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Speaker 1: I get a champs Patrick, Tiffany and Cook you asked

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Speaker 1: for you Project fortnight They get together with two smartest people,

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Speaker 1: Patrick and Tiff and the ex fucking bogan from La Trivalley.

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Speaker 1: Just trying to hang into the conversational brilliance that is

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Speaker 1: these two.

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Speaker 2: Hi, Patrick, Hey he doing.

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Speaker 1: O'm good, I'm good. Good morning Tiff who was up

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Speaker 1: until thirty seconds ago?

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Speaker 2: Me just trying, just trying it on, just seeing how

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Speaker 2: it feels. Be great.

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Speaker 1: For some reason, TIFF's little window on this zoom call

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Speaker 1: said my name, and then she said to me, did

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Speaker 1: you do that? Like I have the skills to do?

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Speaker 2: That's the funny thing. You were touting us as the

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Speaker 2: tech genius is And after our last show where I

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Speaker 2: impersonated Tiff, I jumped into a team meeting with a

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Speaker 2: client and realized that I was still Tiff. So it's

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Speaker 2: all kind of all goes downhill from here, doesn't it.

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Speaker 1: Yeah, Well, you know there's there's the claim, there's the brand,

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Speaker 1: and then there's the reality behind the claim. How are

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Speaker 1: you Patrick, I'm good?

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Speaker 2: And I was just thinking that impersonating you, you could

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Speaker 2: probably sue me.

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Speaker 1: What go on? I feel like this? That was a

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Speaker 1: segue I feel.

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Speaker 2: Mat I's just that Meta is about to have to

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Speaker 2: fork out one point four billion dollars for using facial

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Speaker 2: recognition on the public without checking with them first. So

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Speaker 2: that's that's a big thing that's come through. I think

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Speaker 2: that's one point four billion US by the way, so

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Speaker 2: it's even more of course converted to our currency cocaine.

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Speaker 1: How they how have they been using that in what capacity?

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Speaker 2: So they like they was being social media accounts and

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Speaker 2: using facial recognition. So what they would do is they

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Speaker 2: would say, oh, this is Craig Harper's Facebook account and

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Speaker 2: will now match his face and then look at where

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Speaker 2: all his faces appear on other people's postings and on

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Speaker 2: the internet, all that sort of stuff.

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Speaker 1: It So, oh so then to what end do they

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Speaker 1: do that? Sorry, Like, what's the what's in it for

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Speaker 1: them to do that? Obviously money at some stage.

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Speaker 2: Yeah, look, I think the part of it behind that

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Speaker 2: is to try to develop I guess, an algorithm that's

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Speaker 2: going to keep Craig using Facebook. And if you can

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Speaker 2: refer to Craig and say, oh, look we saw you

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Speaker 2: in this post that was done it was a group

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Speaker 2: shot at Nova or you know, Tiff had a function.

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Speaker 2: I think it's about that connectivity, about trying to link

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Speaker 2: you and to be able to know where you're going,

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Speaker 2: what you're doing, and how you doing it, but then

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Speaker 2: matching that up with because you know, you've had you

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Speaker 2: caught up with Tiff and you might be doing a

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Speaker 2: training session, so you might need a new boxing gloves

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Speaker 2: or something because Tip's going to punch the shit out

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Speaker 2: of you. Go to the dentist.

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Speaker 1: Now, I think I saw somewhere on your list that

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Speaker 1: I saw it on the news the other night. Don't

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Speaker 1: know if it's the same thing, but like a wearable

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Speaker 1: I don't know, electronic brend like.

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Speaker 2: I guess it's imagine if you had a wearable electronic craig.

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Speaker 2: That would be hilarious.

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Speaker 1: People have one. It's called the You Project.

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Speaker 2: This is interesting, Okay, So the idea of incorporating AI

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Speaker 2: into a wearable device that interacts with you, this is

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Speaker 2: so creepy. So it's a ninety nine dollar product that's

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Speaker 2: being launched out into the marketplace. It's only on Apple

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Speaker 2: at the moment, and it's called The Friend and it

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Speaker 2: even can bully you as well, which is interesting. So

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Speaker 2: the promotional video for it basically has attracted a lot

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Speaker 2: of interest ninety thousand views, and it's kind of growing

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Speaker 2: all the time. So what it does is there's actually

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Speaker 2: a scene where this AI model mocks there wear like

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Speaker 2: it bullies them. But what it really monitors what you're doing.

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Speaker 2: So effectively, it's a medallion that you wear, right, It's

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Speaker 2: attached to a lanyard and you you as you walk

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Speaker 2: around and experience your day, it monitors and it just

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Speaker 2: throws in comments about what you're talking about, what you're doing.

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Speaker 2: You can talk to it as well. It's kind of

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Speaker 2: creepy but sounds fun.

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Speaker 1: I don't know about the I wonder about the potential

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Speaker 1: lawsuits when you know this wearable thing is bullying people

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Speaker 1: that are already anxious and depressed.

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Speaker 2: Well, it appears to listen to all your conversations, and

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Speaker 2: it's kind of attached by bluetooth to your phone, and

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Speaker 2: then as you're it kind of starts to using AI.

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Speaker 2: It begins to form its own internal thoughts, so it

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Speaker 2: can give a commentary. It can kind of take in

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Speaker 2: what's happening and understand the context of what you're doing,

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Speaker 2: what you're saying. I just think it would be kind

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Speaker 2: of interesting but I think I just would end up

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Speaker 2: with a grumpy old man sitting around my neck constantly.

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Speaker 1: I could just for me, there's just alarm bells all

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Speaker 1: over this, like a thing that stays attached to my

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Speaker 1: body that records and tracks and listens to everything that

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Speaker 1: I say. I definitely don't want that, because where's all

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Speaker 1: that data going. Talk about fucking invasion of privacy. Where's

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Speaker 1: that going? Yeah?

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Speaker 2: Look, I guess it could be used in other ways,

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Speaker 2: like if you go to the free you reach for

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Speaker 2: the Oreo milkshake instead of the carrot juice. It could

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Speaker 2: kind of have a go at. You couldn't it. You

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Speaker 2: could say, hey, well, yeah.

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Speaker 1: Well I could do that. See I'm okay with that.

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Speaker 1: You know, you fat fuck put the cheesecake down your

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Speaker 1: big fat fuck I mean for me personally, not anyone else.

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Speaker 2: It has to be your part. It has to be.

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Speaker 1: Here's what I'm worried about. Hey, we recorded you saying this,

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Speaker 1: Send us ten grand or we're going to release it

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Speaker 1: like well that No, that's a big fucking fat no.

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Speaker 1: Stick a friend, stick your electronic friend up your ass.

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Speaker 1: You can keep it fuck off. There's already way too

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Speaker 1: much shit we don't need more fucking electronic fucking friends.

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Speaker 1: We need humans.

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Speaker 2: We're wondering what Craig could possibly say outside of the

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Speaker 2: podcast hasn't already said on air live to all of

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Speaker 2: the people, and the terrifying Patrick, The terrifying thing.

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Speaker 1: Is that this has filtered me. This is this is

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Speaker 1: p g me. If I had the R rated podcast,

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Speaker 1: It'd either be the biggest podcast in the world or

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Speaker 1: I'd be in prison. I'm not sure we can do

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Speaker 1: zoom from prison.

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Speaker 2: That's okay, you and Baba.

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Speaker 1: When we're not making number plates and eating fucking rice

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Speaker 1: out of plastic containers, all right, keep going over one.

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Speaker 2: AI. Yeah, so this is another one that I thought's interesting.

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Speaker 2: A friend of mine's an author. You've met our friend

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Speaker 2: Tour who I did passed with. Now she's an author.

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Speaker 2: She's just doing her second book, and I'm actually doing

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Speaker 2: a bit of a preread, which is really exciting. But

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Speaker 2: there's been a stack of books hitting Amazon recently that

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Speaker 2: have all been written by AI. So there are some

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Speaker 2: sneaky people out there who are publishing and putting out cookbooks, particularly,

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Speaker 2: and they're kind of generating all this content and churning

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Speaker 2: out five books a week, and I know you've written,

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Speaker 2: and you've you know, you've got books that have been published,

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Speaker 2: and it's a lot of work that goes putting whatever

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Speaker 2: the book is. If you're a chef and you cookbook,

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Speaker 2: then you spend decades, yes, fine tuning your recipes and

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Speaker 2: you know, life experience and all that sort of stuff.

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Speaker 2: So it's interesting. So this journal went out and decided

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Speaker 2: to just take a look at some of the recipes

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Speaker 2: and there were things like bratwurst ice cream and croc

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Speaker 2: pot mohito so ai has absolutely no blood. The idea

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Speaker 2: what food tastes like, looks like should be combined. There

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Speaker 2: was one recipe I think it was like two gallons

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Speaker 2: of butter. It was one of the things you had

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Speaker 2: to add yes. So obviously they've got it wrong in

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Speaker 2: a very very very big way. So it's an interesting

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Speaker 2: one that it's becoming challenging to work out what's real

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Speaker 2: what isn't real. And I guess the onus really should

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Speaker 2: be on the likes of the publishing community to make

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Speaker 2: sure that you know, when you look at self publishing,

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Speaker 2: which is great a lot of authors who self published,

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Speaker 2: it's fantastic that they can and in the past. You know,

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Speaker 2: a lot of publishing companies had the lion share, and

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Speaker 2: it was really hard to get a book published. The

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Speaker 2: beauty of technology now is that a lot of people

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Speaker 2: can get out there, and some people may only publish

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Speaker 2: one hundred books that they sell to their friends, you know.

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Speaker 2: And that's great that you can do that, But when

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Speaker 2: you're dishing out literally dishing out crap cookbooks, it's not

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Speaker 2: the best thing, you know, Shepherd's Pie sushi. I thought

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Speaker 2: that one sounded interesting.

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Speaker 1: I think I think you mean print a hundred books,

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Speaker 1: not published a hundred books. I know what you mean,

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Speaker 1: because a hundred books would be one hundred different books

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Speaker 1: published the only.

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Speaker 2: One of each.

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Speaker 1: You could do that. That would be a very inefficient model.

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Speaker 1: But you committed. But I do you know what else

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Speaker 1: worries me mate in the same conversation is because obviously

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Speaker 1: AI is becoming better and better, and chat GPT and

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Speaker 1: the like is becoming better and better. I'm hearing lots

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Speaker 1: of stories of people who are basically sailing through undergraduate

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Speaker 1: degrees doing almost no work because you know, like for

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Speaker 1: the stuff that I'm doing, people go ah like with

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Speaker 1: your PhD. But it's really it's no value to me

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Speaker 1: because it's all based on the research that I'm doing.

