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Speaker 1: Welcome to Stuff to Blow Your Mind from how Stuff

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Speaker 1: Works dot com. Hey, welcome to Stuff to Blow your Mind.

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Speaker 1: My name is Robert Lamb and my name is Christian Sager. Hey.

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Speaker 1: Something that people probably don't know about both of us

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Speaker 1: is that outside of doing this show, we're both quote

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Speaker 1: unquote creative people, right Like, even in the show. Well,

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Speaker 1: in the show, we try to be creative. We can't

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Speaker 1: strip the creativity off of cats like that. This is

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Speaker 1: very true. But um, you are a fiction writer, which

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Speaker 1: I always try to mention on the episodes that you're

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Speaker 1: not on so that you're not like somehow embarrassed or

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Speaker 1: anything like that. But I try to plug Robert's books. Uh,

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Speaker 1: And then I do comics and and in the past

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Speaker 1: I've done music as well, and I didn't know that. Yeah,

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Speaker 1: and you were you like in a band. You were

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Speaker 1: in a band, right, Yeah. And I had a period

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Speaker 1: of time I'm gonna talk about in this episode where

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Speaker 1: I dabbled with electronic music as well. Um, but yeah, so,

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Speaker 1: but I was in a band for a couple of years.

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Speaker 1: And Uh, there's this interesting thing going on right now

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Speaker 1: that you saw when you went to the World Science

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Speaker 1: Festival two weeks ago. That is sort of like trying

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Speaker 1: to come to grips with what happens when you teach

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Speaker 1: artificial intelligence or really, I guess the correct term is

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Speaker 1: machine learning to be creative quote unquote, and like, how

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Speaker 1: do people feel about that? And you saw this amazing

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Speaker 1: panel where they had people who were dabbling in that,

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Speaker 1: both in music and culinary arts and drawing, and then

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Speaker 1: they had the psychologist on the panel to who is

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Speaker 1: like the skeptic kind of Yeah, yeah, this was a

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Speaker 1: really good, good panel. I saw it live at the

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Speaker 1: World Science Festival this year, and if you're listening to this,

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Speaker 1: you can watch it as well, because this one not

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Speaker 1: all the talks are available on video, but this one

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Speaker 1: is available on video, and I'll include a link to

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Speaker 1: that talk on the landing page for this episode of

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Speaker 1: Stuff to Blow Your Mind dot Com. But it was

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Speaker 1: titled Computational Creativity. He was moderated by w n y

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Speaker 1: C S John Schaefer, and the panelist included artist Song

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Speaker 1: Win Chung, computer scientists and musician Jesse Ingle, neuroscientist Peter

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Speaker 1: you Rock Say, and engineering theorist A lab of Varshney. Yeah,

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Speaker 1: and so the four of them had this interesting discussion

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Speaker 1: kind of a back and forth about what what they

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Speaker 1: were experimenting with in terms of trying to teach machines

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Speaker 1: to be creative, and then more along the lines of

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Speaker 1: defining creativity, uh, systematizing creativity for human beings, so that

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Speaker 1: you could somehow try to take that and apply it

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Speaker 1: to machines and algorithms. Uh. And that the that's where

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Speaker 1: Peter Rix, say was sort of like the the neuroscientists,

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Speaker 1: not naysayer really, but just like he was doubtful right

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Speaker 1: because he was he could not see a way that

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Speaker 1: quote unquote creativity as he understood it could be replicated

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Speaker 1: uh in machines the way that the other three were

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Speaker 1: pretty excited about. Yeah. He I guess if you were

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Speaker 1: thinking of this as a like a jam band or something.

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Speaker 1: He was the guy with doing the jazzy solos, like

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Speaker 1: all the other three had had really particular examples tied

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Speaker 1: into their research, and then Say would come in and

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Speaker 1: he'd just kind of he'd weigh a little bit. He

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Speaker 1: had very interesting stuff to add to Yeah. Yeah, he

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Speaker 1: was great, um, and it definitely brought some balance to it.

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Speaker 1: So we thought, after you've seen it, you said, you

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Speaker 1: gotta check this out. I watched the video, took notes,

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Speaker 1: and we thought this is worth us having a conversation

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Speaker 1: here on the show about it, especially because we're sort

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Speaker 1: of at the intersectionality of science and creativity. Yeah, so,

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Speaker 1: what we're not gonna do here today, We're not going

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Speaker 1: to just replicate their discussion. We're gonna We're gonna spring

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Speaker 1: off their discussion, uh, talk about our own experience, his

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Speaker 1: own thoughts, and we're gonna drag in a few additional

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Speaker 1: resources as well and in some additional examples, uh, to

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Speaker 1: to bring this conversation to you and then hopefully here

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Speaker 1: back from all of you creative people about your thoughts

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Speaker 1: on machine creativity, machine learning, and the future of human creativity.

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Speaker 1: So along those lines, Robert, like, when you hear the

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Speaker 1: idea of a machine being creative, how does that make

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Speaker 1: you feel as a person who like you know, I

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Speaker 1: I imagine you're like me, Like it's work, you know,

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Speaker 1: being creative, writing, drawing, playing music. It. You know, there's

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Speaker 1: there's something that feels unescapable human about it. Yeah, yeah,

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Speaker 1: you know. I I hear, I hear about it, and

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Speaker 1: and I read articles or studies where they've taken a

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Speaker 1: computer program and it's writing high coups, or it's where

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Speaker 1: it's printing paintings, that sort of thing. I see that,

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Speaker 1: and then I I think about these stories of say

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Speaker 1: journalism jobs like local journalism jobs, jobs being outsourced to

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Speaker 1: another country that I hear about, of course, the impact

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Speaker 1: on manufacturing, of the impact of automation on manufacturing, and

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Speaker 1: that makes me, you know, try and envision the future

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Speaker 1: or where suddenly you're going to have programs and machines

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Speaker 1: that can replicate what I do. Yeah, we've actually exactly

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Speaker 1: to that point. We've actually like encountered that in this business,

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Speaker 1: where like people will occasionally come to us and say, like, hey,

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Speaker 1: you want to make your job easier podcasting. We've come

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Speaker 1: up with this program that will do all the research

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Speaker 1: for you, and all you have to do is just

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Speaker 1: like you know, printed out and go into the studio

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Speaker 1: with it. Uh. And you know that seems really interesting

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Speaker 1: to me. That's I don't like I got that email.

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Speaker 1: I got the one about where you're basically outsourcing it

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Speaker 1: to other researchers. There's that, but then there's also, um, yeah,

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Speaker 1: it'll just basically just pluck stuff for you and put

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Speaker 1: it into a dock. Uh. This has has been pitched

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Speaker 1: to me before at least. And then the other thing

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Speaker 1: that I've heard about is like, you know, some of

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Speaker 1: our listeners are familiar that we do videos occasionally. Uh,

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Speaker 1: the old stuff to blow your mind videos would be

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Speaker 1: a good example of this. So essentially you punch in

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Speaker 1: parameters of what your video would be about. In this

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Speaker 1: software spits out a like video for you that has

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Speaker 1: text on the screen and pulls image clips or video

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Speaker 1: clips out of a library to just make something rather

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Speaker 1: than you're working together with a producer, writing script. All

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Speaker 1: the things that we do when we make those videos

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Speaker 1: just makes me feel like we need to be marketing.

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Speaker 1: Our podcast is a craft podcast, you know, because it's

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Speaker 1: like all the research is done by hand, granted, you know,

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Speaker 1: on a computer screen. A lot of people don't know this.

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Speaker 1: Book Mark Marin, all of his research is done by

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Speaker 1: robot in his garage. Yeah yeah, okay, among the cats

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Speaker 1: I'm imagining it's a robot cat actually yeah, um, but yeah,

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Speaker 1: I kind of feel the same way, especially like when

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Speaker 1: I hear these examples. But then at the same time, like,

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Speaker 1: so I've done commercial art before. I was a graphic

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Speaker 1: designer for a number of years before I got into

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Speaker 1: the podcasting business. And I've done creative stuff where I've

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Speaker 1: been an illustrator, I've worked as a musician, I write, uh,

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Speaker 1: And so the thing that I've learned is that, like

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Speaker 1: the one of the most iportant lessons I've learned about

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Speaker 1: creativity is that you have to impose limits upon your canvas.

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Speaker 1: No matter what medium you're performing in, right or else,

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Speaker 1: You're just gonna like stare into this abyss forever with

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Speaker 1: like trying to figure out where to place your creative ideas, um,

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Speaker 1: because there's this infinite possibility of like what could be right,

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Speaker 1: what could be wrong? Which which things should I use?

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Speaker 1: Which combination of words, which kind of ink all those things? Um?

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Speaker 1: For instance, like writers that I've encountered who have the

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Speaker 1: problem where they'll rewrite the first paragraph that they write

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Speaker 1: over and over and over again and obsessed on making

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Speaker 1: that paragraph perfect before they'll move on, and then they

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Speaker 1: never finished. Right. Yeah, I've I've I've done that before.

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Speaker 1: Like when I first started righting fiction, I was doing

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Speaker 1: that model, and of course the thing I was writing

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Speaker 1: is still to this day incomplete from from that totally. Yeah,

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Speaker 1: I've been there too, and It's like that's one of

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Speaker 1: those things that they don't ever really teach you. At

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Speaker 1: least they didn't teach me very well in composition, yes,

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Speaker 1: like just move on, um. But when I think about

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Speaker 1: creative machines, then I have to wonder, first of all,

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Speaker 1: are they aware of what their limitations are or are

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Speaker 1: they just executing an algorithm? And how do they do

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Speaker 1: they know how to make those limitations work for them

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Speaker 1: creatively the same way that human beings do well. It's

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Speaker 1: interesting because you know, when you when you start picking

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Speaker 1: apart your own creative process, you can you can begin

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Speaker 1: to see how a machine could do some of it,

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Speaker 1: you know, because you're talking about it imposing limits on yourself.

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Speaker 1: Like I'm instantly thinking like, all right, I'm writing, I'm

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Speaker 1: gonna write a I'm gonna write this science fiction short story. Okay,

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Speaker 1: what kind of ideas can I explore within the confines

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Speaker 1: of this story, this setting, maybe this type of character

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Speaker 1: that I want to write? And then you start you

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Speaker 1: start throwing in another parameter. It's like what kind of

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Speaker 1: stories are selling? What? What? What kind of story can

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Speaker 1: I write that doesn't have a vampire in it? You know,

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Speaker 1: stuff like that, And so there's a there's a certain

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Speaker 1: amount of creativity. Is not necessarily this just magical explosion

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Speaker 1: that takes place. There's a lot of computation that goes

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Speaker 1: into it, and therefore there's an entire side to it

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Speaker 1: that could easily be mastered by an inhuman entity, right,

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Speaker 1: especially if like we as humans are able to go

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Speaker 1: to that machine and input the parameters of those limits

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Speaker 1: to it, right and say like, okay, here's your limitations

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Speaker 1: now right this story or here's your limitations now create

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Speaker 1: this music. And that seems to be what they're trying

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Speaker 1: to do. Yeah, And I know when when I think about,

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Speaker 1: you know, these various anxieties about you know, future writers

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Speaker 1: and podcasters that are entirely machine. You know, we all

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Speaker 1: tell ourselves, well, they can replicate a lot of it,

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Speaker 1: but there there's a certain spark, there's something I have

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Speaker 1: that cannot be replicated. And we we also tell that's

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Speaker 1: the same thing we tell ourselves when we're talking about

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Speaker 1: human competition. You can say, all right, well this this

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Speaker 1: individual may be better at at this skill set than me.

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Speaker 1: They maybe have a better voice or what have you,

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Speaker 1: but but there's something special about me, and so do

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Speaker 1: I extend or is that just sort of a self

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Speaker 1: inflating uh fiction that we're telling ourselves all the time anyway.

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Speaker 1: Uh And and so maybe the machines will just swoop

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Speaker 1: in and take all of our creative jobs. Maybe, Although

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Speaker 1: I have to say, like sitting down and listening to

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Speaker 1: this conversation and then like filling out notes and doing

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Speaker 1: a little extra research for this episode, it made me

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Speaker 1: think about my creative process in a systematized way that

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Speaker 1: I haven't before, And it made me really realize that

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Speaker 1: for me at least, I think like one of the

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Speaker 1: big like jumping points for human beings being creative is

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Speaker 1: when they start creating new tools to be creative in

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Speaker 1: different ways. And we'll talk about that later because this

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Speaker 1: is something they discussed in the panel as well. Uh.

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Speaker 1: And I can't see a future yet where AI is

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Speaker 1: doing those things where AI says, I like a guitar,

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Speaker 1: and I can write these songs on guitar, But what

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Speaker 1: if I go over to a tree and I carve

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Speaker 1: an entirely different kind of instrument so that I can

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Speaker 1: get this effect. That's true. That's that's a good point,

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Speaker 1: sort of like meta creativity ideas. All right, so we'll

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Speaker 1: definitely get into the tools here in a bit, but

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Speaker 1: before we roll on, I just want to want to

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Speaker 1: really draw attention to two particular ideas that came up

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Speaker 1: in the discussion um that that I that I thought

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Speaker 1: were particularly striking, and that will come up again and

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Speaker 1: again as we uh as we roll through the episode.

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Speaker 1: First of all, they kept to bring up this analogy

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Speaker 1: of the career of creative technology compared to the airplane.

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Speaker 1: So obviously we make airplanes that fly faster and higher

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Speaker 1: than any bird, but that doesn't mean we fully understand

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Speaker 1: how birds fly, nor does it mean we can replicate

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Speaker 1: the evolved perfection of their powered flight. So technology essentially

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Speaker 1: gives us a version of the same thing that outperforms

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Speaker 1: the organic in many respects, but also underperforms and other areas.

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Speaker 1: And this was al rix Says primary argument sort of

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Speaker 1: against the idea that AI could be quote unquote creative,

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Speaker 1: right because birds airplanes are not birds, right, And so

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Speaker 1: he was saying basically that then subsequently the software couldn't

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Speaker 1: necessarily be human in its creativity, But it depends on

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Speaker 1: how you define creativity too. Yeah, it's a great metaphor,

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Speaker 1: and I think all the panelists ended up, picking up

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Speaker 1: and playing with it a little bit, and well we'll

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Speaker 1: play with it here as well, now chewing. For her part,

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Speaker 1: she highlighted her own projects with creative machine learning, in

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Speaker 1: which she and there's some wonderful like overhead video examples

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Speaker 1: of this, but she draws, uh with or paints with

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Speaker 1: a piano. Believe it was a piano. It looked to

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Speaker 1: me like she was using some kind of stylist tablet thing.

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Speaker 1: But yeah, like basically she from what I could tell

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Speaker 1: as somebody who does illustration, she was drawing on the

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Speaker 1: right half of a of a canvas, and what she

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Speaker 1: would draw, the computer would then draw on the left

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Speaker 1: half of the canvas. But it wouldn't just imitate her.

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Speaker 1: It wasn't like, I mean, you can do this in

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Speaker 1: like Adobe Illustrator where it just like draws the exact

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Speaker 1: same thing you draw, and you've got like a perfectly

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Speaker 1: symmetrical drawing. So it's not a mirror image though. This

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Speaker 1: was this was like creating its own thing, learning from

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Speaker 1: her curves and lines. Yeah, I was playing off her movement,

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Speaker 1: sort of improvising with her to create a work of

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Speaker 1: collaborative art. And so she argues that the notion of

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Speaker 1: a robotic agent that creates with you the dreams with

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Speaker 1: you in harmony is quote an underserved narrative in our culture,

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Speaker 1: and I and I definitely agree with that. She was

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Speaker 1: to me the most compelling person on the panel. I

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Speaker 1: thought they were all really interesting. There was something about

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Speaker 1: her that I was like, this person is going to

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Speaker 1: go on to do like amazing work, and she's gonna

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Speaker 1: come back. Because she was relatively young, I can see

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Speaker 1: her in like ten twenty years coming back to us

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Speaker 1: and just like dropping like some huge revelation on us

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Speaker 1: about creativity and computer learning after the Singularity, after shelded

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Speaker 1: into the mind. They chose me I'm going to be

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Speaker 1: the go between. But she was like she was dropping

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Speaker 1: some really interesting tidbits into the conversation, Like she was

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Speaker 1: thinking about the way that she draws based on competitive gaming.

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Speaker 1: Like she kept bringing up like, and by this we

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Speaker 1: mean like video games where people are playing I don't know,

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Speaker 1: League of Legends or um Overwatch, and so just the

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Speaker 1: idea that she was thinking about how like the AI

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Speaker 1: in those games was compensating for the human movements or

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Speaker 1: or play styles as opposed to how she drew and

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Speaker 1: how the computer drew along with her. That's an very

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Speaker 1: interesting connection that I would never have thought of before. Yeah,

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Speaker 1: that's that's that that is that's a That's a really

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Speaker 1: a really great example because you don't think about like

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Speaker 1: this competitive model being applicable to a creative collaboration. And

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Speaker 1: you and I play a lot of video games, you know,

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Speaker 1: but I don't spend a lot of time thinking about

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Speaker 1: how the AI in the game is compensating for my movements.

