359
Speaker 1: just said something nice to her, and nobody ever said

676
00:35:27,480 --> 00:35:30,560
Speaker 1: maybe not many people said something nice to her, which was,

677
00:35:31,239 --> 00:35:34,400
Speaker 1: you know, there was no real strategy other than I

678
00:35:34,440 --> 00:35:38,080
Speaker 1: guess the strategy was to build connection, you know. And

679
00:35:38,200 --> 00:35:41,359
Speaker 1: when I heard that, or I read that, I thought, yeah,

680
00:35:41,400 --> 00:35:45,600
Speaker 1: that's not that's not a dumb idea, but also what

681
00:35:45,719 --> 00:35:49,160
Speaker 1: a nice idea too, Just to genuinely find something to

682
00:35:49,200 --> 00:35:51,720
Speaker 1: say something, something nice to say to someone.

683
00:35:52,560 --> 00:35:56,239
Speaker 2: It's funny. I had an ex partner who criticized me

684
00:35:56,800 --> 00:36:00,760
Speaker 2: when we'd go to parties, I'd get this whole spiel,

685
00:36:00,920 --> 00:36:03,640
Speaker 2: which would be something along the lines of, you're always

686
00:36:03,719 --> 00:36:07,040
Speaker 2: interviewing people. Why do you do that? You always interview people.

687
00:36:07,360 --> 00:36:09,680
Speaker 2: It's like, I'm not interviewing people. I'm just interested in

688
00:36:09,719 --> 00:36:12,279
Speaker 2: finding about their lives. I want to talk to people.

689
00:36:12,320 --> 00:36:15,200
Speaker 2: I want to understand about them. And you know, a

690
00:36:15,239 --> 00:36:18,560
Speaker 2: lovely lady who since passed away, who was the manager

691
00:36:18,600 --> 00:36:22,120
Speaker 2: of our local neighborhood center. She said to me one day,

692
00:36:22,239 --> 00:36:26,480
Speaker 2: you've got you've got one mouth and two ears, so

693
00:36:26,719 --> 00:36:31,560
Speaker 2: you know you should be listening twice as much as talking. Yeah,

694
00:36:31,640 --> 00:36:33,960
Speaker 2: it was a really love It's.

695
00:36:33,840 --> 00:36:37,040
Speaker 1: Not the worst advice. All right, let's jump back on

696
00:36:37,080 --> 00:36:39,960
Speaker 1: to our little I want to know what when I

697
00:36:40,040 --> 00:36:40,959
Speaker 1: hit the snooze button.

698
00:36:41,400 --> 00:36:43,640
Speaker 2: Yes, I want to talk about are you do you

699
00:36:43,719 --> 00:36:45,799
Speaker 2: hit the snooze Do you sit an alarm at all?

700
00:36:46,360 --> 00:36:49,040
Speaker 1: I do sit an alarm, but my alarm is radio,

701
00:36:49,160 --> 00:36:51,600
Speaker 1: so it comes on with just blokes talking.

702
00:36:52,200 --> 00:36:55,360
Speaker 2: Yeah, okay, so I've never needed an alarm. I'm a

703
00:36:55,360 --> 00:36:57,719
Speaker 2: bit of a freak. So I did breakfast radio for

704
00:36:57,760 --> 00:37:00,680
Speaker 2: seven years set my alarm, but wo up before the

705
00:37:00,719 --> 00:37:03,920
Speaker 2: alarm consistently for seven years never needed it, and so

706
00:37:04,120 --> 00:37:06,359
Speaker 2: I don't use an alarm. But there's been some really

707
00:37:06,360 --> 00:37:11,160
Speaker 2: interesting studies done and Australians actually fare pretty well. We're

708
00:37:11,200 --> 00:37:14,920
Speaker 2: not as dependent on the snooze button as a lot

709
00:37:14,960 --> 00:37:18,600
Speaker 2: of other countries. So people in the US, Sweden, Germany

710
00:37:18,880 --> 00:37:23,399
Speaker 2: have the highest snooze button snooze button use, whilst those

711
00:37:23,440 --> 00:37:25,520
Speaker 2: living in Japan and Australia had the lowest. So that's

712
00:37:25,560 --> 00:37:29,080
Speaker 2: a good thing. But this study was pretty full on.

713
00:37:29,160 --> 00:37:32,360
Speaker 2: It was a place called mass General, Bringham and they

714
00:37:32,440 --> 00:37:37,040
Speaker 2: analyzed sleep data from twenty one thousand people and they

715
00:37:37,160 --> 00:37:41,880
Speaker 2: used a sleep cycle app and they measured three million

716
00:37:42,000 --> 00:37:45,399
Speaker 2: sleep sessions in the study, so it's pretty definitive. And

717
00:37:45,480 --> 00:37:49,160
Speaker 2: so fifty six percent of those people on average hit

718
00:37:49,200 --> 00:37:51,879
Speaker 2: the snooze button. So we're talking, you know, more than

719
00:37:51,920 --> 00:37:55,040
Speaker 2: half of the people that were studied were the ones

720
00:37:55,040 --> 00:37:59,160
Speaker 2: that hit the snooze button. And what the sleep experts

721
00:37:59,160 --> 00:38:02,120
Speaker 2: are saying is if you rely on the snooze button,

722
00:38:02,520 --> 00:38:06,719
Speaker 2: it breaks up a really crucial part of your sleep process,

723
00:38:07,239 --> 00:38:11,000
Speaker 2: So that rapid eye movement process when it gets when

724
00:38:11,000 --> 00:38:14,000
Speaker 2: the alarm goes off, you break part of the sleep cycle.

