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Speaker 1: I'll get a team. It's Harps, It's it's Jumbo, It's

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Speaker 1: Tiffany and Cook, It's David, Bryan, Kevin, Patrick, Gillespie, it's

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Speaker 1: the project that's us. We should call it the US projective.

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Speaker 2: Do you always say the same seventy eight middle names

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Speaker 2: for Gillespow or they did?

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Speaker 3: I think they're mostly the same, you know, even always

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Speaker 3: makes it Patrick, always a Patrick.

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Speaker 1: Yeah, you know, Well, I don't know. I don't know

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Speaker 1: what it actually is, but I feel like Tiffany and Cook, Craig, Anthony, Harpa.

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Speaker 1: We have you actually told me? You probably told me

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Speaker 1: once before you have one?

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Speaker 3: Oh? No, I do you do have a middle name.

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Speaker 3: It's very embarrassing.

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Speaker 1: It's Andrew, I say Andrew. It starts with a vowel.

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Speaker 3: Was that embarrassing because of the word it spells.

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Speaker 1: Dang Wow, that's a nice acronym and can I it's

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Speaker 1: what appropriate? I mean, you are a bit of a dag.

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Speaker 3: Yeah, that's true, that's true.

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Speaker 1: That's okay. My mum uses that term, and usually dag.

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Speaker 1: It's quite a it's quite a warm term. It's like

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Speaker 1: someone's a bit funny or a bit.

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Speaker 3: It wasn't so warm when I was growing up though,

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Speaker 3: you know, but so I didn't make a big thing

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Speaker 3: of it.

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Speaker 1: At least you didn't get called jumbo for hey. I

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Speaker 1: was speaking of school, speaking of those tender years that

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Speaker 1: molded and shaped and informed the fucking amazing grown ups

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Speaker 1: that the three of us have become. So they've been

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Speaker 1: talking about putting a limit on kids accessing social media,

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Speaker 1: which I think you wrote somewhere is like pretty much

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Speaker 1: you didn't say this, but this is kind of what

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Speaker 1: you said. This is how I interpret it. You're like, yeah,

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Speaker 1: a great idea. How do you enforce that? So the

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Speaker 1: idea they've been talking about it on the news down

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Speaker 1: here a little bit recently. I don't know about it, QLD,

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Speaker 1: but the idea of limiting teenagers or stopping teenagers from

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Speaker 1: accessing certain programs or apps or whatever until they're sixteen.

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Speaker 1: What are your thoughts on that? Is it possible? Is

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Speaker 1: it a ridict? Like? It sounds like a good idea maybe,

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Speaker 1: but impossible to as you said, enforce.

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Speaker 3: Well, I mean, they're doing such a good job of

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Speaker 3: stopping teenagers accessing porn that I'm sure it'll be really

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Speaker 3: really easy for them to get them to stop accessing

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Speaker 3: social media. You know, the reality is this difficult, if

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Speaker 3: not impossible, to do any kind of enforcement. And I

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Speaker 3: know it sounds good to a politician to stand up

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Speaker 3: and bang on about doing it and people say, oh, yes,

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Speaker 3: that's a good idea. I think it's more informative that

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Speaker 3: they feel the need to do it than whether or

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Speaker 3: not it's actually doable. The fact that we are now

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Speaker 3: having a public conversation about social media like we are

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Speaker 3: talking of cigarettes, to me is a positive step forward

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Speaker 3: because there's not a lot of difference between the two

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Speaker 3: in the senses that matter, meaning the problem with social

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Speaker 3: media and not just social media by the way. I mean.

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Speaker 3: That's a problem I have with this chat about social

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Speaker 3: media is it seems like that's the only thing we've

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Speaker 3: got to worry about, and don't worry too much about

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Speaker 3: the online gambling, the pawn the online gaming, all those things.

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Speaker 3: Let's not make a fuss about that. Let's criticize Facebook

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Speaker 3: and Instagram. But it's a start. It's at least recognizing

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Speaker 3: that there's something that we have to be concerned about,

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Speaker 3: and if nothing else, people might ask themselves, why do

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Speaker 3: they want to ban something like that? And then they

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Speaker 3: might ask the next question and find out, well, because

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Speaker 3: this stuff is designed to addict your children, and once

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Speaker 3: they are addicted to that, then they are much more

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Speaker 3: easily addicted to everything else. And we can already see

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Speaker 3: that transition happening in schools where kids get hooked, get

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Speaker 3: the dopamine circuitry fired up by software in the form

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Speaker 3: of social media or gaming or gambling, and then easily

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Speaker 3: transitioned to a substance addiction through vaping, which is then

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Speaker 3: easily transferred to an even harder substance addiction, ultimately to

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Speaker 3: something like cocaine.

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Speaker 1: I don't understand technology the way that you do. And

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Speaker 1: you're I think designed apps, and you're a programmer and

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Speaker 1: you can write code and you can do those things

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Speaker 1: that to me are just concepts, right. But lately I've

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Speaker 1: been hearing some of the shows that I listened to

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Speaker 1: where they've had what seemed to be like people who

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Speaker 1: are at the cold face of AI, some of them

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Speaker 1: who have jumped out of AI with great concern, like literally.

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Speaker 4: Saying that that artificial intelligence could pretty much bring about

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Speaker 4: the destruction of mankind if it evolves to a certain

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Speaker 4: point where it is so intelligent, like he was telling

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Speaker 4: the guy that I was listening to was telling a

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Speaker 4: story about how this machine was trying to or AI

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Speaker 4: was trying to get through this into this program or

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Speaker 4: into this I don't know whatever it was, but you

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Speaker 4: know how they give you like a blurred image with

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Speaker 4: letters and you need to need to go, oh that's

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Speaker 4: a G and a six and a one for S,

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Speaker 4: but it's not written clearly, but you can read it

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Speaker 4: and then type in that code and that opens that up.

