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title: "Vladan Joler"
status: "Auto-transcribed by reduct.video"
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James Parker (00:00:00) - I th I think it might be, it might be worth just beginning at the beginning. Vladan uh, and, uh, asking you to introduce yourself again and say a little bit about your, you know, your background and your work. 

Vladan Joler (00:00:13) - Okay. My name is Vladan. I'm based in Serbia. So, uh, yeah, I think this is kind of explanation of, uh, of like attain visibility infrastructure started bitten me, like maybe deeply like five, six years ago. So we were running one organization called shared foundation and organizing some events for these kind of stuff. But the moment we realized that you cannot solve a lot of problems just with like big meeting, interesting people, because we understood that in moments when something was like captain in Q and the region, we didn't have the capacity to react in a way, like more like expert capacity in sense of like, you know, legal analyses, technical analysis, those kinds of stuff. So we started to do, to organize one, like expert group of people and, uh, uh, basically bunch of lawyers, experts, cyber forensics, and song. And this is one moment, uh, I started to do some kind of like, really like personal investigation of like, because I always wanted to, it was always impressed with this kind of internet maps back in like early two thousands and stuff like this.  

Vladan Joler (00:01:54) - And, uh, so I realized that I'm now able to finally to draw those kinds of maps, uh, network maps, internet maps, and stuff like this. And then, you know, with the first map, you know, when you are able to see something, how it looks like, then you are more questions are coming. What are those points of centralization things that I'm seeing in the map? And this is when I basically started to shape my research in some kind of call off, but on the way with like cyber forensic people in order to understand what's going on behind the screen. So first started with like some kind of simple maps and how basically data is flowing, what's going on. What is basically the first question that we asked was like, what is the life of one internet? And then we started to follow stories were coming more and more after that. 

Vladan Joler (00:03:07) - And then, you know, like step by step, we were some kind of, we were like discovering different layers of transparency, being able to go deeper and deeper and deeper behind those infrastructures. And then after that, the more and more complex maps, I was able to create more and more complex maps of those. So the first big one was, what about Facebook cognitive outcomes? This was called Facebook. And that was the beginning of those big black maps that I was like, then make Kinglake for next few years. Like the maps, different kinds of questions that I'm asking. Yeah. So that's kind of a brief intro into what I was doing for the last several years. 

James Parker (00:04:03) - Um, thanks so much. Uh, when you, when you say several years, I mean, how long have you been doing this kind of work? Is it, you know, is it a decade now or, 

Vladan Joler (00:04:12) - Well, I think that the shared lab, I remember I was maybe like, uh, okay. The, the, the same conference and those kinds of big evidence. There were like 2011, let's say. And, but those more kind of like, uh, visualization and visualizing invisible infrastructures, I think it started like around like 2014 or so. 

James Parker (00:04:40) - And would I be right in saying that anatomy of an AI system is the kind of highest profile of those visualizations? 

Vladan Joler (00:04:47) - It somehow became the most visible one and it's a bit different than the ones we were doing before. So in the beginning we were more like interested in, so it's a process of, you know, it's a process of learning what's happening.

Vladan Joler (00:05:04) - So first we go to like being satisfied in a sense, just to getting a picture, you know, seeing the map, seeing the infrastructure, then, you know, more and more, we were like going deeper more and more, the, those maps became like more and more abstract in a sense. And the questions that were asking were different  ...  and AI, it's kind of like different than other ones, because it's not so much about the infrastructure. It's also not so much about like data visualization. It's more like a cognitive map of one really, really complex system. And somehow that might probably resonate better with the general audience. And what was really interesting is that, uh, you know, like there were like so many different audiences that were like accepting this fact as something interesting because in the beginning, like those first Maximus making was mostly like communicated within the tech community and also like, uh, legal and advocacy communities related to this kind of decentralizing, but also like privacy issues. And somehow with the, with the, that, to me, if an AI went into completely kind of different directions, so first people started to realize the, or like accept the system, kind of art design, kind of like a tool for, um, you know, like universities and teachers to explain something. So it went into lots of different directions. So probably this is why it became more known than the other months and went out of these kinds of circles of like internet activists, lawyers. 

