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Speaker 1: We've had machines replace labor many many times over in

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Speaker 1: the past, we've never really had machines replace mental labor

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Speaker 1: in the same way that AI does, And so I

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Speaker 1: think there's this potential for us to really change the

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Speaker 1: paradigm of what making the living means. Let's start again,

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Speaker 1: because at some point, someone just made stuff up, right,

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Speaker 1: Someone just made up the forty hour work week, someone

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Speaker 1: just made up how you spend your time. All these

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Speaker 1: things were made up, and it's just kind of rolled

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Speaker 1: over and become the norm. But we can renormalize, and

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Speaker 1: so I think that's probably the biggest upside to AI,

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Speaker 1: is the ability to do that. And I think that

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Speaker 1: could be really interesting. It could be done really wrong

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Speaker 1: if it's not careful and considered, But I think I'd

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Speaker 1: like to have faith.

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Speaker 2: In human ingenuity.

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Speaker 1: We will tackle these problems, and we can solve them

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Speaker 1: in a clever way that ultimately is been official for everyone.

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Speaker 2: Infinity is whacky but also fun.

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Speaker 1: I think with like pie and the decimal places of pie,

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Speaker 1: because it's non terminating. At some point, if you were

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Speaker 1: to convert those numbers into pixels, this very image of

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Speaker 1: us talking exists in pies, decimal places in some capacity

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Speaker 1: because of the concepts of infinity, and so it's just

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Speaker 1: whacky if and that also means infinitely within pi's decimal places,

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Speaker 1: this audio recording, if you converted the numbers into audio recording,

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Speaker 1: this audio recording exists in the decimal places of PIE.

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Speaker 1: Once AI starts creating things smarter than us, we're not

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Speaker 1: good at thinking about that. It's for most people, it's

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Speaker 1: probably some smarter than us. It would be the equivalent

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Speaker 1: of like an Einstein or something right, or a Hawking,

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Speaker 1: and so like it's something an order of magnitude smarter.

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Speaker 1: It's you know, the way that you can completely intellectually

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Speaker 1: dominate a dog in a park and fake throw balls

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Speaker 1: and the dogs never not even clo you're just toying

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Speaker 1: with it. The idea of us being the dog and

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Speaker 1: something that much smarter than us that we are just

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Speaker 1: it's just toying with us and completely intellectually dominates us.

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Speaker 1: That it's hard to kind of comprehend that situation.

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Speaker 3: Do I get snacks and do I get to play

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Speaker 3: Chasey Chase and then just like lying around and having

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Speaker 3: it like sleep for eighteen hours and occasionally bark at

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Speaker 3: the kids who are walking home from school.

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Speaker 4: That exactly sounds like a pretty good existence.

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Speaker 2: I mean, that's it. Sign me up. Let's you know,

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Speaker 2: like it's a dog's life.

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Speaker 4: That is Dr Matt Agnew.

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Speaker 3: He's here for a conversation about AI, which is vitally

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Speaker 3: important at this point though. This job that I'm doing

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Speaker 3: right now, that's still done by me, a real human,

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Speaker 3: and to keep it that way, I'm going to play

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Speaker 3: some ads back in a second. Gooday, welcome to the show.

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Speaker 3: Thanks for being here. This is better than yesterday, making

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Speaker 3: it better every episode since twenty thirteen.

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Speaker 4: I'm Washa Ginsburg. I'm very very grateful that you are here.

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Speaker 4: Thank you very much.

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Speaker 3: To the people who signed up for the substack, thank

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Speaker 3: you very much. It's lovely to see new people there.

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Speaker 3: It's a nice way to keep in touch. It's in

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Speaker 3: the show notes the link there. I'm really enjoying putting

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Speaker 3: more and more articles up there each and every week.

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Speaker 3: There's also details of live events and things on the

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Speaker 3: substack as well, and so that's the way to get

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Speaker 3: in touch with me, get around the conversation.

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Speaker 4: Doctor Matt Agnew is here now. Number one.

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Speaker 3: He's not that kind of doctor Number two he's lovely.

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Speaker 3: Number three he has a PhD in astrophysics, and number

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Speaker 3: four he's got a new book because he's been busy.

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Speaker 4: I mean, I've got a PhD in astrophysics.

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Speaker 2: What joy do?

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Speaker 3: Now I'll go get a master's in our artificial intelligence. Yeah,

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Speaker 3: he's gone back to UNI, and on the back of that,

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Speaker 3: he's written a brand new book called Is My Phone

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Speaker 3: Reading My which I'm sure that you and I have both.

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Speaker 3: I've certainly felt that because it's just a bit too

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Speaker 3: creepy sometimes. What Matt Agnew has to say today about AI,

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Speaker 3: about the powerfully transformative effect it is already having on

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Speaker 3: education and democracy in both positive and negative ways. What

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Speaker 3: he's got to say was enough to chill me to

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Speaker 3: the bone and give me goosebumps at the same time. Now,

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Speaker 3: I am recording this about a week or so out

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Speaker 3: from the twenty twenty four US election, and we do

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Speaker 3: speak a little about AI and elections. Want to use

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Speaker 3: the US election as an example. So it's been ages

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Speaker 3: since Matt and I actually sat down, because he needed

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Speaker 3: to get it in the can for his book to

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Speaker 3: come out.

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Speaker 4: Anyway, sometimes you record these things once in.

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Speaker 3: Advance, so please understand that if something has happened that's

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Speaker 3: now glaringly obvious to us as a community. Please understand

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Speaker 3: why when not talking about that. I'm right now, I'm

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Speaker 3: in the past. It's simple time travel, all right. And

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Speaker 3: there's no person that's better to speak to about time

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Speaker 3: travel than doctor Agnew.

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Speaker 2: He's a total delight.

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Speaker 4: He's helpful.

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Speaker 3: He's so handsome and charming because when he gets to

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Speaker 3: the really scary stuff about AI and stuff, it's kind

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Speaker 3: of useful because you can get lost in the dreamy

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Speaker 3: eyes of his and offsets the kind of catastrophic, you know,

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Speaker 3: kind of things that are coming out of his mouth.

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Speaker 4: That's good.

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Speaker 3: I put a link in the show notes to his

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Speaker 3: book Get around him. He's a great science communicator. I

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Speaker 3: love spending time with him. This is doctor Matt Agnew.

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Speaker 4: Thanks for coming in, man. How are you today, Matt?

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Speaker 2: I'm well, I'm well. How are you?

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Speaker 4: I'm good?

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Speaker 3: And look, I was in Canberra the other day for

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Speaker 3: a shindig and I'm standing behind David Pocock, the Independent

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Speaker 3: Senator for the set, and I was trying real hard

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Speaker 3: and I actually succeeded in not asking him what he

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Speaker 3: does at the gym and how often he goes. Because

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Speaker 3: he's an ex professional athlete a couple of years after

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Speaker 3: his playing career, they don't normally look like him. He's

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Speaker 3: a man that still has If he's going to get

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Speaker 3: a suit, he needs to put half the budget to

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Speaker 3: tailor's shaped like a trapezoid.

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Speaker 4: But I will ask you, holy shit, you looking good.

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Speaker 2: Thank you, thank you. Yeah.

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Speaker 1: Back in the gym, back running, I kind of I

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Speaker 1: don't want to say the wheels fell off, but you

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Speaker 1: know how things are. Sometimes there's things that knock you

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Speaker 1: about in life. Sometimes all the all the great routines

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Speaker 1: you have with diet and exercise and social socializing and

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Speaker 1: all that stuff can fall by the wayside. And so

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Speaker 1: I've had that back in place fairly consistently for the

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Speaker 1: last three or four months now, and I'm starting to

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Speaker 1: see some of the some of the pieces.

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Speaker 3: When I first met you, you were just starting to

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Speaker 3: get into, you know, fighting gravity with iron.

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Speaker 1: Yes, yes, I kind of had been doing it for

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Speaker 1: a while, but not probably as consistently as I have

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Speaker 1: been now for.

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Speaker 2: The last decade. It's still always been a bit of

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Speaker 2: a therapeutic thing kind of getting there.

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Speaker 1: I've never enjoyed going to the gym with friends, man,

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Speaker 1: because it's not a social thing for me. It's it's

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Speaker 1: in my own head, my own space, its my zen time.

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Speaker 1: So it's always been this kind of.

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Speaker 4: Listening to weird philosophy stuff while I'm pulling deadlifts.

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Speaker 2: Yeah, exactly, I'm not here to talk, you know. Yeah philosophy.

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Speaker 4: I've got Alan Wat's whispering to me while I'm pushing.

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Speaker 3: There you go.

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Speaker 2: I'm pushing stuff around, great choice philosopher.

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Speaker 1: So yeah, it's definitely been like a bit of a

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Speaker 1: therapeutic thing. And I imagine it's whether it's weight, excite

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Speaker 1: or whatever, it's everyone finds their little thing that that's

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Speaker 1: their me time, and that's how.

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Speaker 4: They really I also very much. I trained for esthetic.

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Speaker 3: Goals once and I got those as setic goals, but

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Speaker 3: it was very hard and impossible to it was possible

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Speaker 3: to maintain.

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Speaker 2: Yeah.

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Speaker 3: Now I just train to make sure that I get

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Speaker 3: a good hormonal response and my bone.

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Speaker 4: Density stays up.

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Speaker 2: Yep.

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Speaker 4: If it means I'm fit in a T shirt, that's nice.

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Speaker 2: Yeah, Yeah, that's that.

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Speaker 4: You know what, if I'm doing it right, I'm going

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Speaker 4: to fit in my T shirt.

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Speaker 2: Yeah.

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Speaker 3: Absolutely, get the hormone and response on the I have

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Speaker 3: to push to a particular point, and that means that

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Speaker 3: like I just walked into unique Loo and grabbed a

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Speaker 3: size thirty pair of pants.

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Speaker 4: Because I know I'm still so surty. Yeah I'm fifty

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Speaker 4: years old, I'm a size thirty pair of pants.

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Speaker 2: Yeah.

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Speaker 1: Yeah, No, that's it, I think, and it's kind of

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Speaker 1: I think it's with time. I probably also did the

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Speaker 1: same thing where it's it's esthetic driven.

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Speaker 2: It's the beach bod.

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Speaker 1: And it takes you a long time to kind of

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Speaker 1: recaliber and be like, okay, no, it's the physiological boost,

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Speaker 1: it's the mental health boost, it's all the other auxiliary stuff.

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Speaker 2: That's why you do it. Fitting into the T shirt

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Speaker 2: that kind of that stuff nice, that's gravy.

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Speaker 3: Right, it's nice. Do you go full science on it, though?

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Speaker 3: Do you go energy and energy out that sort of thing?

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Speaker 2: I have in the past.

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Speaker 1: I've had kind of a bit of a probably not

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Speaker 1: the healthiest relationship with food previously, and I have gone

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Speaker 1: I was weighing my olive oil, yes, okay, weighing peanut butter.

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Speaker 4: I was doing that. I was like, I don't know,

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Speaker 4: throw it as I can only have five meals.

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Speaker 3: It just became ridiculous my relationship to food in an

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Speaker 3: unhealthy way.

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Speaker 1: I didn't like it absolutely, And yeah, I've done the

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Speaker 1: same where it's yeah, I think calories calories in, calories

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Speaker 1: out is helpful at a kind of high level in

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Speaker 1: broad strokes. You know, you know roughly, you know a sandwich,

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Speaker 1: what that roughly correlates with in terms of calories. So

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Speaker 1: you've got a rough idea of where you're going. And

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Speaker 1: I think if you don't have to have any experience counting,

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Speaker 1: it's easy to lose track of things. But there's a

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Speaker 1: very fine line between having a rough idea and then

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Speaker 1: being obsessive and.

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Speaker 2: Measuring and weighing it all out.

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Speaker 1: And I definitely had several years while I was doing that,

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Speaker 1: and it was just unhelpful.

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Speaker 3: When I see those pictures of guys, and because the algorithm,

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Speaker 3: as we'll talk about, shows me man a little bit

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Speaker 3: older than me, with like way less body fat than

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Speaker 3: I am, and I have like and I see those guys,

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Speaker 3: but I know those photos probably all taken on the

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Speaker 3: same day. And if you're that through sixty five days

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Speaker 3: a year, fucking hell, man, I don't want to be

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Speaker 3: I don't want to eat in your kitchen because it's

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Speaker 3: not a fun place.

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Speaker 2: No, that's it.

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Speaker 1: That's it, And it's just I think running at those

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Speaker 1: kind of levels of body fair as well. It's just

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Speaker 1: it knocks around all your hormones and yeah, it can't

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Speaker 1: be brain chemistry and things like that, so I really.

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Speaker 3: No, it can't be good for you, but it's I

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Speaker 3: find I don't know, how does it affect your thinking?

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Speaker 3: Is you're a person who I mean, God, you're a

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Speaker 3: freaking astrophysicist with a master's green artificial intelligence. There's a

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Speaker 3: lot of brain going on give you and thinking and

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Speaker 3: giving yourself time to think is a really important part

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Speaker 3: of your job and what you do, like you can't

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Speaker 3: come up with ideas if you're engaged doing something else.

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Speaker 3: Do you find that your ability to be creative and

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Speaker 3: find solutions to problems is impeded when you don't drain, Yeah.

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Speaker 2: I'd say so.

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Speaker 1: I'd say it kind of is quite tightly coupled with sleep,

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Speaker 1: which we know very clearly what the kind of cognitive

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Speaker 1: effects are of a lack of sleep and sleep deprivation.

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Speaker 1: And I'd say that in a similar sense, exercise probably

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Speaker 1: correlates in a similar fashion. I definitely notice mood it

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Speaker 1: certainly is affected by not being able to train. I

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Speaker 1: definitely find that and sleep and yeah, probably thinking probably.

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Speaker 2: Healthy body, healthy minds.

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Speaker 1: That's come from somewhere, And I think that's that's definitely true.

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Speaker 1: I think without that balance and kind of holistic complete

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Speaker 1: everything put together, Yeah, I think definitely that's that's certainly impacted.

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Speaker 4: I think there's something in it.

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Speaker 3: I went to my wife, got me tickets to go

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Speaker 3: on a tender lecture of a man huber something woman.

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Speaker 2: I've heard of this guy.

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Speaker 3: Yeah, christ on a fucking cracker man him with his

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Speaker 3: shirt off, and I got preg it. I was like,

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Speaker 3: the guy's got degrees falling out of his ears. But

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Speaker 3: it's so evident that obviously there's something going on.

