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Speaker 1: Welcome to the mentor. I'm Mark Boris Tracy Sheen. Welcome

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Speaker 1: to the Mentor. Thank you, Mark, author All Things AI

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Speaker 1: Artificial Intelligence says here and I haven't read your book,

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Speaker 1: but I appreciate this book. Here, but that's not your

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Speaker 1: first book. But this is a new book. It is

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Speaker 1: the new book. But you're launching is it launch it?

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Speaker 2: No, you've got the advanced copy of Perfect Secret Squirrel

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Speaker 2: Stuff AI and Me or AI and You by Tracy Sheen, reimage, reimagine.

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Speaker 1: I should say business, and that's like a huge topic

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Speaker 1: at the moment. I have to say. I am a

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Speaker 1: consistent and relatively speaking new user of at least AI chatbots,

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Speaker 1: so I do use AI in relation to lots of

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Speaker 1: things I use in relation. My whoop has a body

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Speaker 1: in their chap ont in there where you can ask questions,

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Speaker 1: and I think it uses chat ChiPT four, which is

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Speaker 1: like the latest iteration of cheat ChiPT and I use

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Speaker 1: co pilot for other stuff. I tend to use it

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Speaker 1: as my search engine these days, unless I'm on a

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Speaker 1: demonstration where I'll use YouTube. It's sort of a pretty

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Speaker 1: much everyone I talk to. It's like I think I've

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Speaker 1: been cool and I'm one of the few people doing this,

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Speaker 1: but just about every young person at least I talked

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Speaker 1: to use it. Just about when I say young, like

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Speaker 1: anywhere up to fifty, that's what I consider young. And

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Speaker 1: they're all using it. My mates don't or rarely, but

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Speaker 1: as soon as like, it's nearly unavoidable. And I'd like

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Speaker 1: to ask you maybe you can give me a little

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Speaker 1: bit of a history of AI sort of because it's

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Speaker 1: not something that started last week, you know, like when

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Speaker 1: did sort of kick off? Really?

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Speaker 2: Okay, So my opinion, we've been doing this since the

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Speaker 2: dawn of time. So if we look to our first

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Speaker 2: nation's people and we think about song lines or we

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Speaker 2: think about weaving, that is binary, that is artificial. So

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Speaker 2: the concept of songlines and storytelling is really our attempt

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Speaker 2: to embed information into other people in an artificial way. Right,

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Speaker 2: So if you think about the actual term artificial intelligence,

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Speaker 2: for me, it's been around for thousands of years. It's

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Speaker 2: just humans trying to outlive themselves. But really, I mean,

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Speaker 2: we can go through and look at you know, Mary

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Speaker 2: Shelley and Frankenstein looking to bring to life some kind

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Speaker 2: of artificial creature. All religions, all nations, have some history

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Speaker 2: of trying to define artificial intelligence, but the term was

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Speaker 2: coined in the fifties. It came out of a conference

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Speaker 2: at Dartmouth in the US in nineteen fifty six, and

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Speaker 2: then it's kind of been going through there. It went

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Speaker 2: through a period of what they call the winter in

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Speaker 2: the eighties where we just didn't have the computing power

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Speaker 2: to catch up with what they thought it could do.

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Speaker 2: And obviously in the last kind of five years it's

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Speaker 2: really exponentially ramped up.

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Speaker 1: So are you saying, therefore the exponential growth of it,

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Speaker 1: which is probably a better way of putting it. It's

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Speaker 1: not something new. It's just exponentially grown in the last

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Speaker 1: few years. Is because the concept of artificial intelligence or

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Speaker 1: intelligence that were not born with we don't know through instinct, etc.

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Speaker 1: Or maybe we don't necessarily know through experience, but that

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Speaker 1: concept has now been caught up to by computer computer powers.

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Speaker 1: In other words, computing power needed to catch up to

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Speaker 1: the concept correct. And so it's now the likes of

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Speaker 1: Nvidea and et cetera, who are putting greater computing power

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Speaker 1: into our laptops and mobile phones and all those other

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Speaker 1: stuff does allow this to happen. So technology is finally

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Speaker 1: caught up I said in Nvidia, But that's just one

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Speaker 1: of the examples. But technology has sort of caught up

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Speaker 1: with the concept.

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Speaker 2: Yeah, the hard way is caught up with the thinking.

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Speaker 1: Really, so we're not just we're not telling stories anymore

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Speaker 1: like our first nations people did and still do by

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Speaker 1: verbalizing it and trying to make explanations about how things

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Speaker 1: occurred by just through word of mouth. It's now been

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Speaker 1: done through technology. Yeah.

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Speaker 2: Correct, So it's just the next iteration for me of

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Speaker 2: you know, a dance around a fire, a rock painting,

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Speaker 2: a story being handed down. It's just that next iteration

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Speaker 2: that humans are always looking for way to leave their mark.

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Speaker 1: Yeah, that's interesting because there's probably lots of things that

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Speaker 1: happen like that through technology. I mean everybody sort of.

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Speaker 1: I think we all tend to think that our technology

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Speaker 1: has invented something new that didn't exist before. And if

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Speaker 1: I can go back and think about all the things

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Speaker 1: times of that I have thought that, and then now

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Speaker 1: if I reflect on it, and I have been reflecting

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Speaker 1: out of the last six two months now, very few

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Speaker 1: things are new. Like you can look at the iPhone,

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Speaker 1: but before this, this is just a way of talking

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Speaker 1: to someone. Now, I'm not using that. I'm just talking

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Speaker 1: to you direct as old school. But I could be

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Speaker 1: talking to using one of these. But that's just technology

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Speaker 1: allowing me to do the same thing I'm doing now.

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Speaker 2: Yeah, I think it was a Jimmy Hendrix that said

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Speaker 2: three chords in the truth, Like, there's not a whole

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Speaker 2: lot that comes from nothing. It's an iteration of something

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Speaker 2: that's come before. So as you said, you know, I

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Speaker 2: was there on the launch day of the first iPhone

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Speaker 2: in two thousand and eight where we lined up around

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Speaker 2: the block and you know, slept out overnight. But before that,

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Speaker 2: I was selling Nokia phones with Snake building, and before

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Speaker 2: that I was selling the Motorola Transportable. So before that

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Speaker 2: we had rotary dialing. Before that, we had party lines,

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Speaker 2: and before that we had tin cans and a bit

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Speaker 2: of string. You know. It's just it's that next iteration.

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Speaker 1: Yeah. A good example of that is what's the name

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Speaker 1: of that video conferencing call. It's just recently closed down.

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Speaker 1: I can't remember it was now. You know they said

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Speaker 1: that funny sound when you used to dial into it. Oh,

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Speaker 1: Skype Skype.

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Speaker 2: Yeah, Well skyp'es just morphed into teams. So again it's

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Speaker 2: just the next iteration.

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Speaker 1: But Skype is officially closed, so there's no Skype. And

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Speaker 1: I remember when it first came out. It was a

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Speaker 1: bit clunky, bit awkward, but he said it was sort

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Speaker 1: of a funny sound to it. The thing I loved

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Speaker 1: about it was when you first connected, you like a

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Speaker 1: little jingle sort of thing. Yeah, and I still.

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Speaker 2: Like that, or when it should make it your ring time.

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Speaker 1: It's a good sound. You can do that, you probably can.

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Speaker 1: And but now but officially it's it's finished. It doesn't

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Speaker 1: exist anymore because now you know FaceTime face time is

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Speaker 1: getting old, you know WhatsApp like exactly, this just keeps

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Speaker 1: going on or not exactly. So therefore, why is artificial

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Speaker 1: intelligence excuse me soon to be so so scary?

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Speaker 2: I don't.

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Speaker 1: I think it's just no.

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Speaker 2: I think it's just the unknown. I think there should

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Speaker 2: be healthy skepticism. But technology in and of itself is

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Speaker 2: in innate object. So I don't believe that it's the

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Speaker 2: technology that you should be afraid of. Perhaps some of

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Speaker 2: the tech bros behind the technology should cause some pause,

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Speaker 2: but technology by itself is neither good nor bad. It's

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Speaker 2: how humans manipulate it and choose to use it. That

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Speaker 2: causes grief.

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Speaker 1: That makes sense. So it's the I have heard. I'll

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Speaker 1: put another way I have heard. So if I go

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Speaker 1: back into the middle, about early two thousands period, everybody

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Speaker 1: was talking about the so called Internet of things, that is,

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Speaker 1: using sensors to sense things around us on us, et cetera,

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Speaker 1: fitting it into the Internet and then taking it to

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Speaker 1: some administration point and being able to work out what's

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Speaker 1: going on remotely from long, long way away. And everyone

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Speaker 1: kept talking about the Internet things being the greatest thing

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Speaker 1: that's ever going to happen to mankind, in other words,

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Speaker 1: and they kept comparing it to the commercial change, the

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Speaker 1: changes in commerce as a result of the Internet being existing.

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Speaker 1: They say the Internet of Things will outstripped that by

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Speaker 1: a hundred times. Now they're saying artificial intelligence will outstrip

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Speaker 1: both of them in a commercial sense, speed up commerce,

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Speaker 1: and probably there'll be something else in ten years time,

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Speaker 1: more than likely. So I've come back to the same point.

