Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey there, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future and says it's like
Speaker 1: a nine and shining armor from a long time ago.
Speaker 1: I'm Johnvin Strickland and Lauren and I'm Joe McCormick. And
Speaker 1: today we want to talk more about about stories and storytelling.
Speaker 1: But you know, in our last podcast we talked about
Speaker 1: interactive storytelling and this idea collaboratively creating a story in
Speaker 1: some way or participating within a story in some way. Now,
Speaker 1: let's talk a little bit about, um, what the stories
Speaker 1: that we wish existed but don't like. Can you imagine
Speaker 1: some of the greatest authors of all time, who who
Speaker 1: maybe died before you felt like they had really produced
Speaker 1: everything they could have produced? Like, do you have any
Speaker 1: favorite authors that you wish had lived longer to tell
Speaker 1: at least one more amazing story? Oh of course. Well,
Speaker 1: actually what first popped into my mind was poets. I'd
Speaker 1: love to have more of Uncle Walton Anti m when
Speaker 1: I'm feeling when I'm feeling patriotic, you know, Walt Whitman,
Speaker 1: Emily Dickinson, I gotta feel the America, you know, coming
Speaker 1: up right bubbling up inside you. But they've been dead
Speaker 1: a long time, right, So, uh well, Lauren, are there
Speaker 1: any authors that you could think of off the top
Speaker 1: of your head where you're thinking, Man, I really wish
Speaker 1: I could have had one more book or story or
Speaker 1: play or poem from this person. I'm a completely terrible person.
Speaker 1: The first thing that just popped into my head was
Speaker 1: George RR. Martin, who is not actually dead as of
Speaker 1: So you want you want an insurance policy to guarantee
Speaker 1: that a Song of Ice and Fire is complete at
Speaker 1: some point, and you want it in George RR. Martin's voice.
Speaker 1: That's really important. Obviously someone else could pick it up,
Speaker 1: but that wouldn't be the same. Yeah, no, no, no
Speaker 1: one else would write. And I'm being actually very earnest
Speaker 1: here the kinds of descriptions of food and dresses into
Speaker 1: my political fantasy that he writes. Well, you guys have
Speaker 1: both made my choice of Shakespeare seemed pedestrian at this point. I'm,
Speaker 1: of course, I am a Shakespearean nut. I love Shakespeare's
Speaker 1: plays and I love his poetry as well. And uh
Speaker 1: we they're already some plays that may or may not
Speaker 1: belong to Shakespeare. We'll talk more about some of those
Speaker 1: in a little bit, but uh, you know, there were
Speaker 1: there's some lost plays that perhaps we might actually have
Speaker 1: if he had lived longer to get to the point
Speaker 1: where he was committing these so that they could be published.
Speaker 1: You know, in Shakespeare's day, he wasn't It wasn't what
Speaker 1: he was writing them down necessarily any anymore than too. Yeah,
Speaker 1: he would get the sides to the actors, but the
Speaker 1: whole purpose of the play was for performance, not for reading.
Speaker 1: So in Shakespeare's day, very few of his plays ever
Speaker 1: were published. It was only after his death when his contemporaries, Yeah,
Speaker 1: his contemporaries began to gather them all together. People who
Speaker 1: worked in the King's Men production company began to gather
Speaker 1: as stuff together. And then even then there were points
Speaker 1: where scholars have argued whether other plays should have been included.
Speaker 1: Perhaps there were plays that Shakespeare wrote when he wasn't
Speaker 1: writing for the King's Men that weren't included. And if
Speaker 1: you have been attributed to to other authors. Sure now
Speaker 1: people think Shakespeare didn't write any of those plays, They're wrong.
Speaker 1: So I would love it if if Shakespeare could turn
Speaker 1: out a few books. I would love it if Dickens
Speaker 1: could finish the Mystery of Edwin Drewd because there's nothing
Speaker 1: more irritating than a mystery that ends halfway through. He
Speaker 1: killed him? Or did he even die? Oh? Yeah, he
Speaker 1: may have just disappeared. There's a there's a science fiction
Speaker 1: book that C. S. Lewis never finished before his death
Speaker 1: that I would very much like to read the ending too.
Speaker 1: I would have liked to have read The Simarillian as
Speaker 1: if Tolkien had actually written it in a form that
Speaker 1: was meant to be read by a human being, as
Speaker 1: opposed to a collection of things that his son pulled
Speaker 1: together from various works and notes and then put into
Speaker 1: some kind of bizarre encyclopedia. Yeah. I mean, don't get
Speaker 1: me wrong, I nuinely love The Simarillian, but I wonder
Speaker 1: what it would have been like had Tolkien actually himself
Speaker 1: put it all out together. So, I mean there are
Speaker 1: lots of different examples, obviously, uh Hunter S. Thompson, Kurt.
Speaker 1: I mean, there's some authors out there I would love
Speaker 1: to have been able to read some more stuff. And
Speaker 1: Charles Dickens, Oh, I already mentioned him. My anglophile traits
Speaker 1: are starting to show, and there is always fan fiction.
Speaker 1: I mean, you know, some some fan authors can write
Speaker 1: very convincingly in an author's voice. Well, what if we
Speaker 1: could create a fan fiction style author that wasn't really
Speaker 1: a person. You know. Before I get into that, we're
Speaker 1: gonna have to talk a little bit about some some
Speaker 1: terms here, like artificial intelligence. Artificial intelligence, What does that
Speaker 1: have to do with literature. Well, we're gonna get there.
Speaker 1: We'll take some steps first. So artificial intelligence or machine intelligence,
Speaker 1: this is a concept that's been around for more than
Speaker 1: half a century at this point. Uh, And it was
Speaker 1: something that was kind of of played with early early
Speaker 1: early days in in the earliest days of computer science,
Speaker 1: in fact, before we even called it computer science. One
Speaker 1: of the fathers of the concept of artificial intelligence, you
Speaker 1: could argue, is Alan Turing. Now Touring was a computer
Speaker 1: scientist before there were was a word for such a thing,
Speaker 1: who worked for the British government. He was working within
Speaker 1: the War Department to help decode messages that were sent
Speaker 1: by Germans using the Enigma machine you guys familiar with.
Speaker 1: He was one of he was on the team, and yes,
Speaker 1: he was highly responsible for cracking the code of the
Speaker 1: Enigma machine. The Enigma machine was a physical mechanical device
Speaker 1: that the Germans were using that you would set to
Speaker 1: a particular setting, had certain reels, and then what you
Speaker 1: do is I had like a typewriter and as you
Speaker 1: would type, the reels would channel a an electric signal
Speaker 1: through so that a particular lightbulb would light up on
Speaker 1: the other side. But it was designed in such a
Speaker 1: way so that would encode eat letters. So if you
Speaker 1: typed a G, one thing you could be sure of
Speaker 1: is that the one bulb that's not gonna light up
Speaker 1: on that board is G. And in fact, that was
Speaker 1: one of the few things that Touring was able to
Speaker 1: figure out that that broke the code was knowing that
Speaker 1: whatever the letter was that you were looking at at
Speaker 1: an encoded message, it was definitely not exactly it'll and
Speaker 1: you would think eliminating one choice would not give you
Speaker 1: that big of an advantage, but that was kind of
Speaker 1: the crack that really helped break it open. Okay, So
Speaker 1: what did Alan Turing have to do with the idea
Speaker 1: of artificial intelligence? So he was very much interested in
Speaker 1: this idea of what is what's the capability of the
Speaker 1: machine world and are there limits? Can it go as
Speaker 1: far as humans can go? Can machines think? Can machines think?
