Speaker 1: Welcome to Stuff to Blow Your Mind production of My
Speaker 1: Heart Radio. Hey you welcome to Stuff to Blow Your Mind.
Speaker 1: My name is Robert Lamb and I'm Joe McCormick. Joe.
Speaker 1: While you were out, I conducted an interview with educational
Speaker 1: technologist Mike Sharple's on the book he wrote with Rafael
Speaker 1: Perezi Perez, Story Machines, How Computers Have Become Creative Writers.
Speaker 1: It's a fascinating read about the nature of storytelling, our
Speaker 1: history of attempting to instill the spirit of storytelling into
Speaker 1: the machine, where we seem to be going with this technology,
Speaker 1: and quite remarkably where we are already. Well, I can't
Speaker 1: wait to hear this interview. All right, Well, without further ado,
Speaker 1: let's jump right in. Hi, Mike, welcome to the show.
Speaker 1: I'm here. It's good to be at. The book, co
Speaker 1: authored with Rafael Perez Perez is Story Machines, How Computers
Speaker 1: Have Become Creative Writers, Publishing July five. It's a terrific read,
Speaker 1: but it has to be stressed from the outside. That's
Speaker 1: the storytelling isn't just a pastime that humans engage in.
Speaker 1: Storytelling is something greater? Right? Yeah, So storytelling we suggest
Speaker 1: in the book is something that's fundamental to human existence
Speaker 1: and has been for millennia. It suggested that instead of
Speaker 1: language coming first and then storytelling involving after that, perhaps
Speaker 1: it's the other way around. That perhaps storytelling started as
Speaker 1: a way of human communication through mine, through expressive gestures,
Speaker 1: and then language followed from that. So we want to
Speaker 1: make the point that storytelling is fundamental at a neural level,
Speaker 1: it's how we make sense of the world. It's at
Speaker 1: a cognitive level about how we create narratives to explain
Speaker 1: our existence, and in the social world, it's the stories
Speaker 1: we tell to each other that makes us who we are.
Speaker 1: So when we're talking about machines engaging in storytelling or
Speaker 1: story creation, we're really getting deep into human creativity and
Speaker 1: human identity. Then, yeah, we are. And it's both a
Speaker 1: fascinating insight into human creativity and it's also something the threat.
Speaker 1: So for centuries, writers have been fascinated about this idea
Speaker 1: that a that a machine might take over their craft,
Speaker 1: that a machine might become a storyteller. It goes right
Speaker 1: back to Jonathan Swift, for example, in Gulliver's Travels Coming
Speaker 1: Across a Weird Academy, where apprentices were manipulating a story
Speaker 1: machine which churned out academic texts right through to modern
Speaker 1: writers such as Roll Dow. He wrote a short story
Speaker 1: about how an author was selling his soul to a
Speaker 1: machine that generated short stories for him. So it's become
Speaker 1: something that particularly professional authors have been fascinated by this
Speaker 1: idea that a machine might be as creative as a
Speaker 1: human and also giving us insight into human creativity. Now,
Speaker 1: of course it's one thing to imagine these machines. You
Speaker 1: point out that they're they're necessary precursors in linguistics, literary analysis,
Speaker 1: and other fields to even get to the point of
Speaker 1: considering asking a machine or or making machine they can
Speaker 1: write a story or novel for you. Yeah, and I
Speaker 1: think there are there are a number of ways we
Speaker 1: can approach this. So one of them is to look
Speaker 1: at language. So we are language machines. We we have
Speaker 1: been trained in how to manipulate language as humans, and
Speaker 1: we can now design machines that could copy that in
Speaker 1: a very expressive way. So I'm sure we'll come onto that.
Speaker 1: But AI systems such as GPT three that are expert wordsmiths.
Speaker 1: So that's one route and other route is simulating characters.
Speaker 1: So some of the newer computer games now have stories
Speaker 1: nonplayer characters that can't tell stories to the human players.
Speaker 1: And then the third way is to build models of
Speaker 1: the creative mind. And that's what my colleague Raphael pre
Speaker 1: Perez has been doing for many years in his Mexican program,
Speaker 1: is trying to build a model of the human creative mind.
Speaker 1: So the different routes for coming at modeling storytelling and
Speaker 1: understanding storytelling with machines. And the one that you mentioned
Speaker 1: that I think is very fascinating is Vladimir props morphology
Speaker 1: of the folk tale. This idea of taking apart of
Speaker 1: folk tale tradition and in figuring out like what are
Speaker 1: the basic strokes, what are the basic elements, and thus
Speaker 1: creating I guess, sort of the palette for recreating stories. Yeah,
Speaker 1: this was back in the early twentieth century that Vladimir
Speaker 1: prop was part of a group of Russian linguists who
Speaker 1: and folk tale academics who became interested in the morphology
Speaker 1: the structure of folk tales, and he realized that Russian
Speaker 1: folk tales had this very similar structure, just as fairy
Speaker 1: tales in other Western traditions have, and so he set
Speaker 1: about trying to ride a set of rules that would
Speaker 1: both analyze these folk tales and show their underlying structure,
Speaker 1: but also could be turned around to generate them. That
Speaker 1: if you use these rules essentially as what we now
Speaker 1: call programs or algorithms, they could be employed to create
Speaker 1: new folk tales. And so he set the foundations for
Speaker 1: the structural analysis of stories way back what a hundred
Speaker 1: and twenty years ago now, And interestingly, some of the
Speaker 1: earliest computer programs to generate story were based on props formalism.