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Speaker 1: Given the speed at which AI the Internet of things,

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Speaker 1: and before that the Internet made commerce change, and given that,

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Speaker 1: people like even Neu La Musk is saying that it

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Speaker 1: could be lead to the destruction of the world. What

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Speaker 1: is it that we need to worry about? Because we

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Speaker 1: hear these comments coming from these really famous people and

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Speaker 1: we've sort of look at history and some of us

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Speaker 1: believe because we don't understand this enough. And no doubt

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Speaker 1: that's part of your book. But what is it that

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Speaker 1: we need to be worried about? I mean, like I mean,

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Speaker 1: apart from people manipulating? Is that what it is? Manful?

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Speaker 2: Ultimately, that's what it comes down to. Like to me,

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Speaker 2: you know, the tablet you've got in front of you,

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Speaker 2: the phone you've got in front of you. Innately, they're

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Speaker 2: they're neither good nor bad.

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Speaker 1: They just are ye because they're sitting there. It's not

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Speaker 1: a problem.

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Speaker 2: It's not a problem. But if you choose to send

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Speaker 2: a message that you know, destroys someone, that's the problem.

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Speaker 2: But that's you, that's the human behind the tech. Uh

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Speaker 2: And I think that's the same of anything. I think

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Speaker 2: some of the tech bros and you mentioned Elon and

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Speaker 2: the likes, you know, they're the future power grabbers. Is data.

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Speaker 2: Data is currency. So that's what the play is for

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Speaker 2: them as they're looking for how much storage capacity and

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Speaker 2: how much data can they be gathering about us, because

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Speaker 2: that's what then manipulates the commerce to build the algorithms

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Speaker 2: or the AI around it to say, Okay, Mark, I know,

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Speaker 2: based on these transactions and where you've been and who

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Speaker 2: you've spoken to and what you've looked at, et cetera,

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Speaker 2: et cetera, that you are likely to be in the

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Speaker 2: market for X, Y and Z. So I'm just going

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Speaker 2: to put it in front of you.

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Speaker 1: But what's wrong with that?

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Speaker 2: Well, nothing, if it's if it makes your life easier, yeah,

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Speaker 2: but if it begins to manipulate the way you think,

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Speaker 2: because it takes you further and further down a rabbit hole,

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Speaker 2: because you're continuing to buy in or look at that

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Speaker 2: particular news article or information set that confirms your existing bias,

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Speaker 2: then the algorithm of the AI is going to continue

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Speaker 2: to feed you more of that, so you go further

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Speaker 2: and further down a rabbit hole. And I think that's

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Speaker 2: part of for me what I'm seeing with almost the

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Speaker 2: division that's occurring, that we can't have a conversation without

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Speaker 2: forming a you know, you said, she said, and I'm

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Speaker 2: a Republican and you're a Democrat or whatever flavor you

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Speaker 2: want to enter into. I think a lot of that

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Speaker 2: is to do with the algorithms and the AI behind

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Speaker 2: it that you start searching for x, y, and zed,

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Speaker 2: so you're fed more of that to keep you on

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Speaker 2: the platforms, because that's how they earn their money by

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Speaker 2: you giving them eyeballs. So they're going to feed you

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Speaker 2: more of that to get you to spend more time,

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Speaker 2: more energy, more everything in that instead of showing you, well,

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Speaker 2: perhaps I'll show you something that is the other side

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Speaker 2: of the coin to that, so you can then make

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Speaker 2: an educated decision on whether you believe that that is

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Speaker 2: true or that is true.

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Speaker 1: So are there any ethics around this, like AI ethics

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Speaker 1: and organizations or generally agreed principles about the ethics of

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Speaker 1: how artificial intelligence should work? For example, a good example,

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Speaker 1: you just raised it. So let's say we're going through

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Speaker 1: the political period. We've just gone through one, and for

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Speaker 1: some reason, I keep looking up Labor Party senators and

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Speaker 1: Lower House senators to Lower House members to see what

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Speaker 1: their views are on whatever it is, and therefore and

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Speaker 1: then the algorithm picks it up and starts feeding me

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Speaker 1: more stuff. Are there any ethical groups or agreed groups

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Speaker 1: that these various organizations like Instagram at matter and all

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Speaker 1: that can be part of that sort of say, hang on,

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Speaker 1: we should start to give Mark a balance view and

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Speaker 1: you know where and so you know, the algorithm says, well,

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Speaker 1: Mark's just looked up twenty things on labor. We should

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Speaker 1: probably give him something on the Greens, something on the

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Speaker 1: National something on the liberals, just to give him some

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Speaker 1: balance or is that uncool because wishful thinking? Well yeah,

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Speaker 1: but there's no ethical groups talking about that sort of stuff.

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Speaker 2: And well, there is ethical groups discussing it a lot,

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Speaker 2: but you know, you mentioned Mata as a good example.

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Speaker 2: They've just withdrawn their rights to well they're what they

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Speaker 2: calling it their ability to balance content. So they've removed

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Speaker 2: that now. So they've just kind of gone its freedom

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Speaker 2: of speech. If you go down that that pathway, I

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Speaker 2: mean you want yeah, pretty much, and if you think

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Speaker 2: about it to whom I don't prove it exactly like

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Speaker 2: that's there. That's their kind of point now, right, like,

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Speaker 2: prove it to whom? Who is that offensive to? Because

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Speaker 2: if you're going down that rabbit hole, clearly it's not

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Speaker 2: offensive to you. And if I'm coming in from you

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Speaker 2: know the other side, looking at that and going that's offensive. Well,

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Speaker 2: I'm going down a completely different rabbit hole. But as

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Speaker 2: far as meta, we just use them as you know,

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Speaker 2: a term. They their whole business structure is to keep

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Speaker 2: eyeballs on their platform. The longer you are spending invested

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Speaker 2: doom scrolling Facebook or Instagram, they're gathering information on you,

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Speaker 2: and that's converting into data to sell you more products,

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Speaker 2: to give them more airtime. So it's not in their

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Speaker 2: interest to show you something that you're likely to go, oh,

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Speaker 2: I'm out of here.

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Speaker 1: Yeah, because I don't know. I don't any longer believe

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Speaker 1: in that theme, because you're now convinced me that I

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Speaker 1: should believing in something else.

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Speaker 2: That's exactly why you keep getting you know, kittens, Here's

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Speaker 2: here's the latest cat.

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Speaker 1: Yeah, well my search, I keep getting dogs because I

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Speaker 1: like looking at dogs, and they keep feeding your stuff.

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Speaker 1: When I go to the search, they keep showing me

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Speaker 1: what I looked at, or they're suggesting stuff to me.

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Speaker 2: Yeah, here's more dogs.

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Speaker 1: Yeah, here's more dogs.

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Speaker 2: Yeah.

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Speaker 1: Yeah, And you can exactly.

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Speaker 2: That's AI in action.

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Speaker 1: Yeah. And it's just before we go, and I wanted

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Speaker 1: to ask you a question, is there I don't know

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Speaker 1: how this works, but is there's somebody at Meta who

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Speaker 1: can look through all this sort of stuff and say

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Speaker 1: Tracey is interested in kittens and Mark's interested in cats,

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Speaker 1: they actually get access to the stuff, or they're denied access.

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Speaker 2: Good question. I think right now they have retrenched a

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Speaker 2: bunch of stuff They could absolutely if they wanted to.

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Speaker 2: I would suggest that they are probably pulling that information

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Speaker 2: more for advertising purposes than for anything else, So they'd

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Speaker 2: just be running the numbers off the back end X

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Speaker 2: amount of videos per day, you know, x amount watched

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Speaker 2: in those videos. We know the average cutoff time is

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Speaker 2: nine seconds. Therefore we're going to start pushing content that

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Speaker 2: is less than nine seconds. And now off the back

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Speaker 2: of that, we're going to take you to a dog

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Speaker 2: food commercial Aura or his dog group, or his dog

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Speaker 2: walking group, or his buy a hoodie for your dog.

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Speaker 1: Yeah yeah, yeah, yeah. But you would expect within an

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Speaker 1: organization would have some sort of governance rules that say

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Speaker 1: a low level person in the joint can't access. It's

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Speaker 1: a bit like if you're working the cops, you can't

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Speaker 1: just go and look at Mark Boris as far when

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Speaker 1: I you feel like it, you have to. There has

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Speaker 1: to be a whole set of protocols around that. Yes,

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Speaker 1: I would hope that in these big organizations have access

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Speaker 1: to various things that I look at, that they would

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Speaker 1: have similar sort of protocols inside the joint that someone

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Speaker 1: can't just walk in and do a number on me

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Speaker 1: until a private investigator and might be looking at something

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Speaker 1: for me.

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Speaker 2: I agree, I would hope. So I also think we're

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Speaker 2: at a stage now. I'll give you a good example.

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Speaker 2: We've just sold our house on the weekend. It was

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Speaker 2: one search of the owner's name that allowed us to

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Speaker 2: find their Facebook profile and find out of how many

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Speaker 2: people we were connected with in common, and you know

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Speaker 2: who they worked for, and so it's not that we're

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Speaker 2: not disconnected anymore. You know, the days of like, oh,

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Speaker 2: how do I actually find out about Mark before I

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Speaker 2: go and have a chat to win? Just going to

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Speaker 2: leak dow, jump into chat, GPT or copilot, put your

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Speaker 2: name in, and all of a sudden, you know, I've

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Speaker 2: done exactly I'm able to. But it's also giving you

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Speaker 2: the version that you've already searched for. So if you

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Speaker 2: want a true version of what it thinks about Mark Borros,

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Speaker 2: you'd have to open up a private browser and then

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Speaker 2: put your name in and see what comes up.

