Listening in the Afterlife of Data (David Cecchetto)
October 17, 2022 |
Listening in the Afterlife of Data (David Cecchetto)
October 17, 2022 |
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If you walk into David Cecchetto ‘s classroom, you might find people wearing audio devices that simulate hearing with a thousand-foot wide head. Or gadgets that swap their ears so that the left ear hears what the right should and vice versa. David is a media theorist who draws on his background as an artist/musician, to create what he calls “engagements,” strange sonic experiments that help him—and his students—understand the nature of our computer-driven lives.
In this episode, we feature an extended chat with David about his recent book, Listening in the Afterlife of Data (Duke University Press). It’s a book about the eternal impossibility of communication and the texture of that impossibility in our current computer-mediated age. David says we live in the afterlife of data, by which he means we know that our data-driven representations of the world don’t really capture the reality of our inner or outer lives, and we know that algorithms perpetuate injustices of all sorts—and yet, we still live our lives as if we do believe in the data. And this is where his engagements come in, the sonic experiments that confront the distortions and fallacies and textures of a data-driven life.
David Cecchetto is Professor of Critical Digital Theory in the Department of Humanities at York University in Toronto, Director of the Graduate Program in Social and Political Thought, he’s President of the Society for Literature, Science, and the Arts. He wrote the book Humanesis: Sound and Technological Posthumanism (2013) and he’s co-authored and edited several others.
For patrons, there’s an extended version of our interview, complete with some outstanding recommendations for reading, listening, and doing. You can get access to that at patreon.com/phantompower.
Ethereal Voice: This is Phantom Power.
David Cecchetto: The world is not coherent with itself.
There’s no single world.
Any theorizing needs, I think, to start by acknowledging that
Mack: And welcome to another episode of Phantom Power, where scholars and artists and musicians tell stories about sound. I’m Mack Hagood and my guest today is David Cecchetto, someone who qualifies as a scholar and an artist and a musician, but he draws on his musical and artistic skills in a very unusual way, creating what he calls engagements, these strange sonic experiments that help him and his students understand the nature of our computer driven lives.
David Ceccheto is Professor of Digital Critical Theory in the Department of Humanities at York University in Toronto. He’s also the director of the Graduate Program in Social and Political Thought there. He’s President of the Society for Literature Science and the Arts or S.A.L.S.A. He wrote the book, Humanesis: Sound and Technological Posthumanism back in 2013, and he’s co-authored and edited several other books.
David is one of those people that I just love having a beer with and talking to because he is such an unorthodox thinker. And so I asked him to have this off-script conversation with me, an extended chat just about his new book entitled, Listening in the Afterlife of Data available from Duke University Press.
This is a book about the eternal impossibility of true communication and the texture of that impossibility in our current computer-mediated age. David says we live in the afterlife of data by which he means we know that our data-driven representations of the world don’t really capture the reality of our inner and outer lives, and we know that algorithms perpetuate injustices of all sorts, and yet we still live our lives as if we did believe in the data.
And this is where his engagements come in, these strange sonic experiments that help David and his students confront the distortions and fallacies and textures of a data-driven life.
If you walk into David’s classroom, you might find people wearing elaborate prostheses that simulate having a thousand-foot-wide head or gadgets that swap your ears so that the left ear hears what the right ear hears and vice versa.
If you’re already feeling a little confused and weirded out, welcome to the counterintuitive world of David Cecchetto. I’ve wanted to have him on the show for a long time. His work is kind of hard to put into a succinct narrative, however, so that’s why he’s our first guest in what we’re calling our “off-script” episodes.
No script, no music, no sound design, just a lengthy and I think fascinating conversation with David about his ideas and practices.
And for our patrons, there’s an extended version of our interview complete with some outstanding recommendations for reading, listening, and doing that David has. You can get access to that at patreon.com/phantompower.
Two final notes about this interview. Number one, we will be using a central term in David’s book “incommunication.” That’s just one word. I N C O M M U N I C A T I O N.
Did I do that right?
Second, we did this interview over the internet and David’s voice gets a little glitchy at times, which is a lovely sonic example of the incommunication that we’re gonna be talking about. But I do apologize nonetheless.
Okay. Here’s my interview with David Cecchetto.
[Robotic Music Fades]
Mack: David, welcome!
David: Thank you. I’m really happy to be here, love the podcast, use it all the time in my teaching, as well as for my personal enjoyment.
Mack: Oh, man. Thank you. That’s great to hear because I think you’re really one of the most original thinkers who are working at this intersection between sound and media theory right now.
I feel like you have really interesting ideas about these things that are challenging and fun. So I’m looking forward to getting into this with you.
Your new book, Listening in the Afterlife of Data: Aesthetics, Pragmatics, and Incommunication, definitely follows its own sort of line of pleasure and curiosity. And the book opens with this question, “How might we listen to computers in their incommunicative profiles?” which, you know, I had to read several times just right there.
But then in the next sentence, you kind of admit that this is an obscure question and that you’re interested in asking obscure questions because a lot of the questions that we ask in a scholarly setting, the answer is already kind of built-in. And so we’re, if I’m reading you correctly, we’re more almost like following an algorithm to get to the desired preordained answer rather than really thinking outside again of these certain kinds of boundaries.
So you set up this kind of strange question, and it requires you, and it’s gonna require us to kind of unpack little by little what that question means. And then maybe by the end of getting to what this question means, we will have in some way answered it.
David: Sounds great. You know I like that. And you know, I think this is the thing about disciplines, right? Is that part of the reason why interdisciplinary is so important because the methods that disciplines use do script in advance what they’re going to find, you know?
And so I really like that as a way of framing this obscure question, and I’ll say, I do love obscure questions, and I think that question is actually, you know, in some ways there’s a really simple idea of what it means, which is just, computers are actually really weird things and there’s a lot that of how they actually work, how a specific computer is working in a specific instance, that’s actually incredibly strange, inconsistent, you know, unreliable, all those sorts of things. Lots of scholars pointed this out,
But then it’s really hard to think about them and not just see them as kind of formal technical tools. So the question of how to listen to them in their incommunicative profiles is, “How does one stay in touch with the strange things that computers are doing?”
The idea is that computers are doing something other than what we’re told that they’re doing. So some of what they’re doing is being done on individual computers. Some of what they’re doing is being done by virtue of the kinds of communication that they make seem natural. Right?
So, we might describe computers as communication technologies, but what idea of communication are we actually building when we do that? This form of asking the question is actually pretty familiar at this point, I think.
So, for example, you know, many people today are asking what is policing actually doing under the guise of keeping people safe? Right? And of course, the answers are that policing maintains the racialized, gendered, ableist, and otherwise power inequities that are part and parcel of the times in places that I’m living in.
So this is not to say that no individual cop never keeps somebody safe, Right? And in the same way, it’s not to say computers never help us communicate. We’re doing this by computer right now. But it’s to say that, you know, a certain policing itself is also doing something else. And a certain kind of computers themselves, in addition to being these sort of communication technologies, are also doing something else.
And so then the question is, how can we actually tune into that? And not in a way that makes it a done deal, say, “well, they also do this,” but what they’re doing is dynamic and lived and part and parcel of all the ways that our cultures are always changing. And so, you know, how do we sort of approach the questions of computation in those ways.
