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The future of computer music

A professor of music and of computer science explains why computers and humans playing together are the future of music.
 A person with headphones using a digital audio workstation on a computer while holding a guitar.
How should we think about the intersection of computers and music? | iStock/Wachiwit

Ge Wang is a professor of music, a computer scientist, and director of the Stanford Laptop Orchestra – an orchestra in which human musicians and computers collaborate to make music.

“I once thought computer music was abstract and inaccessible, but it can be very playful, too,” he says. Humans and computers making music together, it’s the best of both worlds, Wang tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.

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[00:00:00] Ge Wang: Is it still art? If it's not made by a human, um, I don't know, you know, I, at least not today, maybe tomorrow I'll have, I'll feel differently. But what I'm really looking for in anyone working with AI is just, are you being critical, you know, about what you're doing with AI? If you're putting AI, using AI in any way, um, are you being critical? Are you self-examining? Are you asking what do I really want out of this? And does this contribute to a kind of like a world I want to live in? Does this make us as people the kind of people we want to be?

[00:00:41] Russ Altman: This is Stanford Engineering's The Future of Everything, and I'm your host Russ Altman. If you're enjoying the show, or if it's helped you in any way, please consider sharing it with friends. Personal recommendations are one of the best ways to help us spread the word about the podcast. 

[00:00:56] Today, Professor Ge Wang will tell us how AI and computer science can combine with music. You know, you might not expect this, but you can use your iPhone as an instrument because of an app that he wrote. And you can form an orchestra based on laptops. And yet, it's still music. This is The Future of Computer Music.

[00:01:18] Before we get started, A quick reminder to rate and review the show, particularly if you've learned something new or had a good experience with it.

[00:01:32] When we think about music, we think about songs, we think about people singing, we think about people playing instruments, our minds might not always go to the idea of a computer or AI writing music. Yet in this world of ChatGPT and other chatbots and large language models, it is increasingly possible for computers to generate music automatically on their own.

[00:01:56] Well, what do we think about that? What do artists think about that? First of all, there's an issue of copyright. How are we training those systems and are they rewarding the people who wrote the music that's being used to train the AI. And also, what do we think about an AI that generates a song in the style of Mozart, or in the style of Bruce Springsteen, or in the style of Beyonce? There are issues there. 

[00:02:21] Well, Ge Wang is a professor of computer science and music at Stanford University. He's written apps that can play music that sound like a flute. He has written software that allows people to form an orchestra with their laptops. And he thinks a lot about the core values and humanity of music and art and creativity and how they should be reinterpreted and understood in the context of AI.

[00:02:48] Ge, you study music and computers. Which passion came first and how did they wind up merging?

[00:02:56] Ge Wang: I think it started with, uh, actually building just the love of building things. Uh, I remember trying to, I think I fashioned these like bricks out of mud and clay, like when I was like six years old and I told my grandparents with whom I was living that, I've made gold. These are gold bars, and I kept them under the bed, I dry them, and I wrap them carefully in newspaper, and, uh, I feel like that's my, one of my earlier, uh, things that I've, uh, you know, designed. I've designed gold bars, uh, out of mud, um. 

[00:03:27] Russ Altman: Cool. 

[00:03:28] Ge Wang: But then, uh, I think at the age of seven, accordions. My grandparents said, hey, you want to learn the accordion? I'll be like, what's an accordion? They're like, well, we're going to get you one if you want. And, uh, and so they were great like that. They helped me to explore my interests. Uh, but I think somewhere between, I think really music really kicked in around like probably around the age of twelve and thirteen. Uh, when my parents, out of the blue, by this point I had moved from China to, uh, to the United States. Out of the blue, my parents got me an electric guitar, like a used electric guitar. 

[00:04:02] Russ Altman: Woah. 

[00:04:02] Ge Wang: And then, Ge, say, well if you want, we'll get you like weekly lessons at the local music store. And, uh, and I think that was like the perfect, I didn't, I don't remember asking for that. They just gave it to me. And I think probably right around, it was the right age to like, to start playing the electric guitar.

[00:04:20] Russ Altman: So that means music that I'm going to infer that the music came first, it wasn't like, uh, it wasn't perhaps like you were a gamer who then said, I can do this with music. But there was a fundamental familial encouragement of music and a musical life. 

