0:15

Pushkin.

0:24

A few weeks ago, I went to Chicago to interview

0:26

two people on stage about

0:28

creative work they've done using artificial

0:31

intelligence. One of the people

0:33

was Stephen marsh He's a writer. He's

0:36

done nonfiction books, novels, magazine

0:38

articles, and earlier this year,

0:41

he used AI to help him write a

0:43

short novel called Death of an Author. That

0:46

book, by the way, was published in audio

0:48

form by Pushkin Industries, the same company

0:51

that publishes this podcast. The

0:53

other person on stage with us was Lucas

0:56

Contor. Lucas is a composer.

0:59

Among other things, He's won a couple Emmys

1:01

for his work scoring.

1:02

The Olympics for NBC.

1:04

He co produced a Lord song that was

1:06

in one of the Hunger Games movies. And

1:09

the reason he was there talking with us Lucas

1:12

used AI to help him finish Schubert's

1:14

unfinished symphony. It was

1:16

a really interesting conversation and I

1:18

thought it would make a great episode of What's Your Problem?

1:21

So here it is now,

1:23

please join me and welcome our panel.

1:31

So let's do a

1:33

thing you're never supposed to do in narrative.

1:36

Let's answer the question right

1:38

at the beginning. So,

1:41

uh, the sort of headline question

1:44

for this panel is will

1:46

AI kill creativity?

1:49

I want to ask both of you. I

1:51

want you to answer in one word at

1:53

the same time, on the count of three.

1:56

It's gonna be it's not one two three go, You're gonna

1:58

go on what it is? One two three

2:00

go?

2:02

Yeah?

2:02

Okay, will AI

2:04

kill creativity? One two three

2:07

No? No, great done,

2:10

let's go. Yeah, thank you, Thank you very

2:12

much.

2:15

So I'm delighted to be here with

2:17

both of you, in particular

2:20

because you have made things with

2:22

AI. Right there have been countless

2:24

panels of people sort of waving their hands.

2:26

About the theory of AI or the

2:28

future of AI.

2:30

But I love that we're here talking

2:32

about things that you have made, creative

2:35

work that you've made. And so what I want to do

2:37

is, I want to start by talking a

2:39

little bit about process. I love talking

2:42

about how people make creative things.

2:45

And we'll just do that in order, frankly, just because

2:47

I want to get into first of the book and then

2:50

the symphony, and then we can talk more

2:52

generally about AI and creativity and

2:55

humanity, and then we can wave our hands in that classic

2:57

can'd wave away. So Stephen,

2:59

let's start with you. I

3:02

want to read an excerpt from your book,

3:05

in part because this book that

3:07

was written with Ai has a very

3:09

particular, I don't know

3:11

quality to the pros. There's a really

3:14

interesting feel to the pros, and I

3:16

don't know if you'll quite get it from a paragraph,

3:18

but I want to give you something to hold

3:20

on to as we're talking about the book.

3:22

So I

3:24

think I have this right.

3:25

This passage I'm going to read, it's in the first person,

3:27

and it's actually in the book, spoken by

3:30

a digital avatar, an AI

3:32

avatar in the book, who is an avatar

3:35

of a dead author whose

3:38

death is the title of the book. So

3:40

the passage in her voice goes like this,

3:45

I learned the limits of machines when they wanted

3:47

me to fly bombers. They were going

3:49

to force me to push a button that would end

3:51

the world.

3:53

I hope you can.

3:53

Understand that my stance as a pacifist

3:56

wasn't cowardice or principle, but

3:58

a confession. I could never

4:01

bring myself to press that button.

4:03

Human beings cannot stop making buttons,

4:06

and once we've made them, we can't

4:08

stop pushing them.

4:10

Pretty good for a machine, it really pretty good for

4:12

a machine.

4:13

Yeah, I'm gonna read that last sentence again because

4:15

I like it, and because it comes up a couple times in the

4:17

book. Human beings cannot stop

4:19

making buttons, and once we've

4:22

made them, we can't stop pushing

4:24

them. So maybe Stephen,

4:27

we should actually start with that sentence.

4:29

Right.

4:29

It's a great sentence, I think, I or really

4:32

interesting sentence. Sounds like a sentence

4:34

a human being would write. It ends up being important

4:36

in the book sort of thematically. How

4:38

did the machine write

4:41

that sentence?