Mack: So that something else that computers are doing. Maybe we can start picking away at this something else and this term that you use incommunication. So incommunication you draw on a book that I have cited, a lot of people have cited because it’s just such a fantastic book, John Durham Peters’ Speaking Into The Air, and that’s a book that talks about how there’s an impossibility that’s kind of built into communication that is also the basis of our desire to communicate. Right?
And you talk about in your introduction how this relates to two fields that were hugely important in the 20th century, psychology and computer science. So maybe could we talk just a little bit about that because I think it gets at the root of something that the human mind and computers maybe have in common.
David: No, that’s nice. That’s really nice. I mean, I think the key for understanding the impossibility of communication, which, you know, you’re right, it’s a big part of information technology, this idea, it’s a big part of psychology.
It also is a big part of, you know, religious hermanutics. It’s a big part of the way that artists talk about communication. So actually it’s one of these things that sticks to the concept of communication pretty regularly in all sorts of different ways. But you’re right in the book, I’m sort of focusing on those two, or at least gesturing towards those two.
And so the key is that communication is both unavoidable and impossible. And that those two things happen at the same time. I know that’s an obscure way of saying that, but it’s, if you think from a psychological perspective, right? On one hand, we can’t help but communicate constantly, right? If you’re in a group of people, you are communicating, right? You are, whether you’re speaking or not, we’re giving off all sorts of signs and signals and ways that we, you know, people know all the ways that this happens.
In some ways, what psychology is, is, you know, partially what it’s doing is trying to decipher how that works, how people communicate those things. At the same time though, the impossible part is that for psychology to make sense, I have to have a sense of my psyche, me, being different from your psyche, you right?
We have to have some sense of interiority, right. This is me and I’m going to express, I’m going to take the “me” that’s inside and express it, make it outside to communicate an idea to you. And the point is, and the reason why it’s impossible to sort of fully communicate, is because I can’t ever, even the simplest idea, I can’t move from inside of myself to outside of myself to inside of you with, in a complete way, or else there would be no distinction between inside of myself and outside of myself.
If I say “I want a piece of toast,” the “I” there, the “me,” referring to myself, it means that there has to be something about that that I didn’t express to you. Right? Something about who I am. So psychology is built on this, and as Peters sort of notes in passing, maybe this is actually what psychology builds as well, this theory of communication.
So, you know, the book is kind of saying, “Well, okay, that’s great and that’s true. So how does that actually change in different settings?” And one of the other settings where communication is unavoidable and impossible is in information theory. Which, as you know very well, probably better than me, the foundational idea of information theory there’s one way of describing. You’ve got a message that goes through a channel and arrives at a destination, and of course, that channel, the medium, always introduces noise to the message. And many media theorists have said, “Well, actually, the noise means that nothing ever arrives at its destination exactly as it was at its source.”
But it also, in some ways, the noise is the thing that allows those to the source and the destination to connect. So it has to be changed. So it’s unavoidable informatically, you know, two things. As soon as they’re connected, they’re in some sense communicating, but it’s also impossible because there could be no such thing as a message if something were fully communicated.
Mack: I think that’s clear. So the impossibility of ever truly knowing the other, or ever truly knowing the self, is sort of the basis of psychology and the impossibility of a noiseless transmission is the basis of computer technology. Like Claude Shannon was working at AT&T, he develops this information theory because he’s trying to eliminate noise from the phone lines at AT&T and yet that noise is also integral to those phone lines.
So it’s this unavoidable problem, but it’s a very generative problem. We’ve come up with all kinds of theories of the self and of information, and of computers because of this extremely generative problem that you give us a really terrific name for, I think, which is incommunication.
The gloss that I would put on this term, “incommunication,” and then you can kind of fill this out or correct me where I don’t have it right. Is that you’re pushing this realization that noise and miscommunication aren’t just these little moments of breakdown in what would otherwise be a clean process of information or communication.
That actually, incommunication is central to the process of communication itself and our inability to communicate and all of these moments where the communication goes awry is what produces the feeling and the texture of our everyday communication with one another.
David: Yeah, I think that’s totally fair. I guess the key thing I’d say is very little of our seemingly communicative actions are actually explained by the way, that communication is at least in a day-to-day way, talked about, right?
If we think about, if we talk about communication as an exchange of information, as me passing an idea to you and you pass an idea back to me, that actually explains very, very little of what we’re doing as we are unavoidably constantly communicating. Right? So even if we just restrict ourselves to speech, never mind all the bodily and effective things. Right? There’s lots of communication that isn’t speech. But even if we just restrict ourselves to speech, if most of us were to record all the things we say in a day, most of it would not be explainable through the way we talk about communication.
And this, again, it’s not even just talking to ourselves or talking to our cats and things like that, but also if, for example, I go to the coffee shop, right? I start telling the barista about everything I’m going to do that day. I’m gonna go for a bike ride, get some groceries, read for a while, you know, grade, whatever.
The actual exchange wouldn’t be the content of what I’m saying, right? It would be the weirdness of this middle-aged guy giving way too much information to this poor barista who is forced by the sort of exchange or having to have a job basically, to sit there and kind of listen to it and, and be polite. Right?
And actually, even the exchange of coffee for money isn’t in many cases really that. Right? Because I go to the coffee shop for something other than just coffee. So understanding communication as information exchange is ridiculous. It’s just totally inadequate. Right? But, it still matters that that’s what we seem to be doing. Right?
So this is the kind of key I think that incommunication gets at is that it still matters that I seem to be communicating. That’s what allows these more complex, weird interactions that are always happening to sort of continue to take place or not what allows them, but it’s what frames the ways that they take place.
I can give an example if you’d like.
Mack: Yeah, yeah. Give an example. I’m just sitting here mulling this over but Gimme another example.
David: Yeah. I like the way that you explained this actually in the piece you did for Real Life Mag, where you give the example of emojis, right? Which respond to the sort of impossibility of truly knowing other people’s emotions through text.
And in doing so, they give us this new text for communicating and I think people feel this if you just imagine, you know, the difference between writing a letter, writing an email, or texting. One of the things you get with texting is like, you can send emojis or gifs or that sort of thing.
And the key is, nobody would say that an emoji is more precise, right? But it’s different. It feels different. It changes in turn how we feel when we are doing that. Or one that gets talked about a lot is the like function in social media. Nobody would say that liking something on, you know, Twitter or hearting it, I guess, is the same as saying, “I like to eat vegetables.” Right?
Like “the like” doesn’t mean the same thing, but it takes on its own sort of texture of kind of crafting a certain kind of connection that we know what it means, but we also, we can’t kind of quite explicate what it means and what it means is kind of always changing based on how it gets used. Right? I don’t, I
Mack: The emoji example, it’s like, I can’t know what tone of voice you were using in a text message for sure, and the emoji, you know, it gives me a little bit, “Oh, okay. That was a joke.” That gives me a little bit more, and yet that’s still never quite enough. Right?
But, the fact that we couldn’t communicate, created this new form of communication, the emoji, and that becomes part of the texture and now that’s part of the norm of communication. So it’s the incommunication is always producing new forms of communication. Is that right?
