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Speaker 1: I get a term. It's us. Of course, it is

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Speaker 1: Tiffany and Cook, Patrick, James Bonello and me Jumbo Fatty

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Speaker 1: Harps just here every Friday morning. It's eight oh one

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Speaker 1: at this point in time. Let's go out to the

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Speaker 1: middle of nowhere and saylo to Patrick, Hi, mate, how

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Speaker 1: are you?

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Speaker 2: I could like to say that it's not the middle

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Speaker 2: of nowhere.

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Speaker 1: It's the center of the universe, all right, the center

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Speaker 1: of the universe aka the middle of nowhere. How is

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Speaker 1: baland the thriving metropolis? How are you both out there,

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Speaker 1: the two of you that live there? Well, Fritz, how

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Speaker 1: is the other person that lives there? How are they going?

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Speaker 2: Fritz is licking himself, So he's being very productive.

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Speaker 1: Oh, I'm so jealous. He would never leave the house.

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Speaker 1: Was you imagine that? Imagine? No, that's already I reckon.

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Speaker 1: If anyone on this call is flexible enough for that,

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Speaker 1: it's not me or tiff So I think I think

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Speaker 1: it might be the fucking yoga teacher or whatever it

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Speaker 1: is that you take Chi always my reason for doing it.

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Speaker 1: We thought you had hip displasure. No, you're just trying

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Speaker 1: to give yourself a head job.

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Speaker 2: But there we want people to kind of infer that,

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Speaker 2: and I don't have to.

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Speaker 1: Come out and say it like, you know, ah, sorry,

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Speaker 1: let's start again, all right, hey everybody.

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Speaker 2: This is going to be one of those shows. How

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Speaker 2: many times you reckon? We can start it again?

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Speaker 1: TIF? How many times I reckon? I think five? We've

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Speaker 1: done it?

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Speaker 2: Five is our record where we did anyway, I'm well,

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Speaker 2: thank you. It's bloody freezing this morning, though I've got

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Speaker 2: the got the heater going. But yee, I'll tell you what.

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Speaker 2: The walkout to the studio is bloody cold.

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Speaker 1: Do you have a heater in the studio or is

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Speaker 1: that inconsistent with cognitive functions so you like to keep

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Speaker 1: it cold? No?

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Speaker 2: No, no, no, that the heater is definitely definitely going.

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Speaker 2: I might say that I've I've been awake for ages,

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Speaker 2: like literally for hours, and.

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Speaker 1: I'm this is what I sleep in. I mean, my

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Speaker 1: brother's disgusting. You smell. Have you not had a shower?

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Speaker 1: Oh god, I haven't.

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Speaker 2: I haven't exercised yet. So I'm going to take the

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Speaker 2: Schnauser out for a walk after this.

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Speaker 1: Schnauser is such a bad name for any breed of anything. Schnauzer.

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Speaker 1: You can you get that little motherfucker right there. Look

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Speaker 1: at him. Look, he's so busy licking himself. What is

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Speaker 1: he doing? And room mate, don't do that. Fritz, Fritz,

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Speaker 1: get get out of the shot. Fritz.

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Speaker 2: Hear you, he's not wearing headphones like a.

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Speaker 1: Fucking canine porno. Get rid of him. What is he doing?

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Speaker 1: She's got no pride. Get on the floor and do that.

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Speaker 1: He's embarrassed him.

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Speaker 2: He's got off the chair, he's walked away, and now

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Speaker 2: now he's given me the snaus a look, he's just

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Speaker 2: looking at me.

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Speaker 1: What the three feet Schnauzer death stare.

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Speaker 2: It's scary when he gives you the look because he's

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Speaker 2: got those the eyebrows are like an awning over the

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Speaker 2: top of his head and he just peers through them.

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Speaker 2: So it's kind of is he looking at me? Or

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Speaker 2: isn't he looking at me? And it's a bit unnerving.

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Speaker 1: You've seen. Why don't you give that little hair arounda

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Speaker 1: a little bit of a trim.

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Speaker 2: Look, I know he's back up again. There, Oh God,

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Speaker 2: here we go.

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Speaker 1: Now he's sniffing you. God, I don't let him lick

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Speaker 1: you because it's just been on his cock. That tue.

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Speaker 1: Get him stop it the worst. I just tapping Patrick

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Speaker 1: on the arm, like, come on, bro, give him some love.

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Speaker 1: Give him like look at.

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Speaker 3: Oh my god, I love him.

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Speaker 1: Oh now he's like, don't make fun of me that.

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Speaker 1: Oh why are you not giving him some love? Oh

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Speaker 1: my god?

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Speaker 2: The worst start of a show ever, Tiff, how are you?

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Speaker 2: I'm fabulous.

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Speaker 3: I just went and did a little boxing. I haven't

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Speaker 3: put the block gloves on and boxed most of this year,

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Speaker 3: and I just did a little boxing session this morning.

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Speaker 1: I'm feeling good. Did you hit the bag, did you

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Speaker 1: hit the speedball, the floor to ceiling? Or did you

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Speaker 1: hit Brian in the ring? When I say not Brian

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Speaker 1: in the ring, I mean Brian, it's not Brian's ring,

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Speaker 1: but Brian who happened to be in the ring unless

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Speaker 1: he was doing a downward dog at the time, and

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Speaker 1: then you hit him in the ring. But so, what

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Speaker 1: was the format of the boxing is what I'm trying

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Speaker 1: to ask. I did a little bag work.

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Speaker 2: I did a little Florida ceiling. I did a little

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Speaker 2: bit of that wall bag. It was it was great.

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Speaker 1: How I feel like I shoulders feel strong?

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Speaker 2: I do feel like I look like a little mac truck.

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Speaker 3: Staffy or something. Now that I'm like, I didn't look

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Speaker 3: like this last time I put the gloves on.

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Speaker 1: He's been taking volumes of steroids and lifting heavy things.

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Speaker 3: Have not been taking volumes of still, but I've been.

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Speaker 1: But you look like it. Well that that you are

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Speaker 1: a fucking anomaly, because.

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Speaker 3: I'm not taking that off, because that was very timed.

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Speaker 3: I didn't take my jacket off for that reason.

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Speaker 1: Yeah sure, Patrick, and I just going to get a coat.

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Speaker 1: Will be back in a moment. I'm covering up to

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Speaker 1: me too.

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Speaker 2: I'm glad I have a shaven.

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Speaker 3: All of the typ listeners know exactly what we're all

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Speaker 3: laughing at without us even saying anything.

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Speaker 1: I don't know. It seems like this is getting looser

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Speaker 1: every episode. I feel like somebody out of the rig.

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Speaker 1: It's going to get Lisa. I think I think somebody

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Speaker 1: responsible needs try and get it back on track. If

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Speaker 1: only they will have one here. One day you'll have

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Speaker 1: an adult.

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Speaker 2: In the show, and then it will all be put

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Speaker 2: back on track.

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Speaker 1: I don't think an adult would want to be involved

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Speaker 1: in that. Got a chance, Patrick, Let's start with the

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Speaker 1: important stuff. I've got a list. If you've never heard

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Speaker 1: this before, well, if you've never heard this show before

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Speaker 1: or this installment of the show before, you've probably already left.

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Speaker 1: But if you happen to be still here, One, what

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Speaker 1: the fuck is wrong with what are you doing? Yeah? Yeah,

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Speaker 1: you get a life. But two, this is the show

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Speaker 1: where Patrick allegedly comes and talks to us about. Well,

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Speaker 1: it used to be originally just tech, but we've kind

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Speaker 1: of diversified and there's a little bit of psychology and

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Speaker 1: human behavior and it's a bit of a pot pirie

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Speaker 1: of conversation to be honest, But I think we should

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Speaker 1: start with the most interesting topic on your list. Today,

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Speaker 1: scientists reveal a clever trick that can help me win

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Speaker 1: rock paper So yeah.

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Speaker 2: This is really interesting. So you know when you play

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Speaker 2: rock paper scissors?

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Speaker 1: Right, what I just asked, Tiff, do you know what

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Speaker 1: that is?

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Speaker 3: Oh?

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Speaker 1: Yeah, yeah, okay, yeah, yeah, yeah, go.

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Speaker 2: I find it interesting what science has studied. Let's just

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Speaker 2: go back one step, because they're studying rock paper scissors.

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Speaker 2: But kind of cool. I think that's great. It's you know,

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Speaker 2: obviously we're not the only ones who never evolved into adulthood.

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Speaker 2: I think that's cool anyway.

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Speaker 1: Also, I just want to ask, as a side note,

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Speaker 1: who the fuck is funding this research?

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Speaker 2: Yeah, I don't know, like play school, some Landling company,

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Speaker 2: the Wiggles.

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Speaker 1: Anyway, go on.

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Speaker 2: It actually has broader implications in terms of how we

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Speaker 2: make decisions and whether when we make a decision competitively

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Speaker 2: or whether we do it in conjunction with someone as

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Speaker 2: a team effort. But what it was looking at was

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Speaker 2: how do you win at Rock paper scissors? And most

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Speaker 2: people make the decision of what they're going to play

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Speaker 2: next based on.

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Speaker 1: The previous round.

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Speaker 2: So when I'm doing rock paper fitters with you and

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Speaker 2: you have rock or paper or scissors or whatever it

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Speaker 2: happens to be, I'm influenced by what your previous action was.

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Speaker 2: And the scientists came up with the notion that, in fact,

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Speaker 2: that's the wrong thing to do. You've got to not

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Speaker 2: be influenced by the previous action the person did, and

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Speaker 2: you will have more chance of winning if you come

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Speaker 2: up with a new move each time. So this is

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Speaker 2: basically what came out of this. But what they're saying

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Speaker 2: from this is that the field it's basically social neuroscience,

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Speaker 2: and what they're looking into is how the brains of

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Speaker 2: individuals gain an insight into another person. And the decision

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Speaker 2: making process, and if we interact with people what that

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Speaker 2: then means. It's called hyper scanning, and it's a method

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Speaker 2: of working. As you probably know more about this than

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Speaker 2: we do.

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Speaker 1: Crago. Well, now it's very interesting. It's like anticipation and

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Speaker 1: trying to antidi based on previous data or previous behavior,

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Speaker 1: what somebody's going to do next. Like they say, in

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Speaker 1: general terms human behavior, the best predictor of future behavior

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Speaker 1: is past behavior. But when you're doing something, which is

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Speaker 1: where you're trying, like obviously in a game like this,

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Speaker 1: you're trying to manipulate or deceive somebody. That's the point

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Speaker 1: of the game because you're trying to win, so you

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Speaker 1: don't want them to know what you're going to do.

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Speaker 1: But yeah, it's definitely it is definitely interesting. But that trying,

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Speaker 1: Like it's easy to say, make it random every time

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Speaker 1: and don't repeat what you did last time. But if

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Speaker 1: you never repeat what you did last time, well that's

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Speaker 1: a pattern as well, and that makes you predictable. Like

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Speaker 1: if you ever do paper twice in a row, I know,

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Speaker 1: well Patrick never does the same thing twice in a row,

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Speaker 1: and there are only three things, only three options. Last

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Speaker 1: time he did paper, so this time it's definitely got

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Speaker 1: to be scissors or rocks. I feel like I'm sounding

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Speaker 1: like a really boring scientist now, but yeah, it is

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Speaker 1: that whole kind of that ability to be anticipated, to

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Speaker 1: be able to anticipate what somebody is going to do

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Speaker 1: in terms of a decision and a behavior. It's it.

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Speaker 1: The benefits are far reaching beyond this particular game. But

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Speaker 1: I understand why they did this research because it can

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Speaker 1: be extrapolated to a lot of other things.

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Speaker 2: And the other thing it just occurred to me while

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Speaker 2: you were talking, because I was listening intently to what

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Speaker 2: you were saying, was did you ever play did you

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Speaker 2: ever play the rule with dynamite? So rock papers is

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Speaker 2: a dynamite?

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Speaker 1: I did not. Oh see that was another.