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Speaker 1: I have the only time I've thought about this, and

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Speaker 1: this is going to be kind of geeky and and silly,

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Speaker 1: but in playing uh, pro wrestling video games, I've thought

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Speaker 1: about this because because there's like certain wrestling styles that

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Speaker 1: the characters have, well, it basically comes down to this weirdness.

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Speaker 1: Professional wrestling is a simulated combat sport. So yes, sorry spoilers,

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Speaker 1: but as you see it on TV, it's two humans pretending,

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Speaker 1: you know, acting out a combat scenario. We actually have

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Speaker 1: a video about this on KFE that you did a

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Speaker 1: couple of years ago. Yeah, and so when people make

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Speaker 1: a video game of that, they make a fighting game

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Speaker 1: in which too like a huge too humans or a

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Speaker 1: human in an AI or two AI s if you're

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Speaker 1: doing a demo mode, are fighting each other in a

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Speaker 1: competitive in calendar using the moves of professional wrestling. And

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Speaker 1: then on top of that, some players are going to

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Speaker 1: play that video game just like a fighting game. I'm

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Speaker 1: gonna beat my opponent, but others will play it attempting

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Speaker 1: to tell a story, attempting to get the most dramatic match.

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Speaker 1: And in those cases, I don't know that any programmers

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Speaker 1: have really gained that really strange area of video game playing,

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Speaker 1: but like, like that's that's what I think of when

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Speaker 1: she brings up this example, Like, here's an example of

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Speaker 1: somebody trying to not only defeat an AI opponent, but

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Speaker 1: to try and tell a story with an AI opponent. Okay,

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Speaker 1: so this is an episode where we're gonna have a

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Speaker 1: little bit of more digressions than we normally do because

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Speaker 1: of our creative backgrounds and sort of things we can

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Speaker 1: bring to this. I have a story that I think

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Speaker 1: can tie into this that we can then maybe bring

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Speaker 1: back to this AI. Okay, So a friend of mine

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Speaker 1: is currently working on a comic book for a w

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Speaker 1: W E comic. He's just doing like a short story

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Speaker 1: and it's about some missfight between the Undertaker and Mankind

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Speaker 1: is the guy that would okay, and uh so I

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Speaker 1: never saw this. I'm not as bigger a wrestling fan

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Speaker 1: as Robert, but uh when he told me about it,

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Speaker 1: was like, oh, cool, like you could you could do

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Speaker 1: some fun stuff like that, like have them being a

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Speaker 1: slaughterhouse or have them be in a graveyard and they're

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Speaker 1: hitting each other with gravestones or something, right, and he

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Speaker 1: was like, no, we have to do an exact replica

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Speaker 1: in comic form of this infamous fight they hadn't like

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Speaker 1: I don't know, two thousand or something. And I was like, huh,

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Speaker 1: that's that's interesting. But to me that like here we

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Speaker 1: go with limitations, right, Like the limitation there is so

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Speaker 1: limiting that I don't see what would be creative about

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Speaker 1: it or interesting other than obviously my friend and his

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Speaker 1: style and how he draws the thing. But he's not

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Speaker 1: being able to express anything. Mhmm. Yeah, yeah, yeah, I

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Speaker 1: see what you mean. Because the one thing if you

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Speaker 1: could say, I tell this, tell the story, but they

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Speaker 1: actually have magical powers, like they're Undertakers, really an undead

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Speaker 1: creature exactly, That's how I was thinking it would be.

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Speaker 1: I was like, oh, that this is the benefit of

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Speaker 1: doing it in the comics medium is that you can

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Speaker 1: actually give them weird powers and you can like really

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Speaker 1: play with the the goofy narrative of like their their roles. Right. Um.

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Speaker 1: But then this this also leads to the creative challenge,

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Speaker 1: Like sometimes just because the constraints are there doesn't mean

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Speaker 1: there's there's not an opportunity exactly. And that's what I'm

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Speaker 1: looking forward to it because when I hear that, I go, man,

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Speaker 1: there's there's too many limitations there. But you know, who knows,

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Speaker 1: maybe there's a way within all those limitations. I trust

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Speaker 1: that my friend is gonna be able to do this,

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Speaker 1: that that they'll be like a really cool way to

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Speaker 1: depict that fight. All right, but it's gonna take creativity.

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Speaker 1: And so we're gonna we're gonna roll it back a

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Speaker 1: little bit and talk about what creativity actually is, like

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Speaker 1: boiling it down, reconstructing it so that we can understand

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Speaker 1: what we're doing when we're create and in order to

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Speaker 1: uh make a machine engage in the same process. Right,

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Speaker 1: So they come up with some of their uh kind

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Speaker 1: of limited limitations on what creativity can or cannot be.

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Speaker 1: And this is where I think, like Ultimately, like the

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Speaker 1: split between the four of them, or maybe the three

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Speaker 1: of them versus Alri say came down was that Ria

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Speaker 1: has this sort of broader definition of creativity as being

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Speaker 1: this sort of magical thing that happens in the human brain. Right,

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Speaker 1: like he talks about at different times in the discussion,

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Speaker 1: talks about the importance of pain, of intent, of consciousness,

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Speaker 1: various attributes that are currently beyond the scope of of

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Speaker 1: AI and would be problematic if we had them, you know,

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Speaker 1: if you could, would it be It's a whole separate issue,

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Speaker 1: but it would it be ethical to make a you know,

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Speaker 1: a depressed artist robot because why what kind of cruel

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Speaker 1: god are you to make a robot so depressed? You know,

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Speaker 1: it's funny you mentioned that later on in the notes.

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Speaker 1: I have a question on whether robots can be crazy

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Speaker 1: enough to be creative, because, as we know, many creative

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Speaker 1: people suffer mental health issues. Yeah, well that's how we

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Speaker 1: got David and in the Covenant. Well they already answered

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Speaker 1: it for us, Thanks for at le Scott. Alright, So

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Speaker 1: one of the one of these sort of loose models

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Speaker 1: though for creativity that that is thrown out here is

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Speaker 1: it comes from engineering theorist lab Varshny, and he breaks

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Speaker 1: it all down to a comp to a combination of

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Speaker 1: novelty and high quality that comes together in a way

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Speaker 1: that changes your belief. So like a Venn diagram, I'm

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Speaker 1: imagining here of is it novel, is it knew? Is

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Speaker 1: it something different? And is it of high enough quality?

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Speaker 1: And when it comes and does it come together in

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Speaker 1: a way that actually have some sort of meaning to me?

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Speaker 1: I think this is a really interesting way to model

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Speaker 1: aesthetics and that I had not thought of before. And

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Speaker 1: when you try to like sort of lay this on

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Speaker 1: top of like creative artifacts and see like how that works,

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Speaker 1: it's interesting, right, like the first thing I thought of.

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Speaker 1: I was talking to you about this before we went

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Speaker 1: in to the studio, and no spoilers, but the new

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Speaker 1: Twin Peaks is on TV and I've been watching it

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Speaker 1: every week, and as you would expect from David Lynch,

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Speaker 1: it's wacky, right, But in terms of lab varsenyse ven

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Speaker 1: diagram here of novelty and high quality, it's just the

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Speaker 1: right amount of novelty and just the right amount of

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Speaker 1: quality that it works for me. Now maybe for other people,

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Speaker 1: because obviously creativity and artist objective. It doesn't, but but yeah,

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Speaker 1: I was seeing it like, oh, that is the perfect

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Speaker 1: David Lynch is like the perfect metaphor for this, because,

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Speaker 1: let's be honest, like some of David Lynch's stuff, the

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Speaker 1: novelty factor can get a little too big in that

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Speaker 1: ven diagramic situation and you're like and and Varsny actually

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Speaker 1: describes it as noise. He says, like, when either one

401
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Speaker 1: of those things gets too big in comparison to the other,

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Speaker 1: it turns into noise and we're no longer experiencing something

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Speaker 1: like that's communicative. And he also says that if they

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00:21:56,680 --> 00:21:59,439
Speaker 1: both are too if there's too much of both at

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Speaker 1: the same time, right, Like, if it's incredibly novel and

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Speaker 1: it's super high quality, then it's just like that overloads

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Speaker 1: people's circuits. Right, So I'm thinking of like Finnegan's Wake

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Speaker 1: or something like that. Probably well, and it's gonna vary

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Speaker 1: from person to person because you know, obviously, because you're

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Speaker 1: I think we can all imagine movies that we've seen

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Speaker 1: where either like I've seen plenty of movies where there's

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Speaker 1: some great ideas, the quality is not there though you know,

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Speaker 1: maybe the acting is bad, the monster doesn't look right

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Speaker 1: or whatever, and then you see the reverse to some

415
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Speaker 1: beautiful films and you're just like, my eyes are eating

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Speaker 1: up everything that's on the screen, but I feel nothing. Yeah,

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Speaker 1: exactly like you get. You get the opposite too, when

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Speaker 1: like these things are too small in comparison to one another, Right, Like,

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Speaker 1: if it's super high quality, but it's a story that

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Speaker 1: you've seen and it's been done three times before, you're

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Speaker 1: not necessarily going to be that engaged with it, right,

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Speaker 1: So there's got to be a little bit of novel

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Speaker 1: something to it. But at the same time, if it's

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Speaker 1: totally novel, it's this cool idea, it's it's like the

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00:23:00,040 --> 00:23:04,800
Speaker 1: ultimate Hollywood pitch high concept, like the Ocean Walker, the

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Speaker 1: rest of Development, Yeah, something like that. And then the

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Speaker 1: qualities garbage. Well, you know that's not going to do

428
00:23:11,840 --> 00:23:14,320
Speaker 1: very well either. But but to his point about like

429
00:23:14,359 --> 00:23:18,240
Speaker 1: both areas being blown out, both novelty and high quality, well,

430
00:23:18,680 --> 00:23:20,159
Speaker 1: I guess, I guess one example would be if you

431
00:23:20,160 --> 00:23:22,800
Speaker 1: take somebody who does not have much, uh you know,

432
00:23:22,840 --> 00:23:26,440
Speaker 1: of of a bedrock understanding of a modern art or surrealism,

433
00:23:26,600 --> 00:23:28,280
Speaker 1: and you just really throw them into the deep end

434
00:23:28,359 --> 00:23:31,320
Speaker 1: at a modern art museum, So they might encounter a

435
00:23:31,359 --> 00:23:34,959
Speaker 1: piece of work that is, you know, it's highly novel uh,

436
00:23:35,160 --> 00:23:37,840
Speaker 1: you know, created with wonderful you know, high level craftsmanship,

437
00:23:38,440 --> 00:23:40,040
Speaker 1: but they're just not in a place where they're going

438
00:23:40,080 --> 00:23:42,200
Speaker 1: to be able to understand. It's just gonna be essentially

439
00:23:42,240 --> 00:23:45,960
Speaker 1: over their head and therefore, like the it's going to

440
00:23:46,040 --> 00:23:49,560
Speaker 1: be a failure of the art in a way. You

441
00:23:49,600 --> 00:23:51,840
Speaker 1: may have created something wonderful and thought provoking, but if

442
00:23:51,840 --> 00:23:55,600
Speaker 1: it doesn't connect with people, then does it Does it work?

443
00:23:55,760 --> 00:23:59,359
Speaker 1: When he says noise, I immediately think of the image

444
00:23:59,400 --> 00:24:01,440
Speaker 1: of a television and with white noise on it, right,

445
00:24:01,480 --> 00:24:04,119
Speaker 1: so the snow. So that for some people that's what

446
00:24:04,200 --> 00:24:06,280
Speaker 1: it's like looking at modern art is just like staring

447
00:24:06,320 --> 00:24:09,280
Speaker 1: at the screen while while it's playing, you know, the

448
00:24:09,560 --> 00:24:14,080
Speaker 1: staticky snow. But rix Say basically argues that because brains

449
00:24:14,080 --> 00:24:18,480
Speaker 1: and AI are using completely different processes that he doesn't

450
00:24:18,520 --> 00:24:21,359
Speaker 1: even know that you can call what they're doing creativity,

451
00:24:21,600 --> 00:24:25,080
Speaker 1: right because he's defining it as being pretty specific to

452
00:24:25,160 --> 00:24:30,040
Speaker 1: human beings. Uh. And in that human creativity is seen

453
00:24:30,080 --> 00:24:35,040
Speaker 1: as simple problem solving by generating a lot of possibilities

454
00:24:35,280 --> 00:24:39,040
Speaker 1: and then selecting from those possibilities and that's this is

455
00:24:39,040 --> 00:24:42,040
Speaker 1: how we evolved as human beings, right, So we went

456
00:24:42,080 --> 00:24:46,720
Speaker 1: from using stone tools to access to eventually Neanderthal's using

457
00:24:47,200 --> 00:24:50,440
Speaker 1: instruments and actually making cave art. You know, like that

458
00:24:50,520 --> 00:24:55,560
Speaker 1: process of evolution through our our physical evolution also evolved

459
00:24:55,600 --> 00:24:59,240
Speaker 1: our creativity. And he's wondering, like if that topic selection,

460
00:24:59,359 --> 00:25:02,840
Speaker 1: if that type problem solving is even possible for computers,

461
00:25:03,440 --> 00:25:05,960
Speaker 1: but where it really skyrockets. And this is what I

462
00:25:05,960 --> 00:25:09,320
Speaker 1: think is most fascinating about creativity. Here is when you

463
00:25:09,320 --> 00:25:13,840
Speaker 1: start to imagine things that don't really exist. And possibly

464
00:25:14,000 --> 00:25:16,400
Speaker 1: he was saying that this kind of creativity is due

465
00:25:16,480 --> 00:25:19,159
Speaker 1: to the fact that we haven't evolved prefrontal cortex. And

466
00:25:19,160 --> 00:25:20,760
Speaker 1: then the video you get to see, like he shows

467
00:25:20,800 --> 00:25:25,879
Speaker 1: you different um images of various hominids and their skull

468
00:25:25,960 --> 00:25:29,920
Speaker 1: shapes and how they evolved over time, and then creativity

469
00:25:29,960 --> 00:25:32,280
Speaker 1: evolved with them. And then you see examples where there's

470
00:25:32,320 --> 00:25:34,800
Speaker 1: cave art with a with like say, human with a

471
00:25:34,840 --> 00:25:37,720
Speaker 1: beast's head. This is something that doesn't actually exist but

472
00:25:37,920 --> 00:25:40,760
Speaker 1: is a uh, you know, arguably at an example of

473
00:25:40,840 --> 00:25:45,160
Speaker 1: early creativity creating these unreal things that then have various

474
00:25:45,200 --> 00:25:48,480
Speaker 1: meanings and an effect on the viewer and then they like,

475
00:25:48,560 --> 00:25:50,719
Speaker 1: they get to this point in their conversation where they

476
00:25:50,720 --> 00:25:57,239
Speaker 1: start talking about where the AI quote unquote fails at creativity.

477
00:25:57,320 --> 00:25:59,879
Speaker 1: And that was interesting to me too, because they seem

478
00:25:59,920 --> 00:26:01,960
Speaker 1: to be talking about how the machines were failing, but

479
00:26:02,040 --> 00:26:05,920
Speaker 1: not how humans could necessarily fail. Yeah, yeah, they're they

480
00:26:05,920 --> 00:26:08,320
Speaker 1: were talking about how essentially in all of this, you're

481
00:26:08,359 --> 00:26:12,440
Speaker 1: gonna have this machine essentially brainstorming, but there's gonna be

482
00:26:12,480 --> 00:26:14,520
Speaker 1: a person that comes along, and the person is is

483
00:26:14,560 --> 00:26:18,320
Speaker 1: then selecting and judging the ideas in the In the

484
00:26:18,359 --> 00:26:21,720
Speaker 1: case of Varcheny, he's doing a lot of culinary computation.

485
00:26:22,240 --> 00:26:25,800
Speaker 1: So humans are gonna cook the food based on the

486
00:26:25,840 --> 00:26:28,239
Speaker 1: recipe that the computer has come up with, and then

487
00:26:28,240 --> 00:26:30,560
Speaker 1: they're going to taste it. And in some cases it's

488
00:26:30,560 --> 00:26:33,200
Speaker 1: gonna be like, Oh, that's a novel combination that actually works.