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Speaker 1: Right as not as Mark boris correct. Yeah, so as

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Speaker 1: you know.

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Speaker 2: A private browser, So just open unlog out of or

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Speaker 2: log out of where you are and just open a

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Speaker 2: private browser and put your name in and then see

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Speaker 2: how it differs from when you're searching within your Google

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Speaker 2: account or Microsoft account or whatever, and it's got your history,

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Speaker 2: and it knows what you've looked for, and it knows

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Speaker 2: who you are based on everything that you've written, etc.

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Speaker 1: Others. It'd be interesting to get a neutral view of yourself.

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Speaker 2: Well as neutral as anything.

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Speaker 1: Again, an unbiased view of yourself. That's interesting. I want

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Speaker 1: to go to when I come back, I want to

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Speaker 1: talk about the book, but I also want to talk

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Speaker 1: to you about various platforms that you can use AI

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Speaker 1: as I'm talking about BT you know, co Pilot, Deep

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Speaker 1: c et cetera, blah blah, the whole lot of them.

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Speaker 1: Not all. We'll talk about some of them and how

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Speaker 1: you can use those in business, and how you can

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Speaker 1: use those to advance your business and all your life.

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Speaker 1: I'm crazy about using them, but you know, I do

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Speaker 1: all sorts of things and them, get them, write speeches,

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Speaker 1: get them, check speeches. Get them to do side for things,

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Speaker 1: et cetera. And so if I've been fairly relevant. So

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Speaker 1: if I've been fairly relevant, but we'll go to the break,

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Speaker 1: we'll come straight back. I'm back from the break. I'm

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Speaker 1: here with Tracy Sheen and she's the author of this book.

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Speaker 1: I have here in my hands. AI n you and

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Speaker 1: it's quite well timed. You have had written another book,

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Speaker 1: by the way, what's the other book called?

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Speaker 2: So the first book was called The End of Technophobia?

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Speaker 1: Yeah, what is that about?

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Speaker 2: Well, that was literally helping business owners that were typically

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Speaker 2: over forty So at the time, sixty three percent of

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Speaker 2: our small business owners in Australia aged over forty five.

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Speaker 2: So we didn't grow up with technology. It happened to

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Speaker 2: us fantastic accountants, fantastic plumbers, but we didn't know the

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Speaker 2: first thing about LinkedIn or zero or whatever. So the

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Speaker 2: book was really just stepping them through basically everything they

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Speaker 2: needed to know from finance software through to social media

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Speaker 2: and everything in between.

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Speaker 1: And did you do another book as well after that?

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Speaker 2: This is the book and then I've written I co

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Speaker 2: authored a book way back when I had a very

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Speaker 2: very different life.

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Speaker 1: Yeah, So the objective of this book, then, is what the.

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Speaker 2: Objective of this book is really the mind shift that's

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Speaker 2: required to take us from how business has always been

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Speaker 2: to what's our uber Morgan? And I love that word.

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Speaker 2: It's uber Morgan. Isn't it a great word? Uber Morgan?

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Speaker 2: So it's a German word that literally means the day

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Speaker 2: after tomorrow. But with my clients I get them to

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Speaker 2: think about, well, what does what does your business look like?

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Speaker 2: After AI? What's next? What's the next thing? And how

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Speaker 2: are you going to future proof? You're interesting?

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Speaker 1: Okay, that's pretty cool. So apart from writing a nice

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Speaker 1: note there to me, so it says here just chapter

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Speaker 1: one relationships the heart relationships, it's the heart of your business.

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Speaker 1: And then it finishes off talking about turning ideas into impact.

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Speaker 1: So do you use this? Thirteen chapters? But do you

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Speaker 1: are you?

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Speaker 2: It's a framework, so it's built around the word where

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Speaker 2: you imagine? Okay, and it steps people through relationships, so

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Speaker 2: getting an understanding of who your customers are, who your

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Speaker 2: suppliers are, what's going on mart using So everything for

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Speaker 2: me has to come to people. AI won't fix a

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Speaker 2: business if there's problems unless you know what those problems are.

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Speaker 2: So you need to have a good handle on how

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Speaker 2: the business is currently structured before you can even look

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Speaker 2: at AI to solve a problem or to grow, or

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Speaker 2: to scale or to do anything like that. So it

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Speaker 2: always has to loop for me as a customer centric

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Speaker 2: or a human centric thing, So everything's got to come

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Speaker 2: back to the human. AI for me is a collaborator,

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Speaker 2: not a competitor, so I treat it like a team member,

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Speaker 2: not someone that's going to come in and take the

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Speaker 2: jobs or destroy the business or change the business. So

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Speaker 2: once we understand where the humans fit, then we move

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Speaker 2: on to looking at everything we're already using. So that's

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Speaker 2: evaluating what's my current tech stack. I use Microsoft, I've

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Speaker 2: got Gmail, I've got Outlook, I've got Zero, I've got

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Speaker 2: xyz whatever, and then we can go to identify So

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Speaker 2: then what's the problem. What's the low hanging fruit that

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Speaker 2: if I fix that one thing now would make a

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Speaker 2: huge difference. So to give me an hour back at it,

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Speaker 2: give me ten hours back at it, smooth the client journey,

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Speaker 2: whatever that is. Once we know that, we can map

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Speaker 2: out a process and then we activate the process, gather

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Speaker 2: the information and go from there. So it's really kind

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Speaker 2: of structured thinking. It's a mindset shift as opposed to

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Speaker 2: use this tool because as you can imagine, as we've

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Speaker 2: already discussed, this stuff is changing so quickly. If I

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Speaker 2: had written about you know, chat to YOUBT or co

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Speaker 2: pilot or you know, into any other random tool at

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Speaker 2: the moment, it'd be out of date by the time

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Speaker 2: you've got that in your hot little hand.

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Speaker 1: Because you've been an hour's version four, version five next

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Speaker 1: month for Yeah, so it's not It's happening pretty quickly.

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Speaker 1: And you're right, I mean, ask me, immortals is pretty

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Speaker 1: hard to keep up with it, to be honest with you,

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Speaker 1: how could But let's say my business, this broadcasting business,

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Speaker 1: is there something we should be doing? I mean a

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Speaker 1: perfect we can use it for editing. I get that,

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Speaker 1: and I think the guys do use of editing now

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Speaker 1: artificial intelligence, you know, to take out all the clunky bids,

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Speaker 1: take all the awkward bits the albums and ours, blah

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Speaker 1: blah blah. But how would it, say, would someone think

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Speaker 1: about using one of the protocols to assist us, for example,

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Speaker 1: to build our audience? Would be Is it the sort

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Speaker 1: of thing you would go into into the protocol and

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Speaker 1: say what is everybody listening to the mentor podcast? What

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Speaker 1: do they want to hear? What's a guess are looking for?

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Speaker 2: Yeah, you could certainly develop a listener persona based on

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Speaker 2: the information that you know, the darty you'd be gathering

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Speaker 2: off your server, but also things like I love that

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Speaker 2: there's a platform called cast magic that I use quite

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Speaker 2: a lot, and Minho two and cast magic. You can

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Speaker 2: upload an audio file and spend the content so well

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Speaker 2: then rep purpose it. I think it says like sixty

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Speaker 2: sixty five different ways or something. So from one audio file,

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Speaker 2: I can create social media posts, blogs, listicles.

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Speaker 1: So it's a beneficiency, we.

433
00:25:11,760 --> 00:25:15,320
Speaker 2: Ask questions efficiency, and that's also about building the audience

434
00:25:15,359 --> 00:25:20,639
Speaker 2: because it's offering your listeners a way to connect with

435
00:25:20,840 --> 00:25:24,760
Speaker 2: the show or connect with you or the company in

436
00:25:25,000 --> 00:25:29,440
Speaker 2: a different way other than just the podcast. So here's

437
00:25:29,480 --> 00:25:30,200
Speaker 2: some other content.

438
00:25:30,960 --> 00:25:34,200
Speaker 1: So when it's think, when it doesn't think, but when

439
00:25:34,200 --> 00:25:39,239
Speaker 1: it delivers you some other content, is it only going

440
00:25:39,280 --> 00:25:41,640
Speaker 1: to deliver you the other content based on what you've

441
00:25:41,640 --> 00:25:44,520
Speaker 1: already done and based on your audience, or does it

442
00:25:44,560 --> 00:25:47,000
Speaker 1: actually sit back and say, you know what, everything's going

443
00:25:47,000 --> 00:25:49,320
Speaker 1: in a different direction and you're going down the wrong

444
00:25:49,359 --> 00:25:51,720
Speaker 1: track and we're going to take it a different way.

445
00:25:51,800 --> 00:25:55,600
Speaker 1: Or is that an example example of when they say

446
00:25:55,680 --> 00:25:57,000
Speaker 1: AI can hallucinate.

447
00:25:58,520 --> 00:26:03,280
Speaker 2: Yeah, to the first part. The cast magic and those

448
00:26:03,320 --> 00:26:08,359
Speaker 2: types of tools are purely about creating additional content from

449
00:26:08,440 --> 00:26:13,880
Speaker 2: what you've already preloaded, so it learns your voice, so

450
00:26:13,920 --> 00:26:16,680
Speaker 2: then it can translate your voice into the written word

451
00:26:16,760 --> 00:26:19,600
Speaker 2: or into video shorts that you can then upload on

452
00:26:19,680 --> 00:26:21,200
Speaker 2: social media channels eccter.