Speaker 1: It was really if you look at it, it's really
Speaker 1: like a very pair down computer algorithm. Now answer some
Speaker 1: of the first actual text generating machines. You mentioned a
Speaker 1: few different examples of this. Which are the most important
Speaker 1: to mention or which one is the most important to mention?
Speaker 1: So one of the earliest ones was by Christopher Stracci,
Speaker 1: who was a colleague of volenteering working in Manchester, and
Speaker 1: he developed a very very early computer program on one
Speaker 1: of the first prototype computers that generated love letters Victorian
Speaker 1: love letters. Um, and we can speculate on why he
Speaker 1: would want to write a program to generate love letters,
Speaker 1: but he did and pin them up on the wall
Speaker 1: of his lab. And then since then there's been a
Speaker 1: number of people who have particularly taken a linguistic approach,
Speaker 1: and so one of the pioneers was a linguist called
Speaker 1: Sheldon Clade, and Sheldon Klein had this big, grandiose project
Speaker 1: to try and model human language production as a as
Speaker 1: a as an algorithm, as a computer program, and then
Speaker 1: through this trying to understand the origins of language. And so,
Speaker 1: along with a group of colleagues, many PhD students, he
Speaker 1: produced one of the earliest programs that generated stories. It
Speaker 1: generated murder mysteries, a sort of country house Agatha Christie
Speaker 1: type mysteries, and it was part of this big project
Speaker 1: to formalize language in a generative way. The problem was
Speaker 1: the program the stories that generated were pretty trivial. There
Speaker 1: was people gathered at the country house, there was a murder,
Speaker 1: somebody tried to investigate it the end, and so although
Speaker 1: it was seen as a novelty at the time, it
Speaker 1: wasn't really risk acted for the Great linguistic project that
Speaker 1: lasted for twenty or thirty years in the nine nineties,
Speaker 1: and there were a number of attempts to write entire
Speaker 1: novels by computer and probably The most interesting one was
Speaker 1: by Geckll Scott French, who programmed a Mac computer to
Speaker 1: generate a novel in the style of Jacqueline Susan. I've
Speaker 1: got it here. It's called Justice Once, and it is
Speaker 1: a complete published novel in the style of the pot
Speaker 1: boilant author called Jacqueline Susan, and it seems to be genuine.
Speaker 1: There's a picture of him with his Mac computer on
Speaker 1: the cover of the book. And it took him eight
Speaker 1: years to design this very early AI system that would
Speaker 1: mimic the style of this author, and he engaged that
Speaker 1: eyed dialogue with the program to generate an entire three
Speaker 1: hundred page novel. So that was probably the first, I'm
Speaker 1: right up to this day, greatest example of story writing
Speaker 1: with machine. What's fascinating now is that what took him
Speaker 1: eight years to do could now be done in a
Speaker 1: few seconds with the most recent AI UM generator programs. Yeah,
Speaker 1: in the book you you write them just to read
Speaker 1: a quick quote quote. It took just twenty years ago
Speaker 1: from a program that wrote love letters to one that
Speaker 1: created complete short stories. Uh, and a further twenty years
Speaker 1: to a published three page novel in partnership with the
Speaker 1: computer Uh, it's fascinating to think about. You know this,
Speaker 1: this the technological advancement during that time in broad strokes,
Speaker 1: like what I mean, what were the key advancements going
Speaker 1: on here that made this possible. Well, the first advancement
Speaker 1: was to be able to have interactive computer systems that
Speaker 1: you could program in a high level programming which and
Speaker 1: that's what Christopher Straitchy and Alan Turing were working on,
Speaker 1: and that they could then demonstrate this in generating very
Speaker 1: simple love letters. The next stage was to be able
Speaker 1: to produce grammars, generative grammars, and this goes back to
Speaker 1: the work of prop who realized that you could have
Speaker 1: grammars that didn't just generate individual sentences, but could generate
Speaker 1: entire stories. That you could describe the structure of a
Speaker 1: story in terms of something like a formal grammar, and
Speaker 1: that in THEES there were a number of projects to
Speaker 1: put that grammar into a program that would generate a
Speaker 1: short story. That's what Sheldon Klein and his team did.
Speaker 1: And then the next step beyond that was to write
Speaker 1: symbolic AI programs that modeled the style of a particular writer,
Speaker 1: and that was the great achievement of um Scott French.