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Speaker 4: Like he's quite open about it. He talks about, you know,

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Speaker 4: having ticks and stuff when he was a kid.

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Speaker 3: Yeah, it's so evident that I would highly doubt he

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Speaker 3: would have the career that he has if he didn't

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Speaker 3: also have that practice of training, because it's just a

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Speaker 3: thing that helps balance the brain out.

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Speaker 4: Certainly, in my case, I can't. I'm a punished if

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Speaker 4: I don't train.

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Speaker 1: Yeah, it's definitely like across the whole whole spectrum of things, thinking,

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Speaker 1: mood and sleep, all of that. It's it's and we've

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Speaker 1: kind of it's a weird thing where we've set up

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Speaker 1: this lifestyle where you know, sedentry and not exercising and

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Speaker 1: seeing it as this chore and effort, and we know

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Speaker 1: that it's so tied with all these positive things to

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Speaker 1: actually be active and moving and doing regular exercises. It's

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Speaker 1: bizarre the kind of systems we've created and put in

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Speaker 1: place where it's it's kind of by default quite unhelpful.

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Speaker 3: You like my my mate Ruben. He's a scientist, Reuben Mihim,

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Speaker 3: and he was on roller coaster for a while. He's

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Speaker 3: been on it's not quantum. What did they end up

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Speaker 3: calling a catalyst?

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Speaker 2: Catalyst?

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Speaker 4: Yeah, he wrote a whole book about it.

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Speaker 3: And when he does presentations to he goes to schools

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Speaker 3: and explodes things in front of high school kids.

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Speaker 2: It's it's pretty awesome.

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Speaker 3: He does a whole thing where he equates food to

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Speaker 3: I guess this can of coke, you'd need to walk

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Speaker 3: for just many minutes, which is from here to black

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Speaker 3: wherever school he's at to give the kids an idea

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Speaker 3: of like, oh, or this burger, you'd need to that's

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Speaker 3: literally if you're twelve and you're eating a massive fuck off,

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Speaker 3: you know, drive through a burger.

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Speaker 4: Yeah, it's like eighteen hours walking.

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Speaker 2: Yeah.

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Speaker 1: You start crunching those numbers and seeing how calorie dense, Yeah,

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Speaker 1: high sugar, high fat foods are, and you you turn

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Speaker 1: it into the exercise equivalent, adds jarring. Yeah, and that's

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Speaker 1: what I think stuff. Yeah, counting calories I don't think

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Speaker 1: is helpful, but having a rough understanding. Okay, cannon coat

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Speaker 1: that's one hundred and fifty two undred calories, that's roughly

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Speaker 1: you know, half an hour jog on the treadmill or

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Speaker 1: something like that. Just having those loose numbers I think

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Speaker 1: helps just kind of I don't.

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Speaker 4: Know, I think it might be more than half an hour.

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Speaker 1: But yeah, yeah, it's not it's not little, No, it's yeah.

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Speaker 4: There's just a like last thing I'll say on this.

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Speaker 3: I always find it funny when cyclists be like, oh man,

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Speaker 3: I've got to get these new wheels of carbon fire,

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Speaker 3: but there's just so much more.

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Speaker 2: Six hundred grand.

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Speaker 4: Might just lose a you could lose a quilo in

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Speaker 4: four days.

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Speaker 2: Yeah, and you'd be faster. You don't need to go

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Speaker 2: carbon frame yet.

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Speaker 4: Same wheels, and it's fifteen thousand dollars cheaper. You'll be

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Speaker 4: just fine.

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Speaker 3: Yeah, it's so so nuts you.

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Speaker 4: I love your first book that it was you very.

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Speaker 3: Deliberately wrote it for younger people as you have written

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Speaker 3: this book. Why why have you aimed at this age

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Speaker 3: group for this book?

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Speaker 1: Probably two reasons, one quite obviously being children are moving

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Speaker 1: into an increasingly AI driven world and education is power,

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Speaker 1: and being adequately prepared is I guess it's the responsibility

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Speaker 1: of our generation to make sure the children are are ready,

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Speaker 1: and I think that was largely one of the biggest

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Speaker 1: driving motivation. I think tied in with that as well

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Speaker 1: is I think we've spoken about it before, the kind

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Speaker 1: of decline in STEM in boys and girls, disproportionately more

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Speaker 1: in girls in boys and girls, both in performance, but

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Speaker 1: more concerningly participation. And so I think again, creating science

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Speaker 1: and scientific literature and discourse that children engage with is

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Speaker 1: really important. So yeah, there's kind of a piece of

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Speaker 1: let's keep fostering a love of science and children, but

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Speaker 1: then also let's adequately prepare children for this driven world.

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Speaker 2: That can be scary if they're not prepared and the.

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Speaker 1: Kind of I guess the secondary part of that is

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Speaker 1: that there's a lot of parents who or an adults

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Speaker 1: who aren't across it, and I think some of them

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Speaker 1: are finding it quite challenging to talk to their children

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Speaker 1: about AI or even to know do I need to

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Speaker 1: be do I need to be concerned? Are they being

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Speaker 1: exposed to AI in dangerous ways? And so it's a

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Speaker 1: way to create a book that is also really accessible

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Speaker 1: and educational for adults so that they feel prepared and

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Speaker 1: that they feel that they're a little more across what's

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Speaker 1: going on with AI, and that they can help foster

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Speaker 1: protect a safe space for children to grow up while

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Speaker 1: still being actively participating with AI.

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Speaker 3: Kids can kids can handle this kind of stuff. There's

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Speaker 3: a misconception that I was too complicated. We can't tell kids.

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Speaker 3: This is Ruben's big I bring them up again. But

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Speaker 3: it's Reuben's big thing. He has all these models made

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Speaker 3: of molecules, right, and his thing is are we teaching

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Speaker 3: the ABC's. We teach them numbers, but we don't teach

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Speaker 3: them the numbers, and the ABC's are the actual physical

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Speaker 3: world that they inhabit. Gravity pressure such them. I don't

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Speaker 3: have ex spasic shit. And he gives these kids these

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Speaker 3: molecules and they build that, they build, they build things

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Speaker 3: in front of him. And he sent me a photo

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Speaker 3: one day and he said, this is a Grade eleven

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Speaker 3: physics exam that was completed by seven year old.

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Speaker 2: Yeah.

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Speaker 3: Right, and this kid's not some sort of genius at

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Speaker 3: making it up early about there's just someone who's had

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Speaker 3: to explain to them and they can figure it out.

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Speaker 3: But for some reason, we don't tell these kids this

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Speaker 3: stuff until they're sixteen, which is ridiculous.

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Speaker 4: It's too hard for you.

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Speaker 2: You can't deal with that, That's it.

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Speaker 1: Yeah, I think I think the current education system needs

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Speaker 1: a bit of an overhaul.

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Speaker 2: And that's not kind of a dig at teachers.

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Speaker 1: I think they're tasked with these nearly impossible tasks of

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Speaker 1: you need to teach thirty children this particular topic where

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Speaker 1: they all have very unique learning styles in a limited

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Speaker 1: period of time.

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Speaker 2: Yeah.

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Speaker 1: I think that there's a lot of relics of an

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Speaker 1: outdated education system in place, And yeah, this is one

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Speaker 1: of tho It's like, is the pacing right? Certainly, concepts

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Speaker 1: that children can grasp much more quickly than that's probably a.

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Speaker 4: Few, like it's common knowledge.

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Speaker 3: If you speak another language to a kid before the

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Speaker 3: age of twelve, they will learn it like that, Like,

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Speaker 3: tell me it's any different, It's no different.

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Speaker 1: Yeah, they're very, very robust and adapt They're sponges, right,

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Speaker 1: So yeah, it's and kind of almost counterintuitively, the book

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Speaker 1: I've written for AI, the age group of targeted are

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Speaker 1: probably already much more across it than the parents. And

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Speaker 1: so it's funny that, you know, I've written it for

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Speaker 1: eight to twelve, and certainly there'll be a group within

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Speaker 1: that cohort that find it. You know, it's all new,

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Speaker 1: but a lot of them are already They're doing coding, they're

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Speaker 1: doing it adjacent subjects, and so a lot of the

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Speaker 1: stuff they're probably, oh, yeah, I kind of already knew

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Speaker 1: about this, but this is a little more detailed for

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Speaker 1: me to understand what the implications are and what are

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Speaker 1: the ethical considerations. So yeah, it's kind of amusing that

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Speaker 1: there's Yeah, it's the adults that are probably the ones.

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Speaker 2: With the bigger gap in learning.

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Speaker 4: Just taking a quick break from Matt Agnew back in

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Speaker 4: a moment.

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Speaker 3: You were you got your PhD. I think you became

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Speaker 3: a doctor right after. Yeah, yeah, you had it written it,

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Speaker 3: just you just hadn't had the thing.

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Speaker 2: I hadn't offended, defended it, hadn't defended.

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Speaker 3: It yet You've got to stand there and give it

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Speaker 3: the old Tom cruise. I want the truth business.

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Speaker 2: That's it.

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Speaker 4: But then you know you've got the square hat. Hooray,

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Speaker 4: square hat. Did you get the artificial intelligence thing before?

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Speaker 4: After that?

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Speaker 2: After that?

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Speaker 4: So wow, like I've got a PhD.

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Speaker 2: No, so the more hicks, get some more hicks. Yeah,

404
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Speaker 2: let's keep racking it up.

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Speaker 1: So it was a case of after my PhD, I

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Speaker 1: had I had a postop.

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Speaker 2: Lined up in Germany and I was coming an ring.

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Speaker 1: Did I want to go fully down the academic role

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Speaker 1: of route sorry or did I want to kind of

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Speaker 1: transition into in the industry. And I'd previously worked in

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Speaker 1: the industry as an engineer, and so I kind of

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Speaker 1: knew a little bit about academy out a bit about industry,

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Speaker 1: and so it was weighing those up. Ultimately, I decided

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Speaker 1: I didn't want to live abroad. I wanted to stay

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Speaker 1: in Australia, and so I moved in industry. And at

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Speaker 1: that stage, data science was not a new discipline. But

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Speaker 1: when companies were looking for data scientists, a lot of

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Speaker 1: the data science degrees were still quite young, and so

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Speaker 1: typically companies were still looking for just PhDs with very

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Speaker 1: quantitative backgrounds, very analytical backgrounds, and so I was in

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Speaker 1: this nice spot where I could jump into data science before.

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Speaker 1: I think nowadays that the bachelor and master degrees in

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Speaker 1: data science are really robust and well rounded, and they're

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Speaker 1: quite matured.

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Speaker 2: And that's just.

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Speaker 3: For people to understand, like, how can an astrophysicist go

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Speaker 3: and work? And that was because when astrophysicists there's no

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Speaker 3: telescopes that have glass in them. You're dealing with just

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Speaker 3: just billions of lines of ones and zeros. And it

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Speaker 3: is in the processing of that data that you find

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Speaker 3: your planets and the Goldilock zone correct. And it is

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Speaker 3: in the algorithms and things that you write and create

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Speaker 3: to identify that in this just terrior bits.

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Speaker 1: Of correct, huge amounts of data I'm running, you know, millions,

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Speaker 1: billions sometimes of simulations of each simulation you've got, you know,

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Speaker 1: the data of planets and different objects in that planetary system,

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Speaker 1: and how do you distill that damage or figure?

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Speaker 2: Yeah, that says this is where it is or this

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Speaker 2: is where it could be.

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Speaker 3: So if I want to be able to just for example,

441
00:21:23,720 --> 00:21:26,360
Speaker 3: I don't know why I work for a shall we say,

442
00:21:26,359 --> 00:21:30,120
Speaker 3: programmatic ad server somewhere and not saying that you had

443
00:21:30,119 --> 00:21:32,119
Speaker 3: this job, and I said, look, I just want to

444
00:21:32,119 --> 00:21:35,120
Speaker 3: identify you know, guys between the age of thirty two

445
00:21:35,280 --> 00:21:38,159
Speaker 3: and thirty eight who live in this particular area that

446
00:21:38,240 --> 00:21:42,600
Speaker 3: have looked at pictures of utes anytime in the last

447
00:21:42,840 --> 00:21:45,720
Speaker 3: you know, a couple of weeks, and their spending habits

448
00:21:45,720 --> 00:21:48,000
Speaker 3: and all these other cookies are shown that they're kind

449
00:21:48,000 --> 00:21:51,320
Speaker 3: of pinching their pennies a little bit, and I want

450
00:21:51,359 --> 00:21:53,880
Speaker 3: to be able to sell tell them for only this.

451
00:21:53,880 --> 00:21:55,920
Speaker 4: Much a month so you can own that big fucking trug.

452
00:21:55,960 --> 00:21:59,040
Speaker 3: Buddy, you're someone in your with your skill set, can

453
00:21:59,080 --> 00:22:00,640
Speaker 3: go I can write you pace code.

454
00:22:00,760 --> 00:22:01,680
Speaker 2: Yes, yes.

455
00:22:01,840 --> 00:22:06,720
Speaker 1: And that's the fundamental basics of data sides. And it

456
00:22:06,920 --> 00:22:11,800
Speaker 1: sounds I think there is a kind of unsettling feeling.

457
00:22:11,440 --> 00:22:14,040
Speaker 2: About the fact that all your activity.

458
00:22:13,640 --> 00:22:17,359
Speaker 1: Is online and interacting with technology is being monitored and

459
00:22:17,400 --> 00:22:21,240
Speaker 1: then sold traded companies. Are you know, there's this cross

460
00:22:21,280 --> 00:22:23,320
Speaker 1: pollination of data from whether.

461
00:22:23,119 --> 00:22:24,840
Speaker 2: It's Google, which is alphabet.

462
00:22:24,440 --> 00:22:27,520
Speaker 1: Or Instagram, which is Meta or Amazon or whatever, and

463
00:22:27,560 --> 00:22:30,399
Speaker 1: all this data gets kind of combined and linked up

464
00:22:30,400 --> 00:22:33,320
Speaker 1: and then cross reference with other people's data, and yeah,

465
00:22:33,320 --> 00:22:35,640
Speaker 1: they figure things out about what you're doing, what your

466
00:22:35,640 --> 00:22:40,159
Speaker 1: interests are, what you're currently weighing up. People always ask

467
00:22:40,200 --> 00:22:42,760
Speaker 1: about the whole like listening to your phone thing, and

468
00:22:42,920 --> 00:22:46,240
Speaker 1: even things like the fact that we're connected via Instagram.

