Albert Grandy III: Welcome everyone. My name is Albert Grandy III and welcome back to the thread. And I'm here with Dr. Ron Eglash. And today we have a very, very great conversation coming up for y'all. And without further ado, I want to introduce Dr. Ron Eglash. So if you don't mind, take a minute to introduce yourself, let everyone know what you do, where you come from, and how you got into this space.
Ron Eglash: Sure. ⁓ so r ⁓ grew up in ⁓ San Luis Obispo, California, ⁓ total nerd when I was young, and I guess I I've I've kept that as my life theme. ⁓ my father, Albert A. Glash, ⁓ back in the nineteen fifties, was ⁓ here in in ⁓ Michigan. I'm just a few miles away from Detroit. and ⁓ He was working with incarcerated folks, ⁓ returned citizens from prison, ⁓ and ⁓ realized the justice system, you know, wasn't just and came up with an alternative framework he called restorative justice. ⁓ so so that was ⁓ sort of ⁓ a big influence on my my early life, right, growing up with with somebody who had kind of founded this whole framework for rethinking the the justice system around ⁓ restoration and and ⁓ Creative restitution, as dad called it. ⁓ so ⁓ when I started to put together the things I was doing, I was calling it generative justice. ⁓ and so that's the title, this little slideshow I I put together. ⁓ and I can hop right into that if you're ready.
Albert Grandy III: Yeah, we can hop right into it. Like generative justice, I feel like that combines, you know, how we see the world in terms of how we can embrace someone's hopes and dreams to continue pushing forward. So if you want to go into it, we can get into it.
Ron Eglash: Yeah, yeah, yeah. ⁓ all right. So ⁓ you had sent me a list of questions and and I threw those up on a slide here just so I could remember what it was I was supposed to be talking about. ⁓ not ⁓ no guarantees I'm gonna stick to that. ⁓ but let me let me start with ⁓ you know, if you could just rattle off for me what you think are some of the major problems facing humanity today, facing people.
Albert Grandy III: are you talking about just these things on these lists or just overall social problems?
Ron Eglash: No, just just ⁓ just overall what comes to mind.
Albert Grandy III: I would say social problems in terms of communication could be one in terms of how someone can relate to someone in terms of language, in terms of culture, in terms of like what they want to do or what they'd like to do. Another thing is resources. That could be a social problem as well. But yeah, I have those too.
Ron Eglash: Yeah. Yeah, yeah, yeah, absolutely. Yeah, that's gr that's that's a great list right there. ⁓ so so when you mention ⁓ communication problems, the first thing that comes to my mind is ⁓ the political polarization that's happening in this country right now, right? Where you've you've got somebody who's ⁓ low income, working class dude, but you know, he's watching Fox News and feels like the the Democrats are ripping him off and You know, he wants that that that MAGA ⁓ force to come in and make things right. ⁓ so so ⁓ the framework that I apply to this is the framework of value extraction that in every one of these cases ⁓ we're ripping value away from the thing that's generating it. And maybe that's resources that are generated in nature, like ⁓ forests, for example. You know, you you clear-cut the forest and now you've broken its regenerativity. Right, it can't grow back. ⁓ the same thing with labor. You pay people super low wages, right, and give them the these really bad jobs. ⁓ and of course they can barely drag themselves to to work each day. You're extracting that labor value away from them. And the same with whole communities. You know, it used to be your social network was all your friends, now your social network has been commodified. It's been sold on a on a platform through through ⁓ Meta or or Facebook, whatever they're calling it these days. So these are all ways in which value is is extracted from nature, from labor, ⁓ and from communities. ⁓ but we don't have to design systems that way, right? We can design systems instead for value circulation. So if you look at something like organic agriculture, it's not causing soil depletion, right? It's not destroying the land. When you ⁓ if you're an ⁓ organic farmer or small family farm, you'll take all that agricultural waste and you'll put it in a big compost pile and you'll let nature decompose it back into the soil, right? And that enriches the soil and it just happens in this this beautiful cycle. ⁓ the same thing for labor value. So ⁓ if you have worker-owned businesses, they're not gonna take those profits and give it away to a billionaire. They're gonna take those profits and plow it back into the workers. Right. And so you've got these beautiful worker-owned cooperatives and ⁓ businesses around the the country and around the world, like Mondragon and Spain, ⁓ that have shown there are these all very ⁓ beautiful alternatives ⁓ that that don't ⁓ have exploitative labor practices and so on. And the same thing for for whole community. So you can have ⁓ platforms that are owned by the people rather than platforms that are owned by some billionaire that's trying to push a bunch of ads onto there and you know. use algorithms to to optimize clicks or whatever it is they're they're doing to to to make things a terrible space. All right. So so that brings us to the definition of what I've been calling genitive justice. The universal right to generate unalienated value and directly participate in its benefits, the rights of value generators to create their own conditions of production, and the rights of communities of value generators to nurture self-sustaining paths for its circulation. So the question is How do we pull that off? ⁓ And it's not as much of a mystery as we might expect, because we've got some amazing models in indigenous societies. Now, a lot of the folks, when I start describing these problems, they say, ⁓ well, you're just describing the problems of capitalism. If you just get rid of capitalism and had everybody become communists, that problem wouldn't occur. But if you look at these, you know, big authoritarian bureaucracies from the The USSR and Nepal and Cuba, ⁓ they've had the same problems that capitalism has had. They've had pollution and civil rights problems and and poverty and and so on. And that's because they too are extracting value, right? So when Stalin was trying to figure out what should the industry of of the Soviet Union look like, he would say, ⁓ we have the largest iron smelting plant in the world, right? ⁓ I can't do a Russian accent, but you know what I mean. It was it was the all these prestige projects to say that we've got the biggest industry and we're the most we're gonna out out efficiency, efficient capitalism. Right? But if your if your point is to return value to workers, you can't extract it and then expect to be able to d redistribute it like Santa Claus. You've got to come up with a system that never does extraction in the first place.