[00:04:34] Ge Wang: It's true, Russ. That is true. At the same time, I can think also back to when I was eight years old. My mom took me the first time I saw a video game. 

[00:04:40] Russ Altman: Okay.

[00:04:40] Ge Wang: It was like an arcade somewhere in Beijing. The pixels just kind of pulled me and it was like kind of I felt like this is must be what a moth feels like when the moth sees like a fluorescent light. And I could not take my eyes away So from that point, I want to say I was addicted to the pixel if you will .And... 

[00:04:57] Russ Altman: Yeah, yeah. 

[00:04:58] Ge Wang: So maybe it was a, it's hard to say, actually, now that you ask this. This is a great question. It's a hard question. 

[00:05:04] Russ Altman: Well, but this is good. And this, these are like our origin stories, which, you know, it is useful to reflect back on. So let's just, let's fast forward. You, you can have these parallel interests, and then there must have been a time when they started to merge. And was that much later, or a little bit later, um, so you're shredding on the guitar, you're killing it on the video games. When did you say, oh wow, these worlds don't have to be separate? 

[00:05:29] Ge Wang: Probably I would say in undergrad, in college, when I was taking computer science courses. This is Duke University in the late 90s. And by the way, computer science at Duke is at one end of campus, all the way to the end of West Campus. The music building is all the way at the end of East campus. So from LSRC to Biddle building, I had to take a bus. 

[00:05:49] Russ Altman: Bus. 

[00:05:49] Ge Wang: And I remember there, yeah, there's a bus that goes from east to west campus. And so there would be like this frantic trying to get from my CS classes to music classes. And that's when I think these parallel tracks began to really converge. My first computer music class was in junior year in undergrad and taught by Professor Scott Lindroth, who's now the vice provost of the arts at Duke University. And, uh, he, they, I think that was a very mind and eye and ear opening course.

[00:06:18] And, uh, I think it kind of went from there. And then I started looking to grad school and I think it was in grad school, uh, starting the early 2000s, um, that I really, the two things really began to merge music, computer science, studied with the wonderful Perry Cook at Princeton. Uh, this is in computer science and, but I think what I realized, you know, beginning with the computer music class in undergrad and going into grad school was that, you know, it's, I always, up to that point, I thought computer music was this like, really like abstract, like kind of inaccessible kind of like strange world of sounds, which could be interesting. And, uh, but also realize you could, one, there's computer music. They could simply like. Two, you know, and then Perry is so good at teaching others this, that, you know, computer music can be playful. 

[00:07:12] Russ Altman: Gotcha. 

[00:07:13] Ge Wang: The first time I stepped into Perry's lab at Princeton, he was making music on a musical instrument he's made out of a coffee mug, with force sensing resistors and accelerometers, and he's playing like a trumpet. I'm like, wait, this can be research. And Perry's like, this is research. So I think to under, to realize that this research of like putting music and computers together can be so fun and playful, was a, you know, that was a good realization for us. 

[00:07:42] Russ Altman: You know, that is great to hear. So now we can get into, uh, like what you're doing now and what you're thinking about now. And I love that story because for those people who care and love music, you know, when you think about computers, I think a lot of us have, you know, what in medicine we would call antibodies. We say like, wait a minute, like this is the one part of my life where it's very tactile. It's very, there's, yes, there's a computer delivering the sound to me, perhaps like my phone or whatever. But this, and so there's a sense of like, are computers violating something that is otherwise sacrosanct?

[00:08:13] So, but I'm sure, but it sounds like, you know, you have a fundamental love of music. It was there in parallel and before. So how should we think about the role of computers and music of the words that you just used, playful, like are extremely refreshing to hear. Because I think a lot of us worry that this is going down a path that we just don't want it to go down. And, you know, as an amateur musician myself, you know, this is the one thing that's like, it's my fingers and my hands and my ear and my body. And it's, and I spend all day on computers, and this is the one part of my life where there's no computers involved. So help me understand the opportunities and the excitement here.