Speaker 1: It was a symbolic AI expert system of style. And
Speaker 1: then we come right up to date. And there are
Speaker 1: programs like GPT three, which are hugely competent and well
Speaker 1: trained language systems, So they aren't rule based systems. They
Speaker 1: don't have something like the Sheldon client grammar inside them.
Speaker 1: They have been trained on billions of pieces of text
Speaker 1: and they have many millions of interconnections that create an
Speaker 1: internal language model which they then use to generate texts
Speaker 1: in particular styles. You can start it in a style
Speaker 1: and it will continue in that style. You can give
Speaker 1: it an instruction of what sort of story to write
Speaker 1: and it will continue in that story. But the really
Speaker 1: important thing to say is that there are two different
Speaker 1: sorts of AI. So the sort of AI that's got
Speaker 1: French used was writing explicit rules. That's why it took
Speaker 1: him eight years to code these rules to imitate one
Speaker 1: person style. The GPT three type AI transformer programs induce
Speaker 1: infer those rules from being trained on billions and billions
Speaker 1: of pieces of texts. So there are two different sorts
Speaker 1: of AI. Both had their strengths and weaknesses. UH And
Speaker 1: one of the fascinating things of the future is whether
Speaker 1: we can put them together, whether we can merge those
Speaker 1: two different sorts of AI into the sort of universal
Speaker 1: story machine. Wow. And so GPT three is the current model,
Speaker 1: Is that correct? Or are we had four yet? So
Speaker 1: there isn't a GPT four yet, although I'm sure there's
Speaker 1: one in the pipeline that continually revising GPT three. And
Speaker 1: just for those who don't know GPT, the GPT models
Speaker 1: were developed by and still being developed by company called
Speaker 1: open Ai that was founded by a group of entrepreneurs
Speaker 1: including Elon Musk and others, and that company was set
Speaker 1: up to explore the opportunities of AI for social good.
Speaker 1: It has developed a number of different programs. There's one
Speaker 1: that it's developed for art called Dally, which can do
Speaker 1: the same for art and images as GPT three can
Speaker 1: for for words and stories. But the GPT three now
Speaker 1: it's its third generation. In essence, what it's been, what's
Speaker 1: how it works is it's been trained on billions of
Speaker 1: pieces of text. It uses those texts to form an
Speaker 1: internal model of both the surface structure of language but
Speaker 1: also the internal structure language essentially how the world works.
Speaker 1: And then initially the early GPT models were sentenced computers
Speaker 1: like very highly trained, suped up sentenced computers of the
Speaker 1: sort that you've got on your mobile phone. But they
Speaker 1: can look back at the last five words or so.
Speaker 1: They haven't a big attention window, so they know what
Speaker 1: they've written before, and they use this to continue in
Speaker 1: the same style, the same structure. So they give a
Speaker 1: very plausible simulation, a very plausible indication of coherent language,
Speaker 1: as if it were being written by a human. Now,
Speaker 1: the problem is it's highly believable, but it doesn't have
Speaker 1: any sense of itself. So these systems can't reflect on
Speaker 1: what they've written. They can't look back and say, does
Speaker 1: this makes sense? Does this fit with a good model
Speaker 1: of the world? Is it legal on his decent? It doesn't.
Speaker 1: In essence, it's they're a moral They don't have any
Speaker 1: internal sense of what's right and morality. And so as
Speaker 1: story machines, they're great because they can tell fascinating, plausible, engaging,
Speaker 1: sometimes even poignant stories. But for other purposes like writing
Speaker 1: newspaper articles or writing student essays, then they can be
Speaker 1: dangerous because they don't know what they've written, and they
Speaker 1: don't know whether what they've written is decent, honest, and
Speaker 1: truthful than now. Our listeners can can actually get a
Speaker 1: taste of this by going to story slash machines dot net.
Speaker 1: You have an interface here where you can put in
Speaker 1: just a title or also a title and in some
Speaker 1: text like an opening line of a of a story.
Speaker 1: And this is powered by GPT three. Correct. Yeah, that's right.
Speaker 1: So what I did was wrote a website with an
Speaker 1: interface to the most recent GPT three language model. So
Speaker 1: it basically provides a very simple way for you to
Speaker 1: write the title of a story, a few opening words,
Speaker 1: press the create button, and it comes back with about
Speaker 1: a two d and fifty word introduction to a short story.
Speaker 1: So it gives you a taste of what GPT three
Speaker 1: can do. And yeah, I really recommend that you go
Speaker 1: and try it. Um. I launched it just a couple
Speaker 1: of days ago, so people have already been generating fascinating
Speaker 1: short stories using that, So so go and give it
Speaker 1: a try. It's story hyphen machines dot net. Yeah I was.
Speaker 1: I was firing this up when I was reading the
Speaker 1: book yesterday, and yeah, eventually I was having to bring
Speaker 1: my son over and showed to him. I was really
Speaker 1: impressed toying around with it like trying having a title
Speaker 1: that sort of suggested a certain genre, maybe throwing in
Speaker 1: a first sentence that that was the record that the
Speaker 1: reference to certain genre. And then also I was really
Speaker 1: impressed when I would say, put in a title and
Speaker 1: then put in the first line of an existing short
Speaker 1: story by an author. I think I used Clark Ashton
Speaker 1: Smith as an example, and I used um Corny McCarthy.
