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Speaker 1: I'll get a team. Welcome to another install on the show.

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Speaker 1: It's Patrick James Bonello, Tiffany and called Craig Anthony Harper.

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Speaker 1: Let's start with the genius in the bush.

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Speaker 2: Hi, Patrick, Oh sorry, I was waiting for you to say,

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Speaker 2: tiff that would have been the genius in the ring,

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Speaker 2: wouldn't that.

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Speaker 1: You're a genius, Thank you. Yeah, you're a genius of sorts.

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Speaker 1: How are you?

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Speaker 2: I'm actually doing pretty good. I'm sitting here at the

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Speaker 2: moment with Fritz and scientifically, Fritz is actually calming me down.

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Speaker 2: He's lowering my blood pressure and he's causing me to

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Speaker 2: be more relaxed.

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Speaker 1: Do you know that dogs, well, obviously if you love them, dogs, cats, animals,

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Speaker 1: of course, yeah, they increase not only lifespan, but health

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Speaker 1: span as well, which is nice, isn't it.

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Speaker 2: It's amazing, fantastic. I feel I should be doing more.

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Speaker 1: Although I did see a video the other day online

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Speaker 1: of this crazy woman with a cat in a stewdent.

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Speaker 1: I can't remember her name, someone Cook and the cat

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Speaker 1: that she had I might have been Bear the cat.

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Speaker 1: I'm not sure. I can't remember all the.

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Speaker 2: Details, but the common name for a cat, yeah, yeah.

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Speaker 1: Yeah, but this lady swearing like a motherfucker at this

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Speaker 1: poor cat. It was feeline abuse.

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Speaker 3: Bear's very thoughtful, and she realizes that with the cost

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Speaker 3: of living, having such a long life from all these

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Speaker 3: pets in my world is a very big expense. So

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Speaker 3: she's just balancing that out with some high sparks of

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Speaker 3: cortisol every now and then.

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Speaker 1: So tell everyone who's thinking, what is he talking about?

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Speaker 1: Tell everybody one you what happened.

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Speaker 3: If so, I've got banners behind me in the studio

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Speaker 3: that just sit there twenty four to seven. The studio

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Speaker 3: doubles as her bedroom. She's got a little cat towel here.

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Speaker 3: It's quite the playground. But when I when she knows

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Speaker 3: I'm putting up, obviously, when I put my headphones around

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Speaker 3: my ears, she goes into assault mode and pushes the

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Speaker 3: So I was sitting there, I had to record a

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Speaker 3: little video by myself, So I turn on the zoom,

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Speaker 3: I put the headphones on my head, and she just

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Speaker 3: runs in, pushes the banner over behind me, and runs

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Speaker 3: straight out the door. And I abuse her, And I

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Speaker 3: think to myself, my neighbors would hear that abuse all

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Speaker 3: the time and think I'm in a volatile relationship.

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Speaker 1: It's hilarious, and or you're abusing your animals.

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Speaker 3: She does it deliberately though, like it's they're so cunning.

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Speaker 1: It's deliberate and it's thought through.

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Speaker 3: And that banner is in here twenty four to seven.

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Speaker 3: She doesn't touch it unless she wants to irritate me.

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Speaker 2: Well do you reckon?

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Speaker 1: She's like mean cat form? Do you reckon? She real

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Speaker 1: is that you're about to stop focusing on her. Yes,

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Speaker 1: you're about to channel your attention into something else. Yes,

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Speaker 1: that's exactly what happens. Can you two talk amongst yourselves

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Speaker 1: for a moment, Patrick, because all your notes that you

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Speaker 1: sent me have disappeared. Oh no, I found them, found them.

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Speaker 1: I'm back. I'm sorry for that. Yeah. Yeah. My my

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Speaker 1: Monash University account, which is clearly sick of me, just

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Speaker 1: blocked me from my own thing. But I'm back in.

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Speaker 1: Have you been Patrick? What's going on? What's news up there?

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Speaker 2: Look, it's it's got cold really quickly. It was four

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Speaker 2: degrees this morning, so I had to stoke up the fireplace,

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Speaker 2: which is actually one of the very cathartic things that

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Speaker 2: I love to do. I'm not got central heating, but

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Speaker 2: I've also got some I was gonna say, I've got wood,

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Speaker 2: but I've got, you know, a lot of stuff for

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Speaker 2: the fireplace to be able to keep the house warm.

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Speaker 2: So yeah, it's That was one of the things I

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Speaker 2: did first thing this morning because I thought I thought

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Speaker 2: we were recording at seven thirty, so I got up

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Speaker 2: extra early to warm the house and get the fireplace going,

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Speaker 2: get my hot chocolate, and stand by the fire. I

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Speaker 2: was going through the notes, and then I sat down

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Speaker 2: and went to the studio, turned the heater on and

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Speaker 2: sat here at seven thirty and no one was there.

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Speaker 1: And I got a message at seven thirty two, going

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Speaker 1: are we doing a podcast? And I was sitting at

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Speaker 1: the cafe just knees deep. I was going to say

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Speaker 1: something else, knees deep in caffeine. What suits you? It is?

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Speaker 1: Seven thirty suit you better? Or nine?

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Speaker 2: We probably should have this conversation, but not during the podcast.

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Speaker 1: Yeah, let's do it. Let's have a little staff meeting. Now,

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Speaker 1: what are you or nine?

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Speaker 3: I like nine because I like to be able to

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Speaker 3: go and work out first. But you know, I'm just

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Speaker 3: here at press buttons. So it's when you two are

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Speaker 3: most on fire.

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Speaker 2: Yeah, no, that works for me. I can walk for

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Speaker 2: its beforehand.

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Speaker 1: Then you are the button presser Tiff metaphorically and literally.

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Speaker 2: Press push her buttons.

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Speaker 1: Yea, even when she's not in the studio. Yeah, I

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Speaker 1: love a good open fire. But anyway, all right, let's

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Speaker 1: onwards and upwards.

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Speaker 2: I think i've spoken to since we went to jail together.

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Speaker 1: I saw photos of you guys in jail. Was it fun? Patrick?

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Speaker 2: It was awesome. I'd never thought because I didn't know

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Speaker 2: they had cowed jails for one thing. It was great fun.

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Speaker 2: Took me to jail and I wore an orange jumpsuit.

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Speaker 2: Was great.

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Speaker 1: Did you drink any booze?

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Speaker 2: Oh no, I had mocktails. Oh no, it's not true.

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Speaker 2: I did have a shot, but just one, but most

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Speaker 2: of it was mocktails.

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Speaker 1: How long since you've been or have you ever been drunk?

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Speaker 2: Have I ever been drunk? Yes, I have, but decades,

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Speaker 2: it's been decades and decades.

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Speaker 1: Yeah to how long since you've been drunk, give or take.

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Speaker 1: Did you get drunk on your birthday?

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Speaker 3: I definitely didn't. I haven't been drunk for a long time.

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Speaker 3: But yeah, nothing good happens when you're drunk. We call

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Speaker 3: her Francesca and we put her to bed years ago.

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Speaker 1: Who's that drunk you?

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Speaker 3: Yeah, she's a whole different beast. Really, she has no

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Speaker 3: business in any of my life.

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Speaker 1: Tell us about Francesca.

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Speaker 3: She's just wild, you know. She comes out of the bamboo.

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Speaker 3: She's everybody's best friend. She's excited. She's like a two

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Speaker 3: year old that someone's given a packet of red jelly

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Speaker 3: beans too, and then just let loose.

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Speaker 2: She is. If I've met you, you don't need alcohol, exactly, exactly.

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Speaker 1: Yeah, I feel a little bit the same. I'm like,

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Speaker 1: imagine me and booze, or me and drugs that like,

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Speaker 1: I'm a nightmare. Anyway.

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Speaker 2: I had somebody take me aside one day and make

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Speaker 2: me promise that I would never take speed. I wouldn't,

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Speaker 2: but they make me promise that, not for any other reason,

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Speaker 2: but they couldn't imagine me being more hyper than I

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Speaker 2: am now, and they felt that it would probably be scary.

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Speaker 1: Do you not drink coffee? You don't drink coffee, do you, mate?

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Speaker 2: I do. I might have maybe two or three cups

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Speaker 2: a week. I don't drink it every day.

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Speaker 1: No, Well that's a little bit of a stimulant. All right,

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Speaker 1: What are we talking about today technology. Let's open that door,

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Speaker 1: just take us into the wonderland that is your safe space.

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Speaker 2: Well, I was just going to say, we started off

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Speaker 2: chatting about how cathartic. I was sitting here patting my

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Speaker 2: dog and I just figured that, I mean, we've all

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Speaker 2: known that, you know, having animals around can be cathartic. Well, Liss,

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Speaker 2: it's a cat that kind of attacks you. But aside

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Speaker 2: from that, there have been some new studies and what

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Speaker 2: they're saying now is they've done some They definitively got

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Speaker 2: one hundred and twenty two under graduates around the age

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Speaker 2: of eighteen and older and they were experiencing what they

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Speaker 2: were saying was moderate to high stress. So the research

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Speaker 2: is then collected and we're talking full on so saliva, fecal,

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Speaker 2: matter of you know, they did. This was whole biological

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Speaker 2: everything tests. And then what they did was they got

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Speaker 2: they got these people to interact with dogs, and we're

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Speaker 2: talking a discernible change in their stress levels, even seeing

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Speaker 2: the dog come towards them, finding out the dog's name,

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Speaker 2: and then of course petting the dog, interacting, playing with

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Speaker 2: the dog, throwing a ball, giving the dog a treat.

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Speaker 2: And we're talking massive amounts of a change biologically during

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Speaker 2: that interaction. So even just looking at smile or smiling

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Speaker 2: at the dog, calling the dog's name, stress levels dropped,

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Speaker 2: they decreased. And again this was proven and discernible thirty

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Speaker 2: three point five percent.

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Speaker 1: Wow.

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Speaker 2: So that's amazing. So if you've if you've got a dog,

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Speaker 2: but you've got a friend, so Crago, you could just

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Speaker 2: go to TIFF's place and Luna just give Luna a

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Speaker 2: bit of a pat and maybe.

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Speaker 1: I'm very excited. I'm very excited about finishing my study

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Speaker 1: because I'm getting I've just been. I've been every third day.