Speaker 1: He thought about that in such a way It's a
Speaker 1: little different than the way you might initially imagine a
Speaker 1: machine thinking. Uh, Touring said, it wasn't so much. It
Speaker 1: wasn't really important if machines thought the same way that
Speaker 1: people do. In other words, what was necessary from Touring's
Speaker 1: point of view, was that the output of a machine
Speaker 1: would be indistinguishable to what a human would be capable
Speaker 1: of doing. But the process doesn't. Yeah. So in other words,
Speaker 1: if the machine was able to produce something that you
Speaker 1: seem the same as the way a human would produce it,
Speaker 1: it didn't matter how the machine did it. It just
Speaker 1: meant that that machine possessed some form of intelligence. Uh,
Speaker 1: And I'll explain that in a second to In nineteen
Speaker 1: fifty he published a paper Computer Machinery and Intelligence and
Speaker 1: propose the idea of the touring test. Now, in general, today,
Speaker 1: the way we talk about the touring test is, uh. You.
Speaker 1: A simple version would be you have a computer in
Speaker 1: front of your computer terminal. All you get is text,
Speaker 1: and your text is going to someone that's in another room,
Speaker 1: and that might be a person or it might be
Speaker 1: a machine. So you're you're like in a chat set up. Yeah, yeah,
Speaker 1: and you cannot see the other person at all all
Speaker 1: or the machine at all. All you see is whatever
Speaker 1: shows up on your screen and you type in a
Speaker 1: message and a message returns back to you that's generated
Speaker 1: either by a person or a machine. The Touring test
Speaker 1: said that if you were unable to determine to a
Speaker 1: certain degree of certainty whether or not the the the
Speaker 1: thing on the other end was a person or a machine,
Speaker 1: that machine was said to pass the Touring test. So
Speaker 1: if it could imitate human speech through text, yes, So
Speaker 1: if the machine was able to carry on a conversation
Speaker 1: in such a way that it would convince you that
Speaker 1: it's another person, then it could pass the Touring test.
Speaker 1: It would at least seem to possess intelligence. And here's
Speaker 1: how Touring kind of thought about this. It's kind of
Speaker 1: a philosophical way of thinking about intelligence. So I, Jonathan Strickland,
Speaker 1: I know that I possess at least some level of
Speaker 1: intelligence because I have that personal experience. I know what
Speaker 1: it means in an abstract way to myself. When I
Speaker 1: talked to you, Lauren, and you start to display several
Speaker 1: of the same things I associate with an intelligence. I
Speaker 1: then assume you too, possess intelligence of some level, and
Speaker 1: that that is just something that I am granting to Lauren.
Speaker 1: From my perspective, because I cannot inhabit Lauren, I can't
Speaker 1: know what her experience really is. I can only base
Speaker 1: that upon the observations I make of her behavior. Touring says, well,
Speaker 1: if we do that, if you assume that every person
Speaker 1: you encounter has some level of intelligence, why would you
Speaker 1: not offer a machine the same courtesy If it seemed
Speaker 1: to display the same sort of behaviors, You might as
Speaker 1: well say it has intelligence, because there's no way that
Speaker 1: you can inhabit that machine, just as there's no way
Speaker 1: you can inhabit another human being. And in fact, saying
Speaker 1: that we know the machine is different because we programmed
Speaker 1: it might be an example of the genetic fallacy, right
Speaker 1: that the idea that because you know where a phenomenon
Speaker 1: came from, that proves it's not genuine. Sure. Yeah, and
Speaker 1: so when we get to the actual touring test. We've
Speaker 1: had plenty of people design various kinds of of software.
Speaker 1: Usually they're called chatbots. These are just programs that are
Speaker 1: designed to interact with people and respond in a conversational
Speaker 1: way through some context. And uh, and some of them
Speaker 1: have been pretty successful. Surely they could never really trick somebody, right,
Speaker 1: I mean, I've talked a smarter child on instant Messenger.
Speaker 1: I don't think that's a person. It all depends on again,
Speaker 1: the context and how well designed the program was. So
Speaker 1: for example, one of the earliest that was designed was Eliza,
Speaker 1: which was created by Joseph Wisenbaum, and Eliza was effective
Speaker 1: within a certain context, and that it was it was
Speaker 1: presenting a point of view of almost kind of a
Speaker 1: naive person, someone who has very little real world experience,
Speaker 1: and so as long as the real human being had
Speaker 1: that same kind of perspective, it was fairly effective. There
Speaker 1: was another one called Perry p a r R Y
Speaker 1: that was made by Kenneth Colby, and that one simulated
Speaker 1: a paranoid schizophrenic responding to questions. And what they did
Speaker 1: was they ran a whole bunch of conversations between an
Speaker 1: interviewer and actual patients who suffered from paranoid schizophrenia and
Speaker 1: conversations with Perry, and then printed out all those conversations
Speaker 1: handed that to a panel of judges who were made
Speaker 1: up of psychologists, and they had a success rate of
Speaker 1: forty eight percent of saying which one was real in
Speaker 1: which one. Now, in that case, you're talking about a
Speaker 1: specific context that has its own limiting factors, right sure, yeah, yeah,
Speaker 1: you're you're you're talking about two different, um, you know,
Speaker 1: hypotheticals of a human person that aren't the kind of
Speaker 1: conversation that you would normally have straight And in the
Speaker 1: case of Perry, you're talking about a conversation that was
Speaker 1: not conducted by the actual panel of judges. Right, So
Speaker 1: their experience is very different from someone who say, it's
Speaker 1: down at a terminal and it's actually having the conversational moment.
Speaker 1: They're reviewing something that's already happened. That's different too, I
Speaker 1: mean that experience is different. But there are other examples too.
Speaker 1: There's the artificial linguistic Internet computer entity also known as Alice,
Speaker 1: and there's a one that was made by Rollo Carpenter
Speaker 1: called Jabberwocky. Was brilliant, the slightly topes uh that one
Speaker 1: was meant to simulate human chat in a very kind
Speaker 1: of humorous way. I actually tried this one out before
Speaker 1: we came to the podcast. I wish I had been
Speaker 1: able to print out our conversation because I mean, you
Speaker 1: could tell that it was or I could tell it
Speaker 1: was a chat bot. But for one thing, I already knew,
Speaker 1: So that that's problematic, right, I mean I already know
Speaker 1: going into it, So yeah, very scientific, right. I would
Speaker 1: need to have like a double blind test if I
Speaker 1: really wanted to do this properly. But also there were
Speaker 1: other giveaways, like you would it would ask you a
Speaker 1: question and I would answer because it was being snarky.