Speaker 1: I put in a line of Corny McCarthy's work, and
Speaker 1: I was impressed at how well it seemed to model
Speaker 1: a portion of writing as if it were by these authors. Yeah, exactly. Um,
Speaker 1: I'll give you an example, because it's quite quite good
Speaker 1: to show an example. You can cut it out if
Speaker 1: you want to. So so here's an example one that
Speaker 1: I did on Ernest Hemingway in London. The fog came
Speaker 1: in from the river and it was cold for April.
Speaker 1: I walked up past the British Museum and turned down
Speaker 1: towards Holburn. I had a lunch date with Harriet the Criterion,
Speaker 1: and I was early. It was one of those heavy
Speaker 1: grade days that seemed to press down on the city,
Speaker 1: weighing it down. The traffic moved slowly through the streets,
Speaker 1: and the people hurried along with their heads down trying
Speaker 1: to get out of the cold. I glanced in the
Speaker 1: windows of the shops as I walked, looking for something
Speaker 1: to buy Harry for his birthday. I knew he'd appreciate
Speaker 1: a good bottle of scotch, but I wanted to get
Speaker 1: him something else as well. That's just the start of
Speaker 1: as a story in Ernest Hemingway style, and you can
Speaker 1: give it whatever style you want. It's I found it
Speaker 1: works best when you do a weird juxtaposition. So there
Speaker 1: was someone just a few hours ago did one on
Speaker 1: the Sad Sandwich, and it was a really poignant, sad
Speaker 1: story about a poor, neglected sandwich. So try doing some
Speaker 1: some juxtapositions at words like that. Yes, absolutely, and I'd
Speaker 1: love to hear back from from listeners after they've toyed
Speaker 1: around with us and explored it. But of course, recognizing
Speaker 1: the power of of this technology, Uh yeah, we we
Speaker 1: certainly get into this, this area of of anxiety perhaps,
Speaker 1: but also hope and opportunity. Um I guess on the
Speaker 1: anxiety side of things. The first place my mind went
Speaker 1: as I remember seeing Max Tegmark reference a kind of
Speaker 1: an illustration that was like a topography of human abilities
Speaker 1: and jobs with the idea that at the higher elevations
Speaker 1: were going to be more protected from the rising sea
Speaker 1: levels of AI. So chess and jeopardy were already in
Speaker 1: the water, uh, speech recognition, investment, and social interaction there
Speaker 1: in the lowlands they're going next. And then in this
Speaker 1: particular image, we had science as the highest peak, just
Speaker 1: above the peaks of book writing and AI design. So
Speaker 1: I was just wondering, do you feel like this representation
Speaker 1: was accurate or and or have the water has just
Speaker 1: risen so high already? I think every risen high already.
Speaker 1: I think the ones that are going to be protected
Speaker 1: from the rising tide of AI are probably the caring professions, um,
Speaker 1: the nurses, the child careers. But any profession that works
Speaker 1: with words, he's going to find both an opportunity and
Speaker 1: a threat, I think in generative AI. So the opportunity
Speaker 1: is that it's a new kind of tool. The tools
Speaker 1: that we've had up to now have tended to be
Speaker 1: ones that slow down writing, um, like the saurus or
Speaker 1: spell corrector that they make you pause and check. You
Speaker 1: can do them at the end, but there's always a
Speaker 1: temptation to look up a word to slow down. What's
Speaker 1: different about these is that they can be used to
Speaker 1: speed up your writing. You can, and I've tried doing this,
Speaker 1: writing a paragraph when it starts to dry up, them
Speaker 1: handing it over to the machine to write the next paragraph.
Speaker 1: I'm not a fiction writer. I found it quite empowering
Speaker 1: to use GPT three as a writing buddy. So I
Speaker 1: would write a paragraph, it would write the next paragraph
Speaker 1: and probably take it off in some unexpected direction that
Speaker 1: I would then have to follow, and it might introduce
Speaker 1: a new character, a plot twist, and so both as
Speaker 1: a tool for budding writers and also perhaps a prop
Speaker 1: for professional writers, particularly ones with deadlines to meet, then
Speaker 1: that's the opportunity. I think the threat is the inverse
Speaker 1: side of that, that if you either see it as
Speaker 1: a crutch that rather than trying to do your own writing,
Speaker 1: you just hand it over to the machine, then it's
Speaker 1: very easy to become lazy. And also, as I've said,
Speaker 1: they are a moral machine. So if you're trying to
Speaker 1: do scientific writing, or you're trying to do accurate journalists them,
Speaker 1: then beware because they may well throw in some entirely
Speaker 1: fake research study, some entirely inaccurate, fake reference, um, perhaps
Speaker 1: reference to some completely non nonsensical or inaccurate event that's
Speaker 1: happened in the world. So you've always got to be
Speaker 1: aware of the facts that it throws up. And there's
Speaker 1: a good reason for that that it isn't a fact checker.