4:42

Okay, let me see if I can get it exactly

4:44

right. So that

4:47

was the first person from the

4:49

death of an author. So Jacob came to

4:51

me in February and said, we need to release this

4:53

thing.

4:53

This is Jacob Weisberg, actually the

4:56

person who runs Yeah, pushed It. Let's actually

4:58

start at the beginning of the sure and then we'll get to that sentence.

5:00

So right, So Jacob

5:03

Weisberg, who runs Pushkin, which is the company

5:05

where I make a podcast, came to you in February.

5:08

And said, can you write a book

5:10

that's AI that's generated by AI?

5:12

In fact, he said, can you create an AI author and

5:15

then have that author create a book. Now I'd

5:17

been working on this for a while i'd

5:19

been working. I'd wroteen my first you

5:21

know, algorithmically generated story for Wired

5:24

in twenty seventeen, which was before the Transformer,

5:27

so the Dark Ages of AI really,

5:31

and so I said, yes, I can definitely do that.

5:34

It'll be about ninety five percent computer

5:36

generated. I don't want to if I want to change

5:39

heat to the character's name or something like

5:41

that, I want to be able to do that without forcing

5:43

all these iterations and so on. And

5:46

basically I used GBT

5:49

four and I

5:51

would use it to generate texts.

5:54

I knew from having done AI

5:57

AI text before that A is very

5:59

poor at generating plots, okay, and

6:01

it's very poor at certain other tasks.

6:03

It's incredibly good at style, okay.

6:05

Right, So I would, you

6:07

know, have very clear ideas of where the narrative

6:09

what's going. I'd give very specific grammatical

6:11

and syntactical commands write

6:14

a paragraph

6:16

with high variability, like very

6:18

very specific commands like wait, do the whole.

6:21

Give me an example in its entirety

6:23

of a command.

6:24

It would be almost impossible to do because it's exactly like

6:26

do it when you've seen them for visual

6:28

stuff, where it's like they'll just to get really

6:31

interesting AI generated pictures. You often

6:33

have like one hundred different references.

6:36

Like it almost impossible,

6:38

but just give me something.

6:39

Give me something.

6:42

Write a hard boiled detective

6:44

story paragraph

6:47

with a variability between short and long

6:49

sentences and

6:53

clear, elegant syntax,

6:56

containing the following information, and then you write

6:59

out information it would generate that.

7:01

Then you would take that and I would put it into a program

7:04

called pseudo.

7:04

Right, and wait just before we go to the next program,

7:07

when you say containing the following information, like

7:10

that.

7:11

One would be it would be like in this one.

7:13

The author says, well,

7:15

that would be slightly different because with characters,

7:17

I would use a whole different set of commands.

7:20

So you know the author and here

7:22

was basically a combination of Margaret

7:24

Outwood and my dead father. Because I was writing

7:27

this thing fast, so I needed to know something that

7:29

I needed to have a character that I would automatically

7:31

be interested in, and.

7:32

I should say, you're a Canadian, like

7:35

basically the next closest thing after your.

7:38

If women were alive for you, yes,

7:40

right, and so uh, and

7:43

so that I would say write something

7:45

like Sylvia Plath meets Philip

7:48

Roth and meets a bunch of different other things

7:50

and get hurt it.

7:51

So you're doing a very specific character. And

7:53

then do you do all of the sort of exposition

7:56

or plot points, like what what in terms of substance,

7:58

what is an example of what you might put?

8:00

Well, I would that would probably actually be mostly

8:03

the machine, but for plot details would be like she

8:05

walks to a bridge.

8:07

And but this this paragraph about like the

8:10

you know, the buttons. I wouldn't

8:12

press the button, and it's like, how do you It would

8:14

be something like you

8:16

know it to be something like the character.

8:19

Reminisces about her times as a

8:22

UH and and expounds

8:24

philosophically on the

8:27

difference between AI and being a

8:30

fighter pilot.

8:31

And or the character expounds on being a pacifist

8:34

in the military. Exactly okay, right, and

8:36

so sometimes more, sometimes

8:38

less. Tried to get as little as possible,

8:41

but you know you want specificity here,

8:43

like you're the more precise the command,

8:45

the better information, the

8:47

precise the command, the more it's just you writing

8:49

it with the weird kind of intermediation is.