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Speaker 1: I'm like, that's it. I'm getting a German shepherd. No,

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Speaker 1: I'm getting a spooed. I'm getting a whipp it. Somebody

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Speaker 1: told me a shorthired something visla this morning. Oh Murray,

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Speaker 1: who I said every morning is like, no, you've got

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Speaker 1: to get a girl, the female vistla or and they're

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Speaker 1: like a ready brown beautiful. They are so beautiful. And

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Speaker 1: so you know, that's my thing today. But tomorrow I'll

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Speaker 1: be getting a fucking you know, a wolf or something.

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Speaker 1: But I don't know, but I'm yeah, I love dogs,

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Speaker 1: but I think the interesting thing Patrick about all this

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Speaker 1: stuff is too not everyone loves dogs. So you bring

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Speaker 1: a dog into a room that there are some cultures

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Speaker 1: that don't embrace dogs as pets as much as other

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Speaker 1: cultures do, if you know what I mean.

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Speaker 2: Well observably, and I have Indian friends, and I've noticed

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Speaker 2: that when you get a courier come to the door,

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Speaker 2: and I guess couriers and posters are always apprehensive about dogs.

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Speaker 2: But I've found that there are certainly Indian people tend

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Speaker 2: to embrace dogs less when they don't know. I guess

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Speaker 2: it's good for everybody to be wary of any dog

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Speaker 2: that they don't know on their home turf, particularly, I mean,

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Speaker 2: Fritz might lick you to death. But as a rule,

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Speaker 2: dogs can be territorial and they're guarding the pack, so

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Speaker 2: you've got to be careful. I guess posts and couriers

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Speaker 2: can see the worst in dogs too. Mind you, we

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Speaker 2: had a local poster who was friends with every single dog,

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Speaker 2: and he always had a good thing to say, and

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Speaker 2: so the dogs loved him, so that the dogs will

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Speaker 2: be wagging their tails waiting for the post he come around.

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Speaker 2: So there's, you know, one little stigma that actually isn't.

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Speaker 2: Really It's great to see posters who actually love their dogs.

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Speaker 2: But I think that I could see I'm trying to

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Speaker 2: visualize the dog that I would see with Crago, And

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Speaker 2: you know, I wouldn't be surprised if you've got a chihuahua,

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Speaker 2: because then you could you you could fly, You could

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Speaker 2: put it in your pocket, and you could drive your

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Speaker 2: motorbike and it could be sitting in a little napsack

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Speaker 2: around you. You know, I could your bum bag. See

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Speaker 2: it would fit in your bum bag that you always have.

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Speaker 2: Did you reckon tiff I reckon Jack Russell? Oh? Jack Russell?

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Speaker 1: Yeah? Do you know what? I know? You're trying to

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Speaker 1: bait me. That's okay, I'm open. I might get a

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Speaker 1: Chihuahua or a Jack Russell. I love all dogs. I

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Speaker 1: could put a Chihuahua in the pocket of my cargoes

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Speaker 1: and his little head could just pop out the side.

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Speaker 1: That would be do you know what I want to

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Speaker 1: tell you? So I don't know if you know this,

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Speaker 1: but and the I'm ninety eight percent sure about this research,

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Speaker 1: so don't exactly quote me. But dogs are I think

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Speaker 1: the only animal that produce oxytocin the love hormone like

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Speaker 1: humans do, as in when they're around their humans. That

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Speaker 1: could be total bullshit, but I know that they definitely

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Speaker 1: do produce it, whether or not other animals do. I

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Speaker 1: guess I feel like elephants would fuck I'd love an elephant.

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Speaker 2: Dolphins would that? Dolphins? I could fuck it. You know, dolphins,

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Speaker 2: they probably love you too, wouldn't they?

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Speaker 1: No, I don't know. I don't know, but elephants fuck.

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Speaker 1: Elephants are smart. Have you ever seen I saw this

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Speaker 1: footage gest and we'll get onto tech in a moment.

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Speaker 1: But there was this was recently at a zoo. There

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Speaker 1: was an earthquake. Might have been California or somewhere and

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Speaker 1: in the or San Diego, might have been in the anyway,

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Speaker 1: they've got this big elephant enclosure. Now, all these elephants

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Speaker 1: are obviously in captivity. There was an earthquake, all the

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Speaker 1: elephants got around the little elephants, So the big ones

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Speaker 1: and they all stand in a circle facing outwards with

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Speaker 1: all the little elephants in the middle to protect the

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Speaker 1: middle elephants. Little elephants because they don't know what's coming.

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Speaker 1: But whoever taught them more trained than this? Right? And

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Speaker 1: then so there's this circle of elephants like with their

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Speaker 1: bums in the middle and their faces and trunks out

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Speaker 1: and all the little ones being protected by all the

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Speaker 1: big ones. And the earthquake started and they all assembled

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Speaker 1: and got in this position within fifteen twenty seconds. How

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Speaker 1: fucking clever. We think we're smart. Fuck and el animals

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Speaker 1: are so smart.

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Speaker 2: Yeah, I'd be worried though if there was an earthquake

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Speaker 2: in the first thing you did was turn around and

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Speaker 2: shove your bub in my face.

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Speaker 1: Well, that would be to protect you, my little elephant.

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Speaker 1: So as you just keep your trunk to yourself while

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Speaker 1: we're in that position, things will be good.

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Speaker 3: Uh.

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Speaker 2: You remember what the elephants said to the bloke at

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Speaker 2: the nudist colony, who cute? Can you pick up peanuts

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Speaker 2: with it? It's a text, isn't it.

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Speaker 1: No, it's not well at out tip. That's terrible. No,

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Speaker 1: I don't leave it then it Let him suffer, all right?

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Speaker 1: Come on, tell us about technology and science and stuff

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Speaker 1: in the future.

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Speaker 2: Health on the health side of it. Let's stick with

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Speaker 2: the health stuff. This is really fascinating because obviously, when

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Speaker 2: you have surgery, it's very invasive, and scientists now think

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Speaker 2: that they're able to print three D tissue, So they're

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Speaker 2: able to inject a solution into your body and then

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Speaker 2: directly print into your body with no surgery, using ultrasound

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Speaker 2: waves so to form an object. So say you were

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Speaker 2: doing a repair, you don't actually open the body up,

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Speaker 2: you inject this solution in and then using sound waves

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Speaker 2: to three D print using that audio at different levels,

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Speaker 2: and you can create a three dimensional object inside the body.

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Speaker 2: That blew my mind when I saw that. That would

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Speaker 2: be amazing. Wouldn't it to be able to do that?

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Speaker 2: So say you're replacing a joints and you had, you know,

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Speaker 2: you kind of clean out the cartilage and you've got

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Speaker 2: you know, you could potentially inject into the knee joint

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Speaker 2: and then rebuild sections of it using this three D

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Speaker 2: printing technology inside the actual.

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Speaker 1: Joint without even making an incision. Yeah, like where stuff's going.

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Speaker 1: I saw this thing yesterday, This fourteen year old kid, right,

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Speaker 1: I think he was an Indian kid. He invented this

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Speaker 1: app that goes into your phone that monitors your heart

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Speaker 1: right and it can diagnose I think it's forty cardiac

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Speaker 1: medical conditions or forty one or forty two with ninety

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Speaker 1: six percent accuracy, right, And what they were saying is

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Speaker 1: compared to the average accuracy by medical professionals looking at

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Speaker 1: whatever is fifty six and this is like ninety odd percent.

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Speaker 1: So it's incredibly more accurate, more reliable. But just as

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Speaker 1: a kid, yeah, fourteen year old kid, and he's I've

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Speaker 1: got this footage of him doing basically a presentation to

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Speaker 1: all these people about how it works. Yeah, it's incredible.

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Speaker 1: I mean, I don't think we can fully understand or

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Speaker 1: grasp what's coming. And I think it's exponential. I think

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Speaker 1: AI and all the myriad of you know, things that

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Speaker 1: are going to be possible, like even even now, it's ridiculous.

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Speaker 1: Like a friend of mine messaged me that's in Singapore

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Speaker 1: and they had to get they're staying at a hotel

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Speaker 1: in Singapore and they had to get some clothes washed.

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Speaker 1: So they rang room service and said such and such

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Speaker 1: need some shit washed, and they went sure, I don't know,

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Speaker 1: I forget. The name will be up in a moment.

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Speaker 1: And then in a moment the her phone rang or

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Speaker 1: the text rang and said such and such as at

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Speaker 1: your door. She goes to the door. It's a robot, right,

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Speaker 1: and so she opens this thing, puts her thing in

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Speaker 1: the thing, and the robot takes off, and then the

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Speaker 1: robot comes back like four hours later with I mean,

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Speaker 1: and that's yeah, that's not a trial, that's just what's happening.

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Speaker 2: Yeah. Yeah. Incidentally, just staying on this one, this is

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Speaker 2: a California Institute of Technology, and it's called deep tissue

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Speaker 2: in vivo sound printing. So disp dis is what it is.

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Speaker 2: But they've done these exp I haven't done any human

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Speaker 2: being yet, but they've done it in a rabbit's stomach

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Speaker 2: and a mouse's bladder, and they are able to fabricate

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Speaker 2: without any surgical intervention at all. They were able to

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Speaker 2: fabricate using sound technology and three D print internally. That's

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Speaker 2: great stuff, isn't it.

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Speaker 1: Ah? Well, I mean the main the main problem with surgery,

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Speaker 1: or one of the main problems, is the trauma that's

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Speaker 1: caused through cutting through all the tissue. So we eliminate that. Wow.

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Speaker 2: Yeah cool. Hey are you guys readers? I mean you obviously,

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Speaker 2: for what you do, Craigo, you've got to do a

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Speaker 2: lot of reading. I listen to audio books and I

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Speaker 2: do obviously pick up books every now and again as well,

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Speaker 2: But and it's interesting what goes on in our brain

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Speaker 2: when we read, and there's more research going into that.