David: Yeah. Yeah. And or even, I mean, what I like to think about it is with the word alibi. So if you think about it, what is an alibi? If I’m asked to give an alibi for a bank robbery, right? What I’m saying is I’m giving proof that I wasn’t there, Right? I’m saying, “Look, when the bank was being robbed, I was with my cat, and my cat will attest to that.” And you know, which is the other interesting thing about who gets to provide an alibi.
So let’s say instead of with my friend, who might be a more reliable witness for the police saying, my cat. So I give an alibi. But the thing about an alibi is it also proofs or suggests your implication in the scenario that you’re saying you weren’t part of.
So who has to give an alibi under what conditions? Why would I be asked by the police in general where I was during this bank robbery? Right? The fact of the alibi proves I wasn’t there. But it’s still different than having never had to give an alibi at all, right? All the thousands of people who are not even called in to be questioned in that way.
So I think, communication kind of functions in the form of an alibi, right? It’s sort of our way of explaining, “Well, this isn’t quite what I’m saying, but we’re here together, not quite saying this stuff together.”
Mack: So I think that unpacks the incommunicative profiles piece of our question, our opening question, “How might we listen to computers in their incommunicative profiles?”
So maybe we can get deeper into the computer aspect and then we’ll talk about your experiments in listening to computers, which I find fascinating. So your idea here is that data are our primary medium of incommunication, and you have this line that I really like:
“A computational perspective, hallucinates an idea of information as something that would remain unchanged as it moves between contexts, such that data can be raw, pure, and fundamentally un-relational.”
So this abstract notion of data as something portable that remains the same in any context, is one that I’ve kind of been fascinated with in my own work. So I’d like to hear more about this perspective of data, like where does this come from historically? And why are you so concerned with it in this book?
David: Yeah, that’s great. I’ll just say really quickly, first, the word hallucinates is important there because, you know, sometimes when people hear hallucinates they think, “Oh, it’s, it’s not real.” And that’s true, right? Because data is not raw, pure relational. I mean, there nothing is, right? Everything is the opposite of those things.
But, you know, it’s like when you wake up from a dream, let’s say you dreamt you had a fight with your partner. Right? You know, it’s a dream, you know it’s not real, but you still wake up a bit pissed, right? So that’s the key thing about how data hallucinates these things is that even though it’s not real, it could still feel that way.
I feel like I’ve spent more time than most people thinking about this, and it still feels that way, right? So the question of where it comes from, I mean, this is a total cliche. I don’t think I could be the academic guy I am without falling into this cliche. Obviously, it comes from liberal capitalism, right?
Which, I mean, think about it, it would be totally sensible for me to say to you that I decided not to try out the nice new restaurant in my neighborhood because I wanna buy a new bicycle, right? So when I say that I am taking a bicycle and a meal and kind of making them exchangeable with one another, right?
They’re totally and completely different things that are exchangeable for one another. And that’s wild. Like it’s worth staying in touch with that. And computers build on that, right? And lots of people have talked about computers has been sort of meta medial where they allow comparisons of data between things that are unrelated.
So, you know, it makes sense in 2022 to say that a movie is bigger than a song, right? Because the file is bigger. Like if I’m gonna download a song or download a movie, I’ll say, “Yeah, no, the movie’s bigger.” Of course, you know, a question of size with a movie is not really a sensible one. And it’s not also with the song, but somehow computers bring those things and make them exchangeable with one another.
Now that much is, I think, kind of obvious, but one phrase that I suggest my students listen for and that I try to listen for myself, that shows the kind of more subtle ways that this happens is the phrase “At the end of the day,” right? Because most times you hear that phrase, you’re about to hear someone try to convince you of some kind of equivalence that is hallucinated, right?
And again, that doesn’t mean it’s not active and powerful and doing real things, but it’s not a fact. So a politician might say as someone just did here in Toronto, “Yes, I think that everyone deserves to be housed, but at the end of the day…” (that’s the key) “At the end of the day, that would have to be paid for by taxes that people, you know, aren’t willing to pay.”
So, you know, this makes an equivalent between somehow like my tax rate and somebody being able to be housed. And actually this is why housing activists insist on housing as a right, because the discourse of rights at least suggests that there might be something outside of this exchangeability.
So the thing is that this way of thinking just permeates everything, right? It’s so hard to hold onto the qualitative differences, the differences of kind or of texture, omits this kind of constant quantitative impulse. Right? In philosophy, they have this idea of the Trolley Problem where basically you decide is one person gonna die or a bunch of people to die.
And that Trolley Problem as many people say is like a, you know, it’s the false framing of how we live in the world. And it’s, but it’s really hard. It’s so seductive. And, and just to kind of conclude, this informatic perspective, which I think is, you know, capitalism has a whole other way, other things it’s doing about exploiting labor and, and you know, all of that.
I think today we live, of course, still under capitalism, but I think that this informatic perspective, calling informatic perspective rather than a capitalist perspective, really gets in touch with the role that data or information specifically plays, which is slightly different. And it’s important that it doesn’t just fail to capture reality.
It creates realities that perpetuate injustices, right? So this is, this is the point about any failure of communication is that it’s always also doing something else. So if you think about the way that standardized testing, right? Crafts intelligence and education in ways that perpetuate race, class, ability, and gender-based biases.
The problem isn’t just that these tests are designed in ways to make some folks more likely to succeed and others to fail. Although of course that’s true too. But also that the very existence of the tests conceptualizes intelligence as a question of data, as a question of measurement, right?
Which as anyone who spends any time with other people knows that’s an impoverished understanding of what intelligence is, right? So standardized tests, or actually grades in general. Grades are like highly incommunicative things, standardized tests incommunicate, they prescribe a set of values under the alibi of describing what they’re measuring. Right?
And yeah, maybe I’ll just leave it there. So I think that’s where computers enter into this question of incommunication.
Mack: So I’m hearing you say that our current mode of incommunication in our present moment is, is data. That’s sort of the, the prominent mode of incommunication. Data comes out of this, you know, what you describe in the book as a white patriarchal capitalist individualism and, and that, that kind of thinks of in individual thought as property, as it thinks of as the individual as being more important than the community and kind of functions around this kind of exchangeability that you’ve described, which it allows us to equate things that don’t sensibly equate with one another.
And then one of the, the, the points that I think maybe you haven’t highlighted yet in, in that is you, you talked about how data becomes an alibi for like, for classism or racism or ableism and you gave that standardized test example.
But even if we doubt the data, we don’t believe that alibi somehow we can’t really overcome it. So can you talk about that? Because it seems like you, you, you say in the book that critiquing the data isn’t enough, or the way the data are arranged is, is, is not enough. So can you get into that a little bit?
David: Yeah, absolutely. My favorite, you know, this might be slightly apocryphal, but I, I heard several years ago about a study that suggested that placebos work, even when,you know you’re in the control group given, you know, a sugar pill. Right?
Which is an awesome thought. It’s like, cuz you know, the way that placebos were always talked about, at least in my mind is that, you know, they worked because you think you’re being given the medicine.
But actually maybe they work just, maybe just cuz you’re just having somebody pay attention to it, I don’t know. I don’t know what the explanation would be for that, but that seems to be the case. And, and I, you know, again, I won’t, if somebody listening says no no, that’s wrong. I, I won’t contest that.
