WEBVTT - Do Robots Write of Electric Sheep?

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<v Speaker 1>Brought to you by Toyota. Let's go places. Welcome to

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<v Speaker 1>Forward Thinking. Hey there, and welcome to Forward Thinking, the

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<v Speaker 1>podcast that looks at the future and says it's like

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<v Speaker 1>a nine and shining armor from a long time ago.

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<v Speaker 1>I'm Johnvin Strickland and Lauren and I'm Joe McCormick. And

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<v Speaker 1>today we want to talk more about about stories and storytelling.

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<v Speaker 1>But you know, in our last podcast we talked about

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<v Speaker 1>interactive storytelling and this idea collaboratively creating a story in

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<v Speaker 1>some way or participating within a story in some way. Now,

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<v Speaker 1>let's talk a little bit about, um, what the stories

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<v Speaker 1>that we wish existed but don't like. Can you imagine

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<v Speaker 1>some of the greatest authors of all time, who who

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<v Speaker 1>maybe died before you felt like they had really produced

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<v Speaker 1>everything they could have produced? Like, do you have any

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<v Speaker 1>favorite authors that you wish had lived longer to tell

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<v Speaker 1>at least one more amazing story? Oh of course. Well,

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<v Speaker 1>actually what first popped into my mind was poets. I'd

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<v Speaker 1>love to have more of Uncle Walton Anti m when

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<v Speaker 1>I'm feeling when I'm feeling patriotic, you know, Walt Whitman,

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<v Speaker 1>Emily Dickinson, I gotta feel the America, you know, coming

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<v Speaker 1>up right bubbling up inside you. But they've been dead

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<v Speaker 1>a long time, right, So, uh well, Lauren, are there

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<v Speaker 1>any authors that you could think of off the top

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<v Speaker 1>of your head where you're thinking, Man, I really wish

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<v Speaker 1>I could have had one more book or story or

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<v Speaker 1>play or poem from this person. I'm a completely terrible person.

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<v Speaker 1>The first thing that just popped into my head was

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<v Speaker 1>George RR. Martin, who is not actually dead as of

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<v Speaker 1>So you want you want an insurance policy to guarantee

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<v Speaker 1>that a Song of Ice and Fire is complete at

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<v Speaker 1>some point, and you want it in George RR. Martin's voice.

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<v Speaker 1>That's really important. Obviously someone else could pick it up,

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<v Speaker 1>but that wouldn't be the same. Yeah, no, no, no

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<v Speaker 1>one else would write. And I'm being actually very earnest

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<v Speaker 1>here the kinds of descriptions of food and dresses into

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<v Speaker 1>my political fantasy that he writes. Well, you guys have

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<v Speaker 1>both made my choice of Shakespeare seemed pedestrian at this point. I'm,

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<v Speaker 1>of course, I am a Shakespearean nut. I love Shakespeare's

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<v Speaker 1>plays and I love his poetry as well. And uh

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<v Speaker 1>we they're already some plays that may or may not

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<v Speaker 1>belong to Shakespeare. We'll talk more about some of those

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<v Speaker 1>in a little bit, but uh, you know, there were

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<v Speaker 1>there's some lost plays that perhaps we might actually have

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<v Speaker 1>if he had lived longer to get to the point

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<v Speaker 1>where he was committing these so that they could be published.

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<v Speaker 1>You know, in Shakespeare's day, he wasn't It wasn't what

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<v Speaker 1>he was writing them down necessarily any anymore than too. Yeah,

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<v Speaker 1>he would get the sides to the actors, but the

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<v Speaker 1>whole purpose of the play was for performance, not for reading.

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<v Speaker 1>So in Shakespeare's day, very few of his plays ever

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<v Speaker 1>were published. It was only after his death when his contemporaries, Yeah,

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<v Speaker 1>his contemporaries began to gather them all together. People who

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<v Speaker 1>worked in the King's Men production company began to gather

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<v Speaker 1>as stuff together. And then even then there were points

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<v Speaker 1>where scholars have argued whether other plays should have been included.

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<v Speaker 1>Perhaps there were plays that Shakespeare wrote when he wasn't

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<v Speaker 1>writing for the King's Men that weren't included. And if

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<v Speaker 1>you have been attributed to to other authors. Sure now

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<v Speaker 1>people think Shakespeare didn't write any of those plays, They're wrong.

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<v Speaker 1>So I would love it if if Shakespeare could turn

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<v Speaker 1>out a few books. I would love it if Dickens

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<v Speaker 1>could finish the Mystery of Edwin Drewd because there's nothing

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<v Speaker 1>more irritating than a mystery that ends halfway through. He

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<v Speaker 1>killed him? Or did he even die? Oh? Yeah, he

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<v Speaker 1>may have just disappeared. There's a there's a science fiction

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<v Speaker 1>book that C. S. Lewis never finished before his death

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<v Speaker 1>that I would very much like to read the ending too.

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<v Speaker 1>I would have liked to have read The Simarillian as

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<v Speaker 1>if Tolkien had actually written it in a form that

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<v Speaker 1>was meant to be read by a human being, as

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<v Speaker 1>opposed to a collection of things that his son pulled

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<v Speaker 1>together from various works and notes and then put into

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<v Speaker 1>some kind of bizarre encyclopedia. Yeah. I mean, don't get

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<v Speaker 1>me wrong, I nuinely love The Simarillian, but I wonder

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<v Speaker 1>what it would have been like had Tolkien actually himself

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<v Speaker 1>put it all out together. So, I mean there are

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<v Speaker 1>lots of different examples, obviously, uh Hunter S. Thompson, Kurt.

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<v Speaker 1>I mean, there's some authors out there I would love

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<v Speaker 1>to have been able to read some more stuff. And

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<v Speaker 1>Charles Dickens, Oh, I already mentioned him. My anglophile traits

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<v Speaker 1>are starting to show, and there is always fan fiction.

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<v Speaker 1>I mean, you know, some some fan authors can write

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<v Speaker 1>very convincingly in an author's voice. Well, what if we

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<v Speaker 1>could create a fan fiction style author that wasn't really

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<v Speaker 1>a person. You know. Before I get into that, we're

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<v Speaker 1>gonna have to talk a little bit about some some

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<v Speaker 1>terms here, like artificial intelligence. Artificial intelligence, What does that

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<v Speaker 1>have to do with literature. Well, we're gonna get there.

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<v Speaker 1>We'll take some steps first. So artificial intelligence or machine intelligence,

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<v Speaker 1>this is a concept that's been around for more than

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<v Speaker 1>half a century at this point. Uh, And it was

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<v Speaker 1>something that was kind of of played with early early

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<v Speaker 1>early days in in the earliest days of computer science,

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<v Speaker 1>in fact, before we even called it computer science. One

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<v Speaker 1>of the fathers of the concept of artificial intelligence, you

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<v Speaker 1>could argue, is Alan Turing. Now Touring was a computer

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<v Speaker 1>scientist before there were was a word for such a thing,

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<v Speaker 1>who worked for the British government. He was working within

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<v Speaker 1>the War Department to help decode messages that were sent

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<v Speaker 1>by Germans using the Enigma machine you guys familiar with.

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<v Speaker 1>He was one of he was on the team, and yes,

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<v Speaker 1>he was highly responsible for cracking the code of the

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<v Speaker 1>Enigma machine. The Enigma machine was a physical mechanical device

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<v Speaker 1>that the Germans were using that you would set to

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<v Speaker 1>a particular setting, had certain reels, and then what you

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<v Speaker 1>do is I had like a typewriter and as you

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<v Speaker 1>would type, the reels would channel a an electric signal

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<v Speaker 1>through so that a particular lightbulb would light up on

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<v Speaker 1>the other side. But it was designed in such a

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<v Speaker 1>way so that would encode eat letters. So if you

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<v Speaker 1>typed a G, one thing you could be sure of

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<v Speaker 1>is that the one bulb that's not gonna light up

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<v Speaker 1>on that board is G. And in fact, that was

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<v Speaker 1>one of the few things that Touring was able to

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<v Speaker 1>figure out that that broke the code was knowing that

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<v Speaker 1>whatever the letter was that you were looking at at

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<v Speaker 1>an encoded message, it was definitely not exactly it'll and

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<v Speaker 1>you would think eliminating one choice would not give you

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<v Speaker 1>that big of an advantage, but that was kind of

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<v Speaker 1>the crack that really helped break it open. Okay, So

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<v Speaker 1>what did Alan Turing have to do with the idea

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<v Speaker 1>of artificial intelligence? So he was very much interested in

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<v Speaker 1>this idea of what is what's the capability of the

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<v Speaker 1>machine world and are there limits? Can it go as

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<v Speaker 1>far as humans can go? Can machines think? Can machines think?

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<v Speaker 1>He thought about that in such a way It's a

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<v Speaker 1>little different than the way you might initially imagine a

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<v Speaker 1>machine thinking. Uh, Touring said, it wasn't so much. It

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<v Speaker 1>wasn't really important if machines thought the same way that

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<v Speaker 1>people do. In other words, what was necessary from Touring's

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<v Speaker 1>point of view, was that the output of a machine

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<v Speaker 1>would be indistinguishable to what a human would be capable

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<v Speaker 1>of doing. But the process doesn't. Yeah. So in other words,

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<v Speaker 1>if the machine was able to produce something that you

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<v Speaker 1>seem the same as the way a human would produce it,

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<v Speaker 1>it didn't matter how the machine did it. It just

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<v Speaker 1>meant that that machine possessed some form of intelligence. Uh,

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<v Speaker 1>And I'll explain that in a second to In nineteen

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<v Speaker 1>fifty he published a paper Computer Machinery and Intelligence and

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<v Speaker 1>propose the idea of the touring test. Now, in general, today,

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<v Speaker 1>the way we talk about the touring test is, uh. You.

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<v Speaker 1>A simple version would be you have a computer in

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<v Speaker 1>front of your computer terminal. All you get is text,

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<v Speaker 1>and your text is going to someone that's in another room,

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<v Speaker 1>and that might be a person or it might be

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<v Speaker 1>a machine. So you're you're like in a chat set up. Yeah, yeah,

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<v Speaker 1>and you cannot see the other person at all all

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<v Speaker 1>or the machine at all. All you see is whatever

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<v Speaker 1>shows up on your screen and you type in a

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<v Speaker 1>message and a message returns back to you that's generated

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<v Speaker 1>either by a person or a machine. The Touring test

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<v Speaker 1>said that if you were unable to determine to a

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<v Speaker 1>certain degree of certainty whether or not the the the

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<v Speaker 1>thing on the other end was a person or a machine,

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<v Speaker 1>that machine was said to pass the Touring test. So

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<v Speaker 1>if it could imitate human speech through text, yes, So

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<v Speaker 1>if the machine was able to carry on a conversation

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<v Speaker 1>in such a way that it would convince you that

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<v Speaker 1>it's another person, then it could pass the Touring test.

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<v Speaker 1>It would at least seem to possess intelligence. And here's

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<v Speaker 1>how Touring kind of thought about this. It's kind of

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<v Speaker 1>a philosophical way of thinking about intelligence. So I, Jonathan Strickland,

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<v Speaker 1>I know that I possess at least some level of

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<v Speaker 1>intelligence because I have that personal experience. I know what

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<v Speaker 1>it means in an abstract way to myself. When I

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<v Speaker 1>talked to you, Lauren, and you start to display several

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<v Speaker 1>of the same things I associate with an intelligence. I

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<v Speaker 1>then assume you too, possess intelligence of some level, and

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<v Speaker 1>that that is just something that I am granting to Lauren.