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Speaker 2: And it's funny because I you know, if you have

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Speaker 2: like I mean, I read every day, and what we're

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Speaker 2: now finding is that you know, letters, words, sentences, and texts,

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Speaker 2: whether you're reading aloud or just silently reading, your brain

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Speaker 2: is doing some amazing stuff when you put it all together,

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Speaker 2: and being lost in a book can can just change

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Speaker 2: your brain chemistry and it's it's it's amazing. So science

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Speaker 2: really doesn't know what goes on about you know, your

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Speaker 2: brain when you transform those letters on a page into

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Speaker 2: words or you visualize something. So there is more studies

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Speaker 2: going on now into what actually does happen when we

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Speaker 2: pick up a book and you know, you're transported to

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Speaker 2: another location if you think about reading, you know, say

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Speaker 2: you're really in depth into a personal motivation type book

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Speaker 2: and you start to feel that almost euphoric sense of

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Speaker 2: you know, being monivated. You want to get out there

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Speaker 2: and exercise, or you want to get out there and

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Speaker 2: start doing all the things as it's suggesting or for me,

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Speaker 2: you know, as a fan, as someone who loves fantasy

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Speaker 2: and sci fi, being transported and visualizing that place and

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Speaker 2: the place, you know, wherever it happens to be that

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Speaker 2: you're you know you're thinking about in that book, particularly

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Speaker 2: in sci fi and fantasy, because the terms of reference

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Speaker 2: are slightly different. You know, you're creating a world in

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Speaker 2: your mind in what the author is thinking, or even

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Speaker 2: getting into the head of an author. You know, that

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Speaker 2: blows me away when you read a book that was

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Speaker 2: written two hundred years ago and the concept and the

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Speaker 2: thoughts and the words that someone's put on a page

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Speaker 2: is brought back to life in your brain. So it

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Speaker 2: makes sense that they're kind of researching what's going on,

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Speaker 2: and you know, whether it's short form or long form.

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Speaker 2: You know, a lot of people now and I see

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Speaker 2: this as a frightening thought that a lot of young

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Speaker 2: people tend to not have that focus to read anymore.

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Speaker 2: They're stimulated by TikTok videos that go fifteen seconds, you know,

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Speaker 2: as opposed to sitting down and really, you know, diving

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Speaker 2: into a book. I was walking with my friend yesterday

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Speaker 2: who's a big listener to the show, and she said

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Speaker 2: she's reading three books at once. I can only read

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Speaker 2: one book at a time. What about you, guys? Can

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Speaker 2: you read more than one book at any time.

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Speaker 1: I'm a listener. Was I was an avid reader for

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Speaker 1: you is like physically holding a book obviously, because we

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Speaker 1: didn't have audio books, and people are going to go,

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Speaker 1: people are going to think, how on earth do you

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Speaker 1: have time for this? But in my like in my

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Speaker 1: walking hours, I listened to books. Right, So this morning

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Speaker 1: I was up at quarter to six walking listening to

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Speaker 1: a book. And I've listened to ten books in the

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Speaker 1: last twelve weeks. Yeah, so, yeah, So I consume lots

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Speaker 1: of content. And I've never and I've said this once

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Speaker 1: before on the show, but I've never really consumed fiction.

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Speaker 1: Not interested. It's all got to be you know, data

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Speaker 1: information da. And then I said to myself, you're a

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Speaker 1: boring fuck. Just listen to something for the sake of

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Speaker 1: list you don't not everything's got to be a fucking

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Speaker 1: learning moment, right, And so I listened to this book

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Speaker 1: that was recommended called The Gray Man, and which is

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Speaker 1: about the world's best assassin who's got a moral compass.

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Speaker 1: Of course it is, and of course he has, and

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Speaker 1: of course I'm interested, right, And now I'm up to

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Speaker 1: I'm currently listening to book ten and great love it

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Speaker 1: I love the audio books.

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Speaker 2: Yeah, I'm at book eight of a series I only

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Speaker 2: started about two weeks ago because I just plow through them,

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Speaker 2: you know what I'm doing, cause I'm walking, Fritz. But

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Speaker 2: the interesting thing with this study was that reading activates

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Speaker 2: the left hemisphere, so that's the language center of the brain.

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Speaker 2: Letters and text reading engage the visual and motor areas

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Speaker 2: of the brain, and words and sentence reading activate language,

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Speaker 2: classic language what they call language clusters also in the

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Speaker 2: left hemisphere. And they didn't know this, but the cerebellum

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Speaker 2: has never really been thought of to be engaged when

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Speaker 2: you know, we're reading. And in fact, the right cellar

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Speaker 2: about cerebellum is active in all types of reading, and

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Speaker 2: that's out loud reading, so with speech production. And then

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Speaker 2: the left side of the cerebellum is more active during

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Speaker 2: the word reading, like you know, you know, when we're

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Speaker 2: trying to get concepts, So we're trying to get meaning

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Speaker 2: out of whatever it is that we're reading, you know,

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Speaker 2: when we're thinking, really concentrating from focusing really closely on

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Speaker 2: whatever it is we're trying to make sense of.

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Speaker 1: M It's interesting because you can read the same thing,

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Speaker 1: but it won't you and I can read the same thing,

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Speaker 1: but it won't produce the same neurological response in the brain,

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Speaker 1: or biochemical response or emotional response, because there's like the

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Speaker 1: variable with everything is how you interpret things or how

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Speaker 1: you know, Like with food, it's how your biology interacts

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Speaker 1: with that food or that drug. And then with like

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Speaker 1: what we would call, you know, content or you know,

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Speaker 1: cognitive input or data or information. Yeah, it's how how

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Speaker 1: you respond and interact with that as well.

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Speaker 2: But I was we've been involved through my business, so

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Speaker 2: we've we've been involved recently in an amazing organization and

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Speaker 2: a horse welfare organization called Project Hope, and they rescue horses.

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Speaker 2: Their members take the horses on that have been neglected

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Speaker 2: or abused or whatever, and I have the real privilege

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Speaker 2: of working with them to do their annual report. I've

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Speaker 2: just done their third annual report. But there was one

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Speaker 2: I don't know and I don't know whether it was

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Speaker 2: a vulnerability at the time, but my colleague Robert was

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Speaker 2: in the office and it was early in the morning.

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Speaker 2: We just started work and I picked up the document

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Speaker 2: and I was pulling images in so the clients had,

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Speaker 2: you know, put a whole lot of images of horses

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Speaker 2: they've rescued, and there was one picture of a horse

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Speaker 2: and I'm not even going to describe what had happened

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Speaker 2: to it, but I actually walked away from the computer

431
00:24:40,440 --> 00:24:44,119
Speaker 2: and started crying. It was so it may have just

432
00:24:44,160 --> 00:24:47,719
Speaker 2: been a vulnerable moment for me and seeing an animal

433
00:24:47,720 --> 00:24:50,800
Speaker 2: and distress or seeing anything in distress, but I was

434
00:24:51,080 --> 00:24:55,360
Speaker 2: so taken aback from seeing that. You don't just see

435
00:24:56,119 --> 00:25:00,199
Speaker 2: the physical you know, you know what that horse is

436
00:25:00,240 --> 00:25:02,359
Speaker 2: going through physically in terms of what you're seeing on

437
00:25:02,400 --> 00:25:05,359
Speaker 2: the page, but your brain takes you to the scenario

438
00:25:05,440 --> 00:25:08,280
Speaker 2: as well, so the pain that might be associated with

439
00:25:08,320 --> 00:25:11,199
Speaker 2: the you know, the neglect that may have been associated

440
00:25:11,200 --> 00:25:13,920
Speaker 2: with so our brains work in a really amazing way,

441
00:25:13,960 --> 00:25:16,200
Speaker 2: so we're not just looking at a picture and feeling distressed.

442
00:25:16,920 --> 00:25:19,119
Speaker 2: I went to a totally different place where I started

443
00:25:19,160 --> 00:25:22,119
Speaker 2: seeing more than just that, and it was you know,

444
00:25:22,160 --> 00:25:24,159
Speaker 2: and we talked about it. I talked about it to

445
00:25:24,200 --> 00:25:27,399
Speaker 2: the clients because you know, they're celebrating fifty years of

446
00:25:28,040 --> 00:25:32,679
Speaker 2: rescuing animals, rescuing horses, and you know the amount of

447
00:25:32,680 --> 00:25:36,160
Speaker 2: horses over the years that have been rescued through this organization,

448
00:25:36,240 --> 00:25:39,560
Speaker 2: and it's staggering that. You know, we thought, well, I've

449
00:25:39,600 --> 00:25:42,480
Speaker 2: always had a challenge with putting those sorts of confronting

450
00:25:42,520 --> 00:25:45,080
Speaker 2: images into their annual report. But the reality of it

451
00:25:45,160 --> 00:25:47,120
Speaker 2: is you've got to show both. You know, it's great

452
00:25:47,119 --> 00:25:48,920
Speaker 2: to see all these horses that have been rescued, but

453
00:25:48,960 --> 00:25:51,399
Speaker 2: when you see the neglect as a contrast, like the

454
00:25:51,440 --> 00:25:55,719
Speaker 2: before and after, it's very very powerful. So the written

455
00:25:55,720 --> 00:25:58,800
Speaker 2: word or the visuals can take our brain down a

456
00:25:58,840 --> 00:26:02,120
Speaker 2: real rabbit hole and can can really be emotional. I've

457
00:26:02,119 --> 00:26:04,639
Speaker 2: set on trams before reading a book laughing out loud,

458
00:26:05,600 --> 00:26:09,159
Speaker 2: you know, so you know, those sorts of as you

459
00:26:09,200 --> 00:26:12,000
Speaker 2: said before Crago, I can read something and be moved

460
00:26:12,000 --> 00:26:13,640
Speaker 2: by it. I don't know if we've spoken this about

461
00:26:13,640 --> 00:26:14,880
Speaker 2: this on the show. You know, it was the best

462
00:26:14,920 --> 00:26:17,280
Speaker 2: of times, it was the worst of times. I think.

463
00:26:17,400 --> 00:26:21,800
Speaker 2: I'm sure we've quoted that from A Tale of Two Cities.

464
00:26:22,040 --> 00:26:27,159
Speaker 2: You know, some of these profound written dialogue. Ever, you know,

465
00:26:27,240 --> 00:26:32,480
Speaker 2: it's such an amazingly moving, you know, opening paragraph. When

466
00:26:32,520 --> 00:26:35,000
Speaker 2: you think about, you know, an opening sentence to a book.

467
00:26:36,000 --> 00:26:39,760
Speaker 1: Yeah, you raise an interesting point, and this is kind

468
00:26:39,800 --> 00:26:42,800
Speaker 1: of off topic for tech, but it's a philosophical and

469
00:26:44,400 --> 00:26:50,360
Speaker 1: I think it's like an ethical and a moral issue.

470
00:26:51,160 --> 00:26:54,879
Speaker 1: So there's a podcast which I'm happy to promote good podcasts.