[00:08:52] Ge Wang: Well, uh, I would say that as computer scientists, I would say it's actually all too easy to like not be playful in the way that we actually work with CS, computer science, engineering, uh, in service of music. Um, and I think you have to be very intentional about being playful in the things you do. And, but I think part of that is to kind of, it's, I think it's a, it's some kind of like humanistic view on, what do you want to do with this technology? It's not just, what can you do with technology? Can the technology, like what problems does it solve? It's actually something more than that. You know, I always like to think that, I build tons of, I've done a lot of stuff like from ocarina on the iPhone to laptop orchestra instruments.

[00:09:37] Russ Altman: Yeah. I want to hear about those. Those are key. Maybe take a minute now, since you mentioned them, to describe them. 

[00:09:43] Ge Wang: So, uh, once upon a time I designed, uh, a musical instrument on the phone and you basically play by blowing into the microphone and use multi touch control pitch and it's, you literally are blowing into the microphone of the phone to play like a flute like instrument. And, um, well, if you like Russ, I can give you like a really quick demo here. Hopefully. 

[00:10:05] Russ Altman: If it's easy. And if it's sound. 

[00:10:08] Ge Wang: It does make sound. I'm holding up my iPhone here, as you can see, and, uh, I can play this by blowing into the microphone at the bottom of the phone. So

[00:10:24] Russ Altman: Unbelievable.

[00:10:38] Ge Wang: For example, now I'm literally blowing into the microphone. Um, tilt on the phone controls, um, vibrato and, uh, and of course, multi touch controls pitch. 

[00:10:48] Russ Altman: Yes, because for those who aren't watching, um, with your fingers, you were playing the screen a little bit like a trumpet. 

[00:10:55] Ge Wang: Exactly. And I'm holding my iPhone like. I might hold a sandwich and I'm kind of blowing with like just an inch apart from the microphone into the microphone at the bottom of the iPhone and I'm tilting the phone whenever I want one vibrato. And I think this would be an example of kind of playfulness in designing. 

[00:11:12] Russ Altman: What's that app called?

[00:11:13] Ge Wang: This is Ocarina. 

[00:11:15] Russ Altman: Okay. We'll put a link in that. 

[00:11:16] Ge Wang: It's an oldie. It came out in 2008 and, uh, people still come up to me and say Ge, Ocarina was one of the first apps I downloaded on the iPhone back in the day. This is really when the app store first came online. 

[00:11:30] Russ Altman: The other thing I think you said was something about an orchestra.

[00:11:34] Ge Wang: Oh yeah. So the laptop orchestra. So I direct the Stanford Laptop Orchestra or SLOrk. This is a, uh, an ensemble of people and computers. And special speakers we fashioned out of, these speakers look like basically the top half of like R2 D2 from Star Wars. And they have six speaker drivers in them. We built them out of IKEA salad bowls.

[00:11:57] And, uh, and the idea is that the sounds they're making keeps the sound of the computer close to the human performer. So it's more like an acoustic, it makes the laptop more into an acoustic instrument.

[00:12:08] Russ Altman: I see.

[00:12:08] Ge Wang: Right. It's trying to say, we have all this power with computers, but let's not forget that physicality matters. That are, you know, our, our bodies matter and that presence and the sense of intimacy of sound, all that matters. So we're trying to kind of, trying to find the best of both worlds, if you will. 

[00:12:26] Russ Altman: I mean, I really like that because from, again, from my experience playing instruments, there are none of them that don't make my body vibrate when I play them. Like the piano, the electric bass, the acoustic bass, the cello, they all vibrate your body. And that's a key part. In fact, this key part of knowing if you're even playing in tune and if you're even playing the right note. And so I can see that having that local experience for the person on the laptop, so they know if they're playing the right note and playing the right tune and stuff. I'm guessing that's a critical part of the feedback for their performance.

[00:12:56] Ge Wang: It is. And you can see people often leaning towards their speaker to see what kind of sound we're putting. Like I said, putting their hands on the speaker to see if, oh, am I, and this is in the context of a lot of like fifteen to twenty such speakers laid out across the stage. Uh, so for the performer, it's a feedback and for the listener, it's sound that's coming from all around you rather than from say loudspeakers, you know, from around you, but this is sound coming inside out from, you know, from the stations and the performance in front of you. 

[00:13:27] Russ Altman: Okay. So this is great because just in that little detour that we took, these are two descriptions of like the computers become the instrument. I mean, you, when I was watching you play your iPhone a moment ago, that was clearly an instrument. And you were articulating with it just like you would a trumpet or whatever.