Speaker 1: I was being snarky. I answered in a snarky way,
Speaker 1: and then it didn't know how to deal with that,
Speaker 1: like it didn't have enough keywords to work off of,
Speaker 1: so it would go with a uh, like a stock
Speaker 1: response that was just a generic response. And in fact,
Speaker 1: that's the way a lot of these chatbots work is
Speaker 1: that they search for keywords and the things that the
Speaker 1: person the interviewer is typing in, and then it generates
Speaker 1: a response based upon those keywords. And if none of
Speaker 1: the keywords that it normally quote unquote knows as in
Speaker 1: it keywords that are in its database, then it will
Speaker 1: generate some other form of response, either a generic response,
Speaker 1: or it will repeat something that it had already said
Speaker 1: previously in the conversation. So what are some of the
Speaker 1: ways that we think a chat bought could get really convincing?
Speaker 1: Because I'd imagine that we're not there yet, right, but
Speaker 1: we're getting better. We are getting better, um um. And
Speaker 1: obviously there's some kind of hurdle we'd have to get
Speaker 1: over there. There's some like strategy for supplying these things
Speaker 1: with conversation asian rules that we haven't quite achieved yet.
Speaker 1: I mean, would uh, would machine learning have anything to
Speaker 1: do with it? Sure, like the idea of maybe mining
Speaker 1: thousands of other conversations to establish rules about how real
Speaker 1: people interact, or even having every time the machine has
Speaker 1: a conversation that it ends up reviewing that afterward and
Speaker 1: learning from its own experiences. I mean, these are right
Speaker 1: because you know, it's human. Human vocabulary and human understanding.
Speaker 1: Don't stop when you learn a language. We're continually learning
Speaker 1: our own languages. We're learning new rules for it, we're
Speaker 1: learning new vocabulary for it, we're creating things like metaphor.
Speaker 1: And similarly, we're creating these ideas, these abstract ideas that
Speaker 1: makes sense to people who are native speakers of that language,
Speaker 1: who have had exposure to this sort of thing, But
Speaker 1: to something that is just creating, you know, working out
Speaker 1: of say, a very strict dictionary of words, it would
Speaker 1: be meaningless or confusing. This reminds me of that time
Speaker 1: that that IBM t um. Remind me to remind me
Speaker 1: the name of the computer. Um. They yes, they taught
Speaker 1: Watson Urban dictionary. Oh no, yeah, oh no. Yeah. They
Speaker 1: then then after after Watson started talking back at them
Speaker 1: a little bit too much, they were just like, let's
Speaker 1: just nuke that portion of Watson from more. Yeah, it's
Speaker 1: the only way to be sure. Yeah. It turned out
Speaker 1: that Watson Watson developed a bit of a potty mouth, like, Um,
Speaker 1: are you familiar with the show Breaking Bad? Do you
Speaker 1: know how Jesse ends most of his sentences. That's pretty
Speaker 1: much how Watson was ending his sentences. Um. So yeah,
Speaker 1: they found out it was I think Urban Dictionary, and
Speaker 1: it was one other thing too that I think it
Speaker 1: might have been. It may have been Wikipedia, but I'm
Speaker 1: not sure. But they fed that into how to talk. Yeah,
Speaker 1: well then it just becomes unintelligible, right, hacks or yeah, okay,
Speaker 1: so uh I think I've seen I could be wrong.
Speaker 1: About this. What I think I remember seeing is that
Speaker 1: most recently, Ray Kurzwild said he thought that chat bots
Speaker 1: would reliable, you be able to beat the Turing test.
Speaker 1: And to be fair, when we say beat the Turing tests,
Speaker 1: there's no hard and fast rule. I think what that means,
Speaker 1: reliably fool the judges, right, yeah, because there's it all
Speaker 1: depends upon whom you ask, Right. Some people say you
Speaker 1: need to fool people at least thirty percent of the
Speaker 1: time and then you beat the Turing test. Other people say, well,
Speaker 1: Touring had suggested that you show it to a panel,
Speaker 1: and if the majority of the panel thinks that it's
Speaker 1: a real person, when in fact it's a computer, then
Speaker 1: the computer beats the Turing test. It all depends on
Speaker 1: how you frame it. There is not like a magical
Speaker 1: This is the way the test should be administered, and
Speaker 1: this is the only way you can tell if you
Speaker 1: pass or fail. I just want to make that clear
Speaker 1: because when we say passing the Turing test, that's so fuzzy.
Speaker 1: We really are talking about if you the listener, you
Speaker 1: you who are listening right now, if you were to
Speaker 1: have a conversation with one of these machines, you would
Speaker 1: not be completely certain whether or not that was a
Speaker 1: machine or a person. We see this actually used a
Speaker 1: lot in corporate settings for things like UM customer service.
Speaker 1: So if you ever have one of those customer service
Speaker 1: things pop up when you are on a website and
Speaker 1: it says do you need some help? Often this ends
Speaker 1: up being a chatbot that actually just has a very
Speaker 1: deep series of keywords that relate back to the products
Speaker 1: and services of that company, so that when you start
Speaker 1: typing things in, it can start sending you two links
Speaker 1: that possibly can solve your problem, but more likely will
Speaker 1: cause you to go into a red hot rage, burning
Speaker 1: brighter than a thousand exploding suns. Of course, they're the
Speaker 1: goal is to get you to the information or or
Speaker 1: service that you need. It's not really to trick you
Speaker 1: into thinking like, wow, I really just formed an emotional
Speaker 1: connection with this person, or the goal is to cut
Speaker 1: down on the cost of manning actual human beings and
Speaker 1: customer support roles. But that your your point stands either way. UM,
Speaker 1: so I would like to imagine a future where we're
Speaker 1: proposing the next Turing test. Okay, so we've gotten to
Speaker 1: the point where we can have a versation and not
Speaker 1: be sure if it's a person or a machine. Yes, so,
Speaker 1: either because the machines have become really smart or people
Speaker 1: aren't just dumb as bricks. Imagine we get there. Let's
Speaker 1: say it's nine kurs wild was right. Um now we've
Speaker 1: got chat bots that people cannot tell the chat bought
Speaker 1: from a human that every single time, you know, it's
Speaker 1: statistically and significant. Um uh. Once we're there, what's the
Speaker 1: next step. What's the next hardest thing in that same
Speaker 1: sort of realm of imitating human intelligence? Well, Joe, since
Speaker 1: this whole podcast is about storytelling, I'm going to take
Speaker 1: a wild stab in the dark and say it's storytelling.