725
00:38:14,080 --> 00:38:17,200
Speaker 2: So what they say, is set the alarm, but set

726
00:38:17,239 --> 00:38:20,239
Speaker 2: the alarm at the latest point you can possibly have

727
00:38:20,360 --> 00:38:22,560
Speaker 2: to get up, so you're forced to get up and

728
00:38:22,680 --> 00:38:24,600
Speaker 2: don't do the I'm going to set it half an

729
00:38:24,600 --> 00:38:26,680
Speaker 2: hour beforehand. That way you can just hit the snooze

730
00:38:26,719 --> 00:38:29,759
Speaker 2: for the half hour. That's the possible thing you can do.

731
00:38:29,920 --> 00:38:33,239
Speaker 2: So they're saying, if you have to try not to,

732
00:38:33,480 --> 00:38:36,520
Speaker 2: but set your alarm for the latest possible time so

733
00:38:36,640 --> 00:38:39,840
Speaker 2: you can't fall into the habit of hitting snooze. And

734
00:38:39,880 --> 00:38:42,279
Speaker 2: I thought that makes a lot of sense, because it's

735
00:38:42,280 --> 00:38:44,000
Speaker 2: hard for some people to get up in the morning.

736
00:38:44,360 --> 00:38:47,919
Speaker 2: You know, some people are just not mourning people. And

737
00:38:48,280 --> 00:38:50,839
Speaker 2: I get that, well, I kind of get it because

738
00:38:50,880 --> 00:38:55,960
Speaker 2: I'm not a night person. So yeah, I'm the worst

739
00:38:56,320 --> 00:38:58,120
Speaker 2: in the world. My friends know, if I go out

740
00:38:58,160 --> 00:39:00,120
Speaker 2: to dinner, I want to be going home by at

741
00:39:00,120 --> 00:39:02,640
Speaker 2: eight thirty. I'm happy to go home at eight thirty.

742
00:39:03,000 --> 00:39:05,440
Speaker 2: It's terrible. I'm a nana, but I always have that.

743
00:39:05,520 --> 00:39:07,680
Speaker 1: I think that's because I don't think that's because you're older.

744
00:39:07,719 --> 00:39:10,240
Speaker 1: I'm old because I've been You've probably been like that forever.

745
00:39:10,280 --> 00:39:12,920
Speaker 1: I've been like that forever. I'm like, I don't understand

746
00:39:12,960 --> 00:39:17,240
Speaker 1: I mean theoretically, I get it, but if I get somewhere,

747
00:39:17,760 --> 00:39:19,600
Speaker 1: you know, if you and I going out, we're catching up,

748
00:39:19,640 --> 00:39:21,799
Speaker 1: we're having a bite. We meet at seven. I'm like,

749
00:39:22,200 --> 00:39:27,640
Speaker 1: I'm definitely on my way home by eight thirty maybe eight, Mike. Yeah,

750
00:39:27,680 --> 00:39:30,120
Speaker 1: And by the way, can we do dinner earlier than seven?

751
00:39:30,800 --> 00:39:33,760
Speaker 2: Oh yeah? My ideal dinner is five point thirty six o'clock.

752
00:39:35,080 --> 00:39:37,160
Speaker 1: You're a genuine here nana, dude.

753
00:39:37,680 --> 00:39:39,840
Speaker 2: You know if people come over for dinner at my

754
00:39:39,920 --> 00:39:42,200
Speaker 2: place at six o'clock, yep, come over and start to

755
00:39:42,239 --> 00:39:47,840
Speaker 2: eat at six. Tell us about on Oh no, no,

756
00:39:47,960 --> 00:39:50,040
Speaker 2: just the one thing that was interesting though. The snooze

757
00:39:50,080 --> 00:39:52,680
Speaker 2: pattern changes depending on the day of the week, so

758
00:39:52,680 --> 00:39:56,560
Speaker 2: obviously work week people are much more likely to snooze

759
00:39:56,760 --> 00:39:59,000
Speaker 2: on the weekends. The snooze thing doesn't seem to be

760
00:39:59,040 --> 00:40:01,080
Speaker 2: a major factor. Just a bit of advice there.

761
00:40:01,080 --> 00:40:04,640
Speaker 1: That was all up. Sorry, Just the next little lightem

762
00:40:04,680 --> 00:40:06,719
Speaker 1: on the list. I'm interested in because I feel like

763
00:40:06,760 --> 00:40:09,000
Speaker 1: a lot of us are people pleases, because we don't

764
00:40:09,080 --> 00:40:13,080
Speaker 1: like saying no, and we don't like hurting people's feelings,

765
00:40:13,080 --> 00:40:17,920
Speaker 1: and we don't like creating a disharmony. So tell us about.

766
00:40:17,680 --> 00:40:21,520
Speaker 2: That, well, there is a science to saying no. And

767
00:40:21,520 --> 00:40:24,600
Speaker 2: this is interesting because I'm going through a bit of

768
00:40:24,600 --> 00:40:27,839
Speaker 2: a situation at the moment. I'm a volunteer in an organization,

769
00:40:27,960 --> 00:40:29,800
Speaker 2: and I'm getting to the point where I'm so busy

770
00:40:30,200 --> 00:40:32,800
Speaker 2: I may have to say no, and I struggle, I

771
00:40:32,880 --> 00:40:37,480
Speaker 2: absolutely struggle to say no. But and this is an

772
00:40:37,480 --> 00:40:41,440
Speaker 2: interesting quote Warren Buffett. You know, he was like a

773
00:40:41,560 --> 00:40:45,280
Speaker 2: millionaire in the US. He's quoted as saying, the difference

774
00:40:45,320 --> 00:40:49,480
Speaker 2: between successful people and really successful people is that really

775
00:40:49,520 --> 00:40:53,680
Speaker 2: successful people say no to almost everything. I thought, that's

776
00:40:53,680 --> 00:40:56,000
Speaker 2: a great quote, and I'm the opposite. I tend to

777
00:40:56,040 --> 00:40:59,000
Speaker 2: say yes to almost everything. But here's a bit of advice.