[00:13:46] And you described this laptop orchestra and the individual experience of the players. So let's move a little bit from that just because of time to think about, you know, as you are well aware, and you've written a lot about, now we have AI and AI can enable instruments like what we were just talking about, but it also can kind of be the composer and the player and the instrument. But as you said before, what you can do and what you want to do might be different. So talk to me about how the AI revolution, if at all, has changed your approach to these kinds of projects? 

[00:14:20] Ge Wang: In a way, I don't think it has, uh, but it certainly raised a lot more questions, right? And, uh, and if we're actually go back to my grad school kind of days, I remember, I mean, the reason I went to grad school, I remember writing in my statement of purpose, this is back in like 2000. And I said, I want to do computer music in computer science, because I want to build, I think I said something like the world's most badass algorithmic composition engine.

[00:14:44] And these, in today's parlance, it would be generative AI for music. But back in the day, I was like, I'm going to build the world's most bad-ass composition engine. 

[00:14:51] Russ Altman: That was your vision. That was your vision. 

[00:14:53] Ge Wang: Vision going into grad school, on my way to grad school, I stopped in at a band, house band in Washington, DC. The band was tight and I was having a great time afterwards. I said, you know, y'all rock. And I think I was talking to the guitar player and he was asking me, hey, where are you going? I was like, I'm going up to, I'm going up to grad school in computer science. I'm going to go build the world's most badass algorithmic composition engine. And he looked at me and very earnestly, he simply asked, what's the point? 

[00:15:20] And that took me like by surprise, but in a really great way. And I was like, that's a really good question. I don't have an answer for that. Let me think about that. So I think I took that question with me and I went to grad school and I did not end up building or trying to build the world's most badass algorithmic composition engine. Instead, I ended up building a programming language for music. That's ChucK. This is still something I'm still working on twenty years later. 

[00:15:46] Russ Altman: ChucK. ChucK. 

[00:15:46] Ge Wang: Nice and simple. Nice and simple. It's like the verb to throw carelessly. That's kind of where the name comes from. And it's a program that lets you write code, you synthesize sound you can make, I mean, Ocarina actually, that's the sound you hear in Ocarina that was made in ChucK. Much of the sounds we make with instruments are created with ChucK in the Laptop Orchestra. And actually ChucK is running on like more than, I don't know how many millions of phones inside Ocarina actually. That's, there's actually ChucK running in there. So I ended up building a tool, Russ. It's like, you know, I couldn't answer the question, what's the point of building this like oracle that just produces music on demand? Instead, I want to, you know, I figured maybe others can have an answer to this question, but I want to build, I want to make a tool so that maybe others can explore that for themselves.

[00:16:31] But also, in the meantime, I can use this tool to build playful musical expressive things. So that was kind of the, you know, that was twenty years ago. And I would say today, I think I'm still not sure what the point is of building such a thing. You know, we have more impressive generative AI for music technology than we've ever had. And there's a whole history of kind of music composition in AI to this point. But, you know, as technology get more and more technically impressive, I think the question still remains. I think it's still, what do we really want from that? 

[00:17:06] You know, um, and Russ, man, I can tell you to be honest, at least today, and I don't know how I'll feel tomorrow, but today, as far as I can tell, like as an engineer, I feel like the people who most are interested in generative AI for say music and really just artistic, you know, things that we call human creativity, I feel like most of these people are like engineers. They're not the artists. The artists are not asking for this. Right? They're not, they're like, we didn't ask for this. They're artists working with AI. Many of them aren't actually working with AI, but most of them are using AI to say, comment on AI and technology. But the vast majority of artists I know, uh, are, you know, are like, yeah, we did not ask for this. And, um, and in a way this. So I think the point really, the question still stands. What's the point? And what do we really want from it? And this is me as a computer scientist who went to grad school thinking I was going to wanting to build that, but then kind of veering away from that path. And I think that crisis that I felt twenty years ago, I think I'm still in crisis, Russ. 

[00:18:18] Russ Altman: This is The Future of Everything with Russ Altman. We'll have more with Ge Wang next.

[00:18:28] Welcome back to The Future of Everything. I'm Russ Altman, and I'm speaking with Professor Ge Wang of Stanford University. 

