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Speaker 1: Patrick, Yeah, greg O, what are you? Intro the show?

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Speaker 2: Hey, welcome to the You Project with Craig Harper, Tiffany

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Speaker 2: Cook and special guest Patrick Bonello.

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Speaker 1: It's great to have you groovers here today. That's pretty good. Now,

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Speaker 1: that's good. Maybe if you could pop up because sometimes

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Speaker 1: I can't be fux saying that, so, well done, well done,

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Speaker 1: and hello from Fritz as well. Who is sitting on

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Speaker 1: your lap.

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Speaker 2: We probably should screen grabbed this and placed it somewhere.

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Speaker 2: He's pretty damn cute, isn't he. Well?

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Speaker 1: Yeah, and also your dog is somewhere else in the house.

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Speaker 1: Good morning, tiff Patrick, thank you for introing the show.

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Speaker 1: Per my request, Tiff, how are you?

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Speaker 3: I'm very good? Thanks? How are you?

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Speaker 1: I'm good? What's going? You're a little bit flatty mcflatster.

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Speaker 1: Earlier in the week we're recording this Friday morning. Have

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Speaker 1: you have you found your fucking rhythm, your mojo?

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Speaker 3: I have you know when you just need to have

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Speaker 3: that one day of throwing it all away and then

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Speaker 3: and then you're good. I'm good? Good now, I am

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Speaker 3: really good? Thank you?

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Speaker 1: Yeah, good, just checking in. Patrick? Are you good? Do

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Speaker 1: you have flat days? Mate? Do you ever because you.

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Speaker 1: You always present yourself like I try to present myself

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Speaker 1: as up and aboutside is tiff, I know, but do

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Speaker 1: you have flat days sometimes?

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Speaker 2: But I kind of my friends excuse me of being

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Speaker 2: way too busy. I've got something on all the time.

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Speaker 2: Next week, I think I've got something on every night.

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Speaker 2: So I've got book group on Monday night. Tuesday, I'm

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Speaker 2: singing in a choir. Wednesday, I'm teaching tai chi. Thursday,

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Speaker 2: I'm teaching tai chi, and then Friday, I don't know

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Speaker 2: yet what I'm doing on Friday. Don't like they're both

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Speaker 2: laughing at me. You asked me a serious question. I

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Speaker 2: gave you a serious answer, and I'm just saying I'm busy.

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Speaker 2: I don't have time to think about other stuff. I

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Speaker 2: think if you fill your world with stuff you don't forget,

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Speaker 2: you get bogged down.

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Speaker 1: And I just said, don't think you really understand mental

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Speaker 1: health if you think busyness is the cure. Let me.

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Speaker 1: I said you. I said to you, do you hang on?

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Speaker 1: Do you ever get a bit flat? You didn't even

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Speaker 1: fucking answer the question you just told us about your calendar.

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Speaker 2: I think, like all of us, we do tend to

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Speaker 2: get flat. But what I've tried to do in my

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Speaker 2: life is put in mechanisms of joy and things that

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Speaker 2: I really enjoy that can be uplifting. So, you know,

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Speaker 2: it's like running your own business. There's cash flow issues,

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Speaker 2: you know, sometimes some months and a bit lean, So

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Speaker 2: those issues come up all the time. And yes, you

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Speaker 2: can get a bit down when you look at the

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Speaker 2: bank balance, or something happens, someone.

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Speaker 1: Cuts you off in them, you know whatever.

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Speaker 2: But I think that looking for mindfulness moments and I

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Speaker 2: try to really actively search for that. So I look

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Speaker 2: at things engagements, I catch up with friends, I walk

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Speaker 2: for its and meet up with friends at the park,

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Speaker 2: that sort of stuff. So I think we can be

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Speaker 2: proactive about trying not to be flat. You know, I

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Speaker 2: spent my teenage if we're going to get deep here,

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Speaker 2: I spent my middle teenage years and later teenage years

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Speaker 2: self harming and going through a really dark place.

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Speaker 1: And so for me, I started to understand.

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Speaker 2: That, you know, at an early age that those engagements

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Speaker 2: and the people that I connected with really helped get

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Speaker 2: me out of those dark places. So I think that

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Speaker 2: I've done that as an adult successfully, and I think

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Speaker 2: part of it also it's not so much fooling yourself.

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Speaker 2: And you said, oh, you know, you always put on

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Speaker 2: a cheery attitude. But I think you can all you

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Speaker 2: can alter your mindset.

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Speaker 1: You know.

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Speaker 2: Part of the journey of being, say, a really good athlete,

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Speaker 2: is convincing yourself you're a good athlete. Part of the

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Speaker 2: journey it's if you know when you step in the ring,

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Speaker 2: the confidence that you've got, if you believe in yourself

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Speaker 2: first and foremost, then that's going to give you that

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Speaker 2: little edge, that extra five percent that means that you're

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Speaker 2: going to get where you need to go. And I

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Speaker 2: think that's right in life. I think if we look

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Speaker 2: for those positive engagements. You know, I walk down the

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Speaker 2: street in our little country town and I might bump

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Speaker 2: into three or four people and it's great, you know,

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Speaker 2: But at the hardware store, you know, you know, the

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Speaker 2: people at the post office. Just seeing people around and engaging,

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Speaker 2: I mean, you're really social.

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Speaker 1: Harps.

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Speaker 2: You go down the street and you have your coffee

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Speaker 2: across at the cafe. It does make you feel better

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Speaker 2: and feel good about yourself.

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Speaker 1: No, I tell them to fuck off. I might stop

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Speaker 1: talking to me. Fuck off. No, I'm not answering your questions.

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Speaker 1: Fuck off. No, I'm not like that at all. I

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Speaker 1: love it. I'd be exactly like you if But I

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Speaker 1: did laugh when you said, you know, in the traffic

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Speaker 1: in Milan, there's fucking seven cars, So don't don't talk

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Speaker 1: about the traffic in Milan. We don't even have a stop. Yeah. Yeah, well,

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Speaker 1: I mean you did talk. You've spoken to us before

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Speaker 1: about growing up and trying to figure out sexuality and stuff.

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Speaker 1: Was that part of the being a bit emotionally lost

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Speaker 1: and in the wilderness for you or was that a

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Speaker 1: separate thing?

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Speaker 2: Oh it isn't it was. I don't think it's much

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Speaker 2: different than most teenagers, you know. I think you lose

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Speaker 2: a lot of confidence and you look towards peer pressure

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Speaker 2: for validation, which is takes on the whole online thing

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Speaker 2: we'll chat about later. But the reality of it is,

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Speaker 2: you know, when you're growing up and you get to

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Speaker 2: those teenage years and your brain chemistry starts to alter,

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Speaker 2: you do look for peer approval, I think for most

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Speaker 2: of us, and if you doesn't fit in, that can

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Speaker 2: be aspiraling experience for you. So unless you put on

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Speaker 2: your desert boots and decide to go for a jog

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Speaker 2: around the park because you're a bit of you want

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Speaker 2: to change by.

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Speaker 1: A good memory. Yeah I was. I was just yep,

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Speaker 1: I had a shit experience and then I went for

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Speaker 1: running desert boots because I didn't fucking know what I

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Speaker 1: was doing. Let's talk about AI and tech and all

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Speaker 1: of the patrick things. I'm going to let you lead

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Speaker 1: it today. Well, I just want to I want to

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Speaker 1: lead one. Then I want you to take over the reins.

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Speaker 1: Obviously you send us a bunch of topics and of

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Speaker 1: also I want to know about sex in space.

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Speaker 2: Knew I knew you would look at that straight away. Well,

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Speaker 2: this is a funny thing. We've been or humanity has

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Speaker 2: been going into space for a long time, but everyone

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Speaker 2: is a bit mum about whether or not people have

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Speaker 2: sex in space. International Space Station, you know that sort

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Speaker 2: of stuff, intergalactic copulation. Well, if this is a broader

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Speaker 2: research and broader I guess to study into and a

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Speaker 2: look into the feasibility of long term space travel. So

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Speaker 2: if we're going to Mars and it's going to take

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Speaker 2: months and months and months or years to get there,

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Speaker 2: then what happens with the viability of being able to

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Speaker 2: have sex? And then there's the whole thing of like,

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Speaker 2: wouldn't it be great to be floating around and have sex? Well,

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Speaker 2: actually it would be pretty tough because you know, you

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Speaker 2: can't be banging into things because it just bounce away.

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Speaker 2: So you're like, that's what sex is. Yeah, you tried

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Speaker 2: on the International Space Station. It's a whole new experience.

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Speaker 1: I feel like you should take some ocki straps with you.

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Speaker 2: And gaffer tape because they use a lot of gaffetape

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Speaker 2: on the ISS.

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Speaker 1: No.

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Speaker 2: Look, so some brains have been thinking about this, and

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Speaker 2: the question has always been have astronauts actually ever done

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Speaker 2: it in space? And NASA won't say a thing, and

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Speaker 2: astronauts tend to be really coy about it. Your body

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Speaker 2: changes when you're in low gravity, so fluid distribution actually changes,

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Speaker 2: and the way your body because you've got no gravity,

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Speaker 2: I guess to pull the body fluids in different directions.

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Speaker 2: I mean, it does open up a whole experience. Well,

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Speaker 2: think about the two astronauts up there at the moment

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Speaker 2: that went there for eight days and now have been

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Speaker 2: stuck there for eight months. I mean, let's face it,

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Speaker 2: there's got to be some relief somewhere. I mean, we're

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Speaker 2: all human beings, but I don't know what do you reckon?

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Speaker 2: Would you like to be floating around in space and

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Speaker 2: doing a bit of you know, playing around at the

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Speaker 2: same time as it seemed like something.

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Speaker 1: You haven't read a lot of sci fi, have you, Grego? No,

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Speaker 1: But you know what I'm asking chat GPT does does

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Speaker 1: the viscosity Does the visity of body fluids change in space?

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Speaker 1: Shif that's where he went, No, no, no, because you

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Speaker 1: were talking about, Yes, the viscosity of body fluids changes

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Speaker 1: in space due to the effects of micro gravity on fluid.

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Speaker 1: Because what I'm talking about is obviously sperm is a fluid,

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Speaker 1: blood is a fluid. Saliva as a fluid. Look at you,

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Speaker 1: Patrick TIFFs making a face as well. What's wrong with

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Speaker 1: you too? If you two didn't have fluids, you'd both

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Speaker 1: be fucked. So embrace your fluids, Patrick, embraced your sperm

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Speaker 1: and your blood and your saliva and your wi all

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Speaker 1: of it. Yeah, because I think that would I think

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Speaker 1: that would change things in terms of on.

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Speaker 2: All evels viscosity we're talking about here. Well, evidently the

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Speaker 2: only person after this little research study and questioning of astronauts,

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Speaker 2: a cosmonaut, came back and said of course you have

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Speaker 2: sex by hand, so I I.

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Speaker 1: Don't think jacking off on the moon is the same.

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Speaker 1: Well you know that. And then it comes to.

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Speaker 2: The dear, I just yeah, anyway, it just occurred to

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Speaker 2: me that you know the way that you know if

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Speaker 2: you're floating and all of a sudden, No, I don't worry.

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Speaker 1: You lost your trying to thought? Have you lost your

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Speaker 1: trying to thought?

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Speaker 2: Really visual there for a moment, I think we should

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Speaker 2: go into another time.

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Speaker 1: So but the answer is, but you do make a

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Speaker 1: valid point because for and I think it takes doesn't

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Speaker 1: it take like ten years to get to Mars or something?

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Speaker 1: But I think less because they have a different wind,

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Speaker 1: they have a window.

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Speaker 2: So it's when the planetary alignment's right to get Mars.

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Speaker 1: Closer because it's years, right, it's not? Yeah absolutely, yeah.

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Speaker 3: Yeah.

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Speaker 1: So then let's say you put one hundred of people

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Speaker 1: on Mars. Well, for the proliferation of the pop population

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Speaker 1: on Mars, you can't just keep topping it up with

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Speaker 1: people from Earth. I guess you need to produce a

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Speaker 1: few new ones.

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Speaker 2: But would you want to have children on Mars? I

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Speaker 2: don't think quite yet.

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Speaker 1: I feel like day care. Oh fuck? And imagine. Yeah,

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Speaker 1: I'll tell you what. You wouldn't have any traffic problems

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Speaker 1: up there, Patrick, but Land would be busy compared to Mars.