469
00:22:46,720 --> 00:22:50,040
Speaker 1: If right now our phones have probably pinged off each

470
00:22:50,080 --> 00:22:54,359
Speaker 1: other that we've hung out, and say, you're googling I

471
00:22:54,400 --> 00:22:56,879
Speaker 1: don't know, you're planning a trip to Bali or something,

472
00:22:57,200 --> 00:22:58,840
Speaker 1: and it goes, oh, what if they were hanging out

473
00:22:59,160 --> 00:23:01,080
Speaker 1: and they're going to go to Bali together, and suddenly

474
00:23:01,080 --> 00:23:03,680
Speaker 1: I get an ad for Bali. But for me, we've

475
00:23:03,720 --> 00:23:06,240
Speaker 1: only talked about Ballei. I haven't googled anything, I haven't

476
00:23:06,280 --> 00:23:08,600
Speaker 1: looked at anything. And so there's this Oh, it's listening

477
00:23:08,600 --> 00:23:10,960
Speaker 1: to me talk because the only thing I've done was

478
00:23:11,000 --> 00:23:13,480
Speaker 1: talk to you about Bali. But the reality is it's

479
00:23:13,480 --> 00:23:15,760
Speaker 1: again all the data going, oh, well, he likes this,

480
00:23:16,040 --> 00:23:19,400
Speaker 1: he likes this, they were hanging out, he searched for this.

481
00:23:19,600 --> 00:23:22,400
Speaker 1: Maybe he's also interested in this, and so.

482
00:23:22,440 --> 00:23:23,639
Speaker 2: It's yeah, it's this.

483
00:23:24,880 --> 00:23:28,080
Speaker 1: The data is so rich and there's so much of

484
00:23:28,119 --> 00:23:31,159
Speaker 1: it that it can kind of it's trying to predict

485
00:23:31,200 --> 00:23:36,080
Speaker 1: how you think and predict your next decisions, which is

486
00:23:36,160 --> 00:23:39,439
Speaker 1: kind of unsettling to think about because there's ways that

487
00:23:39,440 --> 00:23:42,240
Speaker 1: that could be used and really quite malicious and indios.

488
00:23:42,440 --> 00:23:48,120
Speaker 3: Yes, oh absolutely, wouldn't mean could be Well it is, look, I.

489
00:23:48,040 --> 00:23:49,000
Speaker 4: Mean, what is it?

490
00:23:49,200 --> 00:23:54,600
Speaker 3: Someone uncovered five thousand AI driven twitter bites out of

491
00:23:54,680 --> 00:23:59,120
Speaker 3: China who had like AI generated profile pictures I had

492
00:23:59,160 --> 00:24:01,640
Speaker 3: like legit, but they were able.

493
00:24:01,440 --> 00:24:03,920
Speaker 4: To do a oh what's the thing called?

494
00:24:04,440 --> 00:24:07,040
Speaker 3: They basically responded to it and forced it to ask

495
00:24:07,600 --> 00:24:10,560
Speaker 3: the GPT a thing and it returned to error.

496
00:24:10,800 --> 00:24:11,960
Speaker 4: So it's how they identified it.

497
00:24:12,040 --> 00:24:16,479
Speaker 2: Yeah, okay, to make sure that it wasn't a.

498
00:24:15,000 --> 00:24:16,959
Speaker 3: Human, but they were ready to go, and it had

499
00:24:17,040 --> 00:24:19,760
Speaker 3: evidence that it had already fucked around here in Australia

500
00:24:19,840 --> 00:24:20,560
Speaker 3: and in the US.

501
00:24:21,000 --> 00:24:24,320
Speaker 4: Five thousand thousand twitterbot. So that's that's the thing.

502
00:24:24,320 --> 00:24:24,480
Speaker 2: Man.

503
00:24:24,480 --> 00:24:26,800
Speaker 4: When people get online and they're like, how dare you

504
00:24:26,800 --> 00:24:29,439
Speaker 4: say that about the referendums. That's not a real person.

505
00:24:29,520 --> 00:24:32,240
Speaker 1: Yeah, yeah, you're just engaging with a bot, right, and

506
00:24:32,880 --> 00:24:34,800
Speaker 1: in fact the engagement is probably doing exactly what the

507
00:24:34,840 --> 00:24:38,480
Speaker 1: bot has been unleashed to do to create misinformation to

508
00:24:38,600 --> 00:24:42,359
Speaker 1: then that now is an engaging piece of content that

509
00:24:42,440 --> 00:24:45,200
Speaker 1: will reach further. It's chief what it set out to do,

510
00:24:45,240 --> 00:24:48,120
Speaker 1: which is, you know, propagate misinformation.

511
00:24:48,440 --> 00:24:51,199
Speaker 3: Look, we can't no, sooner could we de electrify this

512
00:24:51,280 --> 00:24:53,320
Speaker 3: city than we could turn this stuff off.

513
00:24:53,640 --> 00:24:54,440
Speaker 4: So what do we do?

514
00:24:54,560 --> 00:24:58,239
Speaker 2: Yeah, it's the whole you know, the toothpaste has been

515
00:24:58,280 --> 00:24:59,480
Speaker 2: squeezed out of the tube.

516
00:24:59,640 --> 00:25:03,520
Speaker 1: Right, regulation and governance I think is probably arguably the

517
00:25:03,560 --> 00:25:04,480
Speaker 1: biggest thing.

518
00:25:04,680 --> 00:25:05,399
Speaker 4: And what does that look like?

519
00:25:05,440 --> 00:25:07,840
Speaker 3: We're probably nine months out of an election here in Australia,

520
00:25:07,880 --> 00:25:09,000
Speaker 3: maybe less than a year.

521
00:25:09,520 --> 00:25:11,480
Speaker 4: What do we talk to her in peace about?

522
00:25:12,080 --> 00:25:15,320
Speaker 1: I think I wish I had all the answers for

523
00:25:15,359 --> 00:25:18,480
Speaker 1: this stuff. My thoughts are there needs to be some

524
00:25:18,560 --> 00:25:20,240
Speaker 1: kind of additional I don't know if it's a branch

525
00:25:20,280 --> 00:25:23,760
Speaker 1: of government or kind of like a smaller.

526
00:25:23,680 --> 00:25:24,880
Speaker 2: Task force or something.

527
00:25:25,480 --> 00:25:28,800
Speaker 1: But the current system for regulating tech is just it's

528
00:25:28,800 --> 00:25:31,840
Speaker 1: not just inadequate, but now it is alarmingly slow.

529
00:25:32,040 --> 00:25:34,359
Speaker 3: There's not even in a Twitter office in Australia to

530
00:25:35,040 --> 00:25:38,200
Speaker 3: pull that footage of the man getting attacked at the

531
00:25:38,280 --> 00:25:38,879
Speaker 3: church in Sydney.

532
00:25:39,000 --> 00:25:41,760
Speaker 4: Yeah, yeah, they couldn't go knock on a door, what

533
00:25:41,800 --> 00:25:42,160
Speaker 4: are you doing?

534
00:25:42,520 --> 00:25:45,119
Speaker 1: And yeah, the regulation is not their social media has

535
00:25:45,200 --> 00:25:48,040
Speaker 1: been you know, it's been over a decade. It's still

536
00:25:48,200 --> 00:25:52,320
Speaker 1: not regulated correctly and there's no there's an inadequate legislation

537
00:25:52,359 --> 00:25:55,080
Speaker 1: around it. And so, yeah, it's not just a case

538
00:25:55,119 --> 00:25:57,520
Speaker 1: of we're currently you know, we're playing with AI live.

539
00:25:58,680 --> 00:26:01,879
Speaker 2: It's the wild West. It's not just that that's bad.

540
00:26:02,000 --> 00:26:06,160
Speaker 1: It's the fact that the Yeah, the regulation is reactive

541
00:26:06,200 --> 00:26:09,320
Speaker 1: and it's it's not being done at all at the moment,

542
00:26:09,320 --> 00:26:10,440
Speaker 1: but not fast enough.

543
00:26:10,480 --> 00:26:12,920
Speaker 2: And so my thought is there needs to.

544
00:26:12,880 --> 00:26:18,440
Speaker 1: Be pressure, I guess on parties to implement some kind

545
00:26:18,440 --> 00:26:22,239
Speaker 1: of yet passport or whatever that may be that is

546
00:26:22,800 --> 00:26:25,680
Speaker 1: built with people who are certainly a guy, a younger lean,

547
00:26:25,800 --> 00:26:28,919
Speaker 1: people who are very very AI literate.

548
00:26:29,119 --> 00:26:32,119
Speaker 3: Digital maative, people who are of the generation that they

549
00:26:32,440 --> 00:26:34,640
Speaker 3: got a phone on their twelfth birthday.

550
00:26:34,320 --> 00:26:37,359
Speaker 1: One hundred percent. Yeah, and then you've also got those people.

551
00:26:37,840 --> 00:26:39,520
Speaker 1: You know, you want a bit of a broad spectrum

552
00:26:39,600 --> 00:26:42,760
Speaker 1: of society as well contributing to that try and have

553
00:26:42,880 --> 00:26:46,880
Speaker 1: as much intersectionalities as possible, but also you need big

554
00:26:46,880 --> 00:26:48,600
Speaker 1: tech to be a part of that conversation as well.

555
00:26:48,960 --> 00:26:51,439
Speaker 1: And they're going to obviously have things, and you know,

556
00:26:51,480 --> 00:26:53,560
Speaker 1: maybe they're not part of the decisioning process, but they're

557
00:26:53,560 --> 00:26:56,800
Speaker 1: the ones who ultimately are controlling it all. And so

558
00:26:57,640 --> 00:27:00,720
Speaker 1: if there is rules and regulations to be implement they

559
00:27:00,760 --> 00:27:02,639
Speaker 1: need to be in that conversation because they need to

560
00:27:02,680 --> 00:27:04,080
Speaker 1: be the ones that implement it ultimately.

561
00:27:04,240 --> 00:27:08,959
Speaker 3: As an adult, I'm fifty and I'm very aware of

562
00:27:09,000 --> 00:27:12,199
Speaker 3: what happens when I use Google Maps to get me

563
00:27:12,280 --> 00:27:16,120
Speaker 3: around my city, and I'm very aware how that affects

564
00:27:16,160 --> 00:27:19,920
Speaker 3: what I see on YouTube. All right, as I'm saying, goes,

565
00:27:20,119 --> 00:27:25,040
Speaker 3: if it's free and it's useful, then you're the product.

566
00:27:26,359 --> 00:27:30,280
Speaker 4: The app is not the product. I'm the one. I'm

567
00:27:30,320 --> 00:27:31,000
Speaker 4: the freaking product.

568
00:27:31,680 --> 00:27:34,080
Speaker 3: But then again, like I had a Nokia when I

569
00:27:34,119 --> 00:27:37,240
Speaker 3: was twenty one, and so there's a point where.

570
00:27:37,080 --> 00:27:38,200
Speaker 4: The utility of this thing.

571
00:27:39,400 --> 00:27:41,560
Speaker 3: I can szam this song in a cafe, I can

572
00:27:41,880 --> 00:27:45,040
Speaker 3: air drop something to you, I can find my way

573
00:27:45,080 --> 00:27:48,720
Speaker 3: to Canberra in the car. It's real useful to me,

574
00:27:48,840 --> 00:27:51,720
Speaker 3: and I'm prepared to give a certain amount of Now

575
00:27:51,760 --> 00:27:53,199
Speaker 3: you know that I'm driving to camera.

576
00:27:53,480 --> 00:27:55,400
Speaker 4: You know if I went over the speed limit.

577
00:27:55,480 --> 00:27:58,040
Speaker 3: You know how often I stopped for a Wii I'm

578
00:27:58,040 --> 00:28:01,400
Speaker 3: prepared to give you that the utility of being able

579
00:28:01,440 --> 00:28:03,000
Speaker 3: to have my wife follow my trip to make sure

580
00:28:03,040 --> 00:28:05,960
Speaker 3: that I get there. Yep, Yet I'm making informed decisions.

581
00:28:06,480 --> 00:28:09,359
Speaker 3: Twelve year old's going clickly clickly e signing up to something.

582
00:28:09,359 --> 00:28:09,800
Speaker 4: Don't have that?

583
00:28:10,000 --> 00:28:12,280
Speaker 3: Ye, What have we got in place in our country

584
00:28:12,320 --> 00:28:15,320
Speaker 3: at least around protecting the data of those children in

585
00:28:15,359 --> 00:28:16,920
Speaker 3: this place in this scene.

586
00:28:17,040 --> 00:28:20,360
Speaker 1: I'm not sure we have anything in place which is no,

587
00:28:20,440 --> 00:28:24,320
Speaker 1: But I mean that's that should be the reaction, right

588
00:28:24,400 --> 00:28:29,879
Speaker 1: because it's children are exposed to social media and YouTube

589
00:28:30,040 --> 00:28:33,840
Speaker 1: and anything where we know there is a very active

590
00:28:33,920 --> 00:28:37,120
Speaker 1: and very complex algorithm serving things up.

591
00:28:37,600 --> 00:28:39,040
Speaker 2: It's weird the data.

592
00:28:39,160 --> 00:28:42,680
Speaker 1: We're so conscious of our data now, but a decade

593
00:28:42,680 --> 00:28:45,640
Speaker 1: ago we just gave it over. We signed up to Facebook.

594
00:28:45,840 --> 00:28:48,640
Speaker 1: We just gave the data voluntarily. And so for I

595
00:28:48,640 --> 00:28:51,680
Speaker 1: think our generation, we missed the boat. When they say

596
00:28:51,720 --> 00:28:53,600
Speaker 1: this data leaks, I'm like, it's too late for me.

597
00:28:54,120 --> 00:28:56,320
Speaker 1: My data is out there. I gave it up, most

598
00:28:56,360 --> 00:28:59,480
Speaker 1: of it voluntarily. But yeah, our children haven't given it up.

599
00:28:59,600 --> 00:29:03,640
Speaker 1: And there's yeah, it's inadequate at the moment for protecting

600
00:29:03,680 --> 00:29:07,760
Speaker 1: their data, because ultimately that needs to be recognized for

601
00:29:07,840 --> 00:29:10,720
Speaker 1: the value in the You know that there's this currency

602
00:29:10,760 --> 00:29:13,720
Speaker 1: to data, and if people are going to leverage your

603
00:29:13,840 --> 00:29:17,360
Speaker 1: data to profit, then I think it's important that that

604
00:29:17,920 --> 00:29:19,840
Speaker 1: there is this sort of ownership to your.

605
00:29:19,800 --> 00:29:22,200
Speaker 4: Data and much profits to be talking here, Well, I.