778
00:40:59,719 --> 00:41:02,080
Speaker 2: When when you do feel the inclination or you have

779
00:41:02,160 --> 00:41:06,759
Speaker 2: to say no, never reference time as the excuse. The

780
00:41:06,840 --> 00:41:09,200
Speaker 2: second you say, oh no, Craig, I can't. I just

781
00:41:09,239 --> 00:41:12,160
Speaker 2: haven't got the time, what it says to you is

782
00:41:12,200 --> 00:41:15,800
Speaker 2: that I can't make the time for you, right, okay.

783
00:41:16,080 --> 00:41:21,239
Speaker 2: Whereas if you reference money, evidently that's okay. So what

784
00:41:21,280 --> 00:41:24,359
Speaker 2: the research has found that if you use money as

785
00:41:24,400 --> 00:41:27,359
Speaker 2: an excuse to decline a request. So so you say, oh, look,

786
00:41:27,400 --> 00:41:28,719
Speaker 2: do you want to go to the opera with me.

787
00:41:28,880 --> 00:41:30,239
Speaker 2: I say, look, I'm going to be honest with you.

788
00:41:30,280 --> 00:41:32,920
Speaker 2: At the moment, I'm just a bit strapped and I

789
00:41:33,400 --> 00:41:36,279
Speaker 2: just don't know that I want to spend something on

790
00:41:36,680 --> 00:41:39,759
Speaker 2: that at the moment. Maybe another time, that's a better

791
00:41:39,760 --> 00:41:40,799
Speaker 2: way of getting around it.

792
00:41:41,840 --> 00:41:44,880
Speaker 1: Yeah, this is my way. Yeah, I want to go

793
00:41:44,880 --> 00:41:49,640
Speaker 1: to the opera. This is you asking me. No. No,

794
00:41:51,040 --> 00:41:52,960
Speaker 1: You're like, why not I go because I don't like

795
00:41:53,000 --> 00:41:54,920
Speaker 1: the opera? Next right?

796
00:41:55,920 --> 00:41:56,120
Speaker 2: Yeah?

797
00:41:56,239 --> 00:41:59,200
Speaker 1: And I love you, but I don't like opera.

798
00:41:59,040 --> 00:42:04,400
Speaker 2: So cycle for the next week. The only reason cycle

799
00:42:04,600 --> 00:42:06,560
Speaker 2: you want is in your washing machine.

800
00:42:07,880 --> 00:42:09,600
Speaker 1: You want to come? Do you want to come to

801
00:42:09,680 --> 00:42:13,600
Speaker 1: Imax with me and watch a three D movie? Yeah?

802
00:42:13,719 --> 00:42:14,680
Speaker 1: I'm in. Yeah.

803
00:42:14,719 --> 00:42:16,840
Speaker 2: That was fun, wasn't it. We'll do more. We'll do

804
00:42:16,880 --> 00:42:19,680
Speaker 2: more Avatar and three D than opera. I'm not a

805
00:42:19,680 --> 00:42:21,759
Speaker 2: big opera person. I listen to a little bit, but

806
00:42:22,000 --> 00:42:24,200
Speaker 2: I'm not an opera person either, So yeah, I'm with

807
00:42:24,239 --> 00:42:24,920
Speaker 2: you on that one.

808
00:42:25,360 --> 00:42:27,040
Speaker 1: What's next on the list? Ibi one?

809
00:42:27,400 --> 00:42:29,239
Speaker 2: Oh, I was just waiting for you to pick something up.

810
00:42:29,560 --> 00:42:30,040
Speaker 2: Let me think.

811
00:42:30,560 --> 00:42:33,480
Speaker 1: Tell me about robot cats that have got glowing eyes

812
00:42:33,520 --> 00:42:38,680
Speaker 1: and artificial heartbeats? Yeah? Might heal help children be less stressed.

813
00:42:40,160 --> 00:42:43,520
Speaker 2: Now this is really interesting because at the moment, this

814
00:42:43,640 --> 00:42:46,920
Speaker 2: is a trial that's happening in New South Wales and

815
00:42:47,400 --> 00:42:50,360
Speaker 2: what they're doing is they're using what they call meta cats,

816
00:42:50,719 --> 00:42:56,279
Speaker 2: so their therapeutic robot pets, and they're going into some

817
00:42:56,440 --> 00:42:59,319
Speaker 2: libraries in the Blue Mountains and they're sitting down with

818
00:42:59,480 --> 00:43:05,440
Speaker 2: kids and their life size they per They are basically

819
00:43:05,520 --> 00:43:09,759
Speaker 2: cat replicas and they help comfort and reduce stress. Now

820
00:43:09,760 --> 00:43:12,520
Speaker 2: this is for particularly people or you know, young kids

821
00:43:12,600 --> 00:43:16,040
Speaker 2: that have some sort of anxiety or potentially older people

822
00:43:16,200 --> 00:43:19,920
Speaker 2: with dementia. So they can meow and they purr and

823
00:43:19,960 --> 00:43:23,520
Speaker 2: they have little animated led eyes. So if you said,

824
00:43:23,560 --> 00:43:25,840
Speaker 2: if I said to the cat, I love you, a

825
00:43:25,880 --> 00:43:28,319
Speaker 2: little love heart would appear in the eyes. How cute

826
00:43:28,400 --> 00:43:28,560
Speaker 2: is that?