8:52

My creation, right, this is a tool

8:54

which you will I will say the same thing.

8:57

So just the same as if like this is the thing people

8:59

don't understand, right, it's like, of

9:02

course this is a creative act. It's just a different creative

9:04

act, right, Like it's this is one hundred

9:06

percent me. It's just I didn't

9:08

write the words right

9:10

like like like so that's like

9:13

like that's weird, Like I am, yeah,

9:15

it's very weird, like I am.

9:16

Don't you didn't write the words that ended up in the

9:18

book word you weren't

9:21

the words that were the instructions to the machine

9:23

to write the words that.

9:23

Well, so good as any computer that's true, any

9:26

computer program.

9:26

So so okay, so I want to get back to the specific

9:29

sort of process narratives. So you put this very

9:31

specific prompt into GPD

9:34

four, which is basically chat GPT.

9:36

I would say, it's actually better fine,

9:38

and chat EPT four is now it was

9:40

better than what chatchat is now fine

9:42

for creative stuff.

9:44

Uh. Then you get some output,

9:46

you get the paragraph for it, and

9:48

then what and then.

9:49

It usually it's very bad, right, And

9:51

then you take that and you put it in a program called pseudo

9:54

right. Okay, and pseudo right is a stochastic

9:56

writing instrument. So you could you then select

9:58

the text and you say shorten lengthen

10:01

you say and then it has another button, which is a customized

10:04

feature, which is make it sound

10:06

like X. So, make

10:08

it sound like Ernest Hemingway, make

10:11

it sound like f Scott Fitzgerald, and

10:13

and and you know, the of course,

10:15

the thing I figured out very quickly is that if you

10:17

want something to sound like Margaret out With, the very

10:20

last thing you should do is put in make

10:22

it sound like markered out.

10:23

That's not enough course to me.

10:25

Well, of course, because markered

10:27

Outwood is in trying to sound like Margaret Out would she's trying

10:29

to sound like Sylvia Plathmas Philip

10:32

Roth meets, it meets

10:34

a bunch of other things.

10:35

Right, then you ultimately always

10:37

get back.

10:38

Yeah, And so that when you the way you get interesting

10:40

things in this text is by essentially folding

10:43

these layers of style onto

10:45

each other.

10:46

Now I also use and then so

10:48

pseudo right has some output. Yeah,

10:50

and then is that output what we're reading in the

10:52

book?

10:52

Correct?

10:54

Or you know, if I don't like it, I

10:56

just try again, just refresh,

10:58

refresh, refresh until I guess something that I like.

11:00

And so so this is very much a creative

11:02

act.

11:03

And you're doing that basically a paragraph

11:05

at a time.

11:06

Yeah, Well, with dialogue, it would

11:08

go like die would be a lot longer, right,

11:11

like, because you want flow and

11:13

you want so I could do up

11:15

to maybe five hundred words of dialogue

11:17

at a time. Uh huh, So that would have been part of

11:19

a much longer series of instructions.

11:21

So this sentence human beings cannot

11:23

stop making buttons, and once we've made

11:25

them, we can't stop pushing them. A nice sentence,

11:28

you know, big idea. I certainly didn't think

11:30

of that.

11:30

You didn't. It just came out of some refreshment,

11:33

yeah, fresh, and.

11:34

It was in some I mean, obviously

11:36

I made it, and I authorized

11:38

it too. You know, I've compared it in the

11:40

Atlantic to doing hip hop

11:42

in the sense that you're you're folding

11:44

things on top of each other, right,

11:47

You're folding styles and

11:49

metrics and effects on top

11:51

of each other until you get

11:53

something new and weird.

11:55

Right.

11:56

And I would say about twenty

11:58

times during the course of writing it, I felt

12:00

like I was, you know, putting

12:02

my hand up against something new

12:05

and weird.

12:05

That's fun, right, like something.

12:08

But you know this is for most

12:10

of the process, it's just a writing tool,

12:13

right, Like, it writes it for you. You decide

12:15

if it works, right, and you tell

12:17

it's you tell it what to write

12:20

in.

12:20

A very granular way.

12:21

The more granular, just like writing

12:23

normally, the more you know about the

12:26

bigger planning. The more planning you have for

12:28

any essay, the better the essay is going

12:30

to be right. And in this case,

12:32

so you have a plan and then you have the editing

12:34

process, and in between there's this machine.