Vladan Joler (00:07:08) - And I'm kind of lucky that we managed to, to break those other bubbles and be present on different places 

Joel Stern (00:07:16) - I was going to ask, um, flat and what, and this is, you know, again, I feel a bit silly cause there's sort of some things we talked about last week, but I think it's, it's, it's good to cover that ground in the, in the hope of sort of progressing a bit too, but just to say something about why you chose the Amazon echo is that as the object of study and, and, and what it is about that particular device that, that, um, made you feel like, um, such such a complex map would be possible. Um, but, but more than that, um, you know, sort of productive and generative, 

Vladan Joler (00:07:51) - Well, it was some kind of, you know, there was like lots of different, uh, aspects of that came together in like why we chose Amazon. First of all, were like, uh, involved in some of these sorts of projects, Mozilla foundation about like those kinds of voice interfaces in a way, like, for me, you know, like this was like kind of topic that we were like researching and, and those Amazon echo at that time was one of the most known and one of the most exposed commercial objects that's died. And then I met, uh, the same, like, um, working process. I met Jake and, and then we kind of started like together to explore this idea of visualizing what's going on behind that specific for me in general, it was maybe not so important is it like Amazon echo or any other device, but somehow like, because it belongs to, because at the end, like those complexities are similar to many different options. 

Vladan Joler (00:09:09) - So in a sense, like if we think about anathema and AI, the map will not be much more different if we speak about like an iPhone or any other CD, for example, or something like this. But in a sense, I think it was good to choose, uh, Amazon product because Amazon it's like one gigantic company and, you know, like going through those kinds of jungles of Amazon company, there's a lot of interesting things to discover, you know, like starting from this kind of like distribution centers treat their workers and those kinds of automatization of labor. So it is a really, really, and in the sense, like, you know, develop this man is owning this in the world is owning this company. So like, it's a good example to explore this complexity of contemporary capitalism, contemporary.

Vladan Joler (00:10:15) - No production chains. Yeah. I think we, we, it was a good choice compared to some other device. 

Sean Dockray (00:10:27) - Can you talk a little bit about how you went about doing the research or like what information went into the material to make them? 

Vladan Joler (00:10:37) - Yeah. Yeah. So the, the, the, the middle part of it, I could open the map. 

Sean Dockray (00:10:41) - I was more just thinking, you know, where you look like, what a database is, what I'm. 

Vladan Joler (00:10:51) - Hmm. So, Sarah, there are many, many, you know, for me, uh, to those segments of the bank can be in a way it's a, it's a story for itself. So there is no one way of trying to deal with all of those things, because like, for example, if we start with, with, and this is in a way similar to how we, how I progressed in my research, you know, it always starts with you and then trying to go deeper and deeper to those like different layers of transparency. So I spent like, let's say first five years in, in doing this, doing this middle box. And then when I started to throw the, the, the, the anatomy of an AI, I knew all of that in a sense. So that was not the problem. The middle part of the map was never in that moment. It was not the problem for me. 

Vladan Joler (00:11:57) - So, so for example, just to explain your different kinds of like the first you try to open the bikes, you know, to see what's going on inside, then you'll realize that it's not much to see. You know, then after that, you're trying to understand how these devices connected to my mentor and what kind of life that kids have, what type of data is going out from device and entering into device. Then you're trying to follow those packets to understand how those packets and where they're flying and destination. So this is linked. For example, then you are researching the locations of those like data centers was trying to understand how this in the visible infrastructure of Amazon data centers is working, which data center you belong and your data have belong. And for each of those steps, you are using different tools, different types of like different methodology. 

