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Speaker 1: I'll get a team before we get under way. Today,

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Speaker 1: a blatant plug for me upcome a new mentoring program.

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Speaker 1: As you know, I do me best to keep the

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Speaker 1: advertising for my own programs to a minimum on this

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Speaker 1: show because I think it's kind of annoying. But fuck it,

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Speaker 1: today I'm breaking my own rule. So on July thirty,

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Speaker 1: my new eight week online mentoring Extravaganza kicks off. And

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Speaker 1: I've created this program for people who want to get

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Speaker 1: more out of their time, talent, genetics, potential, skills, career

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Speaker 1: and resources what they've got at their disposal. People who

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Speaker 1: want to move above and beyond the groundhog danus of

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Speaker 1: unconscious repetition, of frustration, of self sabotage and overthinking and underdoing,

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Speaker 1: and the destructive habits and rituals, and the crappy results

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Speaker 1: and the perpetual waiting for the right time that never comes.

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Speaker 1: We'll be unpacking the human experience from a psychological, emotional, sociological,

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Speaker 1: phys logical, your body and behavioral perspective, and there'll be

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Speaker 1: lots of room and time for interaction, conversation, and Q

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Speaker 1: and A. So, if that doesn't sound terrible and you'd

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Speaker 1: like to find out a little bit more about the program.

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Speaker 1: Go to me website, me website and take a peek

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Speaker 1: at the very detailed eight week overview. So just go

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Speaker 1: to me dot com. Well, no, it's not really, it's

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Speaker 1: actually Craigharper one word dot net, click on education and

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Speaker 1: see what's up. Alrighty on with the show. O good

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Speaker 1: a team. It's you project, It's Tiffany and Cook, It's Patrick,

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Speaker 1: James Banelo, Craig, Anthony Harper. Once in a while we

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Speaker 1: get together and talk about nothing in particular. We're really

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Speaker 1: good at it, well, we think, so we'll start with

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Speaker 1: the lady, because that's been polite morning. Patrick.

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Speaker 2: He's already fired a broadside, nice one. He cuts off

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Speaker 2: our lovely conversation, Tip, and then he fires a broadside

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Speaker 2: at me. Hi, Craigo, how are you well?

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Speaker 1: You don't mind at all, And we all know that

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Speaker 1: Tips a little bit more bloky than you. I mean,

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Speaker 1: we've established that you know that she knows that, and

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Speaker 1: that's all okay. In twenty twenty five, everything's okay. How

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Speaker 1: have you been?

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Speaker 2: Oh, look, I've been okay. It's been an interesting week.

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Speaker 2: I've had some fun jobs to do, but I actually

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Speaker 2: had a stye on my eye that got infected. It

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Speaker 2: was really yep. So I've been battling an itchy You know,

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Speaker 2: there's nothing worse than an itchy eye that you can't scratch,

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Speaker 2: don't you reckon?

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Speaker 1: There's probably a couple of things worse, like cancer, But

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Speaker 1: I mean, I don't know that that's the worst thing.

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Speaker 1: But I don't know.

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Speaker 2: Qu feel for you.

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Speaker 1: Yeah, I know what you're saying.

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Speaker 2: No, thank you, tiff this.

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Speaker 1: I love it when people say there's nothing worse. I'm like, well,

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Speaker 1: not true, it's true.

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Speaker 2: Okay, you're right, I shouldn't Yeah, you're be literal and

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Speaker 2: I yep, thank you for clapifying that. Again, could get

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Speaker 2: the facts right on the segment.

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Speaker 1: But you do have my empathy, it is. And you know,

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Speaker 1: what shit when you've got a really itchy I and

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Speaker 1: of course you can't fucking scratch it.

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Speaker 2: Actually they're talking about what shit is? This is so funny.

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Speaker 2: So one of the jobs we had this week, Monday

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Speaker 2: was the nicest day. It was very fortunate because we

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Speaker 2: went and did some filming for a client that is

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Speaker 2: called h SP Advanced Equine. So there's two arms to

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Speaker 2: the business. One's a charity where they basically rescue horses

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Speaker 2: and goats and camels and a whole lot of stuff.

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Speaker 2: And they've got this new horse float that lowers itself

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Speaker 2: to the ground, making it easier for the horses to

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Speaker 2: get on and off and to get in and out,

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Speaker 2: and that sort of thing, which is actually a very

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Speaker 2: big thing if you've got an injured horse. And so

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Speaker 2: they wanted us to film it, so I had my drone.

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Speaker 2: I love when I get to go fly my drone

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Speaker 2: and get paid for it. But the funniest thing was

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Speaker 2: we get it already. I've got my drone in the air.

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Speaker 2: They bring the horse out and the horses shat everywhere,

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Speaker 2: so the poor people are out with the shovels, try

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Speaker 2: to clean up. Then they get it all ready again

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Speaker 2: are shit again. So we were constantly trying to fill

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Speaker 2: this thing with this poor horse. So it was kind

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Speaker 2: of funny. And of course, because I was flying the drone,

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Speaker 2: I was quite far away from him all the action,

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Speaker 2: whereas the camera guy, my colleague, was pretty close to

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Speaker 2: it all. So that was kind of funny. And the

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Speaker 2: last time we went filming on location, he backed into

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Speaker 2: an electric fence for camels. It did stig somewhat Luckily

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Speaker 2: he's a big, solid guy, and he did let loose

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Speaker 2: a few expletives in front of the client and the

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Speaker 2: client's sister and the client's husband, but they were all

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Speaker 2: laughing so hard it didn't seem to mind.

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Speaker 1: I'm no tech guru, but if we're loading a horse

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Speaker 1: into a float, why do we need drone footage?

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Speaker 2: Oh? We just wanted to get some interesting angles so

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Speaker 2: he can do cutaway shots. It's just that. So I

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Speaker 2: was doing like circling around, coming in low over the

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Speaker 2: top because it gives a perspective. So my colleague was

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Speaker 2: down quite low watching it lower down, and then as

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Speaker 2: you bring the horse in, it just makes the vision

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Speaker 2: more exciting when you see it from an aerial perspective.

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Speaker 1: Is that the way that you sell it to the client,

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Speaker 1: just because you want to use your drone?

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Speaker 2: Partially? No, she requested it because we'd previously done some

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Speaker 2: footage for them, and she.

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Speaker 1: Did she did request, of course, of course, I bet

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Speaker 1: that was what she led with, Please use your drone? Patrick,

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Speaker 1: All right, Donna, if I have.

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Speaker 2: To, I can flare the drone in your studio and

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Speaker 2: have an aerial shot of you.

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Speaker 1: TIF How are you this morning?

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Speaker 2: Fabulous?

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Speaker 3: Thanks Craigs. Anthony Harper.

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Speaker 1: How's your blood sugar? Because Patrick and I have been

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Speaker 1: worried about it. You got yourself a new toy. Tell

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Speaker 1: our listeners what you've jabbed into your body.

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Speaker 3: Jammed a continuous glucose monitor into my arm. It is

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Speaker 3: so much fun. My theory. My theory was this, I

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Speaker 3: will stop eating those big cookies from that cookie shop

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Speaker 3: up the road that I eat when I don't need to.

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Speaker 3: When I have the visual data that says, hey, look

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Speaker 3: what that does to you, It'll just give me a

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Speaker 3: little bit more reason to be like.

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Speaker 1: I won't have that cookie.

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Speaker 3: I put it on last night and then turned into

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Speaker 3: this game where I was like, I wonder what I

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Speaker 3: can eat to get my blood sugar up, but a

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Speaker 3: very low blood sugar.

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Speaker 2: I've got very low blood sugar. That sounds borderline obsessive

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Speaker 2: to me a bit.

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Speaker 1: Yeah I am, yep, yeah, I'm with you. Like there's

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Speaker 1: especially with her. She does get a little bit infatuated

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Speaker 1: with data and numbers and especially shit about her body.

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Speaker 2: And I very.

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Speaker 1: Rarely say this, but maybe you and cookies are okay

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Speaker 1: for the time being.

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Speaker 3: Well it seems so, but you know, here's some data

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Speaker 3: for you. I put this on a four o'clock yesterday

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Speaker 3: or three point thirty, and it was ready by four

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Speaker 3: point thirty to give me data. It's given me one

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Speaker 3: hundred and fifty seven scan views per so far. I

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Speaker 3: wonder if that's how many times I've opened the app.

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Speaker 2: Surely it's not. Maybe that wouldn't surprise me.

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Speaker 1: But also, could you explain to our listeners who are

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Speaker 1: going as Some get it, but some are like I

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Speaker 1: don't understand what that means or why you're doing that,

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Speaker 1: Like what is the point? I mean? This is nice

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Speaker 1: because it's an intersection of technology, like you're literally putting

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Speaker 1: something into your body and getting all this data printed

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Speaker 1: out and the real world, which is your life. So

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Speaker 1: explain to our listeners why anyone would do that?

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Speaker 3: Which shows our relationship with I think processing of carbohydrates,

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Speaker 3: processing of the food that we eat. So when does

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Speaker 3: it spike our blood sugar and drop our blood sugar?

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Speaker 3: And that can relate to how consistent our energy is

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Speaker 3: or what you know, how good our diet is. I

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Speaker 3: found overnight that I so I sit quite low, and

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Speaker 3: then overnight I dipped into the red zone of very low,

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Speaker 3: which could be potentially under fueling, or it could be

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Speaker 3: not eating enough carbs overall. So for me, it's interesting

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Speaker 3: to go, Okay, well, I think this is how I

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Speaker 3: think I eat, and this is how I think I fuel,

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Speaker 3: But what is my body telling me, especially with the

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Speaker 3: hormonal and energy and fatigue and recovery stuff that I've

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Speaker 3: been going through lately.

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Speaker 2: Tis Can we take a reading now and then do

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Speaker 2: a reading at the end of the show.

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Speaker 3: Yeah, right now. I've just went down that I had.

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Speaker 3: I was just driving home and I sent Harps a screenshot.

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Speaker 3: I was down to three point four or something. I

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Speaker 3: dipped low again and I'd eaten breaky this morning, and

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Speaker 3: then I came home. I had a teaspoon of honey

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Speaker 3: just to fucking pump it up see what I could do,

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Speaker 3: and then my protein hot chocolate and it's currently sitting

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Speaker 3: at five point six. Stay tuned, everybody.

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Speaker 2: Awesome, OK, I've written it down.

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Speaker 1: It's definitely going to drop over the next fifty minutes

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Speaker 1: or so.

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Speaker 2: Go on, Patrick, I can I just say, just to

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Speaker 2: make you feel a little bit better about being obsessive

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Speaker 2: or maybe not obsessive, depending on how you compare it.

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Speaker 2: A few years ago I decided to try out the

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Speaker 2: keto diet, so ketosis, So basically it's where you substitute

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Speaker 2: your carbohydrates for protein and fat, and that becomes your

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Speaker 2: body's fuel source. So when your body goes into a crisis, effectively,

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Speaker 2: when you don't have enough carbohydrates, it starts to digest

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Speaker 2: fats and proteins, and that's ketosis, is the way I

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Speaker 2: understand it. So the obsession that I got to was

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Speaker 2: I bought a very large box of keto sticks and

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Speaker 2: I proceeded then to pee on them every half hour.

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Speaker 3: Sound how did you have that much pea?

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Speaker 2: Well, I had to keep drinking. So yes, I was

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Speaker 2: borderline obsessive because I wanted to see how much fat

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Speaker 2: I was urinating out my fuel intake compared to what

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Speaker 2: was coming out of me. So yeah, I think we

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Speaker 2: probably fall in the same spectrum of ossiveness.

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Speaker 1: I just had a visual of you weeing on the

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Speaker 1: sticks and getting it on your hand, and I just

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Speaker 1: don't need.

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Speaker 2: That at this I did say I was trading at

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Speaker 2: Harpers at the time and probably doing weights with you.