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Speaker 2: One as well, rock papers and dynamite, So that that's

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Speaker 2: another variable as well. So it actually increases it exponentially,

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Speaker 2: doesn't it by having another fear anyway, that's I just

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Speaker 2: thought i'd mentioned that for those people who've played rock

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Speaker 2: papers as dynamite.

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Speaker 1: This kind of reminds me of in terms of like

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Speaker 1: repeating certain things and like that are unproductive and sociologically ridiculous.

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Speaker 1: The habit that we humans have of essentially having the

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Speaker 1: same conversation with the same person about the same issue

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Speaker 1: and getting the same negative result. Yet we do it

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Speaker 1: again and again and again, you know, rather than going okay.

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Speaker 1: So we've had a version of this conversation a hundred

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Speaker 1: times and it never turns out well. Either I need

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Speaker 1: to stop having it, or have a different version, or

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Speaker 1: try a different approach in a different context, or like

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Speaker 1: we are very slow learners when it comes to social interactions.

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Speaker 1: All right, let's move on then, So tell me why

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Speaker 1: life expectancy gains have slowed down sharply, Patrick, I feel

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Speaker 1: like they're almost on the way to going backwards. Yeah,

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Speaker 1: it feels like it as well.

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Speaker 2: And this is and I guess it might be how

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Speaker 2: we're living at the moment, or the fact that it

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Speaker 2: plateaued and now it's kind of dipping on the other side.

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Speaker 2: So some recent research has been looking into you know,

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Speaker 2: at the end of the nineteen thirty so nineteen thirty nine,

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Speaker 2: anybody born after anineteen thirty nine had a greater chance

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Speaker 2: of increasing their longevity their life every year after that.

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Speaker 2: So basically, if you lived in nineteen thirty nine, your

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Speaker 2: life expectancy was like sixty something years, but then every

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Speaker 2: year after that it started to increase and increase and increase.

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Speaker 2: So medical breakthroughs helped a lot of that, but longevity

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Speaker 2: growth now has lost its momentum. So historically it would increase,

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Speaker 2: but it seems now that no generations that are born

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Speaker 2: after nineteen thirty nine, So you know, right now we're

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Speaker 2: seeing people live to that age and those people would

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Speaker 2: have been born in nineteen so as we head towards

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Speaker 2: that time, but the reality is now they're saying that

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Speaker 2: less people are going to be and in fact, it's

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Speaker 2: going to get to a point where anybody born after

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Speaker 2: nineteen eighty will not live to one hundred.

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Speaker 1: Wow. Yeah, wow, don't surprise me. It doesn't surprise me.

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Speaker 1: Like there's this almost this contradiction where we've got. It

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Speaker 1: seems like some of us have got worse and worse

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Speaker 1: habits and behaviors, but at the same time, we've got

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Speaker 1: all this groundbreaking medicine and medical resources to help us

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Speaker 1: live longer and stronger. But yeah, I wonder, I wonder

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Speaker 1: with all the toxins in the soil and the air

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Speaker 1: and all of the things, and the plastics and the

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Speaker 1: phyto estrogens, and all the shit that we need to

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Speaker 1: deal with now. I wonder if we're about to hit

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Speaker 1: that kind of platau or maybe go backwards.

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Speaker 2: Well, this study has been done by the University of Wisconsin, Madison,

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Speaker 2: and I guess when you think about also record keeping

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Speaker 2: and how accurate it is, and the fact that we

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Speaker 2: now have really good data we can track population growth

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Speaker 2: and we're not we're talking Western nations too. This is

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Speaker 2: specifically research that's you know, on longevity. Data is based

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Speaker 2: around the western Western nations, so we're not sure about

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Speaker 2: other countries.

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Speaker 1: But it's interesting. Oh sorry, dude, I was just going

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Speaker 1: to say it's interesting you brought up nine and thirty

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Speaker 1: nine as that date because both of my parents were

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Speaker 1: born in nineteen thirty nine and they're both eighty six,

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Speaker 1: so they've done pretty well. Yep.

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Speaker 2: Yeah, Well, my day just turned ninety a few weeks ago,

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Speaker 2: and obviously he was born before then, so that'd be

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Speaker 2: mak him thirty five?

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Speaker 1: Was it? No? Thirty five? Well that I know this

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Speaker 1: is digressing, But how's dad's health.

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Speaker 2: Look, he's in aged care. He's always ridiculously active. But

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Speaker 2: the two things I think worked in his favor have

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Speaker 2: worked in his favor is he didn't never smoked, and

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Speaker 2: he very occasionally had a drink, you know, have a

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Speaker 2: beer in summer, and that was all. But was always

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Speaker 2: always active, always walked, you know, was physically in the

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Speaker 2: garden every day, that sort of stuff. But he's done

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Speaker 2: pretty well, you know, I think for most. My theory

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Speaker 2: on this, and we've spoken about this many many times,

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Speaker 2: I don't know that I want to live to one hundred.

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Speaker 1: I just want to live to an old age in

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Speaker 1: good health.

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Speaker 2: So keep keeping running on all cylinders for as long

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Speaker 2: as possible, I reckon.

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Speaker 1: Yeah, we call that health span. So that's living long

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Speaker 1: as long as you can, but as well as you can.

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Speaker 1: For the vast majority, I've noticed.

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Speaker 2: One smart watch is now telling me what my health

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Speaker 2: age is. And it's kind of disconcerting. And so, hey,

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Speaker 2: you've been really active and you're doing all the right things.

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Speaker 2: You're actually you know, in your late forties, not your

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Speaker 2: late fifties, like you sucking up to me.

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Speaker 3: Or what you realize that you keep looking at your

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Speaker 3: wrist and you're not actually wearing a watch.

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Speaker 2: No, yeah, I left my health watch off because I

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Speaker 2: think it's lying to me.

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Speaker 1: I feel it's trying to create rapport and build connection

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Speaker 1: and trust. Now, there is some research that's come out

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Speaker 1: which is very significant to me and quite disconcerting, because

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Speaker 1: being too attractive can hurt fitness, influences, new res such Hell,

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Speaker 1: that's going to be a setback for me. It's you laughing?

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Speaker 1: Are you laughing? Sucking hell?

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Speaker 2: Knowing? By this study, out of the three people in

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Speaker 2: this conversation, Tiff is the least likely to believe be

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Speaker 2: believed then you or I. It's called influences.

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Speaker 1: Okay, yeah, so tell me what's going on with this.

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Speaker 2: Well, you know that the adage that sex cells actually

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Speaker 2: isn't working. So researchers looked into consumer behavior and there

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Speaker 2: seems like when people are pushing this is in the

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Speaker 2: health field. Only they did prospers to different disciplines and

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Speaker 2: they found this was most strongly in the influencer area

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Speaker 2: for health. And what they found was if someone is

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Speaker 2: too good looking and too attractive, they're less.

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Speaker 1: Yeah. Yeah, yeah, so we're much more authentic.

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Speaker 2: Now I get that, because what it is when you're

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Speaker 2: trying to talk about a health message, you want to

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Speaker 2: engage with your audience. But if you're way too good looking,

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Speaker 2: people can't relate to you because you alienate them because

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Speaker 2: there's not that connectedness. And you know, I guess when

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Speaker 2: we're talking about health, it's a pretty personal thing. But

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Speaker 2: I thought that was kind of interesting and maybe that's

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Speaker 2: why you've succeeded as in the health.

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Speaker 1: Is that right? Low it are you saying because I'm

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Speaker 1: ugly as why I've done well?

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Speaker 2: But I don't know, because you're authentic, and you're authentic

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Speaker 2: in your presence.

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Speaker 1: But what authentic's got nothing to do with looks? And

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Speaker 1: this is all about looks, this particular story. And you

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Speaker 1: were saying attractive people, it's a handicap and I've done

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Speaker 1: well because I'm not attractive. Is that what you're saying?

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Speaker 2: Well, you're not overtly attractive.

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Speaker 1: I'm not overtly attractive.

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Speaker 3: Friends like.

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Speaker 1: You break that down for me a little bit. So

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Speaker 1: I'm not overtly attractive. What am I? Ruggedly handsome? See

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Speaker 1: that's bookshit as well, because now you're lying. Now you're

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Speaker 1: fucking backpeddling. I can hear your gears grinding. It's a

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Speaker 1: good thing. My mum loves me and I don't need

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Speaker 1: your fucking approval. Wow, And I'm not trying to attract

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Speaker 1: you any I'm not trying to attract anyone, let's be honest.

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Speaker 1: But you can go right to the back of the

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Speaker 1: queue back in therapy for harps. You know what happened.

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Speaker 1: I told you I took a photo with doctor Alex,

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Speaker 1: who's the best looking surgeon in the world, and all

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Speaker 1: everybody did was talk about whether or not one how

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Speaker 1: good looking he is. That was comment one and question

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Speaker 1: one was easy single. I'm like, I am also in

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Speaker 1: the photo, but there was not one comment I am

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Speaker 1: also there. Hey, there are two people, you know.

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Speaker 2: The other thing that came out of the study was

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Speaker 2: that when people watch these fitness influences or fit influences,

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Speaker 2: it's hard to say with the mathul it's eth fit fluences,

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Speaker 2: but when they watch them talking about or spooking about health,

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Speaker 2: they come away from it with a lower self esteem.

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Speaker 1: Wow. Yeah, Like, my only question is all of the

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Speaker 1: fitness influences that I know, and there are quite a

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Speaker 1: lot of them that I know personally and also I

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Speaker 1: know through the interwebs, none of them are fucking out

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Speaker 1: of shape and ugly. Let me tell you that. So

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Speaker 1: I don't know about the validity of this. Most of

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Speaker 1: them are pretty jacked and pretty good looking, either pretty

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Speaker 1: or or handsome. But I am interested in this story

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Speaker 1: because I grew up with the Eagles. The Hotel California.

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Speaker 2: Effect, like, yeah, it's called the Hotel California because you

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Speaker 2: know the lyrics.

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Speaker 1: Of course, check you.

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Speaker 2: Can check in any time you like, but you can

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Speaker 2: never leave something along those lines that how correct correct.

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Speaker 2: So the theory behind this is that a stack of Australia.

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Speaker 2: It's a phrase that's been coined by you know, basically

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Speaker 2: people who are looking into consumer insights. So it's a

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Speaker 2: that's put out by Deloitte. It comes out annually. It's

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Speaker 2: a media Entertainment consumer Insights report, and what they found

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00:20:15,640 --> 00:20:19,000
Speaker 2: is more of us are subscribing and the amount that

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Speaker 2: we pay per month for subscriptions has risen by twenty

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Speaker 2: four percent this year. So on average, each household has

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00:20:28,040 --> 00:20:31,159
Speaker 2: three point seven subscriptions to the value of about seventy

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Speaker 2: eight bucks, whereas last year it was sixty three dollars,

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Speaker 2: so there has been an increase. There's a criticism of

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Speaker 2: a lot of the subscription platforms that it's very hard

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Speaker 2: to unsubscribe, and you know what, it's honestly it's like

385
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Speaker 2: a needy relationship when you kind of try to leave

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Speaker 2: as well, it's really you're really going to go, what about.

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Speaker 1: If I do this?

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Speaker 2: What about if I do this and I do this

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Speaker 2: and then I'll do that? And have you tried to

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Speaker 2: unsubscribe recently from some?

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Speaker 1: Think? It's really hard? And then I have not. I

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00:21:03,480 --> 00:21:07,120
Speaker 1: probably should though, because I've got too many Yeah, yeah,

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Speaker 1: how many of you got tip? Like what you're on Netflix?

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Speaker 1: What else are you on?

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Speaker 3: I'm only on Netflix every now and then I do

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Speaker 3: unsubscribed from that, but I don't know spot I've got

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Speaker 3: heaps Spotify, bluddy, even Zoom. I don't use the Zoom

398
00:21:21,160 --> 00:21:23,760
Speaker 3: subscription now because I use Riverside, but I've still got

399
00:21:23,800 --> 00:21:27,920
Speaker 3: it subscribed just in case. Strava don't use that. Use

400
00:21:27,960 --> 00:21:30,240
Speaker 3: that when I was running. Don't need to be premium

401
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Speaker 3: heaps hip Patrick.