12:37

But how much of that, how much does

12:39

that matter? Is actually I

12:42

don't know if it's like twenty times it did

12:44

matter where it was like, oh that's not something I would

12:47

have written, but.

12:47

It's very beautiful.

12:48

Yeah, and it's very strange, and

12:50

it's you know, there's a there's

12:52

a Danish journalist who deals with go players who

12:54

play ai go against

12:56

each other, and they say it's like listening to an

12:58

alien make music right, because

13:00

it's like it's not how they would play go, it's not

13:02

how a human could play go, but it's

13:05

obviously makes sense on some level. Similarly,

13:08

that's how I felt like most

13:10

of the time, it's just a writing machine that does what

13:12

I tell it and then I correct it. But

13:14

then maybe twenty times you feel this

13:17

new presence. That's what's

13:19

exciting.

13:22

We'll be back in a minute to hear how Lucas

13:24

Contour used AI to help

13:26

him finish Schubert's unfinished symphony.

13:44

Okay, back to the conversation in Chicago

13:46

with Stephen Marsh and Lucas Contour.

13:49

Lucas's story of using AI

13:51

to finish Schubert's unfinished symphony

13:53

goes back to twenty nineteen. He

13:56

was approached by a Chinese tech company called

13:58

Huawei. They said, we

14:00

want our phone, which runs AI,

14:03

to finish Schubert's on Finnish symphony. And

14:06

they didn't know what that meant. They had a

14:08

tech team in place that was running the

14:10

AI and I knew those people.

14:12

That's why they, I think brought me in. I was told

14:14

that. So my friend, the technologist who

14:16

brought me in on this project, told me that he thought

14:19

that I would be a good fit because I have a

14:21

corporate friendly bio where they could say,

14:23

oh, he can do it. And

14:25

he said, I know they you don't

14:27

have to say that part. You don't have to say that part.

14:29

He said, uh, But he said I. He said

14:32

that you, I know you can command an orchestra,

14:35

but I don't think you'll be precious about the project,

14:37

meaning that I won't be. He didn't think I would

14:39

say like, oh, well, this is heresy. We

14:41

shouldn't take Schubert's perfect work that

14:44

was so perfect that he didn't even finish it and

14:47

do something with it. And

14:49

uh yeah. So I think

14:51

they thought they would just that I would press

14:53

a button on the phone and a symphony would come

14:55

out and somehow a bunch of musicians would

14:57

play.

14:58

So they need you for it. They

15:01

just pushed the button.

15:02

So this is the conversation we had, and eventually

15:04

I had to I was on a call with them and I said, look,

15:07

this is this is not I mean, what you're asking for in

15:09

principle doesn't exist, like you can't And

15:12

I mean, what do you even want the machine to do? Do you want it to generate

15:14

audio for you? Do you want it to generate a score? Do

15:16

you want it to perform the score? So, I mean,

15:18

right off the bat, this was a

15:20

fascinating project because I had to think about

15:23

the very nature of music to even really

15:25

get started. I don't know if that answers the

15:27

question about I think it does.

15:29

I mean, I just wanted you to set yourself

15:31

up, and I think you've done it.

15:32

You want to I think I'm set up, so I'm

15:34

gonna try something new for you today. So on the on

15:37

the prep call for this event, we

15:40

discussed I said something

15:42

that I don't often say out loud, but I realized as a hallmark

15:44

of my presence on stage, is that I like to

15:46

do things that might spectacularly

15:49

fail in the hopes that they will

15:51

be entertaining to an audience. So I'm going to

15:54

do one of them for you.

15:54

Now.

15:55

I'm going to I wrote a little thing about the

15:57

Unfinished Symphony. I'm going to

15:59

explain it while I'm playing some music in the background

16:01

and basically scoring it as

16:04

i'm talking. So you know, wish me

16:06

luck and hopefully it'll be interesting. This

16:12

is how the Unfinished Symphony starts.

16:29

A symphony has four movements,

16:31

but Schubert only wrote two and sketched

16:34

a third of his eighth Symphony, the Unfinished

16:36

Symphony. No one knows why

16:38

he abandoned the Unfinished Symphony, but

16:40

he did, and now it's probably his most

16:43

famous work, along with his greatest hit, Ave

16:45

Maria. Some

16:50

scholars believe that Schubert couldn't find a

16:52

way to fit the Eighth Symphony into the orthodoxy

16:55

of the time. Which forbade three movements

16:57

in a row in triple meter meters

16:59

like three, four and sixty eight.