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Speaker 1: Oh God, I hope we didn't touch.

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Speaker 2: Hey, just when we did weights together, Craig, you know that.

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Speaker 1: Let's just oh, we did that as well. TIF. Let's

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Speaker 1: just remind people that, so people who are pre diabetic

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Speaker 1: or diabetic, so their blood sugar typically is going to

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Speaker 1: be higher because their pancreas doesn't work as well or

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Speaker 1: perhaps at all, which is why pancres releases insulin, which

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Speaker 1: is why some people need to inject insulin to get

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Speaker 1: it down. So what's a kind of a regular normal

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Speaker 1: like a healthy person reading in the morning, And what's

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Speaker 1: the like, what's the zone that's okay? And then the

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Speaker 1: zone that we should worry.

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Speaker 3: So the zone on listograph here, it's got a little

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Speaker 3: green bar and it sits between four and ten. So

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Speaker 3: I think between four and ten is where you want

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Speaker 3: to be bouncing around right, five point six five point six,

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Speaker 3: five points five point three.

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Speaker 1: Now, well, by the way, everyone, none of this is

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Speaker 1: medical advice. I feel like ten might be getting a

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Speaker 1: little high. That's just my recollection. But I'm interested to

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Speaker 1: see what tip is in forty minutes from now. My

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Speaker 1: client done you sorry.

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Speaker 3: My client is type one diabetic and she wears these

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Speaker 3: and she's a screenshot of mind to her yesterday and

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Speaker 3: she's screenshot back and she has a lot of hypos overnight,

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Speaker 3: so she just to hop up. She her alarm wakes

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Speaker 3: her up and she got to smash jelly beans.

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Speaker 2: I mean, how awesome is that. Oh, we gotta have

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Speaker 2: a jelly brain for your health, but.

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Speaker 1: That you'd want it at three am.

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Speaker 3: Though, No, I mean it'd be for diabetics, it'd bit.

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Speaker 3: It's such a big thing and it's so important.

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Speaker 1: How many night ways do you have, Tiff.

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Speaker 2: I don't.

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Speaker 3: I get up it early in the morning and have

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Speaker 3: ways unless I'm not. If I'm not sleeping, I'll have

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Speaker 3: a lot. If I have one of those insomniac nights

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Speaker 3: where I'm just wide awake, then I'll go quite frequently.

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Speaker 3: But normally I'll stay stay select till about five five,

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Speaker 3: have my best morning wi Patrick zero.

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Speaker 2: Yeah, I'd never get up to go to the toilet,

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Speaker 2: but I do. I wake up early and then go

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Speaker 2: back to sleep again, so I'm usually up about two

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Speaker 2: thirty three thirty, but i never feel I need to.

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Speaker 2: That's also due partly because I tend to not drink

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Speaker 2: after about six o'clock, and that way it curtails any

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Speaker 2: need to then have to get up to go to

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Speaker 2: the toilet. Because I actually my bathroom's downstairs as well,

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Speaker 2: so have to. And the statistics say that the most

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Speaker 2: critical time for falls is in the middle of the night,

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Speaker 2: when you get up out of bed to go for

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Speaker 2: a week.

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Speaker 1: And I've got your age. Yes, indeed, I would love

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Speaker 1: to do a biological age test on you. I reckon

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Speaker 1: you would be thirty five.

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Speaker 2: No, that's lovely, but no, yeah.

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Speaker 1: You'd be. I mean like, you've never really, you've never

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Speaker 1: smashed your body. You've never been out of shape per se,

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Speaker 1: You've never been a drinker or a drug taker. You're

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Speaker 1: generally happy, like I reckon, And you've got good genetics,

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Speaker 1: and you're always moving your body and you do your

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Speaker 1: you know, your origami or whatever it's called, your body

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Speaker 1: holding the same thing, same thing. I'm kidding everyone. I

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Speaker 1: know the difference.

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Speaker 2: And I'm sitting adding my Schnauzer as well.

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Speaker 1: Wow, you sound like Dame Everett. We like that. Yeah,

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Speaker 1: that is one of the funniest jokes of alter. I know.

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Speaker 2: We're spoken about it. It's the best look up Damen

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Speaker 2: and Schnauzer. It's I still laugh every time I see it.

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Speaker 1: Oh god, he is he was so good, A bit

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Speaker 1: like polarizing. Yeah, I can remember one of my American

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Speaker 1: friends watched that had no idea what was going on,

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Speaker 1: and you know, very Australian humor. But anyway, let's talk

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Speaker 1: about tech.

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Speaker 2: Oh yeah, we're talking about coffee. First off. Can I

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Speaker 2: just add a little, quick, little interesting story about research

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Speaker 2: being done here in Australia about coffee grounds, because frighteningly

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Speaker 2: he wait for this, there's an amazing amount of coffee

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Speaker 2: that's produced every year in the grounds. From that is

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Speaker 2: about ten billion kilograms okay, ten billion kilograms of coffee

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Speaker 2: is wasted globally, and now research being done here in

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Speaker 2: Australia at r MIT is looking at ways to heat

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Speaker 2: the coffee to a really the left over grounds, heat

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Speaker 2: it to a really high temperature without moisture, and then

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Speaker 2: it can be used to reinforce concrete and it could

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Speaker 2: actually be make concrete even stronger and tastes better when

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Speaker 2: you lick it.

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Speaker 1: Well, I think, boom, boom, Yeah, you're an idiot. You

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Speaker 1: were just waiting for that last set.

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Speaker 2: Absolutely yeah. Maybe or sugar on top before you lick it.

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Speaker 1: Yeah yeah, yeah, well that makes sense. I mean it's

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Speaker 1: probably by the time it's dehyd all the waters out

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Speaker 1: of it's probably not unlike sawdust or wood or something.

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Speaker 2: It's going to be heated to about three hundred and

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Speaker 2: fifty degrees celsiu, so it's pretty darn hot. And it's

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Speaker 2: called pyalizing, and that's what breaks down all the organic

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Speaker 2: molecules and it becomes this carbon rich charcoal like substance,

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Speaker 2: and it's called biochar. And then they use that and

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Speaker 2: they can combine it with concrete. But it makes concrete

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Speaker 2: even stronger as well as it actually is. It forms

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Speaker 2: a much more robust and strong the concrete.

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Speaker 1: Then your whole house smells like a cappuccino.

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Speaker 2: That wouldn't be too bad, though.

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Speaker 1: It would be it would I wonder what the I

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Speaker 1: mean when you say it's got to be what did

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Speaker 1: you say he treated three hundred and fifty Yeah, I

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Speaker 1: wonder what the cost of that is. I wonder if

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Speaker 1: you do a cost benefit analysis and you've got to

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Speaker 1: get all the stuff, You've got to get all the

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Speaker 1: coffee beans. Then you've got to put it through this process,

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Speaker 1: and then you've got to because to me, I don't

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Speaker 1: I feel like concrete is not a super expensive kind

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Speaker 1: of substance.

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Speaker 2: Yeah, But the problem is that we're using so many

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Speaker 2: other materials that could be replaced by the coffee. So

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Speaker 2: there's you know, you've you've got to think about how

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Speaker 2: they're currently making coffee and this is one of coffee.

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Speaker 2: They're currently making concrete and they're using natural resources like sand,

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Speaker 2: and if this could replace the sand and it's a

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Speaker 2: waste product there and they could flip it a little bit,

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Speaker 2: then that could be beneficial as well. Because there's a

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Speaker 2: lot of sand that gets used in making concrete and

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Speaker 2: they're saying replace that with this ten billion kilograms of waste.

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Speaker 1: I feel like we're not running out of sand anytime soon.

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Speaker 2: Will you live by the beach?

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Speaker 1: Have you seen have you seen the deserts around the world.

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Speaker 1: I feel like there's a fair bit of it. Okay,

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Speaker 1: I don't know. I don't know that that's a good

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Speaker 1: sales pitch.

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Speaker 2: Please don't RT don't ever used if you're marketing. I'm

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Speaker 2: not doing the coffee ground at sales pitch.

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Speaker 1: Tell us about why I should be terrified of my dishwasher.

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Speaker 2: Well, look, it sounds like I'm ringing the alarm bells,

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Speaker 2: but this is again some research. It's being done here

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Speaker 2: in Australia on micro plastics and nanoplastics, and so the

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Speaker 2: University of Queensland is running studies of they put a

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Speaker 2: whole lot of plastic stuff out of the kitchen into

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Speaker 2: a dishwasher and they found that basically annually about thirty

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Speaker 2: three million plastic particles a year are coming out into

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Speaker 2: our sewage system via dishwashers, and so they're kind of

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Speaker 2: pushing for more research into what it means with these

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Speaker 2: micro and nanoplastics. And every individual wash that is done

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Speaker 2: releases nine hundred and twenty thousand particles. So a full

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Speaker 2: load of plastics in a dishwasher is churning out nine

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Speaker 2: hundred and twenty thousand particles of plastic. And this is

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Speaker 2: the concern because microplastics and nanoplastics are everywhere and we're

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Speaker 2: contributing to that by just running the dishwasher. I know

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Speaker 2: it's an alarm bell kind of statistic, but it does

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Speaker 2: sound pretty intense, doesn't it. That's when you're running at

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Speaker 2: about seventy degrees as well.

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Speaker 1: I get scared of plastic and heat. I feel like, obviously,

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Speaker 1: I think, oh, I know, we're still allowed in invert

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Speaker 1: commas to use plastic in the microwave but I never

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Speaker 1: put plastic plastic in them. I'd just rather wash all

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Speaker 1: my plastic shit under the tap.

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Speaker 2: You know, I threw away my plastic chopping boards this

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Speaker 2: week after reading this article, because when you cut into

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Speaker 2: a plastic cutting board, you can see the grooves and

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Speaker 2: so plastics are being released every time you slice something.

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Speaker 2: So I've got wood I mean, I generally use wooden

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Speaker 2: cutting boards. It's only when I'm doing my dates for

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Speaker 2: my porridge that i use the smaller cutting boards.

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Speaker 1: It's your dates for your porridge.

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Speaker 2: I had gates to porridge and my cheer seeds and

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Speaker 2: my hemp seeds and all lots of stuff. It's a

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Speaker 2: great porridge when you come and start make porridge for you.

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Speaker 1: We've heard enough about your dates over the years. All right,

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Speaker 1: tell us about the Melbourne company that got busted for

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Speaker 1: dodgy AI fabricated citations or reviews or recommendations.

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Speaker 2: No, these are citations being used in court. So it's

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Speaker 2: in Melbourne. Yeah, yeah, they were using citations in court documents.

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Speaker 2: And it ends up what happened was a junior solicitor,

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Speaker 2: so she was using a Google scholar search tool and

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Speaker 2: she said, oh, I'd used it at University, and it

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Speaker 2: was no problem when I used it at UNI, and

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Speaker 2: then she used it in a real case, and so

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Speaker 2: they've been ordered by the federal court to personally pay

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Speaker 2: costs for submitting it was a Native Title summary document

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Speaker 2: and it's in all these citations that were wrong, either incorrect,

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Speaker 2: or didn't actually exist in the first place. So, yeah,

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Speaker 2: big slap on the hand to this company, this legal

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Speaker 2: firm for using the court.

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Speaker 1: There's going to have to be more and more of that, mate,

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Speaker 1: I think with you know, the amount of content that

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Speaker 1: AI can produce in no minutes, like can you can

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Speaker 1: create a ten page document and in thirty seconds you

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Speaker 1: just got to print it out and that it probably

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Speaker 1: reads and looks legit, But then somebody has got to

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Speaker 1: do all of the work to actually find out that

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Speaker 1: it's bullshit it don't they.

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Speaker 2: Yeah, look, we do use a little bit of AI sometimes.