I also won’t contest, you know, if somebody wants to insist that psychology is still, you know, as important as information or as important as, as data, that’s fine. You know, I’m not, I don’t actually need to make a grand historical argument about the role of computers to, to say that they’re clearly, you know, saturating our, our daily lives.
But what I would say in terms of this placebo effect of data is that, you know, well actually there’s a, there’s a scholar named R. Joshua Scannell. He’s one of many people who has written about predictive policing, the PredPol technology. And one of the things that he, he sort of shows is that, so this is a technology that police departments use all sorts of ways for recidivism, but also to predict where crimes are gonna take place.
And basically it, it prints out a map that says, this is where, you know, based on all these factors, we should have officers with boots on the ground in order to be present because this is where crime is likely to happen today. And you know, Josh has pointed out that the cops know that this technology doesn’t work properly.
One of the hilarious things about the technology is the first iterations of it showed that the places where crimes are gonna happen are all police stations. Because of course, there’s a whole bunch of crimes of people being brought in, and they’re not actually booked until they’re in the police stations.
So it’s like suddenly it looks like police stations are these sign, you know, these sites and crimes. So the cops know that there’s all sorts of technical things wrong with the data. They also know that this data is based on prior policing data, right? So they know that. And you know, Toronto Police Department just issued an apology, probably an empty apology last week because all of their data shows that, you know, policing is racist. Turns out, right?
So they know that the data that this is based on the crimes, the ways that crimes have been policed in the past are informing what crimes will be policed in the future and that that is biased. The cops know this, and yet it’s still so seductive. Because if you know that going to location A is more likely to lead you to be able to intervene, even if you’re a nice guy, like even if you’re no part of you, no bone in your body, imagines yourself to be, you know, racist, you’re, you still wanna be effective at your job.
And it’s like, well, somebody robbing a convenience store is in one way something that is racialized, but it also in another way is just a crime that if you are a cop, you kind of imagine your role to be, to stop. Right. So, so it’s so seductive because in some weird ways, uh, it works. There’s a scholar named Sun-ha Hong is a, I don’t know if you know the book, Technologies of Speculation, I think it’s called, it’s a really, really nice book.
Really, really, It’s, it’s a weird book, but really responsibly researched. So I, I envy him in that. And he says basically, you know, what’s being sold to us with data and what’s being sold is not what it allows us to do, but actually, or sorry, what the data will do for us, but rather what it allows us to do in the name of data.
And I think that’s that kind of seductive thing that happens all the time. Right. Just and you know, and again, it’s like everybody knows that IQ tests don’t work. But I think, if I’m being honest, I think if I scored like a 60 on an IQ test, I’d still be disappointed. I think some part of me would be like, “Is there something wrong with me that I’m scoring so low?” Right?
Mack: And this is, and this is where the title of of your book comes from, Listening In The Afterlife Of Data. You’re, you’re suggesting that we’re at this point where we don’t naively believe in data anymore, and yet they still hold sway over us. And the example that you use, I love, is about dating apps.
Can you just briefly tell us about that one?
David: Yeah, I, I like the example too. It’s, if somebody, you know, let’s say I get a 97% match, now I’m told on most of the apps now, it’s just, it’s a different matching system. But back in the day, if you get a 97% match with somebody on, on one of these websites, you know that.
I don’t even know what that percentage means. Right. And nobody does, including incidentally, the dating app company, because part of what they’re doing is, you know, they know that increasing that number makes people more likely to pursue and actually may even sort of prime us when we do meet the person to be like, “Well, it doesn’t feel right, but I got this 97% match, so maybe I just need to give it more time. Maybe it’s more likely to be successful.”
But nobody knows what it means. Nobody, that the person does not exist on the planet who knows what, like, it is not a sensible number. There’s no percentage, you know, percentage is per 100. This is not, you know, this is not a sensible number. And yet it still feels compelling in a way that like a 22% match doesn’t.
That’s it. That’s it in a nutshell I think. In terms of the afterlife of data, I actually came to this term “afterlife” originally through a scholar named Eugene Thacker, who has a book called After Life, But that was many years ago this idea, but it’s more closely associated now by far, with a really, truly brilliant scholar named the Saidiya Hartman.
And Hartman uses the phrase “afterlife of slavery” to theorize how the subjection of black people persists after the legal end of enslavement. How this perspective, in fact, continues the logics of slavery, you know, in, in a contemporary moment. There’s actually a really nice book too, by a fellow named the Rinaldo Walcott that takes up a, I think a related argument, not exactly the same thing, but under the phrase “the long emancipation.”
And so that book is making an argument about, about what freedom means after freedom is legally granted to enslaved people. And you know, his argument is that it’s not accomplished yet. So I’m using the phrase “afterlife of data.” In a related way, but obviously slightly differently to describe this cultural moment when the hegemony of data, the way that data organizes our ideas of communication, organizes ideas of power where persists, even though we know that, you know, most or all of our data is hugely flawed.
And even though we know we don’t necessarily know what we’re talking about when we talk about data, and yet it, it persists as a real organizing principle in our lives.
Mack: Right. Okay. So I think at this point we have really covered computers and they’re incommunicative profiles. So I just want to go to the beginning of that sentence now, which is “How might we listen to computers in their incommunicative profiles?”
So why listen to computers?
David: You know, I have a background in music and in sound and the way that I think is almost always through experiments, artworks, engagements, actually trying to activate the, you know, just trying to activate the thoughts. I’m, I’m never, I’m not smart enough to be able to kind of be one of these thinkers who makes a full schematic of their thinking and says, you know, it says this is how everything in the world works and, you know, Logic Point A and Logic Point B connect in this way.
And, and now I’ve explained everything. I tend to be, I, I have to actually think through a problem in its specificity. And for me, that involves often coding or making sounds or testing something in a, in a given setting. So, you know, part of it is just listening in that way. I have lots more to say about listening, but listening is the topic on which I become inaccessibly obscure. So I try to limit myself.
Mack: Well, well let’s, so let’s talk about these experiments, or as you call them, in the book, you call them “engagements.” There are several of them in the book, and I would like to just kind of go through, each one. And have you kind of describe what this engagement was, what did you do with sound?
And then what did this highlight for you about our life in the afterlife of data, having gone through this process? And I know some of these are, are things that you did with your students, so that, that might be interesting to hear about as well. Like how you actually use these techniques and methods, either in the classroom or in collaboration with other scholars.
Because like I said earlier, like that’s one of the aspects of your work that I, I just find so refreshing. So let’s talk about this project called Exurbia. What was Exurbia?
David: With Exurbia, it’s an old piece. I think basically all of the pieces I’m gonna talk about in the book are connected in some way to this fellow William Brent, who, without whom I, you know, he’s a professor of audio technology.
He helps me a lot through, through the, the particulars of this stuff. Although, you know, I, I do a lot of it myself as well. But Exurbia we made it, and it’s basically an online sound editing tool. So it’s, it’s, it’s a version of a digital audio workstation, like GarageBand or something like that. But it has four distinct features that make it very weird.
Unfortunately, the website is down now. We, we made it originally in 2011 and just, it would’ve required full, full recoding for contemporary web standards. But, but basically the four things that are distinct about it is that the interface is mostly only in real-time, and we think of it as a kind of non-visual interface.