Vladan Joler (00:13:02) - So for example, for, I dunno, you can try to find the blueprints of device, and then you are entering into some kind of shady websites in which like sharing the blueprints of different objects. And then you realize, okay, this is the first layer of transparency, because like most of those companies doesn't want you to be able to have a blueprint of their conflict in a sense of not just that this is some kind of like a business secret or something like they don't want you to be able to repair devices. Well, you know, most of the cases, for example, in case of Apple and stuff, then I'm spending a lot of time to try to find the blueprint, for example, to be able to make some kind of drawing of that, then next step it's like, for example, like  ... , you're using the, and then you're trying to understand, like dropped her off the back ends and what kind of data is fine. 

Vladan Joler (00:14:12) - So those are in the middle of middle of this might, it's mostly like a tech research. So we are using different technical tools to explore. But then in the, in the moment we, this metrics pendants on both on the left and right side. And this is basically with the one friend of mine and, uh, John really, you know, uh, infected me with this idea of like materiality of infrastructure. And then she gave me the book from about geology of media. And then I was completely impressed by this idea of, of being able to look all of those infrastructures, but in some kind of deep time from deep dive view. And this is when we expanded this mapping to some kind of light.

Vladan Joler (00:15:13) - Stop geological process elements. And then when, when I, when I stopped that, so it's a different end research that it's a different methodology, completely different methodology, that middle part of the map. So in, in this case, it's mostly like, you know, trying to feed somebody, searched from other people, trying to collect enough information about suppliers. So it's mostly, let's say investigative journalism on one side, but also some kind of like, because I had the chance to more or less piggyback on this research from what was the line in the sense of like, I was going to go around the world and doing somebody to IOT. And then I was running around, around in the way, because we were going to India, China places where basically materiality of this kind of production is happening. So I was running away and trying to do my own investigation, trying to visit mining sites, go mining, or trying to Foxconn in China, trying to, not trying to see all of those places. 

Vladan Joler (00:16:40) - And for me, that was like the really, really important parts. Even most of those things you cannot see on this map, either you read in the text at the end, that's kind of expedience. So they're being, they're trying to enter into some building complex. So it was really important for me to be able to feel the thing really going on. But in a sense, you know, like most of the left part of the map is mostly like, uh, let's say investigative journalism and maybe some kind of academic working late, fine to collect as much information. 

James Parker (00:17:21) - Can I ask Vladan um, did you get into Foxconn or any of these places and, and what was it like? What did you learn specifically? You know, you said it was very important. What was the, what, what was it that was important? 

Vladan Joler (00:17:43) - We, we, I didn't manage to Foxconn. I was in front of the building and then looking out those like workers and stuff like you, I didn't have enough connections or whatever, get inside the folk school, but we managed to go into one of the biggest Amazon houses where those robots are going to cut down and picking stuff. I managed to went to a few, uh, mining sites and I managed to go to one really amazing place where for example, all of those ships are dying. So it's some kind of graveyard of ships in India, but not in, not into Foxconn at the end, but, um, yeah, maybe next time, like, because for example, in case of Amazon warehouse, I think it's important to understand it's different when you're looking at this from some pictures or looking at this place from like, by reading some patterns it's different when you're, when you aren't there. 

Vladan Joler (00:19:03) - Uh, you know, physically, because for example, in the, I was never imagining, for example, this kind of, almost of the big house could be so noisy so that the level of noise it's like amazing and all of those people there. And I'd say it's like really, really feels you're in some kind of inside the body of some Machine. And I mentioned to here, I forgot to tell him like the last question, the question before, one of my favorite methods of investigation, it's also like digging through different patterns. And this is in a way we did like a big bar to the Facebook map, but the old, uh, three was also like really present doing this one.

Vladan Joler (00:20:00) - What was it like really also like really important for me, in a sense it's like, okay, you get there enough data. You'll know how to parts of this puzzle, but in a way, like really important it's cow at the end, you are shaping all of these into something that is visual. And in the sense like this is like for me, what was really helpful in school, Daisy can try to think about these map through those, uh, triangles that were really present. And they are basically the main, uh, let's say visual tool for, for, for, for this map and this something that I kind of as a, as a, from one book also from pretty standard books about, uh, uh, digital democracies and in a sense, those triangles helped me to, to build this story. So, so basically like, uh, the main idea behind those triangles. 