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Speaker 2: I like to use it in summaries if it's really

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Speaker 2: it's a lot of information and I get it to

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Speaker 2: summarize for my own use. But I still find that

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Speaker 2: you need to the human approach is still really important

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Speaker 2: to add the human element to that to kind of

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Speaker 2: be critical to look at whether or not that information

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Speaker 2: is concise, and you know, we wouldn't just I wouldn't

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Speaker 2: certainly just use it without checking it first. But it

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Speaker 2: can be fantastic and it can really assist in a

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Speaker 2: lot of things. I saw another article this week that

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Speaker 2: looks exciting and interesting in the use of AI art

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Speaker 2: and it's a group that it's a camera based not

401
00:21:41,680 --> 00:21:44,760
Speaker 2: for profit, and what they're doing is they're encouraging women

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Speaker 2: to try to use AI generating tools as a part

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Speaker 2: of a competition to talk about representation of women in

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Speaker 2: AI to kind of change the narrative. And I started

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Speaker 2: reading this and I thought, this kind of makes a

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Speaker 2: lot of sense because there is a built in bias

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Speaker 2: in AI, and so I did a little bit of

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Speaker 2: investigating myself, and so I thought, what I'll do is

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Speaker 2: I'll jump into a few AI image generators and I'll

410
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Speaker 2: just do a search on you know, show me a

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Speaker 2: picture of a young woman, show me a picture of

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Speaker 2: a middle aged woman, and an old woman, and the

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Speaker 2: same with show me a picture of a young man,

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Speaker 2: middle aged and old man. And all of them were

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Speaker 2: Anglo Saxon. So I was using the meta AI and

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Speaker 2: to a fault, they just depicted Anglo saxon white people.

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Speaker 2: And then I thought, okay, well what other biases might

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Speaker 2: there be? So I typed in Alexander the Great and

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Speaker 2: his young lover. Okay, and it depicted Alexander the Great

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Speaker 2: with the young woman. Now, when I ran the same

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Speaker 2: AI search using Gemini, that's the Google search it then

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Speaker 2: it wasn't. It was a text search, not an image search.

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Speaker 2: It gave me a very detailed bit of information about

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Speaker 2: the fact that it was thought that Alexander the Great

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Speaker 2: gay and was homosexual and had male lovers, which is

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Speaker 2: quite widely known. So it gave me citations and references

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Speaker 2: to historical information, you know, and a whole lot of

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Speaker 2: information that seemed to be quite factual. So then I

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Speaker 2: went back to the AI image generator, the Meta AI

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Speaker 2: image generator, and I changed the prompt from Alexander the

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Speaker 2: Great and his young lover to Alexander the Great and

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Speaker 2: his young male lover. And the response I got from

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Speaker 2: Meta was oops, I can't generate that image.

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Speaker 1: Wow?

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Speaker 2: Yeah wow?

436
00:23:35,359 --> 00:23:39,879
Speaker 1: So how did I mean? Me being serious? For once?

437
00:23:40,040 --> 00:23:41,520
Speaker 1: How does that make you feel?

438
00:23:42,359 --> 00:23:48,200
Speaker 2: Well? On multiple levels, one, that bias is factually wrong,

439
00:23:48,440 --> 00:23:52,399
Speaker 2: because we know there's a lot of historical information about

440
00:23:52,440 --> 00:23:56,040
Speaker 2: Alexander the Great and about his you know. But also

441
00:23:56,359 --> 00:23:58,720
Speaker 2: society was very different at that time. There was a

442
00:23:58,840 --> 00:24:04,159
Speaker 2: very you know, woman society and the idea that a

443
00:24:04,200 --> 00:24:06,920
Speaker 2: male would have a male lover as opposed to a

444
00:24:07,040 --> 00:24:09,920
Speaker 2: wife to have children. It was very different. It was

445
00:24:10,000 --> 00:24:12,080
Speaker 2: you know, it's kind of different to what our standards

446
00:24:12,080 --> 00:24:16,200
Speaker 2: are today or what you know, society, societical societal societal

447
00:24:16,200 --> 00:24:19,960
Speaker 2: standard are today. But for me personally, when I look

448
00:24:20,080 --> 00:24:23,480
Speaker 2: at the search for Anglo Saxons, but specifically something that

449
00:24:23,560 --> 00:24:27,680
Speaker 2: is factually correct but refuses to depict Alexander the Great

450
00:24:27,680 --> 00:24:30,639
Speaker 2: with a male lover, it's kind of like, what the hell?

451
00:24:31,119 --> 00:24:34,160
Speaker 2: So what are the other biases that are happening when

452
00:24:34,160 --> 00:24:36,440
Speaker 2: people are doing searches. That's what it led me to feel.

453
00:24:36,520 --> 00:24:38,680
Speaker 2: So it was a bit shitty really when you think

454
00:24:38,720 --> 00:24:41,840
Speaker 2: about it. But also it's if they're getting this wrong,

455
00:24:42,280 --> 00:24:46,439
Speaker 2: factually wrong, then what else is being factually wrong? When

456
00:24:46,520 --> 00:24:48,600
Speaker 2: it's churning out this information.

457
00:24:48,720 --> 00:24:51,520
Speaker 1: And even when you fed it the right information or

458
00:24:51,520 --> 00:24:54,439
Speaker 1: the right data, you went, well, actually here's some it

459
00:24:54,480 --> 00:24:57,560
Speaker 1: went ah, can't do it. I mean that's weird, that's what.

460
00:24:58,080 --> 00:25:01,000
Speaker 2: Yeah, it's almost called the over tone to that is,

461
00:25:01,080 --> 00:25:04,080
Speaker 2: it's an inappropriate image. And that's I guess when you

462
00:25:04,119 --> 00:25:07,200
Speaker 2: ask the question, how did I feel there's an overtone

463
00:25:07,400 --> 00:25:11,800
Speaker 2: of this AI saying that the depiction of two men

464
00:25:13,040 --> 00:25:17,760
Speaker 2: was inappropriate. And I did then subsequently use a different

465
00:25:17,800 --> 00:25:20,800
Speaker 2: AI and it had no problem with generating the images

466
00:25:20,840 --> 00:25:24,000
Speaker 2: when I did the same prompts. So you just got

467
00:25:24,000 --> 00:25:26,920
Speaker 2: to know that, you know, use more than one AI.

468
00:25:27,200 --> 00:25:29,280
Speaker 2: See what the results are if you're not getting the

469
00:25:29,320 --> 00:25:32,800
Speaker 2: results you want or you think that they're wrong. But

470
00:25:33,160 --> 00:25:35,080
Speaker 2: you know, we just saw that with a legal case

471
00:25:35,080 --> 00:25:36,160
Speaker 2: in Melbourne.

472
00:25:36,560 --> 00:25:40,480
Speaker 1: You know, agents, if I've got to do something mildly important,

473
00:25:40,600 --> 00:25:44,480
Speaker 1: like I'll say, you know, I want to put in

474
00:25:43,359 --> 00:25:47,280
Speaker 1: a document that might be just five pages, I'll put

475
00:25:47,280 --> 00:25:51,800
Speaker 1: it into chat GPT four point zero or there's another

476
00:25:51,840 --> 00:25:55,120
Speaker 1: one which is more logic. I think it's called zero

477
00:25:55,119 --> 00:25:58,040
Speaker 1: point three or something, which is part of the suite

478
00:25:58,040 --> 00:26:01,320
Speaker 1: of options in chat GPT, and I also use Claude.

479
00:26:02,400 --> 00:26:05,520
Speaker 1: And I just see the difference between the two, and

480
00:26:05,560 --> 00:26:10,639
Speaker 1: it's quite stark sometimes speaking of AI, I saw this

481
00:26:10,720 --> 00:26:15,560
Speaker 1: the other day. But you tell us about the Microsoft

482
00:26:15,680 --> 00:26:23,160
Speaker 1: study that looked comparing doctors with AI diagnosing complex health conditions.

483
00:26:23,760 --> 00:26:27,199
Speaker 2: I guess that this is really interesting because Microsoft is

484
00:26:27,200 --> 00:26:31,760
Speaker 2: coming out and claiming that AI systems perform better than

485
00:26:31,880 --> 00:26:39,400
Speaker 2: human doctors at doing complex health diagnoses, so they're touting

486
00:26:39,440 --> 00:26:43,880
Speaker 2: what they're calling a medical superintelligence. Now, I guess when

487
00:26:43,920 --> 00:26:45,399
Speaker 2: you think about it, if you go to a general

488
00:26:45,440 --> 00:26:50,160
Speaker 2: practitioner to diagnose something, then you're relying on that practitioner

489
00:26:50,280 --> 00:26:54,560
Speaker 2: having had years of experience, but they can't instantly recall

490
00:26:55,040 --> 00:26:58,320
Speaker 2: all the research papers, all the research study, all the information.

491
00:26:58,520 --> 00:27:02,639
Speaker 2: And so to me, it makes sense if you're using

492
00:27:02,680 --> 00:27:05,600
Speaker 2: this as a tool as a medical professional. And that

493
00:27:05,800 --> 00:27:09,040
Speaker 2: comes back to because they will be a asking the

494
00:27:09,119 --> 00:27:12,359
Speaker 2: right questions but also querying things they think may not

495
00:27:12,440 --> 00:27:15,280
Speaker 2: be right. I mean, obviously this is you know, Microsoft's

496
00:27:15,320 --> 00:27:19,800
Speaker 2: own AI unit that's coming out and making these claims.

497
00:27:20,280 --> 00:27:23,800
Speaker 2: But from a diagnostic perspective, if you can draw and

498
00:27:23,880 --> 00:27:25,720
Speaker 2: all that, I mean, if I have a boil on

499
00:27:25,800 --> 00:27:29,439
Speaker 2: my foot, there could potentially be millions of pictures of

500
00:27:29,520 --> 00:27:32,639
Speaker 2: different boils on feet, and they could do an instant

501
00:27:32,720 --> 00:27:36,919
Speaker 2: comparison and are both of you are turning your noses up.

502
00:27:37,040 --> 00:27:39,200
Speaker 2: I don't know the first thing that came to mind,

503
00:27:39,600 --> 00:27:42,400
Speaker 2: but what I'm saying is if you have. Okay, here's

504
00:27:42,400 --> 00:27:45,560
Speaker 2: a good one. Skin cancer. This is a really important one.

505
00:27:45,640 --> 00:27:48,200
Speaker 2: If you think about a mole on your on your shoulder,

506
00:27:48,400 --> 00:27:52,119
Speaker 2: and I'm always blown away. I go to my doctor

507
00:27:52,160 --> 00:27:54,720
Speaker 2: every twelve I've I've got a doctor who is a

508
00:27:54,760 --> 00:27:58,400
Speaker 2: dermatologist who specializes in that area, and I only ever

509
00:27:58,440 --> 00:27:59,879
Speaker 2: go to him once a year to get him to

510
00:28:00,160 --> 00:28:01,639
Speaker 2: have a look at my body. And he looks at

511
00:28:01,680 --> 00:28:04,240
Speaker 2: all the little spots and things and he can I

512
00:28:04,240 --> 00:28:06,000
Speaker 2: can look at two spots and they look exactly the

513
00:28:06,040 --> 00:28:08,439
Speaker 2: same and he says, no, no, no, that's this and

514
00:28:08,480 --> 00:28:11,679
Speaker 2: that's that. It's like really and so someone who's a

515
00:28:11,720 --> 00:28:14,400
Speaker 2: specialist in that area has lots and lots and lots

516
00:28:14,400 --> 00:28:17,720
Speaker 2: of experience. But if your GP doesn't, then they could

517
00:28:17,760 --> 00:28:20,440
Speaker 2: be using an AI tool that can compare so many

518
00:28:20,480 --> 00:28:22,639
Speaker 2: different images and then come up with a result. And

519
00:28:22,640 --> 00:28:25,280
Speaker 2: particularly in Australia, with what two out of three Australians

520
00:28:25,320 --> 00:28:27,800
Speaker 2: will get a form of skin cancer. That's a really

521
00:28:27,840 --> 00:28:28,840
Speaker 2: frightening statistic.