17:01

But I don't believe this.

17:03

Schubert showed little reverence for orthodoxy

17:05

during his short life, and the AI

17:08

that I used to finish Ubert's on Finnish Symphony

17:10

didn't believe it either. At

17:19

first, we trained the AI on

17:21

recordings of Schubert's entire catalog, then

17:25

prompted it with the first two movements of the unfinished

17:27

symphony. Seems like a reasonable strategy,

17:29

right, This was the result

17:37

sounds like Kat's walking on a piano,

17:42

But this was actually pretty logical.

17:45

Recorded music has almost no mathematically

17:47

discernible patterns to it, so

17:49

from the AI's perspective, the input

17:52

was nonsense, so more nonsense was

17:54

a logical output. Music

18:04

as an abstraction is math, but

18:06

music in practice is convention. Music

18:10

is understood by groups of humans, and like

18:12

any language, music doesn't have objective

18:14

meaning. Music is emotionally

18:17

inert left

18:22

myself. A water break is symphony. A

18:25

symphony is like a skyscraper. It's

18:27

enormous, but every inch of it is designed

18:29

in meticulous detail. It's

18:31

beautiful on the outside, but the inside

18:34

is filled with utilitarian solutions to simple

18:36

problems. A

18:38

skyscraper has electrical

18:40

columns to distribute power throughout the building,

18:43

It has plumbing, it has elevators, but

18:45

you don't see any of this essential detail when you

18:47

admire the building from outside. A

18:50

symphony is like a skyscraper, but

18:53

a recording of a symphony is

18:55

like a skyscraper's facade.

19:03

There is no way to tell from photos

19:05

of even a million facades that skyscrapers

19:08

should have electricity, bathrooms and a

19:10

way for humans to move from one floor to another.

19:13

Similarly, there is no way to tell

19:15

from the morass of frequencies that is a piece of

19:17

recorded music which frequencies are the most

19:19

important.

19:30

There we go.

19:33

So analyzing recorded

19:35

music got us nowhere, and

19:38

I thought that the best way to proceed

19:41

was to simplify the task and

19:43

just train the AI on

19:46

the blueprints of music rather than a finished

19:48

building. So train the AI on a blueprint

19:50

rather than a finished building. So what you just heard, what you're

19:52

hearing now is the main theme from the unfinished

19:55

symphony. Here it is again, just

19:57

really listen and try to listen for the melody.

20:10

And here is that same theme reduced

20:12

to its blueprint. This

20:22

structure, this blueprint in music,

20:25

is just a simple melody. So

20:27

my team and I went to work extracting just

20:29

the melodies from as much of Schubert's music as

20:31

we could get our hands on. These

20:34

are some examples of the melodies we extracted. These

20:39

sound robotic because they are.

20:42

They sound emotionally inert. But

20:44

these are Schubert's melodies reduced to their simplest

20:46

forms, the forms that human

20:48

composition students would use when beginning a study

20:50

of Schubert. Your ear knows how

20:53

to pick a melody out of a dense arrangement, but

20:55

an untrained AI cannot

20:57

do this. The

21:02

reason that, since the results we wanted were simple, we

21:04

needed to train the AI on simple data.

21:08

We trained on hours of these simple melodies

21:10

and then prompted again. We prompted

21:12

it with the unfinished symphony reduced to

21:15

its blueprint, and these were some of the results.

21:23

So this is what it suggested might be

21:25

something that Subert would have written.

21:29

These are simple, but much more musical

21:31

than the cats walking on a piano that came from the audio

21:33

only training data.

21:39

This one, for some reason, caught my attention. Let's

21:41

hear it again. I

21:49

liked it, so I selected it for embellishment.

21:52

I decided to use this. I decided

21:54

to use this blueprint. This

22:05

melody is a bit more modern sounding than

22:07

any of Schubert's work. If

22:09

Schubert lived to old age, these sonorities

22:11

would have been available to him.

22:17

The orthodoxy around triple meters

22:19

and other constraints of form would have given

22:22

way to the exploration of

22:24

the Romantic period. Providing

22:33

simple singable melodies is

22:35

perhaps not how most people would imagine

22:38

that an AI would be useful in writing

22:40

a symphony. But what is a symphony?