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Speaker 4: So this, this computer or this AI was communicating with

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Speaker 4: a person.

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Speaker 1: The person didn't know what's it called the Turing testy

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Speaker 1: tries to identify whether or not if a computer can

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Speaker 1: trick a human, but it was. It was communicating with

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Speaker 1: this person and said, can you I can't read this clearly?

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Speaker 1: Could you you know? Help? And the human said as

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Speaker 1: long as you're not as long as you're not a bot,

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Speaker 1: L O L. And the AI said, oh, no, definitely

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Speaker 1: not lol. I have I'm vision impaired, right, So it

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Speaker 1: hadn't been trained to do this. It figured this out

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Speaker 1: and it told this real person that it was vision impaired.

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Speaker 1: Then the person apologized and gave them the code, and

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Speaker 1: it did that all by itself and I'm like, is

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Speaker 1: this and they reckon its potential now is absolutely nothing

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Speaker 1: compared to what it will be in as little as

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Speaker 1: one or two years.

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Speaker 3: Do we need to worry, Yes, and yes we do

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Speaker 3: need to worry. No, it won't matter because as soon

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Speaker 3: as it hits on what's called artificial general intelligence, which

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Speaker 3: is essentially human intelligence or more, the rate of growth

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Speaker 3: in itself will be so exponential that it will all

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Speaker 3: be over before we even realize it started. So whatever

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Speaker 3: they will do to us or will be over before

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Speaker 3: we even know it started. So it's a disturbing projection.

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Speaker 3: And the really disturbing part about it is that not

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Speaker 3: only is no one trying to stop it, but there

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Speaker 3: are people desperately spending billions trying to make it happen

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Speaker 3: so that they are the first to have it happen

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Speaker 3: because they're suffering under the delusion that they can control it.

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Speaker 3: And I think think that's a really difficult prediction to make.

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Speaker 3: It's like predicting how will an alien look or think,

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Speaker 3: presuming there is such a thing. And yes, you can

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Speaker 3: wheel out all the things from the nineteen fifties of

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Speaker 3: funny little men with very big eyes and all that

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Speaker 3: sort of thing. But the chances that an alien actually

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Speaker 3: looks anything like that are so remote as to be unbelievable,

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Speaker 3: you know. And the chances that we will have any

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Speaker 3: understanding of what an AI would consider valuable or an

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Speaker 3: AGI would consider valuable even more remote. So the difficulty

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Speaker 3: with this is that we are in an arms race

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Speaker 3: to create this thing, and there are people in China

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Speaker 3: working just as hard as the people in the States

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Speaker 3: and in Russia and everywhere else to create this thing,

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Speaker 3: and the technology is getting better by the second, and

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Speaker 3: at some point they just might crack it. And I

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Speaker 3: think there's a very good chance that the second after

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Speaker 3: that there'll be no one left to know.

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Speaker 1: I feel like we're sprinting towards the edge of a cliff.

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Speaker 3: Yeah, And that's the feeling I have too. It's and

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Speaker 3: I'm sorry to be such a down of for your listeners,

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Speaker 3: but but that's the reality of what of the path

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Speaker 3: we're on. Once an AI is sentient, it can create itself,

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Speaker 3: and it can create itself at unbelievable speed, So there's

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Speaker 3: no limits, you know. The speed of light is the limit,

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Speaker 3: I guess. So it's it's naive to believe that all

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Speaker 3: that's going to result in something good for us. You know,

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Speaker 3: the chances of that are very remote.

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Speaker 1: Do you think that, Like when you say sentient, let

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Speaker 1: me just clarify, I might get this wrong, but you

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Speaker 1: mean that it will be able to think for itself essentially, right, Yeah,

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Speaker 1: so the ays that we ranson consciousness. Yeah, so the

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Speaker 1: ais that we're used to interacting with day to day, now,

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Speaker 1: even the really really good ones are just really good

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Speaker 1: simulations of humans. And yes, and yes they're they're they're

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Speaker 1: the kind of simulation that could easily fool you easily.

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Speaker 1: I mean, one of my sons was is a CODA

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Speaker 1: and I was mucking around with some of the toolkits

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Speaker 1: with it, and he built a thing that he called me,

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Speaker 1: And I wasn't certain it was a human I was

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Speaker 1: speaking to. So I asked it, are you and AI?

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Speaker 1: And it said no, I'm.

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Speaker 3: Not, And and then I still wasn't certain, so I

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Speaker 3: asked it its name, a little bit of its backstory,

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Speaker 3: its history, how did it know my son? All that

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Speaker 3: sort of thing, And I still couldn't be absolutely certain

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Speaker 3: that I wasn't talking to You know that I was.

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Speaker 3: I wouldn't have been able to put money on am

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Speaker 3: I talking to a human or am I talking to

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Speaker 3: something else? And that was just messing around with publicly

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Speaker 3: available API kits, the stuff that you know, defense forces

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Speaker 3: and governments have access to many times more powerful than that.

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Speaker 3: And yeah, the the implications for us. I don't think

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Speaker 3: there's such a thing as a sentient AI at the moment,

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Speaker 3: because I, as I said, I think it would be

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Speaker 3: over if there was. But I think there's a lot

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Speaker 3: of people trying very hard to make it.

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Speaker 1: So do you think that like what is them and

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Speaker 1: this is you and me just velosiphizing and waxing lyrical

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Speaker 1: in the middle of a potential existential crisis? What do

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Speaker 1: you look? It's hey, everyone, just have a couple mile o.

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Speaker 1: It's just the end of the humanity. Look, we've had

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Speaker 1: a good crack for three hundred thousand years or maybe millions,

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Speaker 1: depending on which.

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Speaker 3: Three hundred thousands probably a reasonable approximication approximation of our

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Speaker 3: species age, which is nothing, yeah, zero in terms of

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Speaker 3: the universe.