Speaker 1: That's my guess. I think that'd be really interesting. I mean,
Speaker 1: so we can sort of imagine, like the chat bots
Speaker 1: we've interacted with today, they can't really do conversation, but
Speaker 1: we can imagine it. We should see it storytelling. Now,
Speaker 1: on one hand, I can imagine how an AI story
Speaker 1: bought would work. But on the other hand, it seems
Speaker 1: so alien into me. Surely they couldn't really create a
Speaker 1: piece of AI that could tell stories that seemed as
Speaker 1: good as human stories, right, I think it would be
Speaker 1: a huge challenge, But I don't see anything that's fundamentally
Speaker 1: impossible about it. Now, well, I mean if you take
Speaker 1: the fact that there are programs these days that can
Speaker 1: create music or or create works of visual art, then
Speaker 1: you know, and those are based on a few thousand
Speaker 1: rules about what makes a good piece of whatever media. Right. Okay, Well,
Speaker 1: well let's let's set it up. Now, let's describe the
Speaker 1: literary touring test. Okay that what what kind of test
Speaker 1: would a story bought have to pass? Well, you'd have
Speaker 1: to be able, I would say, to read the story
Speaker 1: and feel like it was saying satisfied, for lack of
Speaker 1: a better word, organic that that it did not it
Speaker 1: was not just a series of choppy sentences where a
Speaker 1: person goes through some mundane task. It would need to
Speaker 1: be uh, it would need to be engaging. I know
Speaker 1: you love that word, Joe, I keep using it because
Speaker 1: of that. But it would need to It would need
Speaker 1: to captivate an audience in some way, either because of
Speaker 1: the actual plot or the characterization or some combination thereof.
Speaker 1: And it would need to It would need to have
Speaker 1: a narrative flow. It would need to have internal consistency. So,
Speaker 1: in other words, things that happen earlier in the story
Speaker 1: could not be contradicted later in the story. Let's say
Speaker 1: that you have a story about a father and uh,
Speaker 1: the father experiences the heartbreak of a child dying, and
Speaker 1: that's a terrible thing. This is something that we would
Speaker 1: encounter in a story on novel whatever. But then three
Speaker 1: chapters later, the child's there and nothing has gone wrong,
Speaker 1: and there's it's not like it's mystery or I think
Speaker 1: it's just that the child had never died. And and
Speaker 1: either you know, either adhering to those rules, or if
Speaker 1: it breaks those rules, to be breaking them in a
Speaker 1: way that seems conscious and purposeful. Exactly, it couldn't just
Speaker 1: be breaking rules indiscriminately. Here's my blanket rule for the
Speaker 1: literary touring test I've imagined. Um, it doesn't have to
Speaker 1: create stories that are as good as your favorite author.
Speaker 1: It just has to do in the same way that
Speaker 1: the original Turing test didn't have to be as great
Speaker 1: a conversationalist as your most interesting friend. It just has
Speaker 1: to be good enough that you think this is probably
Speaker 1: written by a human being. Sure, I've read some awful stories,
Speaker 1: so the bar is set fairly low. Um, we're going
Speaker 1: back to that fan fiction thing now, But you've read
Speaker 1: some awful stories. But even people who are bad writers
Speaker 1: write better stories than than we could generate now with
Speaker 1: a I some really bad stories. I've read some really
Speaker 1: bad things. Well, let's let's just play this way. The
Speaker 1: way the way a computer would generate a story right now,
Speaker 1: the only way I think you could have one where
Speaker 1: it would have any sort of of actual sense to it,
Speaker 1: unless you were to have an incredibly powerful computer and
Speaker 1: hundreds and hundreds and hundreds of hours of programming time
Speaker 1: built into it, would be if you went the mad
Speaker 1: Libs approach, where you had a story outline that already
Speaker 1: existed and the computer was just filling in the blanks.
Speaker 1: And even then it's not necessarily going to make a
Speaker 1: whole lot of sense. Of course, that that wouldn't be generating, No,
Speaker 1: that would. In fact, I've seen generators that do this
Speaker 1: kind of thing where they they follow very simple rules. So,
Speaker 1: for example, my father created a program in Apple Basic
Speaker 1: back in nine five, I want to say, so, yes,
Speaker 1: we had computers back then, uh, And he created this
Speaker 1: program that was a science fiction novel title generator, and
Speaker 1: it had just very simple rules where it would take
Speaker 1: some ridiculous adjective and ridiculous nouns and pair it up
Speaker 1: together so that you would get a sentence or or
Speaker 1: title that would just sound like yeah, I could totally
Speaker 1: see that being on the store shelf in the science
Speaker 1: fiction fantasy section, the burning lamp, Shade of Venus and
Speaker 1: exactly that would be. That would be a great example.
Speaker 1: It reminds me of I saw online a while back
Speaker 1: at James Bond movie title Generator, but that one spit
Speaker 1: out horrible things. It was like the Last Day to
Speaker 1: Gun or like, oh yeah, because it was my favorite
Speaker 1: was ball Eyes. It's because it was taking it was
Speaker 1: taking existing James Bond titles and breaking them up and
Speaker 1: then putting them back together. So instead of instead of
Speaker 1: creating a really deep, uh database of words to work from,
Speaker 1: it was just like, let's put in every title of
Speaker 1: j so Thunderball and Golden I maybe or something like that. Yeah.
Speaker 1: So so. So the components that we're trying to smush
Speaker 1: together here to to create this literary Turing test passing
Speaker 1: machine are are that that vocabulary. You want to have
Speaker 1: enough vocab so that you're not just ball Eyes to
Speaker 1: the next century. The most basic it would just have
Speaker 1: to know what words mean in a robust way, right right. Uh,
Speaker 1: you know, it needs to be able to work with
Speaker 1: the tropes and metaphors of of the culture that it's
Speaker 1: writing for. I mean, because you know, a lot of
Speaker 1: books will contain a lot of stories, contain references to
Speaker 1: countless other stories, and are using um, the bits and
Speaker 1: pieces the devices and conventions of storytelling that a culture
Speaker 1: is used to write. There either either deliberately playing on
Speaker 1: those tropes or they're deliberately defying them. But in either case,
Speaker 1: it's something where the awareness is important both for the
Speaker 1: storyteller and the audience. It's got to have an advanced
Speaker 1: level of machine learning, which you guys mentioned earlier, which
Speaker 1: is which is the science of getting a computer to
Speaker 1: do something without explicitly having told it to do it, right,
Speaker 1: because there are just too many rules you could not
Speaker 1: possibly code them, right. Sure, Sure, it's it's you know,
Speaker 1: machine learning is what's behind something like telling a Google
Speaker 1: car to go down a street without it ever having
Speaker 1: seen that street before. Um, you know, it's it's also
Speaker 1: what goes into into web search and lots of other
Speaker 1: you know, the Human g Project, everything like that is
Speaker 1: based on machine learning. UM. And and then a basic
Speaker 1: I mean not basic because this is actually really huge.
Speaker 1: And ontology a semantic or abstract model of data UM
Speaker 1: that that builds upon databases which are logical or logical
Speaker 1: or physical UM. So you know, it's the term comes
Speaker 1: from philosophy, where it's used to describe studies of nature
Speaker 1: or existence, and it was picked up by early AI researches.