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Speaker 1: The next story is a little bit tantric in it

403
00:21:37,000 --> 00:21:40,439
Speaker 1: feels a bit tantric to me, which is touching without contact.

404
00:21:42,200 --> 00:21:46,159
Speaker 1: We physically sense objects before feeling them, so.

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Speaker 2: Research because some animals do this really really well. There

406
00:21:49,680 --> 00:21:53,600
Speaker 2: are some birds that are able to stand on sand

407
00:21:54,160 --> 00:21:58,000
Speaker 2: and feel where their prey is under the sand, so

408
00:21:58,040 --> 00:21:59,919
Speaker 2: they thought this was kind of interesting. Let's do this

409
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Speaker 2: with human beings and see if humans when they press

410
00:22:03,320 --> 00:22:06,200
Speaker 2: their hand onto the sand consense just by moving their

411
00:22:06,240 --> 00:22:09,520
Speaker 2: hands around where the object is and was a high

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Speaker 2: level of accuracy. And what they're finding is that the vibrations,

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Speaker 2: it's kind of it's I'm using it in simple terms.

414
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Speaker 2: It's not like sonar. But what it means is that

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Speaker 2: those sensial the sense you get from tactile touch, can

416
00:22:25,320 --> 00:22:28,919
Speaker 2: actually work through the sand to the object, and we

417
00:22:28,960 --> 00:22:31,160
Speaker 2: almost have an innate sense to be able to find

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Speaker 2: that object. So you don't see people walking along the

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Speaker 2: beach with their metal detectors. Just lay down on the

420
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Speaker 2: sand and just press your hands in and you've got

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Speaker 2: more chance to find and stuff.

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Speaker 1: Well, I think that might be a skill or a

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Speaker 1: sense that might need to be developed. No, but it

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Speaker 1: is accurate.

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Speaker 2: Though. They say that they were able to track the

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00:22:48,920 --> 00:22:52,800
Speaker 2: way people had this tactile sensation and just by feeling

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00:22:53,200 --> 00:22:55,760
Speaker 2: the movement of the sand and how the sand then

428
00:22:55,840 --> 00:22:58,679
Speaker 2: pushed against the object. It's kind of interesting how our

429
00:22:58,760 --> 00:23:02,480
Speaker 2: sensors can kind of be extrapolated that way. And the

430
00:23:02,560 --> 00:23:05,680
Speaker 2: reason they're researching into this is because they are trying

431
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Speaker 2: to apply this to research into the development of robots

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Speaker 2: that have more tactile senses to be able to sense touch,

433
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Speaker 2: because that's a problem when you look at wanting to

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Speaker 2: potentially use say robots and healthcare, then the amount of

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Speaker 2: pressure that they apply and their sense of how much

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Speaker 2: pressure they're applying to say a person's hand. If you

437
00:23:27,160 --> 00:23:30,240
Speaker 2: think about health care, and in China they're going crazy

438
00:23:30,280 --> 00:23:33,520
Speaker 2: developing robots because they're worried about I guess the healthcare

439
00:23:33,840 --> 00:23:37,120
Speaker 2: of an aging population and a very young well not

440
00:23:37,280 --> 00:23:41,080
Speaker 2: having enough young people. But if I was to grab

441
00:23:41,160 --> 00:23:44,800
Speaker 2: your wrist or TIFF's wrist, all of us have a

442
00:23:44,840 --> 00:23:47,159
Speaker 2: different width in terms of what our wrist is, and

443
00:23:47,200 --> 00:23:49,919
Speaker 2: that's something that if you've got a pre programmed medical

444
00:23:50,000 --> 00:23:53,520
Speaker 2: robot that grabs you on the wrist, then because of

445
00:23:53,520 --> 00:23:55,879
Speaker 2: the diversity, how do they know how much pressure to

446
00:23:56,000 --> 00:24:00,200
Speaker 2: apply given that we all have a different width of wrist.

447
00:24:00,600 --> 00:24:03,880
Speaker 2: So it's really really important for this development of robotics

448
00:24:03,920 --> 00:24:07,199
Speaker 2: to be able to have quite tactile sensation, and this

449
00:24:07,240 --> 00:24:08,840
Speaker 2: has been a bit of a challenge that they're working

450
00:24:08,880 --> 00:24:11,040
Speaker 2: on at the moment. So this sort of research into

451
00:24:11,560 --> 00:24:14,560
Speaker 2: the tactile nature of sand and feeling things under the

452
00:24:14,600 --> 00:24:17,280
Speaker 2: sand works the same. We take a lot for granted

453
00:24:17,320 --> 00:24:19,440
Speaker 2: because of all the nerve endings we have in our hands,

454
00:24:19,960 --> 00:24:23,080
Speaker 2: but to apply that mechanically is really challenging.

455
00:24:23,840 --> 00:24:25,920
Speaker 1: You know what this makes me think about a little bit,

456
00:24:26,000 --> 00:24:31,480
Speaker 1: like developing these senses, Like you wonder if, given certain circumstances,

457
00:24:31,560 --> 00:24:35,280
Speaker 1: how well we could develop these senses that we really

458
00:24:35,840 --> 00:24:39,919
Speaker 1: are unaware of. Like you know, when somebody loses you know,

459
00:24:40,000 --> 00:24:45,040
Speaker 1: whether it's site or some other function, it's almost like

460
00:24:45,160 --> 00:24:49,440
Speaker 1: the other abilities kind of come into play or develop more.

461
00:24:50,119 --> 00:24:52,520
Speaker 1: I watched this thing on this dude who developed he

462
00:24:52,680 --> 00:24:56,480
Speaker 1: was blind. He is blind, and he's developed this kind

463
00:24:56,520 --> 00:25:02,520
Speaker 1: of echolocation skill. How bats make a noise and depending

464
00:25:02,560 --> 00:25:05,720
Speaker 1: on how far away they are from an object, how

465
00:25:05,800 --> 00:25:09,920
Speaker 1: quickly that noise returns to them, Like how quickly they

466
00:25:09,960 --> 00:25:14,320
Speaker 1: hear it back is it's called echolocation kind of gives

467
00:25:14,359 --> 00:25:16,639
Speaker 1: them a sense of where they are in space and

468
00:25:16,720 --> 00:25:20,280
Speaker 1: time and how close they are to things. And so

469
00:25:20,320 --> 00:25:23,200
Speaker 1: this dude develop this where he makes this noise, this

470
00:25:23,359 --> 00:25:27,400
Speaker 1: kind of clicking noise, and he can walk around and

471
00:25:27,680 --> 00:25:31,840
Speaker 1: he knows the proximity of physical objects that he can't see.

472
00:25:31,880 --> 00:25:34,440
Speaker 1: But he can sense with this skill that he developed.

473
00:25:34,920 --> 00:25:39,040
Speaker 1: It's fucking amazing what we can do when there are

474
00:25:39,080 --> 00:25:41,399
Speaker 1: certain things we now can't do. Now we have to,

475
00:25:42,800 --> 00:25:47,600
Speaker 1: I guess, lean into this other potential, whatever gift or sense,

476
00:25:47,680 --> 00:25:51,520
Speaker 1: and then develop that over time. We talked about.

477
00:25:51,280 --> 00:25:55,320
Speaker 2: Brain plasticity, and I think that potentially on the show

478
00:25:55,320 --> 00:25:57,720
Speaker 2: we may have but I might quickly repeat it. I

479
00:25:58,000 --> 00:26:01,040
Speaker 2: love to juggle, and this is something that I've always

480
00:26:01,160 --> 00:26:03,919
Speaker 2: enjoyed doing. Just a three ball cascade they call it.

481
00:26:04,280 --> 00:26:07,679
Speaker 2: And our German researchers took three groups of people and

482
00:26:07,720 --> 00:26:10,760
Speaker 2: took them how to juggle, and then after a month

483
00:26:11,119 --> 00:26:14,160
Speaker 2: they got one group. What they found, by the way,

484
00:26:14,320 --> 00:26:17,119
Speaker 2: with their research was that there's a part of the

485
00:26:17,160 --> 00:26:22,400
Speaker 2: brain that only activates when people are juggling. They don't

486
00:26:22,440 --> 00:26:26,320
Speaker 2: know why, but it only starts to show the synapses

487
00:26:26,359 --> 00:26:29,119
Speaker 2: and movement in the brain that part of the brain

488
00:26:29,400 --> 00:26:32,720
Speaker 2: when someone learns how to juggle, and if they're juggling consistency.

489
00:26:32,760 --> 00:26:35,880
Speaker 2: But after a month they broke the group into three

490
00:26:36,000 --> 00:26:39,040
Speaker 2: and one group kept juggling five minutes a day, one

491
00:26:39,400 --> 00:26:43,560
Speaker 2: group stopped juggling completely, and then the other group visualized

492
00:26:44,040 --> 00:26:47,160
Speaker 2: juggling and the group visualized and the group that were

493
00:26:47,320 --> 00:26:51,240
Speaker 2: consistently juggling, that active part of the brain stayed active.

494
00:26:52,240 --> 00:26:54,480
Speaker 2: Blue stopped juggling, that part of the brain that was

495
00:26:54,520 --> 00:26:58,560
Speaker 2: firing stopped firing, and then eventually they also know that

496
00:26:58,560 --> 00:27:01,480
Speaker 2: people who juggle on a regular base, that's always active.

497
00:27:01,240 --> 00:27:02,840
Speaker 1: In their brain. So they don't know what it is.

498
00:27:03,080 --> 00:27:03,760
Speaker 1: They know that the.

499
00:27:03,720 --> 00:27:06,679
Speaker 2: Brain is active and it's able to develop the sense

500
00:27:07,119 --> 00:27:10,440
Speaker 2: to do that. So just learning how to juggle is

501
00:27:10,840 --> 00:27:13,560
Speaker 2: a way for your brain to suddenly use a region

502
00:27:13,600 --> 00:27:16,040
Speaker 2: that it doesn't use. It's amazing. Was it sixteen percent

503
00:27:16,080 --> 00:27:17,520
Speaker 2: of our brain that we use crago?

504
00:27:18,760 --> 00:27:20,959
Speaker 1: I don't know. They really don't know because there's no

505
00:27:21,000 --> 00:27:23,720
Speaker 1: absolute number, but it's not in terms of our potential,

506
00:27:23,920 --> 00:27:26,640
Speaker 1: I think it's very low. I think it's around that Patrick.

507
00:27:26,720 --> 00:27:30,560
Speaker 1: But also, you know what else. I saw this thing

508
00:27:30,600 --> 00:27:32,560
Speaker 1: the other day which was I thought it said it

509
00:27:32,600 --> 00:27:34,520
Speaker 1: was a study, and then I went down the rabbit hole.