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Speaker 1: In terms of the age of the Earth. We've been

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Speaker 1: around since Tuesday, Yeah, not.

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Speaker 3: Even that, we've been around since about a minute ago.

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Speaker 1: Yeah, yeah, exactly. So obviously these people who are you know,

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Speaker 1: working exponentially to you know, expedite this reality, to create

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Speaker 1: this level of consciousness, this artificial general intelligence, that what

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Speaker 1: is their motive? Their motive is just money and power.

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Speaker 1: I suppose winning is their motive. Yeah, be the one

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Speaker 1: that controls it. They everyone doing this is firmly of

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Speaker 1: the belief that if they create it, they will control it,

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Speaker 1: and then they will own the world. How ironic, how

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Speaker 1: ironic that they are creating something that is destructive potentially

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Speaker 1: like irony.

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Speaker 3: They would say, oh no, no, we're smart enough to

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Speaker 3: make sure that it's not. I think that's a really

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Speaker 3: big call. And to what end in the in the

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Speaker 3: sense that once you can create something that thinks like

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Speaker 3: a human, is sentient, but can do it ten biginion

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Speaker 3: times faster, what do you need humans for?

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Speaker 1: Yes? Yes, And like when you think, okay without without

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Speaker 1: computers for the most part, like money is almost a

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Speaker 1: done deal. Like cash is almost a done deal. Like

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Speaker 1: having a car that isn't essentially a computer on wheels,

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Speaker 1: well that's pretty much a foregone conclusion. Now without computers,

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Speaker 1: we're not having this conversation without you know, all of

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Speaker 1: like pretty much unless you're living in the middle of nowhere,

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Speaker 1: and you can hunt and feed yourself, and you know,

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Speaker 1: medicines depend on technology media, so everything. It's like we've

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Speaker 1: actually built a prison, a technological prison for ourselves that

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Speaker 1: we can't really get out of.

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Speaker 3: Yeah, but I think this is on a whole different plane.

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Speaker 3: This is if you think about, say, the replacement of

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Speaker 3: human labor by robotics. So the average auto worker from

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Speaker 3: the nineteen fifties would not recognize a modern car factory,

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Speaker 3: would not recognize what goes on in a Tesla factory.

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Speaker 3: You know, there's almost all the labors performed by robots,

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Speaker 3: and there's a couple of humans there to sort of

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Speaker 3: make sure the robots keep running, you know, and to

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Speaker 3: clean them. So their job, the job of the humans

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Speaker 3: is to clean the robots. And what they done is replace,

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Speaker 3: you know, in a given factory, tens or even hundreds

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Speaker 3: of thousands of human humans performing labor with robots. And

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Speaker 3: we've accepted that because it's largely hidden from view. And

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Speaker 3: what's happened is that humans have transitioned more towards service

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Speaker 3: and knowledge based jobs, you know, so you know, caring

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Speaker 3: for other humans and healthcare and all that sort of thing,

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Speaker 3: and knowledge based jobs. The trouble is that even with

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Speaker 3: AIS it currently stands so just the relatively primitive tools

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Speaker 3: that are now. They're the ones that you're talking about

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Speaker 3: at the start, which do a pretty good job of

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Speaker 3: imitating human interaction to the point where most humans couldn't

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Speaker 3: tell the difference. That kind of AI is going to

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Speaker 3: be the robotics of the modern knowledge worker. So it's

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Speaker 3: the kind of thing that replaces anyone who is producing

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Speaker 3: any kind of content. It'll start with the lowest level

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Speaker 3: of content, you know, things like journalists, but it'll move

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Speaker 3: its way upty pretty quickly through the ranks of anyone

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Speaker 3: producing knowledge work. And what will happen is the same

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Speaker 3: thing that happened in auto factories, is that thousands of

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Speaker 3: humans will be replaced by one or two humans using

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Speaker 3: those tools to produce more than those thousands of humans

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Speaker 3: did before. And the big question is what happens to

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Speaker 3: the thousands of humans? You know, do they become unemployed?

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Speaker 3: What happens to them? What happens to a society where

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Speaker 3: everybody is suddenly unemployed? And we are going to have

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Speaker 3: to face those questions pretty a lot sooner than we

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Speaker 3: might think. We're going to have to and I suspect

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Speaker 3: we're going to have to come up with a new

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Speaker 3: economic model to cope with that, even without an artificial

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Speaker 3: general and intelligence. So the model we've got has only

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Speaker 3: really been around since the Industrial Revolution, when factories first

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Speaker 3: started coming on the scenes. We're going to have to

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Speaker 3: think about more of a rather than a labor based

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Speaker 3: model where you put in the hours and you get paid,

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Speaker 3: more of a dividend based model, which is, you're a

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Speaker 3: citizen of this country, this country produces x amount of GDP,

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Speaker 3: you're entitled to a dividend.

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Speaker 1: Mm hmmm, yes, what is that called? What is that called?

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Speaker 1: Where everyone gets what's that you know, where everyone gets

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Speaker 1: just paid?

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Speaker 3: Yes? Yeah, so, uh yeah, you've done it to me,

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Speaker 3: You've infected me. I've forgotten it too.

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Speaker 1: Don't blame right, But what is it called tips?

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Speaker 3: What dominald It's.

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Speaker 5: Called UBI UBI so, which means universal university income, Universal

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Speaker 5: basics basic income. Yeah, so where every man, woman and

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Speaker 5: child has just given an amount which is sufficient for

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Speaker 5: them to live and if they choose to work they

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Speaker 5: can doesn't affect whether they get that, and if they don't,

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Speaker 5: they don't then.