Vladan Joler (00:21:11) - So you always have some kind of means of production, so resources and tools, and you have a labor and with two of them, you are getting a product of the Lake. And in a way, it is how I shaped them in the sense of like some kind of like continuation of those triangles. For example, you're starting with, uh, you know, some kind of, uh, elements, and then you will, you are, you are mining those like, uh, metals or things, and these four become some kind of resource. And again, using some labor sense of smell of thing. And basically following this map, this map, you're following some story that is held to those triangles. That was kind of one of the main core visual elements. And let's just be as relevant logical arguments with these maps. 

Joel Stern (00:22:18) - And w what happens to those triangles when, when we sort of move into the central spine of the map, they start to become, you know, sort of much more complex in a way. And there's one diagram that my, my eye is always drawn to, which I think is the, um, Amazon voice services diagram sort of debt down towards the bottom there, which is that sort of an amazing image of hundreds of intersecting points and on a, on a sort of node that the AVS image, um, uh, sort of need, if you could sort of, um, say something about what that image is, um, sort of representing to you and, and sort of how you kind of produced it from the material. So basically this is the moment 

Vladan Joler (00:23:04) - In this doctor when we were getting into the AI and machine learning, uh, level of the story. So basically this picture is some kind of like, let's say the traditional way, how to represent a machine learning system. And so we took those notes and some kind of, you know, this is like the story about the, either part of the, the part of the system. And he says, yes, the kind of structure it's different in the middleman, or part of the life can drive out because left and right. They're mostly presented through some kind of different way of storytelling. And in the sense that I don't know the most complex part of this, those two drawings on the bottom, and I really spent weeks or months, and like trying to deal with that, because I know from, from here, we are seeing some kind of, you know, the left side of the map. We were seeing some kind of process of flotation of nature and exploitation of human labor. But here from the bottom, we are seeing some process of exploitation of, of date quantification of nature. And then I had this kind of idea of how to, you know, let's try to classify what kind of like the ties being quantified in a sense of like first I.

Vladan Joler (00:25:00) - Uh, tools to, to separate two different fields like quantification of human body actions and behavior, and then quantification of human made products, because idea was like, you know, everything that can be quantified real big because like this kind of, this is this kind of offline data education, everything that, that data is being understood in the sense of like our body and what we can do. And they becomes this territory that need to be invaded. And in a sense here, I tried to classify all of those different, different types of like, so we have like, for example, quantification of our in individual bodies or social buckets, then we have human body biometrics, medical, forensic, psychological behavioral profiling.  ...  

Vladan Joler (00:25:59) - lots of different things because like, when you throw this map, this kind of thing, it's about CPGs. And this is really hard because like, when you have, you know, like, uh, three lines coming from something, you know, this is a statement, this is like your own classification system, you know, all in a way when you have like this human being and four lines, this is how you define something. This is your claim. It's, you're saying like there's four types of law. And in that sense, like it's really hard to struggle or all the time with these kinds of like, you know, like every line leads to some kind of statement of something. And then I spend a lot of time in, in like trying to understand how different people present or in classified something, you know, in order to, in order to be more and more precise in how I'm drawing something, how many lines that are coming from each of those points. 

James Parker (00:27:14) - I absolutely love those, um, those spirals of the bottom of the diagram as well. They're really, really provocative. I mean, apart from just being beautiful on the, on, on the, at the eye as well, and they sort of invite you to get in and sort of turn your head slightly and sort of read, you know, to read them, they sort of invite a slightly different relationship with the diagram, but they're also, you know, they're, they're the bit where the data extraction happens. And as, as you're saying, that's, you know, that's crucial that I was just wondering if you could say a little bit more about how we should think about data in relation to extractivism or, you know, a lot of people saying, uh, you know, um, data is data, production is a form of labor. We need to valorize data production and we can produce a kind of a relationship with the capitalists and so on where we get paid for our data reduction. 