471
00:26:54,880 --> 00:26:58,040
Speaker 1: There's a guy called Sean Ryan who's an ex Navy

472
00:26:58,080 --> 00:27:02,920
Speaker 1: seal blah blah blah, but quite a deep, deep thinking,

473
00:27:03,200 --> 00:27:07,000
Speaker 1: like to me, a really good human And he had

474
00:27:07,000 --> 00:27:09,800
Speaker 1: a guy on called Tim Tebow yesterday other day before,

475
00:27:10,200 --> 00:27:14,359
Speaker 1: who's the next Heisman Trophy winner, which just means he

476
00:27:14,400 --> 00:27:19,679
Speaker 1: was the best college football player regarded maybe ever went on,

477
00:27:19,760 --> 00:27:23,919
Speaker 1: played in the NFL, didn't really crush it, but just

478
00:27:23,960 --> 00:27:26,959
Speaker 1: a good human being. And he's been renowned as just

479
00:27:27,000 --> 00:27:32,880
Speaker 1: this good dude. And he started this thing highlighting all

480
00:27:32,920 --> 00:27:37,399
Speaker 1: of this stuff around child exploitation and all the stuff

481
00:27:37,400 --> 00:27:39,399
Speaker 1: that I don't even really want to mention, but you

482
00:27:39,440 --> 00:27:43,240
Speaker 1: can imagine, right, And I just saw these two on

483
00:27:43,280 --> 00:27:46,000
Speaker 1: a clip yesterday. It came up and they were talking

484
00:27:46,240 --> 00:27:52,439
Speaker 1: and like it is, so it makes me so sad

485
00:27:52,480 --> 00:27:55,520
Speaker 1: for the children. It makes me so fucking angry at

486
00:27:55,560 --> 00:27:59,159
Speaker 1: the people that do this shit, right, And I just

487
00:27:59,320 --> 00:28:02,040
Speaker 1: I was nearly going to turn off because I I

488
00:28:02,080 --> 00:28:05,159
Speaker 1: almost can't cope, right, I don't. I don't want to know.

489
00:28:05,960 --> 00:28:10,560
Speaker 1: And then and then Tim Tebow said when he starts

490
00:28:10,600 --> 00:28:14,679
Speaker 1: to tell he starts to talk. A lot of people go, no, no, no,

491
00:28:14,720 --> 00:28:15,800
Speaker 1: I don't want to know. I don't want to know.

492
00:28:15,880 --> 00:28:18,560
Speaker 1: I've got children, I don't don't tell me, right, And

493
00:28:18,600 --> 00:28:21,160
Speaker 1: I'm like, that is because that's it. I don't even

494
00:28:21,320 --> 00:28:25,359
Speaker 1: have children. And it fucking that. There's so many emotions

495
00:28:25,359 --> 00:28:27,280
Speaker 1: that that brings up in me. And I think to

496
00:28:27,359 --> 00:28:30,479
Speaker 1: your point about the horses, of course, and then all

497
00:28:30,560 --> 00:28:35,000
Speaker 1: of the other myriad of horrible things, domestic violence against women,

498
00:28:35,240 --> 00:28:39,240
Speaker 1: you know, all of the fucking horrible things, as painful

499
00:28:39,240 --> 00:28:42,520
Speaker 1: and as it as uncomfortable as it is, we need

500
00:28:42,600 --> 00:28:46,920
Speaker 1: to fucking throw the door open. And I think, I

501
00:28:47,000 --> 00:28:50,440
Speaker 1: hate looking at it, but I think, and then you go, okay,

502
00:28:51,360 --> 00:28:54,080
Speaker 1: well now I know this. What can I do? Can

503
00:28:54,120 --> 00:28:57,040
Speaker 1: I do anything? I know I can't turn it around,

504
00:28:57,080 --> 00:29:01,440
Speaker 1: but can I do anything? And I think one of them, sorry,

505
00:29:01,480 --> 00:29:04,760
Speaker 1: one of the you know, one of the opportunities that

506
00:29:05,080 --> 00:29:08,800
Speaker 1: social media presents, if used in the right way, is

507
00:29:08,840 --> 00:29:10,840
Speaker 1: that we can shine a light on some of this

508
00:29:11,040 --> 00:29:14,680
Speaker 1: dark shit that happens and maybe do some good that way.

509
00:29:14,920 --> 00:29:19,640
Speaker 2: One of my favorite favorite quotes is by an author

510
00:29:19,640 --> 00:29:22,120
Speaker 2: by the name of Edmund Or. It's attributed to Edmund

511
00:29:22,160 --> 00:29:25,960
Speaker 2: Burke and it says the only thing necessary for triumph,

512
00:29:26,040 --> 00:29:28,280
Speaker 2: for the for triumph of evil is for good men

513
00:29:28,320 --> 00:29:28,880
Speaker 2: to do nothing.

514
00:29:29,480 --> 00:29:31,440
Speaker 1: Yeah. Yeah, I quite that all the time.

515
00:29:31,480 --> 00:29:34,400
Speaker 2: It's so amazing, is it's.

516
00:29:34,680 --> 00:29:39,200
Speaker 1: Yeah, yeah, all it's necessary for evil to triumph is

517
00:29:39,240 --> 00:29:41,600
Speaker 1: for good men to do nothing. Yeah. It's like it's

518
00:29:41,600 --> 00:29:45,520
Speaker 1: so true. It's so and it's like almost like not

519
00:29:45,640 --> 00:29:50,280
Speaker 1: my problem, you know, not my problem. It's like, well,

520
00:29:51,800 --> 00:29:57,840
Speaker 1: stop being a selfish prick because some people, some people

521
00:29:57,960 --> 00:30:01,280
Speaker 1: can't look after themselves. Some people just need other people

522
00:30:01,320 --> 00:30:02,600
Speaker 1: to fucking help, you know.

523
00:30:03,440 --> 00:30:04,040
Speaker 2: Yeah.

524
00:30:04,160 --> 00:30:07,600
Speaker 1: Anyway, so that took.

525
00:30:07,400 --> 00:30:10,560
Speaker 2: A philosophical I know, I didn't kind of know where

526
00:30:10,560 --> 00:30:11,000
Speaker 2: to go with that.

527
00:30:12,320 --> 00:30:15,640
Speaker 1: I think that's okay. Not every moment on a podcast

528
00:30:15,720 --> 00:30:17,920
Speaker 1: needs to be comfortable or cheesy or fun, you know.

529
00:30:18,280 --> 00:30:21,560
Speaker 1: I just think like, and the three of us all

530
00:30:21,640 --> 00:30:24,960
Speaker 1: have a small voice in a fucking ocean of voices,

531
00:30:25,560 --> 00:30:27,960
Speaker 1: and sometimes you've got to go, yeah, okay, well this

532
00:30:28,120 --> 00:30:30,400
Speaker 1: is where we are. But what do you think you know?

533
00:30:30,760 --> 00:30:36,520
Speaker 2: So, yeah, definitely no problem there. I was. I know

534
00:30:36,600 --> 00:30:40,200
Speaker 2: that because you both are in the fitness world, I

535
00:30:40,280 --> 00:30:43,600
Speaker 2: often think about you know, real age, biological age, you

536
00:30:43,680 --> 00:30:45,960
Speaker 2: know what what does it mean as we get older

537
00:30:46,000 --> 00:30:49,120
Speaker 2: and you know, how do we know whether we're doing well,

538
00:30:49,160 --> 00:30:51,560
Speaker 2: whether we're on track, and you know, we're doing all

539
00:30:51,600 --> 00:30:54,240
Speaker 2: the right things for our body. I remember reading a

540
00:30:54,280 --> 00:30:57,200
Speaker 2: study and I tell my tai chies students this all

541
00:30:57,200 --> 00:30:59,880
Speaker 2: the time that when you get over the age of fifty,

542
00:31:00,240 --> 00:31:04,240
Speaker 2: it's it's fairly well documented that doing twenty minutes of

543
00:31:04,360 --> 00:31:08,240
Speaker 2: really vigorous exercise and say doing twenty minutes of something

544
00:31:08,280 --> 00:31:12,120
Speaker 2: like meditation, tai chi yoga can be just as beneficial.

545
00:31:12,480 --> 00:31:15,040
Speaker 2: So over a certain age, we don't have to you know,

546
00:31:15,120 --> 00:31:18,200
Speaker 2: knock ourselves out and go super hard. Not that that's

547
00:31:18,240 --> 00:31:21,800
Speaker 2: a bad thing, but you know, doing some form of

548
00:31:21,840 --> 00:31:26,520
Speaker 2: exercise is really important. But there's an interesting little article

549
00:31:26,520 --> 00:31:28,320
Speaker 2: that I read and I thought, oh, you know, it

550
00:31:28,360 --> 00:31:32,280
Speaker 2: was talking about how to measure, you know, your fitness level,

551
00:31:32,480 --> 00:31:34,920
Speaker 2: and one of the things they talk about was grip strength.

552
00:31:34,960 --> 00:31:37,920
Speaker 2: Have you ever heard that how grip strength actually says

553
00:31:38,000 --> 00:31:40,400
Speaker 2: a whole lot about how the rest of your body

554
00:31:40,440 --> 00:31:43,560
Speaker 2: is functioning and how on track you are. Have you

555
00:31:43,600 --> 00:31:44,600
Speaker 2: have you heard that one? Kraigo?

556
00:31:45,440 --> 00:31:48,600
Speaker 1: Yeah, that's a that's a standard mate, Yep, that's fairly

557
00:31:48,640 --> 00:31:52,120
Speaker 1: well known. Yeah. Also stride length. You know, there's a

558
00:31:52,120 --> 00:31:54,760
Speaker 1: bunch of things, but grip strength is a is an

559
00:31:54,800 --> 00:31:55,760
Speaker 1: indicator for sure.

560
00:31:56,080 --> 00:31:58,440
Speaker 2: Yeah. So the interesting thing is that you don't have

561
00:31:58,560 --> 00:32:01,760
Speaker 2: to because there are obviously you know, you can. What's

562
00:32:01,800 --> 00:32:03,840
Speaker 2: the measurement? How do you the device you use for

563
00:32:03,880 --> 00:32:09,120
Speaker 2: measuring grip? It's a dynamometer, Yeah, it's yeah, but it's pounds.