489
00:26:33,200 --> 00:26:35,560
Speaker 1: I wouldn't think it would. In other cases you would

490
00:26:35,560 --> 00:26:38,320
Speaker 1: say they would say, all right, we'll slow down their

491
00:26:38,880 --> 00:26:44,640
Speaker 1: machine overlords, because this is not really all that tasty. So, yeah,

492
00:26:44,720 --> 00:26:47,600
Speaker 1: you get into this idea where there are are failures. Uh,

493
00:26:47,920 --> 00:26:50,280
Speaker 1: But failures and mistakes are of course an important part

494
00:26:50,280 --> 00:26:53,840
Speaker 1: of the creative process as well. For humans. Right. This

495
00:26:53,920 --> 00:26:55,800
Speaker 1: is where like I stepped back and I was thinking

496
00:26:55,800 --> 00:26:59,199
Speaker 1: about my creative process because I was like, look, I

497
00:26:59,320 --> 00:27:02,440
Speaker 1: know that I spend a good amount of time quote

498
00:27:02,520 --> 00:27:06,000
Speaker 1: unquote failing on every project that I work on. I mean,

499
00:27:06,040 --> 00:27:08,840
Speaker 1: that's just part of being creative is like doing a

500
00:27:08,920 --> 00:27:11,040
Speaker 1: thing and then realizing that's not what it is, crossing

501
00:27:11,040 --> 00:27:13,280
Speaker 1: it out, moving onto the next thing. You know. It's

502
00:27:13,320 --> 00:27:15,560
Speaker 1: basically like what they were talking about before, where you're

503
00:27:15,600 --> 00:27:18,480
Speaker 1: generating all these possibilities and then you're selecting from them.

504
00:27:18,600 --> 00:27:21,639
Speaker 1: But you have to generate some possibilities that you're not

505
00:27:21,680 --> 00:27:24,040
Speaker 1: going to use, that you're going to see as failures

506
00:27:24,080 --> 00:27:26,080
Speaker 1: before you can eventually get to the thing that you

507
00:27:26,119 --> 00:27:29,080
Speaker 1: see as a success. Right. Yeah, I mean I think

508
00:27:29,080 --> 00:27:32,320
Speaker 1: anybody out there who has has engaged in a creative process,

509
00:27:32,320 --> 00:27:34,600
Speaker 1: you've come up with some They can't all be singers,

510
00:27:34,600 --> 00:27:37,120
Speaker 1: as they say, You're gonna come up with some duds,

511
00:27:37,160 --> 00:27:38,760
Speaker 1: and for a while, you might think that dud is

512
00:27:38,760 --> 00:27:42,439
Speaker 1: pretty amazing because maybe it is really novel, you know,

513
00:27:42,560 --> 00:27:46,320
Speaker 1: or maybe it is really high quality, but the balance

514
00:27:46,400 --> 00:27:50,040
Speaker 1: is is perhaps not there. Yeah, Now there's a there's

515
00:27:50,080 --> 00:27:53,200
Speaker 1: also the notion of intent in all of this feelings,

516
00:27:53,640 --> 00:27:56,000
Speaker 1: the later of which is is you know, obviously currently

517
00:27:56,200 --> 00:28:00,240
Speaker 1: uh uh not present in artificial intelligence and machine learning,

518
00:28:00,600 --> 00:28:03,160
Speaker 1: but perhaps you're we're just talking about two different sorts

519
00:28:03,160 --> 00:28:06,160
Speaker 1: of flight. To come back to the bird airplane scenario.

520
00:28:06,520 --> 00:28:10,280
Speaker 1: And indeed, moderator John Shaffer points brought up the point

521
00:28:10,600 --> 00:28:14,520
Speaker 1: of apophenia. So this is a this is this is interesting.

522
00:28:14,520 --> 00:28:18,160
Speaker 1: This is a concept coined by German scientists Class Conrad

523
00:28:18,359 --> 00:28:22,280
Speaker 1: in nineteen fifty eight, and it's the opposite of an epiphany,

524
00:28:22,359 --> 00:28:24,600
Speaker 1: and epiphany being you know, the you know, a true

525
00:28:24,760 --> 00:28:29,680
Speaker 1: intuition of the world's interconnectedness. And in statistics, apophenia is

526
00:28:29,760 --> 00:28:32,919
Speaker 1: essentially a type one error or false positive, where you

527
00:28:32,920 --> 00:28:36,560
Speaker 1: think something's connected and it's not. Uh In. In psychology,

528
00:28:36,560 --> 00:28:40,600
Speaker 1: according to Conrad, it's the stuff of schizophrenia, right, And

529
00:28:40,640 --> 00:28:44,760
Speaker 1: that's where it's primarily being discussed in present day as

530
00:28:44,800 --> 00:28:49,960
Speaker 1: like a psychological problem. It's when unrelated details seem to

531
00:28:50,000 --> 00:28:53,400
Speaker 1: be saturated with connections and meaning, but they're those are false.

532
00:28:53,440 --> 00:28:56,280
Speaker 1: They ultimately lead to nowhere. Right. And I've absolutely had

533
00:28:56,280 --> 00:28:58,960
Speaker 1: this experience with writing before I'm like, oh, I'm totally

534
00:28:59,040 --> 00:29:01,160
Speaker 1: on the right path, and then like I look back

535
00:29:01,160 --> 00:29:02,960
Speaker 1: at it a week later and like that was garbage,

536
00:29:03,320 --> 00:29:06,480
Speaker 1: you know. Yeah, And in the like the schizophrenia case, uh,

537
00:29:06,520 --> 00:29:08,160
Speaker 1: this would be like if you see the same person

538
00:29:08,200 --> 00:29:10,880
Speaker 1: on the subway twice and you're convinced that if someone

539
00:29:10,960 --> 00:29:14,200
Speaker 1: following making a connection that isn't there, and it might

540
00:29:14,720 --> 00:29:19,520
Speaker 1: it might take on you know, a pathological um uh energy.

541
00:29:19,720 --> 00:29:21,840
Speaker 1: What's interesting to me here, though, is that I think

542
00:29:22,040 --> 00:29:25,840
Speaker 1: this type one error definition of it is sort of

543
00:29:26,160 --> 00:29:30,600
Speaker 1: connected to the idea of creativity back to so it's

544
00:29:30,600 --> 00:29:34,800
Speaker 1: it's defined as believing something is real when it isn't. Okay,

545
00:29:34,840 --> 00:29:37,000
Speaker 1: that could be said to be part of the creative

546
00:29:37,040 --> 00:29:40,800
Speaker 1: process when you're imagining something that isn't real, at least

547
00:29:40,800 --> 00:29:43,880
Speaker 1: not yet, right either, Like like if you're writing a

548
00:29:43,920 --> 00:29:47,680
Speaker 1: fantasy story and it's like, Okay, my main character has

549
00:29:48,200 --> 00:29:51,040
Speaker 1: wings in the head of a lion and carries a

550
00:29:51,080 --> 00:29:54,720
Speaker 1: flaming sword, right, like, like that doesn't exist, it probably

551
00:29:54,760 --> 00:29:58,880
Speaker 1: won't ever exist. But that doesn't mean necessarily that it's

552
00:29:59,400 --> 00:30:03,040
Speaker 1: not create native, right, But in this sense, it's the

553
00:30:03,080 --> 00:30:05,680
Speaker 1: actual belief that it's real, right Like that if you

554
00:30:05,720 --> 00:30:08,680
Speaker 1: are like, so, there's this angel with a lion's head

555
00:30:08,720 --> 00:30:12,120
Speaker 1: that's following me, around everywhere the flaming sword. Then I

556
00:30:12,160 --> 00:30:15,560
Speaker 1: can see how that would be schizophrenia. Yeah. So yeah,

557
00:30:15,560 --> 00:30:18,280
Speaker 1: in real life and in statistics this is a problem.

558
00:30:18,320 --> 00:30:22,400
Speaker 1: But but in again creativity, uh as John Shaffer points out,

559
00:30:22,480 --> 00:30:26,600
Speaker 1: like our brain engages with it. Our brain uses apithania

560
00:30:26,720 --> 00:30:30,240
Speaker 1: essentially to forward connections where there where there isn't one,

561
00:30:30,600 --> 00:30:32,640
Speaker 1: and a lot of that is where we end up,

562
00:30:33,480 --> 00:30:37,320
Speaker 1: you know, creating something unique. You know, like, for instance,

563
00:30:37,360 --> 00:30:39,320
Speaker 1: the whole why does the human have have a lion

564
00:30:39,400 --> 00:30:40,920
Speaker 1: for a head and a flaming sword? You feel in

565
00:30:40,960 --> 00:30:43,040
Speaker 1: the details and you can reach the point where it's like, oh,

566
00:30:43,040 --> 00:30:45,680
Speaker 1: well now it makes sense I have I have sown

567
00:30:45,720 --> 00:30:51,240
Speaker 1: these two things together and now I have this complete form. Yeah.

568
00:30:51,320 --> 00:30:55,320
Speaker 1: And so when you make those associations in specific ways

569
00:30:55,400 --> 00:30:59,720
Speaker 1: with different media, right, So, like I'm thinking here of

570
00:30:59,800 --> 00:31:03,520
Speaker 1: a bum art, because you listen to the music, that's

571
00:31:03,560 --> 00:31:07,840
Speaker 1: the artifact that the album is ultimately four, But then

572
00:31:07,880 --> 00:31:10,600
Speaker 1: there's album art that's created kind of as part of

573
00:31:10,600 --> 00:31:16,840
Speaker 1: the creative package, but also as marketing and ultimately like

574
00:31:16,960 --> 00:31:20,000
Speaker 1: it gives you, there's connections between those two things, like

575
00:31:20,280 --> 00:31:22,080
Speaker 1: when you buy them. It's interesting to me since I

576
00:31:22,120 --> 00:31:24,600
Speaker 1: moved away from buying physical records and I mainly just

577
00:31:24,600 --> 00:31:27,800
Speaker 1: get digital music. Now I realized that I had so

578
00:31:27,840 --> 00:31:31,960
Speaker 1: many emotional connections with music purely because of the artwork

579
00:31:32,160 --> 00:31:34,080
Speaker 1: that it came with, because of the packaging that it

580
00:31:34,160 --> 00:31:38,200
Speaker 1: came with, and I wasn't simply just judging the music

581
00:31:38,320 --> 00:31:42,440
Speaker 1: on its own merits outside of any kind of visual thing. Well,

582
00:31:42,560 --> 00:31:44,680
Speaker 1: so here's a quick question on this, this idea of

583
00:31:44,680 --> 00:31:47,920
Speaker 1: of albums in their album art. What's an example in

584
00:31:47,920 --> 00:31:51,479
Speaker 1: your opinion where the album art and the music like

585
00:31:51,640 --> 00:31:54,920
Speaker 1: perfectly matched. Oh. You know, it's funny, as I was

586
00:31:54,960 --> 00:32:00,280
Speaker 1: actually listening to this record this morning, thinking of you, uh, Tools,

587
00:32:00,520 --> 00:32:05,640
Speaker 1: is it undertow? Yeah, that like weird sculpture that's on

588
00:32:05,680 --> 00:32:08,800
Speaker 1: the cover of that. And then okay, surrounding that album

589
00:32:08,880 --> 00:32:11,440
Speaker 1: coming out, there were like at least three videos that

590
00:32:11,560 --> 00:32:16,320
Speaker 1: used that similar kind of like gothic claymation style. Right,

591
00:32:16,960 --> 00:32:20,400
Speaker 1: None of those things were the music themselves, But when

592
00:32:20,400 --> 00:32:24,080
Speaker 1: I hear that music, now, for whatever reason in my head,

593
00:32:24,560 --> 00:32:28,960
Speaker 1: are these like swirling clay sculpture forms. Yeah, I think

594
00:32:28,960 --> 00:32:31,680
Speaker 1: that's a great example. My example would have been Tools

595
00:32:31,760 --> 00:32:35,600
Speaker 1: on Automa. Actually, they're certainly a band that has always

596
00:32:35,680 --> 00:32:37,600
Speaker 1: put a lot of thought into how their art and

597
00:32:37,720 --> 00:32:41,360
Speaker 1: music coming together. Now that being said, there are plenty

598
00:32:42,160 --> 00:32:44,800
Speaker 1: of albums out there where the art and the music

599
00:32:44,880 --> 00:32:47,800
Speaker 1: seemed to be on separate planets, and you did a

600
00:32:47,840 --> 00:32:51,400
Speaker 1: fascinating experiment for this episode. I'm thrilled about this, and

601
00:32:51,480 --> 00:32:53,720
Speaker 1: I can't wait for you to tell the audience. Because

602
00:32:54,080 --> 00:32:57,040
Speaker 1: it's interactive, they can go and listen and look at

603
00:32:57,080 --> 00:33:00,200
Speaker 1: this album art as well. That's right. If you think

604
00:33:00,200 --> 00:33:03,080
Speaker 1: back to our episode on Ntrarch, Gieger, we mentioned how

605
00:33:03,200 --> 00:33:06,880
Speaker 1: he was generally cool with just about anybody using his

606
00:33:07,120 --> 00:33:10,040
Speaker 1: art for their album, provided they went through official channels.

607
00:33:10,360 --> 00:33:12,280
Speaker 1: You know, it was, you know, on the level. So

608
00:33:12,320 --> 00:33:15,360
Speaker 1: there are a number of different albums out there that

609
00:33:15,440 --> 00:33:19,000
Speaker 1: have Geeger covers and the music doesn't always match up

610
00:33:19,320 --> 00:33:23,560
Speaker 1: with Or, I would argue, rarely matches up with the

611
00:33:23,560 --> 00:33:27,479
Speaker 1: the aesthetic energy that he possessed. Yeah, with Geeger, like,

612
00:33:27,600 --> 00:33:30,240
Speaker 1: I assume that it's going to be something that's kind

613
00:33:30,240 --> 00:33:35,239
Speaker 1: of kind of industrial or metallic in someone, right. So

614
00:33:36,000 --> 00:33:37,680
Speaker 1: what I did is I went on Spotify. I made

615
00:33:37,680 --> 00:33:41,760
Speaker 1: a playlist with like one song from every album I

616
00:33:41,760 --> 00:33:45,680
Speaker 1: could find on Spotify that had a Geeger cover, like

617
00:33:45,680 --> 00:33:48,800
Speaker 1: like or Or in one case came out at at

618
00:33:48,840 --> 00:33:51,360
Speaker 1: a at a certain point with geeger Art promoting it.

619
00:33:52,040 --> 00:33:55,600
Speaker 1: So I then listened to each example and I tried

620
00:33:55,640 --> 00:33:57,760
Speaker 1: to decide, all right, is this something where the album

621
00:33:58,000 --> 00:33:59,880
Speaker 1: art makes sense for me? Does it do? Is there

622
00:33:59,880 --> 00:34:03,160
Speaker 1: a an actual connection in my mind between Giger and

623
00:34:03,200 --> 00:34:07,200
Speaker 1: this artist? Is they're totally not? And can I detect

624
00:34:07,200 --> 00:34:09,960
Speaker 1: any moments where I feel apophania kicking in and this

625
00:34:10,080 --> 00:34:14,160
Speaker 1: creative process in my brain bringing the two together, finding

626
00:34:14,200 --> 00:34:16,960
Speaker 1: and forging the connections. So that's what I did, and

627
00:34:17,280 --> 00:34:19,479
Speaker 1: you can do this too. I'm gonna have the link

628
00:34:20,000 --> 00:34:23,080
Speaker 1: for this Spotify playlist on the landing page for this

629
00:34:23,080 --> 00:34:26,080
Speaker 1: episode of Stuff to Blow Your Mind dot Com. But yeah,

630
00:34:26,080 --> 00:34:28,799
Speaker 1: I started listening to these. I listened to Emerson Lake

631
00:34:28,840 --> 00:34:33,560
Speaker 1: and Palmer's Brain Salad Surgery, which, uh, we're talking about

632
00:34:33,560 --> 00:34:35,719
Speaker 1: this before. I don't know a lot about them, but

633
00:34:35,760 --> 00:34:37,360
Speaker 1: I just kind of assume it's sort of like a

634
00:34:37,880 --> 00:34:41,479
Speaker 1: like a Prague post hippie band. It's prog rock, for sure,

635
00:34:41,520 --> 00:34:44,440
Speaker 1: it's it's many people love them. I think Joe's a

636
00:34:44,520 --> 00:34:47,239
Speaker 1: big fan of it. Loves the Tarkus cover for sure.

637
00:34:47,239 --> 00:34:49,319
Speaker 1: He talks about that a lot. Yeah, so it's it's

638
00:34:49,320 --> 00:34:52,000
Speaker 1: a bit too jazzy from my personal taste. And so

639
00:34:52,040 --> 00:34:53,920
Speaker 1: this was an example where I'm like, yeah, I'm just

640
00:34:54,000 --> 00:34:57,360
Speaker 1: not I'm not not feeling this Geeger, Emerson Lake and

641
00:34:57,360 --> 00:35:02,280
Speaker 1: Palmber connection. The same thing with Debbie Harry's uh Cuckoo album.

642
00:35:02,920 --> 00:35:06,680
Speaker 1: I'm straining to make that connection. The same with Steve

643
00:35:06,800 --> 00:35:10,800
Speaker 1: Simons Electric Playboys, and with the Dead Kennedy's album as well.

644
00:35:10,960 --> 00:35:13,600
Speaker 1: And I think on the Dead Kennedy's one, because that

645
00:35:13,719 --> 00:35:18,000
Speaker 1: was like so hotly contested with the pr MC, it

646
00:35:18,080 --> 00:35:20,840
Speaker 1: was only in the interior they have like a different

647
00:35:20,880 --> 00:35:23,560
Speaker 1: cover that's like I think like Shriner's and like little

648
00:35:23,840 --> 00:35:25,759
Speaker 1: roller Cars or something. Yeah, And then I think on

649
00:35:25,800 --> 00:35:30,520
Speaker 1: the inside is that notorious controversial thing the landscape with

650
00:35:30,920 --> 00:35:36,040
Speaker 1: a lot of yeah. Yeah. So those are so those

651
00:35:36,040 --> 00:35:37,879
Speaker 1: are examples where I'm all, right, no connection at all.