So if you wanna adjust, say the volume in a specific section of a piece that you’re editing, you have to press play from the beginning of the piece and basically ride the volume knob that you’re using, or a slider in this case, and, you know, if you wanted to, to increase it at the one and a half minute mark of the piece.
You just wait, and then at the one and a half minute mark, you increase the volume and then, you know, that’s it, it plays through. So it’s real-time in that way. The other really wonderful thing is that editing is destructive. So any change you make, you’ve made a change. It doesn’t, unlike in, you know, the beauty of GarageBand and all of these, and Pro Tools and all of that is that you, you get the opposite.
You have the undue feature, and then also the software is made up basically as a sample-based software. So you upload sound samples and then you can manipulate them. But all of the source materials that anyone uploads are shared between everyone. So, you know, if you were to upload a excerpt from this podcast, then everybody would have access to that excerpt from the podcast.
It’s actually, they’re uploaded into a, a limited bank, so you know, there’s like a thousand samples at a given time. So once that bank fills up, then if you upload Sample 1001 that will overwrite Sample One in the bank, which is the fourth thing because any change to the source material on any individual’s computer affects all instances of that source material.
So suddenly, if Sample One had previously been a, a sample of a violin playing a scale, and now is me droning on and on every instance of a violin playing a scale in anybody’s piece, who’s on the Exurbia network is now gonna be me droning on and on whatever else they’ve done to the sample. So, it creates this kind of weird communal editing thing in terms of how it relates to incommunication and those sorts of things.
I mean, again, part of it is just fun, but also the idea came out of thinking about how, you know, both William and I use digital audio workstations, DAWS, like GarageBand, Pro Tools, Reaper, et cetera. We used them a lot and, and I was curious how it was impacting how I listen, right? You probably know this from editing the podcast and you kind of dump your files into one of these things and there’s a bunch of edits you can make before you even listen once, right?
You cut off all the silence at the beginning. Maybe you run a tool to eliminate some background noise. All those sorts of stuff that, that you just kind of, it, it’s just part of how you, it’s part of what you, we might think of as the visuality of the interface where vision is what we see. Visuality is the sorts of ways of understanding that come about from seeing in certain ways.
So after you play around with Exurbia, you’re really able to feel a lot of these assumptions that are packed into digital audio workstations. You’re able to feel how frustrating it is to have to edit everything in real-time for example. And yet that’s something you know, that, you know musicians when, when you’re working acoustically, you know, you have to kind of plan for that.
I mean it’s a real thing I will say. Also the, for me, the most interesting is the question of memory, because I get on to Exurbia, I haven’t played with it in a month. When I get on, I listen to my piece. There’s no record as to how it has been changed by other people’s actions.
So I might be like, ah, I think it sounds different, but actually I can’t be sure whether it’s sounding different is just, I haven’t listened to it in a month. Or whether it’s sounding different is, you know, is because somebody else has substituted a sample somewhere in this complex mix of weird stuff I’m making.
And that matters because it, it points out that actually part of what these online communities, part of how online communities function is actually through verifying and documenting changes as much as you know, what they, what they seem to be doing. Right? And many years ago, many years ago, this guy Alexis Madrigal was talking about Facebook and he said, you know, “What did we get?”
It seems like Facebook is the exchanges. I give up my privacy or some parts of my privacy in exchange for being able to share material. And he says, “But you know, there’s all sorts of ways to share material before Facebook.” This is just in an article in the Atlantic or something like that, but he says actually what you get is a record of having shared right.
You get a kind of a way of documenting and a searchability and an archive of your connections and your sharing, which is a really different thing. And it’s not better, it’s not worse, but it’s a really, really different thing. So I think Exurbia sort of broadly pulls out some of these weird things that change as we start to think about communal activities or as communal activities happen via visual versions of online networks as opposed to other ways that the internet could connect us, for example.
Mack: Yeah, I think it, I think this really, this project really pulls out that atemporality and abstraction that sort of exists in the way that we handle audio in the computer and that versioning, you know, in this, this, this whole, I think it really makes me think a lot about like what, what a file is and that we’re taking music, which is this temporal form and we’re visualizing it as something, atemporal that’s just spread out on a screen before us that can be edited in the abstract outside of time, so to speak.
And, and all of these commitments that you’re forcing people to have, like not being able to go back and, and having to edit in real-time. And, you know, all of these things really highlight the affordances of the computer, but also the strangeness of the affordances of the computer.
David: Yeah. Yeah. And I like that “outside of time” thought because of course, you know, it’s not actually outside of time.
This is, again, one of the kinds of hallucinations that we get.
Mack: But it’s outside of the musical time of the piece. Which is a place that you can’t go when you’re playing live music with others. Like, so your creativity comes, your creativity doesn’t leave the domain of time, obviously, but it leaves the domain of the time of the piece of music, which is, which is an interesting thing to think about.
Alright. Well, let’s, let’s talk about another piece that you mentioned in this book. I don’t even know if piece is the right word for this actually. I suppose an engagement is definitely a better term for it, and I know this is something that you have engaged with students in doing so, the, the label you gave to this was “Fat Head.”So talk about that.
So “Fat Head” is a wearable device just to say you can put it on and walk around. Again, William helped me with some iterations of this. It’s a wearable device that simulates how things would sound if your head was a thousand feet wide, or how things might sound if your head was a thousand feet wide.
And I chose that distance because, you know, under “neutral” conditions at sea level sound travels at about a thousand feet per second. So, you know, if you’re wearing a Fat Head and somebody whispers into your left ear, the sound will arrive at your right ear, assuming it’s loud enough to arrive there, arrive there a second later.
It’s lots of fun. Right? And also, I also designed it as a way to, you know, for listening to music. So I’ve spent a lot of time listening to like, uh, you know, sort of classics things like The Right Of Spring or in the Buck Gold Variations, wearing the Fat Head and then just sort of turning my head cuz you get these cool doppler effects, you know, like the sounds that when an ambulance passes by and the siren does that funny glissando thing, you get these cool doppler effects and it can make it fun. You know, it can allow you to listen to these pieces again for the various first time.
Mack: So let’s, let’s, let’s just jump a little bit deeper into the, the technology so people can picture this. What exactly are folks wearing in order to hear a second of time differential between their left ear and their right ear and create this, this kind of, I don’t know, what would we call it? Like a, like a phenomenological experience of having a thousand-foot wide head.
David: Yeah, Which is, you know, awesome if you’re also, if you’re wearing it and you’re doing something like playing a piece on the piano, you realize every time you turn your head, you know, of course you, you have to imagine that you were playing on a thousand foot wide piano as well.
But every time you turn your head, you get to these crazy doppler effects and you realize how much your head is involved with playing an instrument as you’re playing it.
But what people are wearing in the kind of most basic version, probably my favorite because it’s a little bit absurd, are some noise canceling earphones with a iPod that is elegantly taped to the top, where the, where the two earphones join. So atop of your head.
And so in the kind of most basic version, what it has is you can, you can press the screen by tapping the top of your head and that will give you it, the center point basically. And so then as you turn your head to the right or turn your head to the left, the software uses the potentiometer in the iPod to register those shifts.