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Speaker 1: But you can certainly write a paragraph and then run

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Speaker 1: it through chat GPT. But academic writing at that level

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Speaker 1: s differ. But at an undergraduate level where you might

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Speaker 1: have to do you know, let's say a three thousand

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Speaker 1: word assignment on you know, the metabolism of carbohydrate or

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Speaker 1: energy systems or whatever in an exercise science degree, or

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Speaker 1: you know, talk about the major muscle groups of the

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Speaker 1: shoulder and how they work. Whatever it is. Yeah, I

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Speaker 1: mean that can churn that out and with a little

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Speaker 1: bit of tweaking, it will pass through the what's it called,

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Speaker 1: I forget, there's a program. It will come to me

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Speaker 1: after we finish. But there's a program that you turn

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Speaker 1: it in. It's called So if you write something, you

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Speaker 1: can put it through this program and it will give

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Speaker 1: you a turn it in score, and it'll tell you

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Speaker 1: whether or not it's going to pass the you know,

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Speaker 1: the plagiarism test. And people are cranking these out and

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Speaker 1: it's passing that test quite easily. So we're seeing students

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Speaker 1: at all levels are who are producing what would take

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Speaker 1: weeks some work in minutes and they're getting them passed,

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Speaker 1: which is it is going to be moving forward a

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Speaker 1: big challenge. And then you think about something like copywriting

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Speaker 1: for real estate agents where here's the details of the house,

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Speaker 1: write us an ad. Well, that's a good application and

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Speaker 1: no one's really cheating, you're just using But when it

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Speaker 1: comes to academic stuff where people are getting degrees on

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Speaker 1: things that they haven't done the work for or they

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Speaker 1: don't understand, it's terrifying, especially when you think about things

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Speaker 1: like medicine.

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Speaker 2: Well, look, I do agree with what you're saying, and

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Speaker 2: we do use AI sparingly. We use it visually probably

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Speaker 2: the most, and I think I might have mentioned this

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Speaker 2: in one of our shows where sometimes when you're designing

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Speaker 2: a website, you want an image to span the entire width.

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Speaker 2: We call it a banner. And if you got a

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Speaker 2: photograph that doesn't have the right aspect ratio, and say

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Speaker 2: you've got, you know, some people in one area and

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Speaker 2: then some cars and some trees or whatever it happens

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Speaker 2: to be, you can use AI to stretch the photo out,

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Speaker 2: which would take hours and hours using normal techniques in photoshop,

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Speaker 2: but you can extend hills and scenes and we do

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Speaker 2: it for our clients all the time. You know, we've

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Speaker 2: got a plumber that we do a lot of social

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Speaker 2: media for and they send us portrait photos that just

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Speaker 2: need to be stretched out a little bit. And there

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Speaker 2: was one of their apprentices actually they had him working

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Speaker 2: on a toilet. It was really narrow and so we

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Speaker 2: just widen the shot and it was great. So we

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Speaker 2: used the original photo. It was just a bit of

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Speaker 2: the sides that we added to. But there was an

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Speaker 2: article in The Guardian only a couple of weeks ago

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Speaker 2: and it basically said that academics are saying, we're now

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Speaker 2: thinking that AI has put the integrity of Australian universities

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Speaker 2: at risk. There is so much cheating. And you alluded

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Speaker 2: to this as well, that it's the quality and the

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Speaker 2: standard of what you know when people are leaving university

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Speaker 2: with degrees, what does actually even mean?

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Speaker 1: Well, that's exactly right, and I think you know what

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Speaker 1: you talk about, how you would use it in your work.

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Speaker 1: I mean, to me, that's a legitimate, intelligent, positive use, right.

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Speaker 1: But that's not the stuff we're worried about. The stuff

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Speaker 1: we're worried about is people taking credit and getting credit

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Speaker 1: for shit they haven't done. I mean, that's basically lying

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Speaker 1: and deception. Well, we know it's becoming harder and harder

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Speaker 1: to monitor.

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Speaker 2: We were approached by a client and when we build

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Speaker 2: a website, one of the things we do is we

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Speaker 2: sit down and we do it quite a rigorous kind

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Speaker 2: of Q and A try to work out what they

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Speaker 2: want to achieve, how they're going to get the content together,

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Speaker 2: how they're going to put it together, imagery, all that

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Speaker 2: sort of stuff. But we had a client say, well,

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Speaker 2: can't you look at other sites in the same industry

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Speaker 2: and just use AI to rewrite their content?

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Speaker 1: Right?

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Speaker 2: I mean we said no, but you know that's what

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Speaker 2: the mindset is out there. Why do it? You know,

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Speaker 2: why do the hard yards if you can do it easy?

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Speaker 1: Well, I mean it like if there's no downside. I mean,

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Speaker 1: you know, with the evolution of everything, right, when something

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Speaker 1: new comes in, something old goes out. Like there are

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Speaker 1: so many jobs now that are becoming redundant. There are

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Speaker 1: so many new jobs that are becoming part of the

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Speaker 1: commercial landscape. And you know, as we've spoken about before,

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Speaker 1: you've showed us images of aiart versus real art, and

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Speaker 1: it's like it's those creative spaces where it might take

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Speaker 1: somebody you know, like Dylan Keyes who I follow Chuck

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Speaker 1: old by Dylan, who's an awesome artist. Follow him on

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Speaker 1: He's been on the show on Instagram, Like, his work

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Speaker 1: is fucking amazing. It take him fifty hours of fucking

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Speaker 1: like super focused work to produce one image that's brilliant.

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Speaker 1: And you can produce something that looks just like that

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Speaker 1: in thirty seconds with chat JPT and it's pretty much indistinguishable,

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Speaker 1: and you're like, and you know that one person did

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Speaker 1: it and the other one was synthetic, so to speak.

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Speaker 1: But yeah, if I was a creative I would be worried.

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Speaker 2: I think I totally agree. Look, I haven't authored any books,

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Speaker 2: but I've done a lot of writing over the years

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Speaker 2: as a journalist and creative pieces. Have written some short

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Speaker 2: stories the idea of someone just taking that material. Even

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Speaker 2: when I've written for clients and we've written copy for websites,

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Speaker 2: you've still spent the hours doing it. You know, you've

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Speaker 2: thought about it, you've spoken to the client. We had

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Speaker 2: one particular client recently, a lovely, lovely couple who you know,

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Speaker 2: were a fencing company. Then they started making steel fence

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Speaker 2: posts and they've revolutionized a lot of the production process

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Speaker 2: of standard domestic wooden fences because they've got metal posts

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Speaker 2: that they have in between all the spacing. So the

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Speaker 2: thing is they don't write they you know. So I

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Speaker 2: spent probably about two and a half hour interviewing them,

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Speaker 2: getting information from them so that I could write the

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Speaker 2: copy for them. Now, if someone else now produces a

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Speaker 2: steel fence post website and they just troll through our

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Speaker 2: site with all that original copy that I wrote, well,

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Speaker 2: you know that's that's not fair. You know, all that

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Speaker 2: hard work that went into it, and you know, whether

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Speaker 2: it's something that you've come up with yourself, whether someone

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Speaker 2: goes through all the you project sites podcasts and says, well,

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Speaker 2: I can kind of copy this and create my own podcast.

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Speaker 2: I don't know.

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Speaker 1: Do you know how many of my whiteboard lessons have

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Speaker 1: been ripped off? I really like, no hundreds, thousands, like

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Speaker 1: yesterday there was one. Anyway, I get sent them all.

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Speaker 1: Pretty much every day someone sends me one of my

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Speaker 1: whiteboards that I wrote. If you don't know what they are,

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Speaker 1: go to my Instagram page where it's just me writing

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Speaker 1: on a whiteboard. I take a photo of it. I

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Speaker 1: kind of brighten it up a bit, but it's my handwriting,

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Speaker 1: and I put it up thousands of times. In fact,

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Speaker 1: you know one I wrote called twelve fucking Rules for Success. Well,

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Speaker 1: that's been that's been done in different languages. There's details

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Speaker 1: of it, there's mugs with it on it, there's posters,

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Speaker 1: there's you know, it's been turned into product that other

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Speaker 1: people are selling, like my intellectual property. And I've you know,

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Speaker 1: I've opened the door on how do I There's really

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Speaker 1: not a lot unless you want to spend hundreds of

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Speaker 1: thousands of dollars that you may or may not, you know,

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Speaker 1: in terms of legal kind of pursuit. But it's I

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Speaker 1: just I don't like it, but I just go all right,

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Speaker 1: it's going to happen, you know, And I think, anyway,

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Speaker 1: let's move on something other than this. I want to

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Speaker 1: know about like Google have gotten. I'll go on.

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Speaker 2: There's one more thing of this is a new phrase

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Speaker 2: and you're gonna love it. It's called AI cannibalism, all right,

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Speaker 2: what is it? So what's happening is there's so much

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Speaker 2: AI generated content on the Internet. There's now concerns that

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Speaker 2: with new AI models being trained on the content on

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Speaker 2: the Internet that potentially they're now scraping AI model content.

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Speaker 2: So what they're doing is they're learning from other AI

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Speaker 2: and then as they scrape through there's so much AI

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Speaker 2: crap out there, and they're learning from the AI crap

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Speaker 2: that it's including the quality of the product. So the

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Speaker 2: best way to describe it is if if you say,

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Speaker 2: AI draw an elephant, and then AI learns off that

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Speaker 2: picture of an elephant that's been drawn by AI, and

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Speaker 2: there are subtle, weird, creepy, different things that just don't

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Speaker 2: feel right the AI is using that. So I just

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Speaker 2: loved the term AI cannibalism. It's such a great way

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Speaker 2: to describe what effectively is you know, I guess the

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Speaker 2: demise of AI models potentially the demise of AI models

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Speaker 2: if they're training them on information that's been generated by AI,

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Speaker 2: because now AI doesn't even know what AI.

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Speaker 1: Is well, and also AI doesn't know because if AI

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Speaker 1: is being programmed by programmers who have a particular bias,

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Speaker 1: which is everyone in the world, then you know, like

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Speaker 1: somebody put up a thing the other day and they

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Speaker 1: asked chat GPT, what happened on whatever the date was

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Speaker 1: that Donald Trump got shot what happened on that date

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Speaker 1: in Pennsylvania? And it said, and chat GPT said the

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Speaker 1: next president had something thrown at him, but no one

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Speaker 1: was hurt, like, which is objectively untrue. Yeah, you know.