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<v Speaker 1>From my perspective, because I cannot inhabit Lauren, I can't

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<v Speaker 1>know what her experience really is. I can only base

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<v Speaker 1>that upon the observations I make of her behavior. Touring says, well,

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<v Speaker 1>if we do that, if you assume that every person

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<v Speaker 1>you encounter has some level of intelligence, why would you

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<v Speaker 1>not offer a machine the same courtesy If it seemed

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<v Speaker 1>to display the same sort of behaviors, You might as

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<v Speaker 1>well say it has intelligence, because there's no way that

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<v Speaker 1>you can inhabit that machine, just as there's no way

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<v Speaker 1>you can inhabit another human being. And in fact, saying

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<v Speaker 1>that we know the machine is different because we programmed

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<v Speaker 1>it might be an example of the genetic fallacy, right

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<v Speaker 1>that the idea that because you know where a phenomenon

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<v Speaker 1>came from, that proves it's not genuine. Sure. Yeah, and

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<v Speaker 1>so when we get to the actual touring test. We've

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<v Speaker 1>had plenty of people design various kinds of of software.

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<v Speaker 1>Usually they're called chatbots. These are just programs that are

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<v Speaker 1>designed to interact with people and respond in a conversational

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<v Speaker 1>way through some context. And uh, and some of them

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<v Speaker 1>have been pretty successful. Surely they could never really trick somebody, right,

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<v Speaker 1>I mean, I've talked a smarter child on instant Messenger.

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<v Speaker 1>I don't think that's a person. It all depends on again,

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<v Speaker 1>the context and how well designed the program was. So

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<v Speaker 1>for example, one of the earliest that was designed was Eliza,

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<v Speaker 1>which was created by Joseph Wisenbaum, and Eliza was effective

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<v Speaker 1>within a certain context, and that it was it was

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<v Speaker 1>presenting a point of view of almost kind of a

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<v Speaker 1>naive person, someone who has very little real world experience,

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<v Speaker 1>and so as long as the real human being had

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<v Speaker 1>that same kind of perspective, it was fairly effective. There

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<v Speaker 1>was another one called Perry p a r R Y

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<v Speaker 1>that was made by Kenneth Colby, and that one simulated

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<v Speaker 1>a paranoid schizophrenic responding to questions. And what they did

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<v Speaker 1>was they ran a whole bunch of conversations between an

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<v Speaker 1>interviewer and actual patients who suffered from paranoid schizophrenia and

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<v Speaker 1>conversations with Perry, and then printed out all those conversations

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<v Speaker 1>handed that to a panel of judges who were made

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<v Speaker 1>up of psychologists, and they had a success rate of

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<v Speaker 1>forty eight percent of saying which one was real in

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<v Speaker 1>which one. Now, in that case, you're talking about a

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<v Speaker 1>specific context that has its own limiting factors, right sure, yeah, yeah,

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<v Speaker 1>you're you're you're talking about two different, um, you know,

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<v Speaker 1>hypotheticals of a human person that aren't the kind of

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<v Speaker 1>conversation that you would normally have straight And in the

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<v Speaker 1>case of Perry, you're talking about a conversation that was

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<v Speaker 1>not conducted by the actual panel of judges. Right, So

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<v Speaker 1>their experience is very different from someone who say, it's

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<v Speaker 1>down at a terminal and it's actually having the conversational moment.

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<v Speaker 1>They're reviewing something that's already happened. That's different too, I

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<v Speaker 1>mean that experience is different. But there are other examples too.

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<v Speaker 1>There's the artificial linguistic Internet computer entity also known as Alice,

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<v Speaker 1>and there's a one that was made by Rollo Carpenter

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<v Speaker 1>called Jabberwocky. Was brilliant, the slightly topes uh that one

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<v Speaker 1>was meant to simulate human chat in a very kind

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<v Speaker 1>of humorous way. I actually tried this one out before

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<v Speaker 1>we came to the podcast. I wish I had been

0:12:29.760 --> 0:12:32.520
<v Speaker 1>able to print out our conversation because I mean, you

0:12:32.520 --> 0:12:35.000
<v Speaker 1>could tell that it was or I could tell it

0:12:35.040 --> 0:12:37.240
<v Speaker 1>was a chat bot. But for one thing, I already knew,

0:12:37.320 --> 0:12:40.560
<v Speaker 1>So that that's problematic, right, I mean I already know

0:12:40.840 --> 0:12:44.720
<v Speaker 1>going into it, So yeah, very scientific, right. I would

0:12:44.760 --> 0:12:46.960
<v Speaker 1>need to have like a double blind test if I

0:12:46.960 --> 0:12:50.000
<v Speaker 1>really wanted to do this properly. But also there were

0:12:50.000 --> 0:12:53.040
<v Speaker 1>other giveaways, like you would it would ask you a

0:12:53.160 --> 0:12:55.640
<v Speaker 1>question and I would answer because it was being snarky.

0:12:55.679 --> 0:12:57.680
<v Speaker 1>I was being snarky. I answered in a snarky way,

0:12:57.720 --> 0:13:00.440
<v Speaker 1>and then it didn't know how to deal with that,

0:13:00.480 --> 0:13:02.800
<v Speaker 1>like it didn't have enough keywords to work off of,

0:13:03.200 --> 0:13:06.720
<v Speaker 1>so it would go with a uh, like a stock

0:13:06.840 --> 0:13:09.400
<v Speaker 1>response that was just a generic response. And in fact,

0:13:09.480 --> 0:13:11.839
<v Speaker 1>that's the way a lot of these chatbots work is

0:13:11.880 --> 0:13:14.440
<v Speaker 1>that they search for keywords and the things that the

0:13:14.480 --> 0:13:18.480
<v Speaker 1>person the interviewer is typing in, and then it generates

0:13:18.480 --> 0:13:22.440
<v Speaker 1>a response based upon those keywords. And if none of

0:13:22.440 --> 0:13:25.680
<v Speaker 1>the keywords that it normally quote unquote knows as in

0:13:25.760 --> 0:13:28.680
<v Speaker 1>it keywords that are in its database, then it will

0:13:28.760 --> 0:13:33.119
<v Speaker 1>generate some other form of response, either a generic response,

0:13:33.400 --> 0:13:36.080
<v Speaker 1>or it will repeat something that it had already said

0:13:36.080 --> 0:13:39.680
<v Speaker 1>previously in the conversation. So what are some of the

0:13:39.720 --> 0:13:43.520
<v Speaker 1>ways that we think a chat bought could get really convincing?

0:13:43.600 --> 0:13:47.200
<v Speaker 1>Because I'd imagine that we're not there yet, right, but

0:13:47.440 --> 0:13:51.640
<v Speaker 1>we're getting better. We are getting better, um um. And

0:13:51.760 --> 0:13:54.400
<v Speaker 1>obviously there's some kind of hurdle we'd have to get

0:13:54.440 --> 0:13:59.000
<v Speaker 1>over there. There's some like strategy for supplying these things

0:13:59.080 --> 0:14:02.600
<v Speaker 1>with conversation asian rules that we haven't quite achieved yet.

0:14:02.800 --> 0:14:05.920
<v Speaker 1>I mean, would uh, would machine learning have anything to

0:14:05.960 --> 0:14:09.520
<v Speaker 1>do with it? Sure, like the idea of maybe mining

0:14:09.760 --> 0:14:14.160
<v Speaker 1>thousands of other conversations to establish rules about how real

0:14:14.280 --> 0:14:18.560
<v Speaker 1>people interact, or even having every time the machine has

0:14:18.600 --> 0:14:22.480
<v Speaker 1>a conversation that it ends up reviewing that afterward and

0:14:22.600 --> 0:14:25.120
<v Speaker 1>learning from its own experiences. I mean, these are right

0:14:25.160 --> 0:14:28.520
<v Speaker 1>because you know, it's human. Human vocabulary and human understanding.

0:14:28.600 --> 0:14:31.920
<v Speaker 1>Don't stop when you learn a language. We're continually learning

0:14:31.920 --> 0:14:34.360
<v Speaker 1>our own languages. We're learning new rules for it, we're

0:14:34.560 --> 0:14:38.880
<v Speaker 1>learning new vocabulary for it, we're creating things like metaphor.

0:14:38.920 --> 0:14:43.040
<v Speaker 1>And similarly, we're creating these ideas, these abstract ideas that

0:14:43.120 --> 0:14:46.200
<v Speaker 1>makes sense to people who are native speakers of that language,

0:14:46.200 --> 0:14:48.600
<v Speaker 1>who have had exposure to this sort of thing, But

0:14:48.720 --> 0:14:52.000
<v Speaker 1>to something that is just creating, you know, working out

0:14:52.040 --> 0:14:55.440
<v Speaker 1>of say, a very strict dictionary of words, it would

0:14:55.760 --> 0:14:58.960
<v Speaker 1>be meaningless or confusing. This reminds me of that time

0:14:58.960 --> 0:15:01.480
<v Speaker 1>that that IBM t um. Remind me to remind me

0:15:01.520 --> 0:15:04.800
<v Speaker 1>the name of the computer. Um. They yes, they taught

0:15:04.840 --> 0:15:08.720
<v Speaker 1>Watson Urban dictionary. Oh no, yeah, oh no. Yeah. They

0:15:08.960 --> 0:15:11.840
<v Speaker 1>then then after after Watson started talking back at them

0:15:11.840 --> 0:15:13.400
<v Speaker 1>a little bit too much, they were just like, let's

0:15:13.440 --> 0:15:16.360
<v Speaker 1>just nuke that portion of Watson from more. Yeah, it's

0:15:16.360 --> 0:15:17.880
<v Speaker 1>the only way to be sure. Yeah. It turned out

0:15:17.880 --> 0:15:22.400
<v Speaker 1>that Watson Watson developed a bit of a potty mouth, like, Um,

0:15:22.440 --> 0:15:24.960
<v Speaker 1>are you familiar with the show Breaking Bad? Do you

0:15:25.000 --> 0:15:28.360
<v Speaker 1>know how Jesse ends most of his sentences. That's pretty

0:15:28.400 --> 0:15:32.640
<v Speaker 1>much how Watson was ending his sentences. Um. So yeah,

0:15:33.240 --> 0:15:35.600
<v Speaker 1>they found out it was I think Urban Dictionary, and

0:15:35.640 --> 0:15:37.760
<v Speaker 1>it was one other thing too that I think it

0:15:37.840 --> 0:15:40.200
<v Speaker 1>might have been. It may have been Wikipedia, but I'm

0:15:40.200 --> 0:15:44.240
<v Speaker 1>not sure. But they fed that into how to talk. Yeah,

0:15:44.880 --> 0:15:50.240
<v Speaker 1>well then it just becomes unintelligible, right, hacks or yeah, okay,

0:15:50.560 --> 0:15:53.280
<v Speaker 1>so uh I think I've seen I could be wrong.

0:15:53.280 --> 0:15:55.320
<v Speaker 1>About this. What I think I remember seeing is that

0:15:55.680 --> 0:15:59.000
<v Speaker 1>most recently, Ray Kurzwild said he thought that chat bots

0:15:59.040 --> 0:16:01.720
<v Speaker 1>would reliable, you be able to beat the Turing test.

0:16:03.400 --> 0:16:06.040
<v Speaker 1>And to be fair, when we say beat the Turing tests,

0:16:06.160 --> 0:16:10.520
<v Speaker 1>there's no hard and fast rule. I think what that means,

0:16:11.840 --> 0:16:16.160
<v Speaker 1>reliably fool the judges, right, yeah, because there's it all

0:16:16.160 --> 0:16:19.040
<v Speaker 1>depends upon whom you ask, Right. Some people say you

0:16:19.120 --> 0:16:21.720
<v Speaker 1>need to fool people at least thirty percent of the

0:16:21.760 --> 0:16:24.480
<v Speaker 1>time and then you beat the Turing test. Other people say, well,

0:16:24.520 --> 0:16:27.120
<v Speaker 1>Touring had suggested that you show it to a panel,

0:16:27.200 --> 0:16:29.280
<v Speaker 1>and if the majority of the panel thinks that it's

0:16:29.280 --> 0:16:31.680
<v Speaker 1>a real person, when in fact it's a computer, then

0:16:31.720 --> 0:16:34.160
<v Speaker 1>the computer beats the Turing test. It all depends on

0:16:34.160 --> 0:16:36.560
<v Speaker 1>how you frame it. There is not like a magical

0:16:36.920 --> 0:16:38.880
<v Speaker 1>This is the way the test should be administered, and

0:16:38.920 --> 0:16:40.200
<v Speaker 1>this is the only way you can tell if you

0:16:40.240 --> 0:16:42.240
<v Speaker 1>pass or fail. I just want to make that clear

0:16:42.320 --> 0:16:45.400
<v Speaker 1>because when we say passing the Turing test, that's so fuzzy.