Speaker 1: Researchers to um to define the the objects, concepts, and
Speaker 1: other entities that are presumed to exist in in some
Speaker 1: area of interest, and the relationships that are held among them. Right, so, uh,
Speaker 1: let's make this concrete. Let's imagine a story. Okay, say
Speaker 1: there's a story where there's like a murder or something. Uh,
Speaker 1: you know, Colonel Mustard kills Mr. Body and and this
Speaker 1: is a murder mystery about the story, Well it the
Speaker 1: computer that's generating the story would have to have such
Speaker 1: a robust understanding not just to what a word like
Speaker 1: knife means, like not just that it's a noun or whatever,
Speaker 1: but that it has relationships. So it can be found
Speaker 1: in the kitchen, it can be used for murder. Maybe
Speaker 1: it's found in Mr. Body. Yeah it can. But it
Speaker 1: can also be a clue. Sure, but there's also metaphors
Speaker 1: about being on a knife's edge or so to understand. Yeah,
Speaker 1: it wouldn't just have to have knife in like its library,
Speaker 1: but it would have to have a connected web of
Speaker 1: relationships to other terms. Furthermore, you would have other relationships
Speaker 1: you'd have to determine, like the fact that you would
Speaker 1: have uh, you know, uh, Colonel Mustard as murderer. Mr
Speaker 1: Body is victim, but you might have Mr Green as suspect.
Speaker 1: You might have Scarlett as suspect. You know this. These
Speaker 1: would be these would be different definitions. And then not
Speaker 1: only do you define everything, but then you have to
Speaker 1: relate them all to one another. Right. That's the big
Speaker 1: thing that would make it even more complex, because in
Speaker 1: order to have good fiction you have to have strong
Speaker 1: characterization and relationships between the characters. So you need ontologies
Speaker 1: that defined characters by like is in love with or
Speaker 1: has a grudge against, is afraid of that kind of thing?
Speaker 1: Or uh, you know, for example, in our our clue
Speaker 1: or clue, oh example, we could go back to uh,
Speaker 1: not only is Mr Green a suspect and Colonel Mustard
Speaker 1: is the murderer, but Colonel Mustard views Mr Green as
Speaker 1: a patsy. Now, the other characters would not view Mr
Speaker 1: Green as a patsy, and within the narrative of a story,
Speaker 1: that would be clear to the audience but not clear
Speaker 1: to the characters within the context of that story. It's
Speaker 1: a very simple thing for us to think about as
Speaker 1: human beings. This is something that comes very naturally to us. Obviously,
Speaker 1: when you're watching a movie or reading a book, you
Speaker 1: are aware of certain things that other characters are not
Speaker 1: aware of, and if those characters magically become aware of them,
Speaker 1: that pulls you out moment. I could see that being
Speaker 1: a very challenging thing to to quote unquote teach a
Speaker 1: computer to know that you have traumatic Irony exists and
Speaker 1: can be used in these ways, but not in these ways.
Speaker 1: And because and just because one character is aware of
Speaker 1: something does not necessarily mean that another character is aware
Speaker 1: of that same thing. I mean I I had an
Speaker 1: experience like that this week where I was watching something
Speaker 1: and I thought, wait a minute, how does this character
Speaker 1: even know this? And I totally pulled me all the story. Yeah, um,
Speaker 1: so it's clear just how difficult this is. How how
Speaker 1: I mean it, I'm gonna say frankly, it seems impossible
Speaker 1: to me now, but I wouldn't say it's actually impolsible.
Speaker 1: I would have said that a computer beating Jeopardy champions
Speaker 1: at Jeopardy would have been impossible, you know, a couple
Speaker 1: of years ago, and then IBM with Watson totally proved
Speaker 1: me wrong. And they did it in a way where
Speaker 1: Watson was not even connected to the Internet. It was
Speaker 1: all self contained. So the fact that there could be
Speaker 1: a self contained database of enough information to be able
Speaker 1: to anticipate practically anything that Jeopardy can throw at you.
Speaker 1: And Jeopardy is a game that includes things like wordplay,
Speaker 1: where it's not just a standard here is your answer,
Speaker 1: what's the what's the correct question? You may have to
Speaker 1: interpret that answer beyond just what jokes and and et cetera.
Speaker 1: Now there might be puns, there can be references. So
Speaker 1: the fact that we were that we the fact that
Speaker 1: IBM was able to create a computer that was capable
Speaker 1: of doing this, I I hesitate to say impossible. I
Speaker 1: think it's an incredibly difficult task. I would hesitate to
Speaker 1: go so far as to say it's it's an uncrackable task. Yeah,
Speaker 1: so so, so what's it like right now? This is
Speaker 1: what we are imagining in the future. I hear that
Speaker 1: you have some examples of computer generated poetry. Yeah, well, okay,
Speaker 1: so obviously I think it's pretty clear poetry is easier
Speaker 1: I think for machines to generate than fiction at this point,
Speaker 1: because because the rules of poetry are a little bit looser, Um,
Speaker 1: we expect poetry to be a lot of people expect poetry,
Speaker 1: you know, you can expect it to be associative rather
Speaker 1: than narrative. And it's actually I mean, it's got to
Speaker 1: be really difficult to get a computer to generate a
Speaker 1: coherent narrative and less it's just following like a pre
Speaker 1: supplied skeletal structure. Yeah, yeah, to tell a computer, Hey,
Speaker 1: tell me a fantasy story. Here are four characters you have,
Speaker 1: and here is one, uh, one important piece of information
Speaker 1: that you need to incorporate. Go. That would be like
Speaker 1: like you said, Joe, right now, that's really hard to image.
Speaker 1: It's impossible, you know, it wouldn't be able to I mean,
Speaker 1: the computer just doesn't understand events and the progression of
Speaker 1: events enough to tell a coherent narrative from one you
Speaker 1: know that that feels meaningful in any way. I mean
Speaker 1: why didn't the eagles just give them a ride to
Speaker 1: more to or at the very beginning if they could? All? Right? Right,
Speaker 1: but but but speaking speaking as one who basically majored
Speaker 1: in poetry in college and very nearly went on to
Speaker 1: do an m f a in poetry, Um, writing good
Speaker 1: poetry is really hard. It's hey, I am, I am
Speaker 1: right there with you. I also studied poetry and I
Speaker 1: love poetry. Um. The difference is that you can get
Speaker 1: away with more associative stuff. Now, it might not be
Speaker 1: poetry that anybody really cares to read more than once,
Speaker 1: but it can pass for a poem. Um. So this
Speaker 1: is a I found this book. It was published in
Speaker 1: two thousand eleven by Pure Press, edited by a computational
Speaker 1: linguist named are Eli Hirtelo. And this is a book
Speaker 1: of poems called Discourse dot c PP that was created
Speaker 1: by a computer that was designed to generate ontologies. So
Speaker 1: what we were talking about before, you know, the idea
Speaker 1: of association, establishing meaning for terms that it finds on
Speaker 1: the internet and relationships with other terms. Uh. And so
Speaker 1: the way that this book was created by it was
Speaker 1: by machine learning. It was by looking at tons and
Speaker 1: tons of text on Wikipedia and trying to establish relationships
Speaker 1: between terms and then using that to auto generate poems
Speaker 1: based on the semantic relationships that it had determined. I
Speaker 1: bet it's poem about citation. Question mark is amazing. Okay.
Speaker 1: The umbrella. Okay, this is when we have not heard yet.