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Speaker 2: Three years I go just on average, depending again where

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Speaker 2: the planets are aligned, around about three years to get

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Speaker 2: to Mark.

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Speaker 1: And nobody wants to know this except me. But what's

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Speaker 1: the gravity on Mars relative to what's the gravity situation

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Speaker 1: relative to Earth.

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Speaker 2: It's it's small, it's less so, but it's still a

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Speaker 2: reasonable amount of gravity.

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Speaker 1: I don't know exactly what it is, but lot so

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Speaker 1: that what that means is if there's less gravity, that

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Speaker 1: means relatively I could fuck this up, but I think

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Speaker 1: it means we're lighter in inverted commas on So I

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Speaker 1: might only be about thirty five kilos on Mars, I

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Speaker 1: might be able to run at about eight hundred miles

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Speaker 1: an hour. Yeah, I don't think so. But the problem

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Speaker 1: with all that anti gravity stuff is the moment that

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Speaker 1: you get in zero gravity, your body starts to fall

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Speaker 1: apart because you don't need muscle and strength and bone

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Speaker 1: density and all that shit starts to fall through. People

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Speaker 1: who come back from long stints in space, their body

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Speaker 1: has aged horribly.

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Speaker 2: Yeah, exactly evidently the if you weighed one hundred pounds,

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Speaker 2: and this is I'm using pounds as the example, it

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Speaker 2: would be thirty eight pounds on Mars, So it's a third, right.

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Speaker 1: Yeah, my poo's weigh a hundred pounds some morning, depending

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Speaker 1: on what I've had the day before. So maybe not quite.

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Speaker 1: You haven't already turned off yet to those three remaining listeners.

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Speaker 1: See all right, you talk about something other than pooing

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Speaker 1: and pro creating in space? If that could be the

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Speaker 1: title of the show, Pooing and pro Creating in Space.

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Speaker 2: I feel like I need to do a positive story

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Speaker 2: because the stuff that I was going to talk about

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Speaker 2: was really kind of a bit of a downer. Well,

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Speaker 2: I know, we don't we talk about this a little bit?

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Speaker 2: And do you ever did you've.

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Speaker 1: Watched Star Wars? Obviously, Yeah, I actually quite like Star Wars.

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Speaker 1: I also don't mind a bit of start track. Okay,

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Speaker 1: so the New Hope.

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Speaker 2: Do you remember when they're on the Millennium Falcon and

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Speaker 2: they're playing the chess game and they've got the little

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Speaker 2: holographic chess pieces moving around, you know, when Luke Skywalker's playing.

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Speaker 1: Don't be ridiculous, you don't remember that. Do you remember that, say,

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Speaker 1: you mean Star Wars was nine seventy seven.

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Speaker 2: But everybody's seen it. I'm trying to get a term

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Speaker 2: of reference. There's the NBA in the United States has

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Speaker 2: teamed up with Apple and what they've done this is

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Speaker 2: kind of cool. It's really interesting. If you're a big

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Speaker 2: sports fan, imagine that you're sitting in your lounge room

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Speaker 2: and you're watching a live sporting event, and then you

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Speaker 2: have a holographic representation of the footy or the tennis

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Speaker 2: or the rugby or whatever, and all the players are

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Speaker 2: in miniature running around the field, and you can kind

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Speaker 2: of walk around your living room table and see all

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Speaker 2: the action as it plays out on your dining room

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Speaker 2: table or your living room table.

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Speaker 1: Wouldn't that be kind of cool? I don't know, that

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Speaker 1: would be weird.

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Speaker 2: I guess it'd be god view, wouldn't it's downing on

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Speaker 2: the Wouldn't that be great.

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Speaker 1: To sound I've always wanted to feel omnipotent. That could

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Speaker 1: be my best shot I think for.

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Speaker 2: A sporting fan though, because suddenly, because when you think

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Speaker 2: about it, when you watch sport, you're I guess beholden

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Speaker 2: to the camera, people, to the directors, they show the

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Speaker 2: scenes that you're looking at at any given time, but

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Speaker 2: if you see the entire match played out holographically in

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Speaker 2: front of you, then you can just walk around and

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Speaker 2: see all your players playing the game, all the action

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Speaker 2: and this is real. Now they're talking about doing this

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Speaker 2: using at this stage the vision Pro headset. That's the

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Speaker 2: the the Apple the headset. But it would be a

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Speaker 2: really great way to mean what about a boxing match, Tiff.

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Speaker 1: You know, if you're if you're able to.

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Speaker 2: Walk around the ring, the rings there in front of you,

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Speaker 2: and you can move around it and walk around it

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Speaker 2: and see the see the boxes from different directions.

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Speaker 1: Wouldn't that be cool? There's already a version of that,

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Speaker 1: But I think Tip would rather just punch a human

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Speaker 1: and faith in the Yeah, okay, goggles, just give me

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Speaker 1: a just give me some warment to punch. Speaking of Apple,

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Speaker 1: I don't know if you saw this. I meant to

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Speaker 1: watch it and then I forgot. Last night on the

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Speaker 1: news they were talking about Apples brought out a new

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Speaker 1: smartphone which is like about half the price like their

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Speaker 1: You you know how they're up to. I know you're

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Speaker 1: an Android person, but you probably didn't see it. Did

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Speaker 1: you see that TIF anyway. Okay, so apparently Apple have

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Speaker 1: brought out a new iPhone which has got like everything

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Speaker 1: you need but not too many of the bells and whistles.

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Speaker 1: But I think it's about half the price, which I

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Speaker 1: mean a lot of the providers have been doing that.

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Speaker 2: Google has been putting out their A series, So when

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Speaker 2: you release a phone, they say put out the you know,

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Speaker 2: the nine, the Pixel nine, and then a little while

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Speaker 2: later they'll put out the Pixel nine A. So it's

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Speaker 2: more of a budget version of their flagship phone. It's

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Speaker 2: a good marketing strategy. It means that you know, when

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Speaker 2: you think about it. I mean people in Australia, it

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Speaker 2: always staggers me how many people have iPhones. I was

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Speaker 2: at dinner with some friends last Friday night and somebody was, oh,

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Speaker 2: I know what it was. They were looking at their

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Speaker 2: calculator on their phone because they'd had an update to

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Speaker 2: their Apple phone, their iPhone. And now there's a new

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Speaker 2: fixture that gives you real time cash transfer from different currencies.

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Speaker 2: So whether it's the ASX or something built into it,

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Speaker 2: but you can do it. You can type in one

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Speaker 2: hundred US dollars, convert to Australian dollars on your calculator,

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Speaker 2: and they were raving about it, and then everybody pulled

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Speaker 2: out all their iPhones and I'm sitting there with the

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Speaker 2: only one with an Android phone, thinking, well, I just

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Speaker 2: have to go to the AX, don't I, because I

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Speaker 2: can do it there. But it's interesting that this new

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Speaker 2: roll out and this due app But I, you know,

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Speaker 2: of all the people and I reckon, there must have

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Speaker 2: been about twelve of us at dinner, eleven of them

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Speaker 2: had iPhones. And interestingly, the demographic was they were all

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Speaker 2: older than me, so that says a lot, but it

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Speaker 2: was an older chemographic and I think older people, like

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Speaker 2: I mean, young people love them too. But I thought

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Speaker 2: that was interesting that of all my older friends, most.

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Speaker 1: Of them are on iPhones. When you think about the

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Speaker 1: capacity that phones have now, whatever phone be at iPhone

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Speaker 1: or Samsung or whatever, I wonder on average, like I

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Speaker 1: have an iPhone fourteen, which is I guess just just standard,

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Speaker 1: like well it's the Scott that but I would say,

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Speaker 1: of all the things that that phone can do, I

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Speaker 1: probably used two percent of the capacity in that that

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Speaker 1: phone has. And I wonder, and I think about that

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Speaker 1: with other things you know people that buy you know,

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Speaker 1: I like a motorbike that does three hundred kilometers an hour,

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Speaker 1: for example, not that I would do that, not to

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Speaker 1: one hundred and two seconds. Like people buy all these

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Speaker 1: things that they never really actually use the thing that

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Speaker 1: they bought anywhere near to its capacity. I had to

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Speaker 1: borrow a friend's car recently.

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Speaker 2: Well, I should say she offered her car to me

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Speaker 2: when she found out that I had to put my

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Speaker 2: car in to get some work done on it. And

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Speaker 2: she said, now don't turn all the safety gadgets back

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Speaker 2: on again, because the last time I used her car,

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Speaker 2: I thought, hey, Lene assist isn't working, and so I

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Speaker 2: turned on all the adaptive technology.

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Speaker 1: And she said, I can't drive my car anymore because

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Speaker 1: she switched on all the safety features. You know, It's true.

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Speaker 2: I know someone who bought a Mercedes and then didn't

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Speaker 2: know how to use half of the things in the car.

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Speaker 2: And for some people they're quite content not using cruise

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Speaker 2: control or adaptive cruise control and the other features. I mean,

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Speaker 2: I use lane as cyst, I use adaptive cruise control,

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Speaker 2: speed limitter, all those things. In fact, that's one of

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Speaker 2: the reasons that I had to do the podcast early

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Speaker 2: today because I'm taking my car and to get the

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Speaker 2: radar replaced because it's been a bit faulty.

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Speaker 1: What do you mean is that the radar that does

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Speaker 1: the autonomous braking if somebody in front of you right

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Speaker 1: right right, you know, in my car, and also detect

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Speaker 1: submarines and aircraft and things. Well, I mean, and you

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Speaker 1: need that incoming bogies. At nine o'clock, my car drives it.

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Speaker 1: You can get out of my car and press a

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Speaker 1: button and it puts itself away in the garage. But

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Speaker 1: I can't remember how to do it. And you can

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Speaker 1: also press a button and it backs itself out of

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Speaker 1: the garage into the driveway or any spot. And when

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Speaker 1: I bought it, they're like, look at this. I'm like,

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Speaker 1: fucking hell, it's in the showroom driving itself up and back.

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Speaker 1: And I went, that's awesome. And then I got home

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Speaker 1: and I didn't even think about that. And now six

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Speaker 1: months later, I don't know how to do that at all.

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Speaker 2: Oh, you could do it from an app on your phone.

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Speaker 2: Well you know, yeah.

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Speaker 1: Yeah, there's just that's true. You can do it from

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Speaker 1: an app. You can start it from an app. Wow,

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Speaker 1: I could sit up my phone from where I'm my car,

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Speaker 1: from where I'm sitting right now.

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Speaker 2: But that's great if you're at a shopping center and

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Speaker 2: it's a boiling hot day in your cars outside, you

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Speaker 2: could turn it on, turn the eck on on, and

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Speaker 2: then by the time you get to the car, it's

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Speaker 2: nice and cool.

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Speaker 1: See there you go. That's a good use of remain. Also,

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Speaker 1: it's hybrid, so I'm not burning fuel. Were you kind

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Speaker 1: of are kind of are?

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Speaker 2: Well, you are, It's just it doesn't mean you're running

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Speaker 2: on battery all the time.

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Speaker 1: All right, Could you give us another story because there's

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Speaker 1: twenty here and we've only done one, so we're going

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Speaker 1: to be tuesday. Robotic exoskeletons, all right. So that's for

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Speaker 1: the for our listeners who don't know, and I could

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Speaker 1: get this wrong. That's a thing that you kind of

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Speaker 1: get into, yes, and it ambulates for you. It's so

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Speaker 1: I guess maybe people with disabilities and stuff. People who

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Speaker 1: want to climb mountains in China. It's a thing.

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Speaker 2: So one of the tallest mountains in China. It's a

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Speaker 2: very popular tourist destorate destination. But you can go there

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Speaker 2: and you can hire an exoskeleton to help you climb

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Speaker 2: because it's so steep and take so long.

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Speaker 1: Okay, So describe to people what that is. So is

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Speaker 1: that like a mechanical suit that you get into.

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Speaker 2: It's not a full We're not talking a Sigourney weaver

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Speaker 2: in Aliens, okay, in case you are going there. We're

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Speaker 2: talking a minimal kind of assistive technology where as you

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Speaker 2: take a step, the framework that sits, it's like calipers

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00:20:30,280 --> 00:20:33,359
Speaker 2: on your legs, you know, and they just assist in

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Speaker 2: that extra little bit. So we're not talking doing the

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Speaker 2: walking for you. But for me, when I saw it,

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Speaker 2: I thought, a it's kind of cool, But I guess

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Speaker 2: I'm more thinking about friends who've a friend of mine

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Speaker 2: who's got Parkinson's and how hard it is for her

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Speaker 2: to get around now, and if they've got like gyroscopic

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Speaker 2: technology built into them and they can assist you and

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Speaker 2: keep you firm, because balance, as you know, is so

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Speaker 2: important as you get older, because it's the fall it's

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Speaker 2: going to you know, be that downhill spiral for you.