0:16:45.600 --> 0:16:50.160
<v Speaker 1>We really are talking about if you the listener, you

0:16:50.160 --> 0:16:51.960
<v Speaker 1>you who are listening right now, if you were to

0:16:52.000 --> 0:16:54.160
<v Speaker 1>have a conversation with one of these machines, you would

0:16:54.200 --> 0:16:56.840
<v Speaker 1>not be completely certain whether or not that was a

0:16:56.880 --> 0:16:59.240
<v Speaker 1>machine or a person. We see this actually used a

0:16:59.280 --> 0:17:03.520
<v Speaker 1>lot in corporate settings for things like UM customer service.

0:17:03.880 --> 0:17:06.320
<v Speaker 1>So if you ever have one of those customer service

0:17:06.359 --> 0:17:08.639
<v Speaker 1>things pop up when you are on a website and

0:17:08.680 --> 0:17:11.360
<v Speaker 1>it says do you need some help? Often this ends

0:17:11.400 --> 0:17:13.520
<v Speaker 1>up being a chatbot that actually just has a very

0:17:13.640 --> 0:17:16.640
<v Speaker 1>deep series of keywords that relate back to the products

0:17:16.680 --> 0:17:19.400
<v Speaker 1>and services of that company, so that when you start

0:17:19.440 --> 0:17:22.679
<v Speaker 1>typing things in, it can start sending you two links

0:17:22.720 --> 0:17:26.840
<v Speaker 1>that possibly can solve your problem, but more likely will

0:17:26.880 --> 0:17:29.600
<v Speaker 1>cause you to go into a red hot rage, burning

0:17:29.600 --> 0:17:32.439
<v Speaker 1>brighter than a thousand exploding suns. Of course, they're the

0:17:32.520 --> 0:17:35.960
<v Speaker 1>goal is to get you to the information or or

0:17:36.040 --> 0:17:38.439
<v Speaker 1>service that you need. It's not really to trick you

0:17:38.480 --> 0:17:41.360
<v Speaker 1>into thinking like, wow, I really just formed an emotional

0:17:41.359 --> 0:17:44.000
<v Speaker 1>connection with this person, or the goal is to cut

0:17:44.040 --> 0:17:46.679
<v Speaker 1>down on the cost of manning actual human beings and

0:17:46.720 --> 0:17:51.119
<v Speaker 1>customer support roles. But that your your point stands either way. UM,

0:17:51.160 --> 0:17:55.040
<v Speaker 1>so I would like to imagine a future where we're

0:17:55.080 --> 0:17:58.720
<v Speaker 1>proposing the next Turing test. Okay, so we've gotten to

0:17:58.720 --> 0:18:00.760
<v Speaker 1>the point where we can have a versation and not

0:18:00.880 --> 0:18:03.080
<v Speaker 1>be sure if it's a person or a machine. Yes, so,

0:18:03.160 --> 0:18:05.280
<v Speaker 1>either because the machines have become really smart or people

0:18:05.280 --> 0:18:08.120
<v Speaker 1>aren't just dumb as bricks. Imagine we get there. Let's

0:18:08.119 --> 0:18:13.000
<v Speaker 1>say it's nine kurs wild was right. Um now we've

0:18:13.040 --> 0:18:17.280
<v Speaker 1>got chat bots that people cannot tell the chat bought

0:18:17.280 --> 0:18:20.160
<v Speaker 1>from a human that every single time, you know, it's

0:18:20.160 --> 0:18:26.439
<v Speaker 1>statistically and significant. Um uh. Once we're there, what's the

0:18:26.480 --> 0:18:30.120
<v Speaker 1>next step. What's the next hardest thing in that same

0:18:30.160 --> 0:18:34.280
<v Speaker 1>sort of realm of imitating human intelligence? Well, Joe, since

0:18:34.320 --> 0:18:37.160
<v Speaker 1>this whole podcast is about storytelling, I'm going to take

0:18:37.200 --> 0:18:39.520
<v Speaker 1>a wild stab in the dark and say it's storytelling.

0:18:39.640 --> 0:18:42.320
<v Speaker 1>That's my guess. I think that'd be really interesting. I mean,

0:18:42.400 --> 0:18:46.200
<v Speaker 1>so we can sort of imagine, like the chat bots

0:18:46.240 --> 0:18:49.560
<v Speaker 1>we've interacted with today, they can't really do conversation, but

0:18:49.600 --> 0:18:52.920
<v Speaker 1>we can imagine it. We should see it storytelling. Now,

0:18:52.960 --> 0:18:56.800
<v Speaker 1>on one hand, I can imagine how an AI story

0:18:56.880 --> 0:18:59.240
<v Speaker 1>bought would work. But on the other hand, it seems

0:18:59.320 --> 0:19:03.159
<v Speaker 1>so alien into me. Surely they couldn't really create a

0:19:03.160 --> 0:19:06.800
<v Speaker 1>piece of AI that could tell stories that seemed as

0:19:06.800 --> 0:19:11.679
<v Speaker 1>good as human stories, right, I think it would be

0:19:11.720 --> 0:19:14.600
<v Speaker 1>a huge challenge, But I don't see anything that's fundamentally

0:19:14.840 --> 0:19:17.440
<v Speaker 1>impossible about it. Now, well, I mean if you take

0:19:17.440 --> 0:19:19.680
<v Speaker 1>the fact that there are programs these days that can

0:19:19.720 --> 0:19:23.360
<v Speaker 1>create music or or create works of visual art, then

0:19:23.520 --> 0:19:25.439
<v Speaker 1>you know, and those are based on a few thousand

0:19:25.560 --> 0:19:30.520
<v Speaker 1>rules about what makes a good piece of whatever media. Right. Okay, Well,

0:19:30.560 --> 0:19:33.159
<v Speaker 1>well let's let's set it up. Now, let's describe the

0:19:33.200 --> 0:19:36.919
<v Speaker 1>literary touring test. Okay that what what kind of test

0:19:37.320 --> 0:19:40.840
<v Speaker 1>would a story bought have to pass? Well, you'd have

0:19:40.920 --> 0:19:44.119
<v Speaker 1>to be able, I would say, to read the story

0:19:44.280 --> 0:19:48.720
<v Speaker 1>and feel like it was saying satisfied, for lack of

0:19:48.720 --> 0:19:51.600
<v Speaker 1>a better word, organic that that it did not it

0:19:52.200 --> 0:19:56.040
<v Speaker 1>was not just a series of choppy sentences where a

0:19:56.040 --> 0:19:58.639
<v Speaker 1>person goes through some mundane task. It would need to

0:19:58.720 --> 0:20:02.600
<v Speaker 1>be uh, it would need to be engaging. I know

0:20:02.640 --> 0:20:04.760
<v Speaker 1>you love that word, Joe, I keep using it because

0:20:04.760 --> 0:20:06.479
<v Speaker 1>of that. But it would need to It would need

0:20:06.520 --> 0:20:09.920
<v Speaker 1>to captivate an audience in some way, either because of

0:20:10.080 --> 0:20:13.560
<v Speaker 1>the actual plot or the characterization or some combination thereof.

0:20:14.040 --> 0:20:15.520
<v Speaker 1>And it would need to It would need to have

0:20:15.720 --> 0:20:19.000
<v Speaker 1>a narrative flow. It would need to have internal consistency. So,

0:20:19.040 --> 0:20:21.600
<v Speaker 1>in other words, things that happen earlier in the story

0:20:21.720 --> 0:20:25.280
<v Speaker 1>could not be contradicted later in the story. Let's say

0:20:25.320 --> 0:20:29.120
<v Speaker 1>that you have a story about a father and uh,

0:20:29.160 --> 0:20:33.560
<v Speaker 1>the father experiences the heartbreak of a child dying, and

0:20:33.560 --> 0:20:35.480
<v Speaker 1>that's a terrible thing. This is something that we would

0:20:35.520 --> 0:20:38.400
<v Speaker 1>encounter in a story on novel whatever. But then three

0:20:38.480 --> 0:20:41.040
<v Speaker 1>chapters later, the child's there and nothing has gone wrong,

0:20:41.080 --> 0:20:43.520
<v Speaker 1>and there's it's not like it's mystery or I think

0:20:43.520 --> 0:20:46.400
<v Speaker 1>it's just that the child had never died. And and

0:20:46.480 --> 0:20:49.440
<v Speaker 1>either you know, either adhering to those rules, or if

0:20:49.440 --> 0:20:52.479
<v Speaker 1>it breaks those rules, to be breaking them in a

0:20:52.520 --> 0:20:57.040
<v Speaker 1>way that seems conscious and purposeful. Exactly, it couldn't just

0:20:57.080 --> 0:21:01.879
<v Speaker 1>be breaking rules indiscriminately. Here's my blanket rule for the

0:21:02.119 --> 0:21:06.520
<v Speaker 1>literary touring test I've imagined. Um, it doesn't have to

0:21:06.560 --> 0:21:10.200
<v Speaker 1>create stories that are as good as your favorite author.

0:21:11.000 --> 0:21:13.359
<v Speaker 1>It just has to do in the same way that

0:21:13.400 --> 0:21:16.280
<v Speaker 1>the original Turing test didn't have to be as great

0:21:16.280 --> 0:21:20.840
<v Speaker 1>a conversationalist as your most interesting friend. It just has

0:21:20.880 --> 0:21:23.720
<v Speaker 1>to be good enough that you think this is probably

0:21:23.720 --> 0:21:27.760
<v Speaker 1>written by a human being. Sure, I've read some awful stories,

0:21:27.800 --> 0:21:31.240
<v Speaker 1>so the bar is set fairly low. Um, we're going

0:21:31.280 --> 0:21:34.200
<v Speaker 1>back to that fan fiction thing now, But you've read

0:21:34.200 --> 0:21:37.120
<v Speaker 1>some awful stories. But even people who are bad writers

0:21:37.600 --> 0:21:41.119
<v Speaker 1>write better stories than than we could generate now with

0:21:41.240 --> 0:21:47.160
<v Speaker 1>a I some really bad stories. I've read some really

0:21:47.160 --> 0:21:49.040
<v Speaker 1>bad things. Well, let's let's just play this way. The

0:21:49.040 --> 0:21:51.320
<v Speaker 1>way the way a computer would generate a story right now,

0:21:52.119 --> 0:21:55.040
<v Speaker 1>the only way I think you could have one where

0:21:55.160 --> 0:21:59.360
<v Speaker 1>it would have any sort of of actual sense to it,

0:22:00.520 --> 0:22:03.080
<v Speaker 1>unless you were to have an incredibly powerful computer and

0:22:03.280 --> 0:22:06.600
<v Speaker 1>hundreds and hundreds and hundreds of hours of programming time

0:22:06.600 --> 0:22:09.080
<v Speaker 1>built into it, would be if you went the mad

0:22:09.160 --> 0:22:13.280
<v Speaker 1>Libs approach, where you had a story outline that already

0:22:13.320 --> 0:22:15.560
<v Speaker 1>existed and the computer was just filling in the blanks.