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Speaker 1: And there was another one six months ago which you

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Speaker 1: would have remembered. I think they said, you know, show

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Speaker 1: us a picture of the founding forefathers of America, you know,

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Speaker 1: like all the old presidents and shit. And it brought

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Speaker 1: up and what it showed was some pictures of Native

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Speaker 1: Indigenous American women, right because it didn't want to show

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Speaker 1: white men. I'm like, okay, but now now, so now

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Speaker 1: there's another conundrum, And the conundrum is how we need

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Speaker 1: to be able to filter through what is objective truth

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Speaker 1: like fact, data, information, and what is something that is

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Speaker 1: being you know, like politically correctified or or there's a

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Speaker 1: there's a particular bias where oh, now this is not

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Speaker 1: this is not information, this is opinion or this is

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Speaker 1: an agenda. And now when it's when that agenda or

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Speaker 1: that opinion on either side, on all sides, right, when

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Speaker 1: that's being presented as objective information, and then over time

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Speaker 1: that starts to become the norm, then fucking hell, we

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Speaker 1: lose a grip on what is real, and it's you know,

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Speaker 1: then it just becomes a slippery slope.

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Speaker 2: You could know. And he's doing Malaysia, Who's doing in Malaysia?

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Speaker 2: They want an Internet kill switch?

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Speaker 1: What is that? That seems like I don't even know

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Speaker 1: what it is, but I think we need one.

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Speaker 2: Yeah. I don't think they even know yet what it is,

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Speaker 2: but they want one. So their parliament at the moment

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Speaker 2: is looking at new legislation to create, so they haven't

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Speaker 2: even created it yet. They want to create an internet

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Speaker 2: kill switch because saying that social media is out of control.

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Speaker 2: They if they've got more than an eight million people

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Speaker 2: in their country that are using a specific platform, they

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Speaker 2: now need to get a license to be able to

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Speaker 2: run in that country. But what they're saying is they

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Speaker 2: want to be able to turn it off. They just

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Speaker 2: want to flick a switch. I like the idea of

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Speaker 2: a big red button because I think big red buttons

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Speaker 2: that glow the best way to do it. And it

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Speaker 2: sits in you know, someone's department there, the president's department.

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Speaker 1: You know we're not in an episode of Get Smart, right, Yeah,

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Speaker 1: you know you know that it's not a nine and

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Speaker 1: sixty sitcom, right. I just wanted this is irrelevant, but

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Speaker 1: it's a little bit elevant to what we're talking about

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Speaker 1: creativity and the danger for creatives when you know AI

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Speaker 1: takes over. But I saw this quote from Einstein that

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Speaker 1: I don't know why I've never heard it. It's one

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Speaker 1: of the greatest quotes I've ever heard from Einstein this week.

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Speaker 1: And the quote is creativity is intelligence having fun.

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Speaker 2: Isn't that beautiful? That is so good?

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Speaker 1: That is love that?

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Speaker 2: Yeah, I love that one do.

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Speaker 1: I think I saw that on Lisa Stevenson's platform, so

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Speaker 1: shout out to her.

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Speaker 2: But what a great quote, isn't it funny. One of

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Speaker 2: the smartest guys that I know who I've interviewed on

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Speaker 2: my own podcast, a guy who is a rocket scientist,

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Speaker 2: and my running joke with him was that the reason

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Speaker 2: I hung out with him is because he brings my

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Speaker 2: IQ average up so high because he's IQ so I

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Speaker 2: and his response to that was, but I hang around

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Speaker 2: with you because it brings up my emotional intelligence.

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Speaker 1: Now that's probably true. I mean, yeah, well, I think you.

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Speaker 1: I hate to say it because I like hanging shit

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Speaker 1: on you, but I think you go okay, I think

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Speaker 1: IQ and EQ I reckon you're doing okay? Tell me

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Speaker 1: about because I feel like I need a robotic massage

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Speaker 1: table in my house. That yeah, this is like the

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Speaker 1: two point zero of the chair that you sit in.

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Speaker 2: It seems I know, this is so much better. It

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Speaker 2: kind of See there's two robot II robot driven machines

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Speaker 2: that I want to talk about in the show today.

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Speaker 2: One is a luge table. So this is I guess

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Speaker 2: when you think about it, there's so many different skill

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Speaker 2: sets that are needed to be able or massage, right,

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Speaker 2: and you've had met I mean one year.

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Speaker 1: I'm a massage I'm a massage slut. This is like

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Speaker 1: the ultimate present for me. H.

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Speaker 2: There we go. So this therapeutic massage has been around

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Speaker 2: for thousands of years. We know that it works, like

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Speaker 2: absolutely works. So there's a type of massage and I

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Speaker 2: think it's called a escape and it's a five thousand

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Speaker 2: year old massage practice. And so what they're trying to

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Speaker 2: do is effectively use an AI driven machine, effectively robotic

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Speaker 2: massage table. But it is going to be able to

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Speaker 2: mimic some of the techniques of massage. So, I mean,

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Speaker 2: what was the massage that you gave me for a birthday? Reason?

434
00:24:23,160 --> 00:24:25,280
Speaker 1: Like a couple of years it was called a hand job.

435
00:24:25,480 --> 00:24:31,879
Speaker 1: It was oh god, so technically not massage but high massage.

436
00:24:32,160 --> 00:24:32,480
Speaker 2: Idiot.

437
00:24:32,560 --> 00:24:35,640
Speaker 1: By the way, happy birthday, big boy.

438
00:24:36,400 --> 00:24:39,960
Speaker 2: Sorry, TIFFs is choked just having a drink of water.

439
00:24:40,040 --> 00:24:41,200
Speaker 1: Then I nearly died.

440
00:24:44,080 --> 00:24:47,000
Speaker 2: You call it a massage high message. I call it

441
00:24:47,040 --> 00:24:49,800
Speaker 2: a happy birthday. They must know that you go there

442
00:24:49,840 --> 00:24:52,560
Speaker 2: regularly because it's got a really big sign. We don't

443
00:24:52,600 --> 00:24:59,000
Speaker 2: give happy.

444
00:24:58,359 --> 00:25:00,960
Speaker 1: I do. I go to the I don't go as much.

445
00:25:01,000 --> 00:25:04,719
Speaker 1: I used to go a lot, but I like what

446
00:25:05,200 --> 00:25:07,560
Speaker 1: It's got to be good though, like tie massage. The

447
00:25:07,600 --> 00:25:12,480
Speaker 1: problem with massage therapists, whatever kind of style, it's like

448
00:25:12,560 --> 00:25:15,959
Speaker 1: everything else. There's some are fucking amazing, Tiff, you know this,

449
00:25:16,400 --> 00:25:20,399
Speaker 1: Some have got amazing hands and some are terrible. Like some.

450
00:25:20,560 --> 00:25:24,600
Speaker 1: If I within three minutes, I know, within thirty seconds,

451
00:25:24,640 --> 00:25:26,159
Speaker 1: I know if this is going to be good or

452
00:25:26,200 --> 00:25:29,439
Speaker 1: a bad hour. And I feel ripped off when I

453
00:25:29,520 --> 00:25:32,320
Speaker 1: lie down and someone's got clunky hands, like they've got

454
00:25:32,400 --> 00:25:33,280
Speaker 1: no feel for it.

455
00:25:33,480 --> 00:25:35,359
Speaker 2: So you're going to love this device because what it

456
00:25:35,400 --> 00:25:38,840
Speaker 2: does as you lay down on the table on the platform,

457
00:25:39,160 --> 00:25:43,160
Speaker 2: it uses infrared sensors to scan your body and then

458
00:25:43,760 --> 00:25:47,720
Speaker 2: by being powered by AI, it delivers a personalized experience,

459
00:25:48,119 --> 00:25:50,919
Speaker 2: so it knows you know, I guess potentially where there

460
00:25:51,000 --> 00:25:52,840
Speaker 2: might be not why are you laughing at me?

461
00:25:54,840 --> 00:25:56,840
Speaker 1: I'm thinking about the title of the show and how

462
00:25:56,880 --> 00:25:59,200
Speaker 1: I can weave hand job in there.

463
00:25:59,240 --> 00:26:03,159
Speaker 2: Gone, I'm trying to be serious. Yeah, So anyway, it

464
00:26:03,200 --> 00:26:06,760
Speaker 2: will scan your body beforehand, so potentially that could be

465
00:26:06,880 --> 00:26:09,800
Speaker 2: those mystical, wonderful hands that you've been dreaming of.

466
00:26:09,920 --> 00:26:14,880
Speaker 1: Great well, firstly available in Australia, and I'm imagining this

467
00:26:15,000 --> 00:26:16,120
Speaker 1: is not a cheap product.

468
00:26:16,440 --> 00:26:18,720
Speaker 2: Look, it won't be. I don't know. I didn't actually

469
00:26:18,720 --> 00:26:20,280
Speaker 2: see a price with it. I don't know if it's

470
00:26:20,359 --> 00:26:22,440
Speaker 2: it's I don't think it's a proof of concept. I

471
00:26:22,440 --> 00:26:25,560
Speaker 2: think that it's actually won a design award, so I'm

472
00:26:25,560 --> 00:26:26,960
Speaker 2: thinking that it's out there. I'll have to do a

473
00:26:27,000 --> 00:26:29,399
Speaker 2: bit of research for you, so I'll look into it

474
00:26:29,440 --> 00:26:31,439
Speaker 2: and see what the cost is going to be. But

475
00:26:31,480 --> 00:26:33,359
Speaker 2: the other thing that scared the Jesus out of me,

476
00:26:33,400 --> 00:26:35,679
Speaker 2: and I wouldn't do this in a million years. I

477
00:26:35,680 --> 00:26:38,159
Speaker 2: don't think about notes. It might have been something I

478
00:26:38,200 --> 00:26:43,480
Speaker 2: read earlier, but the world's first fully automated robot dentist

479
00:26:43,800 --> 00:26:46,960
Speaker 2: has done surgery on a human is that it.

480
00:26:46,840 --> 00:26:50,240
Speaker 1: Seems like a fuck that tiff shaken a head way.

481
00:26:52,040 --> 00:26:54,960
Speaker 2: So I five movies. If you watched where they've got

482
00:26:55,040 --> 00:26:58,080
Speaker 2: robots with needles and stuff pointing at you, and that

483
00:26:58,240 --> 00:27:02,119
Speaker 2: was the TORTUD device. Look it's done, it's happened. A

484
00:27:02,160 --> 00:27:06,120
Speaker 2: person actually had a procedure done. One of the benefits

485
00:27:06,119 --> 00:27:08,560
Speaker 2: of this, it says that they can do crowns and

486
00:27:08,640 --> 00:27:11,520
Speaker 2: things in fifteen minutes instead of an hour and speed

487
00:27:11,600 --> 00:27:14,679
Speaker 2: up the process. He's the crap out of me.

488
00:27:14,960 --> 00:27:18,600
Speaker 1: Yeah, I'm not after speed, I'm after fucking human hands.