[00:18:34] In the last segment, Ge told us about computers, music, and how they can be combined in amazing ways to create new instruments and even new orchestras. 

[00:18:43] In this segment we get a little bit more personal where he talks about his concerns about the future of music, especially in the setting of AI, and what that means for his brand new two-month-old daughter. He'll end, however, with a model of people that's pi-shaped. He'll tell us what the pi is, and he'll tell us why that's an aspirational goal for all of us. 

[00:19:05] So Ge, I happen to know that you have a new baby in the house, and I know that that's affected how you're thinking about a lot of these issues. How does the new baby connect to computer, music, and the future of AI?

[00:19:18] Ge Wang: Well, uh, we are two months and change into being new parents, and right now I feel like I'm in a time warp filled with diapers and things that I didn't know I'd ever be thinking about worrying about, uh, it's all coming up and it's definitely recompiling my brain. But I think one of the things I think about so much these days Russ is just, you know, what kind of a world is my daughter going to be growing up into? Right? In whatever that world is. I feel like that is a world I can barely imagine. And you know, this is a, someone who is like been working day in, day out with all the technology, uh, you know, in a music and in creative expression context, I'm just looking at, well, I guess the future of everything, Russ, is that, what's that going to look like for my daughter? So that's where my head's at these days. 

[00:20:19] Russ Altman: So, and I know you've written about, and I've heard you talk about your concerns about AI. We talked a little bit about generative AI. You know, there are issues of, there are real on the ground issues of like, whose music is being used to train generative AI? Are they being compensated for their, the use of their creative product? Um, there's the question of, um, who, uh, of copyright issues. And as you know, there are lawsuits everywhere from both musicians and artists, but also from the New York Times. What in your world, what, which of these concerns rise up to the ones that you kind of find the most worth thinking about and maybe worrying about.

[00:20:59] Ge Wang: Yeah. I mean, kind of like all of the above. But if I were to just say a few is that one, yeah, it's this kind of, you know, from the point of view of kind of music and all this training on data from artists without consent nor compensation for the artists, that's a huge concern. That's a huge ethical concern. But it actually gets worse in my mind than even livelihood, right?

[00:21:23] Beyond like livelihood. Livelihood is, that's an issue, but it gets even worse. It's not just, the worry, I think, isn't just that we as artists would be replaced by generative AI, is that we might be replaced by something that's actually far more generic and far less interesting. And that would, and the world might be okay with that because, well, generic might make somebody a lot of money. 

[00:21:48] Russ Altman: Ahh.

[00:21:48] Ge Wang: I think the question then becomes. Well, what kind of a culture does that leave us with? That's a huge concern of mine is there's an ethical component and then there's this huge cultural, it's like, what kind of a world do we want to live in? That's the thing that's I think haunting my mind when it comes to generative AI in the realm of human creativity. 

[00:22:13] Russ Altman: No, I really do hear you because, you know, artists are often the vanguard of critical thinking about what's happening in society and like reflecting what they're seeing in ways that make people think, you know all of the reasons that we all like to engage with whatever art we like to engage with. And so if it's being created by an AI, is that, well, I don't even want to ask the question because it sounds so trite. But like, is that actually even count as art if there's not a human behind it with a perspective? 

[00:22:43] Ge Wang: Yeah. You know, I teach, uh, the music and AI course here at Stanford. And, uh, that's in the music and cross-listed computer science, and we build stuff, but also it's a critical making class. So that back to that word that you mentioned, it's critical. So we're trying to, we're building stuff. We're also trying to think as broadly as possible about these. And the question is, is this still art? That's on the table now, right? And it's, it actually goes back to us to actually think critically about what art really means to us and who makes art and is art made by humans? And, you know, I think the question, these new questions are, which used to be in the realm of philosophy, you know, still is in the realm of philosophy, but it's also now very front and center, very practical. And, but it's actually through philosophical lenses that we probably could think through these things, is it still art? If it's not made by a human. Um, I don't know. You know, I, at least not today, maybe tomorrow I'll have, I'll feel differently. Um, but what I'm really looking for in anyone working with AI is just, are you being critical, you know, about what you're doing with AI? If you're putting AI, using AI in any way, are you being critical?