564
00:32:09,240 --> 00:32:12,720
Speaker 2: It's like pounds of force, I think, yep. Yeah. So

565
00:32:13,880 --> 00:32:16,680
Speaker 2: this little article I was reading recently was talking about

566
00:32:16,680 --> 00:32:18,959
Speaker 2: just grabbing a tennis ball and how long you can

567
00:32:19,040 --> 00:32:21,280
Speaker 2: compress and hold the tennis ball. And then the other

568
00:32:21,280 --> 00:32:23,160
Speaker 2: thing was if you go to a pull up bar

569
00:32:23,520 --> 00:32:25,960
Speaker 2: and just hang from the pull up bar, how long

570
00:32:26,000 --> 00:32:28,280
Speaker 2: you can hang from the pull up bar is another

571
00:32:28,320 --> 00:32:30,520
Speaker 2: one as well, just to measure it, and I think

572
00:32:30,560 --> 00:32:33,920
Speaker 2: for women it's over thirty seconds or thirty to forty seconds,

573
00:32:33,960 --> 00:32:36,280
Speaker 2: and for men it's over sixty seconds. So if you

574
00:32:36,320 --> 00:32:39,000
Speaker 2: can just hang for over sixty seconds as a bloke

575
00:32:39,600 --> 00:32:41,920
Speaker 2: or thirty to forty seconds as a woman, then that

576
00:32:42,560 --> 00:32:45,080
Speaker 2: says that you know you're doing pretty well. So just

577
00:32:45,120 --> 00:32:47,560
Speaker 2: a little simple test you can do at home, But

578
00:32:48,000 --> 00:32:50,200
Speaker 2: that fascinates me. I mean, I don't know that I'm

579
00:32:50,440 --> 00:32:52,520
Speaker 2: obsessed by it. But I you know, obviously I'm in

580
00:32:52,520 --> 00:32:54,440
Speaker 2: a room of people when I talk about a room,

581
00:32:54,480 --> 00:32:57,880
Speaker 2: a virtual room of people who are pretty focused on

582
00:32:58,360 --> 00:33:01,080
Speaker 2: staying healthy and looking good as they grace as they

583
00:33:01,160 --> 00:33:04,800
Speaker 2: age gracefully, and doing all the right things or trying

584
00:33:04,840 --> 00:33:07,560
Speaker 2: to do the right things. So it is something I guess,

585
00:33:07,920 --> 00:33:10,280
Speaker 2: you know, the people who listen to the podcast to

586
00:33:10,320 --> 00:33:12,680
Speaker 2: listen to the You Project tend to be from my

587
00:33:12,800 --> 00:33:16,080
Speaker 2: understanding and interaction when I see the Facebook group and

588
00:33:16,480 --> 00:33:19,880
Speaker 2: the chats that people have, it's people who get motivated

589
00:33:20,080 --> 00:33:23,840
Speaker 2: and like to be motivated to maybe have a better

590
00:33:23,880 --> 00:33:26,800
Speaker 2: thought process or do better with their lives, look for

591
00:33:26,880 --> 00:33:27,880
Speaker 2: ways to be motivated.

592
00:33:28,280 --> 00:33:28,400
Speaker 1: You know.

593
00:33:28,440 --> 00:33:31,719
Speaker 2: It's like people who pick up books on motivation. You know,

594
00:33:31,760 --> 00:33:33,480
Speaker 2: why do we back them up in the first place,

595
00:33:33,680 --> 00:33:35,480
Speaker 2: Because we want to do better. We want to be better,

596
00:33:35,520 --> 00:33:37,320
Speaker 2: we want our lives to be better. And for me,

597
00:33:37,440 --> 00:33:39,880
Speaker 2: in a lot of ways, technology does that. You know.

598
00:33:39,920 --> 00:33:42,280
Speaker 2: I love that we can use tech and make our

599
00:33:42,320 --> 00:33:44,880
Speaker 2: lives better. And you know, I know that you use

600
00:33:44,960 --> 00:33:47,840
Speaker 2: chat GPT and some of the prompts and chat GPT,

601
00:33:47,960 --> 00:33:51,040
Speaker 2: and actually that kind of opens to the to the

602
00:33:51,160 --> 00:33:53,560
Speaker 2: little topic I wanted to chat about is for people

603
00:33:53,600 --> 00:33:56,800
Speaker 2: who do use chat GPT, the prompts you use. And

604
00:33:56,840 --> 00:33:59,160
Speaker 2: I have this funny story. So this week I went

605
00:33:59,200 --> 00:34:03,320
Speaker 2: to the local super market and the I started, you know,

606
00:34:03,560 --> 00:34:06,240
Speaker 2: take took my stuff to the register and they started

607
00:34:06,280 --> 00:34:08,239
Speaker 2: scanning through the stuff and then I had mean, I

608
00:34:08,280 --> 00:34:11,840
Speaker 2: think I had like a carrot and some szucchini and

609
00:34:11,880 --> 00:34:13,840
Speaker 2: some other bits and pieces. But when they went to

610
00:34:14,040 --> 00:34:17,879
Speaker 2: weigh them, there was something wrong with the scales and

611
00:34:18,320 --> 00:34:20,319
Speaker 2: they could meet. They could weigh it, but it wasn't

612
00:34:20,360 --> 00:34:23,960
Speaker 2: translating to their first their pose system, the port of

613
00:34:24,040 --> 00:34:26,800
Speaker 2: sales system, And so the woman was standing there. We

614
00:34:26,840 --> 00:34:29,000
Speaker 2: went to a different register. She put the stuff on

615
00:34:29,040 --> 00:34:33,000
Speaker 2: the scales, it still wouldn't interact. And then the senior

616
00:34:33,080 --> 00:34:35,279
Speaker 2: lady there said, we'll work out what it is per

617
00:34:35,360 --> 00:34:38,680
Speaker 2: kilo and you know, and try to work it out.

618
00:34:38,760 --> 00:34:41,319
Speaker 2: So if a carrot is three dollars sixty akilo and

619
00:34:41,360 --> 00:34:44,640
Speaker 2: you've got a one hundred and sixty gram carrot, try

620
00:34:44,680 --> 00:34:46,520
Speaker 2: to work it out. And of course the first thing

621
00:34:46,640 --> 00:34:48,440
Speaker 2: I did was I just jumped on the chat GPT

622
00:34:48,600 --> 00:34:52,520
Speaker 2: on my phone and like all the girls were all

623
00:34:52,560 --> 00:34:54,759
Speaker 2: just looking dumbfounded. It's like, how did we work that out,

624
00:34:54,960 --> 00:34:57,000
Speaker 2: and I don't think off the top of my head,

625
00:34:57,000 --> 00:34:58,840
Speaker 2: I probably could have worked it out without Jack GBT.

626
00:34:58,920 --> 00:35:02,640
Speaker 2: I'm gonna be honest, full transparency here. Chat CHPT did

627
00:35:02,640 --> 00:35:04,239
Speaker 2: it for me. And so there I am standing at

628
00:35:04,239 --> 00:35:07,240
Speaker 2: the cash register going through and we were working out

629
00:35:07,360 --> 00:35:09,200
Speaker 2: you know, if I have a carrot and it's one

630
00:35:09,280 --> 00:35:11,960
Speaker 2: hundred and sixty grams and it costs you know, three

631
00:35:12,000 --> 00:35:15,640
Speaker 2: dollars sixty a kilo, what is my carrot worth? You know?

632
00:35:15,680 --> 00:35:18,319
Speaker 2: And it was kind of fun that we sold our

633
00:35:18,360 --> 00:35:23,520
Speaker 2: little problem using chat GPT in a practical sense, but

634
00:35:23,719 --> 00:35:26,000
Speaker 2: some of the prompts you put into chat GPT can

635
00:35:26,040 --> 00:35:28,400
Speaker 2: be really interesting. So if you've got a.

636
00:35:28,600 --> 00:35:31,759
Speaker 1: Ton I tell you, can I tell you for future use? Yeah,

637
00:35:31,840 --> 00:35:34,759
Speaker 1: go for it, go three sixty times point one six.

638
00:35:35,160 --> 00:35:37,360
Speaker 2: Yeah, thanks for that. It was easier with chat.

639
00:35:37,560 --> 00:35:41,359
Speaker 1: T Nah, not really, not really.

640
00:35:42,000 --> 00:35:44,279
Speaker 2: No, Actually it was interesting. Chat GPT not only gave

641
00:35:44,320 --> 00:35:46,359
Speaker 2: me the answer, but it showed me how to do it.

642
00:35:46,440 --> 00:35:52,240
Speaker 2: So yeah, but yeah, so creating a better to do list?

643
00:35:52,719 --> 00:35:54,960
Speaker 2: So what you know, this this is an article that

644
00:35:55,000 --> 00:35:56,880
Speaker 2: I was reading and it was just kind of giving

645
00:35:56,880 --> 00:35:59,480
Speaker 2: you some prompts that you can use for AI to

646
00:35:59,600 --> 00:36:03,000
Speaker 2: try to you know, streamline what you do. So obviously

647
00:36:03,000 --> 00:36:06,359
Speaker 2: summarizing documents is fantastic. If you have a document, throw

648
00:36:06,400 --> 00:36:07,960
Speaker 2: it in there and just say, can you just give

649
00:36:08,000 --> 00:36:11,799
Speaker 2: me a one paragraph overview of what this document is.

650
00:36:12,080 --> 00:36:14,440
Speaker 2: And it doesn't necessarily mean you're getting get to do

651
00:36:14,520 --> 00:36:17,000
Speaker 2: all the work for you, but it could be things

652
00:36:17,200 --> 00:36:20,080
Speaker 2: as simple as you know, one of my staff recently

653
00:36:20,360 --> 00:36:22,240
Speaker 2: sent out an email that had a lot of errors

654
00:36:22,239 --> 00:36:24,560
Speaker 2: in it, and so you know, it had already gone

655
00:36:24,600 --> 00:36:26,600
Speaker 2: out to the client. He'd copied me in and it

656
00:36:26,680 --> 00:36:29,800
Speaker 2: felt a little bit uncomfortable to have the conversation. You know,

657
00:36:29,840 --> 00:36:33,120
Speaker 2: I'm a grown purse, grown man, but I said to him, look,

658
00:36:33,280 --> 00:36:36,520
Speaker 2: just run it through chat GPT, get chat GPT to

659
00:36:36,600 --> 00:36:41,160
Speaker 2: check for errors and look for formatting. And that's not cheating.