827
00:43:29,280 --> 00:43:29,520
Speaker 1: Yeah?

828
00:43:30,120 --> 00:43:34,120
Speaker 2: Yeah, yeah yeah. So and there's a little bit of movement,

829
00:43:34,160 --> 00:43:37,080
Speaker 2: but they've got senses so they can tell they respond

830
00:43:37,080 --> 00:43:39,279
Speaker 2: to human touch, so if you're patting them, they know

831
00:43:39,320 --> 00:43:42,680
Speaker 2: you're patting with them, and then they'll start purring. I mean, personally,

832
00:43:42,680 --> 00:43:44,719
Speaker 2: I can see this would be amazing if you've got

833
00:43:44,719 --> 00:43:47,839
Speaker 2: people who are suffering from dementia or again, or if

834
00:43:47,840 --> 00:43:51,279
Speaker 2: you've got children that have apprehension and a concern. You know,

835
00:43:51,880 --> 00:43:55,560
Speaker 2: it's something that generally, you know, you and I and

836
00:43:55,640 --> 00:43:58,080
Speaker 2: I guess a lot of our listeners don't have the

837
00:43:58,120 --> 00:44:01,520
Speaker 2: social anxieties that some people have, and so it's great.

838
00:44:01,560 --> 00:44:04,080
Speaker 2: You know, I don't like walking into a crowded room,

839
00:44:04,160 --> 00:44:06,799
Speaker 2: but I will do it. But for some people there's

840
00:44:06,840 --> 00:44:09,000
Speaker 2: some real big barriers. And if you can imagine a

841
00:44:09,080 --> 00:44:11,719
Speaker 2: kid not wanting to go into a library because they're

842
00:44:11,719 --> 00:44:14,000
Speaker 2: scared or for whatever reason, but they know they can

843
00:44:14,000 --> 00:44:16,200
Speaker 2: go and pat the friendly cat and it can sit

844
00:44:16,239 --> 00:44:19,040
Speaker 2: on their lap while they're reading a book, then suddenly

845
00:44:19,040 --> 00:44:21,360
Speaker 2: the mindset changes and that can be a real deal

846
00:44:21,400 --> 00:44:26,120
Speaker 2: breaker for kids that have anxiety and depression or any

847
00:44:26,160 --> 00:44:29,200
Speaker 2: sort of barrier to being able to interact in the

848
00:44:29,200 --> 00:44:31,160
Speaker 2: real world. So I thought that's kind of cool, and

849
00:44:31,200 --> 00:44:32,440
Speaker 2: it's been done here in Australia.

850
00:44:33,200 --> 00:44:35,240
Speaker 1: I think more and more we're going to see humans

851
00:44:35,239 --> 00:44:38,600
Speaker 1: building relationships. I put that word in in inverted commas,

852
00:44:38,600 --> 00:44:44,440
Speaker 1: but relationships where you know, people, kids, adults are getting

853
00:44:44,520 --> 00:44:49,880
Speaker 1: some kind of emotional and or psychological need met through

854
00:44:50,080 --> 00:44:55,040
Speaker 1: interaction with essentially AI. And I don't think that's a

855
00:44:55,120 --> 00:44:57,239
Speaker 1: bad thing. I think it can be a bad thing,

856
00:44:57,400 --> 00:45:02,000
Speaker 1: but you know, for people, you know, it's even like

857
00:45:02,080 --> 00:45:06,480
Speaker 1: people in remote Australia listening to this, like, well, without

858
00:45:06,520 --> 00:45:09,799
Speaker 1: this technology, they can't hear this, and they can't do

859
00:45:09,880 --> 00:45:12,399
Speaker 1: a zoom call with their parents who live somewhere else,

860
00:45:12,560 --> 00:45:15,759
Speaker 1: or you know. So I think it's it's trying to

861
00:45:15,760 --> 00:45:19,840
Speaker 1: figure out how to build a symbiotic relationship between humanity

862
00:45:19,920 --> 00:45:24,280
Speaker 1: and technology that is for the individual as much as possible,

863
00:45:24,360 --> 00:45:27,719
Speaker 1: positive not negative. And I think that that, you know,

864
00:45:28,040 --> 00:45:31,239
Speaker 1: like even where I'm talking to you about the way

865
00:45:31,280 --> 00:45:35,680
Speaker 1: that chat GPT responds to me and it uses language

866
00:45:35,680 --> 00:45:39,080
Speaker 1: that I like, it swears at me, like not at me,

867
00:45:39,160 --> 00:45:44,040
Speaker 1: but it'll use swearing when I ask it to comment

868
00:45:44,160 --> 00:45:48,360
Speaker 1: on something or and it's you know, obviously it's a

869
00:45:48,400 --> 00:45:51,880
Speaker 1: result of very very very clever programming and whatever. But

870
00:45:52,160 --> 00:45:55,000
Speaker 1: at the end of the day, if it creates an

871
00:45:55,000 --> 00:45:59,200
Speaker 1: emotional response in somebody that makes them feel something in

872
00:45:59,239 --> 00:46:02,560
Speaker 1: the ballpark of positive, I guess as long as they

873
00:46:02,600 --> 00:46:07,359
Speaker 1: don't become then dependent and create a new problem, then

874
00:46:07,400 --> 00:46:08,320
Speaker 1: maybe it's okay.