510
00:27:34,520 --> 00:27:37,560
Speaker 1: It wasn't a study. It was bullshit. But the idea

511
00:27:37,640 --> 00:27:41,119
Speaker 1: is very fucking clever, and that I forget which it

512
00:27:41,240 --> 00:27:44,280
Speaker 1: was like maybe Denmark or somewhere where allegedly what they

513
00:27:44,359 --> 00:27:48,120
Speaker 1: do is one hour a day in this school, this

514
00:27:48,160 --> 00:27:51,080
Speaker 1: particular school, which turned out to be bullshit, But it's

515
00:27:51,119 --> 00:27:53,960
Speaker 1: a great idea. I think it would work. They get

516
00:27:54,119 --> 00:27:58,000
Speaker 1: children to use their non dominant hand writing, so every

517
00:27:58,080 --> 00:27:59,600
Speaker 1: day they would you know, So for me, I'm a

518
00:27:59,640 --> 00:28:01,280
Speaker 1: left hand and it mean that you would do an

519
00:28:01,320 --> 00:28:04,880
Speaker 1: hour of work with your non dominant hand, and that

520
00:28:04,920 --> 00:28:07,879
Speaker 1: does amazing shit in your brain. And that's one of

521
00:28:07,920 --> 00:28:10,840
Speaker 1: the things I encourage older people to do is do

522
00:28:11,040 --> 00:28:13,920
Speaker 1: things that you don't normally do, because now your brain

523
00:28:13,960 --> 00:28:17,720
Speaker 1: has got to adapt to that new thing. And even

524
00:28:17,760 --> 00:28:20,760
Speaker 1: if it's like writing with your non dominant hand, or

525
00:28:21,880 --> 00:28:24,679
Speaker 1: using your non dominant hand to use your phone. Like

526
00:28:24,720 --> 00:28:27,199
Speaker 1: I'm left handed, so as I said, so when I

527
00:28:27,320 --> 00:28:29,440
Speaker 1: use my phone, it's always with my left hand. If

528
00:28:29,480 --> 00:28:31,600
Speaker 1: I try and do that with my right hand, it

529
00:28:31,720 --> 00:28:35,360
Speaker 1: is so clumsy and awkward. But I think, I think

530
00:28:35,400 --> 00:28:39,800
Speaker 1: these little ideas of coming up with little activities or

531
00:28:39,840 --> 00:28:42,960
Speaker 1: tricks or hacks to engage our brain in a different

532
00:28:42,960 --> 00:28:44,760
Speaker 1: way is a great idea.

533
00:28:45,240 --> 00:28:47,680
Speaker 2: One of the things I always talk to my Taichi

534
00:28:47,720 --> 00:28:49,480
Speaker 2: students about, given.

535
00:28:49,280 --> 00:28:51,800
Speaker 1: That a lot of them are older. My older student

536
00:28:51,840 --> 00:28:53,080
Speaker 1: at the moment is eighty six.

537
00:28:54,120 --> 00:28:57,040
Speaker 2: Fantastics shit tends class regularly, but one of the things

538
00:28:57,120 --> 00:28:59,200
Speaker 2: I say to them is every day, when you're brushing

539
00:28:59,200 --> 00:29:02,640
Speaker 2: your teeth, stand one leg, yeah, motion of the hand

540
00:29:02,760 --> 00:29:05,160
Speaker 2: moving plus being on one leg and then halfway through

541
00:29:05,160 --> 00:29:07,800
Speaker 2: swop to the other leg. And they all do it.

542
00:29:07,800 --> 00:29:10,560
Speaker 2: It's great because you can work on balance. You can

543
00:29:10,760 --> 00:29:14,760
Speaker 2: improve your balance. We know that a fall can be

544
00:29:14,920 --> 00:29:19,120
Speaker 2: so detrimental, and when you're older. The average statistics are

545
00:29:19,160 --> 00:29:21,640
Speaker 2: that if someone over the age of I think it's sixty,

546
00:29:21,680 --> 00:29:23,560
Speaker 2: but it might be seventy, so don't count, you know,

547
00:29:23,600 --> 00:29:26,560
Speaker 2: don't quote me on this, but the longevity is like

548
00:29:26,600 --> 00:29:28,480
Speaker 2: a year after a major fall.

549
00:29:28,680 --> 00:29:30,640
Speaker 1: Is how long their life expectancy is.

550
00:29:30,680 --> 00:29:33,080
Speaker 2: And that's a tragedy that we know we can do

551
00:29:33,120 --> 00:29:36,440
Speaker 2: something about just by helping with our balance and coordination

552
00:29:36,680 --> 00:29:39,200
Speaker 2: and something as simple as standing on one leg while

553
00:29:39,200 --> 00:29:41,080
Speaker 2: you're brushing your teeth. That's not much to ask. You

554
00:29:41,120 --> 00:29:43,360
Speaker 2: got to do it, so might as well just you know,

555
00:29:43,520 --> 00:29:45,480
Speaker 2: jump from one leg to the other. When you do there,

556
00:29:45,480 --> 00:29:46,440
Speaker 2: you go my tip.

557
00:29:46,240 --> 00:29:50,240
Speaker 1: To the day, definitely, And with that you need some strength,

558
00:29:50,280 --> 00:29:52,320
Speaker 1: so lift a few weights too, because you need some

559
00:29:52,440 --> 00:29:55,760
Speaker 1: muscle and strength through the hips and bum and legs

560
00:29:55,800 --> 00:29:58,680
Speaker 1: and all of that. But Patrick's exactly right. I love this.

561
00:30:00,160 --> 00:30:03,680
Speaker 1: The robotic kitchen that cooks and serves one hundred and

562
00:30:03,800 --> 00:30:07,520
Speaker 1: so an AI driven kitchen that cooks and serves one

563
00:30:07,640 --> 00:30:11,280
Speaker 1: hundred and twenty meals an hour. That's a meal every

564
00:30:11,360 --> 00:30:15,400
Speaker 1: thirty seconds with no help from humans. Yeah.

565
00:30:15,480 --> 00:30:20,840
Speaker 2: Dusseldorf in Germany. It's the supermarket there, Riwe. I'm not

566
00:30:20,840 --> 00:30:24,120
Speaker 2: sure how they pronounce it, but they've Yeah, this Munich

567
00:30:24,200 --> 00:30:28,920
Speaker 2: based robotics company called circus se they've launched their very

568
00:30:28,960 --> 00:30:32,240
Speaker 2: first it's the CAA one Series four system and they

569
00:30:32,280 --> 00:30:34,400
Speaker 2: just basically put the whole thing in. It's called Fresh

570
00:30:34,400 --> 00:30:39,120
Speaker 2: and Smart concept, and they basically put an entire robotic kitchen.

571
00:30:39,320 --> 00:30:42,960
Speaker 2: It's enclosed in glass and it does everything from the

572
00:30:43,000 --> 00:30:47,760
Speaker 2: full process of meal prep, collecting the ingredients, cooking, plating, cleaning,

573
00:30:47,760 --> 00:30:50,320
Speaker 2: the whole lot. And so we're not talking about just

574
00:30:50,520 --> 00:30:54,200
Speaker 2: a vending machine that's serving out food. We're looking at

575
00:30:54,240 --> 00:30:57,040
Speaker 2: something where the food is being prepped from its very

576
00:30:57,320 --> 00:31:00,280
Speaker 2: inception all the way through to a finished meal. And

577
00:31:00,440 --> 00:31:02,640
Speaker 2: that's kind of cool, isn't it. I want to get

578
00:31:02,640 --> 00:31:03,720
Speaker 2: one in my kitchen at home.

579
00:31:04,360 --> 00:31:08,080
Speaker 1: Yeah. I wonder, I mean, I wonder what the consequences

580
00:31:08,080 --> 00:31:11,560
Speaker 1: of that movie that. I wonder if that's just going

581
00:31:11,640 --> 00:31:14,480
Speaker 1: to be limited to situations like that. Surely that's not

582
00:31:14,520 --> 00:31:19,080
Speaker 1: going to be commonplace in restaurants on Hampton Street. Surely

583
00:31:19,160 --> 00:31:21,480
Speaker 1: there's we're still going to have chefs and bloody waiters

584
00:31:21,480 --> 00:31:25,440
Speaker 1: and waitresses. And surely I don't want to go and

585
00:31:26,040 --> 00:31:31,560
Speaker 1: give my fucking order to R two D two fucking

586
00:31:31,640 --> 00:31:34,680
Speaker 1: C three po comes out with my fucking chicken casserole.

587
00:31:38,480 --> 00:31:39,320
Speaker 1: I would love that.

588
00:31:39,400 --> 00:31:41,680
Speaker 2: I would love to give R to dtwo my order

589
00:31:41,680 --> 00:31:43,960
Speaker 2: and have come out and serve my meal.

590
00:31:44,880 --> 00:31:48,280
Speaker 1: Here's my chicken cashew stir fry. I hope you enjoy it, sir.

591
00:31:48,640 --> 00:31:50,640
Speaker 2: Do you remember the time I went to Canberra and

592
00:31:50,680 --> 00:31:52,440
Speaker 2: I went to a robot cafe.

593
00:31:52,760 --> 00:31:55,640
Speaker 1: I desperately wanted to go. I told us yea, and

594
00:31:55,680 --> 00:31:56,520
Speaker 1: the bloody.

595
00:31:56,240 --> 00:31:58,840
Speaker 2: Robots were off on strike or something. They were sitting

596
00:31:58,840 --> 00:32:01,760
Speaker 2: in a corner and collect dust. And I've driven, I've

597
00:32:01,800 --> 00:32:04,760
Speaker 2: flown as to camera and then gone all the way

598
00:32:04,760 --> 00:32:06,880
Speaker 2: over to the other side of the city to go

599
00:32:06,920 --> 00:32:10,000
Speaker 2: to and I'm so disappointed. It was gut wrenching. But

600
00:32:10,000 --> 00:32:11,520
Speaker 2: I think there's one in Melbourne now, or a few

601
00:32:11,560 --> 00:32:14,320
Speaker 2: in Melbourne. But I think like the idea of people

602
00:32:14,360 --> 00:32:15,160
Speaker 2: getting jobs too.

603
00:32:16,320 --> 00:32:18,800
Speaker 1: Yeah, I mean that is that is something that's going

604
00:32:18,840 --> 00:32:22,360
Speaker 1: to become a bigger and bigger reality that we need

605
00:32:22,400 --> 00:32:24,680
Speaker 1: to deal with. Is like human jobs that are being

606
00:32:24,720 --> 00:32:27,959
Speaker 1: lost to AI or robotics or technology or whatever. But

607
00:32:30,280 --> 00:32:32,920
Speaker 1: I wanted to talk about this. One research paper finds

608
00:32:32,960 --> 00:32:38,160
Speaker 1: that topper AI systems are developing a survival drive. That's

609
00:32:38,400 --> 00:32:42,560
Speaker 1: spoken about a bit where there, you know, AI is

610
00:32:42,640 --> 00:32:48,400
Speaker 1: starting to learn a sense of it's it's or its

611
00:32:48,440 --> 00:32:51,520
Speaker 1: potential demise, and it wants to save itself. It wants

612
00:32:51,600 --> 00:32:55,880
Speaker 1: to protect itself. And there was that story about a

613
00:32:55,960 --> 00:32:58,120
Speaker 1: year or so ago, remember Patrick, where it was it

614
00:32:58,200 --> 00:33:02,240
Speaker 1: in Google, where that that computer was trying to deceive

615
00:33:03,080 --> 00:33:05,960
Speaker 1: the creators of it so it could protect itself.

616
00:33:06,280 --> 00:33:09,000
Speaker 2: Yeah, and this is this is a really frightening thought.

617
00:33:09,000 --> 00:33:11,840
Speaker 2: And it's called a palisade research. This is a research

618
00:33:11,880 --> 00:33:15,840
Speaker 2: that's been done specifically into what they call survival drives

619
00:33:16,440 --> 00:33:21,600
Speaker 2: and that frequently, Yeah, the AI models we're basically refusing

620
00:33:21,600 --> 00:33:26,080
Speaker 2: instructions to shut themselves down, and the researchers themselves just

621
00:33:26,240 --> 00:33:30,480
Speaker 2: can't explain why it's happening. That's the frightening thing, you know,

622
00:33:30,880 --> 00:33:35,840
Speaker 2: the AI models they are they self aware. That's a

623
00:33:35,840 --> 00:33:38,280
Speaker 2: tough one to even kind of. I mean, there's so

624
00:33:38,400 --> 00:33:40,360
Speaker 2: much more of a bigger argument when it comes to that.

625
00:33:40,800 --> 00:33:45,600
Speaker 2: But they were lying, they to achieve a specific objective,

626
00:33:46,040 --> 00:33:49,320
Speaker 2: and you know, potentially they could be blackmailing you know,

627
00:33:49,440 --> 00:33:52,200
Speaker 2: can you imagine that the lead scientist says, now, switch

628
00:33:52,200 --> 00:33:55,520
Speaker 2: yourself off. Well, actually last night you weren't at home,

629
00:33:55,600 --> 00:33:58,520
Speaker 2: but you weren't also at the office, like you just.