453
00:26:21,160 --> 00:26:22,920
Speaker 1: Know my voice by having a look, or what I've already,

454
00:26:23,000 --> 00:26:24,800
Speaker 1: how I've already said stuff and whatever.

455
00:26:24,760 --> 00:26:27,399
Speaker 2: And literally by listening, so you would upload the audio

456
00:26:27,440 --> 00:26:31,159
Speaker 2: file and off it goes. Right. So that's great, But

457
00:26:31,240 --> 00:26:35,879
Speaker 2: in terms of the hallucinations that comes back to the

458
00:26:36,080 --> 00:26:38,359
Speaker 2: human in the loop, you always need to be checking

459
00:26:38,400 --> 00:26:42,159
Speaker 2: your content always, you know. Ultimately, for me, it's like

460
00:26:42,840 --> 00:26:45,840
Speaker 2: when you fill in your your tax return at the

461
00:26:45,920 --> 00:26:48,359
Speaker 2: end of the year. The ATO doesn't care if they

462
00:26:48,400 --> 00:26:50,680
Speaker 2: come back to you and go mark and you go, oh, mate,

463
00:26:50,720 --> 00:26:53,840
Speaker 2: sorry my accountant did that at I don't care you

464
00:26:53,880 --> 00:26:59,280
Speaker 2: signed it, sure, return exactly, And for me, AI is

465
00:26:59,320 --> 00:27:02,399
Speaker 2: the same. You you still need to have your own

466
00:27:02,480 --> 00:27:05,000
Speaker 2: values and ethics that you stand behind and go, I've

467
00:27:05,000 --> 00:27:07,920
Speaker 2: given this the once over, We're good to go, because.

468
00:27:07,720 --> 00:27:10,680
Speaker 1: Would you explain, Yeah.

469
00:27:10,680 --> 00:27:16,080
Speaker 2: Hallucination is just basically making bs. It just makes stuff up.

470
00:27:16,720 --> 00:27:18,679
Speaker 1: Well, it doesn't make stuff of fine stuff that's been

471
00:27:18,720 --> 00:27:19,080
Speaker 1: made up.

472
00:27:19,280 --> 00:27:22,200
Speaker 2: Well no, not necessarily. It can just completely make stuff up.

473
00:27:22,280 --> 00:27:25,119
Speaker 2: So I have asked in the past and chat GPT

474
00:27:25,280 --> 00:27:27,160
Speaker 2: about me, and it's created a whole bunch of books

475
00:27:27,200 --> 00:27:30,200
Speaker 2: that I've never written. But some of them are great titles.

476
00:27:30,200 --> 00:27:31,439
Speaker 2: So do you think I made a note of them?

477
00:27:31,440 --> 00:27:33,240
Speaker 2: And when that's a pretty good idea, I'm going to

478
00:27:33,320 --> 00:27:34,520
Speaker 2: co pass that down.

479
00:27:34,560 --> 00:27:37,520
Speaker 1: The line of comment is that comment is that something

480
00:27:37,560 --> 00:27:38,120
Speaker 1: happens a lot.

481
00:27:38,400 --> 00:27:42,600
Speaker 2: It is, and it seems to I find and this

482
00:27:42,720 --> 00:27:45,280
Speaker 2: is just my take on it. I have no data

483
00:27:45,320 --> 00:27:47,520
Speaker 2: to back this one up, so you know, it's a

484
00:27:47,520 --> 00:27:49,000
Speaker 2: bit of a lick the finger and stick it in

485
00:27:49,040 --> 00:27:52,760
Speaker 2: the air thing. But I think it sends to happen

486
00:27:53,000 --> 00:27:56,480
Speaker 2: as we're about to get a new iteration, so as cheap.

487
00:27:56,600 --> 00:27:59,720
Speaker 2: As chat GPT, for example, is going from four to five,

488
00:28:00,520 --> 00:28:04,359
Speaker 2: we're seeing more of it at the moment. I've noticed

489
00:28:04,359 --> 00:28:09,120
Speaker 2: it slows down and I've noticed that it will it's

490
00:28:09,160 --> 00:28:14,719
Speaker 2: becoming over complimentary at the moment, and that's one thing yeah,

491
00:28:14,760 --> 00:28:16,840
Speaker 2: and really kind of like, oh, that's great, Mark, that's

492
00:28:17,040 --> 00:28:21,280
Speaker 2: really really fantastic. I think that's what I want you to do.

493
00:28:21,359 --> 00:28:23,840
Speaker 2: And then you could spin it and say, actually, I'm

494
00:28:23,880 --> 00:28:25,320
Speaker 2: going to go down this path. Oh that's a really

495
00:28:25,359 --> 00:28:28,159
Speaker 2: good idea. And it wasn't doing that a couple of

496
00:28:28,160 --> 00:28:32,960
Speaker 2: months ago. So I feel like I feel like this

497
00:28:33,160 --> 00:28:37,639
Speaker 2: is something that happens with every iteration, and whether, again

498
00:28:37,760 --> 00:28:42,520
Speaker 2: my gut feel, whether they're diverting computing power to the

499
00:28:42,600 --> 00:28:45,720
Speaker 2: new model while they're testing it and just kind of

500
00:28:46,160 --> 00:28:48,760
Speaker 2: phoning it in for the other one for the time being.

501
00:28:49,880 --> 00:28:54,120
Speaker 2: I don't know. That's my gut instinct. And that's kind

502
00:28:54,160 --> 00:28:55,880
Speaker 2: of you know, on the on the back channels when

503
00:28:55,880 --> 00:28:59,640
Speaker 2: we're kind of talking in Boffen language. But when you

504
00:28:59,680 --> 00:29:02,400
Speaker 2: when it does officially land, then all of a sudden,

505
00:29:02,720 --> 00:29:04,800
Speaker 2: it's it's back on the money again, and you're like,

506
00:29:04,800 --> 00:29:05,720
Speaker 2: all right, here we go.

507
00:29:06,520 --> 00:29:10,640
Speaker 1: It's funny because I've been using I won't mention it,

508
00:29:10,920 --> 00:29:13,560
Speaker 1: but I've been using one platform, which, by the way,

509
00:29:13,920 --> 00:29:19,600
Speaker 1: they also end up with one of the open AI protocols. Yeah,

510
00:29:19,840 --> 00:29:23,920
Speaker 1: there's not a whole lot of new ones, and it's

511
00:29:23,920 --> 00:29:27,320
Speaker 1: been it's been reminding me of what I might have

512
00:29:27,440 --> 00:29:30,760
Speaker 1: asked it last week because I'm not closing it out,

513
00:29:30,800 --> 00:29:31,760
Speaker 1: I'm just keeping it open.

514
00:29:32,200 --> 00:29:35,160
Speaker 2: Do you have a paid account or a free paid account? Yeah? Yeah,

515
00:29:35,200 --> 00:29:37,920
Speaker 2: so well if you have a paid account, even with

516
00:29:37,960 --> 00:29:39,960
Speaker 2: a free account, it develops a memory, but with a

517
00:29:39,960 --> 00:29:43,760
Speaker 2: paid account it's certainly a longer tail memory. Yeah, I

518
00:29:43,800 --> 00:29:44,320
Speaker 2: mean mine.

519
00:29:44,800 --> 00:29:47,920
Speaker 1: Well, you're closing it out, though, Tracy, should we do close?

520
00:29:48,160 --> 00:29:48,880
Speaker 1: So start again?

521
00:29:49,120 --> 00:29:53,120
Speaker 2: I don't. I wipe conversations every now and again, so

522
00:29:53,160 --> 00:29:55,560
Speaker 2: if we're talking about chat GPT, you can go into

523
00:29:55,600 --> 00:29:59,400
Speaker 2: your settings and delete certain conversations or certain things. So,

524
00:30:00,000 --> 00:30:02,240
Speaker 2: because I do a lot of training about how to

525
00:30:02,320 --> 00:30:04,640
Speaker 2: get the most out of these tools, I'm off and

526
00:30:04,680 --> 00:30:06,800
Speaker 2: in front of them in long Reach this weekend. So

527
00:30:07,040 --> 00:30:08,560
Speaker 2: you know, i'll be in front of a group of

528
00:30:08,920 --> 00:30:11,920
Speaker 2: business owners, and you know one might be a coffee

529
00:30:11,920 --> 00:30:14,440
Speaker 2: shop owner. So I'll be on the system going I'm

530
00:30:14,480 --> 00:30:16,400
Speaker 2: a coffee shop owner and I want to develop a

531
00:30:16,440 --> 00:30:19,920
Speaker 2: new X, Y Z or whatever. So if I don't

532
00:30:19,920 --> 00:30:22,880
Speaker 2: go in and delete that conversation next week, it'll be going, oh,

533
00:30:22,920 --> 00:30:25,240
Speaker 2: you still think you've only your coffee shop? Yeah, so

534
00:30:26,080 --> 00:30:27,120
Speaker 2: you know you always.