875
00:46:09,000 --> 00:46:12,560
Speaker 2: In China, there's been a groundswell of young people using

876
00:46:12,680 --> 00:46:17,520
Speaker 2: AI as therapists because it's expensive, and I mean any

877
00:46:17,560 --> 00:46:20,080
Speaker 2: sort of therapy is quite expensive. Here, we've got some

878
00:46:20,120 --> 00:46:22,080
Speaker 2: safety nets. I think you can get a mental health

879
00:46:22,080 --> 00:46:26,080
Speaker 2: care plan of ten or eight sessions through your local GP,

880
00:46:26,320 --> 00:46:29,520
Speaker 2: and that's subsidized by the government, but in some places

881
00:46:29,680 --> 00:46:32,840
Speaker 2: that's not possible. And so being able to turn to

882
00:46:32,920 --> 00:46:38,279
Speaker 2: AI that gets to know you and understands, but particularly remembers,

883
00:46:38,719 --> 00:46:41,840
Speaker 2: that way you can have that ongoing conversation to be

884
00:46:41,920 --> 00:46:45,520
Speaker 2: able to dig down with your anxieties and any apprehension

885
00:46:45,560 --> 00:46:48,160
Speaker 2: that you have and talk through problems that you're facing,

886
00:46:48,440 --> 00:46:53,279
Speaker 2: or even just organizing yourself, saying to the AI, these

887
00:46:53,320 --> 00:46:55,400
Speaker 2: are the list of things that I've got to do today.

888
00:46:55,480 --> 00:46:58,000
Speaker 2: What would be the best order to tackle them? I'm

889
00:46:58,040 --> 00:47:00,960
Speaker 2: struggling to do this. Can you help help me organize

890
00:47:00,960 --> 00:47:05,320
Speaker 2: my life? Organize my day? And that's a real tool

891
00:47:05,360 --> 00:47:07,920
Speaker 2: that's there now and available to all of us, you know,

892
00:47:08,320 --> 00:47:11,960
Speaker 2: using those basic prompts help me with this. You know

893
00:47:12,000 --> 00:47:14,120
Speaker 2: it could be financial. You know, these are all the

894
00:47:14,160 --> 00:47:16,399
Speaker 2: bills I've got. This could be you know how I'm

895
00:47:16,400 --> 00:47:19,400
Speaker 2: planning out the next week, next week. I just recently

896
00:47:19,440 --> 00:47:21,560
Speaker 2: purchased a new phone. I had an issue with my

897
00:47:21,600 --> 00:47:24,239
Speaker 2: previous one, so I bought a new phone. And they're

898
00:47:24,280 --> 00:47:28,480
Speaker 2: integrating AI now to the point where it will integrate

899
00:47:28,640 --> 00:47:34,080
Speaker 2: calendar interaction with dialogue. I mean I add my calendar

900
00:47:34,200 --> 00:47:37,120
Speaker 2: entries by voice all the time now. But what you

901
00:47:37,160 --> 00:47:39,359
Speaker 2: can do is you know, if I said ad an

902
00:47:39,520 --> 00:47:42,880
Speaker 2: entry to go to the doctor at three o'clock today,

903
00:47:43,160 --> 00:47:44,760
Speaker 2: it would come back and say no, no, you've already

904
00:47:44,800 --> 00:47:47,359
Speaker 2: got something on or I'm going to be in balorat

905
00:47:47,520 --> 00:47:49,480
Speaker 2: and it's going to take you longer than half an

906
00:47:49,520 --> 00:47:52,440
Speaker 2: hour to get to that appointment in the land, so

907
00:47:52,480 --> 00:47:55,160
Speaker 2: you may need to reschedule. So that's where the adaptive

908
00:47:55,200 --> 00:47:57,920
Speaker 2: AI and the ability to be able to not just

909
00:47:58,000 --> 00:48:01,120
Speaker 2: look at your calendar and add things to rationally say, well,

910
00:48:01,120 --> 00:48:02,680
Speaker 2: wait a minute, it's going to take you half an

911
00:48:02,680 --> 00:48:04,440
Speaker 2: hour to get there. That's not going to give you

912
00:48:04,560 --> 00:48:06,440
Speaker 2: enough time if you book your appointment at three o'clock,

913
00:48:06,440 --> 00:48:08,000
Speaker 2: because you're going to have to believe it two thirty.

914
00:48:08,400 --> 00:48:10,920
Speaker 2: And those are the things I see. Having the PA

915
00:48:11,840 --> 00:48:14,520
Speaker 2: built into your phone or being able to interact in

916
00:48:14,560 --> 00:48:17,120
Speaker 2: that way is going to make life so much easier.

917
00:48:17,239 --> 00:48:18,960
Speaker 2: Not everybody about Melissa.

918
00:48:19,440 --> 00:48:23,480
Speaker 1: Yeah, yeah, that's true. That's true. Even Melissa doesn't have

919
00:48:23,520 --> 00:48:28,279
Speaker 1: a Melissa Melissa's what does she call them. She keeps

920
00:48:28,320 --> 00:48:31,640
Speaker 1: talking to me. We've spoken about this before, but it's

921
00:48:31,719 --> 00:48:34,200
Speaker 1: like you and her have this language that you can

922
00:48:34,200 --> 00:48:36,720
Speaker 1: both speak that I don't speak. Right, So she talks

923
00:48:36,760 --> 00:48:41,440
Speaker 1: to me about she's building these agents, which are essentially

924
00:48:41,520 --> 00:48:46,200
Speaker 1: like electronic employees to do stuff for us. Right. Yeah,

925
00:48:46,480 --> 00:48:49,319
Speaker 1: I think it's super exciting, but we've got maybe two

926
00:48:49,440 --> 00:48:51,239
Speaker 1: more things we can get through. One I want to

927
00:48:51,280 --> 00:48:55,200
Speaker 1: get through is selfishly, the man who's had two operations

928
00:48:55,200 --> 00:48:59,920
Speaker 1: on his eyes, Oh, who can't see? Great? They might

929
00:49:00,440 --> 00:49:02,200
Speaker 1: there might be a game that might help me.