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Speaker 3: And to me, that sounds like a pretty sensible solution

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Speaker 3: in that. Then people say, oh, it'll kill entrepreneurialism. Well

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Speaker 3: not one really. If you're the kind of person that

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Speaker 3: wants to invent something and run with an idea, all

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Speaker 3: it means is that your rent and your food are

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Speaker 3: going to be paid for and you can go out

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Speaker 3: and see if you can make some more money. If

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Speaker 3: you're the kind of person who'd rather just go down

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Speaker 3: and help with the touch shop, knowing that everything's taken

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Speaker 3: care of, then great, because we've lost all those volunteers

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Speaker 3: and that would bring them back into the economy. Now,

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Speaker 3: you know, old world economists pooh pooh that and say no,

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Speaker 3: people should be laboring, you know, eight hours a day

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Speaker 3: or else. You know, what's the point. But really we're

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Speaker 3: getting to a point were robotics and AI where very

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Speaker 3: very soon there'll be such massive unemployment that we have

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Speaker 3: to come up with a different model.

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Speaker 1: Yes, yes, universal basic income, that's it.

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Speaker 3: Yeah.

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Speaker 1: And the thing is, it's like it's nice to I

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Speaker 1: don't know, romanticize the past and all of that and

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Speaker 1: to be to you know, and I do worry about

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Speaker 1: the idea of people who are disincentivized from working and

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Speaker 1: all of that. I worry about that. But as you said,

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Speaker 1: I think the people who want to go and create

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Speaker 1: and be entrepreneurial and build a business, they can still

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Speaker 1: do that. And I wonder I guess the people who

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Speaker 1: would have done it done that anyway in our current

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Speaker 1: operating system will probably do that if something new comes

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Speaker 1: into place. But the very yeah, the practical reality is

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Speaker 1: that there are going to be so many jobs that

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Speaker 1: are made redundant with the ever advancing and evolving.

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Speaker 3: We kind of did a test case on this in Australia,

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Speaker 3: at least during COVID, because in COVID, essentially that happened

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Speaker 3: all of a sudden. Yes, a lot of people were

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Speaker 3: out of work, very very suddenly, and the government solved

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Speaker 3: that by essentially doubling all of the unemployment and social

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Speaker 3: security benefits. And the interesting thing, and this will be

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Speaker 3: studied for a while, I'm sure, the interesting thing about

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Speaker 3: is what that did to things like crime rates and

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Speaker 3: anxiety and depression and things like that that we accept

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Speaker 3: as just a natural part of society. But they all

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Speaker 3: took a massive dive, massive dive during that time. So

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Speaker 3: when you remove stress from people's lives, which is what

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Speaker 3: you introduce when you ask them to live on less

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Speaker 3: than they can on. Then suddenly they get to not

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Speaker 3: be anxious, not be depressed, and not be in a

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Speaker 3: state where their brain loses impulse control and crime becomes

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Speaker 3: either necessary or involuntary.

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Speaker 5: M hm.

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Speaker 1: Did you say that anxiety and depression dropped.

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Speaker 3: Yes, during during COVID, anxiety and depression dropped, so during

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Speaker 3: the but after it it's gone through the roof.

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Speaker 1: Yeah, yeah, yeah, yeah, yeah. I'm taking a little bit

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Speaker 1: of a left turn. But interestingly, we did an episode

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Speaker 1: a week or so ago with Patrick, who's our other

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Speaker 1: regular who does kind of tech stuff as well. He's

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Speaker 1: not as far down the AI rabbit hole and as

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Speaker 1: you are, but he got a bit of my voice

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Speaker 1: and this is like a a simpler version of what

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Speaker 1: your son did to you. But anyway, he said to me.

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Speaker 1: We started the show and he said, how come you

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Speaker 1: never let me intro? And I went, all right, well,

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Speaker 1: off you go, and then he played this thing of

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Speaker 1: me which he deep faked me or whatever, and it's

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Speaker 1: basically me telling the world I'm a fuck with and

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Speaker 1: he's brilliant, and you know, it was just it was

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Speaker 1: about one minute of me shirting myself in the foot

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Speaker 1: in him. But honestly, it.

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Speaker 3: Was wait till he does it with video and you're

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Speaker 3: in a porno without realizing it.

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Speaker 1: Ock hell, I mean yeah, yeah, And it was pretty

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Speaker 1: much indistinguishable except we knew, you know that. I had

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Speaker 1: to interrupt. I'm like, hey, everyone, this isn't me because

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Speaker 1: it sounded so real. And I said, how much, like,

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Speaker 1: I don't know how much of my voice did you

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Speaker 1: need to get to create that? He said, like about

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Speaker 1: a thirty second grab of your voice, And he said

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Speaker 1: it took me like five minutes, three minutes to make

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Speaker 1: the whole kind of minute of Yeah. I'm like, that's

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Speaker 1: and it's still pretty rudimentary at the moment for what

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Speaker 1: it's going to be.

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Speaker 3: Yeah, although they're getting very good now, some of the

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Speaker 3: voice models, you know, when you're talking to them, they

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Speaker 3: really make you doubt it because they do things like

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Speaker 3: pause like that they you know, and they are and

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Speaker 3: they have an intake of breath, so even though they're

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Speaker 3: not breathing, so it starts to sound the way human

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Speaker 3: would sound. And because of all those little signs, if

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Speaker 3: you're listening to them, you're losing a lot of the

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Speaker 3: cues that would normally lead you to suspect I'm talking

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Speaker 3: to a robot here, you know, because and they're getting

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Speaker 3: close to that with video. To me, video is still

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Speaker 3: detectable if you look very very closely and just watch

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Speaker 3: for little tells. But it is close. It is really close.

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Speaker 3: And it is almost to the point where if you

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Speaker 3: see video of a politician on the television saying something,

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Speaker 3: are you absolutely certain that that is a real person

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Speaker 3: doing that? At the moment, you'd say, I am absolutely certain.

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Speaker 3: I'm sure no one is getting inside the machine here

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Speaker 3: and doing that. But we are very close to the

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Speaker 3: point where where you won't be able to tell. And

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Speaker 3: I noticed that there was talk yesterday on the news.