22:44

Typically people think about a symphony as something

22:46

that you hear, while the score is just a byproduct

22:49

of the notated sounds.

22:53

But to me, the

22:55

sound is a byproduct, and the symphony

22:58

is something that you see. It's something

23:00

that you read. It's a collection

23:02

of abstract ideas in

23:04

abstract notation. It's

23:07

markings on a page that serve as instructions

23:10

for how to create sounds. A

23:12

symphony itself is a blueprint,

23:15

and those instructions that blueprint

23:18

will be executed differently at every performance.

23:32

Let me just check out this music. It's pretty cool. The

23:37

sounds are a byproduct of the abstractions

23:39

that are expressed in the notation, and that byproduct

23:42

is what the audience experiences as a symphony.

23:46

The byproduct is what you hear.

23:48

I didn't know that I thought about music in this way

23:51

until I had to explain how I think about music

23:53

to a machine. This

23:55

project taught me to

23:57

question the assumptions I make when thinking about

24:00

my own craft. I

24:04

think this is the job of the AI assisted composer

24:06

today to think about

24:08

what we know and to guide our audience

24:11

to rethink what happens inside their own minds.

24:23

I think it's our job to question orthodoxy.

24:27

I think it's our job to use new tools to

24:29

make new art. Today's

24:33

artists are not on the verge of being replaced.

24:36

On the contrary, we are possessed of powers

24:38

so great that we will expose more truth

24:40

about the human mind and the human soul

24:43

than any generation before us. We

24:47

stand on the shoulders of giants. They

24:50

have given us the language, they

24:52

have given us the blueprints, they

24:55

have given us the technology. What

24:58

we build with these tools will be more powerful,

25:00

and more beautiful, and more profound than

25:02

anything we can now imagine.

25:07

Artificial intelligence is nothing like us than

25:09

a prosthetic for the human mind. It

25:16

will enhance art the way writing enhanced memory,

25:18

the way printing enhanced literature, the

25:20

way the steam engine enhanced travel. Artificial

25:24

intelligence is an automobile. We're

25:26

only beginning to emerge from the age of horse and

25:28

buggy. Artificial

25:31

intelligence helped me write the music that you're hearing

25:34

right now. So

25:37

will AI kill creativity?

25:42

No, that's

25:49

really rather Good's that

25:51

more or less worked? I think that's really rather good.

25:54

Thanks.

25:57

We'll be back in a minute to wave

26:00

our hands a little bit about the future of

26:02

AI and creativity.

26:14

That's the end of the ads.

26:15

Now we're going back to the show.

26:17

The reason I knew AI was going to take off

26:20

was when I was writing a piece for The New Yorker about

26:22

GPT three and I

26:25

got it to finish off Coleridge's

26:29

Kubla Khan is great unfinished

26:31

poem, and it did

26:33

it perfectly well. Like I mean,

26:35

if somebody told me, yeah, this is how it ended, I would have been

26:37

like, great, right and so,

26:40

And it did it like that like one second.

26:42

I mean, it was just so incredible to me.

26:43

Just to sort of close this part of the conversation,

26:47

I'm curious. I mean, both of these projects.

26:49

We were very AI forward,

26:51

right, They were like high concept,

26:54

you know, sort of let's explicitly

26:56

wrap this thing in AI.

26:57

Fine.

26:58

Interesting, But presumably

27:01

the real action comes in the things that are

27:03

just what you guys are working on that

27:05

just happens to have AI as a tool, the same

27:07

way say a Google search, which by the way, is

27:09

a kind of AI, is also a tool, right,

27:12

And so I'm curious in your work

27:14

now on other projects that

27:16

are not like, hey, look this was made with AI

27:19

kind of projects. Are you guys using AI? And

27:21

if so, how what do you

27:23

want to go first?

27:24

Yeah? Yeah, first, so

27:26

yeah, obviously of course, like it's in everybody's

27:28

pockets, you use it all the time. And AI

27:31

has done nothing so far

27:33

other than help my career. And I don't mean

27:35

just by doing this, which was fantastic. But when

27:38

I write a piece of music and put it on Spotify, the

27:40

reason you hear it is because an AI recommended

27:43

it to you. You know, that's the only reason you're going to

27:45

find it. And so and these types

27:47

of algorithms that are generating that are keeping

27:49

people out on apps longer and keeping

27:51

people on Netflix and on Spotify longer,

27:54

are putting money not enough money, and

27:56

that's another panel discussion, but putting

27:58

money in our pockets directly?