489
00:27:18,680 --> 00:27:21,800
Speaker 1: But speaking of such medical procedures, I did see during

490
00:27:21,840 --> 00:27:29,440
Speaker 1: the week Patrick James, Kevin Fernando that a surgeon operator

491
00:27:29,520 --> 00:27:33,760
Speaker 1: did an operation, a remote operation on a patient who

492
00:27:33,880 --> 00:27:37,720
Speaker 1: was in a different location. So he I can't remember

493
00:27:37,720 --> 00:27:39,600
Speaker 1: how they do it, but I think he has his

494
00:27:39,800 --> 00:27:42,440
Speaker 1: hands or she. I think it was a he in

495
00:27:42,560 --> 00:27:45,600
Speaker 1: basically these gloves that have got all of these senses,

496
00:27:46,400 --> 00:27:51,080
Speaker 1: and he performs the operation remotely and it's carried or

497
00:27:51,080 --> 00:27:57,840
Speaker 1: it's executed through a robot on site with so again terrifying.

498
00:27:57,880 --> 00:28:02,080
Speaker 1: But in some situations I guess over time, like honestly,

499
00:28:03,800 --> 00:28:07,240
Speaker 1: think about in fifty years, maybe ninety eight percent of

500
00:28:07,240 --> 00:28:09,719
Speaker 1: the procedures are going to be done by robots because

501
00:28:09,760 --> 00:28:13,760
Speaker 1: they don't get tired, they don't get cranky, you know that,

502
00:28:13,800 --> 00:28:18,320
Speaker 1: they don't lose focus and attention, and they don't have emotions.

503
00:28:18,480 --> 00:28:22,919
Speaker 1: And I guess you know, it's just I think for us,

504
00:28:22,960 --> 00:28:27,680
Speaker 1: well me anyway, it's just a mental and emotional hurdle.

505
00:28:28,000 --> 00:28:30,879
Speaker 1: But maybe it's going to be much better outcomes.

506
00:28:31,400 --> 00:28:34,520
Speaker 2: Well, the remote surgery that you're talking about, when you

507
00:28:34,640 --> 00:28:37,840
Speaker 2: think of situations, okay, here's a great one, the International

508
00:28:37,880 --> 00:28:41,760
Speaker 2: Space Station. Now, what if someone had a ruptured spleen

509
00:28:41,920 --> 00:28:46,440
Speaker 2: or something, what do you do? You can't just pack

510
00:28:46,480 --> 00:28:49,480
Speaker 2: them into a capsule and fire and back down to Earth.

511
00:28:50,160 --> 00:28:52,760
Speaker 2: They may not even survive the re entry. So the

512
00:28:52,800 --> 00:28:54,880
Speaker 2: reality of it is if you're working on an oil

513
00:28:54,960 --> 00:28:57,720
Speaker 2: rig out in the middle of the North Sea, and

514
00:28:59,200 --> 00:29:03,440
Speaker 2: so I'm thinking that potentially, you know, you could suddenly

515
00:29:03,520 --> 00:29:09,480
Speaker 2: turn a remote clinic into a full surgical you know, procedural,

516
00:29:10,520 --> 00:29:15,080
Speaker 2: you know, a venue clinic. Because when you think about

517
00:29:15,080 --> 00:29:19,120
Speaker 2: it in Australia, particularly to fly or to travel from

518
00:29:19,160 --> 00:29:21,760
Speaker 2: one location to another, if you've got a drive for

519
00:29:21,840 --> 00:29:26,040
Speaker 2: twelve fourteen hours and whatever you've got, that's wrong with

520
00:29:26,080 --> 00:29:29,240
Speaker 2: you is mission critical, so potentially you you know, this

521
00:29:29,320 --> 00:29:32,560
Speaker 2: is life saving surgery. If you had a device with

522
00:29:32,960 --> 00:29:35,800
Speaker 2: a robotic doctor, I guess I'd be putting my hand up.

523
00:29:36,280 --> 00:29:38,480
Speaker 2: It's either that or die in transit.

524
00:29:39,440 --> 00:29:42,120
Speaker 1: We had we had a guy tif do you remember

525
00:29:42,160 --> 00:29:46,479
Speaker 1: his name, who was from Antarctica. I think you had

526
00:29:46,560 --> 00:29:52,080
Speaker 1: him on your show as well, David David David Knof, Yeah,

527
00:29:52,120 --> 00:29:54,880
Speaker 1: and he was talking to us about that. I mean,

528
00:29:54,880 --> 00:29:58,120
Speaker 1: they're based in Antarctica, Patrick, and so they have to

529
00:29:58,160 --> 00:30:00,240
Speaker 1: have a whole team of people. I can't remember if

530
00:30:00,240 --> 00:30:02,680
Speaker 1: it was him was telling me or someone else. But

531
00:30:03,320 --> 00:30:05,920
Speaker 1: so they have a doctor which is a really good,

532
00:30:06,200 --> 00:30:09,520
Speaker 1: you know doctor who can do pretty much everything, including surgery,

533
00:30:10,560 --> 00:30:14,920
Speaker 1: until the doctor gets appendicitis. And so she had to

534
00:30:14,960 --> 00:30:20,200
Speaker 1: do an appendectomy on herself. So she had to operate

535
00:30:20,240 --> 00:30:24,520
Speaker 1: her on herself. I think it was appendicitis what obviously

536
00:30:24,880 --> 00:30:32,120
Speaker 1: while she was awake. So that's in those kinds of situations. Yeah,

537
00:30:32,280 --> 00:30:35,240
Speaker 1: I mean to be able to and we've spoken before

538
00:30:35,280 --> 00:30:38,440
Speaker 1: about people who live in remote areas of Australia. You know,

539
00:30:38,640 --> 00:30:42,240
Speaker 1: they have access to everything now, you know, education and

540
00:30:43,320 --> 00:30:46,200
Speaker 1: you know, lots of other kind of medical consults and

541
00:30:47,960 --> 00:30:51,640
Speaker 1: mental health consults, and so it's kind of it's quite

542
00:30:51,640 --> 00:30:54,000
Speaker 1: a it's quite a leap, but maybe it's part of

543
00:30:54,040 --> 00:30:54,800
Speaker 1: the next steps.

544
00:30:55,960 --> 00:30:59,479
Speaker 2: Yeah, sounds pretty cool. Has you arguing?

545
00:31:00,720 --> 00:31:02,840
Speaker 1: You know? You know if you know how I know

546
00:31:02,880 --> 00:31:06,320
Speaker 1: when Patrick, he's already onto the next topic is when

547
00:31:06,360 --> 00:31:09,840
Speaker 1: I finish, and he goes, yeah, good, yeah, good, like

548
00:31:09,920 --> 00:31:11,920
Speaker 1: he's I know, he's bought out of it. And I

549
00:31:11,920 --> 00:31:15,640
Speaker 1: can see his eyes fucking glazing over because he doesn't

550
00:31:15,720 --> 00:31:18,160
Speaker 1: he's not even listening to what I'm saying, and he.

551
00:31:18,200 --> 00:31:20,320
Speaker 2: Just goes, yeah good.

552
00:31:20,360 --> 00:31:22,600
Speaker 1: What was your question? How is my what going?

553
00:31:22,760 --> 00:31:24,760
Speaker 2: As your car going? I was thinking about you and

554
00:31:24,800 --> 00:31:27,719
Speaker 2: your car. I fancy adget, So you've got everything.

555
00:31:28,280 --> 00:31:31,000
Speaker 1: No, I don't. I haven't even driven it because i've

556
00:31:31,640 --> 00:31:35,320
Speaker 1: I'm just yeah, I'm just at home and when I

557
00:31:35,320 --> 00:31:39,360
Speaker 1: get around Suburbia, I drive, I ride a motorbike. So

558
00:31:39,400 --> 00:31:42,080
Speaker 1: the answer is I think I've driven it three times,

559
00:31:42,080 --> 00:31:44,320
Speaker 1: and once was on the way home from the dealership.

560
00:31:44,440 --> 00:31:47,120
Speaker 1: So I'm not wearing that motherfucker out. If anyone wants

561
00:31:47,320 --> 00:31:51,120
Speaker 1: really low mileitch, no, no, I'm not selling driven.

562
00:31:51,120 --> 00:31:53,800
Speaker 2: It's a good car. Lady to church on a Sunday.

563
00:31:53,840 --> 00:31:55,920
Speaker 2: That's the ad it's a nice car.

564
00:31:56,000 --> 00:31:58,280
Speaker 1: It's just it's a car. Let's not. It's but it's

565
00:31:58,320 --> 00:32:00,880
Speaker 1: too it's do you know what it's not car? It's

566
00:32:00,920 --> 00:32:04,160
Speaker 1: a computer that doubles as a car. It's a computer

567
00:32:04,280 --> 00:32:09,920
Speaker 1: with four wheels and seats. I you know how like

568
00:32:10,000 --> 00:32:13,640
Speaker 1: the analog, not the analog, but the old Basically the

569
00:32:13,760 --> 00:32:16,280
Speaker 1: dumb phone is making a little bit of a comeback

570
00:32:16,320 --> 00:32:19,120
Speaker 1: in some I reckon. Eventually, we're going to do the

571
00:32:19,120 --> 00:32:21,840
Speaker 1: full circle and we're going to offer cars that essentially

572
00:32:22,480 --> 00:32:26,080
Speaker 1: are like almost an analog car. Where how do you

573
00:32:26,120 --> 00:32:28,720
Speaker 1: turn on the heater? You push your button, how do

574
00:32:28,760 --> 00:32:32,600
Speaker 1: you make it warmer? Your twist and knob right? And

575
00:32:32,680 --> 00:32:34,680
Speaker 1: how do you lock it? You put the key in

576
00:32:35,040 --> 00:32:37,440
Speaker 1: and you lock it, or you press the button and

577
00:32:37,440 --> 00:32:40,960
Speaker 1: then you hold the handle. Like imagine if they made

578
00:32:41,000 --> 00:32:46,920
Speaker 1: a car with no absolutely zero unnecessary tech, only the

579
00:32:46,960 --> 00:32:49,600
Speaker 1: tech that a car needs to get around. I wonder

580
00:32:49,600 --> 00:32:51,240
Speaker 1: if they could sell that for ten grand?

581
00:32:51,720 --> 00:32:53,360
Speaker 2: You would have by my like, ah, I'm selling my

582
00:32:53,440 --> 00:32:55,920
Speaker 2: old miss annex coop remember my miss hand on the

583
00:32:55,920 --> 00:32:56,840
Speaker 2: little target roof.

584
00:32:57,160 --> 00:32:58,600
Speaker 1: Yeah, I'll give you a hundred bucks for it.

585
00:32:58,920 --> 00:33:02,400
Speaker 2: One hundred bucks. That's pristine condition. You can put another

586
00:33:02,480 --> 00:33:03,880
Speaker 2: four zeros at the end of it.