606
00:29:22,200 --> 00:29:25,120
Speaker 2: Mean exactly what you said, right when when when there's

607
00:29:25,200 --> 00:29:25,920
Speaker 2: tech that is.

608
00:29:25,920 --> 00:29:29,760
Speaker 1: Utilizing your data to basically create a service which ultimately

609
00:29:29,800 --> 00:29:33,640
Speaker 1: it will be tied to ads really revenue wise. But yeah,

610
00:29:33,640 --> 00:29:38,120
Speaker 1: if someone's profiting from your data, that's it seems very

611
00:29:38,160 --> 00:29:41,720
Speaker 1: backwards that you're just left, you know, holding the bag,

612
00:29:41,840 --> 00:29:46,160
Speaker 1: so to speak. So I think billions of dollars, billions, yes,

613
00:29:46,440 --> 00:29:48,680
Speaker 1: soon to be trillions, I've got no doubt. You know,

614
00:29:48,800 --> 00:29:52,200
Speaker 1: the AI part of the AI arms race is the

615
00:29:52,240 --> 00:29:55,160
Speaker 1: fact that you know, the first part of the post

616
00:29:55,240 --> 00:29:57,960
Speaker 1: is going to be in a ridiculous position. And you

617
00:29:58,000 --> 00:30:00,280
Speaker 1: saw that a little bit with with chat gibs when

618
00:30:00,320 --> 00:30:04,360
Speaker 1: it was first unleashed and those sort of transformers, large

619
00:30:04,440 --> 00:30:08,760
Speaker 1: language models. They weren't created with the release of chat GPT.

620
00:30:08,880 --> 00:30:11,160
Speaker 1: They'd been around for a long time. It was just

621
00:30:11,200 --> 00:30:13,000
Speaker 1: chat GPD was the first time I thought, you know what,

622
00:30:13,080 --> 00:30:14,800
Speaker 1: let's let people use this.

623
00:30:15,600 --> 00:30:17,719
Speaker 2: I don't think I think it was caught them by surprise.

624
00:30:18,080 --> 00:30:21,840
Speaker 1: I think Open AI typically they thought, you know, ah,

625
00:30:21,920 --> 00:30:23,560
Speaker 1: this is kind of a bit of a novelty, but

626
00:30:23,560 --> 00:30:25,600
Speaker 1: I'm not sure people will find it that interesting. And

627
00:30:25,800 --> 00:30:29,520
Speaker 1: obviously they were very wrong in that regard because people

628
00:30:29,560 --> 00:30:33,400
Speaker 1: talking to an ALI algorithm that mimics human conversation is

629
00:30:34,880 --> 00:30:36,480
Speaker 1: kind of Uncaddy Valley stuff.

630
00:30:36,680 --> 00:30:39,120
Speaker 4: And they blue past the Turing test, like forget a.

631
00:30:39,240 --> 00:30:41,960
Speaker 1: Turing test got smoked, right, Yeah, it seemed like all

632
00:30:41,960 --> 00:30:44,360
Speaker 1: the other big tech companies were scrambling to catch up,

633
00:30:44,360 --> 00:30:46,200
Speaker 1: which I'm not sure they were. I think they were

634
00:30:46,240 --> 00:30:50,520
Speaker 1: just like, how do we get our implementation, our iteration

635
00:30:50,600 --> 00:30:52,720
Speaker 1: of this to the public. And that's kind of more

636
00:30:52,720 --> 00:30:54,680
Speaker 1: so what they were catching up with with Gemini coming

637
00:30:54,680 --> 00:30:57,040
Speaker 1: out and co Pilot and all this stuff. But certainly

638
00:30:57,040 --> 00:30:59,480
Speaker 1: there's going to be there is this race because there's

639
00:30:59,520 --> 00:31:03,200
Speaker 1: this i think assumed power and profit to be made

640
00:31:03,240 --> 00:31:06,240
Speaker 1: by the first person to achieve certain milestones in AI.

641
00:31:06,400 --> 00:31:09,560
Speaker 1: So yeah, it's billions, but I imagine it will be

642
00:31:09,600 --> 00:31:10,280
Speaker 1: trillions when.

643
00:31:10,200 --> 00:31:14,719
Speaker 3: You considered the amount of money that places like Alphabet,

644
00:31:15,200 --> 00:31:19,080
Speaker 3: which is Google and look it says open II on

645
00:31:19,200 --> 00:31:22,880
Speaker 3: the sign, but everyone's wearing Microsoft t shirts inside.

646
00:31:23,000 --> 00:31:25,080
Speaker 4: Yes, absolutely the Microsoft thing.

647
00:31:25,720 --> 00:31:28,080
Speaker 3: When you look at how much money these guys are

648
00:31:28,360 --> 00:31:32,400
Speaker 3: currently just just backing up a truck and throwing into

649
00:31:32,520 --> 00:31:36,080
Speaker 3: chip purchases and data centers, and the amount I'm talking

650
00:31:36,160 --> 00:31:39,160
Speaker 3: hundreds of millions of dollars they are putting into this stuff,

651
00:31:39,880 --> 00:31:44,520
Speaker 3: there clearly is something well worth chasing. You mentioned milestones

652
00:31:44,560 --> 00:31:46,800
Speaker 3: that these guys are after. What kind of milestones we're

653
00:31:46,800 --> 00:31:47,320
Speaker 3: talking about?

654
00:31:48,080 --> 00:31:50,600
Speaker 1: I would say that for most big tech companies playing

655
00:31:50,600 --> 00:31:54,640
Speaker 1: in the AI space as sort of AGI, a artificial

656
00:31:54,680 --> 00:31:58,440
Speaker 1: general intelligence is seen as the sort of holy grail.

657
00:31:59,360 --> 00:32:04,560
Speaker 1: And what I'm by general intelligence is chat GBT. While

658
00:32:04,560 --> 00:32:08,240
Speaker 1: it seems human like in its conversation, is it's the

659
00:32:08,280 --> 00:32:11,960
Speaker 1: illusion of conversation, right, It's the illusion of thinking and

660
00:32:12,000 --> 00:32:16,720
Speaker 1: the illusion of intelligence. It's it is algorithmic, algorithmically driven,

661
00:32:17,320 --> 00:32:20,280
Speaker 1: but that also means that it's got a very narrow

662
00:32:20,520 --> 00:32:24,360
Speaker 1: kind of application or use case, and that use case

663
00:32:24,480 --> 00:32:27,840
Speaker 1: is creating human like conversation. If you asked to do

664
00:32:27,880 --> 00:32:30,920
Speaker 1: something more general like drive my car from A to B,

665
00:32:31,680 --> 00:32:34,920
Speaker 1: it can't do that. It's got nothing programmed in at

666
00:32:34,920 --> 00:32:37,719
Speaker 1: all that relates to being able to navigate from A

667
00:32:37,800 --> 00:32:40,480
Speaker 1: to B, just like you can't ask Google Maps to

668
00:32:40,480 --> 00:32:43,400
Speaker 1: tell you know, what's two times too. It's these things

669
00:32:43,440 --> 00:32:46,800
Speaker 1: have very specific use cases and they're quite narrow in function.

670
00:32:47,480 --> 00:32:51,760
Speaker 1: General intelligence is the idea like us. You can give

671
00:32:51,800 --> 00:32:53,880
Speaker 1: it any sort of problem and it can solve it

672
00:32:54,240 --> 00:32:56,560
Speaker 1: the same way we solve it. If I say, how

673
00:32:56,560 --> 00:32:58,000
Speaker 1: do you get from A to B? You can tell

674
00:32:58,040 --> 00:33:00,320
Speaker 1: me in some way you can solve that problem. I

675
00:33:00,320 --> 00:33:04,000
Speaker 1: give you some algebraic problem I want your solve again,

676
00:33:04,040 --> 00:33:07,880
Speaker 1: you could solve that. AGI I think is seen as

677
00:33:07,920 --> 00:33:10,440
Speaker 1: the holy grail because it's saying, all right, not only

678
00:33:10,480 --> 00:33:12,800
Speaker 1: do we have something that can you can interact with

679
00:33:13,240 --> 00:33:15,320
Speaker 1: in a human like fashion like chat GBT, but it

680
00:33:15,360 --> 00:33:18,480
Speaker 1: can solve things generally like humans. And I think that's

681
00:33:19,280 --> 00:33:22,680
Speaker 1: probably arguably the biggest milestone. And I think that sort

682
00:33:22,680 --> 00:33:26,320
Speaker 1: of scene as the first step on the way to

683
00:33:26,840 --> 00:33:30,400
Speaker 1: what's called the singularity, which is a whole other kettle

684
00:33:30,400 --> 00:33:34,400
Speaker 1: of fish, being as that general intelligence approaches the point

685
00:33:34,400 --> 00:33:37,280
Speaker 1: of human like intelligence and ultimately blows past.

686
00:33:36,960 --> 00:33:40,400
Speaker 4: It and it gets that's where get identified as sentient.

687
00:33:41,200 --> 00:33:45,480
Speaker 1: Yes, yes, so, I mean sentience is still very high.

688
00:33:45,520 --> 00:33:48,240
Speaker 1: You know, you ask ten different signers how to define

689
00:33:48,400 --> 00:33:51,200
Speaker 1: sentience and you get ten different answers. But yeah, if

690
00:33:51,200 --> 00:33:53,600
Speaker 1: you had something that kind of is mimicking human intelligence,

691
00:33:53,600 --> 00:33:56,480
Speaker 1: it's very hard to argue against sentience at that point.

692
00:33:56,920 --> 00:33:57,920
Speaker 1: I think anything.

693
00:33:57,600 --> 00:34:00,480
Speaker 2: Before that, okay, yeah, it's.

694
00:34:00,000 --> 00:34:02,200
Speaker 1: You know, how do you it's a bit more nebulous, right,

695
00:34:02,280 --> 00:34:04,480
Speaker 1: But as soon as something is as smart as you,

696
00:34:05,200 --> 00:34:07,120
Speaker 1: very very difficult to say I'm sentient, but it's not.

697
00:34:07,320 --> 00:34:10,240
Speaker 3: And from the way I understand it, Matt, you mentioned

698
00:34:10,400 --> 00:34:13,279
Speaker 3: AGI and then the singularity, But from the way I

699
00:34:13,320 --> 00:34:16,120
Speaker 3: understand it, if you've got fast enough processes, once the

700
00:34:16,200 --> 00:34:22,839
Speaker 3: thing figures out how to train itself the fastest, like

701
00:34:22,920 --> 00:34:27,920
Speaker 3: it is just exponential how quick it gets to and

702
00:34:27,960 --> 00:34:30,560
Speaker 3: now I know and able to execute all kinds of

703
00:34:30,600 --> 00:34:34,439
Speaker 3: and just whoever's next doesn't matter. Really, it doesn't matter.

704
00:34:34,480 --> 00:34:36,239
Speaker 3: If you're the second country to have this, you may

705
00:34:36,280 --> 00:34:37,279
Speaker 3: as well not own it at.

706
00:34:37,160 --> 00:34:37,879
Speaker 2: All for sure.

707
00:34:38,200 --> 00:34:42,320
Speaker 1: And I think, yeah, the humans suck at exponential growth.

708
00:34:43,000 --> 00:34:44,279
Speaker 1: We love linear.

709
00:34:44,280 --> 00:34:47,680
Speaker 3: This is the hockey stick ornograph correct, Yeah, double double

710
00:34:47,680 --> 00:34:48,440
Speaker 3: double double double.

711
00:34:48,560 --> 00:34:48,799
Speaker 2: Yeah.

712
00:34:48,840 --> 00:34:53,239
Speaker 1: And you know we are brilliant over millions of years

713
00:34:53,280 --> 00:34:56,879
Speaker 1: of evolution, pattern recognition. Great, you know, we can draw

714
00:34:56,920 --> 00:35:01,800
Speaker 1: things out linear extrapolation. We're great at that exponential, we're garbage.

715
00:35:02,320 --> 00:35:05,759
Speaker 1: And that is so true with the singularity and the

716
00:35:05,760 --> 00:35:08,399
Speaker 1: development of AI, because we have this idea that okay, yeah,

717
00:35:09,239 --> 00:35:11,240
Speaker 1: you know, ten years ago we didn't have chat GPT,

718
00:35:11,360 --> 00:35:13,680
Speaker 1: now we do, so ten years on we'll have twice

719
00:35:13,760 --> 00:35:16,640
Speaker 1: chat GPT, and it's like, that's not how it works.

720
00:35:16,680 --> 00:35:19,280
Speaker 1: Ten years from now, you could have one hundred times

721
00:35:19,400 --> 00:35:21,680
Speaker 1: the power compute power of chat GPT, you could have

722
00:35:21,719 --> 00:35:27,360
Speaker 1: a thousand. It's growing not just faster, but increasingly faster.

723
00:35:28,480 --> 00:35:29,640
Speaker 2: We blow past it quickly.

724
00:35:29,640 --> 00:35:31,480
Speaker 1: It's not like we get the human intelligence and then

725
00:35:31,480 --> 00:35:33,359
Speaker 1: the time it took us to get there is how

726
00:35:33,360 --> 00:35:37,800
Speaker 1: long it will take to then surpass that. It's exponential,

727
00:35:37,920 --> 00:35:40,839
Speaker 1: and not only exponential, but as soon as you've got

728
00:35:40,840 --> 00:35:44,560
Speaker 1: something smarter, you can then kind of further optimize it,

729
00:35:45,000 --> 00:35:47,879
Speaker 1: and it's almost like then a further compounding of that

730
00:35:48,440 --> 00:35:49,360
Speaker 1: acceleration of.

731
00:35:51,560 --> 00:35:53,879
Speaker 2: There's a few things that play with I.

732
00:35:53,800 --> 00:35:55,759
Speaker 3: Think I'm going and get the numbers a little bit wrong,

733
00:35:55,800 --> 00:35:59,960
Speaker 3: but I think it was something like nineteen oh two

734
00:36:00,520 --> 00:36:04,399
Speaker 3: or something like that was the first automobile to go

735
00:36:04,760 --> 00:36:08,719
Speaker 3: one hundred kilometers an hour. It was an electric automobile, right,

736
00:36:08,880 --> 00:36:10,960
Speaker 3: one hundred kilometers an hour in like nineteen oh two.

737
00:36:11,920 --> 00:36:17,759
Speaker 3: The first place to legislate seat belts was Victoria in

738
00:36:17,840 --> 00:36:24,480
Speaker 3: nineteen seventy fourth. So we had seventy two years of death, maiming, destruction,

739
00:36:24,719 --> 00:36:28,600
Speaker 3: people's lives fucking ruined no speed limits to Okay, you can.