28:00

Let me let me ask a more precise

28:02

version of the question in response to that clever

28:05

answer.

28:05

Do you use generative AI?

28:08

Yes?

28:09

And also this is a terminology

28:11

problem.

28:12

But you know what do you

28:14

use music?

28:15

Do you use AI to generate musical

28:17

ideas for you?

28:19

Yes?

28:19

But also like what is a musical idea?

28:21

I use a parametric eque that I mean they

28:23

were using a they were using this was there

28:25

was probably good. I'm

28:27

trying, well the answer the answer is yes.

28:30

I know what you're saying. But I feel like you know

28:32

what I'm saying.

28:33

Well, yes, I'm The reason I'm trying to drill

28:35

down here is because this there tell

28:37

me how to ask the question I want to asking

28:39

doesn't have the answer that you want?

28:42

Right, So fair, what's the what?

28:45

What's the smarter version of the question?

28:47

I'm not well enough equipped to ask.

28:50

I don't know if I can.

28:51

I don't know if I can help you with that.

28:53

I don't

28:58

let.

28:58

Me ask the question to you.

29:00

Thank you for your Stephen.

29:02

Do you you use generative AI when

29:04

you're writing with other things?

29:06

Okay, here's the thing, and I think this is sort of

29:08

where we're going. Like I would when

29:10

I write something for a magazine or newspaper

29:12

or novel that I'm working on, I would never use chatchipt.

29:15

Even to get an idea because here

29:17

or whatever they because I'm

29:19

so much smarter than chat GPT.

29:22

Right, And I'm like when you

29:24

and what you have to also have to understand is chatchypt.

29:26

The reason it's so successful is exactly that it has

29:28

been banalified, like when

29:30

you use other generative ais that

29:32

we have access to, because you realize

29:35

that like these are the ones

29:37

that the public uses are very poor

29:39

creatively, like they're actually.

29:41

But you have access to the good ones, to

29:43

the good stuff.

29:43

Here's the thing you can't get on when when you use

29:46

the good stuff.

29:46

What the good stuff is going to be used

29:49

for stuff that doesn't exist yet.

29:51

What we're seeing here is the birth of a

29:53

new medium, right and what

29:56

and so when it comes to write an essay,

29:59

what people want when they write, when they

30:01

read an essay, is a human being communicating

30:04

their thoughts and feelings, right, they

30:07

don't want like they don't That's why they go

30:09

to it. And a generative AI cannot

30:11

do that generative Like it's

30:13

sort of like asking, like do you use film

30:16

to make theater? Like at

30:18

first, you know, when you when film was invented,

30:20

all they did was cannibalized theater and they were putting

30:22

on weird shows or they were recreating news

30:24

events and things like this. That's where

30:27

we're at right now. This is going to be used for

30:29

new art forms that don't exist,

30:32

and that's that's the exciting stuff.

30:34

And it's also why it's almost impossible to do.

30:36

You mean, like the book that is never done, the book

30:38

where it can or like what like.

30:40

I'm written that I have written a short story

30:42

that is infinite art

30:44

forms?

30:45

Like what do you have in your mind when you say it?

30:47

Well, like, for example, I'm working

30:49

with cohere to recreate the

30:51

Oracle at Delphi. Right there's a

30:53

large amount of information that you can glean from

30:55

that, and there's also pretty interesting historical

30:57

record.

30:58

And so you'll ask it a question and it will

31:00

answer, yes.

31:01

We're try and recreate the experience

31:03

of going to the Oracle at Delphia as closely as we

31:05

can use effects.

31:07

Yeah, it's a perfect use of AI and so oracles.

31:09

This is one of the things that has come up in my research is

31:11

that we use oracles because we're bad

31:13

at doing things randomly. So if

31:16

we're out in the wilderness, we'll just

31:18

go hunt in the same place over and over and over

31:20

again, right, And eventually animals figure

31:22

it out and they say, just don't hang out there.

31:23

And you won't get eaten by the humans.