Speaker 1: It isn't a Wikipedia or even a Google search. It
Speaker 1: is a language machine. It's loves, you know, anthropomorphic sense.
Speaker 1: It loves playing with words, but those words don't necessarily
Speaker 1: make sense. So if you're going to use tools like
Speaker 1: GPT three as AIGs for writing, then you have to
Speaker 1: be very careful that you cross check the facts and
Speaker 1: the output of that machine to make sure that it
Speaker 1: is accurate and honest and truthful. My mind also went
Speaker 1: to um the possibility of of of almost accidental plagiarism
Speaker 1: because I put in the first line of a Clark
Speaker 1: Ashton Smith story and it it threw in a fascinating
Speaker 1: plot twist that was not in the original story, and
Speaker 1: I don't think I've ever seen in a story, and
Speaker 1: so part of me was wondering like, well, well, you know,
Speaker 1: I should I should latch onto this, maybe I could
Speaker 1: use this. But then the other part, you know, the
Speaker 1: lights coming on in my mind were saying, but hold
Speaker 1: on this. Just because I haven't read it doesn't mean
Speaker 1: it doesn't already exist out there. Might Uh we run
Speaker 1: into situations where, uh, the AI is is reproducing something
Speaker 1: you know, perhaps you honestly, if we want to use
Speaker 1: that term when in in in actuality, it may exist
Speaker 1: out there in some story or another. Yeah, I mean,
Speaker 1: I think the first thing to note is that it's
Speaker 1: not working at this sort of word and sad tense level.
Speaker 1: So it's not copying bits of text from the web
Speaker 1: or from published books. It's working below that, basically at
Speaker 1: the phone name level. It's putting together pieces of words,
Speaker 1: but it's putting together these pieces of words in a
Speaker 1: hugely proficient way. So I've tried taking the output of
Speaker 1: GPT three and doing Google search on phrases and sentences,
Speaker 1: and you don't find them. So it seems like they
Speaker 1: are genuinely producing novel pieces of text. So, for example,
Speaker 1: if students are going to use these for writing essays,
Speaker 1: which is already happening, they're already companies that are advertising
Speaker 1: the services of AI generators for students to write their essays.
Speaker 1: Plagiarism checkers won't detect them. I've tried putting them through
Speaker 1: plagiarism checkers and they of sort of nineties five originality.
Speaker 1: So they're not copying bits of text from the web.
Speaker 1: They are genuinely generating new language. Now, of course, there
Speaker 1: are phrases that may pre exist, and if you give
Speaker 1: highly constrained styles like Shakespeare's sonnets, then it may come
Speaker 1: up with previous lines from other Shakespeare sonnets. But providing
Speaker 1: you give it a broad enough brief, providing you give
Speaker 1: it a general enough style, or even if it's from
Speaker 1: a particular author, then it will generate original text. And
Speaker 1: it's still a bit scariest too. You know, I've talked
Speaker 1: about this with other people and said, but surely it's
Speaker 1: copying from the web. No, it isn't. It's generating. It's
Speaker 1: generating new text in the style of that author, or
Speaker 1: in the style of that piece of fiction or piece
Speaker 1: of journalism. So you already touched on like this the
Speaker 1: collaborative possibilities here. But having touched on in school papers
Speaker 1: and such, what do you think are the the educational
Speaker 1: opportunities with this technology. I think the main educational opportunities
Speaker 1: are for beginning writers. It's a way to explore expressivity
Speaker 1: and creativity. One of the problems when you're beginning writing
Speaker 1: is you tend to see everything as being a linear process.
Speaker 1: You write some words, you write some more words. There's
Speaker 1: a flow of writing. It's very difficult to get out
Speaker 1: of that flow and to think about alternative ways of
Speaker 1: expressing something, how it might be different. And what machines
Speaker 1: light GPT three can do is help you to see
Speaker 1: another way of continuing, another way of expressing your ideas.
Speaker 1: It will look back over the last five words or
Speaker 1: so that you've written and perhaps take it in new directions.
Speaker 1: So it's a way for buddying writers to explore possibilities.
Speaker 1: And you can take what you've written so far and
Speaker 1: press the create button a number of times, and each
Speaker 1: time it will take your writing in a different direction.
Speaker 1: So that's one way. Another way is for in a
Speaker 1: class situation, for a teacher to generate a number of
Speaker 1: different articles on a topic. So to give a topic
Speaker 1: like what's the effect of climate change on rising sea
Speaker 1: levels and get it to generate a number of different
Speaker 1: articles and then to critique them because as I say,
Speaker 1: it doesn't always get its facts right. And so to
Speaker 1: look on these as pieces of journalism you might find
Speaker 1: on the web and to take a critical stance. So
Speaker 1: it's it's a good tool for a teacher to give
Speaker 1: some generated articles to students and say, criticize these. We
Speaker 1: know they're written by machine, so what's wrong with them?