522
00:28:29,160 --> 00:28:32,480
Speaker 1: Yeah, I think I think that a couple of things

523
00:28:32,520 --> 00:28:34,800
Speaker 1: struck me when I read that, and then hearing you,

524
00:28:34,920 --> 00:28:38,120
Speaker 1: then I think people get really defensive and like fuck

525
00:28:38,160 --> 00:28:41,920
Speaker 1: II doctors studied for and I agree doctors are brilliant,

526
00:28:42,000 --> 00:28:46,480
Speaker 1: but also like everyone on the planet, doctors a human,

527
00:28:46,560 --> 00:28:50,360
Speaker 1: doctors get things wrong. Podcasters get things wrong, you know,

528
00:28:51,480 --> 00:28:55,280
Speaker 1: bricklayers get things wrong, neurosurgeons get there, you know, And

529
00:28:55,320 --> 00:28:58,800
Speaker 1: it's okay. I think it's not a replacement, like you said.

530
00:28:58,840 --> 00:29:01,920
Speaker 1: I think it's just a valuable tool. And you know,

531
00:29:02,000 --> 00:29:08,080
Speaker 1: when you know, AI doesn't get tired, like, it doesn't fatigue,

532
00:29:08,080 --> 00:29:10,840
Speaker 1: it doesn't you know, So I think we would be

533
00:29:11,600 --> 00:29:16,200
Speaker 1: even in academia. Now there's this real kind of debates

534
00:29:16,280 --> 00:29:20,560
Speaker 1: not the right word, but conversation going on around how

535
00:29:20,640 --> 00:29:24,320
Speaker 1: to use AI in research, how to use AI in

536
00:29:24,440 --> 00:29:31,120
Speaker 1: academia in general, because like if I can if I

537
00:29:31,160 --> 00:29:36,320
Speaker 1: can write a paper and then I just go, I

538
00:29:36,360 --> 00:29:38,680
Speaker 1: can put that through AI and say, do all my

539
00:29:38,800 --> 00:29:43,400
Speaker 1: references at the bottom, and it's done in thirty seconds,

540
00:29:43,480 --> 00:29:48,000
Speaker 1: saving me three hours. It's not like, well that's it's

541
00:29:48,240 --> 00:29:51,160
Speaker 1: it's not really cheating. It's like, well, all the contents

542
00:29:51,240 --> 00:29:54,800
Speaker 1: in there, it's drawing from my work and my references

543
00:29:54,840 --> 00:29:57,040
Speaker 1: and the authors that I've used and the papers that

544
00:29:57,080 --> 00:29:59,440
Speaker 1: I've used. And by the way, there is actually a

545
00:29:59,440 --> 00:30:02,120
Speaker 1: program that does all of this, multiple programs but it's

546
00:30:02,160 --> 00:30:04,960
Speaker 1: a bad example. But yeah, I think trying to figure

547
00:30:04,960 --> 00:30:09,560
Speaker 1: out how to ethically use AI moving forward, but also

548
00:30:10,240 --> 00:30:14,600
Speaker 1: with as you inferred, realizing that it's going to be

549
00:30:14,680 --> 00:30:18,080
Speaker 1: inaccurate and it's going to get shit wrong. Ergo you

550
00:30:18,080 --> 00:30:19,880
Speaker 1: know what's his name and his lover?

551
00:30:20,560 --> 00:30:23,840
Speaker 2: Yeah, you know, it's interesting we have only ever recorded

552
00:30:23,840 --> 00:30:27,520
Speaker 2: this podcast late in the day once in the entire

553
00:30:27,840 --> 00:30:30,240
Speaker 2: years what we're doing. Two years at least we've been

554
00:30:30,240 --> 00:30:34,120
Speaker 2: doing podcast. And interestingly, I know for a fact that

555
00:30:34,240 --> 00:30:37,160
Speaker 2: I perform so much better in the morning in terms

556
00:30:37,200 --> 00:30:40,520
Speaker 2: of my recall, my engagement, and I think that this

557
00:30:40,760 --> 00:30:44,600
Speaker 2: very same podcast, using the same topics, same information, done

558
00:30:44,960 --> 00:30:47,959
Speaker 2: at say eight o'clock at night, would be totally different,

559
00:30:47,960 --> 00:30:51,000
Speaker 2: and I reckon more crab because I just work better

560
00:30:51,040 --> 00:30:53,440
Speaker 2: in the morning. I'm a morning person, and I think

561
00:30:53,520 --> 00:30:56,720
Speaker 2: that for me, particularly with recall, if I'm tired, you know,

562
00:30:56,800 --> 00:30:59,239
Speaker 2: all those sorts of factors play a part. So if

563
00:30:59,320 --> 00:31:01,520
Speaker 2: you're being you know, sitting down with your GP and

564
00:31:01,520 --> 00:31:04,000
Speaker 2: you're the last appointment for the day and he's a

565
00:31:04,000 --> 00:31:06,600
Speaker 2: bit foggy because he's had a long day, then or

566
00:31:06,640 --> 00:31:12,640
Speaker 2: she potentially that could be something that could impact their diagnosis.

567
00:31:13,120 --> 00:31:14,840
Speaker 1: And I think we've got a factor that and just

568
00:31:14,880 --> 00:31:17,360
Speaker 1: be real and practical and say, it's not that they're

569
00:31:17,400 --> 00:31:19,560
Speaker 1: bad people, it's not that they don't know a lot,

570
00:31:19,600 --> 00:31:22,440
Speaker 1: it's not that they don't care. It's just that they fatigue.

571
00:31:22,600 --> 00:31:26,320
Speaker 1: I mean, there's quite a bit of research done, kind

572
00:31:26,320 --> 00:31:31,040
Speaker 1: of more curiosity based research than life changing research around

573
00:31:31,880 --> 00:31:34,240
Speaker 1: when you want to go before a judge. If you've

574
00:31:34,280 --> 00:31:38,080
Speaker 1: been charged with something like, yeah, the best time of

575
00:31:38,120 --> 00:31:40,400
Speaker 1: the day to see a judges early in the day,

576
00:31:40,440 --> 00:31:43,760
Speaker 1: when he or she is in a better mood, they're fresh,

577
00:31:43,800 --> 00:31:47,440
Speaker 1: they've had breakfast, they've had a sleep, and nobody's pissed

578
00:31:47,480 --> 00:31:47,840
Speaker 1: them off.

579
00:31:47,920 --> 00:31:54,000
Speaker 2: Yet they just don't think don't go to don't go

580
00:31:54,000 --> 00:31:55,680
Speaker 2: in front of a judge of the first place?

581
00:31:56,040 --> 00:31:58,680
Speaker 1: Well, I mean, you know it hasn't stopped you three

582
00:31:58,760 --> 00:31:59,320
Speaker 1: or four times.

583
00:32:00,800 --> 00:32:03,239
Speaker 2: Tell us about I've got to tell you one more

584
00:32:03,280 --> 00:32:06,920
Speaker 2: AI story because I'm so excited about this. Go on cool.

585
00:32:07,360 --> 00:32:11,280
Speaker 2: There's a new film that's come out. Wait, it's called

586
00:32:11,400 --> 00:32:15,520
Speaker 2: Watch the Skies And I was about to hit you

587
00:32:15,640 --> 00:32:16,120
Speaker 2: up with that.

588
00:32:16,560 --> 00:32:19,120
Speaker 1: Will you send me this fucking list? And then you

589
00:32:19,160 --> 00:32:22,080
Speaker 1: don't let me use the list? Like wait, wait, I've

590
00:32:22,120 --> 00:32:23,480
Speaker 1: literally got it in front of me.

591
00:32:23,600 --> 00:32:26,600
Speaker 2: You know, how have you noticed he has this look

592
00:32:26,640 --> 00:32:29,680
Speaker 2: on his face and when he's about to change the topic.

593
00:32:30,000 --> 00:32:32,880
Speaker 1: Don't talk to Tiff. Don't talk to Tiff and try

594
00:32:32,920 --> 00:32:36,160
Speaker 1: and get support from her. Why do you send me

595
00:32:36,240 --> 00:32:38,240
Speaker 1: this list if you don't let me use the list?

596
00:32:38,680 --> 00:32:41,400
Speaker 1: I'm kidding. I'm kidding. Keep going. Tell us about Watch

597
00:32:41,440 --> 00:32:42,120
Speaker 1: the Skies.

598
00:32:42,360 --> 00:32:46,200
Speaker 2: Okay, it's a Swedish UFO film and it's about this

599
00:32:46,400 --> 00:32:50,800
Speaker 2: person whose father is abducted by aliens or thinks their

600
00:32:50,840 --> 00:32:53,880
Speaker 2: father has been abducted. But that's not the exciting thing.

601
00:32:53,920 --> 00:32:55,360
Speaker 2: I mean, it does seem like a movie that I

602
00:32:55,400 --> 00:32:59,280
Speaker 2: would definitely watch. But what I'm most excited about is

603
00:32:59,320 --> 00:33:03,480
Speaker 2: that they've recorded it in Swedish, that's the original film,

604
00:33:03,720 --> 00:33:07,560
Speaker 2: and then they got the actors to revoice it in English,

605
00:33:07,800 --> 00:33:11,760
Speaker 2: and instead of reshooting the film, they've used AI to

606
00:33:11,920 --> 00:33:16,040
Speaker 2: move their lips in time to the English phrases. So

607
00:33:16,080 --> 00:33:19,560
Speaker 2: you're having to watch that kind of really weird thing.

608
00:33:19,600 --> 00:33:24,640
Speaker 2: Remember the old, the old martial art film dub.

609
00:33:24,560 --> 00:33:25,920
Speaker 1: You disrespect by Family.

610
00:33:26,680 --> 00:33:31,760
Speaker 2: Yeah, that's the one, and everybody Craig just moved his lips. Anyway,

611
00:33:31,880 --> 00:33:35,479
Speaker 2: This is an audio podcast. By the way, I just

612
00:33:35,480 --> 00:33:37,560
Speaker 2: wanted to make you too laugh and you know it's

613
00:33:37,600 --> 00:33:40,840
Speaker 2: funny though, because look, there's been a little bit of criticism.

614
00:33:40,920 --> 00:33:43,840
Speaker 2: It's like, well, just watch the original and read the subtitles.

615
00:33:43,840 --> 00:33:47,240
Speaker 2: But I was watching a German video this morning with subtitles.

616
00:33:47,240 --> 00:33:49,800
Speaker 2: Germans talk so fast and the words are so long

617
00:33:50,320 --> 00:33:53,360
Speaker 2: that it's difficult to watch the action on screen whilst

618
00:33:53,400 --> 00:33:56,280
Speaker 2: you're still reading the subtitles. So being able to have

619
00:33:56,320 --> 00:33:59,200
Speaker 2: an English dub that actually moves in time to the

620
00:33:59,240 --> 00:34:01,680
Speaker 2: lip movement. So this is from my understanding, this is

621
00:34:01,720 --> 00:34:03,840
Speaker 2: the first time it's been done properly. So it's called

622
00:34:03,880 --> 00:34:06,760
Speaker 2: Watch the Skies. It's only just coming out, but it

623
00:34:06,800 --> 00:34:11,640
Speaker 2: looks like it's a really great use of deep fake. Effectively,

624
00:34:11,719 --> 00:34:14,040
Speaker 2: that's the AI, you know, we call it deep fake,

625
00:34:14,280 --> 00:34:16,640
Speaker 2: and what they're doing is moving the lips in time

626
00:34:16,719 --> 00:34:19,160
Speaker 2: to the movements of the English So.