Speaker 1: We heard one earlier that actually Lauren and I both
Speaker 1: agree like we can imagine a terrible poet. We'll get
Speaker 1: to it, Jonathan, No, no, no, no, I don't want
Speaker 1: you to read that one. I want you to read
Speaker 1: just ones I have not heard yet. The umbrella. You
Speaker 1: want an umbrella and all you have is a flannel
Speaker 1: handkerchief and a sponge. That's that's it. That's that's probably bicycles. No, no, no,
Speaker 1: I'm still I'm still taking an umbrella, flannel handkerchief and
Speaker 1: a sponge that's written by a computer. Go another one.
Speaker 1: Bicycles by the computer. The cycles dominate the street, an
Speaker 1: infantry of two wheeled implements, tricycles and rickshaws, most of
Speaker 1: them far left crutch carriages. The pilots are no duffers.
Speaker 1: Visitors rent higher priced engine powered recumbent three seaters, flashlight
Speaker 1: and tulip included. All commute as one would skim. Pretty sure,
Speaker 1: E Cummings wrote something like that. Um, but hey, no,
Speaker 1: I want to read the good one, this one, this
Speaker 1: one actually, this was kind of amazing, the only one
Speaker 1: that we had heard actually like this one. If if
Speaker 1: a human wrote this, I would think it was kind
Speaker 1: of interesting. Okay, it's called love to love rather like
Speaker 1: prefer and wish to want first, bother then approach, chase, catch,
Speaker 1: eat and kill, thank you, to love of to remind
Speaker 1: and remember, to know and to forget. Oh, that last
Speaker 1: line is kind of a chiller. I just like to
Speaker 1: know the bother part because, as I said earlier in
Speaker 1: our pre meeting, I said, this sounds like the middle
Speaker 1: school approach to romance, like bother the person first and
Speaker 1: then get the kind of or the Anakin Skywalker school
Speaker 1: of courtship. Um, so that's where we're where. Yeah, And
Speaker 1: I think that what we can kind of take from
Speaker 1: this is that it's easier to write poetry about um,
Speaker 1: about something abstract like love, than it is about something
Speaker 1: concrete like a bicycle, right, like a narrative right right
Speaker 1: to see. I would really be curious to see what
Speaker 1: the first computer generated stab at something like bail Wolf
Speaker 1: would be like an epic saga. Well, I would say probably,
Speaker 1: So you have poetry on two sides. It's probably easier
Speaker 1: or a computer to write abstract or lyrical poetry than
Speaker 1: it is to write prose fiction. But it's probably even
Speaker 1: harder for a computer to write narrative poetry than it
Speaker 1: would be for the computer to write prose fiction. Right, Yeah,
Speaker 1: I would imagine so, because then you've got you've got
Speaker 1: to deal with all those difficulties of coherent progressive narrative,
Speaker 1: and you've also got to deal with whatever, you know,
Speaker 1: poetic tropes you're using, like not to makes you have
Speaker 1: a length of a line, right, exactly, like how much alliteration,
Speaker 1: especially for something like Old English where it was all
Speaker 1: alliterative and not rhyming poetry. So one of the things
Speaker 1: that you brought up in the video, and I think
Speaker 1: it's a really interesting idea, is let's say that we
Speaker 1: get to the point where computers are able to simulate
Speaker 1: to a certain extent the ability to tell a story,
Speaker 1: and it will really depend heavily upon what rules the
Speaker 1: programmer creates. Right, So, in other words, if I were
Speaker 1: to program and artificially intelligent story Heller. The stories that
Speaker 1: would come out of that computer would reflect the rules
Speaker 1: I had created, which might be different from the rules
Speaker 1: you create, Joe. You might think that, you know, especially
Speaker 1: something as simple as whether or not you value one
Speaker 1: aspect of storytelling over another could make wildly different stories.
Speaker 1: But we could get really granular, like to the point
Speaker 1: of how frequently do I use the word the versus
Speaker 1: how frequently you use it? And that actually matters, That
Speaker 1: actually contributes to somebody's authorial voice. All right, you can
Speaker 1: you can actually, um take a take a genetic imprint
Speaker 1: of an author's voice based on I think, Joe, I
Speaker 1: think you've got a bunch of notes on this one,
Speaker 1: based on like the number of of times that they
Speaker 1: use particular words, and the scope of their vocabulary and
Speaker 1: like yeah, go ahead, I'm sorry, and the grammatic structure
Speaker 1: of their sentences. Okay, Well, let's start with an idea. Um.
Speaker 1: In N six, I think it was, somebody published a
Speaker 1: novel called Imary Colors. The story, well, primary Colors was
Speaker 1: a political novel. It was a Romano left you know,
Speaker 1: which was a thinly veiled story about the Bill Clinton
Speaker 1: campaign with the names changed and stuff like that. But
Speaker 1: it was published anonymously. The author didn't reveal his or
Speaker 1: her name, gotcha um, And so there was a big
Speaker 1: question like, well, you know, we don't know who wrote this,
Speaker 1: but people were really curious. They wanted, you know, all
Speaker 1: they were positing all these names who were insiders on
Speaker 1: the Clinton campaign and stuff like that. Um. There was
Speaker 1: a Vassar College shakespeare professor named Don Foster, who I've
Speaker 1: got this interesting scene in article from two thousand about him, uh,
Speaker 1: and about how he used literary textual analysis to determine
Speaker 1: who he thought the author was, and he thought it
Speaker 1: was the columnist Joe Klein. Uh. And it turned out
Speaker 1: years later Klein admitted that Foster had been correct. So
Speaker 1: how did Foster identify him? While it was just straight
Speaker 1: up textual comparison. He was looking at the text of
Speaker 1: the novel and looking at the idiosyncrasies of the kinds
Speaker 1: of vocabulary and sentenced construction, just how the text read,
Speaker 1: comparing that with all kinds of other famous writers. And
Speaker 1: when he finally stumbled across Joe Klein's column, he said,
Speaker 1: ah ha, here we go. It's this guy, because he
Speaker 1: he uses the same kinds of words in the same order.
Speaker 1: The voice you can just easily identify and he was correct. Now,
Speaker 1: actually you don't need a really skilled Shakespeare professor to
Speaker 1: do something like this these days. So it helps. It
Speaker 1: helps because because as we were talking about at the
Speaker 1: very top of the show, Shakespeare produced a lot of work,
Speaker 1: and some of the work that has attributed to Shakespeare
Speaker 1: is probably a collaboration with other authors. In some cases
Speaker 1: they're known collaborations. In other cases, Um, there are plays
Speaker 1: where uh, it's suspect said that Shakespeare did not start
Speaker 1: the play, but he finished it. And so having someone
Speaker 1: who is uh specialized in examining the way a particular
Speaker 1: writer wrote and being able to compare other works against
Speaker 1: that body of known work or attributed work is very
Speaker 1: very useful when you're trying to determine like, is this
Speaker 1: actual play that we discovered that has no name attached
Speaker 1: to it? Is that actually a lost Shakespearean play or whatever? Yeah. Um,
Speaker 1: so they're all kinds of anonymous works throughout history. Think
Speaker 1: about the Federalist papers, right that they were all all
Speaker 1: these uh what were the articles published in newspapers? I
Speaker 1: guess they were bad um written by John Jay Uh,
Speaker 1: Alexander Hamilton's and James Madison, Right, they're looking at me.