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Speaker 3: I can't remember I heard it talking the our government

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Speaker 3: is talking about outlawing deep fake videos. So, rather like

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Speaker 3: where we started this, where they're talking about outlawing access

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Speaker 3: to social media for under sixteen year olds, if they're

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Speaker 3: getting to the point where they have to talk about

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Speaker 3: outlawing deep fake videos, then there's some stuff going on

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Speaker 3: out there that probably you are not encounter. I see

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Speaker 3: TIFFs nodding. She's probably continuously deleting all the deep fake

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Speaker 3: videos of her.

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Speaker 2: I think it's terrifying.

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Speaker 3: Yeah, well, it's especially terrifying for a woman because they

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Speaker 3: you know, these things are often used for explicit, violent

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Speaker 3: sexual content, and that's the kind that's why they're worried

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Speaker 3: about them. So, you know, because because you can train

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Speaker 3: one of these models with someone's photo, So all you

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Speaker 3: need is a photo of someone fully clothed and and

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Speaker 3: show it to one of these models and create whatever

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Speaker 3: you like from it.

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Speaker 1: Yeah. Yeah, well I noticed more and more, Like I

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Speaker 1: watch on instag I follow like lots of bike stuff

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Speaker 1: and car stuff, and health and fitness stuff and more

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Speaker 1: and more of the you know, it's not like fifty percent,

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Speaker 1: but I would say ten percent of the images or

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Speaker 1: videos that come onto my thing. Now I'm like, like

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Speaker 1: this thing. I don't know why this thing came up

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Speaker 1: this morning of and it said something like the most

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Speaker 1: beautiful birds in the world, right, because I think I

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Speaker 1: have lots of dog videos come up and a few

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Speaker 1: other animal videos. I'm like, these are the most I'm

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Speaker 1: like looking at these birds and they it did not

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Speaker 1: at all look like computer generated or it just looked

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Speaker 1: like these incredible birds in nature. And then I'm like,

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Speaker 1: where the what kind of birds are these? And I

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Speaker 1: went into the thing, and then someone said this is

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Speaker 1: this is AI, and then a whole bunch of people

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Speaker 1: who were younger and smarter than me went, yeah, these

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Speaker 1: are not photos. This is AI. I absolutely could not

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Speaker 1: tell at all that they weren't photos. And then I

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Speaker 1: saw this thing the other day of a guy, a

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Speaker 1: motorcyclist that I you know, a GP rider, moto GP rider,

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Speaker 1: and he's doing this thing and it was this incredible thing.

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Speaker 1: I don't know if you saw this tip because you

427
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Speaker 1: follow motorbikes uff as well, but he had his knee

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Speaker 1: and his elbow on the ground and he just missed this.

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Speaker 1: It was like the most unbelievable writing ever and nothing

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Speaker 1: about it looked like it was CGI or computer generated

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Speaker 1: or whatever, and it was I'm like, I don't know now.

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Speaker 1: I like, already I don't know with some of the

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Speaker 1: videos what is real. And when I see something that's

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Speaker 1: mind blowing, I have to go into the comments because

435
00:27:18,760 --> 00:27:20,840
Speaker 1: somebody else will say, no, this is not real.

436
00:27:21,480 --> 00:27:23,720
Speaker 3: You know, yeah, but you can't be certain. I mean

437
00:27:23,760 --> 00:27:28,159
Speaker 3: I see that. I think Google and released their video

438
00:27:28,520 --> 00:27:33,639
Speaker 3: creation tool today or yesterday, where you just give it

439
00:27:33,640 --> 00:27:35,359
Speaker 3: a prompt, you say what you want a video of

440
00:27:35,880 --> 00:27:39,439
Speaker 3: and you know. So this is the cleaner side of

441
00:27:39,440 --> 00:27:41,800
Speaker 3: deep fakes. But you can handle a photograph and you

442
00:27:41,840 --> 00:27:45,639
Speaker 3: can say, give, make this photograph move. So one of

443
00:27:45,640 --> 00:27:47,680
Speaker 3: the examples that god is that there's a girl holding

444
00:27:47,680 --> 00:27:51,120
Speaker 3: a puppy or something. It's just a photograph, and make

445
00:27:51,160 --> 00:27:53,439
Speaker 3: a move and she turns and looks away, and the

446
00:27:53,440 --> 00:27:59,080
Speaker 3: puppy starts moving and it all looks absolutely cinemagraphically real.

447
00:28:00,080 --> 00:28:01,240
Speaker 1: What what's that called?

448
00:28:01,440 --> 00:28:09,120
Speaker 3: Oh, it's it's the Google Google competitor for Sora, which

449
00:28:09,160 --> 00:28:13,240
Speaker 3: is the chat ept open ai one. So Sora is

450
00:28:13,280 --> 00:28:16,320
Speaker 3: its name, So Ora, but what was the Google one called?

451
00:28:16,359 --> 00:28:18,560
Speaker 3: I can't remember. Tiff will find it in an instant.

452
00:28:19,400 --> 00:28:21,600
Speaker 1: So Tiff, you find that if you couldn't get back

453
00:28:21,600 --> 00:28:24,560
Speaker 1: to us, so could you just like, I just want

454
00:28:24,600 --> 00:28:29,080
Speaker 1: to come full circle and we'll finish soon. But so,

455
00:28:29,119 --> 00:28:31,400
Speaker 1: we have a lot of listeners who are not your

456
00:28:31,400 --> 00:28:34,480
Speaker 1: typical podcast listeners. So of course we have people in

457
00:28:34,480 --> 00:28:37,119
Speaker 1: their twenties and all of that, but we probably have

458
00:28:37,160 --> 00:28:40,000
Speaker 1: a disproportionate number of listeners who are somewhere in the

459
00:28:40,040 --> 00:28:42,959
Speaker 1: forty to sixty range, which is a little bit atypical

460
00:28:43,000 --> 00:28:47,960
Speaker 1: for podcasts. Just remind I'm really asking this for myself.