652
00:35:37,920 --> 00:35:40,960
Speaker 1: I don't get this, the connection between the art and

653
00:35:41,000 --> 00:35:43,560
Speaker 1: the music. But then there's stuff on the other hands

654
00:35:43,560 --> 00:35:47,360
Speaker 1: into the spectrum. So Celtic Frost for example, Uh, that's

655
00:35:47,400 --> 00:35:51,759
Speaker 1: some some early death metal very much feels uh like

656
00:35:51,800 --> 00:35:55,960
Speaker 1: a Geeger soundscape. Same thing with the Danzig three. I

657
00:35:56,120 --> 00:35:59,719
Speaker 1: you know, it's gloomy and dark and you know, as

658
00:35:59,760 --> 00:36:05,280
Speaker 1: all the this focus on you know, weird ideas, it works,

659
00:36:05,320 --> 00:36:07,960
Speaker 1: all right. I totally but totally buy these album covers.

660
00:36:08,840 --> 00:36:12,560
Speaker 1: But the one area, the one example where I felt

661
00:36:12,560 --> 00:36:15,160
Speaker 1: things coming together and I had to like kind of struggling,

662
00:36:15,160 --> 00:36:17,160
Speaker 1: but also I could I could feel the connection taking

663
00:36:17,160 --> 00:36:21,000
Speaker 1: place was with a group called Magma. So they had

664
00:36:21,040 --> 00:36:24,440
Speaker 1: this album called Attack A T T A h K.

665
00:36:24,760 --> 00:36:27,560
Speaker 1: I've never heard of them until today, and it's and

666
00:36:27,600 --> 00:36:29,440
Speaker 1: it has like Geeger. It's one of these where Gieger

667
00:36:29,560 --> 00:36:31,840
Speaker 1: made the album art with the the name of the bands.

668
00:36:31,880 --> 00:36:33,600
Speaker 1: It was like a commission. It looks kind of like

669
00:36:33,640 --> 00:36:36,799
Speaker 1: his Atomic Babies very much. So yeah, but I think

670
00:36:36,800 --> 00:36:41,040
Speaker 1: there's some clothes, some safety pins in there as well, so,

671
00:36:41,120 --> 00:36:43,480
Speaker 1: you know, very much a Geeger piece with Geegers a

672
00:36:43,560 --> 00:36:47,279
Speaker 1: signature symbolism and uh. And at first time like I'm

673
00:36:47,320 --> 00:36:49,360
Speaker 1: not really feeling this connection, but then I started reading

674
00:36:49,400 --> 00:36:53,239
Speaker 1: about the band French prog Rock. Uh. Their album is

675
00:36:53,320 --> 00:36:56,440
Speaker 1: again very weird. The drum the drummer and founder Christian

676
00:36:56,560 --> 00:37:01,640
Speaker 1: vander He composed the lyrics and a constructed language called Kobayan,

677
00:37:02,360 --> 00:37:06,359
Speaker 1: and the whole project came together over an ecological, spiritual

678
00:37:06,440 --> 00:37:09,359
Speaker 1: vision for humanity's future. And so I read about that

679
00:37:09,440 --> 00:37:10,799
Speaker 1: and I listened to it a bit more, and I

680
00:37:10,840 --> 00:37:13,160
Speaker 1: have to say, yeah, I was beginning to feel the

681
00:37:13,160 --> 00:37:18,839
Speaker 1: connection between the track No No and Gieger's art. So

682
00:37:18,960 --> 00:37:21,839
Speaker 1: this makes me think of, so you and I are

683
00:37:21,880 --> 00:37:25,680
Speaker 1: old enough that we're seeing this resurgence with vinyl, and

684
00:37:25,719 --> 00:37:28,000
Speaker 1: it's like, well, that's stuff that we used to listen

685
00:37:28,360 --> 00:37:31,240
Speaker 1: on and then it went away, and now it's coming back,

686
00:37:31,400 --> 00:37:34,400
Speaker 1: and it in a way, it's novel and high quality,

687
00:37:34,520 --> 00:37:39,080
Speaker 1: right and and uh, but the vinyl resurgence seems to

688
00:37:39,120 --> 00:37:43,520
Speaker 1: me to be inherently connected to this apophenia into this

689
00:37:43,640 --> 00:37:47,640
Speaker 1: like creativity that's going on in the consumer's head. Right.

690
00:37:47,719 --> 00:37:51,000
Speaker 1: And uh, my friend Charlie actually talks about this because

691
00:37:51,000 --> 00:37:53,760
Speaker 1: he's a big vinyl fan. He refers to like having

692
00:37:53,800 --> 00:37:57,680
Speaker 1: the artifact as the material oomph of a thing. And

693
00:37:58,000 --> 00:38:01,400
Speaker 1: I I just I stopped acting records because I was like,

694
00:38:01,520 --> 00:38:05,560
Speaker 1: I have too much physical space. I can't carry these

695
00:38:05,600 --> 00:38:07,399
Speaker 1: around with me anymore for the rest of my life,

696
00:38:07,680 --> 00:38:10,640
Speaker 1: and moved almost entirely to digital. But so many of

697
00:38:10,680 --> 00:38:14,560
Speaker 1: my friends are really into this new resurgence of vinyl,

698
00:38:14,640 --> 00:38:17,120
Speaker 1: and I understand it, right, because like if you're sitting

699
00:38:17,160 --> 00:38:20,120
Speaker 1: there between like what you just said about Magma, you

700
00:38:20,600 --> 00:38:22,880
Speaker 1: look at this record art and then like maybe you

701
00:38:22,880 --> 00:38:25,640
Speaker 1: look the liner notes while the album's playing, and then

702
00:38:25,680 --> 00:38:28,000
Speaker 1: maybe you go on Wikipedia or something and you read

703
00:38:28,120 --> 00:38:31,520
Speaker 1: up on them. It creates this like a series of

704
00:38:31,600 --> 00:38:36,480
Speaker 1: connections that may or may not exist but add something

705
00:38:36,480 --> 00:38:39,440
Speaker 1: to the music. Yeah. So with to bring it back

706
00:38:39,480 --> 00:38:43,080
Speaker 1: to machine learning and creativity, to what extent is it

707
00:38:43,120 --> 00:38:46,560
Speaker 1: a case of machines throwing together combinations? Still something begins

708
00:38:46,560 --> 00:38:49,839
Speaker 1: to catch till you have this Magma moment, and then

709
00:38:49,880 --> 00:38:53,920
Speaker 1: the randomness touches on possible synchronicity, and then just as

710
00:38:53,960 --> 00:38:56,480
Speaker 1: our brains try to make sense of it, we engage

711
00:38:56,480 --> 00:38:59,759
Speaker 1: in the creative process of refining the machine made connections.

712
00:39:00,280 --> 00:39:03,440
Speaker 1: So I'll just say this before we take a break,

713
00:39:03,480 --> 00:39:07,799
Speaker 1: which is that whatever the first album is that's created

714
00:39:08,000 --> 00:39:12,200
Speaker 1: entirely by AI, it should have an HR cover. That's true.

715
00:39:12,239 --> 00:39:14,560
Speaker 1: And if it's already out there, the first d A A

716
00:39:14,719 --> 00:39:17,120
Speaker 1: I album, it needs a new cover by g As

717
00:39:17,160 --> 00:39:19,440
Speaker 1: we talked about in the hr Giger episode, that house

718
00:39:19,560 --> 00:39:23,680
Speaker 1: in Switzerland is just full of unused arts. Yeah, contact them,

719
00:39:23,719 --> 00:39:26,959
Speaker 1: commission knock on something and get the rights to it. So, okay,

720
00:39:27,000 --> 00:39:29,160
Speaker 1: let's take a break, and when we come back, we're

721
00:39:29,200 --> 00:39:31,680
Speaker 1: going to talk more about the effects of the actual

722
00:39:31,760 --> 00:39:39,759
Speaker 1: technology on the act of creativity. Alright, we're back. So

723
00:39:39,880 --> 00:39:42,160
Speaker 1: in all of this, we're talking about machine learning and

724
00:39:42,840 --> 00:39:46,600
Speaker 1: robotic arms that are drawing, uh, computer programs that are

725
00:39:46,640 --> 00:39:50,200
Speaker 1: taking the various you know, hierarchies of of of values

726
00:39:50,200 --> 00:39:52,720
Speaker 1: within a particular discipline and then using those to create

727
00:39:53,520 --> 00:39:57,120
Speaker 1: quote unquote art or fiction or you know, or or

728
00:39:57,520 --> 00:40:00,480
Speaker 1: or a recipe. In all of these cases, Oh, it's

729
00:40:00,520 --> 00:40:03,440
Speaker 1: it's essential to point out that the technology is working

730
00:40:03,480 --> 00:40:07,400
Speaker 1: as a tool. And obviously, creativity and tools have always

731
00:40:07,440 --> 00:40:10,080
Speaker 1: co evolved. So think of any great work of human art,

732
00:40:10,360 --> 00:40:12,880
Speaker 1: and you have to contemplate the tools and the technology

733
00:40:12,920 --> 00:40:16,080
Speaker 1: is required to create it. The physical tools for carving

734
00:40:16,200 --> 00:40:20,680
Speaker 1: rock or would working and firing pottery, the chemical technologies

735
00:40:20,719 --> 00:40:24,360
Speaker 1: of paints, varnishes, and laquers. The evolution of musical instruments,

736
00:40:24,400 --> 00:40:30,120
Speaker 1: electrical musical instruments and recording and producing technology, writing technologies

737
00:40:30,160 --> 00:40:33,960
Speaker 1: from clay tablets two pens to the printing press, the typewriter,

738
00:40:34,280 --> 00:40:37,839
Speaker 1: word processors, and beyond. Yeah, and so in this conversation,

739
00:40:37,960 --> 00:40:41,279
Speaker 1: the panelists are essentially arguing that AI. Some of them

740
00:40:41,280 --> 00:40:44,440
Speaker 1: are arguing that the AI would just be a tool

741
00:40:44,719 --> 00:40:47,560
Speaker 1: for humans to use creatively. It's not that the AI

742
00:40:47,640 --> 00:40:50,399
Speaker 1: itself would be creative, but that we would be using

743
00:40:50,400 --> 00:40:52,160
Speaker 1: it in the same way we use a guitar or

744
00:40:52,200 --> 00:40:54,799
Speaker 1: a pen or something like that. Yeah, And and that

745
00:40:55,120 --> 00:40:57,799
Speaker 1: ultimately it's not going to be any more destructive than

746
00:40:58,120 --> 00:41:00,759
Speaker 1: you know, the effects of the synthesizer or any of

747
00:41:00,800 --> 00:41:04,680
Speaker 1: any of these various electronic musical technologies that obviously did

748
00:41:04,719 --> 00:41:09,759
Speaker 1: not destroy traditional music or or or caused judgment day. Yeah,

749
00:41:09,800 --> 00:41:12,120
Speaker 1: or caused judgment day. That they didn't destroy the the

750
00:41:12,280 --> 00:41:16,480
Speaker 1: artistic traditions that came before. They took the existing traditions

751
00:41:16,480 --> 00:41:19,520
Speaker 1: in new directions, sometimes surprising new directions. They bring up

752
00:41:19,560 --> 00:41:22,279
Speaker 1: the example of the drum machine, where when it came out,

753
00:41:22,640 --> 00:41:24,839
Speaker 1: you know it, there were people who were figuring out

754
00:41:24,840 --> 00:41:28,319
Speaker 1: ways to use it where you're creating sounds that did

755
00:41:28,360 --> 00:41:31,680
Speaker 1: you know, they didn't match, They weren't perfectly replicating real

756
00:41:31,880 --> 00:41:34,640
Speaker 1: world drumming, but they were able to to use them

757
00:41:34,640 --> 00:41:37,879
Speaker 1: in novel ways that that brought about some some new

758
00:41:37,960 --> 00:41:40,600
Speaker 1: sounds and hip hop and electronic music. But then one

759
00:41:40,600 --> 00:41:43,120
Speaker 1: of the panelists is the guy from Google and forgetting

760
00:41:43,120 --> 00:41:46,800
Speaker 1: his name right now, but he likened AI to a garden,

761
00:41:47,239 --> 00:41:49,719
Speaker 1: and he said it's where you're growing things, but you're

762
00:41:49,760 --> 00:41:53,560
Speaker 1: growing them with intentionality, and that computer systems themselves are

763
00:41:53,600 --> 00:41:57,720
Speaker 1: not in a state where they can reflect upon that intentionality. Yet,

764
00:41:58,080 --> 00:42:01,319
Speaker 1: So does this count as create activity when they're doing

765
00:42:01,320 --> 00:42:04,000
Speaker 1: it without intentionality? And it really depends on what you're

766
00:42:04,040 --> 00:42:06,759
Speaker 1: focusing on here. Some of them were focusing on the

767
00:42:06,800 --> 00:42:10,759
Speaker 1: process of creativity and others were focusing on the artifacts

768
00:42:10,760 --> 00:42:14,880
Speaker 1: of creativity. Right. So a computer program can certainly currently

769
00:42:15,000 --> 00:42:18,520
Speaker 1: create an artifact, right, whether it's a recipe, a song,

770
00:42:19,239 --> 00:42:22,600
Speaker 1: or a drawing, right, as we saw examples of all

771
00:42:22,840 --> 00:42:27,239
Speaker 1: three in this panel. Um, but it's not necessarily engaged

772
00:42:27,280 --> 00:42:29,279
Speaker 1: in the process. And so that seems to be where

773
00:42:29,320 --> 00:42:33,680
Speaker 1: they're diverging, especially because for the latter, intentionality may not

774
00:42:33,800 --> 00:42:37,160
Speaker 1: actually be necessary. You can make artifacts without having any

775
00:42:37,200 --> 00:42:40,319
Speaker 1: intention right. And to your point on the process, Chung

776
00:42:40,480 --> 00:42:43,200
Speaker 1: points out that the process is vital to what she does,

777
00:42:43,280 --> 00:42:46,800
Speaker 1: like it's really more important than the finished artifact exactly.

778
00:42:47,040 --> 00:42:48,959
Speaker 1: I mean, the painting is one thing, but it's it's

779
00:42:49,000 --> 00:42:53,160
Speaker 1: that that video as well of her interacting with this

780
00:42:53,360 --> 00:42:58,360
Speaker 1: robotic arm that is that is essentially jamming with her. Yeah, yeah, exactly.

781
00:42:58,719 --> 00:43:01,040
Speaker 1: I think this is really interesting because it forces us

782
00:43:01,120 --> 00:43:05,719
Speaker 1: to systematize our own creativity. And ironically, you know, we

783
00:43:05,760 --> 00:43:09,080
Speaker 1: as human beings like to think that our own internal system,

784
00:43:09,200 --> 00:43:12,359
Speaker 1: especially when we're being creative, is not computational, right, but

785
00:43:12,640 --> 00:43:15,920
Speaker 1: maybe it is more than we know. Well. This is

786
00:43:15,920 --> 00:43:18,319
Speaker 1: always one of those areas where you don't want to

787
00:43:18,320 --> 00:43:22,120
Speaker 1: fall into the trap of thinking entirely of human cognition

788
00:43:22,160 --> 00:43:25,920
Speaker 1: in terms of a computer. Um, even though technology is

789
00:43:25,960 --> 00:43:29,920
Speaker 1: always a a handy way to try and uh and

790
00:43:30,040 --> 00:43:33,520
Speaker 1: see our own cognitive processes. But on the other hand,

791
00:43:33,560 --> 00:43:35,480
Speaker 1: you don't want to You can't dismiss the fact that

792
00:43:35,560 --> 00:43:39,200
Speaker 1: there are there are aspects of our cognition that they

793
00:43:39,200 --> 00:43:42,080
Speaker 1: are very much in line with the functioning of a computer. Right.