And it basically, then introduces a delay that is in accordance with the amount of the shift. So if I turn my head 90 degrees to the right, then there’s, it’s triangulated. So it’s not exactly 500 feet, but it’s basically 500 feet because my, my head is the center point. And likewise using an adjusted version of the, what’s called the inverse square law, which is the way that sound diminishes at a distance, it affects the volume as well.
So those are some of the decisions that I made and played around with. If, if you use a kind of pure inverse square law, then you just don’t hear as much because you know, I can’t hear somebody talking 500 feet away. So I wanted, you know, I played around with that in different ways. I also incidentally tried one with just people walking, like, so we found a, a Toronto is, is largely on a grid system, so there are some streets that you can walk in parallel a person on 500 feet on either side.
So we were on different blocks and they were sending wirelessly, sending audio. So the person who was writing 500 feet to my right was sending audio to my right ear. And likewise, 500 feet to my left was sending audio to my left ear. So that was more, the microphone placement was a thousand feet wide as opposed to stimulating. And I was kind of experimenting, moving through the city in that way.
But the most basic version is you’re wearing basically noise canceling earphones with some kind of receiver device that, that in different ways communicates, or, or sorry, different ways that simulates that.
I thought of the Fat Head as a prosthetic initially, and you know, I’ve, I’ve spent a lot of time experimenting with prosthetics and I believe in spending time so, I have attached microphones to my feet or attach microphones to my hands or reverse my stereo field, things like that. And, and walk around. And, and when I do that, I try to spend, you know, let’s say three hours a day every day for a month or something like that, as a way to, to learn it.
And I thought of Fat Head as part one of these. But what I came to realize, and this is really, really key for the book, is that it’s way more interesting how it works in, or how these sorts of prosthetics work in collective settings. So I had the chance, you know, you were talking about working with students.
I had the chance to start teaching the theory stuff that I teach, like the, the contemporary philosophy that I teach. And started teaching seminars where everybody would be around the seminar table. There’d be between five and 10 of us typically, um, wearing different prosthetics.
So I was wearing a Fat Head. Sometimes there’d be one or two other people wearing a different version of Fat Head. But also people were wearing stereo field reverses, reversers, some people were wearing just things that make them only be able to hear out of one ear, all sorts of weird setups. And then what the seminar, so we’re talking about, you know, the thinker we’re talking about, say we’re talking about Mandy-Suzanne Wong’s book or something like that.
But we are, have to do, so we first have to figure out how to relate to one another with this, with our different technical setups. And what you realize is that’s always present in a seminar setup, and actually probably those of us who teach or who have been at school on Zoom for the, you know, during the pandemic, you realize Zoom reconfigures the technical setup of a seminar classroom in a way that means we have to learn different sorts of minor gestures and, and little variations.
And, you know, something like making a little joke, which I often do in a classroom, is highly disruptive on Zoom because of the way the audio is managed on Zoom, it cuts out other people’s audio. So you have to figure out, you know, people start to do that in the chat maybe, or those sorts of ways of having sidebars.
So in some ways, you know, The Fat Head experiment sort of reaches maturity as a way of thinking about how knowledge or information is collectively maintained and collectively created rather than this version of information as something that is inside of me that I then put out and then, you know, I express and somebody takes in.
So it’s a different theory of communication. It’s one that starts with the, with our relational conditions first, and the readings we came up with of the philosophical texts, the things that seem important were dramatically different from when I’ve taught those same texts in, you know, “more conventional ways.”
So that was really the lesson of Fat Headt for me was, was as a way to think about the relationality of communicative scenarios and to, and to really experiment with texturing them in different ways.
Mack: Yeah, I love this, this experiment and, and this kind of pedagogical, playful experience, you know, of having students using these tools to tease out the embodied nature of knowledge, right?
I mean, we’re all differently bodied in ways that perhaps are more subtle than a thousand-foot wide head or what have you. But, but we are always embodied, we’re always implaced, we’re in different spaces. There’s even a time component, certainly on something like Zoom. But even in, when we’re in the same room, there are room reflections and all of these things are contributing to the experience of knowledge generation.
And none of that can be encapsulated in the idea that information is just this discreet little chunk of knowledge that can just be transmitted from person A to person B.
David: Exactly and if we’re embodied. Which we are. And if communication is also inevitable, remember it’s unavoidable and impossible.
The fact that it’s unavoidable means we are always collectively embodied as well. So a lot of times when people think of a body, we think of my body. But actually, as you say, our bodies are not only situated, but actually you could say our situations are embodied.
Mack: What were your course evals like after that first semester that you did this?
David: Well, unfortunately, the dirty secret about course evaluations right, is that they, you know, most of what they depend on is, is whether what kind of grades the students get.
Mack: So if you gave good grades, this was, this was ammunition for approving what a fantastic teacher you are. And if, if, if not, then this is proof of like, what a bizarre professor you were.
David: And you know, of course I, I, I benefit from structural advantages, of course evaluation as well. We know that they, you know, they disadvantaged people of color, they disadvantaged women, they certainly disadvantaged anyone with an accent.
All that sort of stuff. So as much as I will say the students like to do this kind of stuff. Everything I learned, students like to do this kind of stuff. In terms of the evaluation, who knows?
Mack: Yeah, I don’t read them either.
David: Well, at Penn York, actually they’re, at my university. It’s hilarious because in order to account for all the ways that they can be biased. There’s this whole complex system, but so they often don’t get to me until like 18 months later. And so, it’s like at that point I can’t even remember what I was doing. So they’re useless in that way.
One of my goals as a teacher is to try to create a classroom environment where students are comfortable saying what they like and don’t like in real-time. And actually one of the technical, for those who are teachers up there, I love this tip, is I create a course email account and I give them all the login access so they can email my professor’s account anonymously. All I know is that somebody from the course, cuz I get an email from the course email account.
So if they have a complaint or a concern or that kind of thing, they could send me an anonymous email if they’re not comfortable talking with me about it. So that’s worked really well for me, especially during Covid when you know, we’re managing all sorts of iverse needs around protocols and stuff like that.
Mack: That is so cool. Wow. That’s fascinating. And I was totally joking about not reading my evals. I do read them, but yeah. That, that’s amazing. I, I love that. So, let’s see. Oh, another thing that comes to mind here is that the, the course evaluation is a perfect example of the kind of datafication of things that can’t be datafied and, and perpetuate the inequalities that already exist. Right? David: Exactly. And who is, who is the course evaluation for, Right? And they are, you know, they only existence data, but they only get called upon in sort of cases where something else is afoot.
Like if somebody is in danger of not getting tenure or is in danger of getting tenure and, and the university doesn’t want, you know, whatever. But that, those are the sorts of situations where course evaluation, the data is then called forward, but it’s, it’s not, it’s not like it’s called up when you have.
You know, it only, it’s always in service of something else. Right. Other than what it appears to be.
Mack: Right. And it’s a perfect example of one of those things that nobody believes in and yet everyone believes in. Right?
David: Yeah. Yeah. It’s like reading reviews of your book or your, your album. It’s like if they’re all bad, nobody, everybody knows reviews are biased and all that, but they’re all bad.