0:22:15.560 --> 0:22:17.320
<v Speaker 1>And even then it's not necessarily going to make a

0:22:17.320 --> 0:22:21.000
<v Speaker 1>whole lot of sense. Of course, that that wouldn't be generating, No,

0:22:21.119 --> 0:22:23.720
<v Speaker 1>that would. In fact, I've seen generators that do this

0:22:23.800 --> 0:22:27.120
<v Speaker 1>kind of thing where they they follow very simple rules. So,

0:22:27.240 --> 0:22:30.840
<v Speaker 1>for example, my father created a program in Apple Basic

0:22:31.480 --> 0:22:35.400
<v Speaker 1>back in nine five, I want to say, so, yes,

0:22:35.440 --> 0:22:38.439
<v Speaker 1>we had computers back then, uh, And he created this

0:22:38.480 --> 0:22:43.160
<v Speaker 1>program that was a science fiction novel title generator, and

0:22:43.280 --> 0:22:45.720
<v Speaker 1>it had just very simple rules where it would take

0:22:45.800 --> 0:22:50.120
<v Speaker 1>some ridiculous adjective and ridiculous nouns and pair it up

0:22:50.160 --> 0:22:52.560
<v Speaker 1>together so that you would get a sentence or or

0:22:52.800 --> 0:22:55.320
<v Speaker 1>title that would just sound like yeah, I could totally

0:22:55.320 --> 0:22:58.439
<v Speaker 1>see that being on the store shelf in the science

0:22:58.440 --> 0:23:01.800
<v Speaker 1>fiction fantasy section, the burning lamp, Shade of Venus and

0:23:01.960 --> 0:23:04.200
<v Speaker 1>exactly that would be. That would be a great example.

0:23:04.320 --> 0:23:06.880
<v Speaker 1>It reminds me of I saw online a while back

0:23:06.920 --> 0:23:10.320
<v Speaker 1>at James Bond movie title Generator, but that one spit

0:23:10.359 --> 0:23:13.159
<v Speaker 1>out horrible things. It was like the Last Day to

0:23:13.400 --> 0:23:16.000
<v Speaker 1>Gun or like, oh yeah, because it was my favorite

0:23:16.040 --> 0:23:19.480
<v Speaker 1>was ball Eyes. It's because it was taking it was

0:23:19.520 --> 0:23:22.399
<v Speaker 1>taking existing James Bond titles and breaking them up and

0:23:22.440 --> 0:23:25.159
<v Speaker 1>then putting them back together. So instead of instead of

0:23:25.200 --> 0:23:29.600
<v Speaker 1>creating a really deep, uh database of words to work from,

0:23:29.640 --> 0:23:31.760
<v Speaker 1>it was just like, let's put in every title of

0:23:32.119 --> 0:23:37.200
<v Speaker 1>j so Thunderball and Golden I maybe or something like that. Yeah.

0:23:37.520 --> 0:23:40.119
<v Speaker 1>So so. So the components that we're trying to smush

0:23:40.160 --> 0:23:44.320
<v Speaker 1>together here to to create this literary Turing test passing

0:23:44.400 --> 0:23:47.399
<v Speaker 1>machine are are that that vocabulary. You want to have

0:23:47.520 --> 0:23:50.200
<v Speaker 1>enough vocab so that you're not just ball Eyes to

0:23:50.280 --> 0:23:52.760
<v Speaker 1>the next century. The most basic it would just have

0:23:52.880 --> 0:23:57.080
<v Speaker 1>to know what words mean in a robust way, right right. Uh,

0:23:57.119 --> 0:23:58.919
<v Speaker 1>you know, it needs to be able to work with

0:23:59.040 --> 0:24:02.480
<v Speaker 1>the tropes and metaphors of of the culture that it's

0:24:02.560 --> 0:24:05.040
<v Speaker 1>writing for. I mean, because you know, a lot of

0:24:05.040 --> 0:24:08.000
<v Speaker 1>books will contain a lot of stories, contain references to

0:24:08.400 --> 0:24:12.320
<v Speaker 1>countless other stories, and are using um, the bits and

0:24:12.359 --> 0:24:16.240
<v Speaker 1>pieces the devices and conventions of storytelling that a culture

0:24:16.320 --> 0:24:20.680
<v Speaker 1>is used to write. There either either deliberately playing on

0:24:20.800 --> 0:24:24.840
<v Speaker 1>those tropes or they're deliberately defying them. But in either case,

0:24:24.960 --> 0:24:28.960
<v Speaker 1>it's something where the awareness is important both for the

0:24:28.960 --> 0:24:32.800
<v Speaker 1>storyteller and the audience. It's got to have an advanced

0:24:32.880 --> 0:24:35.520
<v Speaker 1>level of machine learning, which you guys mentioned earlier, which

0:24:35.560 --> 0:24:37.679
<v Speaker 1>is which is the science of getting a computer to

0:24:37.720 --> 0:24:40.840
<v Speaker 1>do something without explicitly having told it to do it, right,

0:24:40.880 --> 0:24:43.840
<v Speaker 1>because there are just too many rules you could not

0:24:44.040 --> 0:24:47.080
<v Speaker 1>possibly code them, right. Sure, Sure, it's it's you know,

0:24:47.119 --> 0:24:50.680
<v Speaker 1>machine learning is what's behind something like telling a Google

0:24:50.720 --> 0:24:53.480
<v Speaker 1>car to go down a street without it ever having

0:24:53.520 --> 0:24:56.920
<v Speaker 1>seen that street before. Um, you know, it's it's also

0:24:56.960 --> 0:24:58.840
<v Speaker 1>what goes into into web search and lots of other

0:24:59.000 --> 0:25:01.919
<v Speaker 1>you know, the Human g Project, everything like that is

0:25:01.960 --> 0:25:05.520
<v Speaker 1>based on machine learning. UM. And and then a basic

0:25:06.000 --> 0:25:08.359
<v Speaker 1>I mean not basic because this is actually really huge.

0:25:08.359 --> 0:25:13.199
<v Speaker 1>And ontology a semantic or abstract model of data UM

0:25:13.240 --> 0:25:18.040
<v Speaker 1>that that builds upon databases which are logical or logical

0:25:18.119 --> 0:25:22.240
<v Speaker 1>or physical UM. So you know, it's the term comes

0:25:22.280 --> 0:25:25.320
<v Speaker 1>from philosophy, where it's used to describe studies of nature

0:25:25.400 --> 0:25:28.359
<v Speaker 1>or existence, and it was picked up by early AI researches.

0:25:28.680 --> 0:25:32.760
<v Speaker 1>Researchers to um to define the the objects, concepts, and

0:25:32.760 --> 0:25:36.000
<v Speaker 1>other entities that are presumed to exist in in some

0:25:36.119 --> 0:25:41.400
<v Speaker 1>area of interest, and the relationships that are held among them. Right, so, uh,

0:25:41.480 --> 0:25:44.160
<v Speaker 1>let's make this concrete. Let's imagine a story. Okay, say

0:25:44.200 --> 0:25:47.520
<v Speaker 1>there's a story where there's like a murder or something. Uh,

0:25:47.560 --> 0:25:50.679
<v Speaker 1>you know, Colonel Mustard kills Mr. Body and and this

0:25:50.760 --> 0:25:54.720
<v Speaker 1>is a murder mystery about the story, Well it the

0:25:54.760 --> 0:25:58.120
<v Speaker 1>computer that's generating the story would have to have such

0:25:58.160 --> 0:26:02.040
<v Speaker 1>a robust understanding not just to what a word like

0:26:02.280 --> 0:26:05.760
<v Speaker 1>knife means, like not just that it's a noun or whatever,

0:26:06.240 --> 0:26:10.080
<v Speaker 1>but that it has relationships. So it can be found

0:26:10.160 --> 0:26:14.440
<v Speaker 1>in the kitchen, it can be used for murder. Maybe

0:26:14.480 --> 0:26:18.439
<v Speaker 1>it's found in Mr. Body. Yeah it can. But it

0:26:18.480 --> 0:26:22.520
<v Speaker 1>can also be a clue. Sure, but there's also metaphors

0:26:22.560 --> 0:26:27.920
<v Speaker 1>about being on a knife's edge or so to understand. Yeah,

0:26:27.920 --> 0:26:30.920
<v Speaker 1>it wouldn't just have to have knife in like its library,

0:26:30.960 --> 0:26:33.359
<v Speaker 1>but it would have to have a connected web of

0:26:33.440 --> 0:26:38.200
<v Speaker 1>relationships to other terms. Furthermore, you would have other relationships

0:26:38.240 --> 0:26:40.399
<v Speaker 1>you'd have to determine, like the fact that you would

0:26:40.400 --> 0:26:44.560
<v Speaker 1>have uh, you know, uh, Colonel Mustard as murderer. Mr

0:26:44.600 --> 0:26:48.520
<v Speaker 1>Body is victim, but you might have Mr Green as suspect.

0:26:48.720 --> 0:26:52.040
<v Speaker 1>You might have Scarlett as suspect. You know this. These

0:26:52.080 --> 0:26:55.800
<v Speaker 1>would be these would be different definitions. And then not

0:26:55.800 --> 0:26:57.720
<v Speaker 1>only do you define everything, but then you have to

0:26:57.760 --> 0:27:00.360
<v Speaker 1>relate them all to one another. Right. That's the big

0:27:00.359 --> 0:27:02.879
<v Speaker 1>thing that would make it even more complex, because in

0:27:02.960 --> 0:27:05.600
<v Speaker 1>order to have good fiction you have to have strong

0:27:05.680 --> 0:27:10.800
<v Speaker 1>characterization and relationships between the characters. So you need ontologies

0:27:10.880 --> 0:27:16.200
<v Speaker 1>that defined characters by like is in love with or

0:27:16.560 --> 0:27:21.440
<v Speaker 1>has a grudge against, is afraid of that kind of thing?

0:27:21.800 --> 0:27:24.800
<v Speaker 1>Or uh, you know, for example, in our our clue

0:27:25.000 --> 0:27:28.720
<v Speaker 1>or clue, oh example, we could go back to uh,

0:27:28.960 --> 0:27:31.840
<v Speaker 1>not only is Mr Green a suspect and Colonel Mustard

0:27:31.920 --> 0:27:34.399
<v Speaker 1>is the murderer, but Colonel Mustard views Mr Green as

0:27:34.400 --> 0:27:37.640
<v Speaker 1>a patsy. Now, the other characters would not view Mr

0:27:37.680 --> 0:27:40.320
<v Speaker 1>Green as a patsy, and within the narrative of a story,

0:27:40.640 --> 0:27:43.040
<v Speaker 1>that would be clear to the audience but not clear

0:27:43.119 --> 0:27:46.760
<v Speaker 1>to the characters within the context of that story. It's

0:27:46.760 --> 0:27:48.520
<v Speaker 1>a very simple thing for us to think about as

0:27:48.560 --> 0:27:51.320
<v Speaker 1>human beings. This is something that comes very naturally to us. Obviously,

0:27:51.359 --> 0:27:53.600
<v Speaker 1>when you're watching a movie or reading a book, you

0:27:53.640 --> 0:27:56.280
<v Speaker 1>are aware of certain things that other characters are not

0:27:56.320 --> 0:27:59.120
<v Speaker 1>aware of, and if those characters magically become aware of them,

0:27:59.280 --> 0:28:01.560
<v Speaker 1>that pulls you out moment. I could see that being

0:28:01.600 --> 0:28:04.479
<v Speaker 1>a very challenging thing to to quote unquote teach a

0:28:04.480 --> 0:28:08.640
<v Speaker 1>computer to know that you have traumatic Irony exists and

0:28:08.680 --> 0:28:11.280
<v Speaker 1>can be used in these ways, but not in these ways.