660
00:36:41,360 --> 00:36:43,279
Speaker 2: You've already done all the hard yards. You've got all

661
00:36:43,320 --> 00:36:46,080
Speaker 2: the data in there and all the information. But it

662
00:36:46,080 --> 00:36:48,640
Speaker 2: can be really good. So but creating a smarter to

663
00:36:48,719 --> 00:36:52,400
Speaker 2: do list, I love that idea where it will prioritize,

664
00:36:52,440 --> 00:36:54,440
Speaker 2: so you put your list of things in and then

665
00:36:54,480 --> 00:36:57,920
Speaker 2: it helps you think about how to prioritize, and sometimes

666
00:36:57,920 --> 00:36:59,480
Speaker 2: that can just take a little bit of that emotional

667
00:36:59,480 --> 00:37:01,719
Speaker 2: burden off I don't know about you. When I've got

668
00:37:01,719 --> 00:37:03,120
Speaker 2: my list of things that I need to do for

669
00:37:03,160 --> 00:37:05,600
Speaker 2: the day, it can be a bit overwhelming. So tackling

670
00:37:05,640 --> 00:37:08,080
Speaker 2: the right thing at the right time, I reckon that's

671
00:37:08,120 --> 00:37:12,320
Speaker 2: a great idea for using something like you know, chet GPT,

672
00:37:12,520 --> 00:37:16,560
Speaker 2: or even just troubleshooting a problem, you know, saying well,

673
00:37:16,640 --> 00:37:18,880
Speaker 2: how can I tackle this? What's the best way around.

674
00:37:19,160 --> 00:37:22,799
Speaker 2: I had to write a bereavement note recently and I

675
00:37:22,840 --> 00:37:26,000
Speaker 2: actually wrote down, you know, I started penning my thoughts

676
00:37:26,040 --> 00:37:29,080
Speaker 2: and then I actually asked chat GPT about the best

677
00:37:29,120 --> 00:37:33,600
Speaker 2: way to send this bereavement message and it actually helped.

678
00:37:34,520 --> 00:37:36,480
Speaker 2: You know, it draws from what they call a large

679
00:37:36,600 --> 00:37:41,279
Speaker 2: language model, so the AI has been trained on a

680
00:37:41,320 --> 00:37:44,080
Speaker 2: whole lot of information, so we can take all that

681
00:37:44,120 --> 00:37:46,640
Speaker 2: info put it all together. And it really helped because

682
00:37:46,640 --> 00:37:50,279
Speaker 2: I find that conversation, that dialogue for somebody who's just

683
00:37:50,360 --> 00:37:54,080
Speaker 2: lost someone can be really difficult. And it's happened in

684
00:37:54,080 --> 00:37:56,920
Speaker 2: two instances, one where someone quite young in their thirties

685
00:37:56,960 --> 00:37:59,680
Speaker 2: died suddenly, and the other was a close friend of

686
00:37:59,719 --> 00:38:02,520
Speaker 2: mine who had a partner, a previous partner they'd broken

687
00:38:02,640 --> 00:38:05,600
Speaker 2: up with, but he committed suicide and so you know,

688
00:38:05,680 --> 00:38:10,440
Speaker 2: how do you message somebody and not cause further grief?

689
00:38:10,760 --> 00:38:12,879
Speaker 2: You know, you want to support the person, And I

690
00:38:12,920 --> 00:38:15,160
Speaker 2: find that really difficult. I think I'm pretty good. I'm

691
00:38:15,160 --> 00:38:17,560
Speaker 2: a good wordsmith and I'm good at writing stuff. But

692
00:38:17,840 --> 00:38:21,000
Speaker 2: just having a little bit of helpful oversight from a

693
00:38:21,120 --> 00:38:24,920
Speaker 2: large language model like chat CHAPT I find really really useful.

694
00:38:26,840 --> 00:38:29,800
Speaker 1: Yeah, I think we're just starting to understand the scope

695
00:38:29,960 --> 00:38:34,840
Speaker 1: for and there's a really common concern at the moment

696
00:38:34,880 --> 00:38:38,160
Speaker 1: that we're going to become dumber, it's going to hijack here.

697
00:38:38,320 --> 00:38:41,560
Speaker 1: I don't think that at all. I think like I

698
00:38:41,719 --> 00:38:46,840
Speaker 1: use I use Claude, which is a more academic focused LLM,

699
00:38:47,080 --> 00:38:51,799
Speaker 1: andat GPT as well for different things. Obviously not to

700
00:38:51,880 --> 00:38:56,360
Speaker 1: do my work in terms of my PhD, but where

701
00:38:56,520 --> 00:39:00,640
Speaker 1: I can I can plug in something and say, you know,

702
00:39:00,719 --> 00:39:03,399
Speaker 1: I can plug in someone else's article and say give

703
00:39:03,440 --> 00:39:06,640
Speaker 1: me the three key takeaways from this, or I can say,

704
00:39:06,719 --> 00:39:10,800
Speaker 1: explain this article to me as though I'm a fourteen

705
00:39:10,880 --> 00:39:14,200
Speaker 1: year old who doesn't understand science, and it'll do it instantly.

706
00:39:14,360 --> 00:39:18,480
Speaker 1: Like it really comes down to, like you go, all right,

707
00:39:18,480 --> 00:39:21,600
Speaker 1: a kitchen knife as a tool, a pushbike as a tool,

708
00:39:21,760 --> 00:39:25,040
Speaker 1: you know, an la LAMB, a large learning model or

709
00:39:25,080 --> 00:39:28,560
Speaker 1: AI is a tool. It really comes down to how

710
00:39:28,680 --> 00:39:31,839
Speaker 1: we use it and people who are terrified of it. One.

711
00:39:32,000 --> 00:39:35,719
Speaker 1: I understand that because new things can be terrifying. But

712
00:39:35,840 --> 00:39:38,680
Speaker 1: to figure out I was talking to my friend Christian

713
00:39:38,760 --> 00:39:41,919
Speaker 1: last night at the gym. TI. If you know Christian, tall,

714
00:39:41,960 --> 00:39:44,520
Speaker 1: goofy Christian, and God bless him. He's a great man.

715
00:39:44,560 --> 00:39:46,359
Speaker 1: You know him as well. He used to work at harp.

716
00:39:46,600 --> 00:39:48,839
Speaker 2: Yeah I do, yeah, yeah, yeah yeah.

717
00:39:48,920 --> 00:39:53,879
Speaker 1: And Christian's dyslexic and that's not a you know, that's

718
00:39:54,040 --> 00:39:56,799
Speaker 1: just you know, it's like he's six foot four and

719
00:39:56,800 --> 00:39:59,440
Speaker 1: he's also dyslexic, right, It's just part of who he is.

720
00:40:00,200 --> 00:40:03,760
Speaker 1: And I was explaining to him, you know, for somebody

721
00:40:03,880 --> 00:40:07,359
Speaker 1: like him to just be able to talk rather than

722
00:40:07,400 --> 00:40:11,720
Speaker 1: write or even and so he doesn't use it. And

723
00:40:11,840 --> 00:40:13,839
Speaker 1: so I got it out in the gym and I said,

724
00:40:13,840 --> 00:40:15,960
Speaker 1: look at this. So I just pressed it and I said,

725
00:40:17,400 --> 00:40:19,840
Speaker 1: you know, it explained to me the difference between spiritually

726
00:40:19,880 --> 00:40:22,840
Speaker 1: and religion, because he's quite spiritual. He's going down this

727
00:40:22,960 --> 00:40:26,200
Speaker 1: big spiritual rabbit and it's like, noa, I go ask

728
00:40:26,280 --> 00:40:28,720
Speaker 1: it anything you want, And he asked it a question,

729
00:40:28,840 --> 00:40:31,839
Speaker 1: I go, by the way, this is Christian, and it goes, oh,

730
00:40:31,920 --> 00:40:35,799
Speaker 1: hi Christian, great question, and then it answers and I'm like, dude,

731
00:40:36,120 --> 00:40:40,279
Speaker 1: just talk to it. But it will answer anything you want.

732
00:40:40,360 --> 00:40:42,800
Speaker 1: You don't have to read, you don't have to type.

733
00:40:42,880 --> 00:40:44,800
Speaker 1: All you've got to do is be able to talk.

734
00:40:45,520 --> 00:40:47,400
Speaker 1: It will talk back to you. And by the way,

735
00:40:47,880 --> 00:40:51,160
Speaker 1: whatever it says to you, there's also a written version

736
00:40:51,200 --> 00:40:53,320
Speaker 1: if you do want to happen to cut and paste

737
00:40:53,320 --> 00:40:56,319
Speaker 1: that into a document or something. And so where you

738
00:40:57,520 --> 00:41:01,200
Speaker 1: like I said to it the other day, over the

739
00:41:01,239 --> 00:41:04,960
Speaker 1: next two years, I want to develop you know, this, this, this, this, this,

740
00:41:05,040 --> 00:41:08,640
Speaker 1: and this, Like there's there's academic stuff, there's podcast stuff,

741
00:41:08,680 --> 00:41:11,759
Speaker 1: there's business, there's corporate, there's you know, there's all these

742
00:41:11,800 --> 00:41:14,160
Speaker 1: different components to what I want to do be create

743
00:41:14,200 --> 00:41:16,520
Speaker 1: over the next two years. And it's like, can you

744
00:41:16,560 --> 00:41:19,680
Speaker 1: help me plan this? And it's like absolutely, And then

745
00:41:19,719 --> 00:41:22,600
Speaker 1: it just comes up with all these brilliant like you're

746
00:41:22,680 --> 00:41:27,520
Speaker 1: sitting with a high end business coach, and then it says,

747
00:41:27,520 --> 00:41:29,759
Speaker 1: would you like me to unpack any of these? I go, yeah,

748
00:41:29,800 --> 00:41:32,120
Speaker 1: this bit here, what do you mean by that? Can

749
00:41:32,160 --> 00:41:35,359
Speaker 1: you un And then it goes absolutely. I mean, all

750
00:41:35,400 --> 00:41:38,080
Speaker 1: you need to do, need to know is how to

751
00:41:38,120 --> 00:41:42,040
Speaker 1: best interact with it. It's I mean this is people

752
00:41:42,080 --> 00:41:44,240
Speaker 1: are scared of it. I think people should be excited.