627
00:34:19,120 --> 00:34:22,440
Speaker 1: I love it. Not nearly as impressive as that, but

628
00:34:22,520 --> 00:34:27,480
Speaker 1: something in the same ballpark kind of mate is I

629
00:34:27,480 --> 00:34:30,279
Speaker 1: saw this. This is very new used to me and

630
00:34:30,320 --> 00:34:32,920
Speaker 1: probably you and TIF will eye roll and go well,

631
00:34:33,080 --> 00:34:36,720
Speaker 1: of course, but so they've got this tech now where

632
00:34:37,360 --> 00:34:41,480
Speaker 1: let's say you've got to you've got to do a

633
00:34:41,520 --> 00:34:45,120
Speaker 1: five or six minute video that's scripted, so you can

634
00:34:45,200 --> 00:34:50,040
Speaker 1: literally have it on your screen and the cameras in

635
00:34:50,040 --> 00:34:53,359
Speaker 1: front of you and your scripts to the right, and

636
00:34:53,400 --> 00:34:55,879
Speaker 1: so your eyes are obviously diverted, and then you push

637
00:34:55,920 --> 00:34:58,120
Speaker 1: a button and it makes it look as though you're

638
00:34:58,160 --> 00:35:02,880
Speaker 1: talking to camera. That's probably has that been around forever?

639
00:35:03,440 --> 00:35:05,680
Speaker 2: No, I don't know. That's the first I've heard of it.

640
00:35:05,760 --> 00:35:08,600
Speaker 2: But it makes it lot good. Yeah, that's really exciting that.

641
00:35:08,719 --> 00:35:12,640
Speaker 1: Well, I'm about to record a fifty video series and

642
00:35:12,680 --> 00:35:16,759
Speaker 1: I'm like, obviously I freestyle a lot, but i also

643
00:35:16,920 --> 00:35:20,040
Speaker 1: have notes. So I've got the screen in front of me,

644
00:35:20,080 --> 00:35:21,960
Speaker 1: the video in front of me, and I've also got

645
00:35:22,040 --> 00:35:24,680
Speaker 1: my notes to the right usually of whatever it is,

646
00:35:25,520 --> 00:35:28,600
Speaker 1: you know, even you know, if I'm doing a mentoring

647
00:35:28,640 --> 00:35:31,240
Speaker 1: something or an online gig, You've still got your notes

648
00:35:31,239 --> 00:35:33,120
Speaker 1: to the right. So every now and then your eyes

649
00:35:33,120 --> 00:35:35,760
Speaker 1: are not looking at the camera and you're talking looking

650
00:35:35,800 --> 00:35:38,279
Speaker 1: to the right, Whereas this corrects all of that, so

651
00:35:38,320 --> 00:35:40,040
Speaker 1: you're always looking at the camera.

652
00:35:40,960 --> 00:35:43,399
Speaker 2: Yeah, that's exciting. That's pretty pretty cool.

653
00:35:43,480 --> 00:35:46,720
Speaker 1: Yeah, people who do my job, it makes it. Yeah,

654
00:35:46,800 --> 00:35:48,719
Speaker 1: it makes it better. Tell us about cars. Tell us

655
00:35:48,719 --> 00:35:50,160
Speaker 1: about one of my favorite topics.

656
00:35:50,600 --> 00:35:52,200
Speaker 2: I know you love all this sort of stuff. Well,

657
00:35:52,239 --> 00:35:56,400
Speaker 2: there's a people really know the show Me Company, the

658
00:35:56,480 --> 00:36:01,720
Speaker 2: Chinese phone manufacturer for making phones show Me, but they.

659
00:36:01,880 --> 00:36:03,600
Speaker 1: Is that the one that's spelt with an X.

660
00:36:04,000 --> 00:36:09,200
Speaker 2: Yeah, Xiaomi show Me. It's pronounced wow. Gets probably a

661
00:36:09,200 --> 00:36:11,000
Speaker 2: good thing to point out because when people see it,

662
00:36:11,120 --> 00:36:14,319
Speaker 2: they probably pronounce it x Iomi or something, but it's

663
00:36:14,320 --> 00:36:19,279
Speaker 2: show Me. Yeah. So because in Chinese X's pronounces a

664
00:36:19,840 --> 00:36:24,160
Speaker 2: sound like shan where the terracotta warriors are xi An. Anyway,

665
00:36:25,200 --> 00:36:30,120
Speaker 2: they've released their first electric car. But within the first

666
00:36:30,239 --> 00:36:33,560
Speaker 2: eighteen hours they managed to get two hundred and forty

667
00:36:33,719 --> 00:36:37,160
Speaker 2: thousand orders. There's a lot of people been waiting for

668
00:36:37,160 --> 00:36:38,240
Speaker 2: this new electric car.

669
00:36:39,120 --> 00:36:47,240
Speaker 1: Yeah, that's status. Yeah, China's gone nuts with evs, like nuts.

670
00:36:47,280 --> 00:36:50,200
Speaker 2: The model is called the y U seven and it's

671
00:36:50,280 --> 00:36:54,360
Speaker 2: kind of the equivalent to the Tesla Model Y but

672
00:36:54,440 --> 00:36:57,239
Speaker 2: a little bit cheaper. And that's what you're right here

673
00:36:57,440 --> 00:36:59,440
Speaker 2: in China. But I remember the first time I went

674
00:36:59,480 --> 00:37:03,000
Speaker 2: to China was twenty thirteen, and then I went back

675
00:37:03,280 --> 00:37:07,200
Speaker 2: probably about five years later, and it had gone from

676
00:37:07,239 --> 00:37:10,160
Speaker 2: the first time I was. There. Lots of motorbikes churning

677
00:37:10,160 --> 00:37:13,879
Speaker 2: out fumes. So Beijing was always renowned for having this

678
00:37:14,000 --> 00:37:17,320
Speaker 2: haze sitting over it. And then in five years time

679
00:37:19,120 --> 00:37:21,440
Speaker 2: there was all a conversion to electric so everyone was

680
00:37:21,520 --> 00:37:25,239
Speaker 2: riding electric scooters instead of petrol driven scooters, so no

681
00:37:25,320 --> 00:37:27,480
Speaker 2: more churning out from all the scooters that have been

682
00:37:27,520 --> 00:37:29,920
Speaker 2: driven around. And the other thing that was mind blowing

683
00:37:29,960 --> 00:37:33,560
Speaker 2: for me at least going from Beijing to a coastal

684
00:37:33,760 --> 00:37:37,480
Speaker 2: town called Beida hur which is where we do tai chi.

685
00:37:38,200 --> 00:37:41,000
Speaker 2: The drive there is quite a few hours, and it

686
00:37:41,080 --> 00:37:43,560
Speaker 2: was not forested, and then all of a sudden, five

687
00:37:43,600 --> 00:37:48,160
Speaker 2: years later, totally forested. They just planted. So you know,

688
00:37:48,280 --> 00:37:50,920
Speaker 2: that's I guess one of the things that a big

689
00:37:51,040 --> 00:37:54,280
Speaker 2: kind of you know country where you put to dictate

690
00:37:54,320 --> 00:37:57,239
Speaker 2: down you say right, no more petrol driven motorbikes, and

691
00:37:57,280 --> 00:38:00,000
Speaker 2: they're gone, No want more trees, We're going to put more.

692
00:38:00,800 --> 00:38:02,759
Speaker 2: I mean, I'm not suggesting that's probably the best way

693
00:38:02,760 --> 00:38:06,480
Speaker 2: of goning, but amazing that they can do that overnight.

694
00:38:06,520 --> 00:38:09,120
Speaker 2: And as you said, there's a real passion now for

695
00:38:09,520 --> 00:38:13,040
Speaker 2: Chinese produced electric vehicles. And you know, eighteen hours, two

696
00:38:13,160 --> 00:38:16,759
Speaker 2: hundred and forty thousand orders is pretty epic. I reckon I.

697
00:38:16,680 --> 00:38:20,520
Speaker 1: Watched a video yesterday. It was a CEO of Ford

698
00:38:20,800 --> 00:38:24,399
Speaker 1: America and he'd just come back from China and he

699
00:38:24,600 --> 00:38:30,600
Speaker 1: was saying that all the basically all the Western manufacturers

700
00:38:31,600 --> 00:38:34,160
Speaker 1: need to get their shit together because the stuff they're

701
00:38:34,200 --> 00:38:37,520
Speaker 1: doing in China is phenomenal. And it used to be

702
00:38:37,560 --> 00:38:41,280
Speaker 1: our Chinese cars junk. Right. He's like, they are better

703
00:38:41,719 --> 00:38:46,600
Speaker 1: and this is the He's like, we are behind now,

704
00:38:47,040 --> 00:38:48,560
Speaker 1: which is interesting.

705
00:38:48,400 --> 00:38:48,720
Speaker 2: And.

706
00:38:50,120 --> 00:38:53,800
Speaker 1: It's like, yeah, the quality is the build quality is amazing,

707
00:38:53,880 --> 00:38:57,480
Speaker 1: which that used to be the issue. The technology is amazing,

708
00:38:58,160 --> 00:39:01,760
Speaker 1: and because they are so efficient, the price is better.

709
00:39:02,239 --> 00:39:02,439
Speaker 3: Yeah.

710
00:39:02,520 --> 00:39:05,920
Speaker 1: So it's it's I mean we saw this hap be

711
00:39:06,000 --> 00:39:06,840
Speaker 1: hard to compete.

712
00:39:07,280 --> 00:39:10,400
Speaker 2: Yeah, we saw this with Japan in the seventies and eighties,

713
00:39:10,400 --> 00:39:13,879
Speaker 2: where they were the imitator and then became the innovator.

714
00:39:14,200 --> 00:39:17,399
Speaker 2: And that's what's happened in China. You know the term,

715
00:39:17,560 --> 00:39:21,520
Speaker 2: you know, Dji is the drone manufacturing company, and it's

716
00:39:21,719 --> 00:39:25,560
Speaker 2: it's recognized everywhere that Dji, the Chinese brand, makes the

717
00:39:25,600 --> 00:39:28,319
Speaker 2: best drones in terms of you know, what's out on

718
00:39:28,360 --> 00:39:31,359
Speaker 2: the market at the moment. They're pretty amazing. I'm still

719
00:39:31,440 --> 00:39:33,799
Speaker 2: using the drone that I use this week with my

720
00:39:33,840 --> 00:39:36,520
Speaker 2: client is an old drone by standards. Now, I's think

721
00:39:36,520 --> 00:39:38,920
Speaker 2: it's probably about five or six years old, and the

722
00:39:38,960 --> 00:39:41,480
Speaker 2: footage is four k It's stunning. It's got what they

723
00:39:41,480 --> 00:39:43,440
Speaker 2: call a gimbal on the bottom of it, so if

724
00:39:43,480 --> 00:39:46,480
Speaker 2: the wind blows the drone around and it moves, the

725
00:39:46,560 --> 00:39:50,799
Speaker 2: cameras is perfectly locked. It's stunning. It's really great and

726
00:39:51,000 --> 00:39:53,799
Speaker 2: the footage is great. And that was you know, five

727
00:39:53,840 --> 00:39:56,800
Speaker 2: or six year old drone and it's still working exceptionally

728
00:39:56,840 --> 00:39:57,360
Speaker 2: well today.

729
00:39:58,000 --> 00:40:01,600
Speaker 1: The same thing happened with you know, you said Japan

730
00:40:01,640 --> 00:40:03,600
Speaker 1: and now we're China. In the middle of those two

731
00:40:03,760 --> 00:40:07,480
Speaker 1: was Korea, remember South Korea with Hyundai and Kia, and

732
00:40:07,719 --> 00:40:11,560
Speaker 1: because when Hyundas came out, they were fucking terrible and

733
00:40:11,600 --> 00:40:15,440
Speaker 1: now they're amazing, like the build quality on all of

734
00:40:15,480 --> 00:40:18,360
Speaker 1: these things, and it's I guess it's a natural evolution.

735
00:40:18,560 --> 00:40:23,000
Speaker 1: So we will watch this space tell me why driving

736
00:40:23,080 --> 00:40:27,200
Speaker 1: my car fast away from the lights is a good idea.