587
00:33:04,280 --> 00:33:07,640
Speaker 1: Fuck your Christine condition. Nobody wanted them when they were new,

588
00:33:07,800 --> 00:33:10,880
Speaker 1: especially now they're thirty years old. You should take that

589
00:33:11,000 --> 00:33:14,920
Speaker 1: to the Nissan Museum and they'll go, no, thanks, I'll

590
00:33:14,920 --> 00:33:22,680
Speaker 1: go okay, here's a voucher to the tip. Wow, no,

591
00:33:23,280 --> 00:33:25,960
Speaker 1: what is it this? And NX isn't it? What is it?

592
00:33:26,160 --> 00:33:29,120
Speaker 2: It's an NX coop it was. Actually, you should look

593
00:33:29,120 --> 00:33:31,360
Speaker 2: it up. They're actually quite a cool little car. The

594
00:33:31,400 --> 00:33:34,320
Speaker 2: target roof. The roof comes off and you can zip around.

595
00:33:34,360 --> 00:33:37,560
Speaker 2: It's got a two liter engine. It's the Pulsar trip

596
00:33:37,640 --> 00:33:38,760
Speaker 2: less engine, so the enginees go.

597
00:33:38,920 --> 00:33:40,720
Speaker 1: You know, you know, this is not a forum for

598
00:33:40,760 --> 00:33:43,520
Speaker 1: you to fucking sell your car, right, so back off.

599
00:33:45,720 --> 00:33:47,280
Speaker 1: That's why you brought up your car.

600
00:33:47,520 --> 00:33:48,480
Speaker 2: It was a segue.

601
00:33:48,640 --> 00:33:51,520
Speaker 1: Yeah, that's right. He's like he wanted to tell people

602
00:33:51,520 --> 00:33:54,240
Speaker 1: that he's selling a car, and he thinks I can't

603
00:33:54,280 --> 00:33:56,520
Speaker 1: be too. I know, I'll ask Craig about his car.

604
00:33:56,600 --> 00:33:58,400
Speaker 2: Oh, while we're on cars.

605
00:33:58,160 --> 00:34:02,840
Speaker 1: Guess what I'm selling my forty year old piece of shit?

606
00:34:03,000 --> 00:34:03,960
Speaker 1: Anyone interested?

607
00:34:04,720 --> 00:34:06,800
Speaker 2: A thirty one year old card by the way, and

608
00:34:06,960 --> 00:34:12,439
Speaker 2: the segway actually was for Samsung because I reckon. They've

609
00:34:12,560 --> 00:34:15,680
Speaker 2: now been able to produce a solid state battery for

610
00:34:15,760 --> 00:34:21,120
Speaker 2: evs with a six hundred mile range and it's a

611
00:34:21,320 --> 00:34:24,360
Speaker 2: nine minute charging cycle to be able to get it

612
00:34:24,400 --> 00:34:28,200
Speaker 2: up to speed and it will last twenty years.

613
00:34:29,480 --> 00:34:30,160
Speaker 1: The battery.

614
00:34:30,400 --> 00:34:31,719
Speaker 2: The battery will last twenty years.

615
00:34:31,920 --> 00:34:33,919
Speaker 1: Well, that's a game charge, I mean, if that's put

616
00:34:33,960 --> 00:34:37,000
Speaker 1: it this way. Okay, So if that is real, if

617
00:34:37,000 --> 00:34:40,040
Speaker 1: you can charge it in nine minutes, six hundred miles

618
00:34:40,080 --> 00:34:43,719
Speaker 1: is one thousand kilometers, which is forever, and if at

619
00:34:43,800 --> 00:34:47,919
Speaker 1: last twenty if all of those things are real, then

620
00:34:48,160 --> 00:34:50,799
Speaker 1: that's a game changer for ev Then I would be

621
00:34:51,120 --> 00:34:54,319
Speaker 1: if they could do that just well not that I'd

622
00:34:54,400 --> 00:34:56,920
Speaker 1: be a convert, but I'd be more supportive of that

623
00:34:57,000 --> 00:34:59,799
Speaker 1: because that makes sense. There's still the small conundrum of

624
00:34:59,800 --> 00:35:01,800
Speaker 1: what we do with all those fucking batteries.

625
00:35:02,719 --> 00:35:06,360
Speaker 2: Well, twenty twenty seven is when they're talking about launching

626
00:35:06,400 --> 00:35:10,719
Speaker 2: at Toyota and Samsung have teamed up. They're sung to

627
00:35:10,719 --> 00:35:12,680
Speaker 2: try to develop this new battery, but they reckon twenty

628
00:35:12,719 --> 00:35:16,960
Speaker 2: twenty seven. The solid state battery. It's an oxide solid

629
00:35:16,960 --> 00:35:18,719
Speaker 2: state battery, and they're saying they're going to be able

630
00:35:18,719 --> 00:35:22,440
Speaker 2: to deliver all of this that's a massive boon. Wouldn't

631
00:35:22,440 --> 00:35:23,799
Speaker 2: that be amazing to be able to have that?

632
00:35:24,840 --> 00:35:27,719
Speaker 1: And I tell you, speaking of electric vehicles, what is

633
00:35:27,800 --> 00:35:36,040
Speaker 1: really struggling on the transport landscape is electric motorcycles. Who

634
00:35:36,200 --> 00:35:39,640
Speaker 1: blokes well and women who ride bikes. Motorbikes are more

635
00:35:39,800 --> 00:35:41,520
Speaker 1: men than women. But there are lots of ladies that

636
00:35:41,640 --> 00:35:44,200
Speaker 1: ride bikes. So I'm going to backtrack on them to

637
00:35:44,280 --> 00:35:47,840
Speaker 1: say people who ride bikes are generally not warming to

638
00:35:47,960 --> 00:35:49,320
Speaker 1: electric motorbikes.

639
00:35:49,719 --> 00:35:52,840
Speaker 2: You know why I was excited about electric motorbikes because

640
00:35:52,920 --> 00:35:57,120
Speaker 2: the three top motorcycle manufacturers got together and they said,

641
00:35:57,160 --> 00:35:59,920
Speaker 2: you know what, why don't we produce a standard battery

642
00:36:00,360 --> 00:36:02,359
Speaker 2: and that way you do a swap and go when

643
00:36:02,400 --> 00:36:05,640
Speaker 2: you go to your service station, instead of charging, you

644
00:36:05,760 --> 00:36:07,880
Speaker 2: just swap the battery out, put the new battery in

645
00:36:08,000 --> 00:36:10,399
Speaker 2: drive off. How good is that?

646
00:36:12,280 --> 00:36:14,880
Speaker 1: Yeah? There's one problem though, it makes no sound.

647
00:36:17,239 --> 00:36:19,920
Speaker 2: What the me screaming like the girl as I'm trying

648
00:36:19,920 --> 00:36:21,359
Speaker 2: to right, So.

649
00:36:21,400 --> 00:36:25,280
Speaker 1: You don't understand it's motorbikes. It's about the visceral experience.

650
00:36:25,360 --> 00:36:28,880
Speaker 1: It's about that fucking throbbing between your legs. It's about

651
00:36:28,920 --> 00:36:32,720
Speaker 1: the noise, It's about the what I'm not talking about

652
00:36:32,719 --> 00:36:35,759
Speaker 1: anything central. I'm talking about or it could be a

653
00:36:35,800 --> 00:36:38,720
Speaker 1: bit central the motorbike, but it's that that fucking sitting

654
00:36:38,760 --> 00:36:41,080
Speaker 1: on something while you're at the lights and your whole

655
00:36:41,080 --> 00:36:43,880
Speaker 1: body is vibrating because you're sitting on a two and

656
00:36:43,920 --> 00:36:45,120
Speaker 1: a half liter engine.

657
00:36:45,200 --> 00:36:48,960
Speaker 2: That's what it's about.

658
00:36:48,239 --> 00:36:48,279
Speaker 1: You.

659
00:36:49,880 --> 00:36:51,400
Speaker 2: I'll tell you what vibrator.

660
00:36:52,680 --> 00:36:58,040
Speaker 1: That's right, it's a six hundred pounds vibrator. That's of sorts.

661
00:36:58,680 --> 00:37:00,360
Speaker 1: Can we get on something less actual?

662
00:37:00,800 --> 00:37:03,160
Speaker 2: Because I didn't enjoy my ride on the bike with

663
00:37:03,200 --> 00:37:05,759
Speaker 2: you at all. I was too busy having the shit

664
00:37:05,840 --> 00:37:08,560
Speaker 2: scared out of me when you were weaving through traffic.

665
00:37:09,120 --> 00:37:11,240
Speaker 1: It's because you're a big baby. If you just fucking

666
00:37:11,360 --> 00:37:16,040
Speaker 1: let it go and you just you just sat really close.

667
00:37:16,760 --> 00:37:19,560
Speaker 1: You wanted to just sit on the back without going

668
00:37:20,160 --> 00:37:23,719
Speaker 1: because you just loved being in that position. You were

669
00:37:23,840 --> 00:37:28,640
Speaker 1: enjoying it till we head it off all right now,

670
00:37:28,680 --> 00:37:31,600
Speaker 1: Can you please tell me why Google has got a

671
00:37:31,600 --> 00:37:35,120
Speaker 1: payback of squillion dollars and why they've gotten in trouble

672
00:37:35,239 --> 00:37:39,359
Speaker 1: for something recently. Not that I'm too heartbroken for them.

673
00:37:39,480 --> 00:37:42,560
Speaker 2: It's an antitrust thing in the United States. So one

674
00:37:42,600 --> 00:37:47,880
Speaker 2: of the problems is that Google has such a massive

675
00:37:48,360 --> 00:37:52,640
Speaker 2: market share. You know, we even knew would you google something,

676
00:37:53,160 --> 00:37:56,520
Speaker 2: you for something, it's when you search online, you google it.

677
00:37:56,920 --> 00:37:59,719
Speaker 2: So there's a real concern now with a lot of lawmakers.