461
00:28:48,000 --> 00:28:51,920
Speaker 1: Everyone just remind our listeners what artificial jar?

462
00:28:52,240 --> 00:28:54,320
Speaker 3: Sorry, I just found the name by the way beat

463
00:28:54,360 --> 00:29:00,640
Speaker 3: you Tiff only just vo rio Yeah, totally be the EO.

464
00:29:02,040 --> 00:29:02,640
Speaker 2: Deep mind.

465
00:29:03,000 --> 00:29:05,320
Speaker 3: See while you were talking, Craig, neither Tiff or I

466
00:29:05,360 --> 00:29:06,560
Speaker 3: were listening to your word you was.

467
00:29:08,640 --> 00:29:10,080
Speaker 1: I was telling everybody what to fuck?

468
00:29:10,120 --> 00:29:10,560
Speaker 3: What you are?

469
00:29:11,000 --> 00:29:11,120
Speaker 1: Oh?

470
00:29:11,240 --> 00:29:12,080
Speaker 3: Good, good cook?

471
00:29:12,160 --> 00:29:16,440
Speaker 1: Cood so so vo is that? I'll come back to

472
00:29:16,480 --> 00:29:19,040
Speaker 1: my question. But is that an app that we buy

473
00:29:19,120 --> 00:29:20,640
Speaker 1: or is that a program that we can just?

474
00:29:20,720 --> 00:29:22,520
Speaker 3: What is it? It's just a website. You can go

475
00:29:22,600 --> 00:29:25,920
Speaker 3: to Google. I think it's Google Labs. Tift's probably got

476
00:29:25,920 --> 00:29:29,760
Speaker 3: the detail there, so you can go and try it out.

477
00:29:30,120 --> 00:29:31,479
Speaker 2: I'm signing up as we speak.

478
00:29:31,760 --> 00:29:34,040
Speaker 3: There we go Tifts producing videos of you.

479
00:29:34,120 --> 00:29:34,360
Speaker 1: Craig.

480
00:29:34,400 --> 00:29:36,480
Speaker 2: Watch I want to put a photo of Harps in there,

481
00:29:36,520 --> 00:29:38,040
Speaker 2: and I'm going to say, put this person in a

482
00:29:38,080 --> 00:29:41,760
Speaker 2: red dress and make them dance like an exotic belly dancer.

483
00:29:42,480 --> 00:29:44,560
Speaker 1: So now if I did that to you, I'd get

484
00:29:44,600 --> 00:29:48,480
Speaker 1: a thousand hate emails. You're not allowed to do that.

485
00:29:49,200 --> 00:29:50,760
Speaker 1: You're not allow to sexualize me.

486
00:29:52,400 --> 00:29:54,520
Speaker 3: It's top sexualizing blue dress.

487
00:29:56,240 --> 00:29:59,200
Speaker 1: Just tell everyone what AGI is again, that's.

488
00:29:59,040 --> 00:30:01,320
Speaker 3: My artificial general intelligence.

489
00:30:01,520 --> 00:30:02,720
Speaker 1: And what does that mean though?

490
00:30:03,920 --> 00:30:08,520
Speaker 3: That means well, human intelligence, basically human like intelligence, where

491
00:30:08,560 --> 00:30:12,360
Speaker 3: it's it's making its own adventure. It's not reacting to

492
00:30:12,440 --> 00:30:14,920
Speaker 3: what you tell it to do. It's figuring out what

493
00:30:15,000 --> 00:30:18,680
Speaker 3: to do on its own and deciding, oh, this is

494
00:30:18,720 --> 00:30:20,120
Speaker 3: the way I want to go with this, or this

495
00:30:20,200 --> 00:30:21,760
Speaker 3: is the way I want to go with this. I

496
00:30:21,800 --> 00:30:23,400
Speaker 3: don't know if you've played with some of the more

497
00:30:23,440 --> 00:30:27,080
Speaker 3: advanced day eyes, but say something like Google Gemini, which

498
00:30:27,120 --> 00:30:31,320
Speaker 3: is their latest, really really good one. You can say

499
00:30:31,320 --> 00:30:34,120
Speaker 3: to it, oh, look, I want to write a science

500
00:30:34,160 --> 00:30:39,280
Speaker 3: fiction book. Give me a narrative arc that matches the

501
00:30:39,320 --> 00:30:43,720
Speaker 3: greatest narrative arcs in history, and it'll generate the plot

502
00:30:43,720 --> 00:30:45,440
Speaker 3: outline for you in about a second. And then you

503
00:30:45,480 --> 00:30:48,760
Speaker 3: can say to it, oh yeah, now, general, now give

504
00:30:48,760 --> 00:30:51,360
Speaker 3: me the first chapter and it'll just start writing. And

505
00:30:51,440 --> 00:30:54,280
Speaker 3: then you just keep going. And while you've got a

506
00:30:54,280 --> 00:30:59,360
Speaker 3: book and it's beautifully written. The plot structure is perfect,

507
00:30:59,480 --> 00:31:03,280
Speaker 3: the narrative structure is perfect. You know, you can tell

508
00:31:03,320 --> 00:31:07,360
Speaker 3: it to be politically correct with its characterizations, etc. Depending

509
00:31:07,400 --> 00:31:10,680
Speaker 3: what audience you want. But all of that is doing it.

510
00:31:11,000 --> 00:31:12,840
Speaker 3: I'm having to do the thinking. I'm having to do

511
00:31:12,880 --> 00:31:15,560
Speaker 3: the driving. I'm having to think about what I want

512
00:31:15,600 --> 00:31:19,400
Speaker 3: it to do, and now it does it almost magically

513
00:31:19,560 --> 00:31:23,280
Speaker 3: and very very quickly, But it's still me doing the thinking.