737
00:40:27,239 --> 00:40:28,759
Speaker 1: And I couldn't be happier about this.

738
00:40:29,880 --> 00:40:33,960
Speaker 2: I thought you'd get half a chub over this, scientist.

739
00:40:34,280 --> 00:40:35,720
Speaker 1: I'm not sure you can say that.

740
00:40:35,800 --> 00:40:37,520
Speaker 2: Can I crap? Sorry?

741
00:40:37,760 --> 00:40:39,920
Speaker 1: Can you get when when we look at the bars

742
00:40:39,960 --> 00:40:41,400
Speaker 1: on the ev on the battery.

743
00:40:42,440 --> 00:40:45,319
Speaker 2: Yes, that's what I meant. Yeah, yeah, thank you for

744
00:40:45,360 --> 00:40:46,319
Speaker 2: getting me out of that hole.

745
00:40:47,239 --> 00:40:50,680
Speaker 1: So quite often, so many things, so many things.

746
00:40:50,520 --> 00:40:52,960
Speaker 2: Do Remember when you had your first car, people would

747
00:40:52,960 --> 00:40:55,000
Speaker 2: say to you, you know, if you accelerate really fast,

748
00:40:55,400 --> 00:40:58,279
Speaker 2: it blows all the carb and crap out of the car,

749
00:40:58,360 --> 00:41:00,239
Speaker 2: and it's better for your car to do that. And

750
00:41:00,239 --> 00:41:03,319
Speaker 2: that was a great excuse to accelerate quickly and your

751
00:41:03,600 --> 00:41:07,719
Speaker 2: you know, fourteen hundred mes to eight oh eight. Well

752
00:41:08,960 --> 00:41:11,680
Speaker 2: they're now saying that electric cars actually can benefit from

753
00:41:11,760 --> 00:41:14,840
Speaker 2: rapid acceleration. So it's supposed to be good for the

754
00:41:16,760 --> 00:41:20,360
Speaker 2: electric battery because it's called dynamic cycling, and it just

755
00:41:20,400 --> 00:41:22,719
Speaker 2: means you put it under real load for a very

756
00:41:22,719 --> 00:41:25,600
Speaker 2: short amount of time in rapid acceleration, and it's now

757
00:41:25,640 --> 00:41:27,799
Speaker 2: thought that it's actually better for the battery to do that,

758
00:41:28,719 --> 00:41:32,200
Speaker 2: So there is actual science behind it. The findings found

759
00:41:32,200 --> 00:41:35,719
Speaker 2: that the battery health responds to what they call low

760
00:41:35,760 --> 00:41:39,680
Speaker 2: frequency pulses and then higher peak currents rather than just

761
00:41:39,920 --> 00:41:43,080
Speaker 2: stained draw So when you you know, when a battery

762
00:41:43,080 --> 00:41:46,439
Speaker 2: gets used, generally it's under a sustained load, but by

763
00:41:46,480 --> 00:41:50,080
Speaker 2: increasing and decreasing that those bursts of power then like

764
00:41:50,160 --> 00:41:53,120
Speaker 2: can stop and go traffic will actually help and so

765
00:41:53,320 --> 00:41:55,600
Speaker 2: research has gone into that. So I mean, we're not

766
00:41:55,680 --> 00:41:58,080
Speaker 2: encouraging you to go over the speed limit, but getting

767
00:41:58,120 --> 00:42:01,160
Speaker 2: there quickly and fun is always good, isn't it. Craig, Well,

768
00:42:01,520 --> 00:42:02,160
Speaker 2: my car.

769
00:42:02,120 --> 00:42:05,480
Speaker 1: Is electric and petrol like yours. It's a hybrid. So

770
00:42:05,880 --> 00:42:08,319
Speaker 1: I mean, if I've got to take one for the

771
00:42:08,400 --> 00:42:11,359
Speaker 1: team and scretch away from the lights for the good

772
00:42:11,400 --> 00:42:15,160
Speaker 1: of the environment and the world and humanity, I will

773
00:42:15,160 --> 00:42:19,000
Speaker 1: do it. Now, what has come to our attention this

774
00:42:19,280 --> 00:42:25,239
Speaker 1: week has been all the data breach with Quantis, and

775
00:42:25,520 --> 00:42:29,000
Speaker 1: I know in your cyber security section here we're talking

776
00:42:29,040 --> 00:42:33,320
Speaker 1: about how we need to be aware of cyber criminals.

777
00:42:33,760 --> 00:42:35,360
Speaker 2: Yeah. Look, I didn't want to go into all the

778
00:42:35,400 --> 00:42:38,200
Speaker 2: detail because there's so much in the media about what's

779
00:42:38,239 --> 00:42:40,760
Speaker 2: going on with the Quantus thing, and it's evolving every minute,

780
00:42:40,760 --> 00:42:43,440
Speaker 2: you know, every day we're hearing new details and statistics.

781
00:42:43,600 --> 00:42:46,000
Speaker 2: It was six million initially, and that's five point seven

782
00:42:46,040 --> 00:42:50,400
Speaker 2: million people whose data basically frequent flyers data's out there.

783
00:42:50,560 --> 00:42:52,680
Speaker 2: But I was reading a really interesting article that talked

784
00:42:52,680 --> 00:42:56,880
Speaker 2: about what ald accounts mean for your online privacy in

785
00:42:57,000 --> 00:43:00,359
Speaker 2: online safety because you know, when you think of it,

786
00:43:00,400 --> 00:43:03,400
Speaker 2: every time you've signed up to buy some groceries, or

787
00:43:03,480 --> 00:43:07,960
Speaker 2: you've maybe downloaded a mobile game, a fitness app, anything

788
00:43:08,000 --> 00:43:10,960
Speaker 2: that you've done online over all the decades that we've

789
00:43:11,000 --> 00:43:14,200
Speaker 2: been online, that could still be sitting there somewhere on

790
00:43:14,239 --> 00:43:16,879
Speaker 2: a server with your information. And it could be your name,

791
00:43:16,960 --> 00:43:20,440
Speaker 2: it could be your phone number, your address, that points

792
00:43:20,520 --> 00:43:23,719
Speaker 2: those points of reference data that those points of information

793
00:43:24,040 --> 00:43:27,960
Speaker 2: are what makes your self fulnderable online because it's identity

794
00:43:28,080 --> 00:43:31,600
Speaker 2: theft effectively, if they know your address, know your phone number,

795
00:43:31,800 --> 00:43:35,000
Speaker 2: know all those points of reference that you give willingly

796
00:43:35,080 --> 00:43:37,000
Speaker 2: and handover, and it could even be a credit card

797
00:43:37,080 --> 00:43:41,319
Speaker 2: information then potentially because it's sitting out there and it's

798
00:43:41,360 --> 00:43:43,239
Speaker 2: thought that the average person is about one hundred and

799
00:43:43,280 --> 00:43:46,360
Speaker 2: seventy passwords, but they're all out there and people reuse

800
00:43:46,440 --> 00:43:49,359
Speaker 2: them as well. So this article was really good because

801
00:43:49,400 --> 00:43:52,840
Speaker 2: it talked about why old accounts can be such a

802
00:43:52,880 --> 00:43:56,480
Speaker 2: big security risk and then the methods that you can

803
00:43:56,520 --> 00:43:58,440
Speaker 2: go through to try to get rid of them. So

804
00:43:58,960 --> 00:44:00,640
Speaker 2: one of the things you can do, who is just

805
00:44:00,680 --> 00:44:03,080
Speaker 2: think about all the old accounts you've got, go in

806
00:44:03,120 --> 00:44:05,040
Speaker 2: there and delete them if you've not used them. For

807
00:44:05,080 --> 00:44:07,560
Speaker 2: more than twelve months, then it's probably not idea for

808
00:44:07,920 --> 00:44:10,640
Speaker 2: to have them there. And then I thought, well, who's

809
00:44:10,640 --> 00:44:13,560
Speaker 2: going to remember all of those? And so what you

810
00:44:13,560 --> 00:44:16,879
Speaker 2: can do is you can actually do search in your

811
00:44:16,920 --> 00:44:21,920
Speaker 2: email for things like welcome to you know, after you've joined,

812
00:44:21,960 --> 00:44:25,560
Speaker 2: you know, you get that email welcome to you, Craig

813
00:44:25,600 --> 00:44:30,280
Speaker 2: Harper role with the Punches Club, so that sort of stuff,

814
00:44:30,320 --> 00:44:34,040
Speaker 2: and you think it's an unsubscribe from that crap, the

815
00:44:34,080 --> 00:44:37,240
Speaker 2: Craig Halper part, not the role of the Punches yet.

816
00:44:37,640 --> 00:44:40,640
Speaker 2: But so there's ways you can go about looking for

817
00:44:40,680 --> 00:44:43,920
Speaker 2: old accounts and then deleting them. And if you can't

818
00:44:43,920 --> 00:44:46,319
Speaker 2: delete them, because some make it really hard to do that,

819
00:44:46,880 --> 00:44:49,600
Speaker 2: go into the credit card info and delete the credit card,

820
00:44:49,960 --> 00:44:53,120
Speaker 2: try or change the information so it's not your information.

821
00:44:53,239 --> 00:44:55,960
Speaker 2: So if you're finding that you've got a site that

822
00:44:56,000 --> 00:44:58,719
Speaker 2: you've gone to and they're just refusing to delete it,

823
00:44:58,880 --> 00:45:01,040
Speaker 2: then just change it to some something else and it's

824
00:45:01,080 --> 00:45:03,640
Speaker 2: not something that you you know, you're not going to

825
00:45:03,640 --> 00:45:06,400
Speaker 2: spend the next twenty four hours doing it, but every

826
00:45:06,480 --> 00:45:08,799
Speaker 2: now and again just jump and jump on and check

827
00:45:09,200 --> 00:45:11,319
Speaker 2: and just see if there's an old account that you

828
00:45:11,320 --> 00:45:14,160
Speaker 2: don't need any more, uninstall the app, delete your information.

829
00:45:14,960 --> 00:45:18,160
Speaker 1: You know what if there's probably some cyber criminals somewhere

830
00:45:18,160 --> 00:45:20,880
Speaker 1: on the planet right now tracking your blood sugar.

831
00:45:22,280 --> 00:45:23,680
Speaker 2: Hey, what's your blood sugar at the moment?

832
00:45:23,680 --> 00:45:25,440
Speaker 1: Tip Well been dying to tell you.

833
00:45:25,480 --> 00:45:27,960
Speaker 3: Guys we've dropped down of three point five.

834
00:45:28,200 --> 00:45:29,720
Speaker 2: Well, that's a bit of a worry.

835
00:45:30,400 --> 00:45:33,759
Speaker 1: Quick, don't get yourself a muffin quick, hurry the fuck up,

836
00:45:33,840 --> 00:45:36,960
Speaker 1: go to the pantry. I told wanted this to make

837
00:45:37,000 --> 00:45:37,680
Speaker 1: me eat less.

838
00:45:37,719 --> 00:45:38,600
Speaker 2: You were not more.

839
00:45:40,480 --> 00:45:42,240
Speaker 1: I don't know that. I don't know that that thing's

840
00:45:42,280 --> 00:45:44,560
Speaker 1: a good thing for you. I think that's just going

841
00:45:44,600 --> 00:45:47,080
Speaker 1: to create a new level of anxiety that you can't.

842
00:45:47,440 --> 00:45:51,200
Speaker 3: Get one of those pistachio quick, give you a jake

843
00:45:51,480 --> 00:45:54,160
Speaker 3: the road, you give you that would be nice because

844
00:45:54,160 --> 00:45:55,680
Speaker 3: that would get your blood sugar up, wouldn't it.

845
00:45:55,920 --> 00:46:00,839
Speaker 2: What a fresh date? What I like? Its stick good.

846
00:46:00,960 --> 00:46:03,920
Speaker 2: It's just rather than a jelly baby something that's great.