0:28:11.320 --> 0:28:14.080
<v Speaker 1>And because and just because one character is aware of

0:28:14.119 --> 0:28:17.200
<v Speaker 1>something does not necessarily mean that another character is aware

0:28:17.200 --> 0:28:20.560
<v Speaker 1>of that same thing. I mean I I had an

0:28:20.560 --> 0:28:22.720
<v Speaker 1>experience like that this week where I was watching something

0:28:22.760 --> 0:28:24.600
<v Speaker 1>and I thought, wait a minute, how does this character

0:28:24.640 --> 0:28:29.720
<v Speaker 1>even know this? And I totally pulled me all the story. Yeah, um,

0:28:29.840 --> 0:28:34.520
<v Speaker 1>so it's clear just how difficult this is. How how

0:28:34.720 --> 0:28:38.680
<v Speaker 1>I mean it, I'm gonna say frankly, it seems impossible

0:28:38.680 --> 0:28:42.680
<v Speaker 1>to me now, but I wouldn't say it's actually impolsible.

0:28:42.760 --> 0:28:46.600
<v Speaker 1>I would have said that a computer beating Jeopardy champions

0:28:46.680 --> 0:28:49.680
<v Speaker 1>at Jeopardy would have been impossible, you know, a couple

0:28:49.720 --> 0:28:52.560
<v Speaker 1>of years ago, and then IBM with Watson totally proved

0:28:52.560 --> 0:28:54.280
<v Speaker 1>me wrong. And they did it in a way where

0:28:54.320 --> 0:28:57.040
<v Speaker 1>Watson was not even connected to the Internet. It was

0:28:57.080 --> 0:28:59.440
<v Speaker 1>all self contained. So the fact that there could be

0:28:59.480 --> 0:29:03.680
<v Speaker 1>a self contained database of enough information to be able

0:29:03.720 --> 0:29:06.920
<v Speaker 1>to anticipate practically anything that Jeopardy can throw at you.

0:29:07.200 --> 0:29:10.720
<v Speaker 1>And Jeopardy is a game that includes things like wordplay,

0:29:10.720 --> 0:29:13.520
<v Speaker 1>where it's not just a standard here is your answer,

0:29:13.600 --> 0:29:16.000
<v Speaker 1>what's the what's the correct question? You may have to

0:29:16.040 --> 0:29:20.640
<v Speaker 1>interpret that answer beyond just what jokes and and et cetera.

0:29:20.720 --> 0:29:23.520
<v Speaker 1>Now there might be puns, there can be references. So

0:29:23.840 --> 0:29:26.320
<v Speaker 1>the fact that we were that we the fact that

0:29:26.320 --> 0:29:29.160
<v Speaker 1>IBM was able to create a computer that was capable

0:29:29.160 --> 0:29:33.160
<v Speaker 1>of doing this, I I hesitate to say impossible. I

0:29:33.200 --> 0:29:39.400
<v Speaker 1>think it's an incredibly difficult task. I would hesitate to

0:29:39.400 --> 0:29:42.760
<v Speaker 1>go so far as to say it's it's an uncrackable task. Yeah,

0:29:42.840 --> 0:29:45.160
<v Speaker 1>so so, so what's it like right now? This is

0:29:45.200 --> 0:29:47.720
<v Speaker 1>what we are imagining in the future. I hear that

0:29:47.760 --> 0:29:52.360
<v Speaker 1>you have some examples of computer generated poetry. Yeah, well, okay,

0:29:52.400 --> 0:29:57.280
<v Speaker 1>so obviously I think it's pretty clear poetry is easier

0:29:57.520 --> 0:30:00.720
<v Speaker 1>I think for machines to generate than fiction at this point,

0:30:01.400 --> 0:30:04.960
<v Speaker 1>because because the rules of poetry are a little bit looser, Um,

0:30:05.040 --> 0:30:11.440
<v Speaker 1>we expect poetry to be a lot of people expect poetry,

0:30:11.720 --> 0:30:14.000
<v Speaker 1>you know, you can expect it to be associative rather

0:30:14.040 --> 0:30:16.320
<v Speaker 1>than narrative. And it's actually I mean, it's got to

0:30:16.360 --> 0:30:19.280
<v Speaker 1>be really difficult to get a computer to generate a

0:30:19.320 --> 0:30:21.880
<v Speaker 1>coherent narrative and less it's just following like a pre

0:30:21.960 --> 0:30:25.840
<v Speaker 1>supplied skeletal structure. Yeah, yeah, to tell a computer, Hey,

0:30:26.000 --> 0:30:29.400
<v Speaker 1>tell me a fantasy story. Here are four characters you have,

0:30:29.880 --> 0:30:34.320
<v Speaker 1>and here is one, uh, one important piece of information

0:30:34.360 --> 0:30:37.440
<v Speaker 1>that you need to incorporate. Go. That would be like

0:30:37.440 --> 0:30:40.080
<v Speaker 1>like you said, Joe, right now, that's really hard to image.

0:30:40.080 --> 0:30:42.880
<v Speaker 1>It's impossible, you know, it wouldn't be able to I mean,

0:30:43.200 --> 0:30:47.240
<v Speaker 1>the computer just doesn't understand events and the progression of

0:30:47.280 --> 0:30:50.880
<v Speaker 1>events enough to tell a coherent narrative from one you

0:30:50.880 --> 0:30:53.160
<v Speaker 1>know that that feels meaningful in any way. I mean

0:30:53.200 --> 0:30:55.080
<v Speaker 1>why didn't the eagles just give them a ride to

0:30:55.120 --> 0:30:57.840
<v Speaker 1>more to or at the very beginning if they could? All? Right? Right,

0:30:57.840 --> 0:31:02.280
<v Speaker 1>but but but speaking speaking as one who basically majored

0:31:02.320 --> 0:31:05.320
<v Speaker 1>in poetry in college and very nearly went on to

0:31:05.360 --> 0:31:09.520
<v Speaker 1>do an m f a in poetry, Um, writing good

0:31:09.600 --> 0:31:13.920
<v Speaker 1>poetry is really hard. It's hey, I am, I am

0:31:14.120 --> 0:31:16.440
<v Speaker 1>right there with you. I also studied poetry and I

0:31:16.480 --> 0:31:21.040
<v Speaker 1>love poetry. Um. The difference is that you can get

0:31:21.080 --> 0:31:24.120
<v Speaker 1>away with more associative stuff. Now, it might not be

0:31:24.200 --> 0:31:26.760
<v Speaker 1>poetry that anybody really cares to read more than once,

0:31:27.080 --> 0:31:30.520
<v Speaker 1>but it can pass for a poem. Um. So this

0:31:30.600 --> 0:31:32.880
<v Speaker 1>is a I found this book. It was published in

0:31:32.920 --> 0:31:37.120
<v Speaker 1>two thousand eleven by Pure Press, edited by a computational

0:31:37.280 --> 0:31:42.880
<v Speaker 1>linguist named are Eli Hirtelo. And this is a book

0:31:43.000 --> 0:31:48.320
<v Speaker 1>of poems called Discourse dot c PP that was created

0:31:48.320 --> 0:31:52.160
<v Speaker 1>by a computer that was designed to generate ontologies. So

0:31:52.200 --> 0:31:54.520
<v Speaker 1>what we were talking about before, you know, the idea

0:31:54.560 --> 0:31:59.760
<v Speaker 1>of association, establishing meaning for terms that it finds on

0:31:59.800 --> 0:32:03.920
<v Speaker 1>the internet and relationships with other terms. Uh. And so

0:32:04.480 --> 0:32:06.720
<v Speaker 1>the way that this book was created by it was

0:32:06.800 --> 0:32:10.120
<v Speaker 1>by machine learning. It was by looking at tons and

0:32:10.200 --> 0:32:15.760
<v Speaker 1>tons of text on Wikipedia and trying to establish relationships

0:32:15.840 --> 0:32:20.680
<v Speaker 1>between terms and then using that to auto generate poems

0:32:20.760 --> 0:32:24.120
<v Speaker 1>based on the semantic relationships that it had determined. I

0:32:24.160 --> 0:32:29.040
<v Speaker 1>bet it's poem about citation. Question mark is amazing. Okay.

0:32:29.080 --> 0:32:31.840
<v Speaker 1>The umbrella. Okay, this is when we have not heard yet.

0:32:31.840 --> 0:32:34.360
<v Speaker 1>We heard one earlier that actually Lauren and I both

0:32:34.360 --> 0:32:36.480
<v Speaker 1>agree like we can imagine a terrible poet. We'll get

0:32:36.520 --> 0:32:38.200
<v Speaker 1>to it, Jonathan, No, no, no, no, I don't want

0:32:38.200 --> 0:32:39.520
<v Speaker 1>you to read that one. I want you to read

0:32:39.560 --> 0:32:43.280
<v Speaker 1>just ones I have not heard yet. The umbrella. You

0:32:43.320 --> 0:32:46.480
<v Speaker 1>want an umbrella and all you have is a flannel

0:32:46.520 --> 0:32:55.360
<v Speaker 1>handkerchief and a sponge. That's that's it. That's that's probably bicycles. No, no, no,

0:32:55.480 --> 0:32:58.840
<v Speaker 1>I'm still I'm still taking an umbrella, flannel handkerchief and

0:32:58.880 --> 0:33:03.360
<v Speaker 1>a sponge that's written by a computer. Go another one.

0:33:03.440 --> 0:33:09.720
<v Speaker 1>Bicycles by the computer. The cycles dominate the street, an

0:33:09.760 --> 0:33:14.400
<v Speaker 1>infantry of two wheeled implements, tricycles and rickshaws, most of

0:33:14.440 --> 0:33:18.800
<v Speaker 1>them far left crutch carriages. The pilots are no duffers.

0:33:19.600 --> 0:33:24.840
<v Speaker 1>Visitors rent higher priced engine powered recumbent three seaters, flashlight

0:33:24.920 --> 0:33:30.040
<v Speaker 1>and tulip included. All commute as one would skim. Pretty sure,

0:33:30.080 --> 0:33:33.960
<v Speaker 1>E Cummings wrote something like that. Um, but hey, no,

0:33:34.040 --> 0:33:36.200
<v Speaker 1>I want to read the good one, this one, this

0:33:36.240 --> 0:33:39.680
<v Speaker 1>one actually, this was kind of amazing, the only one

0:33:39.720 --> 0:33:42.600
<v Speaker 1>that we had heard actually like this one. If if

0:33:42.640 --> 0:33:44.280
<v Speaker 1>a human wrote this, I would think it was kind

0:33:44.280 --> 0:33:48.640
<v Speaker 1>of interesting. Okay, it's called love to love rather like

0:33:49.160 --> 0:33:55.720
<v Speaker 1>prefer and wish to want first, bother then approach, chase, catch,

0:33:55.960 --> 0:34:01.320
<v Speaker 1>eat and kill, thank you, to love of to remind

0:34:01.360 --> 0:34:06.680
<v Speaker 1>and remember, to know and to forget. Oh, that last

0:34:06.720 --> 0:34:08.759
<v Speaker 1>line is kind of a chiller. I just like to

0:34:09.160 --> 0:34:11.880
<v Speaker 1>know the bother part because, as I said earlier in

0:34:11.880 --> 0:34:14.680
<v Speaker 1>our pre meeting, I said, this sounds like the middle

0:34:14.719 --> 0:34:17.799
<v Speaker 1>school approach to romance, like bother the person first and

0:34:17.800 --> 0:34:20.800
<v Speaker 1>then get the kind of or the Anakin Skywalker school

0:34:20.880 --> 0:34:29.440
<v Speaker 1>of courtship. Um, so that's where we're where. Yeah, And

0:34:29.920 --> 0:34:32.239
<v Speaker 1>I think that what we can kind of take from

0:34:32.280 --> 0:34:36.960
<v Speaker 1>this is that it's easier to write poetry about um,

0:34:37.000 --> 0:34:39.880
<v Speaker 1>about something abstract like love, than it is about something