678
00:37:59,719 --> 00:38:01,439
Speaker 2: This is kind of happening in the States at the moment,

679
00:38:01,480 --> 00:38:03,319
Speaker 2: but I'm sure it's being thought. You know, a lot

680
00:38:03,360 --> 00:38:07,080
Speaker 2: of people are thinking this through, whether you're an advertiser

681
00:38:07,160 --> 00:38:09,120
Speaker 2: or whether you're somebody who just happens to be looking

682
00:38:09,160 --> 00:38:12,360
Speaker 2: for something on search. The monopoly is totally with Google,

683
00:38:12,440 --> 00:38:14,800
Speaker 2: and I think on phones it's about ninety five percent,

684
00:38:15,200 --> 00:38:18,759
Speaker 2: and on desktop searches it's about ninety percent. But the

685
00:38:18,840 --> 00:38:22,200
Speaker 2: concern is that Google has done a deal with a

686
00:38:22,280 --> 00:38:27,520
Speaker 2: number of phone manufacturers to make their search feature or

687
00:38:27,560 --> 00:38:30,839
Speaker 2: make the Google Search resident on that device. So when

688
00:38:30,880 --> 00:38:33,959
Speaker 2: you unpack the device, charge it up, and start using

689
00:38:34,000 --> 00:38:36,560
Speaker 2: it and you want to do an Internet search, Google

690
00:38:36,640 --> 00:38:39,080
Speaker 2: is the default search parameter the search engine. And this

691
00:38:39,160 --> 00:38:42,399
Speaker 2: is the thing that's concerning some of the lawmakers. They're

692
00:38:42,440 --> 00:38:45,759
Speaker 2: saying that they've got a massive, massive market share, and

693
00:38:45,800 --> 00:38:49,040
Speaker 2: that's the real concern at the moment. So there's Look,

694
00:38:49,040 --> 00:38:52,279
Speaker 2: it's a hard one because I guess we use it.

695
00:38:52,360 --> 00:38:55,240
Speaker 2: We use it every day? And what are the alternatives?

696
00:38:55,280 --> 00:38:56,960
Speaker 2: I mean, you use duck duck go? Don't you?

697
00:38:57,680 --> 00:39:00,279
Speaker 1: Oh use duck duck go sometimes? But I do you

698
00:39:00,280 --> 00:39:02,400
Speaker 1: know what, Like, I'm not a pro or an anti

699
00:39:02,400 --> 00:39:05,439
Speaker 1: Google person. I use Google like nearly everyone I know, Right,

700
00:39:05,520 --> 00:39:09,880
Speaker 1: But I think, well, if they built that, if they

701
00:39:10,000 --> 00:39:13,800
Speaker 1: got that market share, if they did, I mean, who

702
00:39:13,880 --> 00:39:17,359
Speaker 1: is it moral? Is it ethical? Who gives it?

703
00:39:17,440 --> 00:39:17,680
Speaker 2: Well?

704
00:39:17,760 --> 00:39:20,759
Speaker 1: I mean I care? But did they do anything illegal?

705
00:39:20,960 --> 00:39:26,000
Speaker 1: Like do they have a monopoly? Yes? Did they did

706
00:39:26,000 --> 00:39:30,120
Speaker 1: they create that? Yes? And it's like why, I mean,

707
00:39:31,239 --> 00:39:33,440
Speaker 1: and I don't care about the welfare of Google, but

708
00:39:33,440 --> 00:39:36,480
Speaker 1: I'm thinking, wouldn't we go, Hey, well done, that's just

709
00:39:36,680 --> 00:39:40,440
Speaker 1: really good business, Like you've done some great business to

710
00:39:40,520 --> 00:39:44,480
Speaker 1: get this foothold and this leverage and your hands down

711
00:39:44,520 --> 00:39:48,400
Speaker 1: the market leader in that space. And do they have

712
00:39:48,480 --> 00:39:49,120
Speaker 1: a monopoly?

713
00:39:49,239 --> 00:39:49,799
Speaker 2: Yeah?

714
00:39:50,440 --> 00:39:53,480
Speaker 1: Has anyone got a gun to anyone's head to use it?

715
00:39:53,600 --> 00:39:57,120
Speaker 2: No? Well, this is actually a legal case in the US.

716
00:39:57,560 --> 00:40:01,759
Speaker 2: So the judge has ruled that Google actually broke the

717
00:40:01,880 --> 00:40:05,200
Speaker 2: law with its monopoly. Okay, that's where this is the

718
00:40:05,280 --> 00:40:09,400
Speaker 2: sticking point. This has been effectively a course against Google

719
00:40:09,440 --> 00:40:12,560
Speaker 2: for the last twenty years. So what they're saying is

720
00:40:12,560 --> 00:40:15,440
Speaker 2: it's the way that it serves up as search related ads.

721
00:40:16,120 --> 00:40:20,000
Speaker 2: And so a federal court judge in the US has

722
00:40:20,120 --> 00:40:25,160
Speaker 2: effectively put through this ruling to say that their share.

723
00:40:25,680 --> 00:40:27,880
Speaker 2: So this is the Department of Justice. They're saying that

724
00:40:27,920 --> 00:40:32,960
Speaker 2: the Google share is massive, and that Google had paid

725
00:40:33,320 --> 00:40:38,160
Speaker 2: in Australian dollars, had paid over forty one billion dollars

726
00:40:38,200 --> 00:40:40,960
Speaker 2: in twenty twenty one to make sure that its search

727
00:40:41,000 --> 00:40:46,799
Speaker 2: engine was the default smartphone on app on browsers to

728
00:40:46,880 --> 00:40:50,239
Speaker 2: keep it right. So that's the sticking point that it

729
00:40:50,400 --> 00:40:54,200
Speaker 2: was paying off the phone manufacturers to make their search

730
00:40:54,239 --> 00:40:56,200
Speaker 2: engine the default on their devices.

731
00:40:56,719 --> 00:40:58,799
Speaker 1: But why do we use the term paying off? Why

732
00:40:58,800 --> 00:41:01,640
Speaker 1: don't we just say they had a commercial arrangement, Like

733
00:41:01,680 --> 00:41:05,120
Speaker 1: to me, that seems like, hey, make us your your

734
00:41:05,280 --> 00:41:09,080
Speaker 1: preferred search engine a provider, and we'll give you all

735
00:41:09,120 --> 00:41:11,640
Speaker 1: this money. To me, that just seems like business.

736
00:41:12,520 --> 00:41:15,920
Speaker 2: Well, the term that the judge used was monopolist, and

737
00:41:15,960 --> 00:41:20,400
Speaker 2: it acted to maintain a monopoly, so it forced out

738
00:41:20,880 --> 00:41:22,160
Speaker 2: any other competition.

739
00:41:22,719 --> 00:41:27,160
Speaker 1: So they're just doing better. I mean, I don't know.

740
00:41:27,560 --> 00:41:31,040
Speaker 1: To me, that's the point of business. We're the best. Yeah, yeah,

741
00:41:31,080 --> 00:41:33,480
Speaker 1: I know, I don't know. To me, it reminds me

742
00:41:33,560 --> 00:41:36,840
Speaker 1: of al Capone. You remember, do you know what happened

743
00:41:36,840 --> 00:41:39,720
Speaker 1: with al Capone? Like he obviously he was a complete gangster,

744
00:41:40,320 --> 00:41:42,160
Speaker 1: you know, he did all this stuff, right, and they

745
00:41:42,200 --> 00:41:47,040
Speaker 1: couldn't they couldn't convict him on any crime, any of

746
00:41:47,080 --> 00:41:50,680
Speaker 1: the fucking hundreds of murders and all the underworld stuff

747
00:41:50,719 --> 00:41:55,520
Speaker 1: and all the stealing and all the embezzlement and all

748
00:41:55,560 --> 00:41:58,040
Speaker 1: the corruption, and you know what, they ended up getting

749
00:41:58,080 --> 00:41:59,640
Speaker 1: him on and putting him in prison.

750
00:41:59,360 --> 00:42:02,719
Speaker 2: For no I can't remember, but I know this, yeah, gone.

751
00:42:02,600 --> 00:42:03,719
Speaker 1: Yeah, tax evasion.

752
00:42:03,840 --> 00:42:04,879
Speaker 2: Oh yes, that's right.

753
00:42:05,640 --> 00:42:09,560
Speaker 1: It's like they you know, like such a white collar,

754
00:42:09,680 --> 00:42:13,240
Speaker 1: boring crime. But anyway, you know, so, how.

755
00:42:13,080 --> 00:42:15,600
Speaker 2: Many searches do you reckon Google does in a day,

756
00:42:15,840 --> 00:42:18,200
Speaker 2: as in people using the Google search engine? How many

757
00:42:18,200 --> 00:42:20,160
Speaker 2: searches do you reckon have done in a single day

758
00:42:21,200 --> 00:42:23,960
Speaker 2: all over the world? Just a wild guess.

759
00:42:25,400 --> 00:42:27,480
Speaker 1: So I'm going to go, well, there's eight billion people.

760
00:42:27,760 --> 00:42:31,080
Speaker 1: Let's say half the billion half the people use, so

761
00:42:31,160 --> 00:42:35,400
Speaker 1: that's four billion, and I'd say three I'd say we

762
00:42:35,480 --> 00:42:39,640
Speaker 1: all do on average five searches a day. Twenty billion

763
00:42:39,719 --> 00:42:41,919
Speaker 1: is my guess. Twenty billion searches a day.

764
00:42:42,239 --> 00:42:44,279
Speaker 2: Oh, that's a lot of searches. Well, you actually came

765
00:42:44,320 --> 00:42:47,000
Speaker 2: pretty close at the start, were you're just estimating the usage.

766
00:42:47,080 --> 00:42:49,759
Speaker 2: It's actually eight point five billion searches.

767
00:42:50,320 --> 00:42:54,080
Speaker 1: Okay, yeah, so that's two that's two and a bit

768
00:42:54,160 --> 00:42:57,560
Speaker 1: per person. Yeah, if there's four million, or if we

769
00:42:57,600 --> 00:42:59,919
Speaker 1: averaged it out to that's once a day, if we're

770
00:43:00,080 --> 00:43:01,160
Speaker 1: for everyone in the world.

771
00:43:02,160 --> 00:43:06,239
Speaker 2: Yeah, yeah, okay, Well, don't forget though that there are

772
00:43:06,440 --> 00:43:09,040
Speaker 2: like China has a massive population. You can't use Google

773
00:43:09,040 --> 00:43:11,800
Speaker 2: in China because they've got the Great Firewall of China.

774
00:43:12,239 --> 00:43:15,759
Speaker 1: That's right, that's do you? I mean, I don't know

775
00:43:15,760 --> 00:43:17,520
Speaker 1: if you know the I don't know if you know

776
00:43:17,600 --> 00:43:19,920
Speaker 1: the answer to this, but you are a tai Chi teacher,

777
00:43:20,000 --> 00:43:26,839
Speaker 1: so you should do they do they? Well? I mean

778
00:43:26,920 --> 00:43:28,480
Speaker 1: you've you've been to China, haven't you?

779
00:43:28,840 --> 00:43:30,440
Speaker 2: Yeah? China four times?

780
00:43:30,719 --> 00:43:32,800
Speaker 1: Yeah, so I've never been there. So my point is

781
00:43:32,840 --> 00:43:34,279
Speaker 1: you're going to know better than I'm going to know.