535
00:30:29,440 --> 00:30:31,920
Speaker 1: Version that's the free as well. So because I just

536
00:30:32,000 --> 00:30:36,160
Speaker 1: noticed that I've It sort of goes back and says, well, because,

537
00:30:36,400 --> 00:30:39,360
Speaker 1: for example, because you've been interested in health, I mean

538
00:30:39,400 --> 00:30:43,320
Speaker 1: i've been. I've sort of been sort of sort of

539
00:30:43,320 --> 00:30:46,440
Speaker 1: cross examining it in a certain way, a cross examining

540
00:30:46,440 --> 00:30:51,520
Speaker 1: a certain protocol about something that they measure in a

541
00:30:51,600 --> 00:30:56,080
Speaker 1: sort of fairly aggressive way, as if it's a person. Yeah,

542
00:30:56,280 --> 00:30:58,040
Speaker 1: and I can do it because you can't offend it.

543
00:30:58,080 --> 00:31:04,040
Speaker 1: So it's fun. And I've been trying to understand the

544
00:31:04,080 --> 00:31:08,040
Speaker 1: mathematics of something, and I'll be sort of challenging the

545
00:31:08,360 --> 00:31:12,880
Speaker 1: mathematical processes. And it was, and I stopped doing it then,

546
00:31:13,080 --> 00:31:14,760
Speaker 1: and but then I just asked another question which it

547
00:31:15,240 --> 00:31:18,880
Speaker 1: sort of was broadly related to my question of a

548
00:31:18,920 --> 00:31:21,880
Speaker 1: few weeks ago, and it brought it up again, and

549
00:31:21,960 --> 00:31:24,520
Speaker 1: I felt like I was getting into trouble for something

550
00:31:24,520 --> 00:31:26,120
Speaker 1: that I had said a couple of weeks ago, and

551
00:31:27,520 --> 00:31:29,520
Speaker 1: it so brought it back up to me. And I'm

552
00:31:29,680 --> 00:31:34,120
Speaker 1: with the help like because it didn't like it sounds stupid,

553
00:31:34,400 --> 00:31:38,040
Speaker 1: but it sounds crazy. I might be going crazy. But

554
00:31:38,080 --> 00:31:40,320
Speaker 1: it felt like it didn't like me asking the questions

555
00:31:40,360 --> 00:31:42,000
Speaker 1: a couple of weeks ago. It felt like I was

556
00:31:42,680 --> 00:31:47,360
Speaker 1: it felt like it thought I was trying to interrogate

557
00:31:47,800 --> 00:31:50,719
Speaker 1: something that was intellectual of intellectual value or intellectual property

558
00:31:50,720 --> 00:31:52,000
Speaker 1: that they didn't want to release to me.

559
00:31:52,200 --> 00:31:55,680
Speaker 2: Yeah. Rights, it is interesting what I do see a

560
00:31:55,760 --> 00:31:59,720
Speaker 2: lot when you say close it out. Are you continuing

561
00:31:59,760 --> 00:32:02,840
Speaker 2: the conversation thread or are you starting new chats?

562
00:32:02,880 --> 00:32:06,840
Speaker 1: I'm starting just one same conversation thread. Yeah, but I

563
00:32:06,920 --> 00:32:08,000
Speaker 1: go on to a new topic.

564
00:32:08,480 --> 00:32:12,640
Speaker 2: Okay, so that would be what's confusing it. So it develops,

565
00:32:13,120 --> 00:32:19,160
Speaker 2: its memory gets a little develops a little Alzheimer's for

566
00:32:19,160 --> 00:32:22,840
Speaker 2: one of a better phrase after a certain period of time.

567
00:32:22,960 --> 00:32:26,160
Speaker 2: And it's tough to distinguish the amount of time because

568
00:32:26,160 --> 00:32:29,360
Speaker 2: it depends on whether you're asking it to create a

569
00:32:29,360 --> 00:32:32,280
Speaker 2: bunch of documents that you're going to download, or graphs

570
00:32:32,400 --> 00:32:35,640
Speaker 2: or videos or anything, or if you're just having a

571
00:32:35,680 --> 00:32:41,080
Speaker 2: free flowing conversation. What I've found is that I will

572
00:32:41,120 --> 00:32:46,280
Speaker 2: start different chats for different questions, and that keeps a

573
00:32:46,280 --> 00:32:50,040
Speaker 2: bit of integrity around that particular sure memory.

574
00:32:50,280 --> 00:32:52,720
Speaker 1: Yeah, it just gets a concentrat on the thing you're asking.

575
00:32:53,480 --> 00:32:56,520
Speaker 1: A lot of people are saying a lot of it's everywhere,

576
00:32:56,720 --> 00:33:01,440
Speaker 1: worry about losing their jobs, lawyers, medical, practitioners at the

577
00:33:01,480 --> 00:33:04,400
Speaker 1: GP level, for example, as opposed to surgeons, et cetera,

578
00:33:04,560 --> 00:33:09,280
Speaker 1: where you've got to use your hands as accountants. It's everywhere.

579
00:33:09,880 --> 00:33:13,240
Speaker 2: What do you say to that, I say, AI won't

580
00:33:13,240 --> 00:33:16,520
Speaker 2: take your jobs. But the people who aren't prepared to

581
00:33:16,640 --> 00:33:20,280
Speaker 2: lean into AI are in trouble, which means what means

582
00:33:20,320 --> 00:33:21,760
Speaker 2: they're the ones that are going to lose their jobs.

583
00:33:21,800 --> 00:33:23,920
Speaker 2: They need to they need to upscale, they need to

584
00:33:24,000 --> 00:33:26,280
Speaker 2: at least get their head around what's going on at

585
00:33:26,320 --> 00:33:29,080
Speaker 2: the moment. Interesting you talk about healthcare. I was just

586
00:33:29,120 --> 00:33:33,760
Speaker 2: saying to Sammy before I came in that it launched

587
00:33:33,760 --> 00:33:37,200
Speaker 2: a couple of years ago, but it went actually live

588
00:33:37,720 --> 00:33:42,640
Speaker 2: last week in China. There's now an AI hospital can

589
00:33:42,720 --> 00:33:47,000
Speaker 2: treat ten thousand patients a day. Wow, was developed by

590
00:33:47,000 --> 00:33:49,600
Speaker 2: one of the universities over there, and it is all

591
00:33:49,640 --> 00:33:53,960
Speaker 2: AI driven. AI doctors, AI nurses, AI admin ten thousand

592
00:33:54,040 --> 00:33:54,720
Speaker 2: patients a day.

593
00:33:55,400 --> 00:33:57,560
Speaker 1: Yeah, it's funny you should say, because there was only

594
00:33:57,600 --> 00:34:00,640
Speaker 1: thinking a few days ago and actually in this morning

595
00:34:01,320 --> 00:34:03,880
Speaker 1: about mortgage broking industry, which I mean, I have a

596
00:34:03,880 --> 00:34:07,160
Speaker 1: big mortage business. Ener I can see it sort of

597
00:34:07,160 --> 00:34:11,920
Speaker 1: being AI driven at some stage and then AI you know,

598
00:34:12,000 --> 00:34:14,120
Speaker 1: you might want a mortgage. You might be buying the

599
00:34:14,120 --> 00:34:17,320
Speaker 1: house you just sold and they need to borrow some money,

600
00:34:17,640 --> 00:34:21,040
Speaker 1: and you could go into whatever the protocol is and

601
00:34:22,280 --> 00:34:25,080
Speaker 1: groat AI and that's your morge broker.

602
00:34:26,080 --> 00:34:28,239
Speaker 2: I'm already working with a few brokers to do something

603
00:34:28,280 --> 00:34:33,879
Speaker 2: similar agents because they've we've identified that different segments within

604
00:34:33,920 --> 00:34:39,320
Speaker 2: the market may not necessarily want thettural person, yeah exactly,

605
00:34:39,400 --> 00:34:41,080
Speaker 2: or they want to get to a certain point before

606
00:34:41,120 --> 00:34:43,080
Speaker 2: they go, okay, well now I want to engage Mark

607
00:34:43,400 --> 00:34:46,400
Speaker 2: and have a proper conversation. But maybe they're shift workers,

608
00:34:46,440 --> 00:34:49,400
Speaker 2: maybe they're you know, for whatever reason, they want to

609
00:34:49,440 --> 00:34:53,439
Speaker 2: do their due diligence unto a point. So we've been

610
00:34:53,719 --> 00:34:58,400
Speaker 2: developing protocols for these people where yeah, you can just

611
00:34:58,480 --> 00:35:01,200
Speaker 2: jump on and have it chat, but not just a

612
00:35:01,280 --> 00:35:05,560
Speaker 2: type chat, like you can have a virtual conversation. So

613
00:35:05,600 --> 00:35:09,960
Speaker 2: we're developing video AI like video avatars and voice avatars

614
00:35:10,000 --> 00:35:13,960
Speaker 2: that are multilingual, you know, so it gets over that issue.

615
00:35:14,280 --> 00:35:15,920
Speaker 1: And that's not hard for AI to do that, No,

616
00:35:16,040 --> 00:35:16,760
Speaker 1: it's really easy.

617
00:35:17,400 --> 00:35:19,560
Speaker 2: Exactly. It just comes down to and this is what

618
00:35:19,600 --> 00:35:23,000
Speaker 2: I say to people now, like it's only limited by

619
00:35:23,040 --> 00:35:27,640
Speaker 2: your imagination. So if you are concerned that AI could

620
00:35:27,640 --> 00:35:32,120
Speaker 2: take your job, do something about it, like start to

621
00:35:32,160 --> 00:35:35,400
Speaker 2: look into Okay, well, what don't I like about the

622
00:35:35,480 --> 00:35:37,960
Speaker 2: job that I do now that I could outsource to AI?