Speaker 1: Generally the ear face structure is pretty good, the spellings correct,
Speaker 1: the style is good. But the deeper you go into
Speaker 1: these machines generated texts, the more you find problems with them.
Speaker 1: So it's it's a good class exercise. And then lastly,
Speaker 1: I think is that it's going to be another tool
Speaker 1: companion that writers use. Just as in the early days
Speaker 1: of word processes, there was a lot of criticism that
Speaker 1: it was slowing down writing, that you were reading from
Speaker 1: the screen rather than from the page, that style checkers
Speaker 1: were making writing more conformists. There will be quite rightly
Speaker 1: people who say these new tools are forcing a machine
Speaker 1: type creativity. But I think if we use them wisely
Speaker 1: to extend and to critique our own creativity, then their
Speaker 1: own resting her and exciting opportunities. Do you think we
Speaker 1: better understand human creativity for having gone through this technological journey.
Speaker 1: That's why I started on this h this journey with
Speaker 1: my colligraph I am. It's because I started work as
Speaker 1: a PhD student on trying to understand children's creative writing
Speaker 1: and to develop tools for children to develop their creativity.
Speaker 1: I became fascinated by machine creativity to try and explore
Speaker 1: what is it that a machine can do in terms
Speaker 1: of creativity and where does that stop? So what are
Speaker 1: the limits of machine creativity? And beyond those limits, how
Speaker 1: does that relate to human creativity? What is that the
Speaker 1: week we can do that a machine can't. And now,
Speaker 1: over the years, perhaps the gap between machine creativity and
Speaker 1: human creativity is narrowing, but it's still there, and it
Speaker 1: gives us insights into the way in which we write,
Speaker 1: in the way in which we think. And because these
Speaker 1: new generative AI programs don't work in a human light way,
Speaker 1: then it becomes a really interesting challenge to say, what's
Speaker 1: alien about them, what's different about the writing they produce
Speaker 1: that shows they aren't human? And what does that say
Speaker 1: about human experience and human creativity? Than now, looking into
Speaker 1: the future and getting more speculative, UM say, I'm a
Speaker 1: fan of Frank Herbert's done novels. Do you see for
Speaker 1: see a future in which one would just be able
Speaker 1: to ask an AI to generate the final books in
Speaker 1: the series that Frank would have written, Uh, perhaps more
Speaker 1: novels in this universe he created that sort of thing.
Speaker 1: Or say you're a fan of a particular short story
Speaker 1: author and you're you're like, why would I read anything
Speaker 1: other than stories by this this particular author. I'm just
Speaker 1: going to ask the AI to generate more of them
Speaker 1: for me. Um, it might we easily arrived at such
Speaker 1: a future. And if and if so, like, what does
Speaker 1: that mean for us as both consumers and producers of
Speaker 1: creative writing. I think we'll arrive at that at that
Speaker 1: space pretty soon. I think they will be pastichious, and
Speaker 1: but they may be pastigious that you can't tell from
Speaker 1: the original and that there will be certainly fans of
Speaker 1: authors like Frank Herbert who will be happy to accept
Speaker 1: them as generated in Frank Herbert style, particularly if they
Speaker 1: have interests the new characters, interesting new plots that will
Speaker 1: happen with short stories and Neil Gaiman type short stories.
Speaker 1: I'm sure that will happen quite soon. Um, if it
Speaker 1: hasn't happened already, there may be plan fiction forums where
Speaker 1: those sorts of AI generated pastiches are already circulating, but
Speaker 1: I think there's the future is more likely to be
Speaker 1: around interactive fiction. So um. At the moment, computer games
Speaker 1: are kind of reaching a plateau that the graphics are
Speaker 1: becoming more and more realistic, the interaction is becoming more
Speaker 1: and more engaging, but the AI is lagging behind. Soon
Speaker 1: you'll be able to have AI based characters in games
Speaker 1: that can tell stories that you're not only asked to
Speaker 1: solve a problem or guide you to the treasure, but
Speaker 1: you can engage with them as conversational partners. They will
Speaker 1: take the story forwards, and once you do that, then
Speaker 1: you can get onto interactive soap operas, interactive worlds where
Speaker 1: you've got both human and machine partners now that can
Speaker 1: take you into all sorts of dark areas, but also
Speaker 1: into all sorts of engaging aspects of new interaction, new
Speaker 1: immersive fiction, new types of social interaction that involved both
Speaker 1: machines and humans. So I think rather than trying to
Speaker 1: emulate a particular writer, I think developing interactive fiction where
Speaker 1: you have a continual um story that you can dip
Speaker 1: in and out of with other characters human and machine
Speaker 1: will likely to be the the most engaging, and probably
Speaker 1: the most influential use is of story machines in the
Speaker 1: near future. In the book, you go into several different
Speaker 1: wonderful examples of of how we've reached this point, you know,
Speaker 1: along the line along the road with video games examples
Speaker 1: I wasn't familiar with, like Colossal Cave Adventure, Dwarf Fortress, UM. So,
Speaker 1: I guess most of these examples of these been These