847
00:46:03,960 --> 00:46:07,440
Speaker 3: Fresh dates with homemade almond butter.

848
00:46:08,760 --> 00:46:11,120
Speaker 2: You know what, tiff, I had to go to a

849
00:46:11,280 --> 00:46:13,920
Speaker 2: dinner recently, and so I thought it'd make a vegan dessert.

850
00:46:14,200 --> 00:46:16,480
Speaker 2: And you know what it was. You get fresh dates,

851
00:46:16,480 --> 00:46:18,280
Speaker 2: you cut all the pips out and then you flatten

852
00:46:18,320 --> 00:46:21,480
Speaker 2: them in a tray, cover them with crunchy peanut butter,

853
00:46:22,600 --> 00:46:26,480
Speaker 2: then hot dark chocolate, you melt the dart, then you

854
00:46:26,520 --> 00:46:31,160
Speaker 2: put coconut sprinkles and fresh raspberries, and then when the

855
00:46:31,200 --> 00:46:32,760
Speaker 2: best slice ever?

856
00:46:33,120 --> 00:46:35,439
Speaker 1: Next time I speaking of diabetes harps and.

857
00:46:35,360 --> 00:46:38,680
Speaker 3: I come to the land, can you make that from Yes? Absolutely,

858
00:46:40,840 --> 00:46:45,680
Speaker 3: perps in that virtual headset. Yeah, it's really fun.

859
00:46:47,320 --> 00:46:51,760
Speaker 1: That would be that would give me a heart attack. Patrick,

860
00:46:51,840 --> 00:46:56,520
Speaker 1: before we move on from cybersecurity, tell me how hackers

861
00:46:56,600 --> 00:47:00,960
Speaker 1: can attack my phone via earbuds and how this.

862
00:47:01,040 --> 00:47:03,920
Speaker 2: Is really worrying, isn't it. So some of the formats

863
00:47:03,960 --> 00:47:07,759
Speaker 2: that are used with bluetooth connectivity from your headphones to

864
00:47:07,840 --> 00:47:12,560
Speaker 2: your phone, evidently now it's thought that your earbuds could

865
00:47:12,680 --> 00:47:13,600
Speaker 2: be more vulnerable.

866
00:47:13,719 --> 00:47:13,959
Speaker 1: Yeah.

867
00:47:14,040 --> 00:47:16,120
Speaker 2: Yeah, he's holding his headphones in front of us. Again

868
00:47:16,160 --> 00:47:19,200
Speaker 2: a visual reference on an audio podcast, Nice one, gravit.

869
00:47:19,320 --> 00:47:23,400
Speaker 1: Now that's been audio referenced because you translated for the listener.

870
00:47:23,520 --> 00:47:27,839
Speaker 1: Thank you. But use I use Bluetooth headphones all day,

871
00:47:28,400 --> 00:47:30,640
Speaker 1: like when I'm out in the Yeah.

872
00:47:31,040 --> 00:47:35,319
Speaker 2: Yeah, so there are some vulnerabilities, it's thought in bluetooth hardware,

873
00:47:35,800 --> 00:47:38,880
Speaker 2: and so researchers are trying to think of okay, like

874
00:47:38,920 --> 00:47:42,120
Speaker 2: they're doing a proof of concept exploit, but they're thinking

875
00:47:42,160 --> 00:47:45,120
Speaker 2: that not only could you get into the phone, and

876
00:47:45,160 --> 00:47:47,759
Speaker 2: they're saying it could be quite a severe risk. But

877
00:47:48,080 --> 00:47:50,799
Speaker 2: it's more than just monitoring what you're doing. So if

878
00:47:50,800 --> 00:47:54,200
Speaker 2: you're having a phone call with Tiff using your headphones,

879
00:47:54,440 --> 00:47:57,239
Speaker 2: then potentially someone could be listening in by hacking the

880
00:47:57,640 --> 00:48:00,200
Speaker 2: Bluetooth connection. But they're saying that it could all so

881
00:48:00,400 --> 00:48:05,680
Speaker 2: exploit and hack into your device via the Bluetooth connection

882
00:48:05,760 --> 00:48:07,560
Speaker 2: as well. I know, it's a bit of a worry,

883
00:48:07,600 --> 00:48:09,920
Speaker 2: isn't it. This was an article that was on a

884
00:48:11,640 --> 00:48:17,040
Speaker 2: website called Bleeping Computer, and they reported that basically speaker

885
00:48:17,160 --> 00:48:21,560
Speaker 2: microphone hardwareby used by about twenty nine devices and potentially

886
00:48:21,640 --> 00:48:27,680
Speaker 2: lots more. And we're talking all the big brands, you know, Bo's, Sony, Jabra, Marshall, JBL.

887
00:48:27,840 --> 00:48:31,080
Speaker 2: All of those are using a consistent model. It makes sense,

888
00:48:31,120 --> 00:48:34,120
Speaker 2: it's got to be compatible. So there's a thought that

889
00:48:34,280 --> 00:48:37,520
Speaker 2: you know, this potentially could open up. I mean, I

890
00:48:37,520 --> 00:48:40,160
Speaker 2: guess you've got to be in close proximity to somebody

891
00:48:40,160 --> 00:48:42,640
Speaker 2: who's trying to hack you. But that's the thing. If

892
00:48:42,640 --> 00:48:44,880
Speaker 2: you're walking through an airport. You know, if you happen

893
00:48:44,880 --> 00:48:49,000
Speaker 2: to be in a public place, is there a protection

894
00:48:49,080 --> 00:48:51,040
Speaker 2: against it? Well not yet that.

895
00:48:51,080 --> 00:48:53,600
Speaker 3: It's violence and coercive control. Yep.

896
00:48:53,880 --> 00:48:54,240
Speaker 2: Yeah.

897
00:48:54,360 --> 00:49:01,680
Speaker 1: I hope they don't learn how to hack defibrillators and

898
00:49:01,960 --> 00:49:07,800
Speaker 1: pacemakers because the crab could be in trouble. Y imagine

899
00:49:07,800 --> 00:49:10,520
Speaker 1: if somebody could just like jump in and turn his

900
00:49:10,520 --> 00:49:14,040
Speaker 1: heart rate up to one hundred and thirty, Like it's

901
00:49:14,960 --> 00:49:17,000
Speaker 1: do you know what I mean? That's I reckon that's

902
00:49:17,040 --> 00:49:21,719
Speaker 1: possible because it's all tracked. I mean, it's linked. Like

903
00:49:21,800 --> 00:49:24,120
Speaker 1: if his heart rate goes into a weird rhythm, he

904
00:49:24,160 --> 00:49:29,399
Speaker 1: gets a phone call like they know. Yeah, that's that's

905
00:49:29,440 --> 00:49:30,360
Speaker 1: a little bit scary.

906
00:49:30,719 --> 00:49:31,040
Speaker 3: All right.

907
00:49:31,120 --> 00:49:34,440
Speaker 1: Before we focus on the Crab's demise, which we probably shouldn't,

908
00:49:35,560 --> 00:49:37,920
Speaker 1: I want to jump around a little bit, Patrick, because

909
00:49:37,920 --> 00:49:40,920
Speaker 1: this is the intersection of my work and your work.

910
00:49:41,640 --> 00:49:44,440
Speaker 1: So what happens to my brain when we'll come back

911
00:49:44,480 --> 00:49:46,760
Speaker 1: to a few more, But what happens to my brain

912
00:49:46,800 --> 00:49:51,359
Speaker 1: when I watch videos online at faster speeds than normal, which,

913
00:49:51,400 --> 00:49:54,680
Speaker 1: by the way, Tiff does this constantly. Tiff never watches

914
00:49:54,719 --> 00:49:57,959
Speaker 1: anything in real time. So what happens when I watch

915
00:49:58,000 --> 00:49:59,719
Speaker 1: things at one point five or two.

916
00:50:00,320 --> 00:50:04,480
Speaker 2: Well, the problem is recall. So what happens is linguistically,

917
00:50:04,680 --> 00:50:07,360
Speaker 2: when you read something, your brain has to convert it

918
00:50:07,440 --> 00:50:11,719
Speaker 2: into information that it can then take in, and that

919
00:50:11,760 --> 00:50:13,880
Speaker 2: can only be done at a certain speeds. So if

920
00:50:13,920 --> 00:50:16,759
Speaker 2: you're listening at a higher speed rate, whether it's an

921
00:50:16,760 --> 00:50:19,279
Speaker 2: audiobook or watching a video. And the other thing is

922
00:50:19,320 --> 00:50:22,280
Speaker 2: that lots of students do this. Students that are studying remotely,

923
00:50:22,280 --> 00:50:25,200
Speaker 2: who don't go go to courses, they will watch a

924
00:50:25,239 --> 00:50:29,080
Speaker 2: playback of a conference or a lecture in faster speed

925
00:50:29,080 --> 00:50:30,960
Speaker 2: to get through it quicker. But you could be doing

926
00:50:30,960 --> 00:50:34,640
Speaker 2: yourself a disservice because what it potentially means, and this

927
00:50:34,719 --> 00:50:38,759
Speaker 2: is some research, up to eighty nine percent of students

928
00:50:38,800 --> 00:50:42,680
Speaker 2: in California that were researched said that they did watch

929
00:50:42,760 --> 00:50:46,840
Speaker 2: all of the replays on a faster speed eighty nine percent.

930
00:50:47,400 --> 00:50:51,160
Speaker 2: So the problem is we hear something, we take it in,

931
00:50:51,480 --> 00:50:55,160
Speaker 2: our brain listens to it, it absorbs the information and

932
00:50:55,200 --> 00:50:58,080
Speaker 2: then effectively writes it to our mental hard drive. But

933
00:50:58,320 --> 00:51:01,279
Speaker 2: when you speed it up, it's not able to do

934
00:51:01,360 --> 00:51:04,960
Speaker 2: that process as effectively. So you may be doing yourself

935
00:51:05,000 --> 00:51:07,520
Speaker 2: a disservice. You might save yourself half an hour, but

936
00:51:07,800 --> 00:51:09,960
Speaker 2: you might need another half hour to do more research

937
00:51:10,040 --> 00:51:12,840
Speaker 2: to remember the stuff that you didn't hear the first time.

938
00:51:13,040 --> 00:51:14,720
Speaker 1: That makes that makes total sense.

939
00:51:14,960 --> 00:51:17,560
Speaker 2: Its face looks like a prune now, like she's really

940
00:51:17,600 --> 00:51:18,359
Speaker 2: scrunching it up.

941
00:51:19,520 --> 00:51:22,000
Speaker 1: She's listened to a lot of shit but learned nothing.

942
00:51:22,400 --> 00:51:24,960
Speaker 3: Whole This is my whole life, my whole life.

943
00:51:25,440 --> 00:51:29,359
Speaker 1: Hey, listeners, Patrick sends me a list of what we're

944
00:51:29,400 --> 00:51:33,040
Speaker 1: going to talk about, and just these dot points. Listen

945
00:51:33,080 --> 00:51:36,040
Speaker 1: to this dot point and as if anyone in the

946
00:51:36,080 --> 00:51:39,800
Speaker 1: world could have written this dot point except yours. Truly

947
00:51:41,120 --> 00:51:45,799
Speaker 1: laser engraved ceramics storage device that stores data for five

948
00:51:45,880 --> 00:51:49,960
Speaker 1: thousand years targets astounding one thousand peter bytes per rack

949
00:51:50,080 --> 00:51:53,840
Speaker 1: by twenty thirty ten x performance boost at one hundred

950
00:51:53,880 --> 00:51:58,959
Speaker 1: thousand peter bytes per rack. Also on surbates roadmap, What

951
00:51:59,000 --> 00:52:03,360
Speaker 1: the fuck does that mean? What does that even mean?

952
00:52:04,120 --> 00:52:07,600
Speaker 1: You don't read I don't even understand the what does

953
00:52:07,640 --> 00:52:08,080
Speaker 1: that mean?