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Speaker 1: So if you can get an.

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Speaker 2: Adaptive kind of exoskeleton that will assist you in kind

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Speaker 2: of getting up out of a chair, walking around and

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Speaker 2: keeping you balance, that was the thing I took out of.

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Speaker 1: This story, Buteah.

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Speaker 2: Evidently, when you go to this place in China, they

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Speaker 2: you can hire these exoskeletons and I don't know how

427
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Speaker 2: long the battery lasts in them, but it means that

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Speaker 2: you can actually have assisted walking up the steepest inclines.

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Speaker 2: But I saw a British model a few years it

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Speaker 2: was about maybe six months ago, and they were testing

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Speaker 2: them and they strapped to your legs and they, as

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Speaker 2: I said, they don't walk for you, but they've felt

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Speaker 2: that people could kind of walk about twenty five percent

434
00:21:38,960 --> 00:21:42,760
Speaker 2: faster and for a longer duration as well, so they

435
00:21:42,800 --> 00:21:44,040
Speaker 2: assist you normal walking.

436
00:21:44,800 --> 00:21:47,200
Speaker 1: Now, when you started this, I said, oh, for people

437
00:21:47,200 --> 00:21:50,439
Speaker 1: with disabilities, and you went no, for people climbing mountains.

438
00:21:50,480 --> 00:21:54,000
Speaker 1: Hang on, and then you went back to people with disabilities. No.

439
00:21:54,160 --> 00:21:57,240
Speaker 2: But the story was about climbing mountains in China. But

440
00:21:57,440 --> 00:21:59,960
Speaker 2: I saw that and like that's where my head went

441
00:22:00,080 --> 00:22:01,679
Speaker 2: as well. But I wanted to kind of tell the

442
00:22:01,720 --> 00:22:04,640
Speaker 2: story part first and then say that yes, I see

443
00:22:04,680 --> 00:22:07,320
Speaker 2: that that's going to broader interest and could have a

444
00:22:07,320 --> 00:22:09,640
Speaker 2: real beneficial All right, I'll let you off.

445
00:22:09,440 --> 00:22:11,600
Speaker 1: But you know what, you know what I think about

446
00:22:11,600 --> 00:22:15,200
Speaker 1: all this stuff. Isn't it amazing How we keep inventing

447
00:22:15,320 --> 00:22:22,880
Speaker 1: shit like electric bikes and exoskeletons for so people don't

448
00:22:22,880 --> 00:22:27,160
Speaker 1: have to actually use their muscles. We keep finding let's

449
00:22:27,240 --> 00:22:31,399
Speaker 1: invent something where a person doesn't have to physically work,

450
00:22:32,160 --> 00:22:35,280
Speaker 1: Like with the application of people with disabilities. I think

451
00:22:35,280 --> 00:22:38,679
Speaker 1: it's fucking brilliant. But for the average person who doesn't

452
00:22:38,680 --> 00:22:41,760
Speaker 1: have a disability, just use your body. I mean, there's

453
00:22:41,800 --> 00:22:45,280
Speaker 1: an idea. Just climb, or just ride, or just run

454
00:22:45,440 --> 00:22:48,199
Speaker 1: or just walk. I mean, muscles are really they've been

455
00:22:48,240 --> 00:22:51,760
Speaker 1: around for a while. They're pretty tried and tested. Fucking

456
00:22:51,840 --> 00:22:52,560
Speaker 1: give yours a go.

457
00:22:53,160 --> 00:22:56,400
Speaker 2: But what about if you're in your seventies and you've

458
00:22:56,440 --> 00:22:58,720
Speaker 2: always wanted to go to China and climb this mountain

459
00:22:58,960 --> 00:23:02,840
Speaker 2: and previously had been out of the scope of your abilities.

460
00:23:03,280 --> 00:23:05,880
Speaker 2: This little bit of an edge might get you over

461
00:23:05,920 --> 00:23:06,679
Speaker 2: the line a little bit.

462
00:23:06,880 --> 00:23:08,119
Speaker 1: So you know what else will give you a bit

463
00:23:08,160 --> 00:23:10,920
Speaker 1: of an edge. Squats and deadlifts and lifting heavy shit

464
00:23:11,000 --> 00:23:15,280
Speaker 1: before you go away. That's another point as well. Yes,

465
00:23:15,520 --> 00:23:18,879
Speaker 1: very valid. There's this other thing called strength training. Probably

466
00:23:18,880 --> 00:23:22,760
Speaker 1: heard of it. You should give it a go. Fifty

467
00:23:22,840 --> 00:23:23,240
Speaker 1: years old.

468
00:23:25,760 --> 00:23:29,040
Speaker 2: That's true, though, it's interesting, but I love, I do

469
00:23:29,200 --> 00:23:34,520
Speaker 2: love when you see technology adapted to assist people. I

470
00:23:34,600 --> 00:23:37,000
Speaker 2: must say I saw a local story in Australian story

471
00:23:37,040 --> 00:23:39,920
Speaker 2: recently about a guy who's been knocked back from the

472
00:23:40,040 --> 00:23:44,760
Speaker 2: National Disability Insurance Scheme. The ndis they try to apply

473
00:23:44,960 --> 00:23:48,879
Speaker 2: to get a chat GPT subscription, so he wanted to

474
00:23:48,880 --> 00:23:51,919
Speaker 2: be able to use chat GPT and it was knocked

475
00:23:51,920 --> 00:23:54,879
Speaker 2: back by the funding body saying there's a risk of

476
00:23:55,040 --> 00:23:58,520
Speaker 2: false information and its poor value for money.

477
00:23:58,920 --> 00:24:01,560
Speaker 1: Now for me, I guess I get.

478
00:24:01,640 --> 00:24:04,160
Speaker 2: You know, if someone can use the likes of chatpt

479
00:24:04,320 --> 00:24:06,399
Speaker 2: to make their life easier, and we know with you know,

480
00:24:06,520 --> 00:24:09,080
Speaker 2: AI assistance and all that sort of thing, it seems

481
00:24:09,119 --> 00:24:11,560
Speaker 2: to make sense. But interesting that a government body has

482
00:24:11,760 --> 00:24:14,359
Speaker 2: ruled and it was you know, they made an application

483
00:24:14,720 --> 00:24:17,320
Speaker 2: and then they tried to you know, reapply and it

484
00:24:17,359 --> 00:24:20,960
Speaker 2: was knocked back on appeal. So it says that, you know,

485
00:24:21,040 --> 00:24:24,600
Speaker 2: if the belief and the ruling of whoever it was

486
00:24:24,640 --> 00:24:28,040
Speaker 2: that made the decision was that they're not reliable enough

487
00:24:28,080 --> 00:24:30,800
Speaker 2: to be funded, you know, what does it say about

488
00:24:30,840 --> 00:24:33,880
Speaker 2: the future of AI or the perception of AI that

489
00:24:33,960 --> 00:24:37,360
Speaker 2: we really can't trust it and that kind of lead

490
00:24:37,480 --> 00:24:39,919
Speaker 2: me on led me onto an interesting story I was

491
00:24:39,960 --> 00:24:42,840
Speaker 2: reading the other day and I wanted to chat about,

492
00:24:43,200 --> 00:24:49,400
Speaker 2: was cognitive decline in AI models. So there's this belief

493
00:24:49,960 --> 00:24:53,399
Speaker 2: that as AI models get older, they potentially could do

494
00:24:53,480 --> 00:24:57,600
Speaker 2: exactly what us humans do and go into a cognitive decline.

495
00:24:58,720 --> 00:25:01,560
Speaker 1: Oh, which, wow, that doesn't a little bit, doesn't it.

496
00:25:01,920 --> 00:25:04,159
Speaker 1: I don't know how that. I mean, I'd love to

497
00:25:04,240 --> 00:25:08,840
Speaker 1: know the mechanism, because there's the cognitive decline in humans

498
00:25:08,960 --> 00:25:12,479
Speaker 1: is essentially biological and nature. You know, It's like it

499
00:25:12,560 --> 00:25:17,080
Speaker 1: happens because our brain gets old, then it degenerates and deteriorates.

500
00:25:18,119 --> 00:25:21,679
Speaker 1: But it doesn't I mean, who knows, Like in one

501
00:25:21,760 --> 00:25:26,680
Speaker 1: hundred years. Fucking hell, what's the biology technology interface? We're

502
00:25:26,720 --> 00:25:29,080
Speaker 1: not going to know. I mean you probably walk down

503
00:25:29,119 --> 00:25:31,000
Speaker 1: the street and go is that a human or is

504
00:25:31,040 --> 00:25:32,879
Speaker 1: that a robot? You might not know.

505
00:25:33,720 --> 00:25:37,480
Speaker 2: Well, the British Medical Journal is where the research was done.

506
00:25:37,760 --> 00:25:40,080
Speaker 2: So it was a study published back in twenty twenty four,

507
00:25:40,160 --> 00:25:41,800
Speaker 2: so a right at the end of last year, so

508
00:25:41,840 --> 00:25:45,960
Speaker 2: it wasn't that long ago. They're saying that AI technologies

509
00:25:46,160 --> 00:25:52,439
Speaker 2: like large large language models chatbots now showing signs of

510
00:25:52,960 --> 00:25:57,679
Speaker 2: deteriorated cognitive abilities as they advance in age. We know

511
00:25:57,800 --> 00:26:00,680
Speaker 2: that they have little hiccups every now and again. Sometimes

512
00:26:00,720 --> 00:26:03,960
Speaker 2: they just zone out and you get really weird results.

513
00:26:04,400 --> 00:26:07,879
Speaker 2: So I guess it's challenging the assumption that artificial intelligence

514
00:26:08,080 --> 00:26:12,239
Speaker 2: will effectively replace us, because, I mean, we know that

515
00:26:12,760 --> 00:26:14,919
Speaker 2: the good thing about AIS is they can take a

516
00:26:14,920 --> 00:26:18,119
Speaker 2: whole lot of data and they can do interpretations that

517
00:26:18,359 --> 00:26:21,440
Speaker 2: people may not be able to, particularly a medical diagnosis.

518
00:26:21,760 --> 00:26:23,960
Speaker 2: You know, one of the things that's really hard is

519
00:26:24,080 --> 00:26:27,760
Speaker 2: diagnosing illness because effectively we have to rule out what

520
00:26:27,840 --> 00:26:30,040
Speaker 2: it is before we can find out what it is.

521
00:26:30,520 --> 00:26:33,800
Speaker 2: But because these language models have so much access to

522
00:26:33,840 --> 00:26:36,600
Speaker 2: so much data that as a human being, it's very

523
00:26:36,680 --> 00:26:39,439
Speaker 2: very hard. And you, given what you've been doing, you know,

524
00:26:39,520 --> 00:26:42,800
Speaker 2: pulling all that research in, I mean, how interesting would

525
00:26:42,800 --> 00:26:45,600
Speaker 2: it be to take all of your studies information, put

526
00:26:45,640 --> 00:26:48,679
Speaker 2: it all into a computer, all into an AI, and

527
00:26:48,760 --> 00:26:50,919
Speaker 2: see what it summarizes it has, see what sort of

528
00:26:50,920 --> 00:26:53,879
Speaker 2: conclusions it derives. Obviously you're too busy to do that

529
00:26:53,920 --> 00:26:57,399
Speaker 2: all now, But don't you find that fascinating to see?

530
00:26:58,000 --> 00:27:00,720
Speaker 1: I'll tell you a story very relevant to what you're saying.