31:25

And so when we like consult an oracle,

31:27

or roll some dice, or like ask the sacred

31:29

chickens if we should go to war, they're basically giving

31:32

us a random answer.

31:33

That's right. There are randomization engines, see,

31:35

and it's.

31:35

Things of this nature that I think will

31:38

be that I'm excited about

31:40

to use it. We're cannibalizing forms. That's what

31:42

I do writing short stories to It's very interesting.

31:44

But the truth is that what this can be used for

31:47

we don't know yet, and what it's going to be used

31:49

for is some weird and the

31:51

problem is there's absolutely no institutions

31:53

to do it with, right, Like.

31:55

Nobody will buy your oracle of Veli's.

31:58

Supposed to take oracle of Hi.

32:00

My name Stephen, I'd like to recreate the

32:02

oracle at DELFI using generative

32:05

AI. I'm sorry, sir, this

32:07

is a key mark, you

32:09

know what I mean, like like it like it's

32:12

not that's that's not like there's no one to

32:14

go to. So that's that's

32:16

where we're at. To me, Like, I think

32:18

the the the thing that I think is very obvious

32:20

is that when you use generative

32:23

AI, what it is very good at is

32:25

the most stock answer, right.

32:28

And that's why it's so such a

32:30

threat to like the undergraduate essay, right,

32:32

because that there you're basically looking for the

32:34

fulfillment of a stylistic you

32:37

know, set pattern that

32:39

it can do.

32:40

Right.

32:41

But people respond to human

32:43

like there's this weird idea

32:46

that art is something external to our experience

32:48

of it. It isn't. It's just

32:50

we we have we create tools. As

32:53

the moment we find tools, all we're thinking of is

32:55

can we do something weird with it? And I think,

32:57

I mean, one thing that I've really learned doing this is

32:59

that creativity is instructible.

33:03

Like it it doesn't matter what comes

33:05

down technologically, what comes down politically,

33:08

what Like, we are creative

33:11

animals and we have to understand

33:13

that that's just our nature and

33:15

nothing is gonna kill it, nothing, not

33:17

certainly not chat gept.

33:19

Great I can I can sum up the history

33:21

of music from the year sixty

33:24

thousand before present to now with

33:26

one sentence, and maybe you'll agree that this sums up the history

33:28

of art already. It's the search for new sounds.

33:31

Yeah, that's it. That's all there is to

33:33

it. If something exists, nobody cares and

33:35

chat geept I will chat. Chapet doesn't do music.

33:37

But there are many music generative ais, and

33:40

they generate music that, like

33:42

charitably would call insipid. Yeah

33:44

it's fine, like it's music. You would

33:46

recognize it as music, but nobody. You wouldn't listen

33:49

to it. It'll get bad music. It

33:51

won't, so it'll sound better,

33:53

it'll sound better. So this is the but,

33:55

but nobody cares about that. So as soon as like,

33:57

as soon as you can have so Jacob

34:00

for your podcast, as soon as you can have beautiful

34:02

sounding orchestral music like this for free,

34:05

you're gonna want something else because this is

34:07

available and it's everywhere, and so what you're

34:09

gonna what you're gonna want is like the thing where

34:11

like Lucas plays a guitar with a really nice

34:13

sounding reverb. That's gonna be the style and

34:16

you can trace and we have a we have a composer

34:18

in the audience who could, hopefully will agree with me on this, and a

34:20

professor of this kind of thing. But you can

34:22

trace musical styles in media, and it's

34:24

like whatever is ubiquitous just falls out of fashion

34:27

and then that whatever the opposite of it is becomes

34:30

becomes fashionable. So yeah,

34:33

that's my that's my two cents the search for new

34:35

sounds.

34:36

Thanks you guys. This is closure. Yeah.

34:48

My conversation with Lucas Contour

34:50

and Stephen Marsh was organized.

34:52

By Chicago Humanities.

34:56

Today's show was edited by Karen

34:58

Chakerji, produced by Edith Russolo,

35:01

and engineered by Amanda k Wong.

35:04

You can email us.

35:05

At Problem at pushkin dot fm.

35:07

We are always, always, always

35:09

trying to find interesting new guests

35:11

for the show, So if there's somebody who think we should book,

35:13

please let us know. I'm Jacob Goldstein

35:16

and we'll be back next week with another episode

35:18

of What's Your Problem.