0:34:40.040 --> 0:34:45.120
<v Speaker 1>concrete like a bicycle, right, like a narrative right right

0:34:45.360 --> 0:34:48.759
<v Speaker 1>to see. I would really be curious to see what

0:34:48.920 --> 0:34:52.320
<v Speaker 1>the first computer generated stab at something like bail Wolf

0:34:52.320 --> 0:34:56.600
<v Speaker 1>would be like an epic saga. Well, I would say probably,

0:34:57.080 --> 0:34:59.920
<v Speaker 1>So you have poetry on two sides. It's probably easier

0:35:00.040 --> 0:35:03.279
<v Speaker 1>or a computer to write abstract or lyrical poetry than

0:35:03.320 --> 0:35:06.839
<v Speaker 1>it is to write prose fiction. But it's probably even

0:35:06.920 --> 0:35:10.799
<v Speaker 1>harder for a computer to write narrative poetry than it

0:35:10.800 --> 0:35:13.200
<v Speaker 1>would be for the computer to write prose fiction. Right, Yeah,

0:35:13.200 --> 0:35:15.279
<v Speaker 1>I would imagine so, because then you've got you've got

0:35:15.280 --> 0:35:19.680
<v Speaker 1>to deal with all those difficulties of coherent progressive narrative,

0:35:20.040 --> 0:35:22.719
<v Speaker 1>and you've also got to deal with whatever, you know,

0:35:23.040 --> 0:35:26.719
<v Speaker 1>poetic tropes you're using, like not to makes you have

0:35:26.760 --> 0:35:29.680
<v Speaker 1>a length of a line, right, exactly, like how much alliteration,

0:35:31.080 --> 0:35:33.080
<v Speaker 1>especially for something like Old English where it was all

0:35:33.080 --> 0:35:37.960
<v Speaker 1>alliterative and not rhyming poetry. So one of the things

0:35:38.000 --> 0:35:40.920
<v Speaker 1>that you brought up in the video, and I think

0:35:40.960 --> 0:35:43.360
<v Speaker 1>it's a really interesting idea, is let's say that we

0:35:43.400 --> 0:35:46.960
<v Speaker 1>get to the point where computers are able to simulate

0:35:47.360 --> 0:35:49.880
<v Speaker 1>to a certain extent the ability to tell a story,

0:35:50.080 --> 0:35:53.719
<v Speaker 1>and it will really depend heavily upon what rules the

0:35:53.760 --> 0:35:57.359
<v Speaker 1>programmer creates. Right, So, in other words, if I were

0:35:57.400 --> 0:36:01.279
<v Speaker 1>to program and artificially intelligent story Heller. The stories that

0:36:01.320 --> 0:36:04.360
<v Speaker 1>would come out of that computer would reflect the rules

0:36:04.480 --> 0:36:07.160
<v Speaker 1>I had created, which might be different from the rules

0:36:07.200 --> 0:36:10.239
<v Speaker 1>you create, Joe. You might think that, you know, especially

0:36:10.360 --> 0:36:14.439
<v Speaker 1>something as simple as whether or not you value one

0:36:14.560 --> 0:36:18.560
<v Speaker 1>aspect of storytelling over another could make wildly different stories.

0:36:18.560 --> 0:36:21.280
<v Speaker 1>But we could get really granular, like to the point

0:36:21.280 --> 0:36:25.360
<v Speaker 1>of how frequently do I use the word the versus

0:36:25.360 --> 0:36:28.440
<v Speaker 1>how frequently you use it? And that actually matters, That

0:36:28.520 --> 0:36:31.919
<v Speaker 1>actually contributes to somebody's authorial voice. All right, you can

0:36:32.000 --> 0:36:34.600
<v Speaker 1>you can actually, um take a take a genetic imprint

0:36:34.640 --> 0:36:38.239
<v Speaker 1>of an author's voice based on I think, Joe, I

0:36:38.239 --> 0:36:39.799
<v Speaker 1>think you've got a bunch of notes on this one,

0:36:40.160 --> 0:36:42.839
<v Speaker 1>based on like the number of of times that they

0:36:42.920 --> 0:36:45.719
<v Speaker 1>use particular words, and the scope of their vocabulary and

0:36:46.960 --> 0:36:49.840
<v Speaker 1>like yeah, go ahead, I'm sorry, and the grammatic structure

0:36:49.880 --> 0:36:54.360
<v Speaker 1>of their sentences. Okay, Well, let's start with an idea. Um.

0:36:54.440 --> 0:36:58.480
<v Speaker 1>In N six, I think it was, somebody published a

0:36:58.600 --> 0:37:04.960
<v Speaker 1>novel called Imary Colors. The story, well, primary Colors was

0:37:05.040 --> 0:37:08.160
<v Speaker 1>a political novel. It was a Romano left you know,

0:37:08.680 --> 0:37:12.640
<v Speaker 1>which was a thinly veiled story about the Bill Clinton

0:37:12.719 --> 0:37:17.120
<v Speaker 1>campaign with the names changed and stuff like that. But

0:37:17.160 --> 0:37:20.680
<v Speaker 1>it was published anonymously. The author didn't reveal his or

0:37:20.680 --> 0:37:23.799
<v Speaker 1>her name, gotcha um, And so there was a big

0:37:23.880 --> 0:37:27.200
<v Speaker 1>question like, well, you know, we don't know who wrote this,

0:37:27.280 --> 0:37:30.000
<v Speaker 1>but people were really curious. They wanted, you know, all

0:37:30.160 --> 0:37:32.880
<v Speaker 1>they were positing all these names who were insiders on

0:37:32.920 --> 0:37:37.000
<v Speaker 1>the Clinton campaign and stuff like that. Um. There was

0:37:37.040 --> 0:37:41.799
<v Speaker 1>a Vassar College shakespeare professor named Don Foster, who I've

0:37:41.800 --> 0:37:46.640
<v Speaker 1>got this interesting scene in article from two thousand about him, uh,

0:37:46.640 --> 0:37:51.839
<v Speaker 1>and about how he used literary textual analysis to determine

0:37:51.960 --> 0:37:54.600
<v Speaker 1>who he thought the author was, and he thought it

0:37:54.640 --> 0:37:57.799
<v Speaker 1>was the columnist Joe Klein. Uh. And it turned out

0:37:57.920 --> 0:38:01.960
<v Speaker 1>years later Klein admitted that Foster had been correct. So

0:38:02.000 --> 0:38:05.000
<v Speaker 1>how did Foster identify him? While it was just straight

0:38:05.120 --> 0:38:09.080
<v Speaker 1>up textual comparison. He was looking at the text of

0:38:09.120 --> 0:38:12.920
<v Speaker 1>the novel and looking at the idiosyncrasies of the kinds

0:38:12.920 --> 0:38:17.600
<v Speaker 1>of vocabulary and sentenced construction, just how the text read,

0:38:17.960 --> 0:38:20.600
<v Speaker 1>comparing that with all kinds of other famous writers. And

0:38:20.719 --> 0:38:24.879
<v Speaker 1>when he finally stumbled across Joe Klein's column, he said,

0:38:24.920 --> 0:38:27.920
<v Speaker 1>ah ha, here we go. It's this guy, because he

0:38:27.920 --> 0:38:30.920
<v Speaker 1>he uses the same kinds of words in the same order.

0:38:31.160 --> 0:38:35.439
<v Speaker 1>The voice you can just easily identify and he was correct. Now,

0:38:35.960 --> 0:38:40.200
<v Speaker 1>actually you don't need a really skilled Shakespeare professor to

0:38:40.280 --> 0:38:42.880
<v Speaker 1>do something like this these days. So it helps. It

0:38:42.960 --> 0:38:45.440
<v Speaker 1>helps because because as we were talking about at the

0:38:45.480 --> 0:38:48.479
<v Speaker 1>very top of the show, Shakespeare produced a lot of work,

0:38:48.760 --> 0:38:51.160
<v Speaker 1>and some of the work that has attributed to Shakespeare

0:38:51.520 --> 0:38:54.680
<v Speaker 1>is probably a collaboration with other authors. In some cases

0:38:54.760 --> 0:38:58.040
<v Speaker 1>they're known collaborations. In other cases, Um, there are plays

0:38:58.120 --> 0:39:01.960
<v Speaker 1>where uh, it's suspect said that Shakespeare did not start

0:39:02.000 --> 0:39:05.480
<v Speaker 1>the play, but he finished it. And so having someone

0:39:05.520 --> 0:39:09.560
<v Speaker 1>who is uh specialized in examining the way a particular

0:39:09.600 --> 0:39:13.719
<v Speaker 1>writer wrote and being able to compare other works against

0:39:13.920 --> 0:39:17.520
<v Speaker 1>that body of known work or attributed work is very

0:39:17.640 --> 0:39:19.960
<v Speaker 1>very useful when you're trying to determine like, is this

0:39:20.239 --> 0:39:23.720
<v Speaker 1>actual play that we discovered that has no name attached

0:39:23.719 --> 0:39:28.560
<v Speaker 1>to it? Is that actually a lost Shakespearean play or whatever? Yeah. Um,

0:39:28.840 --> 0:39:32.839
<v Speaker 1>so they're all kinds of anonymous works throughout history. Think

0:39:32.840 --> 0:39:37.279
<v Speaker 1>about the Federalist papers, right that they were all all

0:39:37.400 --> 0:39:41.160
<v Speaker 1>these uh what were the articles published in newspapers? I

0:39:41.160 --> 0:39:46.880
<v Speaker 1>guess they were bad um written by John Jay Uh,

0:39:46.960 --> 0:39:50.719
<v Speaker 1>Alexander Hamilton's and James Madison, Right, they're looking at me.

0:39:50.760 --> 0:39:53.960
<v Speaker 1>I'm the English history guy. Yeah. Well okay, but so

0:39:54.000 --> 0:39:58.239
<v Speaker 1>they were published anonymously. UM, but I wonder if you

0:39:58.280 --> 0:40:01.799
<v Speaker 1>could get right, a computer, your program that can look

0:40:01.840 --> 0:40:06.319
<v Speaker 1>at these three works and group them into different author sortings,

0:40:06.360 --> 0:40:10.200
<v Speaker 1>and then compare those different groups two works that we

0:40:10.280 --> 0:40:12.960
<v Speaker 1>know by each of those three authors and figure out

0:40:12.960 --> 0:40:17.400
<v Speaker 1>which ones wrote which. Well, bam, we've got it. So

0:40:17.640 --> 0:40:20.480
<v Speaker 1>this is essentially like fingerprint analysis, where you're looking for

0:40:20.560 --> 0:40:26.440
<v Speaker 1>points of comparison that are identical against multiple criteria, whether

0:40:26.640 --> 0:40:29.600
<v Speaker 1>of using the both the the anonymous work and then

0:40:29.680 --> 0:40:34.399
<v Speaker 1>the known body of work. Right. Yeah. So the one

0:40:34.440 --> 0:40:37.279
<v Speaker 1>example of this type of software is a free piece

0:40:37.320 --> 0:40:41.480
<v Speaker 1>of software called Signature. It's the Stick Signature stylo Metrics

0:40:41.480 --> 0:40:46.839
<v Speaker 1>System UM, and it's a computer program used for textual analysis.