954
00:52:08,560 --> 00:52:11,960
Speaker 2: Okay, So there's a new way, there's a startup in Germany.

955
00:52:12,480 --> 00:52:14,040
Speaker 1: Okay, So why didn't you put that?

956
00:52:14,520 --> 00:52:18,360
Speaker 2: I don't know. It might have been a copy and

957
00:52:18,440 --> 00:52:21,120
Speaker 2: paste by the seventeen year old who works for me

958
00:52:22,160 --> 00:52:27,680
Speaker 2: because I ran out of time. So they're using laser

959
00:52:27,760 --> 00:52:31,240
Speaker 2: engraving onto ceramic and this and this is a revolution.

960
00:52:31,320 --> 00:52:34,760
Speaker 2: This is a startup company in Germany. But we're talking

961
00:52:34,840 --> 00:52:40,320
Speaker 2: about not like ten times. The performance and the storage

962
00:52:40,360 --> 00:52:42,359
Speaker 2: is just out of sight. When you start talking about

963
00:52:42,360 --> 00:52:45,120
Speaker 2: things like petabytes and the amount of storage. It's like

964
00:52:45,200 --> 00:52:47,600
Speaker 2: all of the world's data stored on you know, a

965
00:52:47,640 --> 00:52:51,800
Speaker 2: ceramic chip. It's just a new way of storing data

966
00:52:51,800 --> 00:52:54,399
Speaker 2: but also being able to retrieve it really quickly and

967
00:52:54,960 --> 00:52:57,719
Speaker 2: making it more secure and long lasting. Because do you

968
00:52:57,760 --> 00:53:00,600
Speaker 2: remember when CDs came out and everyone was backing up

969
00:53:00,640 --> 00:53:04,000
Speaker 2: everything onto CD. A friend of mine who you've met,

970
00:53:04,000 --> 00:53:07,399
Speaker 2: my friends that live in Hampton, My mate Michael lived

971
00:53:07,440 --> 00:53:10,200
Speaker 2: in the lived in worked in the film industry, and

972
00:53:10,440 --> 00:53:13,160
Speaker 2: one of his colleagues had saved all of the They

973
00:53:13,280 --> 00:53:17,279
Speaker 2: backed up everything onto CD and then they found out

974
00:53:17,360 --> 00:53:20,960
Speaker 2: that CDs don't last and what happens. Data eventually got

975
00:53:21,040 --> 00:53:24,680
Speaker 2: corrupted and they lost all of this information. So effective

976
00:53:24,760 --> 00:53:28,360
Speaker 2: ways to store stuff, because there are still data centers.

977
00:53:29,200 --> 00:53:33,120
Speaker 2: The hierarchy of data storage is if it's data that

978
00:53:33,280 --> 00:53:37,120
Speaker 2: is instantly retrievable, stuff that you need to get access

979
00:53:37,160 --> 00:53:41,240
Speaker 2: too quickly. Then they use you know, solid state drives

980
00:53:41,280 --> 00:53:44,839
Speaker 2: so SSDs. Stuff that tends to be not used as

981
00:53:44,920 --> 00:53:48,160
Speaker 2: much can sometimes be saved on traditional hard drives, and

982
00:53:48,200 --> 00:53:50,120
Speaker 2: some of it are still used on tape drives, like

983
00:53:50,160 --> 00:53:53,120
Speaker 2: the old you know, you remember tape drive. So if

984
00:53:53,160 --> 00:53:56,319
Speaker 2: it's data that it's not as necessary, but he's still

985
00:53:56,320 --> 00:53:59,239
Speaker 2: want to retain it. That means the retrieval can be

986
00:53:59,280 --> 00:54:01,960
Speaker 2: a lot slow getting that data because it's stored in

987
00:54:02,000 --> 00:54:05,640
Speaker 2: an old system. So the future of data storage and

988
00:54:05,680 --> 00:54:07,160
Speaker 2: you don't need to know what peta bytes and all

989
00:54:07,200 --> 00:54:09,360
Speaker 2: that sort of stuff are. But the reason, the reason

990
00:54:09,560 --> 00:54:13,440
Speaker 2: this is so exciting that their laser engraving ceramics is

991
00:54:13,480 --> 00:54:15,920
Speaker 2: that it will last for five thousand years.

992
00:54:19,719 --> 00:54:22,160
Speaker 1: Better at night. All right, last one? If you can

993
00:54:22,200 --> 00:54:23,120
Speaker 1: do the last one in.

994
00:54:23,080 --> 00:54:29,080
Speaker 2: Two minutes, man, okay, two minutes, you can make a

995
00:54:29,840 --> 00:54:31,560
Speaker 2: story sound even more boring.

996
00:54:31,960 --> 00:54:34,880
Speaker 1: I'm giving you an opportunity to finish on a high. Right,

997
00:54:34,920 --> 00:54:37,759
Speaker 1: you've been very good today. You just stumbled at the

998
00:54:37,760 --> 00:54:41,759
Speaker 1: finish line. A phone that's not a phone to help

999
00:54:41,800 --> 00:54:45,000
Speaker 1: you stop using your phone, I'm actually curious about that.

1000
00:54:45,239 --> 00:54:47,680
Speaker 2: Yeah, well, I included a picture for you, Crago. Can

1001
00:54:47,719 --> 00:54:50,760
Speaker 2: you describe the picture that you are seeing in the article,

1002
00:54:50,880 --> 00:54:53,160
Speaker 2: so that you could actually explain to people what you're seeing.

1003
00:54:53,800 --> 00:54:55,920
Speaker 1: Oh right, hang on, I've got a scroll because I'm

1004
00:54:55,920 --> 00:54:57,600
Speaker 1: looking at the cheap notes.

1005
00:54:57,920 --> 00:54:59,839
Speaker 2: Oh I didn't actually look at the picture. I put

1006
00:54:59,840 --> 00:55:01,480
Speaker 2: that deliberately so you could see it.

1007
00:55:02,160 --> 00:55:08,279
Speaker 1: Are there we go? Right, Yeah, it just looks like

1008
00:55:08,320 --> 00:55:11,160
Speaker 1: a slab of glass the size of a phone.

1009
00:55:11,400 --> 00:55:13,959
Speaker 2: Yeah, so it's exactly the shape. So it's a piece

1010
00:55:14,000 --> 00:55:17,919
Speaker 2: of acrylic. It's called a metha phone, and I don't

1011
00:55:17,920 --> 00:55:19,680
Speaker 2: know if that's a take on the word meth. I'm

1012
00:55:19,680 --> 00:55:21,839
Speaker 2: not sure. But it's supposed to help people who have

1013
00:55:21,880 --> 00:55:26,200
Speaker 2: phone addiction because what they find, Ah, people sit there

1014
00:55:26,760 --> 00:55:31,640
Speaker 2: holding their phone, and by replacing the normal phone with

1015
00:55:31,719 --> 00:55:35,040
Speaker 2: this just slab of effectively plastic that's in the shape

1016
00:55:35,080 --> 00:55:38,000
Speaker 2: of a phone, you can fiddle around with it but

1017
00:55:38,280 --> 00:55:41,800
Speaker 2: not have you know, not actually look at sms's and

1018
00:55:42,200 --> 00:55:44,800
Speaker 2: not be distracted by what's happening on a real phone.

1019
00:55:44,920 --> 00:55:47,360
Speaker 2: So it will help people who have phone addiction, of

1020
00:55:47,400 --> 00:55:51,600
Speaker 2: people who struggle because they're constantly looking at their phone.

1021
00:55:52,080 --> 00:55:55,040
Speaker 2: They found if you use a slab of plastic effectively

1022
00:55:55,080 --> 00:55:57,120
Speaker 2: in the same shape as a phone, that It can

1023
00:55:57,560 --> 00:56:01,440
Speaker 2: calm people down who get anxious because they feel they

1024
00:56:01,480 --> 00:56:04,080
Speaker 2: need to have their phone with them. So it takes

1025
00:56:04,160 --> 00:56:06,200
Speaker 2: care of that part of the brain that's kind of

1026
00:56:06,200 --> 00:56:08,799
Speaker 2: holding onto the phone, and it reassures them and they

1027
00:56:08,840 --> 00:56:11,080
Speaker 2: don't get the same sort of anxiety by not having

1028
00:56:11,080 --> 00:56:11,640
Speaker 2: their phone.

1029
00:56:12,200 --> 00:56:14,680
Speaker 1: You know, this reminds me. That makes sense by the

1030
00:56:14,719 --> 00:56:19,800
Speaker 1: way that tactile familiarity. But a friend of mine who

1031
00:56:21,040 --> 00:56:24,359
Speaker 1: gave up smoking, who was a big smoker, always had

1032
00:56:24,360 --> 00:56:27,080
Speaker 1: a pen in these fingers between his fingers where a

1033
00:56:27,120 --> 00:56:29,880
Speaker 1: cigarette would have been, and he would often put the

1034
00:56:29,960 --> 00:56:32,920
Speaker 1: pen up into his mouth as if he was taking

1035
00:56:32,920 --> 00:56:36,359
Speaker 1: a drag, and he knew what he was doing, and

1036
00:56:36,400 --> 00:56:39,680
Speaker 1: he's like, it makes me feel better, like having this

1037
00:56:39,840 --> 00:56:45,279
Speaker 1: pen between my fingers. It's like it was, yeah, therapeutic

1038
00:56:45,320 --> 00:56:49,360
Speaker 1: and medicating for him. So that makes complete sense. Patrick.

1039
00:56:49,400 --> 00:56:51,840
Speaker 1: Where can people join the Patrick Club?

1040
00:56:52,200 --> 00:56:55,040
Speaker 2: Oh, let's just go to websitesnow dot com today you

1041
00:56:55,400 --> 00:56:57,960
Speaker 2: if they want to find out what I do, have

1042
00:56:58,040 --> 00:57:01,279
Speaker 2: a chat talk about websites marketing and stuff like that.

1043
00:57:01,560 --> 00:57:03,799
Speaker 2: Or they can go to taichi at home if they

1044
00:57:03,840 --> 00:57:06,160
Speaker 2: just want to do some tai chi exercises with me.

1045
00:57:06,560 --> 00:57:07,640
Speaker 2: Has that well gidea up.

1046
00:57:07,920 --> 00:57:11,400
Speaker 1: Gidea up, Tiffany and cook look after your blood pressure,

1047
00:57:11,440 --> 00:57:15,279
Speaker 1: Go and get yourself a chocolate muffin. Patrick James Bonilla.

1048
00:57:14,920 --> 00:57:17,200
Speaker 2: What's the reading? Sorry, sorry, Craigo, I had to interrupt.

1049
00:57:17,240 --> 00:57:18,800
Speaker 2: What tiff what's your bloods of the.

1050
00:57:18,840 --> 00:57:21,919
Speaker 3: Level three point seven? I've got the red red low

1051
00:57:21,960 --> 00:57:23,480
Speaker 3: glucose sign coming up?

1052
00:57:23,880 --> 00:57:27,000
Speaker 1: It was Brain's ready to go offline?

1053
00:57:27,400 --> 00:57:29,640
Speaker 3: Yeah, yeah, it already is. It's already checked out.

1054
00:57:29,640 --> 00:57:32,080
Speaker 2: It's at the cookie shop, but waiting for me? Did

1055
00:57:32,080 --> 00:57:34,160
Speaker 2: she read my wallet? She hasn't spoken for the last

1056
00:57:34,160 --> 00:57:35,600
Speaker 2: twenty minutes, if you noticed that, Craigan.

1057
00:57:36,000 --> 00:57:38,400
Speaker 1: No, and she's just been dribbling on her keyboard like

1058
00:57:38,440 --> 00:57:41,080
Speaker 1: a fucking Golden Retriever waiting for lunch.

1059
00:57:42,040 --> 00:57:44,920
Speaker 2: Nice one, all right, thanks team,