[00:23:56] Are you self-examining? Are you asking, what do I really want out of this? And does this contribute to a kind of like a world I want to live in? This is make us as people, the kind of people who want to be. I think that's a critical mindset. And I think this should extend in my view to all engineers, researchers, users, um, people who are investing in AI to everyone who are, like in this, who touches AI should do their best to be critical about this technology. And in there, and I think we can, if we're critical, we can hopefully find, begin to find what are the benefits that could come from this for. 

[00:24:38] Russ Altman: Yeah. 

[00:24:38] Ge Wang: And, but also to hopefully like steer clear of some of the pitfalls. And I think, you know, Melvin Kranzberg technology historian had this, these laws of technology. The first one that says, you know, technology is neither good, nor is it bad, nor is it neutral. So, you know, I think being critical about how we deploy and how we make use of technology, how we put technology in, how we live with technology. I think that's really, for me, the key. 

[00:25:09] Russ Altman: Great. And so in the context of AI and music, I know you've thought about the difference between the process, the process in which AI can contribute and the products that are produced. And I know you've begun to think about like maybe a division of labor or a way to think about it so that when you're critical in, as you just discussed, in the domain of music, you're starting to kind of create an ontology or a schema for how to proceed. So can you tell me about what kind of progress you've made in that?

[00:25:38] Ge Wang: I don't know if I've personally made any progress, but I have been thinking about this for a long time. Maybe even dating back to grad school twenty some years ago. But I think today, and especially in this current era of generative AI, you know, for things like, for everything really, or at least they're trying, we're trying, I think we are often, too often thinking about AI as producing a product, like a result, and we also naturally seem to want, you know, that result to be something, we think it's good or it's progress if it like somehow matches or outperforms humans. And that has been kind of the de facto benchmark, I think, of much. 

[00:26:17] Russ Altman: Very common, very common.

[00:26:18] Ge Wang: You know, it's, you know, some would refer to as like the Turing trap, right? This, I think for me, like I always ask the questions like, well, is the product the only thing we value? Right. And the answer is actually, no, we can all think about examples, I think in our lives, which we value the process, for example, uh, cooking. Maybe I don't want to cook every meal, um, and I would, it would be great. I can go to my kitchen and be like, kitchen, uh, make me a sandwich. You know, there's a sandwich that I like, but other times I want to go into the pantry. I want to go gather raw ingredients. I want to make a mess of things and there's a joy of cooking. And I think that joy of cooking extends to so much else that we call the human experience, you know, uh, music making Russ. I mean, you're a musician and you like. It's not just the product. It's like this whole process of, I would call it climbing the mountain, like, you know, practicing of wanting to get better, you know, at playing something and playing a passage, playing a piece, playing an instrument. And this becomes a craft that you hone and to probably literally climbing a mountain, you know, like hiking it's, there's some, the joy is actually in the process of doing, and I think that process. 

[00:27:42] Russ Altman: Not to mention the amount of time you spend. I mean, the gig or the performance at the end is nothing compared to everything that went before it in terms of preparing. 

[00:27:51] Ge Wang: That's so right. And by the time that gig hits and you're hoping, yeah, it's the second nature you've now created for yourself by all these vast amounts of practice and preparation. And, it kind of kicks in and you're going for that moment of the sublime, I think, right? That gig is kind of like getting to the top of the mountain after a long and arduous hike. And it, you know, getting up there, getting up the mountain, I think matters. So I think, you know, I think in this age of so much, you know, automation and this idea that, oh, it, wouldn't it be nice if we can just think about something and then just have it happen, you know, who doesn't want that?

[00:28:34] But actually if we'd stop and think about it, there's times where we do want that. Um, if I could snap my finger and I'd be like, well, hey, you know, we now have more insight and more tools to combat climate change or, you know, maybe there are things that medical professionals would be like generative AI can really help us here. And there's already many cases of that too. Like if I can snap my finger, Thanos down, say generative AI, let's increase access to education or to housing, to food, to sanitation, I might be inclined to snap my finger if it was up to me, like in this thought experiment. But when it comes to things like human creativity, um, things that we experientially appreciate, um, to, and also I would add to that education, then I think I would be much more hesitant in actually snapping my infinity gauntlet here. 

[00:29:32] Russ Altman: Gotcha. Gotcha. So to finish up in the last couple minutes, I want to, you've talked about, and I've been, as you know, we've talked about this, uh, off the air. I've been very compelled by this idea of a pi-shaped person.