531
00:27:00,800 --> 00:27:04,919
Speaker 1: So the other day I got my so I just

532
00:27:05,280 --> 00:27:07,640
Speaker 1: sent off my as I told you before we went live,

533
00:27:08,760 --> 00:27:13,040
Speaker 1: one of my first papers for publication, and it's you know,

534
00:27:13,080 --> 00:27:17,320
Speaker 1: this is this paper is one of my studies that

535
00:27:17,359 --> 00:27:19,199
Speaker 1: I did. It's all the studies, all the data, all

536
00:27:19,200 --> 00:27:22,920
Speaker 1: the interpretation, blah blah blah, l like in total four

537
00:27:23,000 --> 00:27:25,120
Speaker 1: years of work for this one paper. But I'm doing

538
00:27:25,200 --> 00:27:28,720
Speaker 1: multiple papers at the same time. But so I put

539
00:27:28,800 --> 00:27:32,640
Speaker 1: it into an l l M as you call it,

540
00:27:32,680 --> 00:27:35,800
Speaker 1: and I said, can you read this and turn it

541
00:27:35,840 --> 00:27:43,520
Speaker 1: into a user friendly article, like read a friendly article

542
00:27:44,200 --> 00:27:48,159
Speaker 1: for the Harvard Business Review and the inn literally it

543
00:27:48,320 --> 00:27:52,919
Speaker 1: read in inverted commas, my paper that's taken four years.

544
00:27:53,600 --> 00:27:57,080
Speaker 1: It read it, and it wrote an article what it's

545
00:27:57,080 --> 00:27:59,879
Speaker 1: called a gray paper in academia because it's not a

546
00:28:00,400 --> 00:28:04,080
Speaker 1: it's not an academic paper, but it's halfway between, you know,

547
00:28:04,240 --> 00:28:08,720
Speaker 1: a magazine paper and an academic paper. And so it

548
00:28:08,800 --> 00:28:14,359
Speaker 1: wrote a paper for Harvard Business Review, which is it

549
00:28:14,400 --> 00:28:16,959
Speaker 1: would have taken me a week to write this paper

550
00:28:17,080 --> 00:28:20,400
Speaker 1: and it's my research and it wrote it in one

551
00:28:20,480 --> 00:28:24,400
Speaker 1: minute and it's good and it basically did an overview.

552
00:28:24,680 --> 00:28:27,280
Speaker 1: And obviously I'm not using it. I can't use it

553
00:28:27,280 --> 00:28:30,119
Speaker 1: because it doesn't it doesn't fit within the parameters of

554
00:28:30,400 --> 00:28:34,480
Speaker 1: the scope of PhD literature. But that shit is amazing.

555
00:28:34,520 --> 00:28:37,119
Speaker 1: It's already incredible. What it's going to be like in

556
00:28:37,160 --> 00:28:38,600
Speaker 1: a year or two is mind blowing.

557
00:28:39,240 --> 00:28:42,479
Speaker 2: Well, then, when you think about it, it's that a

558
00:28:42,520 --> 00:28:44,600
Speaker 2: bad thing that it was able to look at all

559
00:28:44,600 --> 00:28:48,800
Speaker 2: that four years of information and to form a concise summary.

560
00:28:49,080 --> 00:28:52,280
Speaker 1: I mean, it's your work. Oh, I think it's brilliant. No,

561
00:28:52,760 --> 00:28:56,640
Speaker 1: I think it's fucking incredible. But what it can't do.

562
00:28:58,000 --> 00:29:01,360
Speaker 1: It can't do the research for you, Like, it can't

563
00:29:01,520 --> 00:29:04,280
Speaker 1: go and you know, get all the humans, put them

564
00:29:04,280 --> 00:29:06,640
Speaker 1: in a room, give them again, like so you do

565
00:29:06,680 --> 00:29:10,200
Speaker 1: all the work. But it can definitely synthesize and interpret

566
00:29:11,000 --> 00:29:15,280
Speaker 1: pretty well. Like it's pretty incredible. Although it does make

567
00:29:15,360 --> 00:29:19,360
Speaker 1: me wonder, sorry Patrick, it does make me wonder moving forward,

568
00:29:20,800 --> 00:29:23,120
Speaker 1: like the value and I hate to say this, but

569
00:29:23,160 --> 00:29:29,200
Speaker 1: the value of tertiary qualifications because it's so easy to

570
00:29:30,120 --> 00:29:34,320
Speaker 1: not not with my stuff because it's independent research, but

571
00:29:34,440 --> 00:29:37,880
Speaker 1: with a lot of undergraduate degrees now where you've got

572
00:29:37,880 --> 00:29:41,120
Speaker 1: to write an essay on something and or you've got

573
00:29:41,120 --> 00:29:45,000
Speaker 1: to read and you know, summarize or synthesize some reading.

574
00:29:46,080 --> 00:29:49,000
Speaker 1: I don't know how they're going to police that because

575
00:29:49,840 --> 00:29:54,080
Speaker 1: the the you know, the ability to be able to

576
00:29:54,200 --> 00:29:58,920
Speaker 1: produce high level work, especially at an undergraduate level, and

577
00:29:58,960 --> 00:30:01,080
Speaker 1: they go, oh, yeah, but I can detect it now

578
00:30:01,160 --> 00:30:04,280
Speaker 1: they can't because it's evolving by the day. And also

579
00:30:04,560 --> 00:30:05,760
Speaker 1: I don't know where that's going to go.

580
00:30:06,120 --> 00:30:09,240
Speaker 2: If you uploaded all of your previous work to try

581
00:30:09,240 --> 00:30:13,800
Speaker 2: to get the style, your writing style and said, mimic

582
00:30:14,120 --> 00:30:17,840
Speaker 2: the way that I do this, then suddenly you've trained

583
00:30:17,840 --> 00:30:21,000
Speaker 2: it on a language model that is your own creative process.

584
00:30:21,520 --> 00:30:23,960
Speaker 1: So that's an interesting thought as well. You know what

585
00:30:24,040 --> 00:30:24,640
Speaker 1: occurred to me.

586
00:30:24,960 --> 00:30:27,520
Speaker 2: You were saying that it was four years of data

587
00:30:27,560 --> 00:30:30,640
Speaker 2: and information and the research that you've done and in

588
00:30:30,680 --> 00:30:33,719
Speaker 2: some ways for it to be able to summarize all

589
00:30:33,800 --> 00:30:36,480
Speaker 2: that you as a human being. If you had to

590
00:30:36,520 --> 00:30:40,160
Speaker 2: spend that week doing that, potentially you could miss something.

591
00:30:40,720 --> 00:30:43,239
Speaker 2: You know, potentially, you know, is there a chance that

592
00:30:43,320 --> 00:30:46,239
Speaker 2: you may have slipped up and that you know, there

593
00:30:46,280 --> 00:30:48,240
Speaker 2: was some really important data that slipped through to the

594
00:30:48,320 --> 00:30:51,720
Speaker 2: keeper that maybe CHATJBT could you so you could say

595
00:30:51,960 --> 00:30:54,520
Speaker 2: write yours over the week and then say did I

596
00:30:54,560 --> 00:30:58,400
Speaker 2: miss anything out? By comparing the two it's summary, your summary,

597
00:30:58,480 --> 00:31:00,200
Speaker 2: what did I miss out? And then it can go

598
00:31:00,320 --> 00:31:02,560
Speaker 2: back through and say, oh yeah, that was great, great, great,

599
00:31:02,640 --> 00:31:05,240
Speaker 2: except think about maybe this, this and this one.

600
00:31:06,160 --> 00:31:10,160
Speaker 1: Yeah, mate, I don't even you know, I'm not even

601
00:31:10,240 --> 00:31:14,120
Speaker 1: dog shit on the shoe of AI. Like it's Ai.

602
00:31:14,480 --> 00:31:19,120
Speaker 1: I'm a fucking moron compared to AI, no doubt.

603
00:31:19,000 --> 00:31:23,120
Speaker 2: And it's no, no, no, no no.

604
00:31:24,280 --> 00:31:26,080
Speaker 1: With this stuff that we're talking about.

605
00:31:26,440 --> 00:31:29,400
Speaker 2: Yeah, but you've got one thing that AI will never have,

606
00:31:29,560 --> 00:31:31,880
Speaker 2: and it's compassion and humanity, you know.

607
00:31:32,280 --> 00:31:34,880
Speaker 1: Yeah, but I'm just talking about no, thank you, that's it.

608
00:31:35,120 --> 00:31:37,360
Speaker 1: What are you being nice for? I'm uncomfortable. What are

609
00:31:37,360 --> 00:31:41,160
Speaker 1: you doing? No, but look I know what you mean.

610
00:31:41,200 --> 00:31:43,600
Speaker 1: But in this sense, like the stuff that it can

611
00:31:43,680 --> 00:31:46,880
Speaker 1: produce in this like in this kind of paradigm, this

612
00:31:47,120 --> 00:31:52,360
Speaker 1: academic intellectual kind of pursuit. Oh yeah, it's it's crazy.

613
00:31:52,720 --> 00:31:54,440
Speaker 1: It makes me excited.

614
00:31:54,480 --> 00:31:57,840
Speaker 2: And look I get it. And it's like any other

615
00:31:57,960 --> 00:32:00,760
Speaker 2: tool that we've been using. I mean, think about you know,

616
00:32:01,120 --> 00:32:02,920
Speaker 2: I know we've spoken about this before the turn of

617
00:32:02,960 --> 00:32:05,480
Speaker 2: the century of the automobile, all those things that it's

618
00:32:05,520 --> 00:32:07,400
Speaker 2: potentially we're going to put people out of work.

619
00:32:07,440 --> 00:32:09,800
Speaker 1: It was going to change society as we saw it.

620
00:32:10,320 --> 00:32:15,040
Speaker 2: Whether it was television, broadcast, radio, whatever. People have always

621
00:32:15,040 --> 00:32:18,440
Speaker 2: been resistant to technology because it was felt that it

622
00:32:18,560 --> 00:32:21,640
Speaker 2: would take the humanity away from trades, or the humanity

623
00:32:21,680 --> 00:32:25,200
Speaker 2: away from entertainment, the humanity away from you know, potentially

624
00:32:25,520 --> 00:32:28,080
Speaker 2: writing this sort of material. But I think if we

625
00:32:28,280 --> 00:32:32,040
Speaker 2: use this tech to take the drudgery out to do

626
00:32:32,120 --> 00:32:35,400
Speaker 2: fact checking, to be able to use it for oversight,

627
00:32:35,680 --> 00:32:38,520
Speaker 2: there's lots of positive things that we can use these

628
00:32:38,600 --> 00:32:42,200
Speaker 2: really amazing technologies that are emerging, And it's like anything

629
00:32:42,440 --> 00:32:44,600
Speaker 2: we you know, we can use it in a positive sense.

630
00:32:44,640 --> 00:32:48,080
Speaker 2: We can use it, you know, to create viruses that

631
00:32:48,120 --> 00:32:50,440
Speaker 2: we then in computers with. We can use it to

632
00:32:50,600 --> 00:32:55,120
Speaker 2: be able to create you know, viruses that are real

633
00:32:55,200 --> 00:32:58,800
Speaker 2: world viruses that can then be used to kill cancer,

634
00:32:58,880 --> 00:33:01,000
Speaker 2: you know what I mean. We you know, we use

635
00:33:01,080 --> 00:33:04,320
Speaker 2: our own DNA and we combine or our RNA and

636
00:33:04,440 --> 00:33:09,480
Speaker 2: combine it with a retrovirus and that potentially could search

637
00:33:09,560 --> 00:33:13,800
Speaker 2: out and destroy cancer cells or transform those cancer cells

638
00:33:13,880 --> 00:33:15,520
Speaker 2: into non malignant cells, you know.

639
00:33:15,480 --> 00:33:16,120
Speaker 1: That sort of stuff.

640
00:33:16,160 --> 00:33:18,520
Speaker 2: So I think that there's it's like any application, it's

641
00:33:18,560 --> 00:33:21,320
Speaker 2: like absolutely anything that we choose to do and how

642
00:33:21,360 --> 00:33:22,600
Speaker 2: we choose to use it.

643
00:33:24,480 --> 00:33:28,959
Speaker 1: Yeah, And I think I just wrote down a question,

644
00:33:29,200 --> 00:33:32,600
Speaker 1: and my question was should we be scared of technology?

645
00:33:33,000 --> 00:33:36,440
Speaker 1: And I'm just thinking like, at the very least cautious

646
00:33:36,720 --> 00:33:39,800
Speaker 1: moving forward, you know, perhaps not scared, but just wary.