782
00:43:35,440 --> 00:43:38,239
Speaker 1: Do the Chinese have much of an awareness of what

783
00:43:38,400 --> 00:43:42,000
Speaker 1: happens outside? Like, do they I mean, do they know

784
00:43:42,400 --> 00:43:46,040
Speaker 1: about you know, the rest of the world in terms

785
00:43:46,040 --> 00:43:51,160
Speaker 1: of Google and or are they? Are they somewhat manipulated

786
00:43:51,160 --> 00:43:53,520
Speaker 1: and brainwashed, and you know.

787
00:43:54,040 --> 00:43:57,919
Speaker 2: Yeah, that's a really really good question. I think that.

788
00:43:58,120 --> 00:44:00,000
Speaker 2: I mean, if I look at my tai Chi family,

789
00:44:00,120 --> 00:44:02,319
Speaker 2: we call that. That's a lovely term that I really like.

790
00:44:02,320 --> 00:44:04,840
Speaker 2: I've got my ti Chair brothers in China and sisters

791
00:44:04,840 --> 00:44:09,480
Speaker 2: in China. But because we have an ongoing dialogue, they

792
00:44:09,520 --> 00:44:13,000
Speaker 2: do know what's going on around the world. So it's

793
00:44:13,040 --> 00:44:15,560
Speaker 2: not like they're locked in and we talk about the

794
00:44:15,560 --> 00:44:19,480
Speaker 2: Great Firewall of China. But you know, VPNs are still something,

795
00:44:19,520 --> 00:44:21,839
Speaker 2: so they can actually get out of China if they're

796
00:44:21,840 --> 00:44:22,239
Speaker 2: more clearly.

797
00:44:22,480 --> 00:44:25,319
Speaker 1: Tell people what that is. Tell people what VPN is.

798
00:44:26,000 --> 00:44:29,800
Speaker 2: So what you can do is you can use a

799
00:44:29,920 --> 00:44:32,600
Speaker 2: service that can geolocate you anywhere in the world. So

800
00:44:32,640 --> 00:44:36,240
Speaker 2: say you want to sign up to Netflix, you can use.

801
00:44:36,200 --> 00:44:40,240
Speaker 1: Make it easier. People like, what the fuck is geo locate?

802
00:44:40,600 --> 00:44:42,319
Speaker 1: Just speak our language.

803
00:44:43,200 --> 00:44:46,239
Speaker 2: It hides where you are, so the Internet knows where

804
00:44:46,280 --> 00:44:48,320
Speaker 2: you are when you log on. If you use a VPN,

805
00:44:48,400 --> 00:44:50,880
Speaker 2: you can hide where you are and pretend to be

806
00:44:50,920 --> 00:44:51,520
Speaker 2: somewhere else.

807
00:44:51,600 --> 00:44:56,279
Speaker 1: Has that thank you, thank you, professor? Fucking hell? So

808
00:44:56,680 --> 00:44:59,840
Speaker 1: oh can you explain? Yes, so gea lok and everyone's

809
00:44:59,880 --> 00:45:05,000
Speaker 1: just switched off. It's like all right, tell me about space.

810
00:45:05,080 --> 00:45:07,800
Speaker 1: What's going on in the bloody intergalactic weirdness?

811
00:45:07,840 --> 00:45:12,160
Speaker 2: That is us intergalectic weirdness. It's not integlectic or weird.

812
00:45:12,600 --> 00:45:15,279
Speaker 2: This is like you've heard of the vault, that there's

813
00:45:15,320 --> 00:45:18,640
Speaker 2: a vault somewhere in Scandinavia that has all these seeds.

814
00:45:18,640 --> 00:45:19,399
Speaker 2: It's a seed vault.

815
00:45:19,880 --> 00:45:22,360
Speaker 1: I've seen a doco on it. It's under a mountain.

816
00:45:22,560 --> 00:45:28,200
Speaker 1: It's fucking amazing. Have you seen that, Tiff. So there's

817
00:45:28,239 --> 00:45:32,439
Speaker 1: this vault where they keep all of these seeds from

818
00:45:32,560 --> 00:45:35,120
Speaker 1: all of these plants around the world, so that if

819
00:45:35,840 --> 00:45:38,360
Speaker 1: there's an apocalypse and the world falls apart and that

820
00:45:38,480 --> 00:45:42,759
Speaker 1: goes to shit and then you know, one hundred people live,

821
00:45:42,880 --> 00:45:45,399
Speaker 1: there's this vault where people can go and get all

822
00:45:45,440 --> 00:45:50,759
Speaker 1: of these seeds and replant and regrow if everything. So

823
00:45:51,080 --> 00:45:54,759
Speaker 1: but to get to it, sorry Patrick, you you literally

824
00:45:55,120 --> 00:45:59,600
Speaker 1: enter this massive door that goes into a mountain, so

825
00:45:59,719 --> 00:46:04,840
Speaker 1: it's like hundreds and hundreds of feet underground, so that

826
00:46:04,960 --> 00:46:09,400
Speaker 1: it would pretty much survive well you know, we don't know,

827
00:46:09,480 --> 00:46:15,600
Speaker 1: but most cataclysmic events to basically restart the world anyway,

828
00:46:15,680 --> 00:46:17,360
Speaker 1: sorry Patrick, And it's called the vault.

829
00:46:17,920 --> 00:46:21,880
Speaker 2: Well to doomsday vault, and evidently scientists are now saying

830
00:46:22,080 --> 00:46:24,280
Speaker 2: they want to start up another one. But guess where.

831
00:46:26,040 --> 00:46:26,600
Speaker 1: Australia.

832
00:46:27,160 --> 00:46:29,319
Speaker 2: No, I just said space. Wasn't that a hint?

833
00:46:29,840 --> 00:46:31,760
Speaker 1: Oh?

834
00:46:32,160 --> 00:46:35,120
Speaker 2: The Moon? The Moon? They would have put it on

835
00:46:35,160 --> 00:46:37,719
Speaker 2: the moon. They want to because the Moon's cold enough

836
00:46:37,760 --> 00:46:41,280
Speaker 2: to preserve the samples without needing to power it. Right.

837
00:46:41,360 --> 00:46:44,480
Speaker 1: So yes, here's the problem though, if the Earth blows up,

838
00:46:44,560 --> 00:46:46,719
Speaker 1: we've got no fucking rockets to get to the Moon

839
00:46:46,760 --> 00:46:53,400
Speaker 1: to retrieve our samples. Fucking that's great. So we can't

840
00:46:53,440 --> 00:46:53,879
Speaker 1: get there.

841
00:46:55,239 --> 00:46:58,799
Speaker 2: So the seeds are cryo preserve, right, and then you

842
00:46:58,840 --> 00:47:01,759
Speaker 2: don't have to worry about it. So you know, if

843
00:47:01,760 --> 00:47:04,239
Speaker 2: the air conditioning breaks down, well there is no air

844
00:47:04,239 --> 00:47:06,600
Speaker 2: conditioning to worry about because it's so cold on the Moon.

845
00:47:07,120 --> 00:47:09,440
Speaker 1: I think you're missing the point though, If we can't

846
00:47:09,560 --> 00:47:13,680
Speaker 1: get to the moon because we're all dead from starvation

847
00:47:13,880 --> 00:47:16,680
Speaker 1: because the seeds, the seeds are in.

848
00:47:19,880 --> 00:47:22,840
Speaker 2: I think it's not a bad contingency. We're not talking

849
00:47:22,920 --> 00:47:26,000
Speaker 2: the whole earth blowing up here, right, which.

850
00:47:25,840 --> 00:47:30,600
Speaker 1: Isn't we aren't we? No? Did you hear what happened

851
00:47:30,600 --> 00:47:35,120
Speaker 1: to the dinosaurs? You hear about that, you're familiar with

852
00:47:35,160 --> 00:47:36,280
Speaker 1: that story.

853
00:47:36,160 --> 00:47:38,600
Speaker 2: What the Earth's more than six thousand years old?

854
00:47:39,239 --> 00:47:44,640
Speaker 1: Well, well, no, it's not, because the Bible's true. But

855
00:47:45,640 --> 00:47:51,160
Speaker 1: in some alternate evolutionary timeline, perhaps, yeah, in some fictitious

856
00:47:51,239 --> 00:47:57,120
Speaker 1: timeline of dinosaurs, maybe that the Earth blew up once

857
00:47:57,239 --> 00:47:58,160
Speaker 1: or twice.

858
00:47:57,960 --> 00:48:01,520
Speaker 2: It didn't actually blow up. Okay, So Throid collides with Earth.

859
00:48:01,840 --> 00:48:06,000
Speaker 2: Giant plume of dust and dirt and debris enters the atmosphere,

860
00:48:06,200 --> 00:48:09,799
Speaker 2: blocks out the sun, Earth goes into ice age. Dinosaurs die,

861
00:48:09,960 --> 00:48:14,440
Speaker 2: but some animals, like the wily little animals that we

862
00:48:14,600 --> 00:48:17,680
Speaker 2: ended up becoming, when you make it through, and then

863
00:48:17,880 --> 00:48:20,200
Speaker 2: there's a big sign that says, build a rocket, fly

864
00:48:20,320 --> 00:48:22,040
Speaker 2: to the moon. You can get all your seeds done.

865
00:48:22,640 --> 00:48:25,440
Speaker 1: Now, before you start stating all this stuff as a

866
00:48:25,440 --> 00:48:27,720
Speaker 1: matter of fact, like you were there as an observer,

867
00:48:28,239 --> 00:48:33,799
Speaker 1: could you start all of that with allegedly.

868
00:48:30,760 --> 00:48:39,480
Speaker 2: Oh sorry, we allegedly and the Earth allegedly there was

869
00:48:39,520 --> 00:48:42,560
Speaker 2: a plume of dust and allegedly on the dinosaurs die.

870
00:48:42,600 --> 00:48:44,680
Speaker 2: That's all the fossils and all the stuff that we

871
00:48:44,760 --> 00:48:46,880
Speaker 2: know have been there for you know, all those hundreds

872
00:48:46,880 --> 00:48:52,200
Speaker 2: of millions of years, four thousands last week, I was

873
00:48:52,239 --> 00:48:53,480
Speaker 2: it last week? Yeah?

874
00:48:53,600 --> 00:48:57,000
Speaker 1: Can you tell me speaking of nothing to do with crime,

875
00:48:57,040 --> 00:49:01,240
Speaker 1: I'm interested in this story. Record breaking seventy five million

876
00:49:01,320 --> 00:49:06,960
Speaker 1: dollar ransom had to dark an Angels gang. I feel

877
00:49:07,000 --> 00:49:10,880
Speaker 1: like these kind of that was that like an online

878
00:49:10,920 --> 00:49:11,600
Speaker 1: thing mate.

879
00:49:11,960 --> 00:49:15,000
Speaker 2: Yeah, this is an interesting one because this is thought

880
00:49:15,080 --> 00:49:19,520
Speaker 2: to have been the largest ever ransomware payment ever made.