[00:29:43] So pi is, the Greek character pi, which for people who don't know, has a line. Like, it's like a table, there's a tabletop and then there's two legs, and that's what pi looks like. What do you mean when you talk about a pi-shaped person and how does that relate to all? 

[00:29:57] Ge Wang: Yes, indeed. So the pi-shaped person is something I fleshed out in my book, Artful Design: Technology in Search of the Sublime. And… 

[00:30:04] Russ Altman: We will put a link to that as well. 

[00:30:06] Ge Wang: And the reason I put the pi there is that it's really thinking, I was really just thinking about, you know, what kind of an engineer would I want to be? And also as a teacher, what kind of an engineer and builder, designer, would I want to help people become. And I think it's the pi-shaped person is what I've kind of formulated. Now I don't think I originated the pi, but I added to it. So in the pi-shape, at the pi. 

[00:30:31] Russ Altman: Okay.

[00:30:31] Ge Wang: The letter, in the number of pis you've described has these three components. These two legs with this like horizontal tabletop on top. In the pi-shaped person, one of the legs, I would say is disciplinary expertise. Right? That's actually in an education gaining a discipline expertise. For me, that was computer science. The other leg of the pi, I would call that domain expertise, right? And that for me, it was music, but for someone else that could be public health. Now this top, this tabletop, this bar, horizontal bar on top of the pi, um, is what I call the aesthetic lens. And the aesthetic lens is the philosophical, artistic, and moral lens that gives broader meaning and context in bridging these two legs. And what this translates to is for engineers is, can we be someone who is more than a, like a disciplinary rockstar, right? But someone that can think more broadly about the implications of what we build in, about a domain. But also having the aesthetic, social, philosophical, artistic, cultural awareness to actually see like how to contextualize the systems that we build. Um, in the world, communities and in societies in which we deploy these technologies. And the path forward in, for me, in the educational context is that the way to become more of a pi-shaped person, in my opinion, is to actually learn as broadly as possible, right? If I'm studying computer science, then I should also, I don't try to, learn about, I don't know, philosophy, literature, uh, history, a lot of history, um, the social sciences, the humanities, the arts, um, the natural sciences, and just try to learn as much as possible about our world and to give yourself tools to think critically about how we're going to shape technology. So, Russ, that's, that's. 

[00:32:27] Russ Altman: That's the prescription. So just to review, ‘cause this is a great, I mean, this is a really, it's a, it's a little bit of a simplification, but it's something that's actionable. You have, and I'm just going to repeat back what you just told me. It's a disciplinary excellence.

[00:32:39] So you know how to do something, you know, you're a virtuoso, in doing something. Then you have what you call the domain expertise, which is the kind of the area of application or the, the thing that the area in which you produce, uh, and apply your disciplinary skills. But then there's this very important, the tabletop, which you called, and I love this phrase, the aesthetic lens.

[00:33:01] Which gets all of this input from these things that seem irrelevant. I mean, like, it seems like philosophy doesn't relate to music, it seems like, uh, other social science. But the, but your point is that is what gives us our lens to kind of combine the discipline and the domain in kind of meaningful ways that are responsive to like the human experience. Am I close? 

[00:33:21] Ge Wang: You said it beautifully, Russ, absolutely. And this goes back to what we said at the very beginning, which is the importance of play. 

[00:33:26] Russ Altman: Yeah. 

[00:33:27] Ge Wang: You know, play is not a useful thing. Not really. It's in the word. The whole point of play is that you play without needing to walk away with a productive outcome. If you had to do that, it would be called work. But play is so important to us as humans. And it gives us actually perspective, a lens to think about what's good and, you know, kind of like, what do we really want from, from life? 

[00:33:49] Russ Altman: That's it. And I love that play is on the top. 

[00:33:52] Thanks to Ge Wang. That was The Future of Computer Music. Thanks for listening to this episode. With over 250 episodes in our archive, you have instant access to a wide array of discussions about The Future of Everything. If you're enjoying the show, please remember to rate and review it. Tell your friends about it. It'll help the show grow.

[00:34:13] You can connect with me on X or Twitter @RBaltman, and you can connect with Stanford Engineering @StanfordENG.

[00:34:25] Unbelievable.