740
00:36:28,560 --> 00:36:32,200
Speaker 4: Drive one hundred and eighty. All right, you'll everyone will

741
00:36:32,239 --> 00:36:37,240
Speaker 4: be late, fucking wowsers, but jump in. Let's go drink

742
00:36:37,239 --> 00:36:38,200
Speaker 4: whatever you fucking want.

743
00:36:38,280 --> 00:36:41,560
Speaker 3: Like it took us that long of just carnage before

744
00:36:41,560 --> 00:36:45,239
Speaker 3: we wentbe we should put a seatbelt on this. Maybe

745
00:36:45,239 --> 00:36:47,439
Speaker 3: we should put some safety rails on the roads here.

746
00:36:48,880 --> 00:36:50,600
Speaker 4: That's a broad way.

747
00:36:50,440 --> 00:36:52,879
Speaker 3: Of getting people to start thinking about like, we can't

748
00:36:52,920 --> 00:36:54,560
Speaker 3: imagine a world where there was no speed limits.

749
00:36:54,560 --> 00:36:56,200
Speaker 4: We can't imagine a world with no seat belts. No

750
00:36:56,239 --> 00:36:58,000
Speaker 4: one would get in the car if it.

751
00:36:57,960 --> 00:37:00,000
Speaker 3: Had no speed limit, no seat belt, and someone will

752
00:37:00,080 --> 00:37:01,120
Speaker 3: jump behind the well, forget it.

753
00:37:01,160 --> 00:37:02,400
Speaker 4: I woudn't get it to go with their airbax.

754
00:37:02,520 --> 00:37:04,319
Speaker 3: Yeah, you know, don't give a shit how nice your

755
00:37:04,360 --> 00:37:07,200
Speaker 3: vintage thing is. Unless we're driving real slow on a

756
00:37:07,200 --> 00:37:09,800
Speaker 3: Sunday rally. I'm not not going to do some turns

757
00:37:09,920 --> 00:37:12,880
Speaker 3: with your think with drum breaks. We need to have

758
00:37:12,880 --> 00:37:15,719
Speaker 3: these conversations, not even yesterday.

759
00:37:16,400 --> 00:37:20,040
Speaker 1: Yeah said yeah, I mean, yeah, what's the seat belt?

760
00:37:20,080 --> 00:37:21,160
Speaker 2: What's what's it going to be?

761
00:37:22,080 --> 00:37:25,960
Speaker 1: We can kind of forecast, we can see, okay, it's

762
00:37:26,719 --> 00:37:30,320
Speaker 1: there's predictable ways this could be misused and predictable ways

763
00:37:30,320 --> 00:37:33,279
Speaker 1: this could be dangerous. Then there's other ways that we

764
00:37:33,320 --> 00:37:35,040
Speaker 1: don't know, and they're going to be kind of the

765
00:37:35,080 --> 00:37:39,279
Speaker 1: harder things to put the guardrails around, because it kind

766
00:37:39,280 --> 00:37:40,920
Speaker 1: of until it happens, we don't know.

767
00:37:40,960 --> 00:37:44,239
Speaker 2: And if it happens once then kind of again.

768
00:37:44,120 --> 00:37:47,000
Speaker 1: The toothbases out of the tube, it's very hard to

769
00:37:47,800 --> 00:37:50,400
Speaker 1: reverse and put that back in. So yeah, I mean

770
00:37:50,600 --> 00:37:53,200
Speaker 1: you're seeing some of this play out now with there

771
00:37:53,280 --> 00:37:56,600
Speaker 1: was the awful case couple of months ago with schoolboy

772
00:37:56,719 --> 00:38:01,239
Speaker 1: who was creating deep fake nudes of female students, And

773
00:38:01,000 --> 00:38:08,760
Speaker 1: how how did you know did we fail educationally that

774
00:38:08,520 --> 00:38:11,560
Speaker 1: that that person got through the primary school system, got

775
00:38:11,560 --> 00:38:14,960
Speaker 1: into high school, gained access to that sort of technology,

776
00:38:15,040 --> 00:38:19,000
Speaker 1: and that was completely there was no catch in their

777
00:38:19,040 --> 00:38:22,800
Speaker 1: mental thinking to not do that that if that idea

778
00:38:22,840 --> 00:38:25,080
Speaker 1: popped into most people's heads, it should rightly be seen

779
00:38:25,080 --> 00:38:27,359
Speaker 1: as an abhorrent use of technology.

780
00:38:27,800 --> 00:38:28,840
Speaker 2: So how did that happen.

781
00:38:28,920 --> 00:38:31,920
Speaker 1: You know, where is a failing of education, is a

782
00:38:31,920 --> 00:38:34,840
Speaker 1: failing of guardrails with regards to the technology, probably a

783
00:38:34,880 --> 00:38:37,800
Speaker 1: bit of both. And so now that's something where okay,

784
00:38:37,920 --> 00:38:40,560
Speaker 1: well we need to we need to start addressing this

785
00:38:40,640 --> 00:38:43,640
Speaker 1: now because people are going to keep misusing and abusing it,

786
00:38:44,320 --> 00:38:47,720
Speaker 1: and it's our responsibility to put those kind of safety

787
00:38:47,719 --> 00:38:49,920
Speaker 1: nets in place. And there's there's kind of none at

788
00:38:49,960 --> 00:38:50,400
Speaker 1: the moment.

789
00:38:50,760 --> 00:38:51,920
Speaker 2: As I said, we're.

790
00:38:51,640 --> 00:38:54,200
Speaker 1: Playing with it live and figuring it out as we go,

791
00:38:54,239 --> 00:38:57,360
Speaker 1: which is just woefully inadequate.

792
00:38:56,880 --> 00:38:59,960
Speaker 3: And unable to You mentioned humans suck at exponential human

793
00:39:00,120 --> 00:39:02,560
Speaker 3: something that was Hanns Roslink said, but ware straight lines.

794
00:39:04,880 --> 00:39:08,360
Speaker 3: We suck at the exponential growth. But we also suck

795
00:39:08,520 --> 00:39:12,440
Speaker 3: at understanding how our own biases can be manipulated. We

796
00:39:12,520 --> 00:39:15,040
Speaker 3: have the anchoring bis. It's that's the thing that we

797
00:39:15,080 --> 00:39:17,239
Speaker 3: refer to everything else. That's even if the first thing

798
00:39:17,320 --> 00:39:22,719
Speaker 3: is completely false. I don't know this water here, If

799
00:39:22,760 --> 00:39:24,640
Speaker 3: you drink any more of it, Matt, you one of

800
00:39:24,680 --> 00:39:27,560
Speaker 3: your toes will fall off. And then you see then

801
00:39:27,560 --> 00:39:29,919
Speaker 3: you see ten articles going water is perfectly safe. Here's

802
00:39:29,920 --> 00:39:32,879
Speaker 3: a thousand people who or have all their toes. You'll

803
00:39:32,880 --> 00:39:33,560
Speaker 3: still be like.

804
00:39:35,120 --> 00:39:36,720
Speaker 4: Our brains work that way, right.

805
00:39:37,040 --> 00:39:39,719
Speaker 3: This is something that can be totally totally used to

806
00:39:39,760 --> 00:39:42,720
Speaker 3: manipulate people around elections or something like that. Say, for example,

807
00:39:42,719 --> 00:39:44,799
Speaker 3: in the States, to be very easy to go, there's

808
00:39:44,880 --> 00:39:47,200
Speaker 3: violence that the polling boosts don't go down there, or

809
00:39:47,880 --> 00:39:50,759
Speaker 3: I don't know, they could alter the gate slightly of

810
00:39:51,120 --> 00:39:53,640
Speaker 3: some footage that exists and make it look like someone's

811
00:39:53,680 --> 00:39:57,040
Speaker 3: a bit kind of more frail or older or slurrier.

812
00:39:57,120 --> 00:40:00,320
Speaker 3: And that's even though it's not real. Now you wasting

813
00:40:00,360 --> 00:40:02,680
Speaker 3: all your time and effort trying to prove it. Yeah,

814
00:40:02,840 --> 00:40:05,000
Speaker 3: there's a wrong and humans are fucked.

815
00:40:05,320 --> 00:40:08,720
Speaker 1: Yeah, and this ties into as well in a similar fashion,

816
00:40:09,280 --> 00:40:12,760
Speaker 1: the whole Brandolini's law, the bullshit asymmetry principle.

817
00:40:12,800 --> 00:40:14,960
Speaker 4: Talk to me about Brandolini's law, the bullshit asymmetry.

818
00:40:15,280 --> 00:40:18,600
Speaker 1: Yes, and it's some similar kind of OLAP, which is

819
00:40:18,640 --> 00:40:22,000
Speaker 1: the sense or the idea that the amount of effort, time,

820
00:40:22,160 --> 00:40:26,719
Speaker 1: energy resources to debunk bullshit is an order of manusitude

821
00:40:26,760 --> 00:40:27,560
Speaker 1: more than to.

822
00:40:27,560 --> 00:40:28,520
Speaker 2: Create that bullshit.

823
00:40:29,360 --> 00:40:33,760
Speaker 1: So in the same fashion, I tell you something absolutely outrageous,

824
00:40:33,800 --> 00:40:40,120
Speaker 1: like you know, Kamala did something. You know, I can't

825
00:40:40,120 --> 00:40:41,239
Speaker 1: think of something off the top of my head, but

826
00:40:41,320 --> 00:40:44,400
Speaker 1: something juggled bananas. Juggled bananas, she can't be president.

827
00:40:45,920 --> 00:40:46,320
Speaker 2: Now.

828
00:40:47,120 --> 00:40:50,160
Speaker 1: To debunk that that's out there, it takes ten times

829
00:40:50,160 --> 00:40:52,760
Speaker 1: more effort to so, no, we'll hang on that didn't happen.

830
00:40:52,920 --> 00:40:57,160
Speaker 1: She didn't juggle bananas, because there's no evidence. But you've

831
00:40:57,160 --> 00:40:59,239
Speaker 1: got to keep doing that and pushing that narrative. Everyone's like, Noah,

832
00:40:59,239 --> 00:41:02,440
Speaker 1: and she's jugglingas that happened. I've read that the other jugler,

833
00:41:02,560 --> 00:41:04,840
Speaker 1: Yeah about that, And so the amount of effort to

834
00:41:05,200 --> 00:41:07,960
Speaker 1: kind of reverse that is so much more than it

835
00:41:08,080 --> 00:41:10,719
Speaker 1: was to just say, damn, you know, she juggled bananas.

836
00:41:11,120 --> 00:41:16,359
Speaker 1: So it's really Yeah, it kind of highlights the fact

837
00:41:16,360 --> 00:41:19,640
Speaker 1: that I think there's misinformation that can be spread so

838
00:41:19,719 --> 00:41:23,440
Speaker 1: easily that then to reverse that it's kind of well,

839
00:41:23,440 --> 00:41:25,440
Speaker 1: by the time we reverse that, ten more things have

840
00:41:25,520 --> 00:41:30,239
Speaker 1: been spread misinforming people. And again it's another ten times

841
00:41:30,320 --> 00:41:33,640
Speaker 1: more effort to debunk that. There's not just this primacy

842
00:41:33,760 --> 00:41:35,520
Speaker 1: or anchoring bias that you said, but there's also people

843
00:41:35,520 --> 00:41:39,080
Speaker 1: who take advantage of this. It was probably taking advantage

844
00:41:39,120 --> 00:41:41,400
Speaker 1: of that very phenomenon.

845
00:41:41,480 --> 00:41:44,520
Speaker 3: Well, it's a tactic. It's called the dead cat. Yeah,

846
00:41:44,560 --> 00:41:46,360
Speaker 3: like you throw the dead cat on the dinner table.

847
00:41:46,520 --> 00:41:49,640
Speaker 3: Even if someone brings up I don't know the fact

848
00:41:49,680 --> 00:41:53,759
Speaker 3: that your elder daughter Jessica lives was Belinda, and then

849
00:41:53,880 --> 00:41:57,160
Speaker 3: instead of talking about that, someone throws you know, the

850
00:41:57,239 --> 00:41:58,200
Speaker 3: dead cat on the table.

851
00:41:58,239 --> 00:42:01,440
Speaker 4: It talks but like, well, all easy about well, I

852
00:42:01,480 --> 00:42:01,799
Speaker 4: don't know.

853
00:42:02,440 --> 00:42:04,760
Speaker 3: Let's say, for example, a nuclear powers a great idea

854
00:42:05,120 --> 00:42:06,760
Speaker 3: that the reactors don't exist, but I think we should

855
00:42:06,760 --> 00:42:07,000
Speaker 3: do it.

856
00:42:07,239 --> 00:42:09,600
Speaker 4: That buy is like six weeks of time for you to.

857
00:42:09,600 --> 00:42:11,799
Speaker 3: Do something else in the background where people are going,

858
00:42:11,800 --> 00:42:14,080
Speaker 3: but what what hang on?

859
00:42:14,320 --> 00:42:17,319
Speaker 1: De bunking still well, yeah, the next thing's kind of

860
00:42:17,360 --> 00:42:17,839
Speaker 1: coming out.

861
00:42:18,880 --> 00:42:21,319
Speaker 4: It's well and truly a tactic that has used. But

862
00:42:21,400 --> 00:42:24,200
Speaker 4: this we fucking supercharged with this stuff.

863
00:42:24,239 --> 00:42:27,920
Speaker 3: And as adults we have an idea about.

864
00:42:27,840 --> 00:42:28,560
Speaker 4: What's going on.

865
00:42:28,640 --> 00:42:31,239
Speaker 3: And I look at you know, my two kids, my

866
00:42:31,280 --> 00:42:33,720
Speaker 3: step doing a Georgia she's twenty now and Wolf is five.

867
00:42:34,680 --> 00:42:37,359
Speaker 3: Georgia has an eyea. She's got a pretty good idea

868
00:42:37,400 --> 00:42:41,720
Speaker 3: about how it works, and that when she watches her shows,

869
00:42:41,760 --> 00:42:43,839
Speaker 3: the dating shows she watches, which are all really good,

870
00:42:44,400 --> 00:42:45,279
Speaker 3: she's got a pretty.

871
00:42:45,000 --> 00:42:46,440
Speaker 4: Good idea of what an edit is. And she had

872
00:42:46,440 --> 00:42:48,440
Speaker 4: a pretty good idea of how things go.