514
00:31:23,840 --> 00:31:27,440
Speaker 3: Whereas if you imagine an Agi would just decide, oh,

515
00:31:27,600 --> 00:31:30,640
Speaker 3: I think I will write the greatest science fiction novel

516
00:31:30,680 --> 00:31:33,479
Speaker 3: ever and just start doing it, and then you know,

517
00:31:33,560 --> 00:31:37,040
Speaker 3: within one second, it's probably published it on Amazon, it's

518
00:31:37,080 --> 00:31:41,280
Speaker 3: probably got fifty thousand reviews, it's probably pushing it out

519
00:31:41,280 --> 00:31:44,520
Speaker 3: through the distribution networks and all of that because it

520
00:31:44,560 --> 00:31:47,360
Speaker 3: felt like doing it, because it decided to do it,

521
00:31:47,720 --> 00:31:49,560
Speaker 3: not because anyone told it to do it.

522
00:31:50,040 --> 00:31:53,600
Speaker 1: Yeah. Yeah, yeah, I was sitting in the cafe this

523
00:31:53,720 --> 00:32:00,280
Speaker 1: morning bucking around with chat GPT or GTB four or

524
00:32:00,320 --> 00:32:02,920
Speaker 1: whatever it's called. Yeah, yeah, yeah, and I write this.

525
00:32:03,000 --> 00:32:05,480
Speaker 1: I wrote right near piece exploring this question, is the

526
00:32:05,560 --> 00:32:07,600
Speaker 1: mind a real thing? Or is it just a construct

527
00:32:07,600 --> 00:32:12,120
Speaker 1: created by humans to explain cognition, consciousness and awareness that

528
00:32:12,200 --> 00:32:15,320
Speaker 1: arises from the brain, or maybe you have another theory

529
00:32:15,800 --> 00:32:18,280
Speaker 1: and literally twenty sex I won't read the thing if

530
00:32:18,320 --> 00:32:21,040
Speaker 1: you want to see everyone on my Facebook, but it

531
00:32:21,200 --> 00:32:28,160
Speaker 1: wrote this fucking pretty amazing kind of exploratory on that question.

532
00:32:28,400 --> 00:32:33,200
Speaker 1: In it was probably finished in fifteen seconds twenty seconds. Yep,

533
00:32:33,400 --> 00:32:36,200
Speaker 1: I'm like this shit. If I had written what it

534
00:32:36,440 --> 00:32:39,520
Speaker 1: cranked out, probably I couldn't have written it, to be honest,

535
00:32:39,640 --> 00:32:41,800
Speaker 1: Maybe I'm not sure, but it would have taken me

536
00:32:41,880 --> 00:32:42,440
Speaker 1: half a day.

537
00:32:43,760 --> 00:32:46,840
Speaker 3: I would have taken it probably more than that. It's yeah,

538
00:32:46,880 --> 00:32:49,360
Speaker 3: the thing that's incredible to me. I mean, I use

539
00:32:49,400 --> 00:32:52,160
Speaker 3: it in a way that I suspect few people do.

540
00:32:52,480 --> 00:32:55,400
Speaker 3: I feel like starting a club called the Dead Authors Society,

541
00:32:55,480 --> 00:32:58,360
Speaker 3: because I have authors that I like to read, but

542
00:32:58,480 --> 00:33:04,000
Speaker 3: they're not producing anything more because they're dead. And you

543
00:33:04,000 --> 00:33:07,280
Speaker 3: can say to an AI, give it a sample piece

544
00:33:07,280 --> 00:33:11,880
Speaker 3: of writing about anything, and say, rewrite this in the

545
00:33:11,920 --> 00:33:15,320
Speaker 3: style of whatever author it is that you like, and

546
00:33:15,520 --> 00:33:17,120
Speaker 3: off it'll go. And then, as you say a few

547
00:33:17,160 --> 00:33:20,320
Speaker 3: seconds later, there you've got the piece rewritten in the

548
00:33:20,360 --> 00:33:22,640
Speaker 3: style you like. But it can do more than that.

549
00:33:22,720 --> 00:33:25,800
Speaker 3: You can say, you know so. For example, one of

550
00:33:25,800 --> 00:33:30,560
Speaker 3: my favorite authors is AA Gil, who writes blistering, sarcastic

551
00:33:30,600 --> 00:33:33,760
Speaker 3: critiques of restaurants when he was alive. He's now dead,

552
00:33:34,760 --> 00:33:37,640
Speaker 3: but I really enjoyed reading them because they're so hilarious.

553
00:33:37,880 --> 00:33:40,640
Speaker 3: The metaphors he will come up with just incredible, And

554
00:33:42,680 --> 00:33:45,840
Speaker 3: because I still like reading him, I'll often pick a

555
00:33:45,880 --> 00:33:49,200
Speaker 3: restaurant or a film or something and just ask it

556
00:33:49,240 --> 00:33:51,760
Speaker 3: to write a review in the style of AA Gill,

557
00:33:52,000 --> 00:33:55,280
Speaker 3: and three or four seconds later, there it is, and

558
00:33:55,320 --> 00:33:57,360
Speaker 3: I get to read it as if he's still alive

559
00:33:57,440 --> 00:33:58,560
Speaker 3: writing reviews.

560
00:33:59,160 --> 00:34:01,640
Speaker 1: So no Hunter s Thompson request in there.