James Parker (00:28:08) - And some people say, no, no, no, no, no. Um, data extraction, you know, is really like a form of colonialism. And we can understand this kind of expropriation, eh, as part of a continuous history of, um, capital's relationship to colonization. And, and then there's, you know, other people saying, well, no, the problem with data is bias or computational empiricism because it imagines that to be a kind of, uh, you know, a truth to nature that only the algorithm, you know, can reveal, um, through the process of data extraction and so on and so on. And I was just wondering, you know, could you, could you sort of say a little bit more about how precisely you understand the, you know, the, what it is that's going on with his extraction of data? I mean, either in relation to this diagram specifically, or more generally, how does data become a political problem for you? 

Vladan Joler (00:29:06) - Yeah. Okay. I think, I think that's the key question then the key problem, the another one it's exploitation of labor and resources, but this question about this kind of new types of activism that exists, they're like really important to try to be on my, I have all the maps it's still being published, but this one is crazy. So I tried to make, uh, new methods called a new extractivism and try to basically this one is a bit different than the other ones.

Vladan Joler (00:30:07) - Because this one is kind of, uh, it's, I'm kind of like mosaic or assemblers, assemblage of allegories. So what I try to do here, I tried to mix all different, like lots of different ideas that I really like on this topic of new extractivism and exploitation of data into one big, crazy map. And I considered the form. These basically starting with this idea of also from one friend talk is some names through the sea, like speaks about his gravity of, of different platforms. Like for example, Google and Facebook, like, you know, like in the similar video of like, uh, this kind of theory of relativity, like how they're like bending the space and time. And in a sense, I, um, he is talking with this allegory and then like, you have like little God. So it says some kind of a big crazy diagram and it's called the new extractivism and it's, it's basically some kind of mix of different, uh, uh, allegories and some different concepts that are combined into, into, into one story. 

Vladan Joler (00:31:26) - And in the sense, it starting with this kind of idea of, uh, starting with, uh, this idea of gravity, how different like, uh, how basically those companies are bending the space and time. So we have like one little guy who's trying to escape from this, like a black hole that we mentioned that a black hole, for example, in one moment you cross this like point of no return and you are becoming this kind of person who is like, you know, addicted to those like services or personal, who is not able to go out from there anymore. And then from here, this person is falling into some kind of another structure, some kind of combination between the cave from a plateau and of course, an optical. So this person is falling into the cave. Then I'm trying to understand what is the texture of this cave. 

Vladan Joler (00:32:33) - And then so on and on, and following like this, basically the structure going deeper and deeper into this kind of really abstract ideas and obstacles, uh, concepts that basically define, or how we live today. So for example, here, for example, you have this idea of, of like you ask about data so that it creates a goal in a sense like that. And now you have like a different, uh, companies, different corporations that are trying to, you know, conquer this and that in a sense it's like this kind of wild, wild West racing their flags into some kind of data fields, but in a way, most of those data fields are related to our bodies. So it's some kind of contest to conquer deeper and deeper and deeper into our body. And in a sense, what's really interesting in, in, in context of like, you know, development of these things, it's like before you had like more or less kind of like, uh,  ...  earth, let's say like how many different metals, how many oil you're able to extract let's in the sense, like in order to progress this kind of like, uh, uh, 21st century capitalism needed to find a new tenant to conquer. 

Vladan Joler (00:34:13) - And in a sense like the, the, those, like  ...  data, it's this kind of new territory that is able to be, to go like, you know, have this kind of affinity to infinity. So there's like so many different worlds that can be almost, uh, infinite, uh, you know, like some number of worlds that can be, uh, conquered, but in the sensitivity, it's important to understand that those, you know, girls are basically our bodies and our socials and everything about us. And in a way then in, after you, in a moment, they are able to conquer. Then there is this.