753
00:41:44,320 --> 00:41:48,440
Speaker 1: I think AI taking over down the track when it

754
00:41:48,480 --> 00:41:52,680
Speaker 1: becomes sentient, which will be about next Thursday. That's a

755
00:41:52,680 --> 00:41:53,320
Speaker 1: different matter.

756
00:41:57,160 --> 00:41:59,600
Speaker 2: One of the things I'm always mindful of is what

757
00:41:59,640 --> 00:42:05,400
Speaker 2: I'm fing into AI because obviously there's privacy concerns. But

758
00:42:05,840 --> 00:42:08,920
Speaker 2: if you're using the free version of chatchpt and you

759
00:42:09,000 --> 00:42:12,440
Speaker 2: don't log into it as long as so, for example,

760
00:42:12,560 --> 00:42:14,880
Speaker 2: you might have a medical report that you wanted to

761
00:42:14,920 --> 00:42:18,320
Speaker 2: look at, you could paste all you know. I recently

762
00:42:18,320 --> 00:42:20,319
Speaker 2: went to my GP, I got my annual blood test

763
00:42:20,360 --> 00:42:23,239
Speaker 2: done being vegan, and I'm already is always mindful that

764
00:42:23,280 --> 00:42:26,080
Speaker 2: I've got the right nuxperience and that I'm tracking okay.

765
00:42:26,600 --> 00:42:28,640
Speaker 2: And you know, the doctor said to me, this is great.

766
00:42:28,680 --> 00:42:31,239
Speaker 2: You know your figures are really really good. But I

767
00:42:31,280 --> 00:42:35,600
Speaker 2: could potentially put all of those in, but use it

768
00:42:35,640 --> 00:42:39,520
Speaker 2: without logging in, and don't put all your personal details.

769
00:42:39,640 --> 00:42:42,520
Speaker 2: Just put the results so it's not going to attribute

770
00:42:42,560 --> 00:42:44,839
Speaker 2: that to you. If you're concerned, I've got to tell

771
00:42:44,840 --> 00:42:47,719
Speaker 2: you one very quick quote we're talking quotes before. All

772
00:42:47,760 --> 00:42:49,840
Speaker 2: you have to decide is what to do with the

773
00:42:49,920 --> 00:42:52,239
Speaker 2: time that is given to you. Who said that?

774
00:42:53,840 --> 00:42:55,839
Speaker 1: I do not know, but I'm not mad at it.

775
00:42:55,960 --> 00:42:57,640
Speaker 1: I quite like it, isn't it.

776
00:42:57,680 --> 00:42:59,360
Speaker 2: I actually have that printed in my office. It was

777
00:42:59,400 --> 00:43:01,440
Speaker 2: Gandalf from the Lord of the Rings.

778
00:43:03,520 --> 00:43:06,799
Speaker 1: I thought you were going to say Gandy, but okay.

779
00:43:07,800 --> 00:43:09,480
Speaker 2: It's like it's a great quote though, isn't it.

780
00:43:10,160 --> 00:43:13,520
Speaker 1: You know what Gandy said and you're not. One of

781
00:43:13,520 --> 00:43:16,320
Speaker 1: his most famous quotes be the change you want to

782
00:43:16,360 --> 00:43:20,880
Speaker 1: see in the world. Yeah, yeah, a little better than Gandalf,

783
00:43:20,880 --> 00:43:22,920
Speaker 1: but yeah.

784
00:43:23,600 --> 00:43:26,520
Speaker 2: One of the most inspiring piece perpose people I ever

785
00:43:26,640 --> 00:43:31,240
Speaker 2: saw was attending a talk being given by the Dalai Lama.

786
00:43:31,840 --> 00:43:35,359
Speaker 2: Now there's a bloke. There's a bloke who who has

787
00:43:35,440 --> 00:43:38,920
Speaker 2: the magical qualities of Gandalf. He was he was pretty

788
00:43:38,960 --> 00:43:39,680
Speaker 2: amazing to see.

789
00:43:39,719 --> 00:43:41,560
Speaker 1: Do you and I go to that together or no?

790
00:43:41,680 --> 00:43:43,080
Speaker 1: Because I saw him as.

791
00:43:42,960 --> 00:43:45,720
Speaker 2: Well, I wonder if we did. He has an amazing

792
00:43:45,760 --> 00:43:48,080
Speaker 2: giggle as well. And you know the other cool thing

793
00:43:48,200 --> 00:43:51,239
Speaker 2: is that we we we share the same birthday, not

794
00:43:51,280 --> 00:43:54,120
Speaker 2: the same birth year, but the same birth day.

795
00:43:54,440 --> 00:44:00,359
Speaker 1: Yes, well, out to the Dalai Lama. If he listening.

796
00:44:00,440 --> 00:44:03,320
Speaker 1: I know he's a big fan. He doesn't catch every episode.

797
00:44:03,440 --> 00:44:05,080
Speaker 2: No, No, that's right, I.

798
00:44:05,000 --> 00:44:07,600
Speaker 1: Think mainly yours. Let's do one or two more, mate.

799
00:44:07,680 --> 00:44:09,919
Speaker 2: I'll tell you about another AI story because this kind

800
00:44:09,920 --> 00:44:12,920
Speaker 2: of freaked me out, and I'm not until entirely sure

801
00:44:13,239 --> 00:44:17,040
Speaker 2: that I think it's a good idea. But we've heard

802
00:44:17,040 --> 00:44:20,360
Speaker 2: about digital resurrections where you take a whole lot of

803
00:44:20,360 --> 00:44:23,640
Speaker 2: footage of somebody who's died and then you get AI

804
00:44:23,800 --> 00:44:26,719
Speaker 2: to generate an artificial version of that person. It could

805
00:44:26,760 --> 00:44:30,360
Speaker 2: be them saying something. This is really full on. So

806
00:44:31,160 --> 00:44:36,200
Speaker 2: a murder victim has addressed his killer in a court

807
00:44:37,120 --> 00:44:42,000
Speaker 2: thanks to AI resurrection. Okay, So there's the sister of

808
00:44:42,040 --> 00:44:46,640
Speaker 2: this guy who who was murdered. Stacy Wales used AI

809
00:44:46,760 --> 00:44:51,120
Speaker 2: to generate a video of her brother Christopher, and she

810
00:44:51,239 --> 00:44:56,800
Speaker 2: actually got she was able to play this artificial version

811
00:44:56,840 --> 00:45:01,480
Speaker 2: of him to the courtroom in the final sentence because

812
00:45:01,520 --> 00:45:03,680
Speaker 2: he was killed in a road rage incident. This is

813
00:45:03,680 --> 00:45:09,160
Speaker 2: in Arizona, and this is reported on NPR. And she said, look,

814
00:45:09,280 --> 00:45:11,520
Speaker 2: the struggle, the thing she struggled with. This court case

815
00:45:11,560 --> 00:45:14,680
Speaker 2: went over two years, and she said, what she struggled

816
00:45:14,719 --> 00:45:16,680
Speaker 2: with the most was that he didn't get to say,

817
00:45:16,960 --> 00:45:20,680
Speaker 2: he didn't get a chance to speak. So it motivated her.

818
00:45:20,960 --> 00:45:23,880
Speaker 2: Just woke up one day and thought, I've got footage

819
00:45:23,920 --> 00:45:26,680
Speaker 2: of him. I've got some real video of him. I

820
00:45:26,800 --> 00:45:28,759
Speaker 2: want him to address and I want people to know

821
00:45:28,800 --> 00:45:33,400
Speaker 2: who he was. It wasn't. The thing is the interesting

822
00:45:33,440 --> 00:45:37,280
Speaker 2: thing about this artificial version of him, this AI generated version,

823
00:45:37,680 --> 00:45:40,160
Speaker 2: was it wasn't about having a go at the person

824
00:45:40,200 --> 00:45:42,080
Speaker 2: who killed him. In fact, it was the opposite. It

825
00:45:42,120 --> 00:45:46,920
Speaker 2: was he would have tried to show some sort of sympathy.

826
00:45:47,840 --> 00:45:48,040
Speaker 1: You know.

827
00:45:48,120 --> 00:45:51,680
Speaker 2: It was actually quite an amazing generated you know. I

828
00:45:51,760 --> 00:45:53,600
Speaker 2: watched the little video clip and it was it was

829
00:45:53,680 --> 00:45:56,440
Speaker 2: pretty full on to watch this AI video clip. But

830
00:45:56,840 --> 00:45:59,400
Speaker 2: it concerns me that you could do that in a

831
00:45:59,440 --> 00:46:02,480
Speaker 2: court case. And I don't know what the legal systems

832
00:46:02,640 --> 00:46:05,800
Speaker 2: like in the US. And you know, because they have

833
00:46:05,880 --> 00:46:08,600
Speaker 2: what they call the victim impact statements, and so she

834
00:46:09,080 --> 00:46:12,600
Speaker 2: was entered in as the victim impact statement from the

835
00:46:12,680 --> 00:46:15,680
Speaker 2: victim themselves. I think it opens up a big can

836
00:46:15,719 --> 00:46:19,759
Speaker 2: of worms because you're giving voice to something. And as

837
00:46:19,760 --> 00:46:22,719
Speaker 2: I said, this was an accusatory thing. This wasn't evidence.

838
00:46:23,120 --> 00:46:26,200
Speaker 2: This was just this is who I am, and these

839
00:46:26,280 --> 00:46:30,080
Speaker 2: were my ideals, is what they did with this video clip.

840
00:46:30,120 --> 00:46:32,080
Speaker 2: But what a can of worms that is?

841
00:46:32,880 --> 00:46:38,720
Speaker 1: Yeah? That term digital resurrection, that's creepy. That's a creepy

842
00:46:38,880 --> 00:46:44,759
Speaker 1: fucking digital resurrection. It's like, I understand the motivation on

843
00:46:44,800 --> 00:46:50,400
Speaker 1: her part, but whoever scripted it, whoever whoever scripted the

844
00:46:50,440 --> 00:46:53,920
Speaker 1: words using his voice and his image, he didn't do

845
00:46:54,000 --> 00:46:57,680
Speaker 1: it like that. Not, you know, so to go, this

846
00:46:57,760 --> 00:47:00,200
Speaker 1: is what he wants to tell you. No, this is

847
00:47:00,239 --> 00:47:04,480
Speaker 1: what you I understand it though, but it's it's I

848
00:47:04,560 --> 00:47:07,560
Speaker 1: don't know that that legally or practically that that's going

849
00:47:07,640 --> 00:47:11,560
Speaker 1: to have any impact. I mean emotionally, emotionally, I get it.