Speaker 1: I'm the English history guy. Yeah. Well okay, but so
Speaker 1: they were published anonymously. UM, but I wonder if you
Speaker 1: could get right, a computer, your program that can look
Speaker 1: at these three works and group them into different author sortings,
Speaker 1: and then compare those different groups two works that we
Speaker 1: know by each of those three authors and figure out
Speaker 1: which ones wrote which. Well, bam, we've got it. So
Speaker 1: this is essentially like fingerprint analysis, where you're looking for
Speaker 1: points of comparison that are identical against multiple criteria, whether
Speaker 1: of using the both the the anonymous work and then
Speaker 1: the known body of work. Right. Yeah. So the one
Speaker 1: example of this type of software is a free piece
Speaker 1: of software called Signature. It's the Stick Signature stylo Metrics
Speaker 1: System UM, and it's a computer program used for textual analysis.
Speaker 1: And one of the things that's used for is to
Speaker 1: uh uh, is to assess authorship in a book where
Speaker 1: the author is questionable, or to identify an author in
Speaker 1: the case of been anonymous work like we were talking
Speaker 1: about with the Federalist papers. And so how does it work. Well,
Speaker 1: it does the same thing that Don Foster did with
Speaker 1: primary colors, except it has an organized system of analyzing
Speaker 1: text in a in a machine readable way. So it
Speaker 1: can look at idiosyncrasies in the way you structure sentences,
Speaker 1: or in the frequency with which you use certain words,
Speaker 1: or it can look at the frequency of common function
Speaker 1: words like we were talking about before, just using too
Speaker 1: and from and but and if you if you frequently
Speaker 1: misuse a word, that's a dead giveaway. And it does happen,
Speaker 1: you know. UM, And all of the things that go
Speaker 1: together to form an author's voice, you know, a distinctive
Speaker 1: authorial writing style are things that can be used to
Speaker 1: identify the author. And and we're getting better and better
Speaker 1: at this. There's another textual analysis tool that was created
Speaker 1: by grad students at Drexley University, UM, and it called
Speaker 1: Jay Style. Oh and let's say you've got an anonymous
Speaker 1: work here, UM. And what it does is it let's
Speaker 1: say the work is about five hundred words, and you
Speaker 1: have a pool of maybe fifty potential authorial candidates, and
Speaker 1: you have maybe sixty five hundreds of words of text
Speaker 1: from each of the candidates. They say, that's enough for
Speaker 1: it to identify the five hundred words sample. The author
Speaker 1: of the five five hundred words sample with a very
Speaker 1: high degree of certainty. Um and all funny enough, this
Speaker 1: is kind of a side note, but they also created
Speaker 1: something that works the other way. Uh So, if we're
Speaker 1: getting better and better at using software to identify the
Speaker 1: author of anonymously written text, that seems like it could
Speaker 1: put people like whistleblowers at risk. So they came up
Speaker 1: with a program that does the opposite. It's called anana Mouth,
Speaker 1: and it takes text that you write and helps you
Speaker 1: retranslate it out of your voice. Oh. I see, I
Speaker 1: was just about to say, like I could see this
Speaker 1: being a thing that that that hurts anonymity online, something
Speaker 1: that we already know is becoming a precious commodity. Sure, sure,
Speaker 1: but instead of just running it through babel fish and
Speaker 1: into Russian and then Chinese and then back to English.
Speaker 1: Then what you're thinking, like, I think I know what
Speaker 1: you're saying. So this is really interesting. How does this
Speaker 1: all relate to our Ai storyteller? Well, yeah, this idea
Speaker 1: of if if we can work backwards, then can we
Speaker 1: work backwards to say, create the Simari in the way
Speaker 1: that Tolkien would have written it exactly? So you can
Speaker 1: look at the rules that generate an author's voice. And
Speaker 1: if you can derive rules enough to identify an anonymous work,
Speaker 1: you can potentially derive rules well enough to generate new work. Right, Um,
Speaker 1: but you know can can we can we generate a
Speaker 1: story the way that an author would have created a story,
Speaker 1: Because that's that's emergent behavior right there. I mean, that's
Speaker 1: that's something more than the some of its parts. Again,
Speaker 1: it's it's one of those things where I'm I'm pulled
Speaker 1: in both directions. On one hand, I can see how
Speaker 1: this would work. On the other hand, it seems so impossible. Also,
Speaker 1: I mean, I I edit novels freelance, and like I said,
Speaker 1: I was a writing major in college, and the idea
Speaker 1: of a computer writing a story is personally offensive to me.
Speaker 1: Like coming coming into this podcast, I was angry about
Speaker 1: the very thought. Like I was, I was totally ready
Speaker 1: to kill that computer before it took my job. But
Speaker 1: and but but but but doing research for it, I
Speaker 1: found something that Ray Kurtzwil wrote that that kind of
Speaker 1: struck me. And uh. He was talking about Harold Cohen,
Speaker 1: who was a computer programmer and artist who tat a
Speaker 1: program a thousand rules for drawing and and had it
Speaker 1: draw some stuff and kurts while asked, you know, like, Okay,
Speaker 1: so who's the artist? Uh. Cohen claims that he is,
Speaker 1: and that his computer has not been programmed to complain.
Speaker 1: And I just thought that that was a very neat
Speaker 1: way of looking at it, you know, looking at creating
Speaker 1: a program that can itself be regarded as a work
Speaker 1: of art, and that what it creates being something new
Speaker 1: and not necessarily you know. Yeah, I think, uh, Well,
Speaker 1: first of all, I think the idea of recreating work
Speaker 1: in the voice of another author and existing author is
Speaker 1: very interesting. I would be eager to see the first,
Speaker 1: what we would deem truly successful example of that. Uh.
Speaker 1: I think if it's possible at all, it's a long
Speaker 1: way off. Again, I think it's probably possible, But I
Speaker 1: do agree this a long way off. I think actually,
Speaker 1: I think that creating something I think I think that
Speaker 1: the I think actually creating something in a particular person's
Speaker 1: voice is in some ways easier because you are limiting
Speaker 1: the uh, the choices that your computer program can make.
Speaker 1: They cannot choose something that would go outside of that. Uh. Now,
Speaker 1: with some authors, with some people who have created stuff
Speaker 1: that's particularly tricky for example, again I go back to Shakespeare.