It’s still is depressing. And I admit if I suddenly started getting, you know, bad. One of my, one of my students told me they took the class because they looked me up on, on Rate My Professor. And I thought, oh my gosh, I never have occurred to me to look at myself on Rate My Professor. And as I was looking myself up, I realized, I thought if I got like a one out five, I think it’s, I think it’ll bother me.
Like I think, I think if it was, if all, if I was consistently poorly rated, even though I know that, I know that it’s nonsense.
Mack: Yeah, yeah. Absolutely. Um, okay, let’s talk about the third example, which is you were, you know, doing these experiments with the Fourier Integral, or sometimes I hear people pronounce it Forer, but since I’m from New Orleans and you’re from Canada, I think we better go with Fourier.
This is, this is a fundamental technology that sort of underlies sound being able to work on the internet and in computer. So, can you talk about how you’ve been playing around with that?
David: Yeah, so when you, in some ways it takes the longest to explain, but it’s, I think it’s not difficult to explain, but basically when you digitize sound, as many people will know, you use this technology.
Fourier transform is the technology. The integral is the mathematics that underlie it, that allows you to record a sound, what we might call a composite sound. So a sound as a series of component sounds. So sort of like how you can analyze the color purple of say, a particular flower, and then you can analyze that purple, the composite purple, as being made up of the components certain intensities of red and blue, right? And then you can subsequently recreate that by mixing those colors.
So when you’re recording digital audio at, what still gets called CD quality, which is hilarious. But when you’re recording audio at CD quality, basically what, what is happening is you’re taking 44,100 pictures per second of a sound, and each picture contains 16 bits, which is a number, right?
A bit is the amount of, of “information” about the frequencies, the component sounds that are present. And then when we play it back, basically it’s like a flip book animation. People who did like stick figures in the corners of a book when you’re a kid and you do one on each page and then you flip through and it looks like the figure is moving.
Um, so more than a decade ago, I, I undertook this experiment where I was looking at how these things actually work, you know, in action, if you will, in, in, in a computer. So in theory, you should just be able to send a sound through a microphone into a computer. And then it comes out, you know, just like right now, I can hear my voice is going into my microphone, it’s coming outta my computer, and it’s “neutral.”
I know you have done lots of work, many people have done lots of work critiquing the notion of fidelity that is built into that. Ultimately that’s what I’m trying to get at, but from a technical perspective. But anyway, so what I did, the experiment that I did was just, I took four, I took the same recording and played it through four different Fourier transforms in a room and had it sort of loop back on itself over the course of, you know, I said a week.
I actually am not a hundred percent sure. It might have been two weeks, but I, I said a week in the book, so that’s what I’m sticking with. And, you know, I did this and fed the digitized audio back again and again and again. And the key thing about these Fourier transforms is that, remember they’re taking these pictures, right?
44,100 times per second at 16 bits. So the rate of sampling, how often it’s sampling and the length of each sample, which the length of the sample is, you know, how long it takes to take the picture, sort of like in a camera. The length of the sample is called the window size. And basically you take the rate how fast you’re sampling and the window size, and that gives you, together those produce what is called the spectrum resolution, which is basically another word for the quality, the measurable quality of the recording.
So in this case, I had four of these machines recording at the same measurable quality, but they were doing so through different ratios of the sampling rate and the length of each sample. So it’s sort of at like a student, four students can get all, get the same grade on a test, like they can answer specific questions differently, right?
So this is giving you the same measurable quality, but doing so differently. Over the course of the week, doing this process creates this kind of hum sound. So each of the recordings from the feedback end up sounding more or less like a hum because you know the, the recording was 20 minutes long, approximately it was actually a noisy piano piece by a guy named Gordon Monahan, interesting Canadian Sounders composer splits time between Canada and Berlin.
So, you know, it ends up at a hum. But the four different Fourier transform setups each, the hum sort of resolved at a different pitch. So, and, and the process of, of it resolving was different. So again, this is sort of what I was saying at the beginning about like how do we get in touch with the actual processing of a computer?
Part of what you’re hearing then is the result of each specific computer, or you’re hearing each the process of each computer sort of working through it. I will say, and this was interesting to me, so you know, the same measurable, so just sorry to complete that point. The same measurable quality, the same quantity of, of fidelity produced for qualitatively four different types of sounds. Right?
So that was interesting because that flies in the face of how, again, this idea that that of quantity as somehow separate from context and, and you just being a strict measurement. What was really interesting to me when I was doing it, and in case there are sound engineers listening, you know, I was given a hard time and now that I’m older, maybe I can also see this a little bit.
But I was given a hard time by, you know, those who sort of police the terrain of computer science space. And they said, well, you’re getting these hums because you are not doing the proper, what’s called windowing. And so basically when you’re working in digital audio, each of these samples, these 44,100 samples per second, what you tend to sort of do is record a sample that is slightly longer and then have a little bit of overlap between them.
So it’s like constantly crossfading between samples so that you can kind of not have the signal just degrade. Right? And again, this is like the technical part of how it works.
So I was sort of told, well, hey, you know, you’re getting these hums because you’re not doing it right. But the point I would make in response to that is that if I had used the windows, the way they work is by having more information than is needed so that you can make it after the fact and the way they work is very much as an art, not as a science, right?
So this is one of the many, many, many things that makes recording engineers so special is they know how to do this kind of stuff, that to produce the result that they want to hear. In the book, this is actually one of the germs of the entire book and then it ended up just as a footnote, but I wanted to, I retaught myself trigonometry, spent tons of time learning about Fouriers because I had an insight that, you know, in Canada, so Fourier, these transforms are used for ultrasound analyses, which are performed often by nurses.
And what I realized when I had to go for an ultrasound and I saw the, the nurse. You know, adjusting these dials to tune in to see what she was trying to see. And I thought that’s like, that’s an art that’s like, you know, that’s like trying to tune into something perfectly. She knows what she wants to see, and then of course the results don’t get reported until the doctor verifies them.
So we think about this as a technical operation, but it really is a kind of, it’s a, it’s artful. It requires some, this kind of craft skill at, at the very least on the part of the, the nurse or the technician who, at least in Canada, you know, disproportionately women, mostly women of color, and are paid much less than the doctors.
So, if we, if we were to, to acknowledge the artistry that goes into this, we might have to actually then pay them as more skilled laborers. So we pretend that this is instead just a kind of like flicking, you know, a light switch on or off, which it’s not, you can have a better or worse ultrasound technician.
And so, yeah, I was interested in this. And so the point is that this is also something that sounded engineers do with this kind of stuff. They can adjust windowing. To correct or to introduce redundancies. I almost said to correct errors, but actually they don’t correct, windows don’t correct errors.
They introduce redundancies so that you can make the decisions that the errors are not audible. So yeah, the only thing I’d sort of say, it’s not the case then that the hums. So the only other thing I would say is that, you know, these, I, I made the, at the end of the day error, right? I said at the end of the piece, the hums, you know, you hear four different hums, but what’s kind of crucial is how you get there.
And the hums, you know, we might think, oh yeah, there’s a little bit of a kind of room noise or a little bit of something in the recording that then just gets amplified over the course of the hundreds of repetitions, right? But that’s not quite right because part of what’s happening as a computer computes the audio, the computer is also computing itself computing, right?