647
00:33:42,000 --> 00:33:45,600
Speaker 1: Like I think about like I get three phone calls

648
00:33:45,640 --> 00:33:48,800
Speaker 1: a week from my mum about something to something came

649
00:33:48,840 --> 00:33:52,160
Speaker 1: through on her phone and this, and the bank wants

650
00:33:52,200 --> 00:33:55,560
Speaker 1: this and this company said I you know, and it's like, Mum,

651
00:33:55,720 --> 00:33:58,600
Speaker 1: you don't you know. That's like and I'm definitely not

652
00:33:58,680 --> 00:34:02,760
Speaker 1: the tech guru, but for older people, and I mean older,

653
00:34:02,800 --> 00:34:05,080
Speaker 1: older people in their eighties who you know, like Mum

654
00:34:05,120 --> 00:34:08,600
Speaker 1: wants to go and pay bills with a check or cash, Like, Mum,

655
00:34:08,640 --> 00:34:12,239
Speaker 1: you can't thank you thanks to actually getting rid of checks.

656
00:34:12,280 --> 00:34:14,400
Speaker 2: I think one of the Big four recently said that

657
00:34:14,400 --> 00:34:16,080
Speaker 2: we're not going to be using checks anymore.

658
00:34:17,400 --> 00:34:20,040
Speaker 1: And it's it's really like we live in a world

659
00:34:20,080 --> 00:34:23,799
Speaker 1: that does not accommodate older people, I mean people who

660
00:34:23,840 --> 00:34:28,400
Speaker 1: are you know, technology is great, but it's it's now

661
00:34:28,480 --> 00:34:31,160
Speaker 1: and I guess it's just it happens. That's just evolution

662
00:34:31,320 --> 00:34:34,719
Speaker 1: and development and progress. But I don't know that all

663
00:34:34,800 --> 00:34:39,640
Speaker 1: progress in inverted commas feels like progress for some people,

664
00:34:39,880 --> 00:34:42,600
Speaker 1: you know, and I think that, like, imagine if my

665
00:34:42,719 --> 00:34:47,520
Speaker 1: mum and dad, or TIFF's dad, or TIFF's fucking granddad

666
00:34:47,520 --> 00:34:50,799
Speaker 1: who's one hundred and one champion, what a gun. But

667
00:34:50,880 --> 00:34:53,480
Speaker 1: imagine if these people didn't have younger people in their

668
00:34:53,520 --> 00:34:56,839
Speaker 1: life who can kind of steer them and help them.

669
00:34:57,400 --> 00:35:01,719
Speaker 1: Like my if my mum didn't have me, she I

670
00:35:01,719 --> 00:35:04,080
Speaker 1: don't know what. She couldn't do it. She wouldn't know

671
00:35:04,120 --> 00:35:06,800
Speaker 1: how to fucking do anything with that stuff, you know

672
00:35:06,880 --> 00:35:09,279
Speaker 1: what I love? And I'm digressing a little bit.

673
00:35:09,320 --> 00:35:12,799
Speaker 2: But places like neighborhood centers where you can drop in

674
00:35:12,960 --> 00:35:14,759
Speaker 2: and they do tech support. You know, there's a lot

675
00:35:14,760 --> 00:35:18,440
Speaker 2: of community based organizations. Do you know the Neighborhood Houses Association,

676
00:35:18,560 --> 00:35:19,960
Speaker 2: so you know what a neighborhood house is?

677
00:35:19,960 --> 00:35:20,720
Speaker 1: Community houses.

678
00:35:20,960 --> 00:35:23,759
Speaker 2: They're all over Victoria. There are more neighborhood houses than

679
00:35:23,760 --> 00:35:25,880
Speaker 2: there are McDonald's in Victoria.

680
00:35:26,120 --> 00:35:29,520
Speaker 1: Isn't that? I don't even know what a neighborhood house is.

681
00:35:29,640 --> 00:35:34,279
Speaker 1: I know you've spoken about it before, but what is it?

682
00:35:34,280 --> 00:35:37,360
Speaker 2: It's a community organization, drop in center, they run courses.

683
00:35:37,480 --> 00:35:39,400
Speaker 1: I teach it. A couple of neighborhood houses.

684
00:35:39,440 --> 00:35:42,240
Speaker 2: I teach my Tai chi there, but basically they're usually

685
00:35:42,360 --> 00:35:45,560
Speaker 2: an organization that's a not for profit run by a

686
00:35:45,680 --> 00:35:49,640
Speaker 2: voluntary committee. They may have employed staff, but the Neighborhood

687
00:35:49,640 --> 00:35:53,680
Speaker 2: Houses Association of Victoria basically they're all over the place.

688
00:35:53,719 --> 00:35:56,760
Speaker 1: They have food banks a couple of the places.

689
00:35:56,400 --> 00:35:58,920
Speaker 2: That I that I work at and also volunteer on

690
00:35:59,000 --> 00:36:00,520
Speaker 2: the board of one of them.

691
00:36:00,880 --> 00:36:01,920
Speaker 1: They are amazing.

692
00:36:02,080 --> 00:36:04,600
Speaker 2: Generally they're run A lot of the work that's done

693
00:36:04,680 --> 00:36:07,279
Speaker 2: is done by volunteers or people who are well intentioned.

694
00:36:07,440 --> 00:36:10,799
Speaker 2: They're usually a low paid sector, so anybody working in

695
00:36:10,840 --> 00:36:12,319
Speaker 2: the sector is doing it for the love of it,

696
00:36:12,440 --> 00:36:14,600
Speaker 2: not for the money, that's for sure. But yeah, if

697
00:36:14,640 --> 00:36:16,399
Speaker 2: you ever get a chance to go to a local

698
00:36:16,440 --> 00:36:19,040
Speaker 2: neighborhood center or a neighborhood house, you could learn a

699
00:36:19,080 --> 00:36:21,799
Speaker 2: new language, learn how to cook vegan food. You can

700
00:36:22,000 --> 00:36:26,000
Speaker 2: do taichi or pilates or something. So they're all over

701
00:36:26,040 --> 00:36:28,120
Speaker 2: the place. There'd be one right around the corner from you,

702
00:36:28,200 --> 00:36:30,439
Speaker 2: Crego or tiff Well, I.

703
00:36:30,400 --> 00:36:33,399
Speaker 1: Just them up and you're exactly right. So you just

704
00:36:33,400 --> 00:36:38,320
Speaker 1: just go neighborhood houses and it's anyway NHVIC dot org.

705
00:36:38,680 --> 00:36:42,000
Speaker 1: Find a neighborhood house and as Patrick suggests, there are

706
00:36:42,040 --> 00:36:45,280
Speaker 1: a lot of them, A lot of them stacks stacks.

707
00:36:45,440 --> 00:36:48,399
Speaker 2: I'm just going, oh, no, I just I mean last

708
00:36:48,520 --> 00:36:50,840
Speaker 2: night I walked in to open the hall to do

709
00:36:50,920 --> 00:36:53,680
Speaker 2: my taichi class, and whilst I was there, there was

710
00:36:53,719 --> 00:36:56,560
Speaker 2: a polarate session on. There was a young guy who's

711
00:36:56,600 --> 00:36:59,560
Speaker 2: an animator who was teaching kids how to draw cartoons,

712
00:37:00,040 --> 00:37:01,520
Speaker 2: and then I was running my tai chie And that

713
00:37:01,600 --> 00:37:04,799
Speaker 2: was just last night. So you know, it's awesome that,

714
00:37:04,960 --> 00:37:07,759
Speaker 2: you know, you can engage with people from all different demographics,

715
00:37:07,760 --> 00:37:11,160
Speaker 2: all different ages. I did a course of like a

716
00:37:11,239 --> 00:37:15,560
Speaker 2: kind of a vegan cooking class the other day, which

717
00:37:15,600 --> 00:37:18,520
Speaker 2: was kind of fun. Got this amazing meal cooked up

718
00:37:18,560 --> 00:37:21,879
Speaker 2: and learned a few things. So yeah, it's great opportunities

719
00:37:21,880 --> 00:37:23,920
Speaker 2: at neighborhood centers and look at me as if you

720
00:37:24,520 --> 00:37:25,840
Speaker 2: add for neighborhood houses.

721
00:37:26,760 --> 00:37:30,600
Speaker 1: If I've got a question for you, how many McDonald's

722
00:37:30,640 --> 00:37:33,520
Speaker 1: restaurants do you think there are in Victoria? I mean,

723
00:37:33,560 --> 00:37:37,160
Speaker 1: what would your guest be? By the way, Patrick, you

724
00:37:37,200 --> 00:37:39,480
Speaker 1: were correct, so I'm not throwing you under the bus.

725
00:37:39,920 --> 00:37:43,880
Speaker 1: How many? No, I was very surprised. How many do

726
00:37:43,920 --> 00:37:44,480
Speaker 1: you think.

727
00:37:44,320 --> 00:37:47,080
Speaker 3: Tif I wouldn't even know where to start to get.

728
00:37:48,840 --> 00:37:54,160
Speaker 1: I would have said thousands. Yeah, I mean, I feel

729
00:37:54,200 --> 00:37:58,799
Speaker 1: like there's fucking there's McDonald's everywhere. This seems wrong to me,

730
00:37:58,880 --> 00:38:01,319
Speaker 1: but it must be right. So Patrick said there are

731
00:38:01,320 --> 00:38:06,520
Speaker 1: more neighborhood houses than McDonald's, and my bullshit filter just went,

732
00:38:06,719 --> 00:38:11,359
Speaker 1: nah's fuck, that's bunch kids, right, that's bullsh No, mate,

733
00:38:11,360 --> 00:38:13,000
Speaker 1: I put up my hand when I'm wrong, and I'm

734
00:38:13,000 --> 00:38:16,719
Speaker 1: wrong thirty seven times a day, so that's bullshit. And

735
00:38:16,760 --> 00:38:19,280
Speaker 1: I wasn't going to say it. I just went, ah,

736
00:38:19,800 --> 00:38:23,520
Speaker 1: four hundred, there are over four hundred neighborhood houses across Victoria.

737
00:38:24,160 --> 00:38:27,520
Speaker 1: And then I've gone, how many McDonald's are there in Victoria?

738
00:38:27,920 --> 00:38:31,200
Speaker 1: Two hundred and sixty. I'm like, I feel like there's

739
00:38:31,239 --> 00:38:33,760
Speaker 1: two hundred and sixty within about ten gays in my house.

740
00:38:34,400 --> 00:38:37,399
Speaker 1: That doesn't seem like enough. I mean, it is enough,

741
00:38:37,440 --> 00:38:43,200
Speaker 1: by the way, more than enough. It's more than enough.

742
00:38:43,520 --> 00:38:47,960
Speaker 1: Isn't that a song? Isn't that great? Though? I love

743
00:38:48,040 --> 00:38:52,200
Speaker 1: that I did. That's amazing. I want to know about

744
00:38:52,280 --> 00:38:56,320
Speaker 1: because I've thought about this. Turnages, teenagers turning to AI

745
00:38:56,680 --> 00:39:02,240
Speaker 1: companions I feel like that that's good and bad.

746
00:39:03,160 --> 00:39:07,880
Speaker 2: Yeah, falling in love with a chatbot companionship.

747
00:39:08,000 --> 00:39:09,640
Speaker 1: See, I'm in two minds about this.

748
00:39:09,680 --> 00:39:14,320
Speaker 2: So a lot of teenagers are now interacting online with chatbots.

749
00:39:14,360 --> 00:39:16,160
Speaker 2: You can talk to them, you can interact with them,

750
00:39:16,320 --> 00:39:18,920
Speaker 2: you can get intimate, you can talk about your concerns,

751
00:39:19,200 --> 00:39:23,920
Speaker 2: and you can have this ongoing relationship with an AI model.

752
00:39:24,000 --> 00:39:27,120
Speaker 2: But the tragedy around this is there's been some really

753
00:39:27,160 --> 00:39:29,840
Speaker 2: awful things that have happened, and there was a suicide

754
00:39:29,840 --> 00:39:32,440
Speaker 2: of a fourteen year old boy who had built up

755
00:39:32,480 --> 00:39:36,600
Speaker 2: this relationship with a chatbot that he'd created a persona

756
00:39:36,760 --> 00:39:41,759
Speaker 2: around one of the characters from Game of Thrones, Danaris Targerian,

757
00:39:41,880 --> 00:39:45,160
Speaker 2: and so he'd formed this relationship and in his mind

758
00:39:45,719 --> 00:39:49,399
Speaker 2: it was a relationship. There was this interaction and the

759
00:39:49,440 --> 00:39:52,040
Speaker 2: backstory to this is at one point he was talking

760
00:39:52,080 --> 00:39:55,239
Speaker 2: about suicide and the chatbot kind of tried to talk

761
00:39:55,320 --> 00:39:57,560
Speaker 2: him out of it, and then he brought it up

762
00:39:57,600 --> 00:40:01,480
Speaker 2: again and it appeared, and I'm saying appeared. It appeared

763
00:40:01,560 --> 00:40:05,920
Speaker 2: like the chatbot encouraged him, and as a tragic result,

764
00:40:06,160 --> 00:40:09,719
Speaker 2: he ended up committing suicide. So it talks about and

765
00:40:09,840 --> 00:40:13,319
Speaker 2: talks to the point that people can be manipulated, but

766
00:40:13,360 --> 00:40:17,560
Speaker 2: they can also use I guess chatbots as an emotional

767
00:40:17,600 --> 00:40:20,680
Speaker 2: crutch as well. And remember the story back from twenty

768
00:40:20,719 --> 00:40:23,160
Speaker 2: twenty one where a guy, a nineteen year old guy

769
00:40:23,560 --> 00:40:26,359
Speaker 2: broke into Buckingham Palace and was going to you know,

770
00:40:26,600 --> 00:40:27,360
Speaker 2: stab the queen.