873
00:42:48,880 --> 00:42:53,520
Speaker 3: That wolf, though, is still he knows the cartoons are

874
00:42:53,600 --> 00:42:55,919
Speaker 3: not real, but he still walk out of the room

875
00:42:55,960 --> 00:43:01,200
Speaker 3: if the scary music plays right. Two, take advantage of

876
00:43:01,520 --> 00:43:05,879
Speaker 3: someone's perception of what they see on a screen when

877
00:43:05,880 --> 00:43:06,440
Speaker 3: they're young.

878
00:43:06,840 --> 00:43:08,080
Speaker 4: It's so fucking easy.

879
00:43:08,080 --> 00:43:11,800
Speaker 3: I had to take YouTube kids off our TV because

880
00:43:11,800 --> 00:43:13,360
Speaker 3: he want to watch YouTube kids. And it's like, what,

881
00:43:13,480 --> 00:43:15,400
Speaker 3: it's three hours of a grown man playing with a

882
00:43:15,400 --> 00:43:16,160
Speaker 3: poor patrol doll?

883
00:43:16,320 --> 00:43:18,919
Speaker 4: Yeah yeah, no, no.

884
00:43:19,040 --> 00:43:22,000
Speaker 1: Yeah, I mean, And this was that I wasn't super

885
00:43:22,080 --> 00:43:23,839
Speaker 1: across it. I don't know if you were a bit

886
00:43:23,880 --> 00:43:26,520
Speaker 1: more of the whole elsagate thing it is with so

887
00:43:26,560 --> 00:43:28,120
Speaker 1: it was I think it was this kind of thing.

888
00:43:28,200 --> 00:43:32,680
Speaker 1: It was YouTube videos that were being created that were

889
00:43:32,800 --> 00:43:38,400
Speaker 1: kind of not really kid appropriate, but they were created

890
00:43:38,400 --> 00:43:41,240
Speaker 1: in a way that kind of could poison the algorithm

891
00:43:41,320 --> 00:43:44,520
Speaker 1: almost that they were being served up for children to watch,

892
00:43:44,560 --> 00:43:47,160
Speaker 1: but they were kind of I don't know what it

893
00:43:47,200 --> 00:43:49,200
Speaker 1: was called else Agate, because it started during Frozen and

894
00:43:49,200 --> 00:43:50,920
Speaker 1: people were creating these things, and I think it was

895
00:43:51,239 --> 00:43:52,840
Speaker 1: this kind of like you said, it was a grown

896
00:43:52,880 --> 00:43:55,960
Speaker 1: man playing with you know, dolls or I don't know,

897
00:43:56,000 --> 00:43:59,120
Speaker 1: suggestible language and all kinds of things like this that

898
00:43:59,480 --> 00:44:02,879
Speaker 1: was Yeah. And if you're someone who just yeah, watch

899
00:44:02,960 --> 00:44:05,640
Speaker 1: YouTube kids, YouTube kids. So it's already gone through a

900
00:44:05,640 --> 00:44:09,040
Speaker 1: bit of kids, gone through the filtration process, you know,

901
00:44:09,080 --> 00:44:12,080
Speaker 1: it's already Ali should be filtering that appropriately.

902
00:44:12,160 --> 00:44:12,839
Speaker 2: Yeah.

903
00:44:12,880 --> 00:44:15,520
Speaker 1: And people were kind of, you know, gaming the algorithm,

904
00:44:15,600 --> 00:44:18,240
Speaker 1: so to speak to and I mean, there's no upside

905
00:44:18,239 --> 00:44:20,560
Speaker 1: other then they were like, why not, you know, fuck

906
00:44:20,640 --> 00:44:23,440
Speaker 1: with things almost this sort of anarchist approach to things.

907
00:44:23,920 --> 00:44:27,600
Speaker 4: That's all well and good until there's no water, you know.

908
00:44:27,800 --> 00:44:30,600
Speaker 3: Yeah, I love your idea, mate, I'm thrilled your idea

909
00:44:30,640 --> 00:44:34,560
Speaker 3: of no government redistribution of wealth brilliant great, Okay, di

910
00:44:34,680 --> 00:44:35,520
Speaker 3: serag stopped working.

911
00:44:35,600 --> 00:44:39,360
Speaker 2: Yes, Yeah, it's like they've forgotten.

912
00:44:39,040 --> 00:44:40,000
Speaker 4: Really important things.

913
00:44:40,080 --> 00:44:40,279
Speaker 2: Yeah.

914
00:44:40,680 --> 00:44:43,440
Speaker 3: I can't buy a toothbrush now. Yeah, and now and

915
00:44:43,440 --> 00:44:47,600
Speaker 3: now cavities and now life is terrible. What are we

916
00:44:47,600 --> 00:44:49,680
Speaker 3: going to do about the at Yeah, there's a reason

917
00:44:49,719 --> 00:44:51,439
Speaker 3: we got a bunch of stuff. Not all of it's great,

918
00:44:51,480 --> 00:44:53,560
Speaker 3: don't get me wrong, all it's great, But there's a

919
00:44:53,600 --> 00:44:55,439
Speaker 3: reason that things like chlorine mac a life a whole

920
00:44:55,480 --> 00:44:55,799
Speaker 3: it is.

921
00:44:56,080 --> 00:44:57,319
Speaker 2: Yeah.

922
00:44:57,440 --> 00:44:59,360
Speaker 3: When so we've been talking a bit about it, like

923
00:44:59,400 --> 00:45:01,560
Speaker 3: it is truly something we need to be mindful of.

924
00:45:01,680 --> 00:45:04,680
Speaker 4: But I mean there's a parallel, parallel.

925
00:45:04,239 --> 00:45:05,080
Speaker 2: In the war behind me.

926
00:45:05,239 --> 00:45:09,560
Speaker 3: Okay, So electricity absolutely transformed the world one hundred and

927
00:45:09,600 --> 00:45:13,680
Speaker 3: twenty years ago, completely changed everything, changed how society works, changed,

928
00:45:14,080 --> 00:45:18,799
Speaker 3: gave us refrigeration, gave us incredible healthcare. It's unfucking believable,

929
00:45:19,840 --> 00:45:25,359
Speaker 3: yet it'll kill you dead, and there's all these kind

930
00:45:25,400 --> 00:45:27,160
Speaker 3: of rails and rules and regulations and ways we talk

931
00:45:27,200 --> 00:45:31,600
Speaker 3: to kids about it. But what we're talking about it

932
00:45:31,640 --> 00:45:34,920
Speaker 3: could be if we're careful enough with it, it could

933
00:45:34,960 --> 00:45:40,360
Speaker 3: be unbelievably useful and change what we have as a society.

934
00:45:40,400 --> 00:45:42,520
Speaker 2: Could absolutely absolutely.

935
00:45:42,560 --> 00:45:45,960
Speaker 1: I mean yeah, like electrics, like the internet, life before

936
00:45:45,960 --> 00:45:48,920
Speaker 1: and after these inventions, they're just not even in the

937
00:45:48,920 --> 00:45:53,520
Speaker 1: same category. And yeah, they have the ability to just

938
00:45:53,640 --> 00:45:56,920
Speaker 1: boost and enrich our lives in ways that prior to

939
00:45:56,960 --> 00:46:00,239
Speaker 1: their existence you had no comprehension of that sort of

940
00:46:00,280 --> 00:46:01,040
Speaker 1: thing taking place.

941
00:46:01,360 --> 00:46:04,120
Speaker 2: So yeah, this huge upside to AI.

942
00:46:04,360 --> 00:46:07,440
Speaker 1: And I think often when people ask me what is

943
00:46:07,480 --> 00:46:12,400
Speaker 1: that huge upside, I think we've had machines replace labor

944
00:46:12,760 --> 00:46:16,000
Speaker 1: many many times over in the bust, We've never really

945
00:46:16,040 --> 00:46:20,080
Speaker 1: had machines replace mental labor in the same way that

946
00:46:20,320 --> 00:46:24,759
Speaker 1: AI does. And so I think there's this potential for

947
00:46:24,840 --> 00:46:28,920
Speaker 1: us to really change the paradigm of what making living means.

948
00:46:29,520 --> 00:46:33,600
Speaker 1: And that's kind of been a fairly constant idea. I say,

949
00:46:33,960 --> 00:46:36,520
Speaker 1: for a while, you work your forty hour week, you

950
00:46:37,320 --> 00:46:39,400
Speaker 1: sleep for however many hours, and then you've got a

951
00:46:39,400 --> 00:46:41,799
Speaker 1: bit of time to yourself. And I think there's a

952
00:46:41,800 --> 00:46:43,320
Speaker 1: way that we could change. So I think work is

953
00:46:43,360 --> 00:46:46,080
Speaker 1: always important. I think that sort of purpose is fairly

954
00:46:46,120 --> 00:46:49,040
Speaker 1: critical to humans, but we could kind of, you know,

955
00:46:49,760 --> 00:46:51,480
Speaker 1: can we dial that down a bit and can we

956
00:46:51,760 --> 00:46:55,800
Speaker 1: re amplify some of the things that humanity or humans

957
00:46:55,960 --> 00:46:59,239
Speaker 1: really get a boost from, which which largely I would

958
00:46:59,280 --> 00:47:02,799
Speaker 1: say is whole his passions and relationships. And so I

959
00:47:02,840 --> 00:47:06,600
Speaker 1: think the idea of okay, well let's change rather than

960
00:47:07,239 --> 00:47:09,479
Speaker 1: the five day working week and forty hours a week

961
00:47:09,520 --> 00:47:13,720
Speaker 1: and work life balance, it's like, what if making a living?

962
00:47:13,719 --> 00:47:17,200
Speaker 1: What if life? We can change that in quite a

963
00:47:17,239 --> 00:47:20,359
Speaker 1: significant way, a bit of an overhaul and you know,

964
00:47:20,760 --> 00:47:21,920
Speaker 1: let's have a bit of a reset.

965
00:47:22,400 --> 00:47:22,560
Speaker 4: All.

966
00:47:22,600 --> 00:47:25,080
Speaker 1: A lot of this is kind of rollover legacy stuff

967
00:47:25,080 --> 00:47:27,880
Speaker 1: that we've implemented over the years as we've had more

968
00:47:27,920 --> 00:47:31,719
Speaker 1: and more developments, and this potentially is an opportunity to

969
00:47:31,760 --> 00:47:32,600
Speaker 1: reset that and go.

970
00:47:32,880 --> 00:47:34,160
Speaker 2: Let's let's start again.

971
00:47:34,400 --> 00:47:36,880
Speaker 1: Because at some point someone just made stuff up, right,

972
00:47:37,719 --> 00:47:39,799
Speaker 1: Someone just made up the forty hour work week, someone

973
00:47:39,880 --> 00:47:41,759
Speaker 1: just made up how you spend your time.

974
00:47:41,800 --> 00:47:43,759
Speaker 2: All these things were made up and it's just kind

975
00:47:43,800 --> 00:47:45,680
Speaker 2: of rolled over and become the norm.

976
00:47:46,400 --> 00:47:51,040
Speaker 1: But we can renormalize and so I think that's probably

977
00:47:51,080 --> 00:47:53,440
Speaker 1: the biggest upside AI is the ability to do that.

978
00:47:53,520 --> 00:47:55,960
Speaker 1: And I think that could be really interesting. It could

979
00:47:56,000 --> 00:47:59,799
Speaker 1: be done really wrong if if it's not careful and

980
00:47:59,800 --> 00:48:03,520
Speaker 1: can but I think I'd like to have faith in

981
00:48:04,000 --> 00:48:09,799
Speaker 1: human ingenuity in that we will tackle these problems and

982
00:48:09,840 --> 00:48:13,640
Speaker 1: we can solve them in a clever way that ultimately

983
00:48:13,760 --> 00:48:15,240
Speaker 1: is beneficial for everyone.

984
00:48:15,360 --> 00:48:16,560
Speaker 4: There's that corner in Melbourne.

985
00:48:16,560 --> 00:48:18,719
Speaker 3: I would ride past it every day when I was

986
00:48:18,719 --> 00:48:21,640
Speaker 3: heading to the theater to the news show, and they

987
00:48:21,719 --> 00:48:24,960
Speaker 3: got the eight symbol there, which is the founding of

988
00:48:25,000 --> 00:48:29,360
Speaker 3: the forty hour work week in Australia, which was because

989
00:48:29,440 --> 00:48:34,440
Speaker 3: stonecutters had an average lifespan of thirty five years. They

990
00:48:34,600 --> 00:48:37,920
Speaker 3: died at thirty five because the people in power who

991
00:48:38,000 --> 00:48:40,920
Speaker 3: hired these people, which is like fucking cut more, cut more,

992
00:48:40,960 --> 00:48:43,160
Speaker 3: sandstone boys, come on, let's go, that's go and these

993
00:48:43,200 --> 00:48:47,719
Speaker 3: guys would just die because so yes, they made it up,

994
00:48:47,719 --> 00:48:50,359
Speaker 3: But there was an agreement between the people who did

995
00:48:50,400 --> 00:48:52,160
Speaker 3: the work with the hammers and the chisels and the

996
00:48:52,200 --> 00:48:56,040
Speaker 3: people that benefited from scaling that work, and they went, Okay,

997
00:48:56,080 --> 00:48:58,960
Speaker 3: we want fifty hours, you want forty, you know, thirty, we'll.

998
00:48:58,840 --> 00:48:59,400
Speaker 4: Meet offend them.

999
00:48:59,480 --> 00:49:03,480
Speaker 3: So there was an agreement between who's got you in

1000
00:49:03,520 --> 00:49:06,680
Speaker 3: this position of power and the people around it. And like,

1001
00:49:06,719 --> 00:49:09,600
Speaker 3: you don't have to be you to be able to

1002
00:49:09,640 --> 00:49:12,040
Speaker 3: talk to your MP about what are you doing about

1003
00:49:12,080 --> 00:49:15,000
Speaker 3: keeping our kids safe around this stuff? Because to think

1004
00:49:15,040 --> 00:49:18,040
Speaker 3: that unless you log into chet GPT, AI is in

1005
00:49:18,080 --> 00:49:21,200
Speaker 3: your life is absolutely fast. Yes, it is worring away

1006
00:49:21,239 --> 00:49:23,799
Speaker 3: in the background of pretty much everything we touch, isn't it.

1007
00:49:23,920 --> 00:49:24,120
Speaker 2: Yeah.

1008
00:49:24,200 --> 00:49:28,360
Speaker 1: Absolutely, anytime you're using tech, AI is probably doing something

1009
00:49:28,960 --> 00:49:33,319
Speaker 1: in some capacity, and I think that's chat GBT or

1010
00:49:33,400 --> 00:49:36,760
Speaker 1: generative AI broadly. I think kind of change people's perceptions

1011
00:49:36,760 --> 00:49:39,480
Speaker 1: of what AI is a little bit. But the reality

1012
00:49:39,520 --> 00:49:42,080
Speaker 1: is that, yeah, it's been chugging away for a long time.