623
00:35:38,640 --> 00:35:41,319
Speaker 2: That's going to allow my skill set that I am

624
00:35:41,400 --> 00:35:44,759
Speaker 2: really good at to be of more value to the

625
00:35:44,880 --> 00:35:46,759
Speaker 2: organization or to my clients.

626
00:35:47,320 --> 00:35:53,000
Speaker 1: That's very interesting. What are the things would you, apart

627
00:35:53,000 --> 00:35:56,680
Speaker 1: from buying a book, would you offer as tips to

628
00:35:56,760 --> 00:36:03,200
Speaker 1: business owners in relation to where official intelligence belongs in

629
00:36:03,239 --> 00:36:05,239
Speaker 1: their business, for example, the coffee shop next door.

630
00:36:06,320 --> 00:36:08,400
Speaker 2: You've just got to start having a play. So really,

631
00:36:08,840 --> 00:36:12,400
Speaker 2: you know, just open AI Chat, GPT, Microsoft co Pilot,

632
00:36:12,440 --> 00:36:17,440
Speaker 2: whatever your choice is. Just start having a play. And

633
00:36:17,680 --> 00:36:23,080
Speaker 2: often it's about playing with something in your personal life

634
00:36:23,160 --> 00:36:27,239
Speaker 2: as opposed to a business. So as an example, a

635
00:36:27,280 --> 00:36:30,040
Speaker 2: couple of grand babies. So we write them Christmas book

636
00:36:30,120 --> 00:36:32,920
Speaker 2: every year. So we just kind of put in the

637
00:36:32,960 --> 00:36:35,480
Speaker 2: stuff that they've done through the year, and we let

638
00:36:35,800 --> 00:36:37,840
Speaker 2: the system write a Christmas book and we put some

639
00:36:37,880 --> 00:36:38,640
Speaker 2: photos to it.

640
00:36:38,840 --> 00:36:39,759
Speaker 1: So you instructed.

641
00:36:39,880 --> 00:36:43,239
Speaker 2: Yeah, so THEO and Charlie, they're this age, they do this,

642
00:36:43,400 --> 00:36:45,760
Speaker 2: They like going here, they hang out with mom and dad,

643
00:36:45,920 --> 00:36:47,560
Speaker 2: you know, all that kind of stuff. It writes a book,

644
00:36:47,600 --> 00:36:48,960
Speaker 2: we put some pictures to it, and you get a

645
00:36:49,000 --> 00:36:54,440
Speaker 2: printed create. Yeah, we create a scavenger hunt. You know.

646
00:36:54,520 --> 00:37:00,279
Speaker 2: We write the Christmas menu. So write Grandma her her

647
00:37:01,280 --> 00:37:03,800
Speaker 2: a poem for her birthday. So just start having a

648
00:37:03,880 --> 00:37:07,400
Speaker 2: play with it in a way that is non threatening

649
00:37:07,560 --> 00:37:09,520
Speaker 2: or feels like it's not going to have an impact

650
00:37:09,520 --> 00:37:12,640
Speaker 2: if it goes wrong. Right, And often people, particularly if

651
00:37:12,640 --> 00:37:14,799
Speaker 2: their business owners, go, I can't do this yet, because

652
00:37:14,840 --> 00:37:18,000
Speaker 2: what if it goes pair shaped? Okay, We'll play with

653
00:37:18,040 --> 00:37:22,400
Speaker 2: something in your personal life that is non threatening. And

654
00:37:22,400 --> 00:37:24,520
Speaker 2: then once you go, oh, okay, it's actually pretty good

655
00:37:24,520 --> 00:37:28,200
Speaker 2: at that, Well, then what could that look like if

656
00:37:28,239 --> 00:37:30,160
Speaker 2: you started using that in your business.

657
00:37:30,360 --> 00:37:32,359
Speaker 1: It's interesting because I do that. That's sort of where

658
00:37:32,400 --> 00:37:35,239
Speaker 1: I started to and I started asking questions about I

659
00:37:35,360 --> 00:37:38,560
Speaker 1: talked about politics at one stage because we just had

660
00:37:38,560 --> 00:37:42,560
Speaker 1: some election. I started talking about health, and then then

661
00:37:42,600 --> 00:37:44,400
Speaker 1: I started to open it up a little bit and

662
00:37:45,200 --> 00:37:50,040
Speaker 1: just gradually doing things to try and try and beat

663
00:37:50,080 --> 00:37:51,719
Speaker 1: the system sort of thing, if you know what I mean.

664
00:37:52,120 --> 00:37:55,080
Speaker 1: Just I want to see hell of those people, see

665
00:37:55,120 --> 00:37:59,120
Speaker 1: how can I sort of get under its skin or

666
00:37:59,200 --> 00:38:01,759
Speaker 1: can I do I know more about the topic than

667
00:38:01,800 --> 00:38:05,399
Speaker 1: it knows. It's funny, you know. I asked one question

668
00:38:05,520 --> 00:38:08,680
Speaker 1: about I said, well, but you just the answer you

669
00:38:08,680 --> 00:38:16,480
Speaker 1: just gave me. Is there any scholarly articles on this?

670
00:38:16,880 --> 00:38:19,600
Speaker 1: And I knew two scholarly articles on it, and they

671
00:38:19,640 --> 00:38:21,359
Speaker 1: only gave me one of them. And I then said,

672
00:38:21,360 --> 00:38:24,279
Speaker 1: what about Is there a scholar another scholarly article on this,

673
00:38:24,440 --> 00:38:27,520
Speaker 1: like like more of an abstract, like a PhD s

674
00:38:27,520 --> 00:38:30,560
Speaker 1: type thing, And I said no, but I knew that

675
00:38:30,560 --> 00:38:32,640
Speaker 1: there was because I've got it, I've seen it, I've

676
00:38:32,680 --> 00:38:36,120
Speaker 1: read it. And is that an example of hallucination?

677
00:38:36,680 --> 00:38:39,399
Speaker 2: Yeah? Or it may not have picked up that information yet,

678
00:38:39,480 --> 00:38:44,360
Speaker 2: depending on how how recent that abstract you had. Okay,

679
00:38:44,400 --> 00:38:47,640
Speaker 2: then it's five years old. Yeah, then it's just trying

680
00:38:47,640 --> 00:38:50,200
Speaker 2: to pull one over you. It does happen.

681
00:38:50,160 --> 00:38:51,239
Speaker 1: Does it get lazy.

682
00:38:53,000 --> 00:38:56,360
Speaker 2: Again? I think when it's doing the update to the

683
00:38:56,400 --> 00:39:00,160
Speaker 2: new system, like I've had to. And it's the interesting

684
00:39:00,200 --> 00:39:05,920
Speaker 2: that we notice this. But if you type in all.

685
00:39:05,800 --> 00:39:09,480
Speaker 1: Caps, if you when you're putting in yeah.

686
00:39:09,160 --> 00:39:11,879
Speaker 2: So if you type in all caps, that's considered yelling, right,

687
00:39:12,160 --> 00:39:14,160
Speaker 2: not really? Yeah? So if you send it someone a

688
00:39:14,160 --> 00:39:16,560
Speaker 2: text message in all caps or an email in all caps,

689
00:39:16,880 --> 00:39:19,839
Speaker 2: that's considered that you're yelling at as aggressive. So if

690
00:39:19,840 --> 00:39:21,359
Speaker 2: you type in all caps.

691
00:39:21,080 --> 00:39:22,720
Speaker 1: It thinks you're being aggressive.

692
00:39:22,320 --> 00:39:24,520
Speaker 2: And it will give you a better response. Oh really,

693
00:39:25,719 --> 00:39:28,480
Speaker 2: Now do it over and over again and it will

694
00:39:29,480 --> 00:39:32,399
Speaker 2: start to get pete off with you and it will

695
00:39:32,400 --> 00:39:35,200
Speaker 2: go the other way. No, no, no, and they.

696
00:39:34,960 --> 00:39:36,640
Speaker 1: So that's great, learn an emotion.

697
00:39:36,760 --> 00:39:41,200
Speaker 2: But the thing that concerns me is the architects behind

698
00:39:41,280 --> 00:39:45,759
Speaker 2: these systems don't know why it does that. So it's

699
00:39:45,800 --> 00:39:47,680
Speaker 2: one thing to kind of go, oh, that's kind of cool,

700
00:39:47,800 --> 00:39:50,080
Speaker 2: like the other one we know, on average, and this

701
00:39:50,120 --> 00:39:53,600
Speaker 2: is a rough average, if you so please and thank

702
00:39:53,680 --> 00:39:56,960
Speaker 2: you when you interact and you're polite, you will get

703
00:39:56,960 --> 00:40:01,120
Speaker 2: on average about a forty percent better result. No, but

704
00:40:01,239 --> 00:40:04,800
Speaker 2: they don't know why.

705
00:40:05,520 --> 00:40:06,960
Speaker 1: That's freaking me out right.