Vladan Joler (00:35:02) - Second step in the process, it's basically the process of them closer. So they are trying to prioritize and to build the fence around those territories. And once they build a fence, they're able to gain profit and to do the online space based on online disposition. So the, the ECU have these kind of new extract to be some it's like really, really, I think really important, really deep. And in a sense, I also, in some sense, follow this idea of like, you know, like continuation on this kind of whole colonial practices, but the kind of like a lot of the demands. So it's not just about like data is a new field it's combination as if we combine this idea with the things that you're like explaining. And then that, to me, if an AI, then you'll see that all the time you have this kind of mix of different forms of exploitation, human labor, and exploitation of nature. And that, and I think it's like really important to, to speak about that in that context of like, not to have like, you know, to have a cold picture, we need to see the problem more broad in the sense of like covering social relations and labor and stuff. So that's the, but this one it's maybe too crazy for like, maybe we can speak once, like, like in separate topic about this one. 

Joel Stern (00:36:50) - I love this one. Vladan I mean, this is amazing to me. I mean, it sort of goes so far beyond the kind of material infrastructure that you were mapping before. Is this still a sort of work in progress or is this something, 

Vladan Joler (00:37:08) - Hey, you totally need to proofread it too. Like I was like speaking with like friends and colleagues that can comments and stuff. That'd be, so it's almost done. 

James Parker (00:37:25) - I was wondering, I don't know if it's too early to ask this question, um, but it's sort of clear that so much of your work is about a kind of, um, I want to say, you know, a diagnostics or a representation, right. And sort of, there's a sort of, there's a politics to that representation. Obviously, you know, we see the world differently. I, I see the world differently. Having seen anatomy of an air. I can't, I can't get it out of my head. You know, it's, it's a frame through which I now encounter, you know, not just Amazon know Alexa or echo or whatever, but also, you know, now the halo, which, you know, just got launched last week and wants to listen to our emotions apparently, you know, and everything. So it becomes a frame. So there's something obviously strongly political about that, you know, this kind of map that's working at the level of, you know, a little bit more speculatively or something too, but I wonder do you also put on a slightly more expressly, normative hat, um, ever, do you ever think, you know, the, sort of the, what is to be done question because there's a sense in which like the, the maps are sort of, so they, they point to, you know, the whole world and the G you know, the, th there's something overwhelming about them as well. 

James Parker (00:38:48) - So there's, you know, one might one might encounter anatomy of an AI system and, and feel kind of politically disabled, you know, having had one's eyes opened, or what have you, I'm just wondering, do you ever sort of, do you ever step into that sort of slightly more kind of advocacy space? Do you have, do you have a sort of a sensibility about how to confront the, the challenge of whatever you might call it? So, you know, probably not surveillance capitalism, I'm, don't know what you'd want to call it, but you know, the beast, the behemoth that, um, your you're representing in your diagram. 

Vladan Joler (00:39:22) - Well, I, I think like, uh, you're not like I don't feel, I think my mental capacity in a sense of, like, it's kind of limited into like, uh, trying to understand reality. So I, I don't feel confident in like, proposing 

James Parker (00:39:45) - That's quite a big job on itself. It's not that limited. 

Vladan Joler (00:39:51) - Yeah. Because if, for example, I not, you know, I, for example, I'm not too sure should, might keep.

Vladan Joler (00:39:59) - Go to school tomorrow or not. And in that sense, I don't feel comfortable of like proposing how to solve the world's problem. I'm not able to deal with everyday questions. So what time may be good, just trying to understand how reality works from my point of view, and then maybe there will be someone else who will, based on that came to you. No idea. Okay. Maybe we can do it differently now, but I always like to quote like alone, some people are all asking this question for me, but I, 

James Parker (00:40:46) - No, I hate that question when people ask it to me. Uh, yeah, 

Vladan Joler (00:40:50) - But then the cool thing, this, uh, you know, a guy from blade runner, my job is still to find what's wrong, you know, to deal with the, not to propose how things should be done now. So I kind of, for me, it's enough. 

Joel Stern (00:41:08) - It was that was that the blade runner or, or the replicant who said that? I can't remember. 