Speaker 1: And the reason I do is because not that Shakespeare
Speaker 1: was creating brand new plots. We all know Shakespeare took
Speaker 1: almost every single one of his plots either from history
Speaker 1: and then he revised it heavily, or he took it
Speaker 1: from pre existing work but then put it into his
Speaker 1: own voice, with his own motivations and characters. Um, so
Speaker 1: that's not you know, that wouldn't surprise me to be
Speaker 1: able to create another Shakespearean style just based upon the
Speaker 1: previous existing plot. The problem is that Shakespeare was also
Speaker 1: known for creating words. He created words that we use
Speaker 1: in our vocabulary. He created phrases and metaphors that are
Speaker 1: now part of our common vocab in in English speaking nations,
Speaker 1: and UH to be able to recreate that would be
Speaker 1: particularly difficult. So that it would feel natural that you
Speaker 1: would create new metaphors and new UH phrases that would
Speaker 1: become something that people you could see people quoting. I
Speaker 1: mean people are going to quote it. It's a play.
Speaker 1: You gotta quote it if you're doing it as a play.
Speaker 1: So that part is it's hard for me to imagine.
Speaker 1: But I still would be reluctant to say it's impossible. Right,
Speaker 1: So your question is basically, just can a work that
Speaker 1: is created based on a set of rules have meaning?
Speaker 1: Can it have meaning? And can it be true? And
Speaker 1: you know, and I think you know. Meaning is one
Speaker 1: of those things that is found within the reader. You
Speaker 1: get to readers to read the same piece of work,
Speaker 1: and it doesn't matter if that piece of work is
Speaker 1: regarded as the best literature ever written or if it's
Speaker 1: just you know, pulp fiction or anything in between. Some
Speaker 1: one reader might get a lot of it and another
Speaker 1: reader might get nothing out of it, or they might
Speaker 1: get too totally different things, both of value out of it. Right. Um,
Speaker 1: maybe I'm just a sucker, but I felt a little
Speaker 1: bit of meaning in the computer's poem about love. I
Speaker 1: sucker that it wasn't poetry, it didn't even rhyme um,
Speaker 1: But no, I I you know. The The interesting thing
Speaker 1: here again goes back to the touring test. Right, maybe
Speaker 1: the stuff that's produced has meaning, Maybe the machine never
Speaker 1: realizes that there's any meaning. And touring would say, what's
Speaker 1: the problem because we just assume that the person who
Speaker 1: is creating something is seeing meaning there and we cannot
Speaker 1: be sure that that's the case. Great examples there when
Speaker 1: you have literature classes that say, like, the teacher is
Speaker 1: going on and on about how a certain passage in
Speaker 1: a book actually represents this one particular thing, and then
Speaker 1: you talk to the author and like, uh no, that
Speaker 1: part is about a guy having breakfast, which is pretty
Speaker 1: much what I wrote. Actually, this is not a problem
Speaker 1: for me to imagine at all. We are so eager
Speaker 1: to find meaning, right and personifying machines to begin with.
Speaker 1: We find meanings in things that weren't designed by anybody
Speaker 1: at all. I mean, we find meaning in like if
Speaker 1: I walk outside and I'm feeling a certain way about
Speaker 1: I don't know about a relationship I'm in, or my
Speaker 1: job or something, and I see a hawk swoop down
Speaker 1: and grab a mouse. I mean the hawk isn't trying
Speaker 1: to create meaning or entertainment, right, but I will interpret.
Speaker 1: I mean that will become meaningful to me. I'll see
Speaker 1: meaning in this utterly random event. Well, sure, I mean
Speaker 1: people also will create patterns, whether or see things within patterns,
Speaker 1: whether there was no actual thing. They're so looking up
Speaker 1: at the clouds and saying, oh, look, it's very like
Speaker 1: a whale. So it's a Shakespeare reference for you. I
Speaker 1: don't think a person had to necessarily choose a word
Speaker 1: to go in a certain place and a sentence for
Speaker 1: that word to have a meaningful effect on me. Sure, yeah,
Speaker 1: I mean it'll be interesting to see if this, if
Speaker 1: this ever becomes a reality, I assume it will. If
Speaker 1: it is possible it will happen that that we can
Speaker 1: go ahead and just say, because I think it's one
Speaker 1: of those things you can say about the future, is
Speaker 1: that if something is possible, someone will do it. So
Speaker 1: it's just a question curiosity. So it's just a question
Speaker 1: of when, assuming that it is possible, I'm curious to
Speaker 1: read it. Uh, it may I may feel that it
Speaker 1: is a much better novel than and let's see, I'm
Speaker 1: just gonna pick one of the novels I hated reading
Speaker 1: in school, and it's a toss up between Tests of
Speaker 1: the Revels and Ethan Frome. So take whatever you like.
Speaker 1: But anyway, I might read it and think it's a
Speaker 1: novel ten times better than either of those. Well, I'm
Speaker 1: going to guess that I'm I might get some convincingly
Speaker 1: dickinson Ian No, no no, no, not Charles Dickenson. Dickinson. I
Speaker 1: was saying, Emily Dickinson Dickinson. That's why I'm sooner to
Speaker 1: get some convincingly Dickinsonian poetry than you are to get
Speaker 1: some convincing prose narrative. I I think you're I think
Speaker 1: you're on target there. I would imagine that we will
Speaker 1: see this develop over time in various fields, and the
Speaker 1: ones that will be conquered first will be the ones
Speaker 1: that require the least narration. The ones that will be
Speaker 1: conquered last will be really, really good poetry. Because obviously,
Speaker 1: one of the rules here is that the longer the
Speaker 1: form is for a human anyway, the longer the form
Speaker 1: is in general, the easier it is to write. Not
Speaker 1: that it's going to be easy to write a good one,
Speaker 1: but writing a good novel is easier than writing a
Speaker 1: good short story because you have to make every word count.
Speaker 1: Writing a good short story is way easier than writing
Speaker 1: a really good poem because you have even fewer words
Speaker 1: to work with. Here's the thing, too, though, if we're
Speaker 1: considering creating stuff by generating rules in a dead author's voice,
Speaker 1: how much work they have available is going to be
Speaker 1: a big deal. Here, somebody who's got, you know, seventy
Speaker 1: novels that we can feed in to generate rules from
Speaker 1: is going to create a more robust AI voice than
Speaker 1: somebody who published one novel and that's it. Sure, yeah,
Speaker 1: that's very true. So, uh you know, maybe we'll eventually
Speaker 1: get to a point where we have these computers just
Speaker 1: generating things in their own voices, in which case it
Speaker 1: will all be zeros and ones be a fascinating read
Speaker 1: binary the novel. All right, So, uh no, this is
Speaker 1: a fun discussion. I am curious to hear what our
Speaker 1: listeners have to say about this. You should go to
Speaker 1: our website FW thinking dot com. That's where we have
Speaker 1: all the blog post, podcast videos, articles all relating to
Speaker 1: these topics are right there. Um find the one for
Speaker 1: this podcast, the entry we have for the for the episode,
Speaker 1: and let us know what you think about the idea
Speaker 1: of computer generated fiction, and tell us if you think
Speaker 1: that's ever gonna be a reality or what do you
Speaker 1: think the first computer generated book will be about? Now?
Speaker 1: Will it be The Mouse who Loved Me? Maybe it's
Speaker 1: I have no mouse and I'm a screen who knows apologies.
Speaker 1: All right, so let us know and we will talk
Speaker 1: to you again really soon for more on this topic
Speaker 1: and the future of technology. Visit forward thinking dot Com,
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