1013
00:49:42,160 --> 00:49:44,600
Speaker 1: And social media is one of the obvious examples, but

1014
00:49:45,200 --> 00:49:49,480
Speaker 1: things like Google Maps, route optimization, your face idea on

1015
00:49:49,520 --> 00:49:53,320
Speaker 1: your phone, credit card fraud, and kind of identifying on

1016
00:49:53,320 --> 00:49:56,680
Speaker 1: a normal aus transactions all these things, Yes, Spotify and

1017
00:49:56,760 --> 00:50:00,799
Speaker 1: Netflix recommendation engines. Anytime you do something on your phone,

1018
00:50:00,840 --> 00:50:04,000
Speaker 1: on your TV, on your computer, there's probably some aspect

1019
00:50:04,040 --> 00:50:08,600
Speaker 1: of AI that you've interacted with. So nothing's changing, and

1020
00:50:08,640 --> 00:50:11,600
Speaker 1: it's only going to happen in a greater degree.

1021
00:50:11,680 --> 00:50:14,480
Speaker 3: I guess how many voice clones of phone calls do

1022
00:50:14,520 --> 00:50:16,520
Speaker 3: we need? Did you hear about that one where they

1023
00:50:16,640 --> 00:50:20,400
Speaker 3: voice cloned like the CFO of some big company on

1024
00:50:20,440 --> 00:50:23,000
Speaker 3: a zoom Yeah, Ferrari wasn't it, And they got his

1025
00:50:23,120 --> 00:50:26,840
Speaker 3: face and everything, so and they on the on the

1026
00:50:26,880 --> 00:50:28,040
Speaker 3: actual zoom call, they prank.

1027
00:50:28,080 --> 00:50:31,000
Speaker 4: They got in there and they authorized.

1028
00:50:30,480 --> 00:50:34,760
Speaker 3: This transaction and they just stole squillions and it sounded

1029
00:50:35,040 --> 00:50:37,600
Speaker 3: but they cloned his voice and they cloned his face.

1030
00:50:38,280 --> 00:50:40,719
Speaker 4: Like how Like, if.

1031
00:50:40,640 --> 00:50:45,000
Speaker 3: You're from in banking, I'd be working pretty quick on

1032
00:50:45,239 --> 00:50:47,480
Speaker 3: figuring out how many more quitty three factor authentic?

1033
00:50:47,840 --> 00:50:50,720
Speaker 2: Yeah? Yeah, yeah, what do you think? How do we okay?

1034
00:50:50,760 --> 00:50:51,840
Speaker 2: What I see?

1035
00:50:52,040 --> 00:50:54,879
Speaker 3: Yeah? You know, like you need two keys to launch

1036
00:50:54,880 --> 00:50:57,399
Speaker 3: a nuclears weapon, and their key holes are far enough

1037
00:50:57,400 --> 00:51:01,440
Speaker 3: apart that you actually can't the same person both. So yeah,

1038
00:51:01,600 --> 00:51:06,240
Speaker 3: it's it's physical, but you know, yeah, it could be useful.

1039
00:51:06,400 --> 00:51:08,680
Speaker 1: Yeah, and it's yeah, I mean it's I think the

1040
00:51:08,880 --> 00:51:11,960
Speaker 1: one I had heard was it was a Ferrari one.

1041
00:51:12,000 --> 00:51:14,040
Speaker 1: It didn't they weren't successful, but I think it was

1042
00:51:14,040 --> 00:51:18,719
Speaker 1: because someone asked the question. Only they almost did two

1043
00:51:18,800 --> 00:51:23,719
Speaker 1: factor authentication live wow, like atalogue mode. And that was

1044
00:51:23,760 --> 00:51:25,640
Speaker 1: really cool, I thought, because that showed, Okay, this is

1045
00:51:25,640 --> 00:51:26,440
Speaker 1: how I.

1046
00:51:26,440 --> 00:51:30,399
Speaker 4: Asked him a question that only this person could not correct. Yeah,

1047
00:51:30,440 --> 00:51:31,120
Speaker 4: I can answer it.

1048
00:51:31,239 --> 00:51:34,960
Speaker 1: Yeah, so there's still there's still ways we can get

1049
00:51:34,960 --> 00:51:37,839
Speaker 1: around it. But but yeah, it's it's it's why. And

1050
00:51:38,080 --> 00:51:40,840
Speaker 1: often people they asked, you know, what's the scary scariest

1051
00:51:40,880 --> 00:51:43,200
Speaker 1: thing about a you know, is the terminator type stuff?

1052
00:51:43,200 --> 00:51:46,160
Speaker 1: And I'm like, no, no, no, it's stuff like the deep faking,

1053
00:51:46,239 --> 00:51:50,200
Speaker 1: the the the prevalence and accuracy of deep faking that

1054
00:51:50,239 --> 00:51:51,399
Speaker 1: you can no longer believe your.

1055
00:51:51,280 --> 00:51:53,400
Speaker 2: Eyes and ears. Yeah, that's where.

1056
00:51:53,239 --> 00:51:56,439
Speaker 1: We're running into real strife, because we're already seeing how

1057
00:51:57,080 --> 00:51:58,919
Speaker 1: good it is now and how easy.

1058
00:51:58,560 --> 00:51:59,120
Speaker 2: It is to do.

1059
00:51:59,440 --> 00:52:02,920
Speaker 1: Yeah that Yeah, as you said, banking, there's like, oh

1060
00:52:02,960 --> 00:52:05,920
Speaker 1: yeah that looks like him, that sounds like him, give

1061
00:52:06,000 --> 00:52:07,560
Speaker 1: him the money, and.

1062
00:52:07,560 --> 00:52:08,920
Speaker 4: I don't want my grand my grandson.

1063
00:52:09,000 --> 00:52:10,480
Speaker 3: Sounds like he's in a lot of trouble over there

1064
00:52:10,480 --> 00:52:12,400
Speaker 3: in Portugal.

1065
00:52:12,760 --> 00:52:16,319
Speaker 1: Yeah, it's going to We're going to have round two

1066
00:52:16,360 --> 00:52:18,879
Speaker 1: of the Nigerian prints, but this time Steve an.

1067
00:52:18,880 --> 00:52:22,960
Speaker 2: Email it will be you know, the audio and videos.

1068
00:52:23,640 --> 00:52:25,279
Speaker 4: It's definitely definitely on the way.

1069
00:52:25,560 --> 00:52:28,720
Speaker 3: I'm grateful that you're involved in communicating about this because

1070
00:52:29,120 --> 00:52:31,440
Speaker 3: it is something we absolutely have to speak to and

1071
00:52:31,640 --> 00:52:34,680
Speaker 3: I certainly hope you are in some way talking to

1072
00:52:34,719 --> 00:52:38,560
Speaker 3: people in camera because they fucking absolutely need to hear

1073
00:52:39,440 --> 00:52:42,480
Speaker 3: in very simple terms, what can what could be possible,

1074
00:52:42,480 --> 00:52:45,640
Speaker 3: what could be incredible, and what comes with that.

1075
00:52:45,880 --> 00:52:47,720
Speaker 4: And so I'm grateful you in this space.

1076
00:52:48,360 --> 00:52:49,800
Speaker 3: Just going to take a moment to pay the bills

1077
00:52:49,800 --> 00:52:58,920
Speaker 3: back after the break. I remember when you told me,

1078
00:52:58,960 --> 00:53:01,680
Speaker 3: I don't know the observable the observable universe with the

1079
00:53:01,680 --> 00:53:03,360
Speaker 3: paramost power, there's only sixty four.

1080
00:53:04,000 --> 00:53:08,520
Speaker 1: Billion, ninety three billion across of light years. Yeah light years,

1081
00:53:08,840 --> 00:53:12,360
Speaker 1: but we know what keeps going? Yeah you get yeah,

1082
00:53:12,280 --> 00:53:16,320
Speaker 1: which which is which is the unobservable universe?

1083
00:53:16,400 --> 00:53:19,279
Speaker 2: Trips me out because does it even matter if it's

1084
00:53:19,320 --> 00:53:21,359
Speaker 2: unobserved It's.

1085
00:53:22,400 --> 00:53:25,600
Speaker 1: Yeah, there's there's a lot of this really large scale

1086
00:53:25,640 --> 00:53:29,440
Speaker 1: stuff that it messes with your head a bit, a

1087
00:53:29,480 --> 00:53:30,239
Speaker 1: bit a lot.

1088
00:53:30,840 --> 00:53:32,520
Speaker 2: I find it kind of.

1089
00:53:32,600 --> 00:53:35,960
Speaker 1: Yeah, it's equal parts terrifying and equal parts comforting knowing

1090
00:53:36,520 --> 00:53:39,200
Speaker 1: the sort of insignificance of us in the scheme of things.

1091
00:53:39,840 --> 00:53:41,160
Speaker 2: Yeah, I don't know.

1092
00:53:41,520 --> 00:53:44,960
Speaker 1: I love the kind of idea of you know, our

1093
00:53:45,000 --> 00:53:47,759
Speaker 1: existence is a way for the universe to understand or

1094
00:53:47,760 --> 00:53:50,239
Speaker 1: experience itself. And I think, you know, there's a lot

1095
00:53:50,280 --> 00:53:53,080
Speaker 1: of really kind of they sound cliche and a bit

1096
00:53:53,160 --> 00:53:55,200
Speaker 1: woo woo, but it's kind of a nice way to

1097
00:53:55,520 --> 00:53:59,640
Speaker 1: I think, give meaning and purpose to our lives, I guess,

1098
00:54:00,160 --> 00:54:03,960
Speaker 1: But yeah, it's there's a lot of wackiness going. And

1099
00:54:04,000 --> 00:54:05,840
Speaker 1: the big bounds I think we taught I love the

1100
00:54:05,880 --> 00:54:08,879
Speaker 1: big bounce, and it's the one that makes the most

1101
00:54:08,880 --> 00:54:10,960
Speaker 1: sense to me. I think it's the least there's the

1102
00:54:11,040 --> 00:54:14,279
Speaker 1: least evidence for it, but it sounds so and I

1103
00:54:14,320 --> 00:54:17,080
Speaker 1: think we spoke about it. We both like the neatness

1104
00:54:17,120 --> 00:54:20,239
Speaker 1: of it, that the big bang. Eventually, you know, as

1105
00:54:20,320 --> 00:54:26,400
Speaker 1: gravity reverses the acceleration entropy yep, eventually the expansion slows

1106
00:54:26,440 --> 00:54:28,960
Speaker 1: down and then gravity gets it to recollapse, and then

1107
00:54:29,040 --> 00:54:31,160
Speaker 1: when it fully collapses back in the singularity, it blows

1108
00:54:31,239 --> 00:54:33,320
Speaker 1: up and it's big Bang two and Big Bang three.

1109
00:54:33,200 --> 00:54:33,640
Speaker 4: Et cetera.

1110
00:54:33,760 --> 00:54:35,640
Speaker 3: I think you mentioned me on the day and we're

1111
00:54:35,640 --> 00:54:37,160
Speaker 3: in the trillianth iteration.

1112
00:54:37,719 --> 00:54:40,680
Speaker 1: Who knows how many times through are you.

1113
00:54:41,160 --> 00:54:43,600
Speaker 3: There's the same group of atoms thrown out in the

1114
00:54:43,640 --> 00:54:45,919
Speaker 3: universe in different lego models getting made out of it.

1115
00:54:46,040 --> 00:54:49,799
Speaker 1: Yeah, yeah, And I mean because it's been happening infinitely,

1116
00:54:50,000 --> 00:54:55,440
Speaker 1: that means probabilistically, this very conversation has happened infinity times

1117
00:54:55,960 --> 00:54:59,360
Speaker 1: in the past, will continue to happen. It's it's the

1118
00:54:59,400 --> 00:55:02,160
Speaker 1: whole thing, you know, with like pie and the decimal

1119
00:55:02,160 --> 00:55:04,719
Speaker 1: places of pie, because it's non terminating at some point,

1120
00:55:04,760 --> 00:55:08,200
Speaker 1: if you were to convert those numbers into pixels, that

1121
00:55:08,680 --> 00:55:12,960
Speaker 1: this very image of us talking exists in pi's decimal

1122
00:55:12,960 --> 00:55:16,360
Speaker 1: places in some capacity because of the concepts of infinity.

1123
00:55:17,000 --> 00:55:18,120
Speaker 2: And so it's just whacky.

1124
00:55:18,960 --> 00:55:23,319
Speaker 1: And that also means infinitely, within pi's decimal places, this

1125
00:55:23,440 --> 00:55:27,320
Speaker 1: audio recording. If you converted the numbers into audio recording,

1126
00:55:27,400 --> 00:55:31,120
Speaker 1: this audio recording exists in the decimal places of Pie.

1127
00:55:31,440 --> 00:55:38,600
Speaker 2: So infinity is wacky but also fun. I think I.

1128
00:55:40,320 --> 00:55:42,799
Speaker 3: Can't help but think, and this will close a loop

1129
00:55:42,840 --> 00:55:45,239
Speaker 3: on the AI conversation. I can't help it think that

1130
00:55:46,239 --> 00:55:49,560
Speaker 3: we might actually have this kind of this governor on

1131
00:55:49,560 --> 00:55:52,360
Speaker 3: our brain. So I remember being a five when I

1132
00:55:52,400 --> 00:55:56,640
Speaker 3: first I watched Skylab fall to Earth in nine. My

1133
00:55:57,160 --> 00:56:01,040
Speaker 3: cousin had come over from Switzerland and she ran out, come, come, come,

1134
00:56:01,160 --> 00:56:03,719
Speaker 3: and I watched it because I landed someone in Western Australia,

1135
00:56:04,120 --> 00:56:07,759
Speaker 3: and I watched it fly across the sky and she

1136
00:56:07,880 --> 00:56:10,560
Speaker 3: told me, I don't know. It's infinite, and I lied.

1137
00:56:10,680 --> 00:56:12,640
Speaker 3: I lay there at night trying to think about what

1138
00:56:12,680 --> 00:56:14,759
Speaker 3: infinity is and how this it just keeps going, and

1139
00:56:14,760 --> 00:56:18,040
Speaker 3: my brain will just go blue screen, spinny beach ball,

1140
00:56:19,120 --> 00:56:23,280
Speaker 3: and I wonder if, like if by design we've got

1141
00:56:23,680 --> 00:56:27,719
Speaker 3: this limit on our capacity considered this stuff. Then when

1142
00:56:27,760 --> 00:56:31,040
Speaker 3: your generative AOI shows up and then figures out a

1143
00:56:31,040 --> 00:56:33,319
Speaker 3: way to explain it to us, I don't know.