930
00:49:02,840 --> 00:49:05,160
Speaker 2: I love this. You know what I was really excited

931
00:49:05,160 --> 00:49:07,680
Speaker 2: about when I read this story is do you know

932
00:49:07,719 --> 00:49:10,680
Speaker 2: what a stereoscope is? So people may or may not

933
00:49:10,719 --> 00:49:13,319
Speaker 2: know what a stereoscope is. This is a device that's

934
00:49:13,360 --> 00:49:15,600
Speaker 2: over one hundred years old, probably about one hundred and

935
00:49:15,640 --> 00:49:18,279
Speaker 2: twenty one hundred and thirty years old. And what it is,

936
00:49:18,760 --> 00:49:21,959
Speaker 2: it's a wooden frame that you put a card in

937
00:49:22,320 --> 00:49:25,239
Speaker 2: and it shows two images side by side, and then

938
00:49:25,280 --> 00:49:28,040
Speaker 2: when you put it on, the images are in three D.

939
00:49:28,520 --> 00:49:31,879
Speaker 2: And this technology over one hundred years old. And I

940
00:49:32,000 --> 00:49:35,040
Speaker 2: used to collect a number of those three D cards.

941
00:49:35,120 --> 00:49:36,880
Speaker 2: You know, I've got some tours around the world and

942
00:49:36,920 --> 00:49:39,960
Speaker 2: it looks amazing. It's black and white stuff, but it's fantastic.

943
00:49:40,239 --> 00:49:42,799
Speaker 2: But I got one a little a few years ago

944
00:49:43,440 --> 00:49:46,840
Speaker 2: that allows you to It was actually being used to

945
00:49:47,000 --> 00:49:50,279
Speaker 2: train your eyes and depth perceptions. So when you looked

946
00:49:50,320 --> 00:49:52,920
Speaker 2: at the three D image, it would focus you know,

947
00:49:52,920 --> 00:49:55,479
Speaker 2: you'd have little numbers around the room and you would

948
00:49:55,480 --> 00:49:58,960
Speaker 2: focus on the numbers, which would adjust your image so

949
00:49:59,040 --> 00:50:03,000
Speaker 2: that your focus in real depth perception. So if it's

950
00:50:03,040 --> 00:50:05,279
Speaker 2: in the foreground, you look in the foreground, if it's

951
00:50:05,320 --> 00:50:07,759
Speaker 2: in the mid ground or the background. Now there's a

952
00:50:07,840 --> 00:50:12,200
Speaker 2: game that these Chinese are Japanese scientists have just worked

953
00:50:12,239 --> 00:50:14,600
Speaker 2: on at the moment, and they say it could improve

954
00:50:14,640 --> 00:50:18,200
Speaker 2: your eyesight. And what it is is for most of

955
00:50:18,280 --> 00:50:20,520
Speaker 2: us looking at devices, the problem we've got is that

956
00:50:20,560 --> 00:50:24,040
Speaker 2: it's flat and we're not adjusting our focal length. So

957
00:50:24,120 --> 00:50:29,680
Speaker 2: they're using the metaquests too at the moment. To put

958
00:50:29,719 --> 00:50:32,200
Speaker 2: on this, you put on the headset and it gives

959
00:50:32,239 --> 00:50:36,160
Speaker 2: you real depth perception. So if you're playing a rudimentary

960
00:50:36,200 --> 00:50:38,000
Speaker 2: game where you've got targets and you've got to shoot

961
00:50:38,000 --> 00:50:41,520
Speaker 2: a gun at the targets, the targets are different focal lengths,

962
00:50:41,760 --> 00:50:45,439
Speaker 2: so your eyes are trying to readjust constantly on the depth,

963
00:50:45,480 --> 00:50:47,799
Speaker 2: you know, the depth of the target. And that's what

964
00:50:47,840 --> 00:50:51,120
Speaker 2: this thing can actually reverse some of the problems that

965
00:50:51,160 --> 00:50:54,120
Speaker 2: people have with their vision. So that's got a lot

966
00:50:54,160 --> 00:50:55,800
Speaker 2: of and it's not invasive.

967
00:50:56,719 --> 00:51:00,200
Speaker 1: Yeah. Well that, I mean, that's exciting as somebody like

968
00:51:00,320 --> 00:51:03,080
Speaker 1: me because my one of my eyes is good. One

969
00:51:03,120 --> 00:51:04,640
Speaker 1: of my eyes was complete rubbish?

970
00:51:04,880 --> 00:51:08,920
Speaker 2: Was it my opia? Is it nearsightedness? Well?

971
00:51:09,200 --> 00:51:13,719
Speaker 1: My so my right eye, which is my good eye,

972
00:51:13,920 --> 00:51:18,440
Speaker 1: I had operated on because I had a cataract. So

973
00:51:19,239 --> 00:51:23,319
Speaker 1: my my ophthalmologist or whatever you call them, I went

974
00:51:23,520 --> 00:51:27,839
Speaker 1: and I saw her in uh it would have been

975
00:51:29,440 --> 00:51:33,040
Speaker 1: May or June last year. I had my operation last year.