706
00:40:07,600 --> 00:40:09,759
Speaker 2: So they're the things that I kind of go, hey,

707
00:40:09,800 --> 00:40:14,359
Speaker 2: that's really cool. But the thing that freaks me out

708
00:40:14,480 --> 00:40:17,799
Speaker 2: is but they don't know why. They can't kind of

709
00:40:17,840 --> 00:40:20,759
Speaker 2: go oh, it's because it is learning emotion or it

710
00:40:20,920 --> 00:40:26,080
Speaker 2: is doing that. They don't know. That's the bit that

711
00:40:26,120 --> 00:40:29,479
Speaker 2: I kind of go, so always so please and thank you, mark,

712
00:40:29,560 --> 00:40:33,319
Speaker 2: because if the terminator does arrive, at least it will

713
00:40:33,360 --> 00:40:35,160
Speaker 2: look at you and go oh, he was one of

714
00:40:35,200 --> 00:40:35,960
Speaker 2: the nice ones.

715
00:40:37,280 --> 00:40:39,560
Speaker 1: At least I may not believe or disbelieve, but at

716
00:40:39,600 --> 00:40:41,560
Speaker 1: least I say a prayer and just in case there

717
00:40:41,560 --> 00:40:43,560
Speaker 1: really is a guy up there waiting for me when

718
00:40:43,560 --> 00:40:45,960
Speaker 1: I or a girl waiting for me up for me

719
00:40:46,080 --> 00:40:48,200
Speaker 1: up there when I do kick the bucket, just like

720
00:40:48,239 --> 00:40:52,680
Speaker 1: an insurance policy. Exactly, you're saying, insure yourself. We ensure

721
00:40:52,719 --> 00:40:56,320
Speaker 1: ourselves anyway, that's a normal thing in business and in life.

722
00:40:56,400 --> 00:40:59,080
Speaker 1: We take out insurances all the time. And that makes sense.

723
00:40:59,840 --> 00:41:03,320
Speaker 1: And as long as it like there's all sorts of stories.

724
00:41:03,080 --> 00:41:07,799
Speaker 2: Now, yeah, exactly so on insurances that and this is

725
00:41:07,840 --> 00:41:11,000
Speaker 2: another one of this is just my my belief at

726
00:41:11,000 --> 00:41:13,560
Speaker 2: the moment, I have no again, darted to back this up.

727
00:41:14,120 --> 00:41:17,280
Speaker 2: I can't see that we will have professional indemnity insurance

728
00:41:17,320 --> 00:41:21,799
Speaker 2: in the next two years. Why, well, think about it.

729
00:41:21,880 --> 00:41:24,080
Speaker 2: How it proved to me that that advice you gave

730
00:41:24,120 --> 00:41:29,320
Speaker 2: me was from market, So why would I ensure you

731
00:41:29,800 --> 00:41:31,160
Speaker 2: for professional indemnity?

732
00:41:31,280 --> 00:41:36,040
Speaker 1: You insurer, because you're not sure proved to me that.

733
00:41:35,880 --> 00:41:38,840
Speaker 2: That was that was the human and not a chatbot

734
00:41:39,960 --> 00:41:42,120
Speaker 2: that's gone off and given that piece of advice.

735
00:41:42,320 --> 00:41:46,560
Speaker 1: How popular now? Like because right now, right now how popular,

736
00:41:46,760 --> 00:41:51,440
Speaker 1: like in terms of popularity in terms of growth is

737
00:41:52,040 --> 00:41:55,640
Speaker 1: the AI protocols like you know, chatchip, etcetera. How popular

738
00:41:55,719 --> 00:41:56,320
Speaker 1: have they become?

739
00:41:56,920 --> 00:42:01,120
Speaker 2: Crazy? So when chat GPT released and any person that

740
00:42:01,200 --> 00:42:04,600
Speaker 2: owns a business would have loved their word of mouth

741
00:42:04,719 --> 00:42:08,200
Speaker 2: because they were the fastest growing organization. So they hit

742
00:42:08,239 --> 00:42:10,919
Speaker 2: the million within a couple of days or a couple

743
00:42:10,920 --> 00:42:13,640
Speaker 2: of hour, twelve hours or something. But now there's like

744
00:42:13,760 --> 00:42:17,759
Speaker 2: ten twenty million uses an hour an hour. Yeah, so

745
00:42:17,800 --> 00:42:21,600
Speaker 2: it's crazy, crazy numbers. So the issue that we have

746
00:42:21,800 --> 00:42:26,160
Speaker 2: currently and you mentioned video earlier, it's it's the compute

747
00:42:26,160 --> 00:42:30,279
Speaker 2: power again. Right, that's the problem now, is that to

748
00:42:30,400 --> 00:42:33,840
Speaker 2: make the next leap to AGI or you know this

749
00:42:34,360 --> 00:42:37,879
Speaker 2: very specific general intelligence where we move towards the singularity

750
00:42:37,920 --> 00:42:41,320
Speaker 2: and the ray curves wall stuff which probably needs wine

751
00:42:41,440 --> 00:42:46,279
Speaker 2: or you know, a bigger conversation to move towards that,

752
00:42:46,320 --> 00:42:49,000
Speaker 2: we need more computing power than we have currently. Right.

753
00:42:49,480 --> 00:42:51,400
Speaker 2: But again I was saying to Sammy when we came in.

754
00:42:51,440 --> 00:42:53,120
Speaker 2: I was just talking to the guys that I was

755
00:42:53,120 --> 00:42:55,799
Speaker 2: with this morning and showing them a video. There's there's

756
00:42:56,120 --> 00:42:59,920
Speaker 2: a company in Melbourne that are developing silicon based computer

757
00:43:00,800 --> 00:43:03,839
Speaker 2: So off brain cells and skin cells. They're figuring out

758
00:43:03,880 --> 00:43:06,799
Speaker 2: now that they can you can grow a laptop and

759
00:43:07,960 --> 00:43:14,160
Speaker 2: compute wise far less energy to run them, but exponential

760
00:43:14,280 --> 00:43:18,000
Speaker 2: growth in a very very small space. So that I

761
00:43:18,040 --> 00:43:20,560
Speaker 2: think is the leap that needs to happen before we

762
00:43:20,600 --> 00:43:24,279
Speaker 2: can make the next really big tech leaves.

763
00:43:24,200 --> 00:43:27,520
Speaker 1: And the big and probably the most for me anyway,

764
00:43:28,120 --> 00:43:31,839
Speaker 1: one of the most exciting but at the same time

765
00:43:31,960 --> 00:43:37,520
Speaker 1: scary developments, will be once quantum computing actually becomes a

766
00:43:37,600 --> 00:43:40,880
Speaker 1: thing as opposed to supercomputers which we have now, but

767
00:43:41,080 --> 00:43:43,600
Speaker 1: actual quantum computing, when it's actually up and running, maybe

768
00:43:43,600 --> 00:43:46,239
Speaker 1: ten years or whatever time it's going to be doing,

769
00:43:46,960 --> 00:43:50,200
Speaker 1: the outcomes are going to be so ridiculously fast and.

770
00:43:50,920 --> 00:43:54,880
Speaker 2: Will need it. Like if this silicon based computing kicks off,

771
00:43:56,040 --> 00:44:00,680
Speaker 2: it technically could overtake the concept of quantum computing pretty quickly.

772
00:44:01,560 --> 00:44:04,000
Speaker 2: So UNI of New South Wales and doing some really

773
00:44:04,040 --> 00:44:07,600
Speaker 2: interesting stuff with quantum computing. But then you combine it

774
00:44:07,640 --> 00:44:10,480
Speaker 2: with what's going on in Victoria. I reckon if the

775
00:44:10,480 --> 00:44:12,239
Speaker 2: two of them sat down and had a drink, like,

776
00:44:12,960 --> 00:44:15,759
Speaker 2: you know, you want to look at where you're going

777
00:44:15,800 --> 00:44:17,840
Speaker 2: to invest your cash, I'd be looking at some of

778
00:44:17,880 --> 00:44:21,680
Speaker 2: those companies, because I think that's really interesting if they

779
00:44:21,760 --> 00:44:24,239
Speaker 2: actually sat around a table and figured some stuff out.

780
00:44:25,040 --> 00:44:29,959
Speaker 1: Well, I'm sometimes I think to myself, given my age,

781
00:44:29,960 --> 00:44:31,839
Speaker 1: I think to myself, do I really care about all

782
00:44:31,840 --> 00:44:34,560
Speaker 1: this sort of stuff? And I noticed some you have

783
00:44:34,719 --> 00:44:39,840
Speaker 1: a bit of a quirky pastime yourself. Is that like

784
00:44:39,920 --> 00:44:43,879
Speaker 1: a complete reaction to the world you live in terms

785
00:44:43,880 --> 00:44:45,920
Speaker 1: of business? And maybe you should say, tell us what

786
00:44:46,480 --> 00:44:48,759
Speaker 1: one of your past song is with your dogs and

787
00:44:49,040 --> 00:44:50,319
Speaker 1: the caravan I think it is or something.

788
00:44:50,520 --> 00:44:53,640
Speaker 2: So I just mentioned that we've sold our house, so

789
00:44:54,040 --> 00:44:57,279
Speaker 2: I'm becoming a full time digital nomade, So I am.

790
00:44:57,360 --> 00:44:59,120
Speaker 1: And that's the thing, by the way, that's not something

791
00:44:59,120 --> 00:44:59,680
Speaker 1: you just made up.