It’s saying how much, what are my resources that I have to do thisc omputing. This is why if you look at like a progress bar, you know, everybody laughs about this. It goes to 90% in two seconds, and then the last 10% takes 10 minutes or something like that. Yeah. Right. That’s because the computer is sort of always in the background doing essentially a progress bar saying how much, how many, how many resources do I have to dedicate to this task?
And that’s part of what a computer does, it prioritizes tasks. So these remnants are there in the computation. They’re just usually so minor, so small that they wouldn’t become audible. But if you repeat it and repeat it and repeat it, because these, the differences in the computation become audible in this way, they are impacted.
So if you can repeat it multiple times, it may not arrive at the exact same hum. So it’s really, really important actually to, to spend some time thinking about, to me, that was really interesting to think about, right? To think about how a computer doing what seems like a totally mathematical operation, like literally working with these Fourier Integrals is nonetheless doing so in a way that is, it’s not like, you know, it’s not at a consistent rate and because it’s not at a consistent rate and what it’s doing is time-based.
You know, this is sort of like their, their speeding up and slowing down the podcast, except for them, it’s, they’re processing not the podcast.
Mack: And do you relate this to the work of the theorist M. Beatrice Fazi, who talks about how computation can’t be computed. That, that what computation does is kind of take this, this infinite world we live in and process that, represent that as finite numbers.
And that process is always gonna leave something out. There’s a incomputability involved in the act of computation, which relates to your underlying thesis of incommunication, right?
David: Yeah. That’s it. Exactly. I mean, Fazi is absolutely fiercely intelligent thinker. And you know, it’s also, I think, in many respects, a proper philosopher in a way that I could only aspire to be.
So she is interested in the contingency that she calls “internal to computation.” So she’s saying, of course, when we compute something, you know, like in the realm of audio, we all know that there’s no such thing as a perfect recording because we’re, as many scholars have pointed out, you know, what we call a kind of perfect, a recording of perfect fidelity is in fact an idealized listening position.
And there’s lots of baggage that’s built into that about who the ideal listener is and what I’m trying to draw from Fazi’s work on incomputability is to say, well, actually that’s also the case within the computer. So this seemingly kind of flat landscape of computation that just seems like it’s just mechanistic is in fact textured.
Fazi, my favorite quotation from her is, maybe, and she just sort of says, you know, computation is computation, right? And this is kind of like, it seems like, okay, that’s, that’s hilariously simple. But her point in saying that is that it is, at least my understanding of her point in saying that, is that we have to think about computation as its own thing, right?
If you want to get at what the rationality that is enacted by computers, you have to understand that’s different than the rationality that is enacted in formal logic, right? It matters that computers are actually enacting this and the, you know, the work that she draws on to make that point is the foundational work of contemporary computer science.
So like people like Alan Turing and for Turing, this is, you know, the kind of key thing is that a computer can, in theory, compute anything that’s computable. So there’s like kind of definition there of well computer is this thing that does compute. But that’s necessary because without that, the whole thing falls apart.
Mack: Okay. So maybe a final question about sound and listening. Uh, you suggest that computers organize our listening even when we’re not using a computer. Can you explain that?
David: Yeah, it’s a really good question. I think the most basic answer is that one might think of listening as information acquisition. A lot of times that’s what talk gets talked about, right?
Listening as receiving inputs through our ears that are then processed by our brains. So when we think of listening like that, we can miss all of the strange relations and indirections that happen. So even if I just think about what happens in my ears when I’m listening, right? Sound waves are conducted through the auditory osicles, the little bones in my ears, and they are in turn connected to this stapedius muscle, which contracts and expands according to the various stresses that it is under.
So even, you know, just to be clear, listening is not the province of the ears. We listen in all sorts of embodied ways, et cetera, et cetera, and technical ways. But even if we were to accept that it’s about ears and about information reception, you know, the, the point is that our ears, you know, the same sound, will not sound the same ever.
It just, it, it just simply doesn’t Because I mean, and if people, if, if people wanna test this out, you know, just think about going to, walking into a live music venue. You walk in and it sounds super loud and your ears adjust. Right? And that’s a physical process. And what they’re adjusting to is, is changing how receptive or the ways that they’re receptive to the vibrations that they’re encountering.
So maybe most of the time that we hallucinate something of a sound remaining unchanged as it moves across context and through time again, that’s, that, that is a kind of hallucination that is in concert with this idea of communication as information exchange.
Mack: Yeah, and an interesting thing is happening to our mutual hallucination of communicating via Zoom right now, which is your voice is starting to go a little bit robotic every now and then, which I think is totally appropriate to this conversation.
David: Yeah, I wasn’t gonna disclose that I am a cyborg sent here by computers to attest to their strangeness.
Mack: I don’t know if my sons are gaming right now and glitching stuff out or just the you’ve made the internet mad with all your disparaging talk, but
David: Internet. I love you.
Mack: Great. Well, I think we’ve covered the heart of the book. I hope you feel we have. Is there anything that’s kind of been left out in that regard?
David: I guess the one thing that I would try to again emphasize about all of this is that it’s important for me to try to stay in touch with the reality that the world is not coherent with itself.
There’s no single world. Any theorizing needs I think to start by acknowledging that there is no single perspective that is gonna make sense of everything. And so if we, if we start from that place, then I think, and this is what the book tries to just keep in touch with, like it tries, I try to resist saying, this is how things really are.
Try to resist, “look, computers seem to be doing this, but really they’re doing this other thing.” Instead. I try to sort of frame it as they’re always doing something else, and so what are the techniques? These are the techniques that I’ve developed to try to attend to some of those other things that are, are going on, but, but nothing is gonna kind of neatly package the world into something that makes sense.
That’s really the important thing to me. And it’s not to say that the world is nonsense, but that’s what’s important is how to stay in touch with the actual textures of realities, I would say.
So the part of it, again, the part of us that learns to wake up from our dreams and, you know, not be mad at, at the person that just wronged us in the dream is important, right?
Like, that’s, it’s worth, it’s worth having that sociality, but it’s also important to not just simply pretend that you’re not feeling what you’re feeling because that dream is also real and not, it’s not just real because it, you know, it can be interpreted in certain ways, but it also means, you know, you’re probably more likely to feel a bit short with that person that day.
So it helps, you know, like the irrealities are active in different ways and productive of different things.
Mack: Alright. Well, David, thank you. This has been so much fun. I really appreciate you coming on the show.
David: Thanks so much, Mack, I really appreciate it. Look forward to all the future episodes too.
And that’s it for this episode of Phantom Power. Huge. Thanks to David Cecchetto for being on the show. You can see transcripts and links to some of the things we’ve heard and talked about today phantompod.org. You can also subscribe to our show there or wherever you get your podcasts. We’d love it if you’d rate and review us on Apple Podcasts or Spotify or your platform of choice, and we’ve made it super easy to do. Just go to rate this podcast.com/phantom.
Today’s show was written and edited by me, Mack Hagood. Today’s music was also by yours truly. Phantom Power’s production team includes Craig Eley, Ravi Krishna Swami, and Amy Skjerseth, our production coordinator and transcriber is Jason Meggyesy.
Take care and see you next time.
[Robotic Music Fades]