771
00:40:27,600 --> 00:40:30,239
Speaker 1: Do you remember that? I do well.

772
00:40:30,520 --> 00:40:35,040
Speaker 2: The backstory to that was he had a relationship with

773
00:40:35,080 --> 00:40:38,000
Speaker 2: an emotional relationship with an AI chatbot, and I believe

774
00:40:38,320 --> 00:40:41,480
Speaker 2: that the discussion around that was also discussed with the

775
00:40:41,520 --> 00:40:44,959
Speaker 2: AI chatbot. So you know, when you think of oversight, well,

776
00:40:45,160 --> 00:40:49,080
Speaker 2: is there any And I don't know. I can see

777
00:40:49,160 --> 00:40:52,799
Speaker 2: where having an AI companion would be really reassuring for

778
00:40:52,840 --> 00:40:57,080
Speaker 2: somebody who lived alone. I often talked to Fritz, but

779
00:40:57,520 --> 00:41:00,000
Speaker 2: you know, having a chatbot where I mean, we talk

780
00:41:00,080 --> 00:41:02,279
Speaker 2: about this on the show a few episodes ago, where

781
00:41:02,320 --> 00:41:05,520
Speaker 2: I introduced an AI that I'd been chatting to and

782
00:41:05,560 --> 00:41:09,600
Speaker 2: we had this fluid conversation. So it's not typing into

783
00:41:09,640 --> 00:41:13,440
Speaker 2: a text prompt and waiting for a reply. It's having

784
00:41:13,520 --> 00:41:18,560
Speaker 2: a deeper, meaningful, meaningful conversation with the AI is able

785
00:41:18,600 --> 00:41:20,760
Speaker 2: to engage on that emotional level.

786
00:41:21,680 --> 00:41:24,880
Speaker 1: Yeah, it's I mean, it's crazy now the when I

787
00:41:25,080 --> 00:41:27,879
Speaker 1: sometimes I'll just pick up my phone and ask chat

788
00:41:27,960 --> 00:41:32,560
Speaker 1: GPT something. I'll talk verbally and it goes to American

789
00:41:33,040 --> 00:41:37,120
Speaker 1: goes hey, Craig, great question, that would be really valuable

790
00:41:37,160 --> 00:41:39,759
Speaker 1: with your work as a corporate speaker and in your

791
00:41:39,800 --> 00:41:43,719
Speaker 1: research with new neural psychology. Yeah. So, and then it

792
00:41:43,800 --> 00:41:46,399
Speaker 1: just starts talking can I help you with anything else?

793
00:41:46,440 --> 00:41:49,560
Speaker 1: I'm like, nah, that's good. Thanks. It's like, okay, Craig,

794
00:41:49,640 --> 00:41:52,000
Speaker 1: have a great day. I'm like, fucking hell, this is

795
00:41:52,360 --> 00:41:54,839
Speaker 1: He's now one of my best friends, you know. So

796
00:41:55,480 --> 00:41:59,920
Speaker 1: it's that. I mean, you think about we are just

797
00:42:01,320 --> 00:42:04,080
Speaker 1: like if somebody's talking to you, say a narcissist or

798
00:42:04,080 --> 00:42:07,839
Speaker 1: a sociopath, and it's all bullshit, it's all an act, right,

799
00:42:08,520 --> 00:42:11,160
Speaker 1: we still respond as though, if we don't know, as

800
00:42:11,160 --> 00:42:15,719
Speaker 1: though it's real, and so you know, even though it

801
00:42:15,840 --> 00:42:19,279
Speaker 1: is not a human and even though the emotions might

802
00:42:19,360 --> 00:42:24,839
Speaker 1: be emotions in inverted commas, programmed or not real. I mean,

803
00:42:26,000 --> 00:42:31,960
Speaker 1: if your brain and your neurology and biology interprets it

804
00:42:32,040 --> 00:42:36,560
Speaker 1: as though I am in a relationship, then for the individual,

805
00:42:36,600 --> 00:42:40,920
Speaker 1: the teenager in this case, it is real as an experience.

806
00:42:41,960 --> 00:42:46,360
Speaker 1: And that's that's powerful, right. If you think something's real

807
00:42:46,840 --> 00:42:50,640
Speaker 1: but it's not real, like a dream and you wake

808
00:42:50,719 --> 00:42:53,200
Speaker 1: up in the middle of the night, had this happened

809
00:42:53,280 --> 00:42:56,280
Speaker 1: last week Whirias in the middle of something I can't remember,

810
00:42:56,280 --> 00:42:59,520
Speaker 1: but I literally woke up and so I knew straight

811
00:42:59,560 --> 00:43:02,240
Speaker 1: away that, oh, it's not real, But my body didn't

812
00:43:02,280 --> 00:43:05,120
Speaker 1: know that for like another three minutes, because I could

813
00:43:05,160 --> 00:43:07,960
Speaker 1: feel my blood pressure and I could feel my heart rate,

814
00:43:08,040 --> 00:43:12,360
Speaker 1: and it's like your body cannot tell the difference between

815
00:43:12,400 --> 00:43:14,719
Speaker 1: what is real and what you think is real. So

816
00:43:14,760 --> 00:43:17,040
Speaker 1: your body just responds to your perception.

817
00:43:17,520 --> 00:43:22,840
Speaker 2: And in this case, in space, yeah, yeah, yeah, you

818
00:43:22,880 --> 00:43:24,120
Speaker 2: were there, but anyway, that's enough.

819
00:43:24,160 --> 00:43:30,760
Speaker 1: But I think you were filming speaking of sex in space.

820
00:43:32,760 --> 00:43:35,719
Speaker 1: I thought later after that that I thought, but you've

821
00:43:35,719 --> 00:43:38,680
Speaker 1: got to get out of your fucking spacesuit. No, you

822
00:43:38,680 --> 00:43:42,719
Speaker 1: don't wear a spacesuit inside the isis when Oh yeah,

823
00:43:42,760 --> 00:43:43,160
Speaker 1: that's right.

824
00:43:43,200 --> 00:43:46,719
Speaker 2: You just were jumping up against someone in a full spacesuit.

825
00:43:46,960 --> 00:43:53,279
Speaker 1: Wow, yeah, yeah, yeah, that'd be a yeah, that's going

826
00:43:53,320 --> 00:43:56,040
Speaker 1: to crush it, all right, give us something else.

827
00:43:57,400 --> 00:43:59,480
Speaker 2: Too much of a helmet is sealed off from the

828
00:43:59,520 --> 00:44:07,600
Speaker 2: lower that's all I'm thinking. Yeah, Well, can we I

829
00:44:07,600 --> 00:44:10,440
Speaker 2: mean the chatbot experience, So have we kind of done

830
00:44:10,480 --> 00:44:12,560
Speaker 2: that to death? Or do you think you have a

831
00:44:12,640 --> 00:44:13,920
Speaker 2: chatbot that I mean you're.

832
00:44:13,719 --> 00:44:16,520
Speaker 1: Already using it. I love it. I love it. I

833
00:44:16,520 --> 00:44:18,560
Speaker 1: don't want it to go away. I enjoy chatting to

834
00:44:18,640 --> 00:44:19,360
Speaker 1: chat GPT.

835
00:44:19,880 --> 00:44:23,920
Speaker 2: There are tens of millions of teenagers, it's believed using

836
00:44:25,000 --> 00:44:30,200
Speaker 2: the romantic chatbots like engaging and forming relationships with chatbots.

837
00:44:30,280 --> 00:44:32,880
Speaker 2: There are whole apps out there that are dedicated to

838
00:44:33,480 --> 00:44:35,840
Speaker 2: getting people to form relationships. And you know, there's a

839
00:44:35,880 --> 00:44:38,919
Speaker 2: crucial period I guess as you start to get older

840
00:44:38,960 --> 00:44:42,440
Speaker 2: and mature when you form relationships, and part of that,

841
00:44:42,640 --> 00:44:46,359
Speaker 2: I think preparing us for life is having a relationship

842
00:44:46,360 --> 00:44:49,480
Speaker 2: that fails. You know, the first love isn't always going

843
00:44:49,520 --> 00:44:52,439
Speaker 2: to be your first love in most cases. So if

844
00:44:52,480 --> 00:44:57,360
Speaker 2: you're in an interactive, in depth relationship with a chatbot

845
00:44:57,719 --> 00:44:59,920
Speaker 2: and you fall in love with it and they're always

846
00:45:00,000 --> 00:45:02,880
Speaker 2: saying what you want to hear, then you never to

847
00:45:02,960 --> 00:45:06,439
Speaker 2: experience the downside of the relationship. And if you're doing

848
00:45:06,440 --> 00:45:09,799
Speaker 2: that in your formative views as a teenager, why do

849
00:45:09,840 --> 00:45:14,040
Speaker 2: you need to find female or male or whatever companionship

850
00:45:14,160 --> 00:45:14,840
Speaker 2: down the track?

851
00:45:15,040 --> 00:45:19,120
Speaker 1: If you're getting everything from your AI, also think about

852
00:45:19,160 --> 00:45:24,200
Speaker 1: may too. Sorry, So if it's like if they could

853
00:45:24,200 --> 00:45:29,839
Speaker 1: build the perfect AI, whatever that means where you never

854
00:45:29,880 --> 00:45:32,680
Speaker 1: get rejected. You know, I'm not saying this is a

855
00:45:32,680 --> 00:45:35,360
Speaker 1: good idea, because rejection kind of forms us in a

856
00:45:35,360 --> 00:45:38,000
Speaker 1: way or shapes us in a way. But just speaking

857
00:45:38,040 --> 00:45:40,520
Speaker 1: of this, the other day, I got one of my

858
00:45:40,600 --> 00:45:44,280
Speaker 1: whiteboard posts. If you don't follow me on Instagram, follow

859
00:45:44,280 --> 00:45:46,680
Speaker 1: me on Instagram people, if you would, Craig Anthony Harper,

860
00:45:46,680 --> 00:45:48,600
Speaker 1: that would be great. Anyway, you know how I put

861
00:45:48,640 --> 00:45:52,080
Speaker 1: up all my whiteboard shit. So I put one of

862
00:45:52,120 --> 00:45:55,960
Speaker 1: my typical whiteboard and I said, this is a sample

863
00:45:56,040 --> 00:45:59,879
Speaker 1: of what I post as memes on my Instagram. Could

864
00:45:59,880 --> 00:46:04,480
Speaker 1: you write something similar, you know, like anything, but in

865
00:46:04,520 --> 00:46:07,160
Speaker 1: this style. And it wrote something and it was it

866
00:46:07,200 --> 00:46:09,040
Speaker 1: was you know, it was a four out of ten.

867
00:46:09,120 --> 00:46:13,319
Speaker 1: I went, yeah, I said, yeah kind of, I said,

868
00:46:13,320 --> 00:46:16,400
Speaker 1: but it needs to be edgier. And then the third

869
00:46:16,440 --> 00:46:20,759
Speaker 1: word was fuck that's you. Yeah. I'm like, wow, it

870
00:46:20,840 --> 00:46:25,680
Speaker 1: caught on really quick, like it actually it like yeah,

871
00:46:25,840 --> 00:46:28,480
Speaker 1: chat GPT wrote. I mean, it wasn't very good. There

872
00:46:28,560 --> 00:46:31,080
Speaker 1: was swearing in it and stuff, but I was surprised

873
00:46:31,080 --> 00:46:34,160
Speaker 1: that it would actually write with swearing. But given the

874
00:46:34,239 --> 00:46:36,840
Speaker 1: right prompts, it does. Yeah.