794
00:43:42,080 --> 00:43:44,799
Speaker 1: It's the same thing as the bird is not an airplane, right,

795
00:43:45,040 --> 00:43:47,879
Speaker 1: But at the same time they are, they both learned,

796
00:43:47,920 --> 00:43:51,040
Speaker 1: they both carry out the same process. Yeah. Um. I

797
00:43:51,080 --> 00:43:53,319
Speaker 1: think though, like the tool building thing, is where it

798
00:43:53,360 --> 00:43:55,839
Speaker 1: really gets interesting to me and determining whether or not

799
00:43:55,920 --> 00:43:59,200
Speaker 1: computers are creative because for creative purposes, when you actually

800
00:43:59,239 --> 00:44:01,080
Speaker 1: go out and build old the new tool. For me,

801
00:44:01,200 --> 00:44:04,520
Speaker 1: that's a sign that a creator has actually like graduated

802
00:44:04,520 --> 00:44:06,640
Speaker 1: to this other level of making, right. I think I

803
00:44:06,719 --> 00:44:09,799
Speaker 1: referred to it as meta creativity earlier. They're somehow not

804
00:44:10,000 --> 00:44:13,359
Speaker 1: satisfied with the available tools that are there, and they

805
00:44:13,400 --> 00:44:17,560
Speaker 1: need to experiment, and in terms of that novelty quality divide,

806
00:44:17,920 --> 00:44:22,200
Speaker 1: this isn't always successful obviously, right, And it's especially fascinating

807
00:44:22,200 --> 00:44:25,920
Speaker 1: when a creator balances the novelty of that experiment with

808
00:44:25,960 --> 00:44:28,879
Speaker 1: actual aesthetics and creates like a new tool that other

809
00:44:28,920 --> 00:44:30,960
Speaker 1: people end up going on to use. You know, we

810
00:44:30,960 --> 00:44:35,080
Speaker 1: were talking about Burrows, William Burrows and Naked Lunch here.

811
00:44:35,120 --> 00:44:39,239
Speaker 1: I think the cutoup machine is an example where the

812
00:44:39,480 --> 00:44:43,560
Speaker 1: it is a novel approach to take essentially like taking

813
00:44:43,640 --> 00:44:46,759
Speaker 1: these these these paragraphs and phrases and they're literally cut

814
00:44:46,840 --> 00:44:51,040
Speaker 1: up and then may combined. And yet when I've when

815
00:44:51,040 --> 00:44:55,200
Speaker 1: I've read cut up literature, it's noise. It's mostly noise. Yeah,

816
00:44:55,320 --> 00:44:58,440
Speaker 1: And then sometimes I like it, But most of the

817
00:44:58,440 --> 00:45:01,680
Speaker 1: time I it doesn't quite connect from me. And when

818
00:45:01,680 --> 00:45:04,879
Speaker 1: I've tried to do it myself, I've really not not

819
00:45:05,000 --> 00:45:08,160
Speaker 1: liked it. So I experimented with it a while ago

820
00:45:08,719 --> 00:45:10,799
Speaker 1: and what I ended up doing was rather than just

821
00:45:10,840 --> 00:45:13,160
Speaker 1: like doing cut up method, I guess we should explain

822
00:45:13,239 --> 00:45:16,440
Speaker 1: what cut up is. So you write something with intentionality

823
00:45:16,560 --> 00:45:19,200
Speaker 1: and then you cut out all the words up into

824
00:45:19,239 --> 00:45:21,520
Speaker 1: separate little pieces of paper. At least is how Burrows

825
00:45:21,560 --> 00:45:23,120
Speaker 1: did it. I think there are programs that will do

826
00:45:23,160 --> 00:45:27,600
Speaker 1: it for you know, UM, and you literally randomly paced

827
00:45:27,680 --> 00:45:32,799
Speaker 1: them together so they form just ungrammatical, oddly formed sentences,

828
00:45:32,880 --> 00:45:37,279
Speaker 1: and try to see what kind of apothenic connections are

829
00:45:37,320 --> 00:45:40,480
Speaker 1: formed by these words combined the way they are. UM.

830
00:45:40,520 --> 00:45:42,359
Speaker 1: The way I was doing it was I would do

831
00:45:42,400 --> 00:45:45,080
Speaker 1: that and then I would look to see where there

832
00:45:45,080 --> 00:45:47,480
Speaker 1: were interesting connections, and then I would pull those out

833
00:45:47,480 --> 00:45:50,359
Speaker 1: and put them in my writing rather than actually use

834
00:45:50,480 --> 00:45:53,000
Speaker 1: cut up method to write anything. Well. And that that

835
00:45:53,040 --> 00:45:56,080
Speaker 1: comes back around to what what the panels we're talking

836
00:45:56,080 --> 00:45:58,480
Speaker 1: about here? The idea that that it is a tool,

837
00:45:58,760 --> 00:46:01,480
Speaker 1: that that there's still going to be this this human

838
00:46:01,520 --> 00:46:04,560
Speaker 1: at the center of it that's walking in this garden

839
00:46:04,719 --> 00:46:08,759
Speaker 1: of robotic creativity, of machine creativity and saying that's a

840
00:46:08,760 --> 00:46:11,400
Speaker 1: good carrot. I'm going to pick that one. That carrot

841
00:46:11,719 --> 00:46:14,480
Speaker 1: looks like crap. We're gonna leave that that carrot was

842
00:46:14,520 --> 00:46:17,400
Speaker 1: never meant to be. So this whole thing leads me

843
00:46:17,480 --> 00:46:19,560
Speaker 1: to ask a question that I don't think they addressed,

844
00:46:19,560 --> 00:46:23,080
Speaker 1: which is if tool use and making tools is an

845
00:46:23,160 --> 00:46:26,480
Speaker 1: essential part of creativity, right, Like somebody had to invent

846
00:46:26,800 --> 00:46:29,759
Speaker 1: the right kind of crow quill pen to illustrate with,

847
00:46:30,120 --> 00:46:34,320
Speaker 1: or somebody invented a particular style of guitar with pickups

848
00:46:34,360 --> 00:46:37,400
Speaker 1: in the six strings and the exact kind of materials

849
00:46:37,400 --> 00:46:39,440
Speaker 1: that are used in the body and the neck right,

850
00:46:40,040 --> 00:46:43,719
Speaker 1: or narrative style, or even stoves right, particular kinds of

851
00:46:43,760 --> 00:46:47,120
Speaker 1: stoves for cooking, So all of those things are actually

852
00:46:47,239 --> 00:46:51,959
Speaker 1: part of creativity. Our computers then capable of creating their

853
00:46:52,080 --> 00:46:56,200
Speaker 1: own tools for the sake of experimenting and being novel.

854
00:46:56,719 --> 00:46:59,000
Speaker 1: And I think the answer to this would be that

855
00:46:59,440 --> 00:47:01,880
Speaker 1: we would say, well, that's how they write algorithms, right,

856
00:47:01,920 --> 00:47:05,640
Speaker 1: The algorithms are their tools. But what about actual artifacts

857
00:47:05,680 --> 00:47:09,919
Speaker 1: that they're using to make other artifacts? Huh? Yeah, I don't.

858
00:47:10,120 --> 00:47:12,799
Speaker 1: I don't recall any examples of that, whether within this

859
00:47:12,840 --> 00:47:16,759
Speaker 1: discussion or or outside of it. Um that seems to

860
00:47:16,800 --> 00:47:20,360
Speaker 1: me like it might be a good place to draw

861
00:47:20,400 --> 00:47:24,400
Speaker 1: a line of like when AI starts being creative at

862
00:47:24,440 --> 00:47:27,400
Speaker 1: least from my perspective. But then again, I mean I

863
00:47:27,400 --> 00:47:29,960
Speaker 1: think of of great creative minds, and so many of

864
00:47:30,000 --> 00:47:35,120
Speaker 1: them your prefab tools. Yeah, and they're working within the

865
00:47:35,280 --> 00:47:39,000
Speaker 1: confines of their genre. But between you know, within say,

866
00:47:39,040 --> 00:47:42,480
Speaker 1: literary norms in many cases, well in many cases, of course,

867
00:47:42,520 --> 00:47:45,560
Speaker 1: you see someone sort of mastering the norms and then

868
00:47:45,760 --> 00:47:47,480
Speaker 1: figuring out what new spins to put on it. But

869
00:47:47,520 --> 00:47:50,680
Speaker 1: they're not really I would I would ask are they

870
00:47:50,760 --> 00:47:53,600
Speaker 1: actually creating any new tools though, or are they just

871
00:47:54,160 --> 00:47:57,920
Speaker 1: using the existing tools in slightly different ways, breaking the rules? Yeah,

872
00:47:58,000 --> 00:47:59,800
Speaker 1: I don't know. It's it's interesting, though. I wish we

873
00:47:59,840 --> 00:48:03,360
Speaker 1: could to ask those panelists this question. Like Corn McCarthy

874
00:48:03,840 --> 00:48:05,560
Speaker 1: used as a typewriter. I think he uses like an

875
00:48:05,560 --> 00:48:09,719
Speaker 1: old typewriter to knock out his his fiction, but he

876
00:48:09,760 --> 00:48:14,000
Speaker 1: did not create his own. He didn't build his typewriter, like, uh,

877
00:48:14,400 --> 00:48:18,279
Speaker 1: you know, bowl skulls and children's skulls. This is kind

878
00:48:18,280 --> 00:48:22,520
Speaker 1: of what I'm getting at, Yeah, exactly, Like, but but

879
00:48:22,600 --> 00:48:25,520
Speaker 1: you could argue maybe that Cormac McCarthy had created a

880
00:48:25,600 --> 00:48:30,919
Speaker 1: certain kind of prose style, right that is unique to him.

881
00:48:31,040 --> 00:48:34,280
Speaker 1: Um or and I don't I haven't read enough McCarthy.

882
00:48:34,360 --> 00:48:37,680
Speaker 1: So I might be totally off here, but like maybe

883
00:48:37,760 --> 00:48:41,719
Speaker 1: he's like fiddled with narrative rules in such a way

884
00:48:41,800 --> 00:48:44,840
Speaker 1: that it's novel true. Yeah, I mean you can also

885
00:48:44,960 --> 00:48:46,759
Speaker 1: go back to Gieger when we've talked about how he

886
00:48:46,800 --> 00:48:50,239
Speaker 1: was using the airbrush. He did not invente brush, but

887
00:48:50,280 --> 00:48:55,279
Speaker 1: he was using the airbrush in ways that well. I

888
00:48:55,640 --> 00:48:57,319
Speaker 1: don't I don't want to speak to method too much

889
00:48:57,320 --> 00:48:59,440
Speaker 1: because I don't really know much about the methodology of

890
00:48:59,480 --> 00:49:02,520
Speaker 1: airbrush art, but he was doing things with it that

891
00:49:02,640 --> 00:49:04,399
Speaker 1: no one else had done. From what I could tell

892
00:49:04,520 --> 00:49:07,520
Speaker 1: from when we did that hr Geiger episode, he was

893
00:49:08,120 --> 00:49:11,120
Speaker 1: using ink and paint that hadn't been used in an

894
00:49:11,200 --> 00:49:13,920
Speaker 1: airbrush for Yeah, So I think that might be part

895
00:49:13,960 --> 00:49:15,680
Speaker 1: of that. Would that would yeah, I think that would

896
00:49:15,719 --> 00:49:17,960
Speaker 1: support the idea of using a new tool. I mean,

897
00:49:18,000 --> 00:49:20,919
Speaker 1: if you're changing the tool, you are creating a new tool.

898
00:49:21,000 --> 00:49:23,279
Speaker 1: That's That's something they touched on in the in the

899
00:49:23,480 --> 00:49:25,840
Speaker 1: panel discussion of the World Science Festivals that one of

900
00:49:25,840 --> 00:49:28,880
Speaker 1: the things about human tools is that they have evolved

901
00:49:28,880 --> 00:49:31,600
Speaker 1: and we have evolved with them. Our brain, our culture,

902
00:49:31,680 --> 00:49:35,200
Speaker 1: our language, this is all been part of the same

903
00:49:35,280 --> 00:49:38,680
Speaker 1: journey out of out of the Stone Age out of

904
00:49:38,680 --> 00:49:41,400
Speaker 1: the pre Stone Age. As we as we we adapt

905
00:49:41,440 --> 00:49:43,799
Speaker 1: these tools, we figure out new ways to communicate the

906
00:49:43,800 --> 00:49:49,160
Speaker 1: construction of these tools and then create increasingly complex um

907
00:49:50,000 --> 00:49:54,759
Speaker 1: bits of pieces of art, technologies, etcetera with those tools. Yeah.

908
00:49:54,920 --> 00:49:57,719
Speaker 1: So then getting back to limits, right, which is something

909
00:49:57,800 --> 00:50:01,200
Speaker 1: I brought up really before even we out into these panels,

910
00:50:01,840 --> 00:50:05,000
Speaker 1: is that, Okay, a I can use the tools, and

911
00:50:05,080 --> 00:50:08,239
Speaker 1: it can learn the composition traits of art, and it

912
00:50:08,239 --> 00:50:11,320
Speaker 1: can learn those rules. What we're questioning is whether or

913
00:50:11,360 --> 00:50:14,680
Speaker 1: not it can learn how to break those rules. But uh,

914
00:50:14,719 --> 00:50:18,040
Speaker 1: when they're using a tool, they have to kind of

915
00:50:18,040 --> 00:50:21,480
Speaker 1: figure out the same constraints on the system that humans do.

916
00:50:21,800 --> 00:50:24,839
Speaker 1: And so I'm wondering if these patterns are based on

917
00:50:25,080 --> 00:50:28,839
Speaker 1: quote unquote how the world works as we humans see it,

918
00:50:29,280 --> 00:50:34,160
Speaker 1: then that's human culture, and the we're just teaching the

919
00:50:34,200 --> 00:50:38,000
Speaker 1: computers the sort of like mathematics of human culture and

920
00:50:38,040 --> 00:50:42,200
Speaker 1: then having them replicate that rather than the machines looking

921
00:50:42,200 --> 00:50:44,400
Speaker 1: at the world and kind of coming up with their

922
00:50:44,400 --> 00:50:48,840
Speaker 1: own cultural interpretation of it. Maybe I'm leaning more towards Ulrica,

923
00:50:49,040 --> 00:50:53,000
Speaker 1: say than I thought I would. All Right, well, let's

924
00:50:53,040 --> 00:50:55,760
Speaker 1: take another quick break and when we come back, we'll

925
00:50:55,880 --> 00:50:58,399
Speaker 1: talk about what it means for the future and uh,

926
00:50:58,400 --> 00:51:02,040
Speaker 1: and also some more compare between the creative process and

927
00:51:02,360 --> 00:51:10,520
Speaker 1: machine learning and airplanes. Thank thank Alright, we're back. So

928
00:51:10,640 --> 00:51:12,960
Speaker 1: you were really taken with Chung as I was. I

929
00:51:13,000 --> 00:51:15,520
Speaker 1: think like she she just stood up. I mean, everybody

930
00:51:15,520 --> 00:51:18,560
Speaker 1: on the panel was interesting, but Chung just was really

931
00:51:18,600 --> 00:51:20,719
Speaker 1: fascinating the way she talked about her process and then

932
00:51:20,719 --> 00:51:23,520
Speaker 1: when you watched her actual process. Yeah. The second she

933
00:51:23,640 --> 00:51:26,320
Speaker 1: said that bit about like the narrative of machines and

934
00:51:26,400 --> 00:51:29,040
Speaker 1: humans cooperating, I totally agreed with because it lines up

935
00:51:29,120 --> 00:51:31,759
Speaker 1: with a lot of what I've been thinking about with

936
00:51:31,800 --> 00:51:35,959
Speaker 1: science fiction, is that we we continually see the dystopian

937
00:51:36,160 --> 00:51:41,200
Speaker 1: vision of robot overlords and evil robots, evil androids, and

938
00:51:41,239 --> 00:51:43,239
Speaker 1: I love all that stuff, don't get me wrong. But

939
00:51:43,960 --> 00:51:46,319
Speaker 1: when I read in in Banks and his view of

940
00:51:46,360 --> 00:51:52,239
Speaker 1: the culture, uh, this post scarcity, far future um utopian

941
00:51:52,280 --> 00:51:57,800
Speaker 1: society living with computers live basically having this sort of

942
00:51:57,920 --> 00:52:02,440
Speaker 1: hybrid cultural scenaria, you know, with super intelligent machines, Like

943
00:52:02,480 --> 00:52:04,520
Speaker 1: it makes me think, what we should have more of that?

944
00:52:04,560 --> 00:52:06,600
Speaker 1: We should have more of this This side of the

945
00:52:06,760 --> 00:52:11,360
Speaker 1: argument for a post singularity world and and the and

946
00:52:11,360 --> 00:52:13,360
Speaker 1: and not not to say that we shouldn't be concerned

947
00:52:13,360 --> 00:52:17,600
Speaker 1: about the potential dire consequences. I mean, obviously we if

948
00:52:18,000 --> 00:52:21,239
Speaker 1: to whatever extent it's practical, we should try to avoid

949
00:52:22,280 --> 00:52:26,279
Speaker 1: terminator scenarios. But but there's this whole other side. There's

950
00:52:26,280 --> 00:52:28,680
Speaker 1: this whole there's this view of the machine as a tool.

951
00:52:28,920 --> 00:52:33,080
Speaker 1: This machine is a collaborative of process, and I think

952
00:52:33,080 --> 00:52:35,279
Speaker 1: that's that's very important to keep in mind. Yeah, and

953
00:52:35,400 --> 00:52:39,080
Speaker 1: I totally admire their intention, but I wonder if there

954
00:52:39,239 --> 00:52:41,680
Speaker 1: is a point where it's really down to like sort

955
00:52:41,680 --> 00:52:44,759
Speaker 1: of what our linguistic definitions are here. I think there

956
00:52:44,800 --> 00:52:48,840
Speaker 1: might be a little bit of confusing collaboration for tool building.