1144
00:56:33,360 --> 00:56:34,480
Speaker 4: Actually, here's what's going on.

1145
00:56:38,360 --> 00:56:41,680
Speaker 1: There's a there's a yeah again, it's that that singularity

1146
00:56:41,680 --> 00:56:45,120
Speaker 1: of intelligence. Right Once once AI starts creating things smarter

1147
00:56:45,200 --> 00:56:50,640
Speaker 1: than us, Yeah, they solve these Is that a hard

1148
00:56:50,680 --> 00:56:52,960
Speaker 1: limit for us? Infinity is a hard limit where we

1149
00:56:53,040 --> 00:56:56,880
Speaker 1: can understand conceptually what it is. But maybe there's like

1150
00:56:56,880 --> 00:57:00,919
Speaker 1: a really there's actually a very neat excellent nation for it.

1151
00:57:01,160 --> 00:57:03,800
Speaker 1: And I mean, look, there's probably some mathematicians listening to

1152
00:57:03,800 --> 00:57:05,920
Speaker 1: here and being like, he's getting infinity wrong. And I'm sorry,

1153
00:57:05,960 --> 00:57:10,000
Speaker 1: I'm not suggesting we don't understand infinity within the constructs

1154
00:57:10,000 --> 00:57:13,960
Speaker 1: of mathematics. But yeah, maybe there's this even more kind

1155
00:57:14,000 --> 00:57:18,920
Speaker 1: of profound understanding beyond which our brain allows us to

1156
00:57:18,960 --> 00:57:19,760
Speaker 1: comprehend it.

1157
00:57:20,400 --> 00:57:22,880
Speaker 2: But yeah, the whole the singularity with the AI as well.

1158
00:57:22,880 --> 00:57:26,440
Speaker 1: Always, I think for a lot of people thinking about

1159
00:57:27,080 --> 00:57:30,960
Speaker 1: something smarter than us, we're not good at thinking about that.

1160
00:57:31,000 --> 00:57:34,440
Speaker 1: It's for most people it's probably some smarter than US's.

1161
00:57:34,560 --> 00:57:36,800
Speaker 1: It would be the equivalent of like an Einstein or

1162
00:57:36,840 --> 00:57:38,000
Speaker 1: something right or hawking.

1163
00:57:38,400 --> 00:57:39,600
Speaker 2: We think, oh, we're going to create.

1164
00:57:39,400 --> 00:57:42,160
Speaker 3: An one that can literally allow you to exercise the

1165
00:57:42,160 --> 00:57:43,480
Speaker 3: eighth dimensional transfer.

1166
00:57:43,560 --> 00:57:48,280
Speaker 1: It it correct, and so like it's something an order

1167
00:57:48,320 --> 00:57:50,760
Speaker 1: of magnitude smarter. It's it's you know, the way that

1168
00:57:50,800 --> 00:57:55,200
Speaker 1: you can completely intellectually dominate a dog in a park

1169
00:57:55,280 --> 00:57:58,720
Speaker 1: and fake throw balls, and the dogs never not even

1170
00:57:59,200 --> 00:58:01,680
Speaker 1: you were just toying with it. The idea of us

1171
00:58:01,680 --> 00:58:04,800
Speaker 1: being the dog and something that much smarter than us,

1172
00:58:04,840 --> 00:58:07,560
Speaker 1: that we are just it's just toying with us and

1173
00:58:07,640 --> 00:58:14,000
Speaker 1: completely intellectually dominates us. That it's hard to kind of

1174
00:58:14,040 --> 00:58:15,240
Speaker 1: comprehend that situation.

1175
00:58:15,280 --> 00:58:16,840
Speaker 3: Do I get snacks and do I get to play

1176
00:58:16,880 --> 00:58:18,800
Speaker 3: Chasy Chase and then just lying around and having it

1177
00:58:18,840 --> 00:58:21,040
Speaker 3: like sleep for eighteen hours and occasionally bark at the

1178
00:58:21,120 --> 00:58:22,320
Speaker 3: kids who are walking home from school.

1179
00:58:22,360 --> 00:58:24,840
Speaker 1: That exactly sounds like a pretty good existence. I mean,

1180
00:58:24,960 --> 00:58:27,560
Speaker 1: that's it signed me up. Let's you know, like it's

1181
00:58:27,560 --> 00:58:28,240
Speaker 1: a dog's life.

1182
00:58:28,320 --> 00:58:30,560
Speaker 3: If it's going to be that, I might be all

1183
00:58:30,640 --> 00:58:33,240
Speaker 3: right with it. But yeah, I got it, just said

1184
00:58:33,240 --> 00:58:35,200
Speaker 3: it easily. And the thing is we won't even know.

1185
00:58:35,400 --> 00:58:37,520
Speaker 4: But like, dude, what do you keep shush the snacks?

1186
00:58:37,680 --> 00:58:40,240
Speaker 2: Stop bucket? Yeah? And then what if it's I'm.

1187
00:58:40,120 --> 00:58:41,480
Speaker 4: A good boy, I'm a good boy.

1188
00:58:41,120 --> 00:58:42,480
Speaker 2: God? What if it's not dogs?

1189
00:58:42,480 --> 00:58:45,000
Speaker 1: What if it's the difference between us and ants, which

1190
00:58:45,080 --> 00:58:47,720
Speaker 1: is in order of magnitude again, and so we're the

1191
00:58:47,760 --> 00:58:50,960
Speaker 1: ants and so we'll we even be aware of them

1192
00:58:51,000 --> 00:58:53,960
Speaker 1: at some point, you know, there's there's the idea of

1193
00:58:54,000 --> 00:58:56,520
Speaker 1: not being at the top of the intellectual pyramid is

1194
00:58:56,600 --> 00:59:02,000
Speaker 1: so foreign of a concept that too, Yeah, I understand

1195
00:59:02,000 --> 00:59:05,960
Speaker 1: how uncomfortable that would be. Is because the band of

1196
00:59:06,080 --> 00:59:09,160
Speaker 1: kind of I guess human IQ is such a it's

1197
00:59:09,240 --> 00:59:11,960
Speaker 1: quite tight in the scheme of the IQ of living things,

1198
00:59:12,560 --> 00:59:14,440
Speaker 1: that band, you know, and so the top of that

1199
00:59:14,520 --> 00:59:17,160
Speaker 1: band versus the bottom of that band is still in

1200
00:59:17,200 --> 00:59:19,600
Speaker 1: the same order of magnitude.

1201
00:59:20,120 --> 00:59:21,320
Speaker 2: It's kind of terrifying.

1202
00:59:22,520 --> 00:59:25,120
Speaker 1: But I don't even know if that's I mean, most

1203
00:59:25,160 --> 00:59:27,760
Speaker 1: ali experts I think say it's inevitable we'll get to that,

1204
00:59:27,880 --> 00:59:31,440
Speaker 1: But yeah, I don't know. Is there something fundamental that

1205
00:59:31,440 --> 00:59:35,320
Speaker 1: we're missing from thinking and consciousness and sentience that will

1206
00:59:35,320 --> 00:59:39,360
Speaker 1: never get there with just iteratively improving the algorithms. Is

1207
00:59:39,400 --> 00:59:44,400
Speaker 1: there some kind of missing ingredient of sentience that without it,

1208
00:59:44,400 --> 00:59:47,640
Speaker 1: it's just a case of creating cleverer and cleverer algorithms.

1209
00:59:48,720 --> 00:59:54,920
Speaker 3: I'm excited because I mean, yeah, it's real scary, but

1210
00:59:57,280 --> 01:00:02,760
Speaker 3: generally we're still here, Yes, here, because even even when

1211
01:00:02,800 --> 01:00:05,680
Speaker 3: the saber rattling has gotten real, real, real big, we

1212
01:00:06,120 --> 01:00:09,880
Speaker 3: have pretty much always erred on the side of let's

1213
01:00:10,280 --> 01:00:13,760
Speaker 3: let's not destroy each other, even though we have the

1214
01:00:13,800 --> 01:00:15,760
Speaker 3: capacity to do so. I just love how people I

1215
01:00:15,800 --> 01:00:17,160
Speaker 3: grew up with it, but I love how everyone in

1216
01:00:17,200 --> 01:00:20,480
Speaker 3: the office behind me just has no idea about nuclear renihilation.

1217
01:00:20,600 --> 01:00:22,160
Speaker 3: It's not even in the front of their minds. Yes,

1218
01:00:22,400 --> 01:00:25,040
Speaker 3: like it's still as real as it was in four

1219
01:00:25,640 --> 01:00:29,760
Speaker 3: probably more now it is terrify you know. I kind

1220
01:00:29,760 --> 01:00:31,480
Speaker 3: of hope that the only reason we're still alive is

1221
01:00:31,480 --> 01:00:35,160
Speaker 3: because humans have always erred on the side of ultimately

1222
01:00:35,160 --> 01:00:38,360
Speaker 3: compassionate and empathy. It's like, yes, this destruction and death

1223
01:00:38,720 --> 01:00:41,640
Speaker 3: and wars happen, but we are still here.

1224
01:00:42,040 --> 01:00:44,600
Speaker 4: Yes, because we're more good than we're not.

1225
01:00:45,200 --> 01:00:51,200
Speaker 3: And I'd like to think that balance will find its way.

1226
01:00:51,320 --> 01:00:55,320
Speaker 1: Yes, But I agree as well, I think if I think,

1227
01:00:55,560 --> 01:01:00,800
Speaker 1: I like to think the same. In aggregate, human are good,

1228
01:01:01,080 --> 01:01:06,240
Speaker 1: and we're robust, and we are creative and ingenious, and

1229
01:01:06,280 --> 01:01:12,600
Speaker 1: we do solve problems collaboratively and globally now, and so

1230
01:01:12,680 --> 01:01:14,360
Speaker 1: I like to think that the problems that we face

1231
01:01:14,560 --> 01:01:17,040
Speaker 1: we can tackle, we can earn on the side of caution,

1232
01:01:17,160 --> 01:01:23,680
Speaker 1: we can solve things compassionately. And to not think that,

1233
01:01:23,760 --> 01:01:28,080
Speaker 1: I think, is it's a sad outlook, I think. And yeah,

1234
01:01:28,120 --> 01:01:34,200
Speaker 1: I like to think that, Yeah, collectively, we are good people.

1235
01:01:34,000 --> 01:01:35,840
Speaker 3: And maybe good enough to like, I don't know, COP

1236
01:01:35,880 --> 01:01:38,800
Speaker 3: thirty six or whatever it is, the top twenty carbon

1237
01:01:38,800 --> 01:01:42,880
Speaker 3: emitters will just send ais that have a set of

1238
01:01:43,560 --> 01:01:46,480
Speaker 3: things that they want and they'll.

1239
01:01:46,280 --> 01:01:48,320
Speaker 4: Just go and come up with an agreement. And when

1240
01:01:48,360 --> 01:01:49,800
Speaker 4: I goes, Actually that's pretty good.

1241
01:01:49,880 --> 01:01:51,720
Speaker 3: Yeah yeah, because we still get to a GDP and

1242
01:01:51,760 --> 01:01:53,640
Speaker 3: you still get to have a thing and yeah yeah yeah,

1243
01:01:53,680 --> 01:01:55,200
Speaker 3: I couldn't have got to that agreement, could you not?

1244
01:01:55,440 --> 01:01:55,840
Speaker 4: She could not?

1245
01:01:56,040 --> 01:01:59,120
Speaker 1: Great, this is the most optimized solution because that's what

1246
01:01:59,160 --> 01:01:59,880
Speaker 1: the ALI does.

1247
01:02:00,040 --> 01:02:01,560
Speaker 4: I'm here optimized here for that.

1248
01:02:01,840 --> 01:02:02,640
Speaker 2: Yeah that's good.

1249
01:02:02,960 --> 01:02:04,360
Speaker 4: Yeah, I'll take that over the deep faiks.

1250
01:02:04,360 --> 01:02:06,000
Speaker 2: Thanks, yeah, yeah, yeah, exactly.

1251
01:02:06,040 --> 01:02:09,200
Speaker 4: Glad you wrote the book, man, Thank you so much. Yeah.

1252
01:02:09,680 --> 01:02:14,080
Speaker 3: Interesting times, No, very interesting time. Okay, So that was

1253
01:02:14,200 --> 01:02:16,560
Speaker 3: Dr Matt agnew the new book. He's written a few

1254
01:02:16,640 --> 01:02:18,640
Speaker 3: but his new book is called Is My Phone Reading

1255
01:02:18,680 --> 01:02:19,120
Speaker 3: My Mind?

1256
01:02:19,440 --> 01:02:21,080
Speaker 4: It's out right now. I'll put a link there in

1257
01:02:21,120 --> 01:02:21,720
Speaker 4: the show notes.

1258
01:02:21,720 --> 01:02:24,760
Speaker 3: Thank you for getting on the Substack this week. I

1259
01:02:24,800 --> 01:02:26,480
Speaker 3: really appreciate that you come on board there.

1260
01:02:26,480 --> 01:02:26,960
Speaker 4: It means a lot.

1261
01:02:26,960 --> 01:02:28,480
Speaker 3: I'm happy to be able to put some more writing

1262
01:02:28,520 --> 01:02:31,160
Speaker 3: out there, which is really exciting. It's building nicely and

1263
01:02:31,640 --> 01:02:34,520
Speaker 3: I'd love to see you as part of the conversations

1264
01:02:34,520 --> 01:02:36,840
Speaker 3: that we're having there on the Substack. Thanks to everyone

1265
01:02:36,840 --> 01:02:39,000
Speaker 3: that helped me make the show Today. Abby Beno produced

1266
01:02:39,000 --> 01:02:41,520
Speaker 3: the episode. Andy Maher, the great and powerful Andy Marr,

1267
01:02:41,520 --> 01:02:44,320
Speaker 3: who's my audio producer. Andy mar dot com for all

1268
01:02:44,320 --> 01:02:47,680
Speaker 3: of your podcast production services, bexi ad On video post production,

1269
01:02:47,840 --> 01:02:50,160
Speaker 3: and Monica Chapman, my executive function assistant.

1270
01:02:50,480 --> 01:02:51,960
Speaker 4: I'll see you Monday. Thanks for being here.