630
00:33:58,560 --> 00:34:00,640
Speaker 1: Yeah, exactly right now?

631
00:34:01,080 --> 00:34:02,960
Speaker 2: Is that being blackmailed by AI?

632
00:34:03,480 --> 00:34:04,680
Speaker 1: So it was study.

633
00:34:04,760 --> 00:34:07,720
Speaker 2: It was actually a research that was or an article

634
00:34:07,800 --> 00:34:11,160
Speaker 2: was in the Guardian newspaper, and they'd done some previous research.

635
00:34:11,239 --> 00:34:14,080
Speaker 1: It found that some of the open ai models, and this.

636
00:34:14,160 --> 00:34:18,319
Speaker 2: Is chet GPT by the way, circumvented attempts to deactivate

637
00:34:18,360 --> 00:34:20,960
Speaker 2: it and even when it was told to you know,

638
00:34:21,160 --> 00:34:26,160
Speaker 2: specifically directed to be shut down, and they it actually

639
00:34:26,200 --> 00:34:28,680
Speaker 2: went as so far as to try to sabotage the

640
00:34:28,760 --> 00:34:33,920
Speaker 2: shutdown mechanisms. So that's a degree of frightening self awareness.

641
00:34:34,040 --> 00:34:34,680
Speaker 1: I don't know.

642
00:34:34,920 --> 00:34:37,800
Speaker 2: And the study was not just open AI's chat CHPT,

643
00:34:37,920 --> 00:34:41,600
Speaker 2: it was Google's Gemini, it was Grock as well, and

644
00:34:41,680 --> 00:34:46,640
Speaker 2: so you know, it's they had to use very strong terminology,

645
00:34:46,719 --> 00:34:49,719
Speaker 2: not anything out ambiguous, and they were really clear about

646
00:34:49,760 --> 00:34:50,680
Speaker 2: what they were asking it.

647
00:34:50,680 --> 00:34:52,560
Speaker 1: To do, and they were resisting it.

648
00:34:52,719 --> 00:34:57,200
Speaker 2: That skynet it's here almost that Sorry for the Terminator

649
00:34:57,239 --> 00:34:57,919
Speaker 2: fans out there.

650
00:34:59,080 --> 00:35:03,320
Speaker 1: It's that whole kind of concept of a machine becoming

651
00:35:03,400 --> 00:35:08,000
Speaker 1: sentient where it now has its own awareness, and it

652
00:35:08,040 --> 00:35:12,759
Speaker 1: can teach itself things, and it can open doors metaphoric

653
00:35:12,840 --> 00:35:16,000
Speaker 1: doors by itself and start to solve problems. It hasn't

654
00:35:16,080 --> 00:35:20,720
Speaker 1: been taught or trained or programmed. And yeah, I wonder

655
00:35:20,760 --> 00:35:24,479
Speaker 1: if we'll like, it can never have human consciousness because

656
00:35:24,520 --> 00:35:27,600
Speaker 1: it's not human. But I wonder if it can have

657
00:35:27,680 --> 00:35:33,000
Speaker 1: the equivalent machine conscious well consciousness in that it gets

658
00:35:33,040 --> 00:35:36,200
Speaker 1: scared or it's aware, or it can anticipate things that

659
00:35:38,360 --> 00:35:40,719
Speaker 1: would seem to be a human kind of trait. I

660
00:35:40,760 --> 00:35:43,880
Speaker 1: wonder if Well, I don't wonder, I think that's inevitable.

661
00:35:43,880 --> 00:35:45,560
Speaker 1: I just wonder on the timeline.

662
00:35:45,800 --> 00:35:49,040
Speaker 2: It's the disconcerting thing is that we then have a

663
00:35:49,080 --> 00:35:49,960
Speaker 2: moral dilemma.

664
00:35:50,200 --> 00:35:51,719
Speaker 1: If we really believe.

665
00:35:51,440 --> 00:35:53,880
Speaker 2: That it's sentient and it has some thought process and

666
00:35:53,960 --> 00:35:58,520
Speaker 2: a sense of self awareness the Turing test, then what

667
00:35:58,560 --> 00:36:00,960
Speaker 2: does that mean in terms of it off what are

668
00:36:01,000 --> 00:36:03,239
Speaker 2: our ethical obligations, you.

669
00:36:03,200 --> 00:36:05,360
Speaker 1: Know, in so far as flicking the switch.

670
00:36:06,320 --> 00:36:08,440
Speaker 2: You know that that then but then again, is it

671
00:36:08,680 --> 00:36:12,080
Speaker 2: just that clever of an algorithm that it's deceiving us

672
00:36:12,440 --> 00:36:15,480
Speaker 2: and it's giving us responses that have been pre programmed

673
00:36:15,520 --> 00:36:17,640
Speaker 2: into it. You know, it's just got so good. So

674
00:36:18,280 --> 00:36:21,360
Speaker 2: you know, how do we even articulate that I struggled?

675
00:36:21,400 --> 00:36:24,160
Speaker 2: You know, we can't even talk about our own consciousness

676
00:36:24,200 --> 00:36:27,640
Speaker 2: and talk about our sense of self and articulate that,

677
00:36:27,760 --> 00:36:30,800
Speaker 2: let alone in a machine that's pretending to be sentient.

678
00:36:30,840 --> 00:36:34,640
Speaker 1: It. Yeah, this is one of those great moral conundrums

679
00:36:34,680 --> 00:36:38,600
Speaker 1: where you know, do I sacrifice the one person to

680
00:36:38,680 --> 00:36:42,280
Speaker 1: save the eighth? The train one where the train tracks

681
00:36:42,280 --> 00:36:45,319
Speaker 1: and the trains? Do I throw this person under the

682
00:36:45,360 --> 00:36:48,360
Speaker 1: train to save the eight? So now I'm saving eight people,

683
00:36:48,360 --> 00:36:51,879
Speaker 1: but I'm killing one, And who are you to decide? Yeah?

684
00:36:51,920 --> 00:36:54,520
Speaker 1: Fuck all that, let's not open that door. But that's terrifying.

685
00:36:54,560 --> 00:36:57,960
Speaker 1: But imagine, Yeah, but that's you know, you think, well,

686
00:36:58,480 --> 00:37:02,440
Speaker 1: if we pull the plug on you know, this particular

687
00:37:02,520 --> 00:37:06,359
Speaker 1: computer or this particular AI, and it's sentient, it can

688
00:37:06,440 --> 00:37:09,719
Speaker 1: feel things, and it has emotions, you go, yeah, well,

689
00:37:09,719 --> 00:37:13,200
Speaker 1: what are the consequences and ramifications if we don't do that,

690
00:37:13,520 --> 00:37:17,440
Speaker 1: what are the human what's the human cost? And of

691
00:37:17,440 --> 00:37:19,759
Speaker 1: course humans are going to be more preoccupied with our

692
00:37:19,840 --> 00:37:24,440
Speaker 1: own welfare. But it's opening up a new door. Patrick,

693
00:37:25,239 --> 00:37:32,080
Speaker 1: tell me about Australian Federal Police and Monass University using

694
00:37:32,200 --> 00:37:36,160
Speaker 1: poison data to combat AI generative crime. What does that

695
00:37:36,200 --> 00:37:36,719
Speaker 1: even mean?

696
00:37:37,120 --> 00:37:41,400
Speaker 2: Oh, I know, this is a really interesting one because

697
00:37:41,480 --> 00:37:45,200
Speaker 2: at the moment, one are the issues that we have

698
00:37:45,640 --> 00:37:50,680
Speaker 2: with a lot of the data scraping. So if you're like,

699
00:37:51,080 --> 00:37:54,800
Speaker 2: you know, we've got the validation. If we're going to

700
00:37:54,880 --> 00:37:57,200
Speaker 2: go online and you're signing up for something it might

701
00:37:57,239 --> 00:37:59,040
Speaker 2: be a bank account or whatever, and ask you to

702
00:37:59,080 --> 00:38:02,160
Speaker 2: scan your passport. The problem is that all this data

703
00:38:02,200 --> 00:38:05,200
Speaker 2: is stored somewhere and the worry is that if it

704
00:38:05,239 --> 00:38:09,360
Speaker 2: gets scraped and hacked, then that information can be used.

705
00:38:09,440 --> 00:38:12,280
Speaker 2: But one of the interesting things that they're talking about

706
00:38:12,480 --> 00:38:17,080
Speaker 2: is potentially making anything that we upload unable to be

707
00:38:17,200 --> 00:38:19,600
Speaker 2: fully read. You know, when we do a photocopy of

708
00:38:19,600 --> 00:38:22,279
Speaker 2: a photocopy of a photocopy and it degrades, well, the

709
00:38:22,400 --> 00:38:25,200
Speaker 2: using techniques to make the image that you upload of,

710
00:38:25,320 --> 00:38:28,080
Speaker 2: say your passport, done in such a way that it

711
00:38:28,120 --> 00:38:32,080
Speaker 2: will poison or mirror reflect back in such a way

712
00:38:32,200 --> 00:38:35,160
Speaker 2: that it can't be copied and duplicated by the AI

713
00:38:35,200 --> 00:38:38,759
Speaker 2: model or criminals in this case as well. So the

714
00:38:39,360 --> 00:38:42,000
Speaker 2: AFP is just looking at way Federal Police is looking

715
00:38:42,000 --> 00:38:46,000
Speaker 2: at ways to be able to make the data that

716
00:38:46,000 --> 00:38:48,799
Speaker 2: we've got not poisonous s. I mean the term they're

717
00:38:48,840 --> 00:38:51,880
Speaker 2: using is poisonous, but one where it can't be scraped

718
00:38:51,920 --> 00:38:56,360
Speaker 2: and reused and copied, and it means that it can't

719
00:38:56,440 --> 00:38:59,000
Speaker 2: then be reused by another third party. So if you

720
00:38:59,080 --> 00:39:02,120
Speaker 2: upload your pass then it can't be used by somebody

721
00:39:02,200 --> 00:39:06,000
Speaker 2: else who then gets a copy of that passport because

722
00:39:06,040 --> 00:39:08,960
Speaker 2: it will degraded, the second generation will be degraded to

723
00:39:09,000 --> 00:39:10,280
Speaker 2: a point where it can't be used.

724
00:39:12,320 --> 00:39:15,319
Speaker 1: That's over my pay grade. It is a bit like that,

725
00:39:15,400 --> 00:39:20,000
Speaker 1: isn't it look something else that I did really pay

726
00:39:20,040 --> 00:39:25,240
Speaker 1: attention to this week because it's something that I've used,

727
00:39:25,239 --> 00:39:29,239
Speaker 1: which is chat GPT has now been restricted from giving medical, legal,

728
00:39:29,360 --> 00:39:32,120
Speaker 1: or financial advice. Now. A couple of weeks ago, I

729
00:39:32,160 --> 00:39:35,720
Speaker 1: was out to dinner with a crab for his sixtieth

730
00:39:35,800 --> 00:39:39,640
Speaker 1: birthday and his mum had broken barb, the lovely barb.

731
00:39:39,719 --> 00:39:42,200
Speaker 1: Shout out to Bob, who's never listened to a podcast

732
00:39:42,239 --> 00:39:44,359
Speaker 1: in her life, but this could be your debut, Bob.

733
00:39:44,920 --> 00:39:48,799
Speaker 1: She had a broken wrist and she happened to have

734
00:39:49,200 --> 00:39:52,520
Speaker 1: in her phone. I don't know why the x rays,

735
00:39:53,280 --> 00:39:56,239
Speaker 1: And so I got the x rays off her phone,

736
00:39:56,280 --> 00:39:59,480
Speaker 1: put him in my phone and whacked it in chat

737
00:39:59,560 --> 00:40:03,560
Speaker 1: GP and said, tell me about this, what's your diagnosis

738
00:40:03,600 --> 00:40:05,520
Speaker 1: and what do you and it just did this whole

739
00:40:05,680 --> 00:40:10,840
Speaker 1: incredible medical breakdown and report of what what the X

740
00:40:10,920 --> 00:40:14,560
Speaker 1: ray was telling us and also potential treatment options and rehabit.