0:40:46.880 --> 0:40:48.560
<v Speaker 1>And one of the things that's used for is to

0:40:49.120 --> 0:40:54.080
<v Speaker 1>uh uh, is to assess authorship in a book where

0:40:54.080 --> 0:40:59.440
<v Speaker 1>the author is questionable, or to identify an author in

0:40:59.480 --> 0:41:01.480
<v Speaker 1>the case of been anonymous work like we were talking

0:41:01.480 --> 0:41:04.960
<v Speaker 1>about with the Federalist papers. And so how does it work. Well,

0:41:05.320 --> 0:41:08.799
<v Speaker 1>it does the same thing that Don Foster did with

0:41:08.880 --> 0:41:13.600
<v Speaker 1>primary colors, except it has an organized system of analyzing

0:41:13.640 --> 0:41:17.279
<v Speaker 1>text in a in a machine readable way. So it

0:41:17.360 --> 0:41:21.880
<v Speaker 1>can look at idiosyncrasies in the way you structure sentences,

0:41:22.040 --> 0:41:24.800
<v Speaker 1>or in the frequency with which you use certain words,

0:41:25.360 --> 0:41:28.720
<v Speaker 1>or it can look at the frequency of common function

0:41:28.719 --> 0:41:31.600
<v Speaker 1>words like we were talking about before, just using too

0:41:31.719 --> 0:41:35.000
<v Speaker 1>and from and but and if you if you frequently

0:41:35.160 --> 0:41:38.359
<v Speaker 1>misuse a word, that's a dead giveaway. And it does happen,

0:41:39.200 --> 0:41:41.719
<v Speaker 1>you know. UM, And all of the things that go

0:41:41.840 --> 0:41:45.719
<v Speaker 1>together to form an author's voice, you know, a distinctive

0:41:45.840 --> 0:41:49.480
<v Speaker 1>authorial writing style are things that can be used to

0:41:49.520 --> 0:41:52.879
<v Speaker 1>identify the author. And and we're getting better and better

0:41:52.920 --> 0:41:56.160
<v Speaker 1>at this. There's another textual analysis tool that was created

0:41:56.200 --> 0:42:00.319
<v Speaker 1>by grad students at Drexley University, UM, and it called

0:42:00.400 --> 0:42:03.000
<v Speaker 1>Jay Style. Oh and let's say you've got an anonymous

0:42:03.000 --> 0:42:07.000
<v Speaker 1>work here, UM. And what it does is it let's

0:42:07.040 --> 0:42:10.279
<v Speaker 1>say the work is about five hundred words, and you

0:42:10.480 --> 0:42:16.160
<v Speaker 1>have a pool of maybe fifty potential authorial candidates, and

0:42:16.239 --> 0:42:19.840
<v Speaker 1>you have maybe sixty five hundreds of words of text

0:42:19.920 --> 0:42:22.880
<v Speaker 1>from each of the candidates. They say, that's enough for

0:42:22.960 --> 0:42:26.000
<v Speaker 1>it to identify the five hundred words sample. The author

0:42:26.040 --> 0:42:28.480
<v Speaker 1>of the five five hundred words sample with a very

0:42:28.560 --> 0:42:33.320
<v Speaker 1>high degree of certainty. Um and all funny enough, this

0:42:33.400 --> 0:42:36.520
<v Speaker 1>is kind of a side note, but they also created

0:42:36.600 --> 0:42:39.799
<v Speaker 1>something that works the other way. Uh So, if we're

0:42:39.800 --> 0:42:43.000
<v Speaker 1>getting better and better at using software to identify the

0:42:43.080 --> 0:42:46.719
<v Speaker 1>author of anonymously written text, that seems like it could

0:42:46.719 --> 0:42:51.000
<v Speaker 1>put people like whistleblowers at risk. So they came up

0:42:51.000 --> 0:42:54.120
<v Speaker 1>with a program that does the opposite. It's called anana Mouth,

0:42:54.960 --> 0:42:58.359
<v Speaker 1>and it takes text that you write and helps you

0:42:58.480 --> 0:43:01.759
<v Speaker 1>retranslate it out of your voice. Oh. I see, I

0:43:01.800 --> 0:43:03.440
<v Speaker 1>was just about to say, like I could see this

0:43:03.520 --> 0:43:08.600
<v Speaker 1>being a thing that that that hurts anonymity online, something

0:43:08.640 --> 0:43:12.879
<v Speaker 1>that we already know is becoming a precious commodity. Sure, sure,

0:43:12.960 --> 0:43:14.840
<v Speaker 1>but instead of just running it through babel fish and

0:43:15.320 --> 0:43:18.000
<v Speaker 1>into Russian and then Chinese and then back to English.

0:43:18.120 --> 0:43:21.440
<v Speaker 1>Then what you're thinking, like, I think I know what

0:43:21.480 --> 0:43:25.520
<v Speaker 1>you're saying. So this is really interesting. How does this

0:43:25.560 --> 0:43:29.560
<v Speaker 1>all relate to our Ai storyteller? Well, yeah, this idea

0:43:29.719 --> 0:43:32.120
<v Speaker 1>of if if we can work backwards, then can we

0:43:32.160 --> 0:43:35.440
<v Speaker 1>work backwards to say, create the Simari in the way

0:43:35.440 --> 0:43:38.319
<v Speaker 1>that Tolkien would have written it exactly? So you can

0:43:38.440 --> 0:43:43.120
<v Speaker 1>look at the rules that generate an author's voice. And

0:43:43.520 --> 0:43:47.279
<v Speaker 1>if you can derive rules enough to identify an anonymous work,

0:43:48.120 --> 0:43:53.920
<v Speaker 1>you can potentially derive rules well enough to generate new work. Right, Um,

0:43:53.960 --> 0:43:56.200
<v Speaker 1>but you know can can we can we generate a

0:43:56.400 --> 0:43:59.840
<v Speaker 1>story the way that an author would have created a story,

0:44:00.080 --> 0:44:03.439
<v Speaker 1>Because that's that's emergent behavior right there. I mean, that's

0:44:03.480 --> 0:44:06.200
<v Speaker 1>that's something more than the some of its parts. Again,

0:44:06.400 --> 0:44:08.800
<v Speaker 1>it's it's one of those things where I'm I'm pulled

0:44:08.800 --> 0:44:11.680
<v Speaker 1>in both directions. On one hand, I can see how

0:44:11.719 --> 0:44:16.959
<v Speaker 1>this would work. On the other hand, it seems so impossible. Also,

0:44:17.000 --> 0:44:20.680
<v Speaker 1>I mean, I I edit novels freelance, and like I said,

0:44:20.680 --> 0:44:23.320
<v Speaker 1>I was a writing major in college, and the idea

0:44:23.400 --> 0:44:27.520
<v Speaker 1>of a computer writing a story is personally offensive to me.

0:44:27.640 --> 0:44:32.000
<v Speaker 1>Like coming coming into this podcast, I was angry about

0:44:32.040 --> 0:44:35.480
<v Speaker 1>the very thought. Like I was, I was totally ready

0:44:35.520 --> 0:44:39.160
<v Speaker 1>to kill that computer before it took my job. But

0:44:40.040 --> 0:44:42.200
<v Speaker 1>and but but but but doing research for it, I

0:44:42.200 --> 0:44:45.600
<v Speaker 1>found something that Ray Kurtzwil wrote that that kind of

0:44:45.640 --> 0:44:48.160
<v Speaker 1>struck me. And uh. He was talking about Harold Cohen,

0:44:48.200 --> 0:44:51.280
<v Speaker 1>who was a computer programmer and artist who tat a

0:44:51.320 --> 0:44:54.799
<v Speaker 1>program a thousand rules for drawing and and had it

0:44:54.960 --> 0:44:57.840
<v Speaker 1>draw some stuff and kurts while asked, you know, like, Okay,

0:44:57.880 --> 0:45:01.160
<v Speaker 1>so who's the artist? Uh. Cohen claims that he is,

0:45:01.239 --> 0:45:03.640
<v Speaker 1>and that his computer has not been programmed to complain.

0:45:04.200 --> 0:45:06.479
<v Speaker 1>And I just thought that that was a very neat

0:45:06.480 --> 0:45:08.800
<v Speaker 1>way of looking at it, you know, looking at creating

0:45:08.800 --> 0:45:11.319
<v Speaker 1>a program that can itself be regarded as a work

0:45:11.320 --> 0:45:15.479
<v Speaker 1>of art, and that what it creates being something new

0:45:15.640 --> 0:45:19.480
<v Speaker 1>and not necessarily you know. Yeah, I think, uh, Well,

0:45:19.480 --> 0:45:22.840
<v Speaker 1>first of all, I think the idea of recreating work

0:45:22.960 --> 0:45:26.840
<v Speaker 1>in the voice of another author and existing author is

0:45:26.960 --> 0:45:30.760
<v Speaker 1>very interesting. I would be eager to see the first,

0:45:31.160 --> 0:45:35.520
<v Speaker 1>what we would deem truly successful example of that. Uh.

0:45:35.560 --> 0:45:38.160
<v Speaker 1>I think if it's possible at all, it's a long

0:45:38.160 --> 0:45:41.160
<v Speaker 1>way off. Again, I think it's probably possible, But I

0:45:41.200 --> 0:45:43.160
<v Speaker 1>do agree this a long way off. I think actually,

0:45:43.160 --> 0:45:50.279
<v Speaker 1>I think that creating something I think I think that

0:45:50.320 --> 0:45:53.680
<v Speaker 1>the I think actually creating something in a particular person's

0:45:53.760 --> 0:45:57.640
<v Speaker 1>voice is in some ways easier because you are limiting

0:45:57.960 --> 0:46:01.560
<v Speaker 1>the uh, the choices that your computer program can make.

0:46:02.040 --> 0:46:05.799
<v Speaker 1>They cannot choose something that would go outside of that. Uh. Now,

0:46:05.840 --> 0:46:08.760
<v Speaker 1>with some authors, with some people who have created stuff

0:46:08.800 --> 0:46:12.279
<v Speaker 1>that's particularly tricky for example, again I go back to Shakespeare.

0:46:12.480 --> 0:46:14.920
<v Speaker 1>And the reason I do is because not that Shakespeare

0:46:14.960 --> 0:46:17.719
<v Speaker 1>was creating brand new plots. We all know Shakespeare took

0:46:17.719 --> 0:46:20.440
<v Speaker 1>almost every single one of his plots either from history

0:46:20.520 --> 0:46:23.920
<v Speaker 1>and then he revised it heavily, or he took it

0:46:24.000 --> 0:46:26.840
<v Speaker 1>from pre existing work but then put it into his

0:46:26.880 --> 0:46:31.000
<v Speaker 1>own voice, with his own motivations and characters. Um, so

0:46:31.080 --> 0:46:33.680
<v Speaker 1>that's not you know, that wouldn't surprise me to be

0:46:33.719 --> 0:46:36.600
<v Speaker 1>able to create another Shakespearean style just based upon the

0:46:36.640 --> 0:46:40.000
<v Speaker 1>previous existing plot. The problem is that Shakespeare was also

0:46:40.040 --> 0:46:43.200
<v Speaker 1>known for creating words. He created words that we use

0:46:43.239 --> 0:46:46.319
<v Speaker 1>in our vocabulary. He created phrases and metaphors that are

0:46:46.360 --> 0:46:52.480
<v Speaker 1>now part of our common vocab in in English speaking nations,

0:46:53.040 --> 0:46:55.880
<v Speaker 1>and UH to be able to recreate that would be

0:46:55.920 --> 0:46:59.160
<v Speaker 1>particularly difficult. So that it would feel natural that you

0:46:59.200 --> 0:47:03.440
<v Speaker 1>would create new metaphors and new UH phrases that would

0:47:03.440 --> 0:47:05.800
<v Speaker 1>become something that people you could see people quoting. I

0:47:05.840 --> 0:47:07.800
<v Speaker 1>mean people are going to quote it. It's a play.

0:47:08.040 --> 0:47:10.800
<v Speaker 1>You gotta quote it if you're doing it as a play.

0:47:10.880 --> 0:47:16.279
<v Speaker 1>So that part is it's hard for me to imagine.

0:47:16.840 --> 0:47:20.880
<v Speaker 1>But I still would be reluctant to say it's impossible. Right,

0:47:20.920 --> 0:47:24.440
<v Speaker 1>So your question is basically, just can a work that

0:47:24.600 --> 0:47:28.320
<v Speaker 1>is created based on a set of rules have meaning?