Speaker 1: haven't necessarily been part of, like saying, the mainstream of
Speaker 1: video game culture. No, they they haven't been part of
Speaker 1: the mainstream. There's been a kind of tributary. So the
Speaker 1: mainstream has been sort of from Pong and Space Invaders
Speaker 1: onwards in terms of graphics and interactivity, and then we
Speaker 1: get to Grand Theft or to where you have hugely
Speaker 1: realistic simulated worlds m and it's the gameplay, the action,
Speaker 1: the game mechanics that's really important. But there's been another
Speaker 1: tributary that been mainly followed by people who are fascinated
Speaker 1: by stories and words and storytelling. And it started with
Speaker 1: Colossal Cave Adventure, which, for those of you who don't know,
Speaker 1: was in the late nineties seventies, um by UM a
Speaker 1: couple who were cavers and Um the Crowder, I think
Speaker 1: that's his name. I would have to check it. Um
Speaker 1: he developed this program which generated a world that you
Speaker 1: could explore. You could go down and explore a cave system,
Speaker 1: and it was all done entirely through text. So you
Speaker 1: are going through a dark forest, you find a great
Speaker 1: in the forest floor. What do you do next? Your
Speaker 1: type go down. It then comes back with a description
Speaker 1: of where you are. You are underneath the forest floor,
Speaker 1: in the spring of a stream, and then you can
Speaker 1: go left, you can go right, you can go down.
Speaker 1: So you are guiding this character through a textual world,
Speaker 1: and the more you get into it, the more you
Speaker 1: engage with not only descriptions but also characters in that world,
Speaker 1: and you could collect things, do things, So it's a
Speaker 1: textual world that you're exploring. UM. Since then there have
Speaker 1: been other extensions of that which make these textual characters
Speaker 1: more believable, so actually interact with them. They cannot only
Speaker 1: give you things, but they can behave as real agents
Speaker 1: in the world would real humans would. And Dwarf Fortress
Speaker 1: is an example of that where you've got a hugely
Speaker 1: realistic world. And there was an example I gave him
Speaker 1: the book about Drunk Cat, and the designers of Dwarf
Speaker 1: Fortress had created all sorts of properties of animals, but
Speaker 1: the ability of cats to drink alcohol had deliberately not
Speaker 1: been programmed him. But in the tavern and war Fortress,
Speaker 1: there were these cats that were lying dead, and only
Speaker 1: through interacting with the code did they understand it. Did
Speaker 1: they realize that what had happened was that the cats
Speaker 1: went into this tavern, that people in the tavern had
Speaker 1: spilt alcohol in the floor, the cats had walked through,
Speaker 1: the cats had licked their paws, that cats have become
Speaker 1: poisoned by alcohol. So you have these hugely rich and
Speaker 1: realistic worlds that are realized through text. And so it's
Speaker 1: become as a bit of a tributary of game playing
Speaker 1: because you do have to interact with with text with words.
Speaker 1: But as they big into merge now with the mainstream games,
Speaker 1: then you will have spoken dialogue. You will be able
Speaker 1: to meet your favorite characters in soap operas in streaming
Speaker 1: series and you could talk to them, you can go
Speaker 1: on dates with them, you can go holidays with them, um,
Speaker 1: you can be part of their story. So it's bringing
Speaker 1: together those two streams of game design these rich visual
Speaker 1: worlds and now these textual, believable story worlds that I
Speaker 1: think is going to be the next generation of interactive games.
Speaker 1: What's gonna be really exciting to see this come together. Yeah,
Speaker 1: I think it will, and I think one of the
Speaker 1: things in the future. One of the opportunities in the
Speaker 1: future is that you will be able to live in
Speaker 1: the worlds for an extended period of time. So you
Speaker 1: don't just play the game for forty minutes just like
Speaker 1: you have a TV series. You'll have a TV series
Speaker 1: where you live in this world. Now, that's both scary
Speaker 1: and exciting to be able to live for extended time
Speaker 1: in a virtual world where you can talk to and
Speaker 1: engage with the characters. So what do you think the
Speaker 1: machines are going to want to tell stories about? So
Speaker 1: it's been suggested that because machines don't have human experience,
Speaker 1: they will never be able to tell stories that are
Speaker 1: experientially rich. They don't know the human condition, They've never
Speaker 1: been there, they've never fallen in love, they've never seen
Speaker 1: a sunset. So they will reach a plateau where they
Speaker 1: may produce pastiches, but they won't be able to describe
Speaker 1: or to we have the human condition, they won't be
Speaker 1: able to engage you in any deep human way in
Speaker 1: a story. Now, I'm sure that's right. It's possible you
Speaker 1: could get around that by having embodied storytellers, so embodied
Speaker 1: robots that can go out into the world, that can
Speaker 1: gaze at the sunset, that can go for walks, that
Speaker 1: can feel the wind in their metal phases. But there's
Speaker 1: another possibility, which is that already programs have something approaching
Speaker 1: a social life, that they are connected to other entities
Speaker 1: on the web. They are part of a social network.