Vladan Joler (00:41:14) - I think it's a blade that having some form, the, the, the thing is the problem it's like so complex and so diverse that in, in, you know, you know, for example, there's like this kind of thingy about like, you know, ethical AI and stuff like this. And then the question is like, enrich segment. Do you think AI can be ethical? Are we speaking on the level of data set or we are speaking about level of, of like, you know, like workers in Congress, so, and in order to solve the problem deeper, then you need to deal with much bigger problems, like issue of capitalism, issue of like inequality, colonialism, you know, like, so I don't think there is like a simple solution. What we can maybe try to do is to try to improve parts of the map that is not, I don't believe there is like, you know, like one solution that can solve all of these. 

James Parker (00:42:23) - Can I ask, um, similarly speculatively, uh, if you have an intuition about how the pandemic is affecting the, like, if you were to redraw anatomy of an AI, you know, in 2020, do you think that the story would look different or do you think, do you have, have you been sort of following, um, big techs sort of positioning in relation to the pandemic? 

Vladan Joler (00:42:52) - I didn't follow a lot, but what is obviously the most dedicate part of the mappings? Basically those supply chains, because we managed to dependent on like planetary scale systems of production bananas that are coming close to our shops or how tech is being made. So in the sense of, I remember like signing ex-Yugoslavia and during the eighties, because we were like in between two blocks to the Western block and we, uh, we were pushed to, to develop our own, uh, that infrastructure on factory. So we were able to produce, you know, like our own capacity at that time, of course it was not even near to all like, sorry, in a sense, this is like, but it took like decades to able to do that. And I'm not so sure that we, you know, like, uh, because it's really obvious. It's like those like flex supply chains are now collapsing because of, it's not so easy to see like production on different places in the globe. This is something that is going to collapse. And for example, one friend of mine, just five to buy like 10 the sweets. And there is not because there is all like supply week. 

Vladan Joler (00:44:39) - So this is something that continued to be present. We rebuilt, then it will be like some kind of like a moment. Did we realize how fragile and how conflicts are in our supply chains and production chains.

Vladan Joler (00:44:59) - And this is to be a problem, but there is no easy solution. It's not like we can grow our own, you know, computers in the backyard. Like we can grow. That could be interesting, you know, like the complex technology relies on like lots of different materials coming from lots of different parts of the globe. So in a sense, you know, probably if this continue, we will, I'm not so sure that we will be able to produce this kind of sophisticated technology anymore in the sense of like, because like, you know, not each country or each part of the world have all the materials that are needed. Some kind of like, I dunno, it's going to be interesting, but the, in a sense, like, you know, like we should not believe in trumps and like, you know, like these kind of ecology made in USA, it's like, it's almost like even the big, lots of different resources that probably not in this moment to complete the full supply chain within one country. And then on the other side, kind of like, you know, like don't like, uh, what is also interesting field is how all of those, like data collection and surveillance that is bound to be now used for those tracking. 

James Parker (00:46:42) - Yeah. So Sean's question gets to something that I was wondering right at the beginning about the choice of Amazon echo, you know, there's something about, there's something about a voice interface that seems particularly, you know, opaque, we're not a peak, um, immaterial, um, invisible, um, it, it wants to disappear itself and even more profound a way than the computer with the computers that we're talking through now. So I suppose, um, I I'm, I don't want to overrun Sean's question with my own, but yeah, just this idea of the voice, the specificity of the voice in some way, as in relation to visual analysis. 

Vladan Joler (00:47:37) - Yeah, no, but what's really interesting is it's kind of like deconstruction of the interface and visibility of the interface, because before we were able to, you know, see the interface, understand the element and something to face, and then in that sense, you know, becomes some kind of like your office, you know, that can defend and defines what you can do or what you can not do define the length of your, how many characters who can say, or write or what they would. And in a sense, like that sense the visual interfaces are, let's say more transparent to salvage extent in the sense of like, maybe they are more transparent, but in a way what's going on behind them, these kind of invisible infrastructure and the rules them are, again, not so transparent with the voice. We have a similar, even more maybe complex situation because we are not able to see data. 