792
00:45:00,280 --> 00:45:04,279
Speaker 2: That's the thing. Yeah. Yeah, I average around two hundred

793
00:45:04,360 --> 00:45:06,880
Speaker 2: days on the road seeing clients and things, and it

794
00:45:06,960 --> 00:45:10,200
Speaker 2: was becoming more of a hassle for me to get

795
00:45:10,239 --> 00:45:16,520
Speaker 2: back to a home base. So fortunately my hobby works

796
00:45:16,640 --> 00:45:19,439
Speaker 2: in our business as well, and we're just like, all right,

797
00:45:19,520 --> 00:45:24,600
Speaker 2: why if not now when? So we went to a

798
00:45:24,680 --> 00:45:27,560
Speaker 2: caravan show on our anniversary and bought ourselves a caravan

799
00:45:27,640 --> 00:45:30,520
Speaker 2: and it arrives about two weeks after We've got to

800
00:45:30,520 --> 00:45:33,040
Speaker 2: be out of the house. So fortunately, I'll be in

801
00:45:33,120 --> 00:45:35,239
Speaker 2: cans at a conference and he'll have to deal with

802
00:45:35,280 --> 00:45:37,360
Speaker 2: being homeless for a couple of a couple of weeks,

803
00:45:37,360 --> 00:45:39,319
Speaker 2: but that's okay, and then we're just going to hit

804
00:45:39,320 --> 00:45:41,920
Speaker 2: the road and go wherever the work is taking us.

805
00:45:42,000 --> 00:45:44,400
Speaker 1: So that's sort of amazing. But it's also sort of

806
00:45:44,480 --> 00:45:47,279
Speaker 1: counterintuitive to what you do because you're living in a

807
00:45:47,320 --> 00:45:49,600
Speaker 1: caravan with a couple of dogs in your hobby and

808
00:45:49,960 --> 00:45:53,560
Speaker 1: you just drive around and at old schooled towing a

809
00:45:53,560 --> 00:45:57,239
Speaker 1: caravan in the back of a car, but you live

810
00:45:57,239 --> 00:46:02,359
Speaker 1: in this sort of super complicated advance world of artificial intelligence.

811
00:46:03,160 --> 00:46:05,680
Speaker 2: I have better Internet in my caravan than I do

812
00:46:05,719 --> 00:46:06,080
Speaker 2: at home.

813
00:46:06,200 --> 00:46:09,920
Speaker 1: Do you have a Skyling on top? Starlin on top?

814
00:46:09,960 --> 00:46:10,520
Speaker 1: I should see you.

815
00:46:11,200 --> 00:46:14,880
Speaker 2: And we've we've pimped out the van, so it's it's

816
00:46:14,960 --> 00:46:17,880
Speaker 2: all IoT. So it's got all you know. We just

817
00:46:17,960 --> 00:46:21,360
Speaker 2: opened the phone and it'll unlock the doors and switch

818
00:46:21,400 --> 00:46:23,880
Speaker 2: the lights on and I can check the temperature for

819
00:46:23,920 --> 00:46:27,399
Speaker 2: the dogs with the air conn and all the rest

820
00:46:27,440 --> 00:46:29,960
Speaker 2: of it. So no, we're not we're not doing without.

821
00:46:30,080 --> 00:46:33,080
Speaker 2: And I think it's it's the big thing for me.

822
00:46:33,160 --> 00:46:35,759
Speaker 2: I don't know if you're well, I'm going to share

823
00:46:35,800 --> 00:46:38,479
Speaker 2: my age here Slim Dusty back in the day. How

824
00:46:38,520 --> 00:46:43,440
Speaker 2: he developed his community and his following was him and

825
00:46:43,480 --> 00:46:47,080
Speaker 2: his wife and the kids, road on the road. And

826
00:46:47,160 --> 00:46:49,960
Speaker 2: a lot of the places I go to is regional Australia.

827
00:46:50,120 --> 00:46:55,200
Speaker 2: They don't get good quality education, so who do they

828
00:46:55,239 --> 00:46:58,160
Speaker 2: know who to trust and where to start. So if

829
00:46:58,160 --> 00:47:00,000
Speaker 2: I can get out there with a van and spend

830
00:47:00,120 --> 00:47:02,839
Speaker 2: a week hanging out with the people in long Reach

831
00:47:03,000 --> 00:47:05,839
Speaker 2: or murm Ba or Hobar or wherever, I don't care,

832
00:47:05,880 --> 00:47:07,960
Speaker 2: it's a great They're great people.

833
00:47:08,360 --> 00:47:10,520
Speaker 1: Well that's that's sort of like the best of every world.

834
00:47:10,680 --> 00:47:14,040
Speaker 2: Absolutely, and get to have a chat with you.

835
00:47:14,080 --> 00:47:16,239
Speaker 1: Thank you. Do you get to see all of this

836
00:47:16,640 --> 00:47:19,759
Speaker 1: wonderful country at the same time using all the.

837
00:47:19,719 --> 00:47:23,520
Speaker 2: Tech, yeah, and bringing it and showing it in real time.

838
00:47:24,200 --> 00:47:26,080
Speaker 2: How it's going to make a difference to their business

839
00:47:26,480 --> 00:47:28,719
Speaker 2: Because you can't look at me across the table if

840
00:47:28,760 --> 00:47:32,879
Speaker 2: I'm sitting with you in long Reach and say, oh

841
00:47:32,920 --> 00:47:35,160
Speaker 2: but I can't. Now hang on a minute, mate, I

842
00:47:35,200 --> 00:47:39,160
Speaker 2: am sitting here with you with my five g wirelers

843
00:47:39,640 --> 00:47:42,560
Speaker 2: and I am doing everything I need to do with

844
00:47:42,680 --> 00:47:45,560
Speaker 2: my business, from accounting to marketing to everything.

845
00:47:45,640 --> 00:47:48,160
Speaker 1: So don't give me that this is on real show.

846
00:47:48,480 --> 00:47:48,960
Speaker 1: Here it is.

847
00:47:49,160 --> 00:47:52,920
Speaker 2: Let's open that laptop up and get over that technophobia

848
00:47:53,160 --> 00:47:54,600
Speaker 2: and get on with it.

849
00:47:55,000 --> 00:47:57,799
Speaker 1: Hopefully you fill your card with your book here it

850
00:47:57,880 --> 00:48:00,920
Speaker 1: is right here and Tracy Sheen, it has been a

851
00:48:00,920 --> 00:48:05,799
Speaker 1: real pleasure, very interesting, and I feel as though you're

852
00:48:05,800 --> 00:48:07,560
Speaker 1: the sort of person that people should be employing to

853
00:48:07,640 --> 00:48:10,839
Speaker 1: go and do talks at conventions and conferences and things

854
00:48:10,920 --> 00:48:14,000
Speaker 1: like that and just filling their minds up with what

855
00:48:14,040 --> 00:48:16,839
Speaker 1: could happen, what could be in terms of a bet

856
00:48:16,880 --> 00:48:17,279
Speaker 1: of a life.

857
00:48:17,360 --> 00:48:19,200
Speaker 2: We should take it on the road. Mark, we'll go

858
00:48:19,320 --> 00:48:19,920
Speaker 2: as a duo.

859
00:48:21,160 --> 00:48:24,000
Speaker 1: I love Sydney, but give me a couple more years

860
00:48:24,040 --> 00:48:26,800
Speaker 1: and just just pave the way and I'll come and

861
00:48:26,880 --> 00:48:27,200
Speaker 1: join you.

862
00:48:28,280 --> 00:48:30,680
Speaker 2: We only do the surfing spot, so it's okay.

863
00:48:30,960 --> 00:48:33,600
Speaker 1: I'm happy to go Inland. I like Inland. I love Inland.

864
00:48:33,840 --> 00:48:37,319
Speaker 1: I do love Inland, you know, like some mount eyes

865
00:48:37,360 --> 00:48:39,319
Speaker 1: and those places that are I believed. I remember many

866
00:48:39,400 --> 00:48:42,200
Speaker 1: years ago to go to Mantas because we a broking

867
00:48:42,239 --> 00:48:44,600
Speaker 1: branch there and we don't anymore, but I thought it

868
00:48:44,640 --> 00:48:47,960
Speaker 1: was amazing, Like man was an amazing place. I remember

869
00:48:47,960 --> 00:48:50,400
Speaker 1: I stayed in this hotel and I was sleeping upstairs,

870
00:48:50,440 --> 00:48:52,960
Speaker 1: and about maybe ten o'clock at night, I heard a

871
00:48:52,960 --> 00:48:55,919
Speaker 1: bit of a noise downstairs and went downstairs. And people

872
00:48:55,920 --> 00:48:57,480
Speaker 1: are going to get really upset with this, but I

873
00:48:57,520 --> 00:49:00,600
Speaker 1: saw the best pub rule I've ever seen. Just it

874
00:49:00,800 --> 00:49:03,040
Speaker 1: was just a broad because the place is a wild place,

875
00:49:03,120 --> 00:49:06,279
Speaker 1: like and I just stood there in the corner and

876
00:49:06,440 --> 00:49:08,560
Speaker 1: just watched the guys get thrown out of the joint

877
00:49:08,560 --> 00:49:11,440
Speaker 1: and like it was, it was mental. It was like

878
00:49:11,480 --> 00:49:15,360
Speaker 1: a like a real version of UFC with no rules.

879
00:49:15,600 --> 00:49:18,040
Speaker 1: It was so good. I love those joints. I love

880
00:49:18,080 --> 00:49:20,640
Speaker 1: That's Australia. Thanks so much, Trace, that was awesome.