Speaker 1: And if they can tell stories about their worlds, their
Speaker 1: worlds of being entities on the web, their worlds as
Speaker 1: being part of a connected Internet system where there are viruses,
Speaker 1: where there are software breakdowns, breakthroughs, entities that interact with
Speaker 1: each other in a computational way that we can't express,
Speaker 1: then they could be valuable in two ways. One is
Speaker 1: that they could help us to understand this complex system
Speaker 1: that is the World Wide Web. They could help to
Speaker 1: interpret the growing, changing nature of the World Wide Web
Speaker 1: in human language. But also they could tell stories. They
Speaker 1: could tell stories of their travels through the Internet. They
Speaker 1: could tell stories of how they became beings that were
Speaker 1: taught and learned through interaction with other objects in the web.
Speaker 1: So I don't think it's necessary to be embodied to
Speaker 1: have human type experience in order to tell interesting stories.
Speaker 1: That they may tell quite alien stories of life on
Speaker 1: the web. And to me, that's much more exciting and
Speaker 1: interesting than just spouting a pastiche of a human story.
Speaker 1: This reminds me of something you you bring up in
Speaker 1: in the book that that that i've that they really
Speaker 1: ring true and uh and also made me, you know,
Speaker 1: rethink a number of things, and that is that the
Speaker 1: uncanny valley, which is a concept that most of its
Speaker 1: familiar with when it comes to um robots made in
Speaker 1: the in the likeness of a human being, or or
Speaker 1: certainly when we get into computer generated imagery and films.
Speaker 1: But you point out that that this that the uncanny
Speaker 1: valley isn't really a thing in storytelling. In one sense,
Speaker 1: it isn't because stories are meant to be disturbing and unsettling.
Speaker 1: That's why we read science fiction, that's why we read
Speaker 1: crime novels because they're meant to be disturbing. So having
Speaker 1: a machine that is in some sense uncanny or disturbing
Speaker 1: in the language that it produces in the stories. That
Speaker 1: else I think we'll only add to the richness of storytelling.
Speaker 1: But where I think there is an uncanny valley is
Speaker 1: if we then say, was this written by a machine?
Speaker 1: And if so, what kind of machine? So is it
Speaker 1: a machine that was trained on billions of words from
Speaker 1: her the web that has had no experience of the world.
Speaker 1: In that case, how can it be reporting on the world?
Speaker 1: How can it pretend to have a human like experience?
Speaker 1: So if we know that the author is a machine,
Speaker 1: then it becomes unsettling because we then start to judge
Speaker 1: perhaps it's very plausible, very poignant, very evocative prose against
Speaker 1: human experience, and we realize that it hasn't had that
Speaker 1: human experience. So how do we know whether other stories
Speaker 1: are really, you know, the product of human experience? And
Speaker 1: what does it mean to tell a story based on experience?
Speaker 1: So the stories themselves I don't think need to worry, yes,
Speaker 1: because stories do have an uncanniness to them. But I
Speaker 1: think once we start to question the author of those
Speaker 1: stories and whether that story is based on genuine experience,
Speaker 1: then it becomes unsettling. All right, Well, the book again
Speaker 1: is story Machines How Computers Have Become Creative Writers should
Speaker 1: come out July five, and Mike thanks once more for
Speaker 1: taking time out of your day and chatting with us
Speaker 1: on the show. It was a pleasure. I've really enjoyed it,
Speaker 1: so thank you for asking me all right so that
Speaker 1: you have it. Thanks again to Mike Sharple's for a
Speaker 1: taking time out of his day to chat with me again.
Speaker 1: That book is Story Machines, How Computers Have Become Creative
Speaker 1: Writers is available now wherever you get your books. And
Speaker 1: if you want to get a taste of this for yourself,
Speaker 1: you can go to story hyphen machine dot net and
Speaker 1: uh it's experiment a little bit like we've been experimenting
Speaker 1: in the meantime. If you would like to check out
Speaker 1: other episodes of Stuff to Blow Your Mind, you can
Speaker 1: find it in the Stuff to Blow your Mind podcast feed.
Speaker 1: We have Core episodes on Tuesdays and Thursday's, Artifact or
Speaker 1: Monster Fact episodes on Wednesdays, Listener Mail on Mondays, and
Speaker 1: on Friday, we set aside most serious concerns and just
Speaker 1: talk about a weird film. Huge thanks as always to
Speaker 1: our excellent audio producer Seth Nicholas Johnson If you would
Speaker 1: like to get in touch with us with feedback on
Speaker 1: this episode or any other, to suggest a topic for
Speaker 1: the future, or just to say hello, you can email
Speaker 1: us at contact at stuff to Blow your Mind dot com.
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