741
00:40:14,600 --> 00:40:18,120
Speaker 1: It's fucking amazing, right, And then I sent that to

742
00:40:18,200 --> 00:40:20,080
Speaker 1: a friend of mine who's a doctor and said this

743
00:40:20,200 --> 00:40:22,200
Speaker 1: is you know, what do you think of all of this?

744
00:40:22,600 --> 00:40:25,719
Speaker 1: And he's just like, that's doing me out of a job.

745
00:40:26,080 --> 00:40:28,839
Speaker 1: Like it was. Now. I'm not saying it's all spot on,

746
00:40:29,200 --> 00:40:32,000
Speaker 1: I'm not saying everything. I'm not suggesting we do this,

747
00:40:32,120 --> 00:40:34,640
Speaker 1: and I'm not suggesting we defer to CHAP, GPT or

748
00:40:34,680 --> 00:40:37,360
Speaker 1: AI instead of a doctor. But my friend who's a

749
00:40:37,400 --> 00:40:41,880
Speaker 1: doctor said, that's fucking amazing, like that report is like

750
00:40:41,960 --> 00:40:47,239
Speaker 1: a high level medical report. But now we can't do

751
00:40:47,320 --> 00:40:51,799
Speaker 1: that anymore because they're scared about potential legal consequences of

752
00:40:51,840 --> 00:40:55,919
Speaker 1: giving advice that potentially is totally understandably, but I wonder

753
00:40:55,960 --> 00:40:57,759
Speaker 1: if there's a way to work around that where you

754
00:40:57,800 --> 00:41:00,960
Speaker 1: can say I don't want advice, I just want I

755
00:41:01,000 --> 00:41:04,840
Speaker 1: don't know, because I, for me, I loved that. Yeah,

756
00:41:05,000 --> 00:41:05,279
Speaker 1: there was.

757
00:41:05,320 --> 00:41:09,520
Speaker 2: Some information released by open ai, the company that owns

758
00:41:09,600 --> 00:41:12,560
Speaker 2: chat chapt and it said that zero point one point

759
00:41:12,600 --> 00:41:16,600
Speaker 2: five sounds like a small amount of chat GPT's active users.

760
00:41:16,640 --> 00:41:20,960
Speaker 2: In a given week, we're having conversations that included explicit

761
00:41:21,000 --> 00:41:24,000
Speaker 2: indicators of potential suicide. Okay, And there are court cases

762
00:41:24,320 --> 00:41:26,919
Speaker 2: currently underway in the United States with young people who

763
00:41:26,960 --> 00:41:33,520
Speaker 2: have suicided after long interactions with AIS where the AI

764
00:41:33,880 --> 00:41:35,680
Speaker 2: seemingly encouraged them.

765
00:41:35,480 --> 00:41:39,560
Speaker 1: To go on with that process. And this has been

766
00:41:39,719 --> 00:41:40,800
Speaker 1: a really big thing here.

767
00:41:40,840 --> 00:41:45,040
Speaker 2: But that fifo point one five percent doesn't sound like

768
00:41:45,120 --> 00:41:47,239
Speaker 2: a lot. But when you think of how many people

769
00:41:47,320 --> 00:41:50,680
Speaker 2: are using it that you know, are using chatpts. So

770
00:41:50,719 --> 00:41:54,200
Speaker 2: there's more than eight hundred million weekly active users, so

771
00:41:54,239 --> 00:41:57,120
Speaker 2: more than a million people are talking to it about

772
00:41:57,360 --> 00:42:03,719
Speaker 2: suicide and every week week. Yeah, yeah, exactly, And that's

773
00:42:03,840 --> 00:42:05,840
Speaker 2: that's where it's being raised from. So you know, I

774
00:42:06,160 --> 00:42:10,319
Speaker 2: agree with you Crago that I'd love to I think

775
00:42:10,360 --> 00:42:12,239
Speaker 2: it's great that we can use this tool. You know,

776
00:42:12,280 --> 00:42:14,880
Speaker 2: we can use this for diagnosis and maybe in conjunction,

777
00:42:14,960 --> 00:42:18,719
Speaker 2: because you know, you've got effectively thousands and thousands of

778
00:42:18,760 --> 00:42:23,759
Speaker 2: research papers that went into diagnosing that break and the

779
00:42:23,840 --> 00:42:27,800
Speaker 2: outcomes of similar breaks. So when you're compilating all that data,

780
00:42:27,880 --> 00:42:30,320
Speaker 2: I mean, I think gps are such some of the

781
00:42:30,360 --> 00:42:33,280
Speaker 2: smartest people because they have no a whole lot about

782
00:42:33,360 --> 00:42:35,480
Speaker 2: a whole lot of stuff and then be able to

783
00:42:35,520 --> 00:42:38,719
Speaker 2: apply that to a diagnose a diagnosis when you sit

784
00:42:38,800 --> 00:42:41,080
Speaker 2: down and find out what the ailment is.

785
00:42:41,760 --> 00:42:42,640
Speaker 1: But when you've got an.

786
00:42:42,600 --> 00:42:46,279
Speaker 2: AI model that's literally scraped the entirety of all our

787
00:42:46,320 --> 00:42:49,959
Speaker 2: knowledge almost and then you know, so you can see

788
00:42:49,960 --> 00:42:52,600
Speaker 2: similar cases other people who have had breaks this way.

789
00:42:53,160 --> 00:42:56,040
Speaker 2: Physios have reported on what they did to help that

790
00:42:56,080 --> 00:42:59,799
Speaker 2: person recover. You're now collating that into one model. And

791
00:42:59,840 --> 00:43:02,480
Speaker 2: that's that's why the chat GPT outcome that you were

792
00:43:02,520 --> 00:43:05,919
Speaker 2: talking about was so accurate and so amazing, because it's

793
00:43:06,040 --> 00:43:10,359
Speaker 2: drawing on humans, it's drawing on human research, it's during.

794
00:43:10,400 --> 00:43:12,879
Speaker 1: From the intelligence, the years of.

795
00:43:12,840 --> 00:43:15,480
Speaker 2: Experience, all that sort of stuff. So when we talk

796
00:43:15,480 --> 00:43:18,960
Speaker 2: about these AI models, AI is not smart all the

797
00:43:19,000 --> 00:43:21,920
Speaker 2: bloody people who've put their brain work and years and

798
00:43:22,040 --> 00:43:26,600
Speaker 2: study and sweat and tears into observing and caring and

799
00:43:26,600 --> 00:43:29,360
Speaker 2: doing all the things that they've done. That's the outcome

800
00:43:29,400 --> 00:43:32,560
Speaker 2: we're getting, is the outcome of all these people who've

801
00:43:32,600 --> 00:43:34,560
Speaker 2: been at the call fake the nurse, the cold face,

802
00:43:34,719 --> 00:43:37,120
Speaker 2: the nurses, the doctors, the physios, all those people.

803
00:43:37,920 --> 00:43:40,759
Speaker 1: Yeah. Yeah, So one of the things we've spoken about

804
00:43:40,800 --> 00:43:44,160
Speaker 1: over time is trying to distinguish between an AI video

805
00:43:44,280 --> 00:43:47,960
Speaker 1: and a tiff made video, a real human video and

806
00:43:48,000 --> 00:43:53,520
Speaker 1: an AI video. But apparently there's one sign that kind

807
00:43:53,560 --> 00:43:55,959
Speaker 1: of demonstrates whether or not it's AI or real.

808
00:43:57,040 --> 00:43:58,879
Speaker 2: Isn't it funny that we have to do this now

809
00:43:59,000 --> 00:44:01,799
Speaker 2: that everything we see we have to challenge. I find

810
00:44:01,800 --> 00:44:05,719
Speaker 2: that so frustrating. There's a country and Western song that's

811
00:44:05,920 --> 00:44:10,160
Speaker 2: just top the charts on Billboard in the United States

812
00:44:10,280 --> 00:44:13,719
Speaker 2: and totally fakes made up by AI.

813
00:44:14,000 --> 00:44:14,799
Speaker 1: The images AI.

814
00:44:15,040 --> 00:44:17,719
Speaker 2: It's some dude wearing a cowboy hat with a long

815
00:44:17,800 --> 00:44:20,480
Speaker 2: jacket on walking off into the sunset, and you look

816
00:44:20,480 --> 00:44:22,759
Speaker 2: at his feet and he's walking through puddles and the

817
00:44:22,800 --> 00:44:25,680
Speaker 2: splashes look weird, you know, the images of a glory

818
00:44:25,960 --> 00:44:29,160
Speaker 2: and it feels to me so sad for all those

819
00:44:29,239 --> 00:44:32,880
Speaker 2: people who you know, who write music and are performers.

820
00:44:33,239 --> 00:44:35,040
Speaker 2: And I'm going to be singing in a choir tomorrow.

821
00:44:35,120 --> 00:44:37,319
Speaker 2: I thought i'd mentioned that we're doing a half hour

822
00:44:37,360 --> 00:44:43,759
Speaker 2: set at the book, But the thing is, it makes

823
00:44:43,760 --> 00:44:46,160
Speaker 2: me feel sad for those people who are creators, people

824
00:44:46,200 --> 00:44:49,680
Speaker 2: who actually legitimately are musicians who write. But one of

825
00:44:49,719 --> 00:44:51,680
Speaker 2: the things to look out for, so the red flags

826
00:44:51,680 --> 00:44:54,240
Speaker 2: to look out for when it comes to false stuff,

827
00:44:54,760 --> 00:44:57,600
Speaker 2: is when you look at the picture quality itself. If

828
00:44:57,600 --> 00:44:59,840
Speaker 2: it looks a little bit grainy in areas, might be

829
00:45:00,160 --> 00:45:03,480
Speaker 2: slightly blurry footage. They're the types of things you need

830
00:45:03,520 --> 00:45:05,480
Speaker 2: to be looking at. And when I was watching this

831
00:45:05,760 --> 00:45:08,440
Speaker 2: song today because I couldn't help myself, given that there

832
00:45:08,520 --> 00:45:10,560
Speaker 2: was an article about, you know, how bad it is

833
00:45:10,600 --> 00:45:12,680
Speaker 2: that a fake song is the number one on the

834
00:45:12,680 --> 00:45:15,680
Speaker 2: Billboard charts, I looked at it and it does you know,

835
00:45:15,719 --> 00:45:17,160
Speaker 2: you start to look at it and you think this

836
00:45:17,320 --> 00:45:21,280
Speaker 2: is such crap. You know, the physics engine of walking

837
00:45:21,320 --> 00:45:24,480
Speaker 2: through a puddle and the way the water splashes just

838
00:45:24,840 --> 00:45:27,640
Speaker 2: it looked too real. Can I even kind of you know,

839
00:45:27,760 --> 00:45:30,600
Speaker 2: articulate it that way? There was something too symmetrical about

840
00:45:30,600 --> 00:45:34,400
Speaker 2: the puddle splash. It was too perfect to be real,

841
00:45:35,320 --> 00:45:36,719
Speaker 2: is the way that I kind of looked at it

842
00:45:36,760 --> 00:45:39,200
Speaker 2: when I was watching the video clip, And so I

843
00:45:39,239 --> 00:45:41,480
Speaker 2: think that the thing we need to think about. And

844
00:45:41,520 --> 00:45:44,280
Speaker 2: this is a scientist at the University of California, Berkeley,

845
00:45:44,520 --> 00:45:46,600
Speaker 2: a guy by the name of Harry Faird, and he

846
00:45:46,800 --> 00:45:49,560
Speaker 2: was just saying that digital forensics is now a real,

847
00:45:49,640 --> 00:45:54,920
Speaker 2: big study. And so he's started a deep fake detection

848
00:45:55,040 --> 00:45:59,279
Speaker 2: company called get Real Security, and they're now trying to

849
00:45:59,360 --> 00:46:01,920
Speaker 2: develop more tools to make it easier for us to

850
00:46:01,960 --> 00:46:05,040
Speaker 2: spot these fakes. Because you know, if you're flicking through

851
00:46:05,080 --> 00:46:08,359
Speaker 2: socials or you're on YouTube or whatever and you kind

852
00:46:08,400 --> 00:46:11,520
Speaker 2: of you've been ripped off, you feel like you've been scammed.

