Robin Sloan: Chapter 3: How AI Redefines What We Think Is Possible

M-A-C-H-I-N-E-Y-E-S-I-S-T.

It's Machine Yearning from Assist. It's a podcast where we think and dream about the future of AI, the talking internet, and how we're reshaping our culture. This chapter is an excerpt from our longer conversation with Robin Sloan. Robin is a sci-fi writer, social observer, and dedicated AI enthusiast. He's been bootstrapping his way into some fascinating co-writing experiments with the machine. Coming up, Shane and Robin dig into Robin's experiments with using machine learning to write music and how playing Go against the machine taught the game's grandmasters that there were levels of play they had not even considered. In a conversation that was already really fun and thought-provoking, This illustrates the degree to which human-machine collaboration might really be a 1 1 5 reality. So let's get to it.

I also want to talk about, because you're using machine learning for music stuff.

Yeah, a little bit.

Yeah, like, tell me about it.

Where—

when did—

when did the music stuff start? And then how did you— what are you doing with machine learning and music?

Yeah, that, that was one of those funny kind of curveball things that you just can never predict ahead of time. I'm not a musician myself or even super deeply engaged by like music production or anything like that, but I am a writer, a fiction writer, and they do make audiobooks out of basically all novels these days. It's a huge market. It's like the part of publishing that's growing the fastest, and it is actually growing a lot. Really? So what it means is the audiobook producers at the publishers are quite emboldened, and rightly so. And so for both books, they reached out to me and said, "Well, okay, we're getting ready to record this audiobook. Could you write something new to kind of slip into the audiobook?" Or like, "Can you think of anything else that would make this just a little different, a little special?" Because they want to find a way to kind of differentiate it from all the other audiobooks out there. And my new book, a part of the plot actually hinges on this kind of mysterious form of music that I describe in the text. And so it seemed actually, I don't know, obvious that in the audiobook you'd actually get to hear a little bit of this music. Maybe even frustrating if you got through the whole audiobook. I spent a lot of time in this book.

The music from the book?

From the book.

This is Sourdough?

This is Sourdough. Yeah, that's right. And yeah, I actually had the thought because we're talking about the audiobook and I was like, man, if you get to the end of this book and you've heard me describe this mysterious music in like a language that no one could quite understand 10 different times and you never actually get to hear it, you might actually feel a little bit cheated. So, um, I decided it would be a good opportunity to try to apply these tools to something really concrete. Uh, it felt like something I could bite off. It wasn't trying to produce like an album of generative machine learning created music. It was just easy.

That's happening though.

That is, that absolutely. Someone's more than one, I'm sure there's a whole host of people hard at work in their studios trying to be the first person out of the door to, to do that and actually make it popular. No, this was great because it was just these little snippets. It had to be these little bits that we could kind of fit into the text in different places. So it was actually— ended up being a really similar process to the text, except that instead of text, it was audio waveforms. And how do you make it? What are you using?

What tool?

What are you actually doing? So in this case, it was a piece of code. It was, boy, one of those great internet open source stories. There had been a paper describing a system called Sample RNN. And again, the RNN here is for recurrent neural network. So you see, it's all connected. It's all this sort of same underlying core tool. The paper had described these great results with sound. The people who put it out had put out an implementation. I couldn't quite get their implementation to work. Someone else, a like a renegade genius in the UK named Richard Assar, did an implementation of his own, you know, just as as one does. He was like, I am interested in these techniques and I would like to write a version of that in my language of choice. So he writes it, puts it up on GitHub. I find it because I'm looking around for a tool that I can use to do this audiobook thing, get it up and running on my computer, feed in the audio. In this case, it was this sort of Croatian choral singing that I've just loved for a long time and was actually— it's what was in my head when I was writing my descriptions of this fictional music. So there's a kind of a parallel path here. Like, I had used it as my inspiration in a sense for what I put on the page, and then I fed it into this recurrent neural network, and it worked great. I mean, it was actually spooky.

I want to finish with one thing you said earlier, and you talked about the dystopian type future, but you never talked about the other side. What do you think the other side is that is actually a great thing that can happen?

I think we've actually gotten a glimmer of the— not utopian, but really exciting kind of machine learning future with the AlphaGo project.

What is that?

DeepMind wrote a computer program.

They're Google, right?

Based on neural— yeah, it's a wholly owned subsidiary of Alphabet, and they were the first people to write a Go program that could beat the best Go players in the world.

And Go is a game.

Go is a game.

Like chess? Like what is Go?

It's, it's, um, it is like chess in its, I mean, in terms of how seriously some people take it, it's definitely like chess. It is a more ancient game. It's really simple. You, you recognize it. I think it has these black and white stones on a big grid.

It's called checkers, right?

It's like, this is like super, super checkers. Um, it's a hard, it's a really hard game. Um, a lot of strategic depth. Uh, the people who are good at Go are really, really, really, really good at Go. And there had been a lot of debate about how long it would take for a computer program to beat the best Go players. So DeepMind did it. So that's just, you know, interesting and kind of a milestone. But what's heartening, and to me really, really exciting, is actually what happened next. Because these Go players, like the super grandmasters of the world, some of whom got beat by AlphaGo, the program, and others who just kind of watched the matches, their reaction was, "Oh my god, we didn't know you could play Go like that." Like, a few even described it as being somehow analogous to, like, playing Go against an alien. Like, if aliens landed on Earth, they're like, "We are here not to conquer. We want to play your games." And that's amazing that it's not just— it's not actually just a story of, like, clash and conquest and like, "Oh, too bad humans, looks like you're not the top of the food chain anymore." But instead, all these people who spent such— so much time studying this game, loving this game, in fact now realizing there's— there are whole new avenues that they never even contemplated. And in a way, the door was opened by this computer program. Could the same thing happen for music, for writing, for So when you said—

when you said it's not about making writing easier, it's about making it harder. It makes it harder because it actually makes us better.

I mean, that's the hope. And, and you know, it comes to creativity, it's probably less the case that there is better and worse and more just— there's just more. I mean, there's, there's more ways to write, there's more things to learn, more ways to paint, more ways to make music. And so that's— that is the optimistic vision. That just like any other creative tool, these machine learning tools make more things possible and, and people get really good at them.

Wow. Yeah, it's interesting you say that to end this, of like, I never thought about it that way, where what it's actually giving us is a, is a— because it's not a human, it doesn't have the emotion to get mad at, but it gives me another view and of openness that I didn't have before that hopefully makes me more open.

Yeah, I mean, I can report— it's, it's easy to kind of talk in big sweeping generalities and kind of get into pretty heady philosophy about this stuff. I can report from the ground level, working with these tools myself, they've changed the way that I think about my own writing. Wow. They've produced, on the most basic level, they've produced bits of language that I think are really cool and interesting and that are making their way into my writing, you know. My next novel is gonna have little bits of this machine learning generated text in it.

Well, I thank you for coming by today.

Hey, my pleasure. It's fun. Bye.

All right, thanks for listening. Get in touch on Twitter @Assist. DMs are open. We're super interested to hear who you think should appear on the podcast. Please check out the full interview with Robin, subscribe so you never miss an episode, and share this with someone who cares about how we make sense of these changing times. Machine Learning is made by Paul Ciuffo and Michael Alsesser for Limina House. Have a great day!