957
00:52:49,560 --> 00:52:53,960
Speaker 1: Is the machine actually collaborating with Chung or was she

958
00:52:54,080 --> 00:52:56,640
Speaker 1: building a new tool that she could use to create

959
00:52:56,680 --> 00:52:59,160
Speaker 1: new art with. Yeah, because you could argue that in

960
00:52:59,200 --> 00:53:02,440
Speaker 1: the same way that a French horn, you you buzz

961
00:53:02,480 --> 00:53:05,880
Speaker 1: your error into one end, and the technology of the

962
00:53:05,960 --> 00:53:10,400
Speaker 1: horn allows a different sound to come out. So you

963
00:53:10,400 --> 00:53:12,480
Speaker 1: could say that that's what she's doing here. She's essentially

964
00:53:12,520 --> 00:53:17,520
Speaker 1: blowing into a horn, and we're appreciating the the duality

965
00:53:17,560 --> 00:53:21,319
Speaker 1: of her organic sound and in the manufactured sound of

966
00:53:21,320 --> 00:53:24,000
Speaker 1: the horn, right, It's like she's developed a new instrument

967
00:53:24,080 --> 00:53:26,640
Speaker 1: and she's learning how to play it. Yeah, and I

968
00:53:26,680 --> 00:53:28,800
Speaker 1: think that's a valid reading what's going on as well

969
00:53:29,680 --> 00:53:32,640
Speaker 1: now in all of this, uh. Jesse Ingle, the computer

970
00:53:32,680 --> 00:53:36,440
Speaker 1: scientist musician. UH, he spoke a bit about the Google

971
00:53:36,480 --> 00:53:40,080
Speaker 1: Magenta project that he's involved with, and he pointed out

972
00:53:40,120 --> 00:53:43,239
Speaker 1: that with Magenta, there's always a human in the process

973
00:53:43,880 --> 00:53:45,560
Speaker 1: because again the human as a tool. This is the

974
00:53:45,600 --> 00:53:50,719
Speaker 1: gardener walking, you know, amid the the creative machines in

975
00:53:50,760 --> 00:53:52,759
Speaker 1: the garden. But this isn't soil and green. It's not

976
00:53:52,800 --> 00:53:56,120
Speaker 1: like Google is made of people. Right. But I found

977
00:53:56,120 --> 00:53:59,239
Speaker 1: this particularly interesting because it closely mirrors the inner workings

978
00:53:59,280 --> 00:54:02,480
Speaker 1: of military drones. So a few years back I spoke

979
00:54:02,560 --> 00:54:06,400
Speaker 1: with this guy, Knowles Sharkley, as a professor of Artificial

980
00:54:06,440 --> 00:54:09,560
Speaker 1: Intelligence and Robotics of the University of Sheffield and uh

981
00:54:09,920 --> 00:54:13,080
Speaker 1: and and he he had some some interesting revelations about

982
00:54:13,080 --> 00:54:16,839
Speaker 1: the necessary human components of u a v s of

983
00:54:16,840 --> 00:54:20,960
Speaker 1: of unmanned aerial vehicles. He said, quote, these are all

984
00:54:21,080 --> 00:54:24,719
Speaker 1: man in the loop systems, which means essentially there's someone, uh,

985
00:54:24,840 --> 00:54:28,200
Speaker 1: someone controls the applications of lethal force. They're not exactly

986
00:54:28,239 --> 00:54:31,040
Speaker 1: remote control. They're sort of a hybrid. They have a

987
00:54:31,040 --> 00:54:34,120
Speaker 1: certain they have certain autonomous functions, meaning they can be

988
00:54:34,160 --> 00:54:37,520
Speaker 1: programmed to react to their to their GPS, so they

989
00:54:37,520 --> 00:54:40,200
Speaker 1: can go about on their own. They can navigate themselves,

990
00:54:40,360 --> 00:54:42,640
Speaker 1: though a pilot will control their height and that sort

991
00:54:42,640 --> 00:54:46,120
Speaker 1: of thing. It's the first step towards full autonomy. The

992
00:54:46,120 --> 00:54:49,480
Speaker 1: most recent US Air Force documents describe a swarm of planes.

993
00:54:49,480 --> 00:54:52,360
Speaker 1: The term swarm is kind of a technical term in robotics,

994
00:54:52,400 --> 00:54:54,960
Speaker 1: meaning a bunch of robots that interact with one another

995
00:54:55,040 --> 00:54:58,040
Speaker 1: on a local basis. The man on the loop would

996
00:54:58,040 --> 00:55:01,640
Speaker 1: be in an executive control of the swarm, So rather

997
00:55:01,719 --> 00:55:03,799
Speaker 1: than having at least two pilots in charge of a

998
00:55:03,960 --> 00:55:06,719
Speaker 1: predator drone in his example, you'll have one person in

999
00:55:06,800 --> 00:55:10,239
Speaker 1: charge of a swarm of robots. So human technology can

1000
00:55:10,280 --> 00:55:13,680
Speaker 1: be used to kill people or create heart as well. Yeah, yeah,

1001
00:55:13,760 --> 00:55:17,320
Speaker 1: this is interesting, especially in lieu of the other episode

1002
00:55:17,320 --> 00:55:19,640
Speaker 1: that we're working on right now, which is it's gonna

1003
00:55:19,640 --> 00:55:23,239
Speaker 1: come out after this one. But it's about violence in

1004
00:55:23,560 --> 00:55:27,200
Speaker 1: the capacity for human violence, and drones in general, I

1005
00:55:27,200 --> 00:55:30,680
Speaker 1: think are interesting in that respect because they allow us

1006
00:55:30,760 --> 00:55:34,719
Speaker 1: to create violence from a distance and there is sort

1007
00:55:34,760 --> 00:55:40,000
Speaker 1: of an apophenia. I don't know what the right application

1008
00:55:40,040 --> 00:55:43,040
Speaker 1: of apophenia is there, but there's something going on there

1009
00:55:43,080 --> 00:55:47,000
Speaker 1: with that right that allows that disconnection. Yeah, So in

1010
00:55:47,040 --> 00:55:48,880
Speaker 1: the case of the drones, it's you know, very much

1011
00:55:48,880 --> 00:55:52,480
Speaker 1: a person uh, you know, checking in and uh in

1012
00:55:52,640 --> 00:55:56,840
Speaker 1: fine tuning everything. And in the case of say Young's

1013
00:55:56,880 --> 00:55:59,080
Speaker 1: example of working with a with a with a machine

1014
00:55:59,080 --> 00:56:02,080
Speaker 1: collaborator a similar case. So it's it's like a feedback

1015
00:56:02,239 --> 00:56:05,440
Speaker 1: loot process. The machine creates something, the human ads their spin,

1016
00:56:05,560 --> 00:56:07,480
Speaker 1: and the machine adds a spin on that, and the

1017
00:56:07,560 --> 00:56:11,719
Speaker 1: human adds an additional spin and and weeds out what's

1018
00:56:11,760 --> 00:56:14,800
Speaker 1: not working and maybe add some flourishes that that tweak

1019
00:56:14,880 --> 00:56:17,560
Speaker 1: it for human consumption. And this is again what Jesse

1020
00:56:17,680 --> 00:56:21,640
Speaker 1: Engle compared to tending a garden, you know. And this

1021
00:56:21,800 --> 00:56:24,640
Speaker 1: also reminded me of of another conversation I had, and

1022
00:56:24,680 --> 00:56:26,960
Speaker 1: this was back in two thousand and eleven. I spoke

1023
00:56:27,000 --> 00:56:31,560
Speaker 1: with Atlanta area electronic musician Richard Divine uh, who was

1024
00:56:31,600 --> 00:56:33,880
Speaker 1: a really cool guy. I'll include the link to the

1025
00:56:33,880 --> 00:56:37,279
Speaker 1: full interview on the landing page for this episode, but

1026
00:56:38,160 --> 00:56:41,120
Speaker 1: he said there were at the time against eleven, he

1027
00:56:41,120 --> 00:56:44,240
Speaker 1: said there were more electronic music tools on the market

1028
00:56:44,440 --> 00:56:46,600
Speaker 1: that didn't exist when he was starting out, and it

1029
00:56:46,640 --> 00:56:49,800
Speaker 1: was at the point where anyone could really create something

1030
00:56:49,840 --> 00:56:53,040
Speaker 1: and put it out on SoundCloud. And he told me quote,

1031
00:56:53,040 --> 00:56:55,800
Speaker 1: there are so many people trying to emulate specific styles,

1032
00:56:56,040 --> 00:56:58,520
Speaker 1: so now you have hundreds and hundreds of people trying

1033
00:56:58,560 --> 00:57:00,840
Speaker 1: to sound a particular way. I find that there is

1034
00:57:00,960 --> 00:57:04,080
Speaker 1: less and less innovation in music, but more and more

1035
00:57:04,120 --> 00:57:07,680
Speaker 1: people creating it. And he also talked about the use

1036
00:57:07,719 --> 00:57:11,760
Speaker 1: of algorithmic music composition, UH, something that's been around for years.

1037
00:57:12,200 --> 00:57:17,120
Speaker 1: Brian Eno has engaged with it, aw Tecker, the famous

1038
00:57:17,160 --> 00:57:19,280
Speaker 1: I d M group, They've they've used it various to

1039
00:57:19,280 --> 00:57:23,080
Speaker 1: our artists. This is essentially where you you have you

1040
00:57:23,120 --> 00:57:26,800
Speaker 1: have a program ustually have an algorithm for creative output,

1041
00:57:27,280 --> 00:57:30,720
Speaker 1: and you tend it like the garden that is, and

1042
00:57:30,960 --> 00:57:33,800
Speaker 1: and see what it creates. But in those cases, right

1043
00:57:33,840 --> 00:57:37,960
Speaker 1: at least with Eno and Oddiker, they're creating the software themselves,

1044
00:57:38,120 --> 00:57:41,000
Speaker 1: right with a team of people. I believe, so yes,

1045
00:57:41,720 --> 00:57:44,520
Speaker 1: So I asked, I asked Divine about this, and he said,

1046
00:57:44,760 --> 00:57:48,040
Speaker 1: it's really interesting because you're defining the rules and parameters

1047
00:57:48,040 --> 00:57:50,360
Speaker 1: of that environment, and then you can decide how that

1048
00:57:50,480 --> 00:57:54,360
Speaker 1: environment behave. But then he he closed with the following

1049
00:57:54,360 --> 00:57:56,760
Speaker 1: about the limits of your tools, and this is this

1050
00:57:56,800 --> 00:57:58,680
Speaker 1: is key because it gets into what we're talking about.

1051
00:57:58,680 --> 00:58:02,720
Speaker 1: Imposing limits, he said, I don't necessarily think that always

1052
00:58:02,720 --> 00:58:05,760
Speaker 1: helps creatively or makes people more creative. I think it

1053
00:58:05,880 --> 00:58:09,400
Speaker 1: sometimes makes people lazy. When I have too many resources

1054
00:58:09,400 --> 00:58:12,200
Speaker 1: at my fingertips, I have a tendency to get really

1055
00:58:12,280 --> 00:58:14,480
Speaker 1: lazy with the creativity. So for me, I try to

1056
00:58:14,520 --> 00:58:17,479
Speaker 1: limit myself with how many tools I use. I try

1057
00:58:17,520 --> 00:58:20,320
Speaker 1: to keep it to just a couple of pieces of

1058
00:58:20,360 --> 00:58:23,800
Speaker 1: equipment and learn those pieces of equipment really really well. Yeah,

1059
00:58:23,840 --> 00:58:25,320
Speaker 1: this gets back to sort of what I was talking

1060
00:58:25,360 --> 00:58:29,880
Speaker 1: about earlier about like self imposed rotations. Yeah. Um, so

1061
00:58:29,920 --> 00:58:32,400
Speaker 1: this is actually interesting to me because, as I brought

1062
00:58:32,480 --> 00:58:35,760
Speaker 1: up earlier, I did have a little foray in the

1063
00:58:35,800 --> 00:58:39,160
Speaker 1: early two thousand's where I locked myself in my bedroom

1064
00:58:39,240 --> 00:58:42,480
Speaker 1: and made electronic music over a winter. Um, and the

1065
00:58:42,480 --> 00:58:46,120
Speaker 1: way I did it was with this this video game

1066
00:58:46,360 --> 00:58:48,920
Speaker 1: for PlayStation. This is this shows you how far back

1067
00:58:48,960 --> 00:58:51,760
Speaker 1: it's going. It's the original PlayStation It was called the

1068
00:58:51,920 --> 00:58:55,280
Speaker 1: MTV Music Generator. Have you ever heard of this? Yeah,

1069
00:58:55,520 --> 00:58:59,720
Speaker 1: this is branded MTV. Uh and Basically, it was like

1070
00:58:59,840 --> 00:59:03,000
Speaker 1: the the most basic way that you could create your

1071
00:59:03,040 --> 00:59:05,520
Speaker 1: own electronic music. I had like pre built in beats

1072
00:59:05,760 --> 00:59:08,160
Speaker 1: and pre built in like melodies and things like that,

1073
00:59:08,480 --> 00:59:11,120
Speaker 1: and you could add layers of effects and things like

1074
00:59:11,160 --> 00:59:12,880
Speaker 1: that on top of it. But what I thought was

1075
00:59:12,920 --> 00:59:15,760
Speaker 1: really cool was you could take c d s at

1076
00:59:15,760 --> 00:59:18,720
Speaker 1: the time, because the MP three's were just just getting

1077
00:59:18,720 --> 00:59:21,240
Speaker 1: off the ground, um, and you could put them in

1078
00:59:21,600 --> 00:59:24,160
Speaker 1: and you could rip segments off from your c d

1079
00:59:24,400 --> 00:59:28,720
Speaker 1: s and then insert those sound files into these compositions

1080
00:59:28,760 --> 00:59:32,320
Speaker 1: that you've created using mtvs just prefab generator. And so

1081
00:59:32,440 --> 00:59:35,880
Speaker 1: I wrote like five songs I think, and like put

1082
00:59:35,880 --> 00:59:39,320
Speaker 1: them all up online there. I don't know, maybe if

1083
00:59:39,320 --> 00:59:41,160
Speaker 1: the audience is interested, maybe I'll put them on the

1084
00:59:41,240 --> 00:59:43,080
Speaker 1: landing page for this or one or two of them.

1085
00:59:43,280 --> 00:59:47,920
Speaker 1: They're silly, I I wrote under the name Invisible Maniac.

1086
00:59:48,120 --> 00:59:50,520
Speaker 1: And this literally lasted for like three months and then

1087
00:59:50,560 --> 00:59:54,080
Speaker 1: nothing ever came of it. But the thing about it, though,

1088
00:59:54,160 --> 00:59:56,760
Speaker 1: was that that was all I did with the electronic music, right.

1089
00:59:57,040 --> 01:00:01,360
Speaker 1: I never went beyond my CD collection and the PlayStation

1090
01:00:01,520 --> 01:00:04,200
Speaker 1: in this video game that allowed me to do all

1091
01:00:04,240 --> 01:00:08,959
Speaker 1: these things. But uh, the very idea of doing something

1092
01:00:09,000 --> 01:00:12,880
Speaker 1: like Ottoker or uh Brian Eno going out and writing

1093
01:00:12,880 --> 01:00:15,520
Speaker 1: my own software, or going out and recording my own

1094
01:00:15,640 --> 01:00:18,480
Speaker 1: drumbeats or other sounds to work with. It was like

1095
01:00:18,640 --> 01:00:21,960
Speaker 1: way past the point of my creative interest with electronic music. Right.

1096
01:00:22,200 --> 01:00:24,960
Speaker 1: But I take something like writing or or comics the

1097
01:00:25,000 --> 01:00:27,360
Speaker 1: stuff that I work in now, and I spend a

1098
01:00:27,400 --> 01:00:29,920
Speaker 1: lot of time thinking about the tools that are available

1099
01:00:29,920 --> 01:00:32,480
Speaker 1: in the ways I can use them to tell stories differently.

1100
01:00:33,040 --> 01:00:37,520
Speaker 1: I think this all comes back to the systematization of art. Right.

1101
01:00:37,600 --> 01:00:41,080
Speaker 1: So you're you're when you start getting to a point

1102
01:00:41,160 --> 01:00:44,760
Speaker 1: with the art form where you're you're so interested in

1103
01:00:44,800 --> 01:00:46,800
Speaker 1: it and you want to take it a step further

1104
01:00:47,320 --> 01:00:50,200
Speaker 1: that you start figuring out like, Okay, well how does

1105
01:00:50,240 --> 01:00:53,920
Speaker 1: the actual form work, how does it tick? And then

1106
01:00:53,920 --> 01:00:56,480
Speaker 1: how can I take that and apply it in new ways.

1107
01:00:57,240 --> 01:00:59,160
Speaker 1: That's that's something really interesting to me. I mean, like,

1108
01:00:59,320 --> 01:01:01,120
Speaker 1: for instance, like and I played in bands, I never

1109
01:01:01,120 --> 01:01:04,000
Speaker 1: built my own guitars. I never built my own pedals.