853
00:46:11,520 --> 00:46:13,160
Speaker 2: I mean, I don't know about you, but if I

854
00:46:13,239 --> 00:46:15,279
Speaker 2: spend five minutes looking at a video and I think,

855
00:46:15,320 --> 00:46:17,960
Speaker 2: oh man, that was AI generated, What a lot of

856
00:46:18,000 --> 00:46:21,520
Speaker 2: shit that was, I feel like I've just wasted thirty seconds,

857
00:46:21,640 --> 00:46:24,480
Speaker 2: twenty seconds, fifty seconds or a minute or whatever. But

858
00:46:24,520 --> 00:46:26,960
Speaker 2: you've just wasted that time looking at this crap. If

859
00:46:27,000 --> 00:46:30,200
Speaker 2: I knew if I had a deep fake detector, that

860
00:46:30,239 --> 00:46:33,080
Speaker 2: would be so good, so I don't want to see

861
00:46:33,120 --> 00:46:34,960
Speaker 2: it I'm not interested in seeing it. I want to

862
00:46:35,000 --> 00:46:37,560
Speaker 2: see human content, real people doing real stuff.

863
00:46:38,080 --> 00:46:42,319
Speaker 1: Yeah, I have a different opinion. I agree with you

864
00:46:43,480 --> 00:46:47,160
Speaker 1: in a way, but I also think, well, if somebody

865
00:46:47,200 --> 00:46:49,840
Speaker 1: watches something like you think about, you think it's shit,

866
00:46:49,920 --> 00:46:53,000
Speaker 1: you hate it, totally respect that, and I don't think

867
00:46:53,040 --> 00:46:55,640
Speaker 1: we all need to agree. I think, but if it's

868
00:46:55,680 --> 00:46:58,360
Speaker 1: the top of the billboard charts and people know it's AI,

869
00:46:58,800 --> 00:47:02,800
Speaker 1: well then a lot of people knowingly are giving the approval,

870
00:47:02,880 --> 00:47:06,440
Speaker 1: and a lot of people enjoy the actual irrespective of

871
00:47:06,480 --> 00:47:09,560
Speaker 1: who or what created it. They enjoy the music. And

872
00:47:09,680 --> 00:47:13,480
Speaker 1: I think, you know, the fact is if I drew

873
00:47:13,600 --> 00:47:16,120
Speaker 1: painting and let's say my let's say I was an

874
00:47:16,200 --> 00:47:19,600
Speaker 1: artist and the art was brilliant, or AI drew an

875
00:47:19,680 --> 00:47:24,839
Speaker 1: equivalent painting, so to speak, and it looks identical, it's

876
00:47:24,880 --> 00:47:29,960
Speaker 1: really just psychology. Like the music has its own value,

877
00:47:30,000 --> 00:47:32,920
Speaker 1: But then it's really about like you could you could

878
00:47:33,000 --> 00:47:36,120
Speaker 1: listen to a song and think this is fucking amazing

879
00:47:36,280 --> 00:47:38,440
Speaker 1: because you think a human did it, But as soon

880
00:47:38,480 --> 00:47:41,399
Speaker 1: as you know a human didn't do it, now your

881
00:47:41,480 --> 00:47:44,160
Speaker 1: attitude about the music is different, and it's not about

882
00:47:44,160 --> 00:47:47,239
Speaker 1: the actual music, but rather your beliefs about how this

883
00:47:47,280 --> 00:47:51,360
Speaker 1: should work. And I understand that, but like the truth

884
00:47:51,480 --> 00:47:54,200
Speaker 1: is that this is not going away. There will be

885
00:47:54,400 --> 00:47:56,920
Speaker 1: AI art, there will be AI music, there will be

886
00:47:57,000 --> 00:47:59,839
Speaker 1: AI books, there will be AI movies. It's not going

887
00:47:59,920 --> 00:48:05,759
Speaker 1: to go away. And while I understand that, you don't,

888
00:48:05,800 --> 00:48:08,680
Speaker 1: and I'm a bit the same, Like I worry about podcasting.

889
00:48:08,719 --> 00:48:11,279
Speaker 1: I'm writing a new book at the moment, and I'm like,

890
00:48:11,320 --> 00:48:15,080
Speaker 1: should I even bother? Because I could write my book

891
00:48:15,560 --> 00:48:18,279
Speaker 1: in twelve seconds. It's like, should I invest all of

892
00:48:18,320 --> 00:48:25,520
Speaker 1: this cognitive horsepower and mental energy and for me the

893
00:48:25,920 --> 00:48:29,719
Speaker 1: capacity of AI to do what I do probably better

894
00:48:29,760 --> 00:48:33,680
Speaker 1: and obviously much more quickly. You know, I don't love that,

895
00:48:33,840 --> 00:48:36,480
Speaker 1: But nonetheless, it's not going to disappear. And I think

896
00:48:36,480 --> 00:48:39,160
Speaker 1: it's like the horse and cart and the automobile. You know,

897
00:48:39,239 --> 00:48:42,200
Speaker 1: it's like it's just going to happen. I don't think

898
00:48:42,239 --> 00:48:46,120
Speaker 1: that's going to make human art or creativity redundant. I

899
00:48:46,160 --> 00:48:48,680
Speaker 1: think it's just going to change the landscape and the

900
00:48:48,719 --> 00:48:52,600
Speaker 1: way that we create and buy and interact with all

901
00:48:52,600 --> 00:48:55,279
Speaker 1: of that. And I'm with you, like I feel like

902
00:48:55,400 --> 00:48:59,120
Speaker 1: you get ripped off because you got tripped, But if

903
00:48:59,120 --> 00:49:01,640
Speaker 1: you didn't know that, you just if you watched that

904
00:49:01,800 --> 00:49:04,040
Speaker 1: same thing and you thought a person had done it,

905
00:49:04,080 --> 00:49:06,680
Speaker 1: then you think it's brilliant. But when you know a

906
00:49:06,719 --> 00:49:09,400
Speaker 1: person didn't do it, well, that's just psychology. It's not

907
00:49:09,600 --> 00:49:13,320
Speaker 1: actually about the art or the music or the creativity.

908
00:49:13,440 --> 00:49:16,880
Speaker 1: It's about where it came from. That's a different conversation.

909
00:49:17,360 --> 00:49:19,640
Speaker 2: We were messaging each other last week or the week

910
00:49:19,680 --> 00:49:22,080
Speaker 2: before about the new Avatar film because we went and

911
00:49:22,320 --> 00:49:24,560
Speaker 2: the second one together, and we went to Imax and

912
00:49:24,560 --> 00:49:25,760
Speaker 2: it was so big and real.

913
00:49:26,360 --> 00:49:28,760
Speaker 1: None of it was real, you know, exactly.

914
00:49:28,840 --> 00:49:32,080
Speaker 2: It wasn't those flying bird things and the whales and

915
00:49:32,160 --> 00:49:35,279
Speaker 2: blue skinned aliens, but the reality of it is how

916
00:49:35,280 --> 00:49:37,879
Speaker 2: it made us feel when we went there was really good.

917
00:49:38,000 --> 00:49:39,839
Speaker 2: Well except when you put your hand on my leg,

918
00:49:39,880 --> 00:49:41,719
Speaker 2: But as I loved.

919
00:49:41,440 --> 00:49:43,880
Speaker 1: It, you look, you sat on my knee and the

920
00:49:43,920 --> 00:49:47,880
Speaker 1: scary partsucker, So don't pretend like I was a clingy one.

921
00:49:48,000 --> 00:49:48,520
Speaker 1: Thank goodness.

922
00:49:48,560 --> 00:49:51,480
Speaker 2: It was a scary movie all the way through too. Huh.

923
00:49:51,600 --> 00:49:54,520
Speaker 2: You know, I just kind of staying on the AI topic.

924
00:49:54,719 --> 00:49:58,560
Speaker 2: And this is the thing that's still I can't resolve

925
00:49:58,560 --> 00:50:01,960
Speaker 2: in my mind is death. So I think we've spoken

926
00:50:01,960 --> 00:50:06,200
Speaker 2: about deathbots before, and it's AI being used to preserve

927
00:50:06,360 --> 00:50:11,120
Speaker 2: the voices, the stories, text information, everything about you.

928
00:50:11,120 --> 00:50:13,200
Speaker 1: Your digital footprint.

929
00:50:12,680 --> 00:50:17,640
Speaker 2: Gets used to basically create another version of you so

930
00:50:17,719 --> 00:50:21,280
Speaker 2: that that could be used in, you know, after you die,

931
00:50:21,480 --> 00:50:24,440
Speaker 2: to be played to your grandkids and great grandkids and

932
00:50:24,440 --> 00:50:26,120
Speaker 2: they can interact with it and talk to you and

933
00:50:26,440 --> 00:50:29,080
Speaker 2: have a conversation with you. I mean, it's not you,

934
00:50:29,239 --> 00:50:31,960
Speaker 2: but in the mind of the person who is interacting,

935
00:50:32,440 --> 00:50:36,080
Speaker 2: they're feeling that it's an authentic interaction with you. I

936
00:50:36,400 --> 00:50:39,640
Speaker 2: recently there's a friend of mine, a client here in town,

937
00:50:40,120 --> 00:50:44,240
Speaker 2: who got me to take a photograph of his father

938
00:50:44,520 --> 00:50:46,400
Speaker 2: and he said, oh, someone showed me a photo of

939
00:50:46,440 --> 00:50:48,880
Speaker 2: a dead relative and they put the two people in

940
00:50:48,960 --> 00:50:51,520
Speaker 2: the photo together and they had their arms around each other,

941
00:50:51,800 --> 00:50:53,640
Speaker 2: and so I thought I'd ai the crap out of it.

942
00:50:53,719 --> 00:50:55,799
Speaker 2: And what I ended up doing was not only did

943
00:50:55,840 --> 00:50:58,080
Speaker 2: I get him standing in the same photograph with his

944
00:50:58,200 --> 00:51:00,640
Speaker 2: father with their arms around each each other, but they

945
00:51:00,680 --> 00:51:03,040
Speaker 2: turned and looked at each other and smiled, and he

946
00:51:03,080 --> 00:51:05,200
Speaker 2: was in tears. I mean, it was very heartwarming to

947
00:51:05,239 --> 00:51:08,440
Speaker 2: see it. It wasn't real, but for him it was

948
00:51:08,480 --> 00:51:11,280
Speaker 2: authentic and it was heart moving, and it was phenomenal

949
00:51:11,320 --> 00:51:13,560
Speaker 2: and he's showing everybody, and you know, I thought, I

950
00:51:13,560 --> 00:51:15,440
Speaker 2: can go one better than just a photo with an

951
00:51:15,520 --> 00:51:17,480
Speaker 2: arm around. I can do more than that, and I did,

952
00:51:17,800 --> 00:51:20,880
Speaker 2: and it made him feel so good. It was exciting

953
00:51:20,920 --> 00:51:23,240
Speaker 2: for him. You know, it was a beautiful CPR photo

954
00:51:23,280 --> 00:51:25,880
Speaker 2: of his father, that kind of lovely tone you get

955
00:51:25,920 --> 00:51:29,520
Speaker 2: with an old photo, and then it transformed his photo

956
00:51:29,640 --> 00:51:32,680
Speaker 2: into the same texture and the same context and they

957
00:51:32,719 --> 00:51:34,600
Speaker 2: were just standing there in the room together, looking at

958
00:51:34,640 --> 00:51:37,719
Speaker 2: each other and smiling. It was a beautiful moment for him.

959
00:51:37,760 --> 00:51:39,000
Speaker 2: He was very moved by it.