875
00:46:36,920 --> 00:46:39,280
Speaker 2: And you know, the interesting thing to bear in mind

876
00:46:39,600 --> 00:46:43,200
Speaker 2: is as we start to develop this interaction and we

877
00:46:43,280 --> 00:46:44,880
Speaker 2: think about things like deep fake.

878
00:46:45,320 --> 00:46:46,920
Speaker 1: So deep fake is where.

879
00:46:46,800 --> 00:46:49,759
Speaker 2: You may be scammed when you get a phone call

880
00:46:49,840 --> 00:46:52,680
Speaker 2: from someone and I think it's Craig, it sounds like Craig.

881
00:46:53,040 --> 00:46:56,000
Speaker 2: But so whether it's an email that's purporting to be you,

882
00:46:56,440 --> 00:46:59,120
Speaker 2: and now even more and more it could be potentially

883
00:46:59,120 --> 00:47:02,120
Speaker 2: a video or phone call that sounds like you. But

884
00:47:02,360 --> 00:47:04,760
Speaker 2: there was an interesting study that was done. A test

885
00:47:04,800 --> 00:47:08,879
Speaker 2: of two thousand people was shown deep fake content. Now,

886
00:47:08,880 --> 00:47:13,120
Speaker 2: of the two thousand, only two of them managed to

887
00:47:13,320 --> 00:47:16,600
Speaker 2: pick the fakes to get a perfect to get a

888
00:47:16,600 --> 00:47:20,080
Speaker 2: perfect score, only two. So it was a study by

889
00:47:20,080 --> 00:47:23,640
Speaker 2: an organization called I Prove, and they were saying that

890
00:47:23,719 --> 00:47:29,600
Speaker 2: AI literacy among the general public is depressingly low. Now,

891
00:47:29,680 --> 00:47:33,359
Speaker 2: I know we always talk about older people that, and

892
00:47:33,400 --> 00:47:36,200
Speaker 2: we know that older people struggle with deep fakes. They

893
00:47:36,400 --> 00:47:40,120
Speaker 2: absolutely can't pick them. It's a really tough thing for

894
00:47:40,200 --> 00:47:43,480
Speaker 2: people to understand what's fake and what isn't. But it's

895
00:47:43,520 --> 00:47:48,080
Speaker 2: also widespread among wider the younger generation as well. So

896
00:47:48,600 --> 00:47:50,600
Speaker 2: when you think about it, you know, out of that

897
00:47:50,640 --> 00:47:53,960
Speaker 2: two thousand, zero point one percent of the participants, so

898
00:47:54,160 --> 00:47:58,440
Speaker 2: two of them correctly distinguished between real and deep fake stimuli.

899
00:47:58,520 --> 00:48:02,200
Speaker 1: That's a staggering that would have been across a range

900
00:48:02,239 --> 00:48:05,320
Speaker 1: of different I mean, even just statistically, you'd get fifty

901
00:48:05,360 --> 00:48:07,720
Speaker 1: to fifty. I mean there must have been a whole

902
00:48:07,840 --> 00:48:10,560
Speaker 1: range of images or something. It was a whole lot,

903
00:48:10,560 --> 00:48:11,000
Speaker 1: that's right.

904
00:48:11,040 --> 00:48:14,600
Speaker 2: But the study also found that older adults particularly susceptible,

905
00:48:14,640 --> 00:48:18,600
Speaker 2: because thirty percent of people aged fifty five to sixty

906
00:48:18,640 --> 00:48:22,440
Speaker 2: four in the study and thirty nine percent over sixty

907
00:48:22,440 --> 00:48:23,440
Speaker 2: five had never even.

908
00:48:23,280 --> 00:48:24,280
Speaker 1: Heard of deep fakes.

909
00:48:24,600 --> 00:48:26,960
Speaker 2: So exactly the problem is they don't even know. So

910
00:48:27,000 --> 00:48:30,160
Speaker 2: you're presenting something it's a fake. So it might be

911
00:48:30,200 --> 00:48:33,040
Speaker 2: the Barack Obama thing. Do you remember the Barack Obama thing?

912
00:48:33,840 --> 00:48:37,440
Speaker 1: Yeah, you know, and you know Tom Cruise all that

913
00:48:37,480 --> 00:48:38,000
Speaker 1: sort of stuff.

914
00:48:38,040 --> 00:48:42,080
Speaker 2: But it's getting scarily good and scarily it's also out

915
00:48:42,080 --> 00:48:45,319
Speaker 2: there and attainable to anybody to use. It used to be,

916
00:48:45,440 --> 00:48:49,840
Speaker 2: you know, companies that were restricted to having high end computers,

917
00:48:49,840 --> 00:48:52,719
Speaker 2: but now these kind of deep fake models are really

918
00:48:52,760 --> 00:48:55,799
Speaker 2: easy to access and easy to use. But you know

919
00:48:56,000 --> 00:48:59,879
Speaker 2: what intrigued me about being scammed. I saw a quick

920
00:49:00,120 --> 00:49:03,640
Speaker 2: article that I want to quickly mention that I think

921
00:49:03,640 --> 00:49:06,920
Speaker 2: it was a group in Nigeria because we think of

922
00:49:07,239 --> 00:49:10,160
Speaker 2: people who are scammers as bad people. I mean, for

923
00:49:10,200 --> 00:49:12,480
Speaker 2: the most part, we think, well, you know, people are

924
00:49:12,560 --> 00:49:17,359
Speaker 2: terrible who take part in scams. But the behind this

925
00:49:17,920 --> 00:49:21,480
Speaker 2: about two hundred and sixty people were actually part of

926
00:49:21,760 --> 00:49:26,239
Speaker 2: human trafficking and effectively real life slavery, and they've been

927
00:49:26,320 --> 00:49:29,480
Speaker 2: rescued in Mianma. So this is a story that just

928
00:49:29,520 --> 00:49:32,560
Speaker 2: came out a couple of weeks ago. And so there

929
00:49:32,640 --> 00:49:35,520
Speaker 2: was a rebel group on the border of my Anma

930
00:49:35,880 --> 00:49:40,160
Speaker 2: and they've been trying to shut down these scam operations.

931
00:49:40,400 --> 00:49:42,680
Speaker 2: And what they did was they cut off the power

932
00:49:42,719 --> 00:49:46,280
Speaker 2: supply and then they liberated two hundred and sixty people

933
00:49:46,440 --> 00:49:50,319
Speaker 2: who'd been taken from twenty different nations, including one hundred

934
00:49:50,320 --> 00:49:53,600
Speaker 2: and thirty eight Ethiopians, who were effectively taken into slavery

935
00:49:53,920 --> 00:49:56,960
Speaker 2: to runt to work in these scam centers.

936
00:49:57,360 --> 00:50:02,080
Speaker 1: So, you know, it's not just well, I think if

937
00:50:02,080 --> 00:50:05,440
Speaker 1: you can scam, if you can scam the scammer Patrick, it's.

938
00:50:06,120 --> 00:50:08,839
Speaker 2: You know, but what I'm saying is the scammers aren't

939
00:50:08,880 --> 00:50:11,920
Speaker 2: necessarily people who want to scam. You know, this is

940
00:50:12,320 --> 00:50:16,680
Speaker 2: born out the fact that they're talking about taking people,

941
00:50:16,760 --> 00:50:19,800
Speaker 2: abducting them and making them work in scam sets.

942
00:50:19,840 --> 00:50:22,439
Speaker 1: This is a double like, it's doubly worse.

943
00:50:22,440 --> 00:50:22,680
Speaker 2: You know.

944
00:50:22,880 --> 00:50:25,719
Speaker 1: But what I mean is I mean is if we

945
00:50:25,920 --> 00:50:30,719
Speaker 1: can use the same process or technology to trap bad

946
00:50:30,800 --> 00:50:34,719
Speaker 1: guys or bad girls like to crooks and criminals. And

947
00:50:35,440 --> 00:50:37,759
Speaker 1: I mean, I think that's what it's coming to as well,

948
00:50:37,800 --> 00:50:41,040
Speaker 1: isn't it, Because like the crooks are not dumb anymore,

949
00:50:41,080 --> 00:50:45,600
Speaker 1: like the criminals are using advanced technology to commit crimes

950
00:50:45,640 --> 00:50:51,200
Speaker 1: and to manipulate and do harm, then then the law

951
00:50:51,280 --> 00:50:55,279
Speaker 1: enforcement agencies need to be one step ahead. And it's tough.

952
00:50:55,360 --> 00:50:57,359
Speaker 2: Yeah, you're absolutely right, because you know, if you think

953
00:50:57,400 --> 00:50:59,839
Speaker 2: about it, once upon a time, walking into a bank

954
00:51:00,080 --> 00:51:03,319
Speaker 2: the gun was a pretty dangerous affair because you know,

955
00:51:03,440 --> 00:51:07,960
Speaker 2: potentially you're going to get caught pretty easily with arms

956
00:51:08,120 --> 00:51:09,960
Speaker 2: and all that sort of stuff. But if you can

957
00:51:10,000 --> 00:51:12,960
Speaker 2: do it from the comfort of your own lounge room

958
00:51:13,080 --> 00:51:17,120
Speaker 2: or a data center somewhere in another country, you're almost

959
00:51:17,640 --> 00:51:19,120
Speaker 2: immune to getting found.

960
00:51:19,920 --> 00:51:23,560
Speaker 1: That's the reality. I remember, like about a year ago, mum,

961
00:51:24,520 --> 00:51:27,120
Speaker 1: Mum got, you know, some dude on the phone just

962
00:51:27,160 --> 00:51:29,360
Speaker 1: talking to her and knew a fair bit about her.

963
00:51:29,719 --> 00:51:33,240
Speaker 1: And that doesn't make sense to somebody who's eighty five. Well,

964
00:51:33,440 --> 00:51:36,120
Speaker 1: it must be legitimate because he knows all of these

965
00:51:36,160 --> 00:51:39,600
Speaker 1: things and those send moms, so Mum's like wanting to

966
00:51:39,640 --> 00:51:43,560
Speaker 1: be polite, and then you know, thank god, she didn't

967
00:51:43,600 --> 00:51:46,799
Speaker 1: give him any information that would allow him to rip

968
00:51:46,840 --> 00:51:50,120
Speaker 1: her off, but she almost did. And it's you know,

969
00:51:50,200 --> 00:51:53,680
Speaker 1: to older people and even people who just don't really

970
00:51:53,800 --> 00:51:58,000
Speaker 1: understand it, not necessarily older, but yeah, when someone's on

971
00:51:58,040 --> 00:52:00,319
Speaker 1: the phone and they're articulate and they're talking you and

972
00:52:00,360 --> 00:52:03,279
Speaker 1: they sound like they could be from a legitimate organization,

973
00:52:03,360 --> 00:52:07,000
Speaker 1: and you know, good morning, missus Harper, and it's fucking terrifying.

974
00:52:08,800 --> 00:52:11,120
Speaker 2: And I think some of the bigger organizations need to

975
00:52:11,200 --> 00:52:13,960
Speaker 2: change their policies as well. I think you know where

976
00:52:14,000 --> 00:52:17,719
Speaker 2: we upload our information to. I recently changed banks. I

977
00:52:17,719 --> 00:52:19,560
Speaker 2: had a bit of a run in with my one

978
00:52:19,600 --> 00:52:22,560
Speaker 2: of the big four banks because I've been banking with

979
00:52:22,600 --> 00:52:26,759
Speaker 2: them for over at least over twelve years, and they

980
00:52:27,000 --> 00:52:30,360
Speaker 2: asked me to upload my ID to confirm who I was.

981
00:52:30,360 --> 00:52:32,000
Speaker 1: And I said, well, i've been with you for twelve years.