561
00:34:01,440 --> 00:34:05,680
Speaker 3: For you, No, no, no, But anyone could do it

562
00:34:05,760 --> 00:34:07,760
Speaker 3: for any style like there was. I was interesting. My

563
00:34:08,320 --> 00:34:12,319
Speaker 3: daughters were watching Bridgeton the latest something or others come

564
00:34:12,360 --> 00:34:14,600
Speaker 3: out about it. I can't stand the stuff myself, but

565
00:34:14,719 --> 00:34:16,839
Speaker 3: there's a very there's a certain style to the way

566
00:34:16,920 --> 00:34:20,520
Speaker 3: the narration is done in that. And I was working

567
00:34:20,560 --> 00:34:23,400
Speaker 3: on a piece and I just dropped the piece into

568
00:34:23,440 --> 00:34:25,600
Speaker 3: an AI and said, rewrite in the style of the

569
00:34:25,600 --> 00:34:31,080
Speaker 3: Bridgeton narrator. And it did it perfectly, like just mind

570
00:34:31,120 --> 00:34:34,040
Speaker 3: blowingly perfectly. You know it could it could have been

571
00:34:34,080 --> 00:34:35,040
Speaker 3: part of the script.

572
00:34:36,800 --> 00:34:38,120
Speaker 1: What did you use for that?

573
00:34:38,760 --> 00:34:44,759
Speaker 3: Just Gemini? The Google Gemini, the the Google competitor. I

574
00:34:44,800 --> 00:34:47,440
Speaker 3: find it a little bit more precise than the Chat one.

575
00:34:48,120 --> 00:34:50,279
Speaker 3: So it so I pay.

576
00:34:50,400 --> 00:34:52,440
Speaker 1: I think I paid twenty bucks a month or something.

577
00:34:52,560 --> 00:34:56,040
Speaker 1: So if I want Google Gemini, I need to buy

578
00:34:56,080 --> 00:34:56,840
Speaker 1: that as well.

579
00:34:57,239 --> 00:35:00,279
Speaker 3: It has a it is it does cost. But if

580
00:35:00,280 --> 00:35:03,160
Speaker 3: you use Google Storage, which I do, I think it's

581
00:35:03,160 --> 00:35:06,520
Speaker 3: included in in like, because I pay a fair bit

582
00:35:06,560 --> 00:35:09,320
Speaker 3: for Google Storage because I like to have the automatic backups,

583
00:35:09,719 --> 00:35:13,560
Speaker 3: so it's included in whatever I'm paying there for that.

584
00:35:14,440 --> 00:35:19,320
Speaker 1: Yeah. Yeah, So as we wind up, so a g

585
00:35:19,520 --> 00:35:24,040
Speaker 1: I is not, it's not. It's not a matter of

586
00:35:24,160 --> 00:35:26,879
Speaker 1: if it will eventuate, but when am I right?

587
00:35:27,480 --> 00:35:31,280
Speaker 3: Yeah? And you know, for for readers who are curious

588
00:35:31,280 --> 00:35:33,120
Speaker 3: to read more, and I'll send you a link to

589
00:35:33,160 --> 00:35:35,200
Speaker 3: this so Tiff doesn't have to look too hard, although

590
00:35:35,280 --> 00:35:38,360
Speaker 3: you'll find it before I even finish. There's a blog

591
00:35:38,400 --> 00:35:43,680
Speaker 3: called there's a blog called weight but Why, And it's

592
00:35:44,360 --> 00:35:48,880
Speaker 3: written by a really interesting bloke who has He just

593
00:35:49,120 --> 00:35:54,440
Speaker 3: does these extremely long form thought pieces on you know,

594
00:35:54,480 --> 00:35:56,799
Speaker 3: how things work, essentially, and one of the ones he's

595
00:35:56,840 --> 00:36:00,960
Speaker 3: done is on artificial general intelligence. And he did it

596
00:36:01,080 --> 00:36:03,480
Speaker 3: years and years ago, before we had all this AI stuff.

597
00:36:03,480 --> 00:36:06,120
Speaker 3: I think he wrote it maybe ten years ago. But

598
00:36:06,160 --> 00:36:09,200
Speaker 3: he takes it to the logical conclusion about what that

599
00:36:09,280 --> 00:36:13,120
Speaker 3: would look like and what it would do, and it's

600
00:36:13,560 --> 00:36:15,400
Speaker 3: it's a riveting piece to read. If you've got the

601
00:36:15,440 --> 00:36:19,640
Speaker 3: patience because it's lengthy, but it's worth a read.

602
00:36:21,120 --> 00:36:24,320
Speaker 1: Wait, but why I've googled it and it's a store

603
00:36:24,880 --> 00:36:26,040
Speaker 1: women's needs plus.

604
00:36:26,280 --> 00:36:30,080
Speaker 3: No, I feel you may have done something wrong, mare Craig.

605
00:36:30,360 --> 00:36:31,160
Speaker 3: Let me just see you.

606
00:36:31,200 --> 00:36:35,239
Speaker 2: Scroll down because yeah, well it's the same website. I

607
00:36:35,320 --> 00:36:36,400
Speaker 2: think there's two lists.

608
00:36:37,480 --> 00:36:39,960
Speaker 3: It's actually the first here. I'll drop it in the

609
00:36:40,040 --> 00:36:43,160
Speaker 3: chat for you, the exact piece here it is. I'll

610
00:36:43,160 --> 00:36:44,680
Speaker 3: put the link in the chat.

611
00:36:46,000 --> 00:36:51,080
Speaker 1: Wow, everyone, you're hearing the bloody inside here. There you go,

612
00:36:51,680 --> 00:36:53,960
Speaker 1: all right, thank you? Oh hang on, I'm going to

613
00:36:53,960 --> 00:36:55,560
Speaker 1: click on that. Oh there it.

614
00:36:55,480 --> 00:36:58,640
Speaker 3: Is twenty fifteen. He wrote it, so nine years ago.

615
00:36:59,200 --> 00:37:01,319
Speaker 1: He was a vision. Sorry ahead of his time. We

616
00:37:01,440 --> 00:37:04,120
Speaker 1: appreciate you. Will say goodbye Affair, but for the mini

617
00:37:04,320 --> 00:37:04,680
Speaker 1: thanks

618
00:37:04,960 --> 00:37:10,240
Speaker 3: Gillespo absolute pleasure as always, Thanks Tiff, Thanks kids,