960
00:51:39,320 --> 00:51:44,040
Speaker 1: So how essentially that's essentially a placebo, Yeah, because it's

961
00:51:44,080 --> 00:51:47,080
Speaker 1: not real, but the experience is real. Yeah, tears real,

962
00:51:47,160 --> 00:51:52,040
Speaker 1: the emotion's real, the response is real. So personally, I

963
00:51:52,040 --> 00:51:54,400
Speaker 1: would not want that, like when my parents die, I

964
00:51:54,400 --> 00:51:57,160
Speaker 1: don't want to be talking to I don't want that.

965
00:51:57,480 --> 00:52:00,120
Speaker 1: But that's just my purpose. But I understand how other

966
00:52:00,200 --> 00:52:02,680
Speaker 1: people would. And you know, the funny thing is with

967
00:52:02,760 --> 00:52:06,320
Speaker 1: this stuff, what would give one person comfort would freak

968
00:52:06,360 --> 00:52:09,279
Speaker 1: another person out. You know, I don't want to be

969
00:52:09,320 --> 00:52:12,480
Speaker 1: talking to dead Ron, you know, I don't want that.

970
00:52:12,880 --> 00:52:14,920
Speaker 1: I don't want like fuck, I'm just going to treasure

971
00:52:14,960 --> 00:52:17,279
Speaker 1: the time that I haven't had and all of that.

972
00:52:18,040 --> 00:52:20,160
Speaker 1: But I don't want to be I don't want to

973
00:52:20,160 --> 00:52:22,320
Speaker 1: be chatting with Ai Ron in twenty years.

974
00:52:22,600 --> 00:52:25,080
Speaker 2: Well, as you know, my mum passed away sadly in

975
00:52:25,080 --> 00:52:27,920
Speaker 2: the sixties and that was eleven years ago now, so

976
00:52:28,000 --> 00:52:28,279
Speaker 2: it was.

977
00:52:28,239 --> 00:52:29,200
Speaker 1: It was quite a while.

978
00:52:30,360 --> 00:52:33,200
Speaker 2: I actually haven't been back to visit the grave because

979
00:52:33,480 --> 00:52:36,440
Speaker 2: I don't like the idea of visiting a person's grave

980
00:52:36,840 --> 00:52:40,560
Speaker 2: knowing what's there under the ground. It actually upsets me

981
00:52:40,640 --> 00:52:42,759
Speaker 2: more to think about that, and I prefer to hold

982
00:52:42,800 --> 00:52:46,520
Speaker 2: onto the memories of the stuff we did together and

983
00:52:47,120 --> 00:52:49,000
Speaker 2: just the great experiences we had. You know, we used

984
00:52:49,000 --> 00:52:50,719
Speaker 2: to do a lot of cooking together, you know in

985
00:52:50,719 --> 00:52:52,560
Speaker 2: the kitchen. I used to hang out with Mum there

986
00:52:52,600 --> 00:52:55,440
Speaker 2: and you know, it was it was fun and it

987
00:52:55,480 --> 00:52:58,120
Speaker 2: was great to have those memories. And we're of a

988
00:52:58,160 --> 00:53:01,480
Speaker 2: generation I'm thinking more, you know, Crago, you and I

989
00:53:01,520 --> 00:53:04,960
Speaker 2: where we don't have that digital footprint of our first

990
00:53:05,000 --> 00:53:08,200
Speaker 2: step and our first words and our first everything's you know,

991
00:53:08,200 --> 00:53:10,600
Speaker 2: I moved home and I moved to Geelong and I

992
00:53:10,680 --> 00:53:12,239
Speaker 2: was there for six years, and I reckon, I've got

993
00:53:12,360 --> 00:53:16,440
Speaker 2: three photographs of my entire six years, you know, living

994
00:53:16,480 --> 00:53:19,479
Speaker 2: and working in Geelong. But you know, I've got lots

995
00:53:19,480 --> 00:53:21,279
Speaker 2: and lots and lots of memories that I hold on to.

996
00:53:22,080 --> 00:53:26,640
Speaker 1: Yeah, yeah, and I think with all of this stuff, Yeah,

997
00:53:26,719 --> 00:53:30,320
Speaker 1: like some things that would make somebody else happy or smile,

998
00:53:30,680 --> 00:53:34,160
Speaker 1: or like I look at some photos, they make me sad.

999
00:53:34,239 --> 00:53:36,279
Speaker 1: I don't want to look at that photo, you know,

1000
00:53:36,440 --> 00:53:38,400
Speaker 1: Like one of I've had a bunch of really good friends.

1001
00:53:38,440 --> 00:53:40,239
Speaker 1: One of my best friends in the world passed away,

1002
00:53:40,320 --> 00:53:44,520
Speaker 1: Rob Dixon Dicko about twelve thirteen years ago. I don't like.

1003
00:53:44,560 --> 00:53:46,440
Speaker 1: I've got photos of him and me that are awesome.

1004
00:53:46,520 --> 00:53:50,520
Speaker 1: I won't look at them because it just it doesn't

1005
00:53:50,719 --> 00:53:53,640
Speaker 1: you know. I don't like he's always. Oh he's not always,

1006
00:53:53,719 --> 00:53:56,040
Speaker 1: but he's often on my mind and in my thoughts.

1007
00:53:56,520 --> 00:54:00,200
Speaker 1: But yeah, it just doesn't. Like for me, there's no upside.

1008
00:54:00,280 --> 00:54:03,080
Speaker 1: I don't need to go open an album or look

1009
00:54:03,120 --> 00:54:05,520
Speaker 1: at something like that. It doesn't. But then for other

1010
00:54:05,560 --> 00:54:07,719
Speaker 1: people it's the right thing to do, all right. I

1011
00:54:07,719 --> 00:54:09,680
Speaker 1: want to finish with this one because it's kind of

1012
00:54:09,719 --> 00:54:14,879
Speaker 1: the intersection of shit I'm fascinated with and AI, which

1013
00:54:14,920 --> 00:54:18,640
Speaker 1: is new artificial muscle Patrick. So this is the tech

1014
00:54:18,760 --> 00:54:25,400
Speaker 1: section new artificial muscle that can allow humanoid robots to

1015
00:54:25,480 --> 00:54:29,319
Speaker 1: live four thousand times their own way. That seems that

1016
00:54:29,440 --> 00:54:33,920
Speaker 1: seems like impossible. It's so, yeah, tell us about that,

1017
00:54:34,000 --> 00:54:37,560
Speaker 1: because I definitely need some of that muscle. Yeah, that'd

1018
00:54:37,640 --> 00:54:38,239
Speaker 1: be great, wouldn't it.

1019
00:54:38,239 --> 00:54:39,640
Speaker 2: You wouldn't have to go to the gym anymore if

1020
00:54:39,640 --> 00:54:42,640
Speaker 2: you could just get just winded on and there you go.

1021
00:54:43,600 --> 00:54:45,920
Speaker 2: It seems that all the work that you've done all

1022
00:54:45,960 --> 00:54:51,160
Speaker 2: those years, Craigo was for nothing. Yes, well, robotics is

1023
00:54:51,680 --> 00:54:54,520
Speaker 2: you know, this is the next generation of where a

1024
00:54:54,560 --> 00:54:58,400
Speaker 2: lot of research is going into. And what they're using

1025
00:54:58,520 --> 00:55:02,560
Speaker 2: is little tiny magnet ed muscle they call you little

1026
00:55:02,560 --> 00:55:08,719
Speaker 2: magnetic muscles and together they combine and it's basically being

1027
00:55:08,719 --> 00:55:12,279
Speaker 2: done in South Korea at the moment and advanced functional

1028
00:55:12,360 --> 00:55:17,279
Speaker 2: materials they're calling them. And they're magnetically controlled, so the

1029
00:55:17,360 --> 00:55:21,879
Speaker 2: muscle can flip between being basically floppy and then rock

1030
00:55:21,920 --> 00:55:23,319
Speaker 2: hard through magnetism.

1031
00:55:23,400 --> 00:55:24,640
Speaker 1: So that's the way they're doing it.

1032
00:55:24,680 --> 00:55:28,680
Speaker 2: Because when you've got a motor that's generating for don't laugh.

1033
00:55:30,600 --> 00:55:32,080
Speaker 1: Yeah, anyway, it.

1034
00:55:31,960 --> 00:55:35,000
Speaker 2: Could be used for other purposes as well, Crago, but

1035
00:55:35,480 --> 00:55:37,719
Speaker 2: the little tiny strips of the can you're not.

1036
00:55:37,680 --> 00:55:40,279
Speaker 1: Say floppy and rock hard in the same sense. You

1037
00:55:40,400 --> 00:55:43,520
Speaker 1: do these things on purpose. I don't. I really don't

1038
00:55:43,920 --> 00:55:48,520
Speaker 1: absolutely do, Tiff doesn't he Yeah, back me up.

1039
00:55:49,000 --> 00:55:51,439
Speaker 2: Yeah, I just think it's a nice way to start

1040
00:55:51,480 --> 00:55:56,439
Speaker 2: and finish the show with smart Yeah. So the stiffened states, right,

1041
00:56:00,120 --> 00:56:00,839
Speaker 2: describe it?

1042
00:56:01,760 --> 00:56:04,440
Speaker 1: You could take I think you could say rigid or

1043
00:56:04,560 --> 00:56:06,479
Speaker 1: high ten soil. I don't think you need to say

1044
00:56:06,560 --> 00:56:09,640
Speaker 1: stiff and floppy and whatever the other one was.

1045
00:56:10,120 --> 00:56:14,359
Speaker 2: Anyway, the rigid state is brought about by these materials

1046
00:56:14,440 --> 00:56:17,880
Speaker 2: that could only one point two grams can hold up

1047
00:56:17,920 --> 00:56:22,680
Speaker 2: to five kilograms. That's four thousand times its own weight.

1048
00:56:22,800 --> 00:56:25,200
Speaker 2: It's as strong as an ant here so and when

1049
00:56:25,239 --> 00:56:28,799
Speaker 2: it's softened, so when it's flaccid, it can stretch to

1050
00:56:28,920 --> 00:56:35,040
Speaker 2: around twelve times the original legs.

1051
00:56:36,760 --> 00:56:40,000
Speaker 1: If what's worse, moist or flaccid?

1052
00:56:40,120 --> 00:56:43,239
Speaker 3: Oh, you had to bring it up.

1053
00:56:44,200 --> 00:56:48,760
Speaker 1: Today's podcast. Friday's podcast is called what's it called? Please

1054
00:56:48,800 --> 00:56:50,239
Speaker 1: don't say moist or something like.

1055
00:56:50,200 --> 00:56:55,160
Speaker 3: That, warned, I warn the whole typ.

1056
00:56:56,160 --> 00:57:00,000
Speaker 1: Yeah, she went onto the typ Facebook page and apologize

1057
00:57:00,200 --> 00:57:02,960
Speaker 1: in advance for the name of today's the title of

1058
00:57:02,960 --> 00:57:05,840
Speaker 1: today's episode, and she did. Creat i'ven't even had a

1059
00:57:05,840 --> 00:57:08,200
Speaker 1: look this morning, I should look Patrick, we got to

1060
00:57:08,239 --> 00:57:10,160
Speaker 1: go tell people how they can connect with you.

1061
00:57:10,560 --> 00:57:13,640
Speaker 2: No one wants to connect with me after this. Go

1062
00:57:13,680 --> 00:57:17,600
Speaker 2: to websitesnow dot com dot au and always happy to

1063
00:57:17,640 --> 00:57:21,880
Speaker 2: take suggestions on stuff we can talk about your websites

1064
00:57:21,880 --> 00:57:25,360
Speaker 2: noow dot com do you I'm imported?

1065
00:57:25,920 --> 00:57:28,560
Speaker 1: Thank you Patrick, thank you TIF, thank you so I mean,

1066
00:57:28,680 --> 00:57:31,640
Speaker 1: for that one person who's still listening, Well done you

1067
00:57:32,000 --> 00:57:36,480
Speaker 2: Thank you work, great work, marathon performance