Vladan Joler (00:48:53) - We are not able to heal the borders of the interface. It's not written, it's never written, right. But this is the least of the works that this system can understand. For example, in case of Amazon Alexa, like those of 200,000 words that this device can understand. So in a way you don't know the borders, the system, I think that you know, where those borders exist. And then in that sense, those borders are one that are defining what is possible and what is not possible and what is possible for, or different people. So for example, for male or female, for like different decks, probably it's much harder to deal with those kind of like can visit completely invisible, completely invisible interfaces. But I believe there are ways to investigate that as well.

Vladan Joler (00:50:01) - Let's see, I think it carries the similar function of like the normally regional interface in a sense of like, it's kind of cool, active. Maybe it's even more regular than the visual. No, no, no. I didn't went into that direction of trying to understand the Porter, trying to understand the shape of the interface into some other direction. We had like one project that they never managed to finish. But then thinking about the law, it talks about the border. It was about like investigating where is the border, for example, limitation, for example, like, uh, in that case it was about Google. And the question was, is there any war that exists that is behind the border? So that then any, any war that is not captured by this Machine, and then trying to understand what will be behind the border, but try and understand that you were like doing like different kinds of tests. 

Vladan Joler (00:51:15) - And then for example, you find out that there are some kind of limitation. For example, I don't know, it cannot be more that is longer than 125 characters. So in the sense like the Google crawler cannot pick something that is bigger than one 25 characters. And that sense, you know, like all of those worlds that are bigger than 125 characters living in some kind of free world without Google, because they're too big to be captured because all of those smaller words are basically transformed into profit capture on the big horse. Well, in a way there is no, I think there is no bigger words than 50 characters that's about, but then like going through through there, I realize, okay, what if we write without a comma space and adopt and write all the, you know, like things together without separation between then? And I tried. 

Vladan Joler (00:52:21) - And in a sense, like for example, there is no, you should find some kind of longer sentence without spaces. Google will not be able to recognize this. So it's really important to understand where the border is because in the moment we understand what is the border of the capital interfaces, similar thing, then we are able to play with it. We are able to explore what's going on behind and to explore maybe how to protect the species that leaves behind the border, some kind of free zones and stuff like this. So this is what I'm really interested in. And this is why it's really important to try to understand, to understand what are the limitations of those systems, including the Moxy interface. 

Joel Stern (00:53:18) - Yeah, I, I think, um, that that's, that's an amazing sort of thought to, to sort of, um, con to conclude with, because that's something we've been, we've been thinking about and has come up in every, every conversation we've had really, uh, I think around, um, questions of intelligibility and the end, the possibility of escape and the possibility of, you know, um, thresholds, um, beyond which the logic of these machines and devices and sort of extractive systems, um, CA CA can't function. And, and, um, we've been thinking about it a lot in, in, in relation to Machine Listening specifically, and, you know, one of the, um, names we've given to that sort of line of thought is the lessons in how not to be heard, which, um, some of the different people who sort of participating in the project have interpreted sort of as tactics or, or strategies for kind of communicating in ways that say, you know, Listening machines would, which would be unintelligible. So it kind of completely analogous to the kind of, um, practices that would be answered and sort of searchable, but by a Google engine, you know, at the same time have had other people kind of, um, come back and say, well, some of those individual practices of evasion are a bit sort of, um, you know, they're sort of not structurally or politically kind of, um, impactful enough that they're just sort of, you know, ways of creatively.

Joel Stern (00:55:03) - Evading capture temp temporarily or, or training the system to, you know, um, decode ever more complex material, but in any case, I think it's, um, what I really, they took from me from the other document, which, which you showed, you, showed us the new extractivism, which is really amazing. I'm really sort of thankful to see that work in progress with these, uh, that there is a desire to escape it, however impossible or sort of temporary, that escape might be, you know, the black hole is there, but the little guys like tried and still trying to get out of it. We should let you go. Um, I think it's, it's been a great conversation.