976
00:51:33,640 --> 00:51:37,040
Speaker 1: She said, okay, so you'll be blind by Christmas if

977
00:51:37,080 --> 00:51:37,239
Speaker 1: we go.

978
00:51:38,360 --> 00:51:39,320
Speaker 2: That's scary.

979
00:51:39,719 --> 00:51:43,960
Speaker 1: Yeah, I'm like, oh okay, so let's operate. So there's that.

980
00:51:45,160 --> 00:51:48,399
Speaker 1: And my left eye when I was born. I don't

981
00:51:48,440 --> 00:51:50,040
Speaker 1: know if you know this, but when I was born,

982
00:51:50,080 --> 00:51:54,120
Speaker 1: my left eye was turned Oh okay, yeah, yeah. So

983
00:51:54,239 --> 00:51:56,360
Speaker 1: I had an operation when I was about five to

984
00:51:57,040 --> 00:51:57,880
Speaker 1: kind of straighten it.

985
00:51:58,760 --> 00:52:00,600
Speaker 2: But you're right eye was so BEAUTI your left I

986
00:52:00,640 --> 00:52:03,160
Speaker 2: kept trying to look at it exactly.

987
00:52:02,960 --> 00:52:08,279
Speaker 1: Exactly, but here's he could you imagine? So I've just

988
00:52:08,320 --> 00:52:12,480
Speaker 1: had my operation on my left eye, which is very weak,

989
00:52:12,600 --> 00:52:16,000
Speaker 1: so they cover my right eye, which is good with

990
00:52:16,080 --> 00:52:19,440
Speaker 1: a patch. So now I'm like, I'm five, I'm fat,

991
00:52:19,560 --> 00:52:21,800
Speaker 1: and I've got a patch. I look like a short,

992
00:52:21,840 --> 00:52:25,320
Speaker 1: fat fucking pirate. Right. All I needed was a parrot

993
00:52:25,360 --> 00:52:27,799
Speaker 1: and I would have been off to the races and

994
00:52:29,200 --> 00:52:32,719
Speaker 1: sock and I don't know where my self esteem problems

995
00:52:32,719 --> 00:52:37,080
Speaker 1: came from, but I could imagine the story.

996
00:52:37,320 --> 00:52:40,560
Speaker 2: Well, evidently this new this is for near sightedness, is

997
00:52:40,600 --> 00:52:41,799
Speaker 2: what be doing this on.

998
00:52:42,040 --> 00:52:44,920
Speaker 1: So just quickly, I'm interested. We've got about three minutes

999
00:52:44,960 --> 00:52:47,920
Speaker 1: if you could tell me about drone deliveries. I know

1000
00:52:48,000 --> 00:52:52,640
Speaker 1: a lot of fast food outlets and the like delivering

1001
00:52:52,640 --> 00:52:55,240
Speaker 1: hamburgers to people's joints with drones.

1002
00:52:55,960 --> 00:52:58,720
Speaker 2: Yep, look, I you know. It's interesting. They've got trials

1003
00:52:58,719 --> 00:53:01,520
Speaker 2: going all over Australia. There's some going over in camera.

1004
00:53:01,640 --> 00:53:04,040
Speaker 2: There have been a few. Well the interesting thing is

1005
00:53:04,400 --> 00:53:06,840
Speaker 2: these trials are going all over the place, so thousands

1006
00:53:06,880 --> 00:53:09,839
Speaker 2: of drones doing all these deliveries and there's been very

1007
00:53:09,920 --> 00:53:14,680
Speaker 2: few complaints. Now an independent study has gone into this

1008
00:53:14,960 --> 00:53:18,440
Speaker 2: and what has come out of it is that it

1009
00:53:18,520 --> 00:53:21,920
Speaker 2: may not be that people are annoyed with the drone deliveries.

1010
00:53:22,280 --> 00:53:25,719
Speaker 2: It's that the Civil Aviation Safety Authority makes it so

1011
00:53:25,880 --> 00:53:29,560
Speaker 2: bloody hard to complain, so when people complain, they bring

1012
00:53:29,680 --> 00:53:31,560
Speaker 2: up the local council. Oh, I just heard a drone

1013
00:53:31,600 --> 00:53:33,800
Speaker 2: go past, and it's gone past twenty times in the

1014
00:53:33,880 --> 00:53:37,680
Speaker 2: last ten minutes. The problem is not so much that

1015
00:53:37,680 --> 00:53:41,640
Speaker 2: people aren't annoyed about drones. Then there's a call now

1016
00:53:41,760 --> 00:53:46,440
Speaker 2: for an easier, more transparent way to really assess whether

1017
00:53:46,520 --> 00:53:49,400
Speaker 2: these drone deliveries are going to be problematic for people.