1110
01:01:04,040 --> 01:01:06,160
Speaker 1: I have friends who do that though, Like they have

1111
01:01:06,720 --> 01:01:09,520
Speaker 1: you know, little electronics backgrounds, and they build their own

1112
01:01:09,520 --> 01:01:12,920
Speaker 1: pedals and make music that nobody else has ever made before.

1113
01:01:13,680 --> 01:01:16,160
Speaker 1: And I feel like there's a distinction between us. They're

1114
01:01:16,200 --> 01:01:18,400
Speaker 1: like they feel it feels to me like they're more

1115
01:01:18,440 --> 01:01:20,960
Speaker 1: masters of their craft than I am. They go out

1116
01:01:21,000 --> 01:01:24,720
Speaker 1: and they build something new or likewise, uh for instance,

1117
01:01:24,720 --> 01:01:27,480
Speaker 1: like in graphic design, I have friends who are graphic designers.

1118
01:01:27,800 --> 01:01:32,360
Speaker 1: They'll go and write algorithms for Adobe software to help

1119
01:01:32,400 --> 01:01:36,080
Speaker 1: them make graphic design in ways that they haven't been

1120
01:01:36,160 --> 01:01:39,919
Speaker 1: able to before. What's interesting from what we're talking about

1121
01:01:39,920 --> 01:01:43,280
Speaker 1: here is that it seems like in music certainly, and

1122
01:01:43,320 --> 01:01:47,800
Speaker 1: then also in UH, in the use of Adobe, Photoshop

1123
01:01:47,800 --> 01:01:53,840
Speaker 1: and digital um digital art creation tools, like there many

1124
01:01:53,880 --> 01:01:58,800
Speaker 1: steps ahead of of the literary model. Right, so we

1125
01:01:58,800 --> 01:02:02,840
Speaker 1: can try to hype right analogy from earlier is exactly right.

1126
01:02:02,920 --> 01:02:05,320
Speaker 1: It doesn't exactly work the same way tool was now.

1127
01:02:05,440 --> 01:02:09,840
Speaker 1: Now granted, we have we're fabulous word processing options out there.

1128
01:02:09,960 --> 01:02:12,960
Speaker 1: We have we have spell check and grammar checks and all.

1129
01:02:13,000 --> 01:02:15,600
Speaker 1: You know, of course, the what Clippy popping up and

1130
01:02:15,680 --> 01:02:19,160
Speaker 1: giving us tips. But imagine if you know easily it

1131
01:02:19,320 --> 01:02:21,600
Speaker 1: easily imagine I can easily imagine reaching the point where

1132
01:02:21,640 --> 01:02:24,720
Speaker 1: Clippy pops up and says, hey, I see you're writing

1133
01:02:24,720 --> 01:02:27,400
Speaker 1: a short story in the style of Clark Ashton Smith.

1134
01:02:28,040 --> 01:02:30,640
Speaker 1: Would you like to tweak it in this direction, and

1135
01:02:30,680 --> 01:02:32,600
Speaker 1: I wonder, I wonder what it would be like to

1136
01:02:32,640 --> 01:02:36,920
Speaker 1: reach that point where you're essentially you're writing with this

1137
01:02:36,960 --> 01:02:41,160
Speaker 1: machine filter in place. And you know, certainly you can

1138
01:02:41,200 --> 01:02:43,480
Speaker 1: be a very purist about it and say like, well,

1139
01:02:43,480 --> 01:02:46,560
Speaker 1: that's that's cheating if you're you're writing through a machine

1140
01:02:46,640 --> 01:02:48,320
Speaker 1: and something else is coming out on the on the

1141
01:02:48,360 --> 01:02:52,320
Speaker 1: other side, that's that's not the authentic process of writing.

1142
01:02:52,320 --> 01:02:54,440
Speaker 1: But on the other hand, how is it any different

1143
01:02:54,480 --> 01:02:56,600
Speaker 1: than buzzing into a horn and getting that song on

1144
01:02:56,640 --> 01:03:00,600
Speaker 1: the other end? You know, we don't say, ah, who

1145
01:03:00,680 --> 01:03:03,480
Speaker 1: is the great trumpeter dizny Llespie? Yeah, that sounds right,

1146
01:03:03,480 --> 01:03:05,880
Speaker 1: and nobody says, ah, if you heard the sound of

1147
01:03:06,120 --> 01:03:08,560
Speaker 1: him just buzzing his lips, it's awful. It's all that

1148
01:03:08,640 --> 01:03:10,880
Speaker 1: more And it was just it's clearly it's the technology.

1149
01:03:10,960 --> 01:03:15,400
Speaker 1: That's that's who's to just be Disney Galpsie's trumpet credited

1150
01:03:15,400 --> 01:03:18,280
Speaker 1: on the album. No, nobody's doing that, like you credit

1151
01:03:18,320 --> 01:03:21,040
Speaker 1: the lips buzzing into the into the machine. You know

1152
01:03:21,040 --> 01:03:24,280
Speaker 1: what's interesting though, is I think of like the media

1153
01:03:24,320 --> 01:03:28,440
Speaker 1: world that that we exist in today, where in you

1154
01:03:28,480 --> 01:03:30,640
Speaker 1: and I hear this all the time content is king

1155
01:03:31,320 --> 01:03:36,080
Speaker 1: and the idea that just like the the consumption levels

1156
01:03:36,320 --> 01:03:39,800
Speaker 1: for content are so high right now that like almost

1157
01:03:39,880 --> 01:03:42,960
Speaker 1: the human beings creating the content can't keep up right.

1158
01:03:43,040 --> 01:03:44,960
Speaker 1: And so there's all these like businesses that are trying

1159
01:03:45,000 --> 01:03:46,880
Speaker 1: to come up with a lot of like quick and

1160
01:03:46,920 --> 01:03:50,160
Speaker 1: easy ways to to get more content out there. And

1161
01:03:50,360 --> 01:03:52,720
Speaker 1: this seems to me like a way to do that

1162
01:03:52,720 --> 01:03:58,520
Speaker 1: that might not necessarily be aesthetically displeasing to the audience

1163
01:03:58,600 --> 01:04:01,160
Speaker 1: that's that's consuming it. And that's whether or not you're

1164
01:04:01,160 --> 01:04:03,400
Speaker 1: reading something in an RSS feed or you're listening to

1165
01:04:03,440 --> 01:04:08,080
Speaker 1: an MP three file or whatever you're consuming digitally. Uh,

1166
01:04:08,320 --> 01:04:11,320
Speaker 1: having machines that can create content for you, I can

1167
01:04:11,360 --> 01:04:14,240
Speaker 1: totally see in like less than twenty five years that

1168
01:04:14,320 --> 01:04:17,120
Speaker 1: being the thing. I don't think it will replace us necessarily,

1169
01:04:17,600 --> 01:04:21,440
Speaker 1: but it could absolutely be a way for what's the

1170
01:04:21,480 --> 01:04:24,720
Speaker 1: word click farms, for click farms to sort of create

1171
01:04:24,800 --> 01:04:27,120
Speaker 1: more stuff. I'll tell you. Another example that comes to

1172
01:04:27,160 --> 01:04:30,560
Speaker 1: mind is, uh, I enjoyed I enjoyed reading. For a

1173
01:04:30,560 --> 01:04:32,920
Speaker 1: brief time towards the end of his life. Hunter S.

1174
01:04:32,960 --> 01:04:36,320
Speaker 1: Thompson wrote for one of the ESPN websites, and he

1175
01:04:36,360 --> 01:04:39,160
Speaker 1: was comment sports stuff is interesting, yeah, and but in

1176
01:04:39,200 --> 01:04:41,439
Speaker 1: this one he just wrote about whatever. So he's often

1177
01:04:41,440 --> 01:04:44,280
Speaker 1: talking about politics and what have you. For someone who

1178
01:04:44,320 --> 01:04:48,240
Speaker 1: wanted Hunter S. Thompson to essentially come back from the

1179
01:04:48,280 --> 01:04:52,520
Speaker 1: grave and comment on today's political news, you could conceivably,

1180
01:04:52,840 --> 01:04:55,000
Speaker 1: I mean, or I can conceive of a future in

1181
01:04:55,040 --> 01:04:58,240
Speaker 1: which you have the the Hunter S. Thompson AI that

1182
01:04:58,360 --> 01:05:00,600
Speaker 1: and you just drop in the news feeds to it

1183
01:05:00,680 --> 01:05:03,600
Speaker 1: and then it creates commentary in the style of him,

1184
01:05:03,640 --> 01:05:05,520
Speaker 1: and maybe there's a human in the mix as well.

1185
01:05:05,920 --> 01:05:10,160
Speaker 1: Maybe not right, but I mean I can see people

1186
01:05:10,400 --> 01:05:13,320
Speaker 1: digging that even though they know that Hunter S. Thompson

1187
01:05:13,360 --> 01:05:15,480
Speaker 1: has been dead for a long time. What I mean

1188
01:05:15,520 --> 01:05:19,320
Speaker 1: what you just described, if that existed tomorrow, that would

1189
01:05:19,400 --> 01:05:22,880
Speaker 1: be the news of the week other than like you know,

1190
01:05:23,280 --> 01:05:25,280
Speaker 1: big current events and things that are going on. But

1191
01:05:25,360 --> 01:05:28,800
Speaker 1: like in terms of like media, everybody would be sharing

1192
01:05:28,840 --> 01:05:30,880
Speaker 1: it and everybody would be like, look what I plugged

1193
01:05:30,880 --> 01:05:33,160
Speaker 1: into Hunter S. Thompson bought and it came out with this,

1194
01:05:33,240 --> 01:05:36,200
Speaker 1: you know, remember that was the Twitter thing that was

1195
01:05:36,240 --> 01:05:39,360
Speaker 1: created that was like Jonathan Strickling would know this because

1196
01:05:39,360 --> 01:05:41,360
Speaker 1: I think they talked about it on tech stuff. There's

1197
01:05:41,360 --> 01:05:44,240
Speaker 1: like a Twitter bot that was created, and the idea

1198
01:05:44,320 --> 01:05:47,960
Speaker 1: was that it would learn how to tweet from reading

1199
01:05:47,960 --> 01:05:50,800
Speaker 1: other people's tweets, and like, within like a day or something,

1200
01:05:50,920 --> 01:05:54,320
Speaker 1: it eventually was just like swearing and saying like horrible,

1201
01:05:54,480 --> 01:05:58,760
Speaker 1: racist and like things like Like it was like super

1202
01:05:58,840 --> 01:06:03,240
Speaker 1: quick that it just devolved into this monster. Now along

1203
01:06:03,280 --> 01:06:06,160
Speaker 1: these lines, um, an example that came up in the

1204
01:06:06,200 --> 01:06:10,840
Speaker 1: talk is, uh this a Sony CSL research laboratory project

1205
01:06:10,880 --> 01:06:14,480
Speaker 1: where they use their AI flow machines system to create

1206
01:06:14,480 --> 01:06:17,680
Speaker 1: a new Beatles song. So essentially they just loaded in,

1207
01:06:18,160 --> 01:06:22,760
Speaker 1: you know, all the parameters of of the Beatles discography

1208
01:06:22,800 --> 01:06:24,840
Speaker 1: and then they created this song. It wrote this song

1209
01:06:25,040 --> 01:06:28,400
Speaker 1: Daddy's Car, and then they got humans to perform it,

1210
01:06:28,680 --> 01:06:30,280
Speaker 1: because you know, we're not at the point where that

1211
01:06:30,400 --> 01:06:34,240
Speaker 1: the machine can perfectly replicate recording that would sound like

1212
01:06:34,280 --> 01:06:37,720
Speaker 1: the Beatles, and this essentially sounds like a Beatles cover band.

1213
01:06:38,000 --> 01:06:40,720
Speaker 1: I listened to it, and it's very weird, like the

1214
01:06:40,800 --> 01:06:43,560
Speaker 1: lyrics are written by humans. That's another thing that's important

1215
01:06:43,560 --> 01:06:47,920
Speaker 1: to distinguish. But the machine itself decides, you know, what

1216
01:06:48,040 --> 01:06:51,480
Speaker 1: the composition is, and there is something just a little

1217
01:06:53,120 --> 01:06:54,840
Speaker 1: I don't know, I don't know how to like uncanny

1218
01:06:54,920 --> 01:06:58,800
Speaker 1: Valley in music, which I've never thought of before. When

1219
01:06:58,800 --> 01:07:01,280
Speaker 1: you guys talked about Uncanny Alley in that episode, did

1220
01:07:01,280 --> 01:07:04,400
Speaker 1: it come up in any other sense structure other than vision,

1221
01:07:05,160 --> 01:07:07,480
Speaker 1: I do not. I know it didn't come up in

1222
01:07:07,840 --> 01:07:10,240
Speaker 1: terms of music. So yeah, I think this is an

1223
01:07:10,240 --> 01:07:13,440
Speaker 1: interesting example because when I listened to it, I definitely

1224
01:07:13,440 --> 01:07:16,440
Speaker 1: feel that uncanny effect. But how much of that is

1225
01:07:16,520 --> 01:07:19,880
Speaker 1: me knowing beforehand that this is not a Beatles song? Right?

1226
01:07:20,320 --> 01:07:22,440
Speaker 1: So it was presented to me as hey, here's the

1227
01:07:22,480 --> 01:07:24,760
Speaker 1: lost Beatles song. Would I dig it? Would I be

1228
01:07:24,920 --> 01:07:27,680
Speaker 1: all in on it? And then the additional question is

1229
01:07:27,920 --> 01:07:31,720
Speaker 1: will we reach a point where audiences won't care? So,

1230
01:07:31,840 --> 01:07:35,800
Speaker 1: like I mentioned Tool earlier, I've I've had to wait

1231
01:07:35,840 --> 01:07:37,920
Speaker 1: for the last three albums to come out, and that's

1232
01:07:37,920 --> 01:07:41,480
Speaker 1: like a twenty something year span there. Uh, But there

1233
01:07:41,480 --> 01:07:43,240
Speaker 1: are new fans who come online and they're like, all

1234
01:07:43,240 --> 01:07:45,040
Speaker 1: the albums are there and they can start waiting on

1235
01:07:45,080 --> 01:07:47,600
Speaker 1: the next album with the rest office and if and

1236
01:07:47,640 --> 01:07:50,400
Speaker 1: if fake material comes out, we're not going to accept it.

1237
01:07:50,840 --> 01:07:53,680
Speaker 1: I mentioned I mentioned at Tacker earlier. I believe a

1238
01:07:53,880 --> 01:07:56,360
Speaker 1: tecker Is is an example where there have been fake

1239
01:07:56,520 --> 01:07:59,320
Speaker 1: leaks that have come out where someone says, here's the

1240
01:07:59,360 --> 01:08:01,400
Speaker 1: new Autacker and listen to an itch really some other

1241
01:08:02,160 --> 01:08:05,320
Speaker 1: album that somebody else created, and everyone just you know,

1242
01:08:05,480 --> 01:08:07,919
Speaker 1: craps on it. They're like, get this, I can see

1243
01:08:07,960 --> 01:08:12,120
Speaker 1: that being pretty easily not replicable, but like you could

1244
01:08:12,120 --> 01:08:14,160
Speaker 1: get faked out on that pretty easily given their music.