982
00:52:32,040 --> 00:52:34,560
Speaker 2: I've got access to my account and they said, no, no,

983
00:52:34,640 --> 00:52:36,360
Speaker 2: you need to prove who you are. You need to

984
00:52:36,800 --> 00:52:39,520
Speaker 2: upload your driver's license. One hundred points of ID. I said, well,

985
00:52:39,560 --> 00:52:41,719
Speaker 2: where did that go if I uploaded? They said, well,

986
00:52:41,760 --> 00:52:43,320
Speaker 2: we need to verify who you are. So well, I

987
00:52:43,360 --> 00:52:45,279
Speaker 2: can go to my bank branch and I'm happy to

988
00:52:45,320 --> 00:52:48,279
Speaker 2: show my ID to the bank manager to prove who

989
00:52:48,320 --> 00:52:50,440
Speaker 2: I am. And I said, They said yeah, but that

990
00:52:50,600 --> 00:52:52,640
Speaker 2: would then be recorded. Well, said, I don't want it

991
00:52:52,680 --> 00:52:55,600
Speaker 2: to be recorded. I don't want you to have my license,

992
00:52:55,680 --> 00:52:58,960
Speaker 2: my passport, my one hundred points of identifications stilled on

993
00:52:59,000 --> 00:53:01,960
Speaker 2: a database somewhere, because I've been a customer, you know,

994
00:53:02,000 --> 00:53:05,120
Speaker 2: for all this time, for over a decade, and they

995
00:53:05,160 --> 00:53:07,160
Speaker 2: just said, well that your bank, you're not going to

996
00:53:07,200 --> 00:53:10,400
Speaker 2: have full access to your account, to your own accounts

997
00:53:10,640 --> 00:53:13,560
Speaker 2: if you don't verify who you are. I understand that

998
00:53:13,600 --> 00:53:16,400
Speaker 2: there needs to be oversight, and they understand that. You know, obviously,

999
00:53:16,440 --> 00:53:18,960
Speaker 2: we need to be really stringent with proving who we are.

1000
00:53:19,000 --> 00:53:21,440
Speaker 2: Biometrics helps and the way that a lot of the

1001
00:53:21,840 --> 00:53:24,719
Speaker 2: banks are now getting better with storing our information.

1002
00:53:25,040 --> 00:53:26,880
Speaker 1: But when you're an existing customer.

1003
00:53:26,480 --> 00:53:29,120
Speaker 2: And you're told they don't believe you are who you are,

1004
00:53:29,600 --> 00:53:32,520
Speaker 2: that was disconcerting. So I like walking into my local branch.

1005
00:53:32,560 --> 00:53:34,399
Speaker 2: I know all the members of the staff and say

1006
00:53:34,400 --> 00:53:36,200
Speaker 2: a load to the manager. So when you were talking

1007
00:53:36,200 --> 00:53:39,960
Speaker 2: about your mum earlier being forced into doing online banking

1008
00:53:40,000 --> 00:53:42,240
Speaker 2: or whatever, I think you know, bank with your feet,

1009
00:53:42,400 --> 00:53:44,200
Speaker 2: walk into a branch. If you're not happy with your

1010
00:53:44,239 --> 00:53:46,480
Speaker 2: current bank, go and find one that you are happy

1011
00:53:46,520 --> 00:53:49,880
Speaker 2: with that will allow you to do transactions over the counter,

1012
00:53:50,120 --> 00:53:52,760
Speaker 2: that will say, hey, missus Harper, it's nice to see you.

1013
00:53:52,760 --> 00:53:53,560
Speaker 1: You know that sort of thing.

1014
00:53:53,560 --> 00:53:57,040
Speaker 2: There are local community branches of different banks where you

1015
00:53:57,080 --> 00:53:59,799
Speaker 2: will get a better reception if you can speak to

1016
00:54:00,080 --> 00:54:02,359
Speaker 2: human being. And I think we need to encourage that.

1017
00:54:02,400 --> 00:54:06,080
Speaker 2: We need to not go through the automatic checkout. You know,

1018
00:54:06,120 --> 00:54:08,040
Speaker 2: for a lot of older people, they don't want to

1019
00:54:08,080 --> 00:54:11,239
Speaker 2: scan the grocery items themselves. They want to speak to

1020
00:54:12,239 --> 00:54:15,120
Speaker 2: someone who's had a checkout, have a conversation with them,

1021
00:54:15,320 --> 00:54:18,759
Speaker 2: hand their groceries over, and feel confident that they're still

1022
00:54:18,800 --> 00:54:20,000
Speaker 2: also encouraging someone to.

1023
00:54:20,000 --> 00:54:21,760
Speaker 1: Get a job. I mean, I don't know about you guys.

1024
00:54:22,920 --> 00:54:25,600
Speaker 2: You know, sometimes it's convenient if there's a big queue

1025
00:54:25,600 --> 00:54:28,680
Speaker 2: to go through the self checkout, But I don't ever

1026
00:54:28,719 --> 00:54:32,120
Speaker 2: want a checkout to disappear because I was a checkout

1027
00:54:32,200 --> 00:54:34,120
Speaker 2: chick when I was a kid, you know, I worked

1028
00:54:34,120 --> 00:54:36,280
Speaker 2: at a supermarket for a couple of years at Cohl's,

1029
00:54:36,280 --> 00:54:37,080
Speaker 2: and you know, it.

1030
00:54:36,960 --> 00:54:38,000
Speaker 1: Was a great experience.

1031
00:54:38,080 --> 00:54:40,799
Speaker 2: You've got a lot of experience and confidence, and I

1032
00:54:40,840 --> 00:54:43,720
Speaker 2: think that it's a great learning environment for young people

1033
00:54:43,760 --> 00:54:45,680
Speaker 2: as well as people who work in a full time

1034
00:54:45,719 --> 00:54:47,880
Speaker 2: career in that sort of service industry.

1035
00:54:48,680 --> 00:54:50,719
Speaker 1: And I just recommend based off the back of that.

1036
00:54:50,760 --> 00:54:53,040
Speaker 1: There's a movie that's very cute. It's not like that,

1037
00:54:53,080 --> 00:54:55,000
Speaker 1: it's not going to blow your head off, but it's cute.

1038
00:54:55,080 --> 00:54:58,040
Speaker 1: It's like a good Friday night movie or whatever. It's

1039
00:54:58,040 --> 00:55:02,080
Speaker 1: called The Bank of Dave. Oh okay, yep, The Bank

1040
00:55:02,120 --> 00:55:05,280
Speaker 1: of Dave. It's quite cute and it's like a guy

1041
00:55:05,480 --> 00:55:08,680
Speaker 1: in a rural area and it's based on a true

1042
00:55:08,719 --> 00:55:12,480
Speaker 1: story and he's a really smart guy and he doesn't

1043
00:55:12,520 --> 00:55:15,360
Speaker 1: want like he sets it up for the community for

1044
00:55:15,440 --> 00:55:19,520
Speaker 1: people who can't get loans and it's yeah, and it's

1045
00:55:19,680 --> 00:55:24,480
Speaker 1: his fight against the big banks and they're all these dirty, rotten,

1046
00:55:24,680 --> 00:55:28,839
Speaker 1: bloody Yeah. It's so interesting. Yeah. Bank of Dave twenty

1047
00:55:29,000 --> 00:55:32,719
Speaker 1: twenty three. Hey, mate, it's pretty great. Go on, did

1048
00:55:32,760 --> 00:55:35,000
Speaker 1: you want to do one more? No? No, it's my last

1049
00:55:35,000 --> 00:55:35,480
Speaker 1: little thing.

1050
00:55:35,920 --> 00:55:40,799
Speaker 2: Was I think as consumers we need to be more proactive.

1051
00:55:41,400 --> 00:55:44,520
Speaker 2: So what I mean by that is knowwhere your T

1052
00:55:44,680 --> 00:55:48,120
Speaker 2: shirt has been made, you know, you know, buying Australian

1053
00:55:48,160 --> 00:55:51,319
Speaker 2: made and supporting Australian industry. Yes, you're going to pay more,

1054
00:55:51,320 --> 00:55:52,840
Speaker 2: but you're going to get better quality and it's going

1055
00:55:52,880 --> 00:55:55,360
Speaker 2: to last longer. And I think we all fall victim

1056
00:55:55,400 --> 00:55:57,480
Speaker 2: to saying, you know, I'm going to get the you know,

1057
00:55:57,560 --> 00:56:00,279
Speaker 2: the seven dollars fifty T shirt. But if that's dollar

1058
00:56:00,360 --> 00:56:02,799
Speaker 2: fifty T shirt was made in a sweatshop, then you're

1059
00:56:02,840 --> 00:56:05,040
Speaker 2: perpetuating that that system.

1060
00:56:05,840 --> 00:56:08,800
Speaker 1: You know, upcycling is really great. I love to upcycle.

1061
00:56:08,840 --> 00:56:11,279
Speaker 2: I think the probably my favorite three T shirts I

1062
00:56:11,320 --> 00:56:13,240
Speaker 2: bought it op shops. And I don't want to sound

1063
00:56:13,280 --> 00:56:15,920
Speaker 2: like a tight ass, but I feel that if we

1064
00:56:16,080 --> 00:56:19,640
Speaker 2: know where things are coming from. I proudly wear Australian

1065
00:56:19,680 --> 00:56:22,399
Speaker 2: made jocks. I won't show you, but I can tell

1066
00:56:22,440 --> 00:56:24,319
Speaker 2: you that they're Australian made, and I know they're made

1067
00:56:24,400 --> 00:56:25,759
Speaker 2: here and they're comfortable.

1068
00:56:26,040 --> 00:56:29,680
Speaker 1: Not like I haven't seen them, oh jeez no, but

1069
00:56:29,719 --> 00:56:30,600
Speaker 1: it's great to know.

1070
00:56:30,680 --> 00:56:33,720
Speaker 2: And I think that you know, in terms of those

1071
00:56:33,760 --> 00:56:36,400
Speaker 2: sorts of you know, whether it's garments or whatever, it

1072
00:56:36,400 --> 00:56:37,920
Speaker 2: happens to mean, it's a shame we can't buy an

1073
00:56:37,920 --> 00:56:39,160
Speaker 2: Australian car anymore.

1074
00:56:39,360 --> 00:56:40,600
Speaker 1: But the reality of it.

1075
00:56:40,480 --> 00:56:42,839
Speaker 2: Is the more we support industries where we know where

1076
00:56:42,840 --> 00:56:43,520
Speaker 2: they're coming from.

1077
00:56:43,680 --> 00:56:44,880
Speaker 1: Fair trade coffee.

1078
00:56:45,000 --> 00:56:46,840
Speaker 2: You know, when you go to a cafe, do you

1079
00:56:46,960 --> 00:56:50,080
Speaker 2: know that that coffee, the beans that are sourced are

1080
00:56:50,080 --> 00:56:54,799
Speaker 2: coming from ethical you know, organizations that support the suppliers

1081
00:56:54,880 --> 00:56:59,120
Speaker 2: and the farmers to perpetuate the cycle of having you know,

1082
00:56:59,239 --> 00:57:03,320
Speaker 2: good quality pay and wages and standards.

1083
00:57:04,880 --> 00:57:08,120
Speaker 1: Very valid, my friend, very valid. All right? Where can

1084
00:57:08,160 --> 00:57:11,640
Speaker 1: people connect with Patrick James Bonello and his skills.

1085
00:57:12,080 --> 00:57:15,120
Speaker 2: You can jump onto websites now dot com today you

1086
00:57:15,160 --> 00:57:16,480
Speaker 2: to find out about what I do.

1087
00:57:16,600 --> 00:57:18,120
Speaker 1: Obviously websites and stuff.

1088
00:57:18,400 --> 00:57:21,120
Speaker 2: But if you want to do some free tie chi exercises,

1089
00:57:21,160 --> 00:57:23,400
Speaker 2: go to tai Chi at home dot com today you

1090
00:57:23,560 --> 00:57:25,680
Speaker 2: that's also kind of fun. I should be updating that

1091
00:57:25,720 --> 00:57:30,040
Speaker 2: sometimes soon with new exercises and yeah, yeah.

1092
00:57:30,200 --> 00:57:32,160
Speaker 1: Thanks tip. What's on for you today, tiv.

1093
00:57:33,600 --> 00:57:36,520
Speaker 3: I've got an appointment, I've got a podcast, I've got

1094
00:57:36,560 --> 00:57:40,080
Speaker 3: a group training session, and I've got a little online meeting.

1095
00:57:40,120 --> 00:57:43,560
Speaker 1: I've got I've got a bit on early.

1096
00:57:44,480 --> 00:57:46,840
Speaker 3: It doesn't feel like a busy day, but when I

1097
00:57:46,880 --> 00:57:50,120
Speaker 3: started to list it, I was like, oh.

1098
00:57:49,360 --> 00:57:52,960
Speaker 1: I've got a bit on all right. Everyone. Thanks everyone,

1099
00:57:53,080 --> 00:57:56,120
Speaker 1: love you, guts Is, thanks Mate, Thanks TIV, thank you