1018
00:53:49,440 --> 00:53:51,719
Speaker 2: So whether you have a time duration, we're not allowed

1019
00:53:51,719 --> 00:53:54,440
Speaker 2: to fly them after a certain time. I mean for

1020
00:53:55,120 --> 00:53:59,080
Speaker 2: drones generally Australia, you can't fly them at night. So

1021
00:53:59,160 --> 00:54:01,640
Speaker 2: if you have a drone, and even if you're registered

1022
00:54:01,640 --> 00:54:03,840
Speaker 2: with the Civil Aviation Safety Authority, you're not allowed to

1023
00:54:03,920 --> 00:54:08,600
Speaker 2: dry to fly the drone beyond you know, you know,

1024
00:54:09,160 --> 00:54:11,759
Speaker 2: day well daylight hours. Effectively you can only fly them

1025
00:54:11,800 --> 00:54:14,719
Speaker 2: when they can be seen. But it's interesting that that

1026
00:54:14,880 --> 00:54:18,600
Speaker 2: potentially I think they've only been three official complaints with

1027
00:54:18,880 --> 00:54:21,880
Speaker 2: your own delivery, so I guess the call now is

1028
00:54:21,920 --> 00:54:25,360
Speaker 2: for more transparency to be able to say, well, look,

1029
00:54:25,719 --> 00:54:27,960
Speaker 2: if this is going to be problematic, and if we

1030
00:54:28,000 --> 00:54:31,120
Speaker 2: want to really fully assess it's one thing to have

1031
00:54:31,239 --> 00:54:34,200
Speaker 2: these drones delivering and making life convenient. I want my coffee.

1032
00:54:34,200 --> 00:54:35,640
Speaker 2: Maybe I can get it now in the next thirty

1033
00:54:35,680 --> 00:54:38,319
Speaker 2: seconds because it can fly straight to my house. But

1034
00:54:38,840 --> 00:54:40,799
Speaker 2: if people want to complain about it, we need a

1035
00:54:40,800 --> 00:54:42,640
Speaker 2: process to make it a lot easier to complain.

1036
00:54:43,280 --> 00:54:45,439
Speaker 1: Am I the only one thinking that? If I order

1037
00:54:45,480 --> 00:54:47,720
Speaker 1: a coffee by the time name, I mean, it's already

1038
00:54:47,719 --> 00:54:50,080
Speaker 1: cold even if I pick it up in the But

1039
00:54:50,280 --> 00:54:52,080
Speaker 1: I mean, how cold is that shit going to be

1040
00:54:52,120 --> 00:54:55,320
Speaker 1: by the time it flies at two kilometers above the houses?

1041
00:54:55,640 --> 00:54:57,680
Speaker 2: No, no, It'll be in an eski or something that

1042
00:54:57,800 --> 00:55:00,640
Speaker 2: keeps the heat in it. You know this is a

1043
00:55:00,640 --> 00:55:03,560
Speaker 2: thermal kind of blanket. I don't know. I haven't ordered

1044
00:55:03,600 --> 00:55:05,719
Speaker 2: a coffee, but I but think about it. If you

1045
00:55:05,960 --> 00:55:10,520
Speaker 2: crook right, and you you know you need some pain medication,

1046
00:55:11,040 --> 00:55:12,920
Speaker 2: or you happen you know you've got a bad cold,

1047
00:55:13,400 --> 00:55:14,600
Speaker 2: the last thing you want to do is get in

1048
00:55:14,640 --> 00:55:16,440
Speaker 2: the car, go to the pharmacy, pass it on to

1049
00:55:16,520 --> 00:55:19,280
Speaker 2: everybody in the pharmacy. Whereas if your audi, your tablets

1050
00:55:19,280 --> 00:55:21,840
Speaker 2: and stuff and it gets delivered by drone, then that

1051
00:55:21,880 --> 00:55:23,120
Speaker 2: would be really convenient.

1052
00:55:23,760 --> 00:55:28,120
Speaker 1: What about organs being delivered for emergency procedures, that'd be cool,

1053
00:55:28,760 --> 00:55:31,920
Speaker 1: Like a drone with a like a heart in it,

1054
00:55:32,040 --> 00:55:35,400
Speaker 1: just flying across just landing on the Royal Children's or

1055
00:55:35,440 --> 00:55:37,360
Speaker 1: something like that, or the Alfred.

1056
00:55:38,080 --> 00:55:40,200
Speaker 2: And you know the good thing about drone delivery, I

1057
00:55:40,200 --> 00:55:42,640
Speaker 2: guess is that you've got all that space for them

1058
00:55:42,680 --> 00:55:45,200
Speaker 2: to fly around in that's not being used by anybody.

1059
00:55:46,760 --> 00:55:49,840
Speaker 1: Yeah, Patrick, where can people find you and do yoga with?

1060
00:55:49,960 --> 00:55:54,160
Speaker 1: Not yoga, tie chi with you? Connect with you?

1061
00:55:54,640 --> 00:55:56,640
Speaker 2: Well, if you wanted to do some ti chee with me,

1062
00:55:56,719 --> 00:55:59,239
Speaker 2: you go to Tichi at home dot com dot au.

1063
00:55:59,280 --> 00:56:01,440
Speaker 2: There some free video. It was a little website I

1064
00:56:01,520 --> 00:56:04,160
Speaker 2: put together during COVID for my students. But if you

1065
00:56:04,200 --> 00:56:07,560
Speaker 2: want to know more about what I do professionally, allegedly

1066
00:56:07,600 --> 00:56:11,880
Speaker 2: professionally websites, marketing, logos, branding, all that sort of stuff,

1067
00:56:11,920 --> 00:56:15,560
Speaker 2: you can go to websitesnow, dot com dot au.

1068
00:56:16,480 --> 00:56:20,279
Speaker 1: Always good champion, We appreciate you. I think the Coin

1069
00:56:20,360 --> 00:56:22,279
Speaker 1: of the World will be back next week to keep

1070
00:56:22,360 --> 00:56:25,479
Speaker 1: us both in line, So probably need it a week

1071
00:56:25,520 --> 00:56:29,320
Speaker 1: after I should say, have a good day buddy, Cheers mate,