1245
01:08:14,360 --> 01:08:16,920
Speaker 1: Exactly can you imagine the a point in the future

1246
01:08:16,960 --> 01:08:19,160
Speaker 1: where the fakery, if you want to call it, or

1247
01:08:19,200 --> 01:08:21,960
Speaker 1: at least the AI the machine creativity involved is so

1248
01:08:22,040 --> 01:08:24,680
Speaker 1: advanced that they can come up with something that scratches

1249
01:08:24,720 --> 01:08:28,400
Speaker 1: your itch, that itch for say, another Tool album or

1250
01:08:28,400 --> 01:08:32,880
Speaker 1: another mos Art composition, whatever the uh the need is,

1251
01:08:33,520 --> 01:08:35,720
Speaker 1: and maybe on top of that, it even customizes it

1252
01:08:35,800 --> 01:08:38,360
Speaker 1: to your own particular taste within that group, your own

1253
01:08:39,040 --> 01:08:43,679
Speaker 1: real time emotional demands. Well, what this all boils down

1254
01:08:43,680 --> 01:08:46,000
Speaker 1: to that I think is really interesting and didn't come

1255
01:08:46,040 --> 01:08:48,720
Speaker 1: up in their conversation at all, is um Are you

1256
01:08:48,760 --> 01:08:53,519
Speaker 1: familiar with Walter Benjamin or Benjamin sometimes pronounced his nineteen

1257
01:08:54,240 --> 01:08:57,439
Speaker 1: essay Work of Art in the Age of Mechanical Reproduction? No,

1258
01:08:57,520 --> 01:08:59,920
Speaker 1: I don't, I am not. It's this really interesting take

1259
01:09:00,280 --> 01:09:04,760
Speaker 1: on what the difference is between art and copies of

1260
01:09:04,960 --> 01:09:08,439
Speaker 1: art and he throws out this term aura in it,

1261
01:09:08,520 --> 01:09:13,080
Speaker 1: where he essentially argues that mechanically reproduced art is missing

1262
01:09:13,640 --> 01:09:16,760
Speaker 1: this aesthetic uniqueness that the original has. Right, So, if

1263
01:09:16,800 --> 01:09:19,120
Speaker 1: you take the Mona Lisa and then you run it over,

1264
01:09:20,000 --> 01:09:23,160
Speaker 1: you get a like really high definition quality print of

1265
01:09:23,160 --> 01:09:25,720
Speaker 1: it that's not the Mona Lisa though, right, Like, it

1266
01:09:25,760 --> 01:09:28,960
Speaker 1: doesn't have the aesthetic uniqueness there. And so when you

1267
01:09:29,000 --> 01:09:32,960
Speaker 1: apply that here, I'm wondering, is the art of the

1268
01:09:33,000 --> 01:09:38,000
Speaker 1: computer authentic then? Like, so, for instance, they were talking

1269
01:09:38,040 --> 01:09:41,160
Speaker 1: about the recipes that were being created by the by

1270
01:09:41,200 --> 01:09:44,559
Speaker 1: the computer. If that comes out and it tastes great

1271
01:09:44,640 --> 01:09:46,840
Speaker 1: and you're like, oh, this is really good, does it

1272
01:09:47,000 --> 01:09:52,000
Speaker 1: have that aesthetic uniqueness of somebody figuring it out or

1273
01:09:52,160 --> 01:09:55,840
Speaker 1: is it a mechanical reproduction? Huh? But this brings to

1274
01:09:55,920 --> 01:09:59,320
Speaker 1: mind an example that also ties into my trip to

1275
01:09:59,320 --> 01:10:02,719
Speaker 1: New York that I kept thinking about Arnold Bocklin's painting

1276
01:10:02,800 --> 01:10:05,559
Speaker 1: Isle of the Dead, in part because I've just seen

1277
01:10:05,600 --> 01:10:09,400
Speaker 1: Alien Covenant and they referenced the painting in the film. Yeah,

1278
01:10:09,439 --> 01:10:11,680
Speaker 1: there's a scene where it's like one of the castles

1279
01:10:11,680 --> 01:10:15,000
Speaker 1: of the Engineers and David and Walter standing out there

1280
01:10:15,000 --> 01:10:18,160
Speaker 1: having a conversation, and it's clearly inspired by this painting.

1281
01:10:18,160 --> 01:10:21,759
Speaker 1: The The Isle of the Dead coincidentally, is a painting

1282
01:10:21,800 --> 01:10:25,280
Speaker 1: the gieger Um did versions of and in his own style.

1283
01:10:25,400 --> 01:10:27,400
Speaker 1: Is that right? Well, that must be where they got

1284
01:10:27,400 --> 01:10:29,479
Speaker 1: the idea for it from. I would I would assume

1285
01:10:29,800 --> 01:10:31,880
Speaker 1: that's very like when you look at it and you've

1286
01:10:31,880 --> 01:10:34,120
Speaker 1: seen that movie, you can see the influence. Yea. But

1287
01:10:34,280 --> 01:10:36,360
Speaker 1: it's a it has it's a picture. It's an image

1288
01:10:36,360 --> 01:10:39,240
Speaker 1: with a lot of darkness in it, like literal darkness.

1289
01:10:39,760 --> 01:10:42,360
Speaker 1: And there are some fabulous prints out there of it.

1290
01:10:42,400 --> 01:10:44,800
Speaker 1: There's some fabulous digital versions of it online you go

1291
01:10:44,840 --> 01:10:48,040
Speaker 1: on Getty, etcetera. But seeing it, seeing an actual version

1292
01:10:48,080 --> 01:10:50,920
Speaker 1: of it at the met Or it was actually a

1293
01:10:50,960 --> 01:10:54,960
Speaker 1: little lackluster because there's this gleam on the black on

1294
01:10:55,000 --> 01:10:57,720
Speaker 1: the black paint and it's aged a bit. So it

1295
01:10:57,800 --> 01:11:00,400
Speaker 1: was one of these rare cases where seeing the original

1296
01:11:00,960 --> 01:11:04,160
Speaker 1: it felt unique in many in many cases like here's

1297
01:11:04,200 --> 01:11:07,920
Speaker 1: the thing itself. But on the other hand, it was

1298
01:11:08,000 --> 01:11:11,360
Speaker 1: less satisfying. Wow. So that's like a reverse of Walter

1299
01:11:11,439 --> 01:11:16,360
Speaker 1: Benjamine's uh Aura. It's like the aura itself was diminished somehow.

1300
01:11:16,640 --> 01:11:18,960
Speaker 1: And I should add that usually I feel the other

1301
01:11:18,960 --> 01:11:21,559
Speaker 1: way around. Usually if I see the an actual painting

1302
01:11:21,560 --> 01:11:24,000
Speaker 1: that I that I care about, like it's it's amazing

1303
01:11:24,040 --> 01:11:25,800
Speaker 1: to get in there close to it or close as

1304
01:11:25,800 --> 01:11:29,560
Speaker 1: you're allowed and uh and see the details, see the brushstrokes,

1305
01:11:29,560 --> 01:11:32,599
Speaker 1: see that like the physical paint. Another example would be

1306
01:11:32,640 --> 01:11:35,360
Speaker 1: like the difference between seeing a band live and listening

1307
01:11:35,400 --> 01:11:38,960
Speaker 1: to them on recording um. And this makes me think

1308
01:11:39,640 --> 01:11:43,479
Speaker 1: of synthesis and synthesizers because in this talk they brought

1309
01:11:43,560 --> 01:11:46,560
Speaker 1: up synthesizers as being like an early example kind of

1310
01:11:46,600 --> 01:11:50,840
Speaker 1: like the drum machine of this computer generated creativity and

1311
01:11:51,040 --> 01:11:55,439
Speaker 1: synthesis makes me think of blooms taxonomy of learning and

1312
01:11:55,680 --> 01:11:59,040
Speaker 1: within it, synthesis is one of the modes of learning

1313
01:11:59,080 --> 01:12:01,880
Speaker 1: that you're you're supposed to try to achieve, and it's

1314
01:12:01,920 --> 01:12:05,759
Speaker 1: basically one has to put together parts from diverse elements

1315
01:12:05,760 --> 01:12:09,200
Speaker 1: to form a whole. So the process of synthesis creates

1316
01:12:09,200 --> 01:12:14,920
Speaker 1: a unique form of ideas, communication, operations, relations, and sometimes art.

1317
01:12:15,439 --> 01:12:20,000
Speaker 1: So while machines are currently capable of being synthesizers right there,

1318
01:12:20,400 --> 01:12:23,120
Speaker 1: they have a combination of those elements, but the humans

1319
01:12:23,120 --> 01:12:27,400
Speaker 1: are the ones doing the synthesizing. The machines themselves aren't

1320
01:12:27,400 --> 01:12:30,360
Speaker 1: doing that, So that role still seems to be held

1321
01:12:30,400 --> 01:12:34,879
Speaker 1: by humans in this relationship between us and seems inherent

1322
01:12:34,920 --> 01:12:38,080
Speaker 1: to the creative process to me. And they sort of

1323
01:12:38,120 --> 01:12:40,840
Speaker 1: get into this a little bit when they start talking

1324
01:12:40,840 --> 01:12:44,760
Speaker 1: about ownership, because they brought up the idea that computational

1325
01:12:44,880 --> 01:12:47,840
Speaker 1: artifacts are actually not owned by anyone under current law.

1326
01:12:47,840 --> 01:12:51,559
Speaker 1: They're defined as public domain. So if a company can't

1327
01:12:51,840 --> 01:12:56,880
Speaker 1: patent what comes out of a computer that generates art,

1328
01:12:57,360 --> 01:13:01,920
Speaker 1: what's their incentive to fund further develop min of AI creativity.

1329
01:13:01,920 --> 01:13:04,040
Speaker 1: So actually, this goes back to what we were saying earlier,

1330
01:13:04,120 --> 01:13:08,400
Speaker 1: right like if you were going to, uh, let's say,

1331
01:13:08,400 --> 01:13:12,200
Speaker 1: how stuff Works built an army AI robot that would

1332
01:13:12,200 --> 01:13:14,840
Speaker 1: write all our articles for us, and it was able

1333
01:13:14,840 --> 01:13:17,120
Speaker 1: to write like a hundred articles a day based on

1334
01:13:17,240 --> 01:13:21,320
Speaker 1: like whatever it saw coming up in the news. Technically

1335
01:13:21,880 --> 01:13:26,400
Speaker 1: how stuff Works wouldn't own those articles because they'd be

1336
01:13:26,400 --> 01:13:32,519
Speaker 1: in the public domain because they're created by computation. Interesting, Yeah,

1337
01:13:32,600 --> 01:13:34,840
Speaker 1: I mean this brings back them to mind the idea

1338
01:13:34,880 --> 01:13:37,200
Speaker 1: of having a human in the loop, And so I

1339
01:13:37,200 --> 01:13:38,800
Speaker 1: could see the case where you'd have to have a

1340
01:13:38,880 --> 01:13:41,720
Speaker 1: human in the loop just for for legal purposes, just

1341
01:13:41,760 --> 01:13:44,360
Speaker 1: so that it could be a definite owner because then

1342
01:13:44,439 --> 01:13:47,000
Speaker 1: that that that human can have it in their contract.

1343
01:13:47,040 --> 01:13:49,479
Speaker 1: Of course that anything they create, uh you know on

1344
01:13:49,479 --> 01:13:52,960
Speaker 1: the company clock is property of the company. That's pretty standard. Uh.

1345
01:13:53,040 --> 01:13:55,760
Speaker 1: And then I assume the machines contributions to that. The

1346
01:13:55,800 --> 01:13:58,479
Speaker 1: machine would be more of a pure tool in that scenario,

1347
01:13:59,080 --> 01:14:03,360
Speaker 1: but a a self aware tool or if you will,

1348
01:14:03,720 --> 01:14:06,600
Speaker 1: is just going to be outside of the confines of

1349
01:14:06,640 --> 01:14:11,120
Speaker 1: existing law. Yeah, yeah, for now at least. So wow,

1350
01:14:11,200 --> 01:14:15,240
Speaker 1: we've had a pretty extended and a deep discussion about this.

1351
01:14:15,400 --> 01:14:17,640
Speaker 1: I knew that this was going to be interesting, but

1352
01:14:18,000 --> 01:14:21,320
Speaker 1: we really dove deep. And one of the things that I,

1353
01:14:21,560 --> 01:14:24,280
Speaker 1: you know, to close out watching the panel, they sort

1354
01:14:24,280 --> 01:14:26,000
Speaker 1: of said, well, why are we asking this now? Why

1355
01:14:26,040 --> 01:14:28,880
Speaker 1: are we asking these questions about machines and creativity? And

1356
01:14:28,880 --> 01:14:32,400
Speaker 1: they said, well, because this was actually Chung I think

1357
01:14:32,439 --> 01:14:35,799
Speaker 1: said this. She said, we're currently generating these huge amounts

1358
01:14:35,800 --> 01:14:39,440
Speaker 1: of data, right, like just think of like our information systems,

1359
01:14:39,479 --> 01:14:41,639
Speaker 1: just on the Internet in general, all the data that's

1360
01:14:41,640 --> 01:14:44,880
Speaker 1: being generated, and we're trying to wrap our heads around it.

1361
01:14:45,320 --> 01:14:49,439
Speaker 1: So we're using models that we're already familiar with. So

1362
01:14:49,479 --> 01:14:53,439
Speaker 1: in this case, the model of creativity right again, getting

1363
01:14:53,439 --> 01:14:57,240
Speaker 1: back to systematizing it, and so human culture is being

1364
01:14:57,320 --> 01:15:02,120
Speaker 1: applied on top of he chnology. Uh, and that that's

1365
01:15:02,120 --> 01:15:05,040
Speaker 1: what's interesting, I think, is like we've gotten to this

1366
01:15:05,080 --> 01:15:07,559
Speaker 1: point now where we're like, oh my god, there's so

1367
01:15:07,640 --> 01:15:12,439
Speaker 1: much information that even like we cannot process it and

1368
01:15:12,479 --> 01:15:14,879
Speaker 1: figure it out. We need to turn to these machines

1369
01:15:14,920 --> 01:15:16,479
Speaker 1: to try to help us do that, but we need

1370
01:15:16,520 --> 01:15:19,840
Speaker 1: to like layer our cultural understanding of the world on

1371
01:15:19,920 --> 01:15:22,680
Speaker 1: top of that. Yeah, So creativity is not it's not

1372
01:15:22,960 --> 01:15:25,639
Speaker 1: it goes beyond just merely using these machines to make

1373
01:15:25,640 --> 01:15:30,360
Speaker 1: our art, but using these machines to make sense of ourselves. Yeah, yeah, exactly.

1374
01:15:30,680 --> 01:15:34,280
Speaker 1: I mean I guess ultimately that's the point. Well that

1375
01:15:34,439 --> 01:15:39,320
Speaker 1: in like cat videos all right, Well, you know this

1376
01:15:39,360 --> 01:15:40,920
Speaker 1: is gonna be a great one to get some feedback

1377
01:15:40,960 --> 01:15:43,040
Speaker 1: on because I I know for a fact that we

1378
01:15:43,160 --> 01:15:47,599
Speaker 1: have creators out there who create their art while listening

1379
01:15:47,600 --> 01:15:50,800
Speaker 1: to episodes of stuff to blow your mind. So you

1380
01:15:50,840 --> 01:15:54,960
Speaker 1: guys and gals in particular probably have some insightful commentary

1381
01:15:55,200 --> 01:15:58,160
Speaker 1: on on the material discussed here today. Yeah, and we

1382
01:15:58,200 --> 01:16:00,400
Speaker 1: would love to hear from you about at There are

1383
01:16:00,439 --> 01:16:02,000
Speaker 1: a number of ways that you can get in touch

1384
01:16:02,040 --> 01:16:04,559
Speaker 1: with us. We're all over social media. If you want

1385
01:16:04,600 --> 01:16:07,599
Speaker 1: to write us about your creative experiments or your thoughts

1386
01:16:07,840 --> 01:16:12,680
Speaker 1: on computational creativity, you can find us on Facebook, Twitter, Tumbler,

1387
01:16:12,720 --> 01:16:15,960
Speaker 1: and Instagram. And has already mentioned we are a craft

1388
01:16:16,000 --> 01:16:19,599
Speaker 1: podcast and uh and we were. We require your support

1389
01:16:19,920 --> 01:16:23,320
Speaker 1: for our for our art, so we're not asking for money,

1390
01:16:23,360 --> 01:16:25,320
Speaker 1: but we are asking for iTunes reviews. So if you

1391
01:16:25,360 --> 01:16:27,120
Speaker 1: listen to us on iTunes, or even if you don't,

1392
01:16:27,160 --> 01:16:29,120
Speaker 1: you just have an iTunes account, why don't you go

1393
01:16:29,120 --> 01:16:31,360
Speaker 1: on there and leave us a nice review? Throw us

1394
01:16:31,720 --> 01:16:34,760
Speaker 1: five stars, six star, seven stars, however many stars they

1395
01:16:34,880 --> 01:16:38,000
Speaker 1: let you give us, Uh, just give us the maximum

1396
01:16:38,040 --> 01:16:39,920
Speaker 1: and I'll leave a nice review, And that helps us

1397
01:16:39,960 --> 01:16:41,639
Speaker 1: out and helps us to continue to do this show

1398
01:16:41,680 --> 01:16:45,000
Speaker 1: for it and most of our episodes as well. All

1399
01:16:45,000 --> 01:16:48,200
Speaker 1: of our episodes have landing pages on our website, Stuff

1400
01:16:48,200 --> 01:16:50,639
Speaker 1: to Blow your Mind dot com, So we always recommend

1401
01:16:50,680 --> 01:16:52,280
Speaker 1: that you go check those out because they have like

1402
01:16:52,400 --> 01:16:55,040
Speaker 1: interesting links to other stuff that we've worked on that's related.

1403
01:16:55,200 --> 01:16:57,519
Speaker 1: But this one, as we've discussed in the episode, is

1404
01:16:57,520 --> 01:17:00,320
Speaker 1: going to have all kinds of cool stuff, including that

1405
01:17:00,400 --> 01:17:04,000
Speaker 1: playlist that Robert put together of all the Gager album

1406
01:17:04,080 --> 01:17:08,160
Speaker 1: cover songs. Uh so check that out for sure, and

1407
01:17:08,479 --> 01:17:10,400
Speaker 1: as always, you can get in touch with us directly

1408
01:17:10,439 --> 01:17:13,080
Speaker 1: by emailing us at blow the Mind at how stuff

1409
01:17:13,080 --> 01:17:25,840
Speaker 1: works dot com for more on this and thousands of

1410
01:17:25,840 --> 01:17:51,360
Speaker 1: other topics. Is it how stuff works dot com