0:47:28.800 --> 0:47:33.319
<v Speaker 1>Can it have meaning? And can it be true? And

0:47:33.360 --> 0:47:35.520
<v Speaker 1>you know, and I think you know. Meaning is one

0:47:35.560 --> 0:47:38.719
<v Speaker 1>of those things that is found within the reader. You

0:47:38.760 --> 0:47:41.080
<v Speaker 1>get to readers to read the same piece of work,

0:47:41.320 --> 0:47:43.239
<v Speaker 1>and it doesn't matter if that piece of work is

0:47:43.239 --> 0:47:46.359
<v Speaker 1>regarded as the best literature ever written or if it's

0:47:46.440 --> 0:47:50.000
<v Speaker 1>just you know, pulp fiction or anything in between. Some

0:47:50.200 --> 0:47:52.080
<v Speaker 1>one reader might get a lot of it and another

0:47:52.080 --> 0:47:53.920
<v Speaker 1>reader might get nothing out of it, or they might

0:47:53.960 --> 0:47:57.440
<v Speaker 1>get too totally different things, both of value out of it. Right. Um,

0:47:57.960 --> 0:48:00.919
<v Speaker 1>maybe I'm just a sucker, but I felt a little

0:48:00.960 --> 0:48:04.440
<v Speaker 1>bit of meaning in the computer's poem about love. I

0:48:04.640 --> 0:48:14.000
<v Speaker 1>sucker that it wasn't poetry, it didn't even rhyme um,

0:48:14.400 --> 0:48:18.560
<v Speaker 1>But no, I I you know. The The interesting thing

0:48:18.640 --> 0:48:22.280
<v Speaker 1>here again goes back to the touring test. Right, maybe

0:48:22.320 --> 0:48:26.200
<v Speaker 1>the stuff that's produced has meaning, Maybe the machine never

0:48:26.280 --> 0:48:30.359
<v Speaker 1>realizes that there's any meaning. And touring would say, what's

0:48:30.400 --> 0:48:33.239
<v Speaker 1>the problem because we just assume that the person who

0:48:33.320 --> 0:48:36.960
<v Speaker 1>is creating something is seeing meaning there and we cannot

0:48:36.960 --> 0:48:40.000
<v Speaker 1>be sure that that's the case. Great examples there when

0:48:40.040 --> 0:48:44.080
<v Speaker 1>you have literature classes that say, like, the teacher is

0:48:44.120 --> 0:48:46.200
<v Speaker 1>going on and on about how a certain passage in

0:48:46.200 --> 0:48:49.160
<v Speaker 1>a book actually represents this one particular thing, and then

0:48:49.160 --> 0:48:51.520
<v Speaker 1>you talk to the author and like, uh no, that

0:48:51.600 --> 0:48:55.040
<v Speaker 1>part is about a guy having breakfast, which is pretty

0:48:55.120 --> 0:48:57.720
<v Speaker 1>much what I wrote. Actually, this is not a problem

0:48:57.760 --> 0:49:00.320
<v Speaker 1>for me to imagine at all. We are so eager

0:49:00.360 --> 0:49:04.080
<v Speaker 1>to find meaning, right and personifying machines to begin with.

0:49:04.520 --> 0:49:08.200
<v Speaker 1>We find meanings in things that weren't designed by anybody

0:49:08.280 --> 0:49:10.799
<v Speaker 1>at all. I mean, we find meaning in like if

0:49:10.840 --> 0:49:14.359
<v Speaker 1>I walk outside and I'm feeling a certain way about

0:49:14.440 --> 0:49:16.680
<v Speaker 1>I don't know about a relationship I'm in, or my

0:49:16.800 --> 0:49:19.120
<v Speaker 1>job or something, and I see a hawk swoop down

0:49:19.200 --> 0:49:22.080
<v Speaker 1>and grab a mouse. I mean the hawk isn't trying

0:49:22.120 --> 0:49:27.640
<v Speaker 1>to create meaning or entertainment, right, but I will interpret.

0:49:27.719 --> 0:49:30.160
<v Speaker 1>I mean that will become meaningful to me. I'll see

0:49:30.280 --> 0:49:33.319
<v Speaker 1>meaning in this utterly random event. Well, sure, I mean

0:49:33.440 --> 0:49:37.800
<v Speaker 1>people also will create patterns, whether or see things within patterns,

0:49:37.840 --> 0:49:41.080
<v Speaker 1>whether there was no actual thing. They're so looking up

0:49:41.120 --> 0:49:43.719
<v Speaker 1>at the clouds and saying, oh, look, it's very like

0:49:43.760 --> 0:49:46.560
<v Speaker 1>a whale. So it's a Shakespeare reference for you. I

0:49:46.600 --> 0:49:50.320
<v Speaker 1>don't think a person had to necessarily choose a word

0:49:50.480 --> 0:49:52.279
<v Speaker 1>to go in a certain place and a sentence for

0:49:52.320 --> 0:49:55.919
<v Speaker 1>that word to have a meaningful effect on me. Sure, yeah,

0:49:56.000 --> 0:49:58.359
<v Speaker 1>I mean it'll be interesting to see if this, if

0:49:58.400 --> 0:50:02.400
<v Speaker 1>this ever becomes a reality, I assume it will. If

0:50:02.440 --> 0:50:05.359
<v Speaker 1>it is possible it will happen that that we can

0:50:05.400 --> 0:50:08.160
<v Speaker 1>go ahead and just say, because I think it's one

0:50:08.200 --> 0:50:09.719
<v Speaker 1>of those things you can say about the future, is

0:50:09.719 --> 0:50:13.360
<v Speaker 1>that if something is possible, someone will do it. So

0:50:13.400 --> 0:50:16.240
<v Speaker 1>it's just a question curiosity. So it's just a question

0:50:16.280 --> 0:50:19.440
<v Speaker 1>of when, assuming that it is possible, I'm curious to

0:50:19.440 --> 0:50:21.799
<v Speaker 1>read it. Uh, it may I may feel that it

0:50:21.920 --> 0:50:24.600
<v Speaker 1>is a much better novel than and let's see, I'm

0:50:24.640 --> 0:50:28.000
<v Speaker 1>just gonna pick one of the novels I hated reading

0:50:28.000 --> 0:50:31.200
<v Speaker 1>in school, and it's a toss up between Tests of

0:50:31.239 --> 0:50:33.920
<v Speaker 1>the Revels and Ethan Frome. So take whatever you like.

0:50:34.600 --> 0:50:36.719
<v Speaker 1>But anyway, I might read it and think it's a

0:50:36.880 --> 0:50:39.759
<v Speaker 1>novel ten times better than either of those. Well, I'm

0:50:39.800 --> 0:50:44.080
<v Speaker 1>going to guess that I'm I might get some convincingly

0:50:44.680 --> 0:50:49.320
<v Speaker 1>dickinson Ian No, no no, no, not Charles Dickenson. Dickinson. I

0:50:49.360 --> 0:50:52.759
<v Speaker 1>was saying, Emily Dickinson Dickinson. That's why I'm sooner to

0:50:52.840 --> 0:50:56.680
<v Speaker 1>get some convincingly Dickinsonian poetry than you are to get

0:50:56.760 --> 0:51:01.080
<v Speaker 1>some convincing prose narrative. I I think you're I think

0:51:01.120 --> 0:51:03.799
<v Speaker 1>you're on target there. I would imagine that we will

0:51:03.840 --> 0:51:07.120
<v Speaker 1>see this develop over time in various fields, and the

0:51:07.120 --> 0:51:09.560
<v Speaker 1>ones that will be conquered first will be the ones

0:51:09.600 --> 0:51:12.239
<v Speaker 1>that require the least narration. The ones that will be

0:51:12.239 --> 0:51:17.480
<v Speaker 1>conquered last will be really, really good poetry. Because obviously,

0:51:17.760 --> 0:51:19.600
<v Speaker 1>one of the rules here is that the longer the

0:51:19.680 --> 0:51:22.160
<v Speaker 1>form is for a human anyway, the longer the form

0:51:22.239 --> 0:51:25.680
<v Speaker 1>is in general, the easier it is to write. Not

0:51:25.840 --> 0:51:27.759
<v Speaker 1>that it's going to be easy to write a good one,

0:51:28.000 --> 0:51:30.120
<v Speaker 1>but writing a good novel is easier than writing a

0:51:30.120 --> 0:51:32.120
<v Speaker 1>good short story because you have to make every word count.

0:51:32.239 --> 0:51:34.520
<v Speaker 1>Writing a good short story is way easier than writing

0:51:34.520 --> 0:51:37.080
<v Speaker 1>a really good poem because you have even fewer words

0:51:37.120 --> 0:51:39.719
<v Speaker 1>to work with. Here's the thing, too, though, if we're

0:51:39.719 --> 0:51:43.920
<v Speaker 1>considering creating stuff by generating rules in a dead author's voice,

0:51:44.000 --> 0:51:46.360
<v Speaker 1>how much work they have available is going to be

0:51:46.400 --> 0:51:49.840
<v Speaker 1>a big deal. Here, somebody who's got, you know, seventy

0:51:49.920 --> 0:51:52.680
<v Speaker 1>novels that we can feed in to generate rules from

0:51:52.760 --> 0:51:56.480
<v Speaker 1>is going to create a more robust AI voice than

0:51:56.680 --> 0:51:59.880
<v Speaker 1>somebody who published one novel and that's it. Sure, yeah,

0:52:00.400 --> 0:52:03.800
<v Speaker 1>that's very true. So, uh you know, maybe we'll eventually

0:52:03.800 --> 0:52:07.000
<v Speaker 1>get to a point where we have these computers just

0:52:07.040 --> 0:52:09.000
<v Speaker 1>generating things in their own voices, in which case it

0:52:09.040 --> 0:52:12.200
<v Speaker 1>will all be zeros and ones be a fascinating read

0:52:13.360 --> 0:52:17.319
<v Speaker 1>binary the novel. All right, So, uh no, this is

0:52:17.320 --> 0:52:20.160
<v Speaker 1>a fun discussion. I am curious to hear what our

0:52:20.200 --> 0:52:22.839
<v Speaker 1>listeners have to say about this. You should go to

0:52:22.920 --> 0:52:25.360
<v Speaker 1>our website FW thinking dot com. That's where we have

0:52:25.440 --> 0:52:29.520
<v Speaker 1>all the blog post, podcast videos, articles all relating to

0:52:29.600 --> 0:52:32.719
<v Speaker 1>these topics are right there. Um find the one for

0:52:32.760 --> 0:52:36.200
<v Speaker 1>this podcast, the entry we have for the for the episode,

0:52:36.239 --> 0:52:38.520
<v Speaker 1>and let us know what you think about the idea

0:52:38.640 --> 0:52:43.319
<v Speaker 1>of computer generated fiction, and tell us if you think

0:52:43.360 --> 0:52:46.239
<v Speaker 1>that's ever gonna be a reality or what do you

0:52:46.280 --> 0:52:49.920
<v Speaker 1>think the first computer generated book will be about? Now?

0:52:50.000 --> 0:52:52.319
<v Speaker 1>Will it be The Mouse who Loved Me? Maybe it's

0:52:52.440 --> 0:52:56.640
<v Speaker 1>I have no mouse and I'm a screen who knows apologies.

0:52:57.040 --> 0:52:59.680
<v Speaker 1>All right, so let us know and we will talk

0:52:59.719 --> 0:53:06.800
<v Speaker 1>to you again really soon for more on this topic

0:53:06.840 --> 0:53:20.640
<v Speaker 1>and the future of technology. Visit forward thinking dot Com,

0:53:20.760 --> 0:53:23.600
<v Speaker 1>brought to you by Toyota Let's Go Places