881
00:49:20,280 --> 00:49:26,080
Speaker 2: Because look what happens is that ransom were like activists

882
00:49:26,160 --> 00:49:28,640
Speaker 2: or people who go out, their actors go out and

883
00:49:28,680 --> 00:49:33,160
Speaker 2: they infect a system and lock out the entire manufacturing

884
00:49:33,239 --> 00:49:36,200
Speaker 2: process or whatever it happens to be. That freezes out

885
00:49:36,239 --> 00:49:39,200
Speaker 2: the company's information and they say to them, if you

886
00:49:39,239 --> 00:49:41,840
Speaker 2: don't pay a certain amount of money within x amount

887
00:49:41,880 --> 00:49:43,960
Speaker 2: of time, we're going to delete all of your data.

888
00:49:44,800 --> 00:49:47,480
Speaker 2: So it's effectively what ransomware is. It happens on a

889
00:49:47,480 --> 00:49:50,440
Speaker 2: small scale, It happens to everyday people, but it can

890
00:49:50,480 --> 00:49:53,759
Speaker 2: also happen to large corporations and companies. And look, as

891
00:49:53,800 --> 00:49:56,799
Speaker 2: an off the record, I do know that in Australia

892
00:49:56,880 --> 00:49:59,040
Speaker 2: there was a company that made a decision to pay

893
00:49:59,040 --> 00:50:02,399
Speaker 2: their ransomware because they were losing more money every day

894
00:50:02,440 --> 00:50:06,000
Speaker 2: in production than they did to pay the people who

895
00:50:06,960 --> 00:50:10,200
Speaker 2: were behind the ransomware attack. But they're saying this is

896
00:50:11,640 --> 00:50:15,880
Speaker 2: like Threat Labs is a like as a group that

897
00:50:16,200 --> 00:50:19,440
Speaker 2: looks into this sort of stuff. And what they're saying

898
00:50:19,560 --> 00:50:22,400
Speaker 2: is that this is pretty deplorable because the thing is,

899
00:50:22,719 --> 00:50:27,160
Speaker 2: so many legal authorities, so many jurisdictions say don't pay ransomware.

900
00:50:27,160 --> 00:50:30,360
Speaker 2: Don't pay ransomware. It only encourages people to keep the

901
00:50:30,440 --> 00:50:34,200
Speaker 2: system going. And so now this and this group called

902
00:50:34,239 --> 00:50:38,280
Speaker 2: the Dark Angels evidently got a massive payment of seventy

903
00:50:38,280 --> 00:50:41,320
Speaker 2: five million dollars paid to them by an undisclosed victim

904
00:50:41,320 --> 00:50:45,240
Speaker 2: earlier this year, and that massively beat the previous record

905
00:50:45,239 --> 00:50:48,719
Speaker 2: of forty million. So the problem is that this is

906
00:50:48,719 --> 00:50:53,560
Speaker 2: a ransomware report that comes out and it's on the increase,

907
00:50:53,800 --> 00:50:55,719
Speaker 2: and the problem is, you know, do we pay it

908
00:50:55,800 --> 00:50:59,040
Speaker 2: or don't we pay it? The Australian government says don't

909
00:50:59,040 --> 00:51:02,440
Speaker 2: pay it. The American government says don't pay it. But

910
00:51:02,480 --> 00:51:05,399
Speaker 2: it's pretty hard if you're in an organization where you're

911
00:51:05,440 --> 00:51:08,680
Speaker 2: relying on the match, say, the manufacturing process, and allo,

912
00:51:09,280 --> 00:51:11,200
Speaker 2: everything grinds to a holt. What do you do?

913
00:51:12,040 --> 00:51:14,920
Speaker 1: And especially it's all well and good for the government,

914
00:51:14,920 --> 00:51:18,560
Speaker 1: and I understand the intention behind that, and in principle

915
00:51:18,640 --> 00:51:21,600
Speaker 1: I agree with that, but Let's say you're the owner

916
00:51:21,640 --> 00:51:26,960
Speaker 1: of a fifty billion dollar company and you've got to

917
00:51:26,960 --> 00:51:30,719
Speaker 1: pay out fifty million to get the wheels turning again,

918
00:51:30,760 --> 00:51:35,160
Speaker 1: and you're losing ten twenty thirty million a day, and

919
00:51:35,200 --> 00:51:36,920
Speaker 1: you go, well, it's all well and good, but if

920
00:51:36,960 --> 00:51:40,880
Speaker 1: we don't pay it, my business is fucked and I'm fucked,

921
00:51:40,880 --> 00:51:44,239
Speaker 1: and we go broke, and my five thousand employees don't

922
00:51:44,320 --> 00:51:47,239
Speaker 1: have a job. And the easy way to fix that,

923
00:51:47,280 --> 00:51:50,279
Speaker 1: in inverted commas is to do this. It is. It

924
00:51:50,320 --> 00:51:53,719
Speaker 1: is a real conundrum and a you know, like financial

925
00:51:53,840 --> 00:51:58,759
Speaker 1: and moral and ethical and legal dilemma. And while I

926
00:51:58,800 --> 00:52:02,280
Speaker 1: agree with the intention and the sentiment behind the don't

927
00:52:02,280 --> 00:52:06,319
Speaker 1: give in to criminals, I can understand in certain I

928
00:52:06,360 --> 00:52:08,520
Speaker 1: think everything is context dependent.

929
00:52:08,960 --> 00:52:09,200
Speaker 2: You know.

930
00:52:09,280 --> 00:52:13,600
Speaker 1: It's like I would never hurt anyone, but if someone

931
00:52:13,680 --> 00:52:16,560
Speaker 1: was hurting my mum, I would definitely hurt someone. Like

932
00:52:16,840 --> 00:52:20,520
Speaker 1: it depends on what's going on. You know, would you

933
00:52:20,600 --> 00:52:23,040
Speaker 1: ever want to kill anyone? Of course I would never

934
00:52:23,080 --> 00:52:26,200
Speaker 1: want to kill anyone, but you put any person in

935
00:52:26,239 --> 00:52:32,719
Speaker 1: some situations, in an extreme situations, you go, well, if

936
00:52:32,760 --> 00:52:35,920
Speaker 1: it's like, you know, show me a dad or mum

937
00:52:35,960 --> 00:52:39,520
Speaker 1: that won't die to protect their child, or kill someone

938
00:52:39,560 --> 00:52:42,200
Speaker 1: who's trying to hurt their child, you know, I think,

939
00:52:42,239 --> 00:52:48,520
Speaker 1: and that's an extreme stretch, but I think I don't

940
00:52:48,560 --> 00:52:53,080
Speaker 1: think that all of these rules or laws are Yes,

941
00:52:53,120 --> 00:52:55,160
Speaker 1: they're black and white, but in certain things like this

942
00:52:55,200 --> 00:52:57,400
Speaker 1: where you think, well, someone's going to lose everything and

943
00:52:57,480 --> 00:53:00,960
Speaker 1: everyone that works in that organization can pay these crooks,

944
00:53:01,000 --> 00:53:03,759
Speaker 1: and you go, yeah, it doesn't help because now we're

945
00:53:03,920 --> 00:53:07,400
Speaker 1: kind of encouraging the crooks. But yeah, that's one of

946
00:53:07,400 --> 00:53:10,600
Speaker 1: those there is no right and wrong, but it's understandable

947
00:53:10,600 --> 00:53:11,080
Speaker 1: either way.

948
00:53:11,600 --> 00:53:13,960
Speaker 2: I think the key finding out of this one is

949
00:53:14,239 --> 00:53:18,000
Speaker 2: we need to pour more resources into cybersecurity and to

950
00:53:18,520 --> 00:53:21,239
Speaker 2: kind of churning out of I guess universities where people

951
00:53:21,239 --> 00:53:25,680
Speaker 2: are using AI to learn cybersecurity. Now, I'm thinking that

952
00:53:26,080 --> 00:53:29,080
Speaker 2: if the right investment is made to try to encourage

953
00:53:29,360 --> 00:53:32,600
Speaker 2: businesses and not just big business but smaller businesses to

954
00:53:33,440 --> 00:53:36,799
Speaker 2: just do what they can to increase their security, and

955
00:53:37,000 --> 00:53:38,719
Speaker 2: if the government can do that, then we're going to

956
00:53:38,719 --> 00:53:41,000
Speaker 2: prevent it in the first place. It's that whole prevention

957
00:53:41,120 --> 00:53:42,320
Speaker 2: is better than the cure.

958
00:53:43,400 --> 00:53:45,600
Speaker 1: I just realized I've got another appointment in four and

959
00:53:45,600 --> 00:53:48,440
Speaker 1: a half minutes, So Patrick, you're the best. How do

960
00:53:48,520 --> 00:53:50,239
Speaker 1: people find you and connect with you?

961
00:53:50,160 --> 00:53:53,680
Speaker 2: You can go to websites now, dot com, dot au.

962
00:53:53,760 --> 00:53:56,279
Speaker 2: There's all the stuff about what we do. So feel

963
00:53:56,280 --> 00:53:57,799
Speaker 2: free reach out say gooday.

964
00:53:58,840 --> 00:54:02,560
Speaker 1: Thank you for that, thank you and thanks kids. I

965
00:54:02,560 --> 00:54:05,840
Speaker 1: feel like tis been really distracted today. Have you noticed that? Patrick?

966
00:54:06,360 --> 00:54:07,840
Speaker 2: Oh? I know she was just taking it all in,

967
00:54:07,880 --> 00:54:10,759
Speaker 2: weren't you, Ti? I understand loaded dak dack go and

968
00:54:10,840 --> 00:54:13,239
Speaker 2: installed it. To be honest, I.

969
00:54:13,239 --> 00:54:16,560
Speaker 1: Knew she was doing something else because she definitely wasn't

970
00:54:16,560 --> 00:54:17,280
Speaker 1: paying attention.

971
00:54:19,080 --> 00:54:20,320
Speaker 2: Is every time you talk about.

972
00:54:20,120 --> 00:54:21,759
Speaker 1: It, I'm like, I've got to do that, and then

973
00:54:21,800 --> 00:54:23,120
Speaker 1: I forget, and I'm like, well, I'm doing it now

974
00:54:23,160 --> 00:54:23,840
Speaker 1: because I'll forget.

975
00:54:24,160 --> 00:54:26,440
Speaker 2: See that's good, that's great. She actually listens to what

976
00:54:26,480 --> 00:54:27,520
Speaker 2: you're saying, Craigo.

977
00:54:28,239 --> 00:54:31,920
Speaker 1: Yeah, you're only getting paid half of this episode. All right,

978
00:54:32,000 --> 00:54:33,600
Speaker 1: thanks everyone, Thanks mate,