850
00:47:12,080 --> 00:47:16,160
Speaker 2: What if I took every single podcast you've ever recorded,

851
00:47:16,680 --> 00:47:20,200
Speaker 2: put it into a large language model, and asked it,

852
00:47:20,400 --> 00:47:22,960
Speaker 2: what would Craig say? What? You know? What?

853
00:47:23,120 --> 00:47:23,279
Speaker 1: I know?

854
00:47:23,320 --> 00:47:26,120
Speaker 2: What would be amazing would be to isolate you in

855
00:47:26,160 --> 00:47:29,480
Speaker 2: one location and then do a large language model of

856
00:47:29,680 --> 00:47:33,719
Speaker 2: everything you've ever said publicly, all the podcasts and information,

857
00:47:34,200 --> 00:47:37,520
Speaker 2: and then ask a series of ten questions and see

858
00:47:37,560 --> 00:47:41,919
Speaker 2: how much the AI version of you responds to the

859
00:47:41,960 --> 00:47:44,399
Speaker 2: real you. Wouldn't that blow your mind? How interesting would

860
00:47:44,400 --> 00:47:46,680
Speaker 2: that be.

861
00:47:45,840 --> 00:47:49,560
Speaker 1: Interesting to you? Yeah? It would but it's still not

862
00:47:49,760 --> 00:47:53,279
Speaker 1: the actual person in real life, in real time. But yeah, look,

863
00:47:53,400 --> 00:47:56,440
Speaker 1: I mean, and where it's going, who knows it's so

864
00:47:57,600 --> 00:48:01,680
Speaker 1: it's it's it's getting pretty close to where it can

865
00:48:01,800 --> 00:48:04,640
Speaker 1: I guess with a high level of predictability know what

866
00:48:04,680 --> 00:48:07,560
Speaker 1: you're going to say, because you've because you're I mean,

867
00:48:07,600 --> 00:48:10,080
Speaker 1: we're all quite predictable. We like to think we're not,

868
00:48:10,239 --> 00:48:14,040
Speaker 1: but I'm sure I'm very predictable. You know. I don't

869
00:48:14,040 --> 00:48:15,520
Speaker 1: want to think I'm special, but I'm not.

870
00:48:16,280 --> 00:48:18,800
Speaker 2: Well, it's I think a few episodes, well quite a

871
00:48:18,800 --> 00:48:20,479
Speaker 2: few episodes ago. It might have been even a couple

872
00:48:20,520 --> 00:48:24,160
Speaker 2: of years ago, there was a guy who who'd lost

873
00:48:24,200 --> 00:48:27,439
Speaker 2: his father and was so devastated he decided to try

874
00:48:27,480 --> 00:48:30,440
Speaker 2: to build a digital version of his father using you know,

875
00:48:30,560 --> 00:48:33,080
Speaker 2: video and cassette recordings and all that sort of stuff.

876
00:48:33,120 --> 00:48:36,360
Speaker 2: And you know, there is that it is a morbid

877
00:48:36,480 --> 00:48:39,640
Speaker 2: kind of connection, and I guess part of the grieving

878
00:48:39,719 --> 00:48:44,279
Speaker 2: process is letting go and having memories and having a

879
00:48:44,320 --> 00:48:48,960
Speaker 2: digital representation of that person and interacting with them on

880
00:48:49,000 --> 00:48:51,480
Speaker 2: a daily basis. Like he was talking to his dead

881
00:48:51,520 --> 00:48:54,400
Speaker 2: father every day. He was interacting with him, so he

882
00:48:54,520 --> 00:48:57,880
Speaker 2: was never letting go. Of that person because he in

883
00:48:57,960 --> 00:49:02,279
Speaker 2: his mind he'd recreated it was a digital resurrection of

884
00:49:02,320 --> 00:49:04,040
Speaker 2: that person via AI.

885
00:49:05,000 --> 00:49:09,960
Speaker 1: So yeah, I'm very careful to judge how people grieve.

886
00:49:10,160 --> 00:49:13,200
Speaker 1: I reckon whatever works for you. You know, it's like

887
00:49:13,600 --> 00:49:16,839
Speaker 1: how many people talk to their dead partner? How many

888
00:49:16,880 --> 00:49:19,200
Speaker 1: people go to a grave and talk as though they're

889
00:49:19,200 --> 00:49:22,680
Speaker 1: there and they're listening, And how many people you know

890
00:49:22,880 --> 00:49:25,600
Speaker 1: you just don't know. I think it's like when you

891
00:49:25,680 --> 00:49:28,000
Speaker 1: talk to that person that's no longer here, but you

892
00:49:28,160 --> 00:49:31,000
Speaker 1: sense they're here or their energies here, how does that

893
00:49:31,120 --> 00:49:34,880
Speaker 1: make you feel? I feel great, I feel connected. I

894
00:49:34,960 --> 00:49:37,560
Speaker 1: know they're listening. I know this, I know that, And

895
00:49:37,600 --> 00:49:39,560
Speaker 1: then who are we to go? Yeah, well that's not

896
00:49:39,560 --> 00:49:44,239
Speaker 1: true though they're dead. So I think like I don't know,

897
00:49:44,440 --> 00:49:48,040
Speaker 1: Like we don't know. Fuck, I don't know what happens.

898
00:49:48,160 --> 00:49:52,920
Speaker 2: Like one of my dearest friends, she lost her husband,

899
00:49:52,960 --> 00:49:57,480
Speaker 2: who was quite young relatively when he died. Gosh, it

900
00:49:57,520 --> 00:50:00,520
Speaker 2: was twenty thirteen, so quite a while ago. And I

901
00:50:00,560 --> 00:50:03,920
Speaker 2: remember calling her a few days after he died because

902
00:50:03,920 --> 00:50:05,880
Speaker 2: I spent a lot of time with him when he

903
00:50:05,960 --> 00:50:08,400
Speaker 2: was going through cancer, and I'd catch up with him

904
00:50:08,440 --> 00:50:11,120
Speaker 2: once a week and we went from going for coffee

905
00:50:11,120 --> 00:50:14,719
Speaker 2: together to me bringing coffee to him, you know, but

906
00:50:14,800 --> 00:50:17,360
Speaker 2: he wasn't able to leave his home. And then a

907
00:50:17,360 --> 00:50:20,640
Speaker 2: few days after he died, I called my friend and

908
00:50:21,680 --> 00:50:23,680
Speaker 2: she wasn't able to take the call, and it was

909
00:50:23,760 --> 00:50:26,239
Speaker 2: his voice on her message service. And I was so

910
00:50:26,440 --> 00:50:29,239
Speaker 2: confronted because I could hear the voice of this guy

911
00:50:29,320 --> 00:50:31,960
Speaker 2: I had so much respect for and I love her dearly,

912
00:50:32,360 --> 00:50:36,520
Speaker 2: and I thought, oh gosh, it's him on the message

913
00:50:36,560 --> 00:50:39,120
Speaker 2: and it still is, you know, all that time later,

914
00:50:39,360 --> 00:50:43,920
Speaker 2: whenever I'm not as challenged now. I actually I would

915
00:50:43,960 --> 00:50:46,800
Speaker 2: hang up the phone before the message came on because

916
00:50:46,800 --> 00:50:49,720
Speaker 2: it was freaking me out a bit when that first happened.

917
00:50:50,000 --> 00:50:52,120
Speaker 2: So I can't even begin to imagine what it would

918
00:50:52,160 --> 00:50:55,359
Speaker 2: be like to have a digital recreation and interact with

919
00:50:55,440 --> 00:51:00,239
Speaker 2: someone who had passed away. Yeah, I'm challenged by the concept,

920
00:51:00,360 --> 00:51:03,480
Speaker 2: but that grieving process and we need to move on.

921
00:51:03,600 --> 00:51:03,799
Speaker 3: You know.

922
00:51:03,880 --> 00:51:06,480
Speaker 2: One another friend of mine lost her husband three years

923
00:51:06,520 --> 00:51:09,080
Speaker 2: ago and now she's met somebody new. And it's not

924
00:51:09,160 --> 00:51:11,799
Speaker 2: like she loves her husband any less, you know, she

925
00:51:11,880 --> 00:51:15,160
Speaker 2: had this amazing journey and he passed away, but she

926
00:51:15,360 --> 00:51:18,319
Speaker 2: now has a new relationship, so it doesn't lessen how

927
00:51:18,360 --> 00:51:21,000
Speaker 2: much we love that person. I think that again, it's

928
00:51:21,040 --> 00:51:23,040
Speaker 2: part of the grieving process, but we need to move

929
00:51:23,080 --> 00:51:26,840
Speaker 2: on from that and forming those new relationships. You know,

930
00:51:26,840 --> 00:51:29,160
Speaker 2: if a close friend passes away or we lose someone

931
00:51:29,160 --> 00:51:31,719
Speaker 2: in our life, then maybe it just means that it

932
00:51:31,840 --> 00:51:34,640
Speaker 2: makes way for somebody else to take that space or

933
00:51:34,880 --> 00:51:37,360
Speaker 2: a little part of the space that we want for

934
00:51:37,400 --> 00:51:37,880
Speaker 2: that person.

935
00:51:38,680 --> 00:51:43,520
Speaker 1: Yeah. Yeah, its good chatting with you, buddy. Tell people

936
00:51:43,520 --> 00:51:46,920
Speaker 1: how to connect with you and reach out if they

937
00:51:47,000 --> 00:51:47,359
Speaker 1: want to.

938
00:51:48,080 --> 00:51:52,000
Speaker 2: Websites now dot com today you or tie chair at

939
00:51:52,000 --> 00:51:55,680
Speaker 2: home dot com TODAYU. So there's the yin and yang

940
00:51:55,840 --> 00:51:59,120
Speaker 2: of my lifestyle. One is my one is my work,

941
00:51:59,160 --> 00:52:01,239
Speaker 2: and one is my past, and it's nice to have

942
00:52:01,320 --> 00:52:02,040
Speaker 2: them overlap.

943
00:52:03,800 --> 00:52:06,399
Speaker 1: And Tiff, try not to lose your shit at your

944
00:52:06,480 --> 00:52:10,239
Speaker 1: cat in the upcoming days if you would, yes, sir,

945
00:52:12,239 --> 00:52:15,480
Speaker 1: we'll say goodbye affair. But for now, thanks kids, Thanks everyone,