Albert Grandy III: you Okay.
Ron Eglash: So the problem is not whether value is being extracted to ⁓ millionaires or whether the the value is being extracted to the communist state. The problem is the extraction. You've got to come up with ways of doing things from the bottom up instead of the top down. We're so accustomed to thinking of everything as top down that when I say I want things to be organized, folks immediately assume I'm I'm imposing a grid. Right, I'm I'm imposing some kind of manager who's gonna tell everybody, you know, you have to be in this spot producing this thing at this particular moment, right? Right, imposing that control ⁓ to try to iron out any variation. ⁓ but that's not what you see in these indigenous societies. You see just the opposite. They're actually organizing things from the bottom up the way nature does. And those self-organized societies prior to to colonialism, ⁓
Albert Grandy III: Okay.
Ron Eglash: had much more flexibility, much better adaptive response to climatic changes, much better ⁓ democratic potential because they could organize through, you know, small clusters of people democratically deciding things, and then they would send representatives out and they would decide in a tribal council or or however it was arranged. So that's what I found in my work on ⁓ these these architectures in Africa. That they were designed ⁓ from the bottom up rather than imposing a grid from the top down. So it's much more organic forms of growth. ⁓ and by doing that, it could be much more democratic. So ⁓ one of the my first exposures to this was reading about a study in Tanzania where ⁓ women had an unusual amount of autonomy. And this anthropologist was saying, you know, it's so amazing to walk into this village, and it's you know, religion-wise, it's a Muslim village. ⁓ but if the women wanted to divorce their their husbands, they would just build their own house out of the same, you know, ⁓ grass and sticks and mud that everybody else was using. ⁓ and that flexibility in land use was instrumental to a kind of autonomy and democracy. ⁓ even, you know, your kids are bothering you, so you build your teens a an extra house, your mother-in-law is getting on her nerves, you build her a house. That that flexibility in design ⁓ grounded in ⁓ the kind of legal systems that would allow it, right? So it's what what's called usufructory rights. If you can go to the council and say, hey, I have a use for this plot of land, you're you're able to use it. It's not imposing this grid of of ownership on on everything. So those those commons commons-based ⁓ sharing mechanisms prevent monopolies from forming, right? ⁓ and I found that it wasn't just in the architecture, it was also in the
Albert Grandy III: Okay.
Ron Eglash: textiles and in sculpture and in construction techniques for metal and all sorts of things were embodying this spirit of ⁓ organic growth and things, you know, evolving from the bottom up rather than being planned out from the from the top down. So I published that book, African Fractals, ⁓ talked about, you know, even in cases where Christianity had come in, so here in in Ethiopia, you've got these 900-year-old churches. But you can see they've they've kind of taken that fractal structure and combined it with the cross. So you have the cross within the cross within the cross, or these beautiful fractals in the ⁓ processional, big processional crosses that are used in ceremonies. ⁓ Here I'm I'm at the base of one of these big rock churches, and you can see ⁓ it's got this beautiful ⁓ curve, nonlinear curve, ⁓ where the staircase progresses in a a scaling sequence to indicate that. ⁓ the curves of life, right? These organic curves. ⁓ so so ⁓ that leaves us with this question now of translation. So now that we know you have all these advantages from these self-organized bottom-up systems, how do we now translate that into something ⁓ in modern circumstances? ⁓ and the basis for that was ⁓ understanding the kind of circular economy
Albert Grandy III: Okay.
Ron Eglash: That they create. So this is my old friend ⁓ Gabriel Boace, passed away just a few years ago, unfortunately, ⁓ in the village of Antonso in Ghana. And he's got bark from the body tree, Braidelia is the Latin name. And they boil that down in these big pots ⁓ and then they strain out the bark. And the leftover bark, once the pigment has been taken out, the leftover bark goes back into the the sacred forest or into composting for farming, and that completes that circular economy, right? But if you if you look at the symbols, ⁓ and I've got they they carve these beautiful little stamps, right? And then they they they stamp it onto the ⁓ the cloth. ⁓ so if you look at the symbols that are are being stamped, it's things like Fun Tun Funafu. ⁓ this symbol here is ⁓ two crocodiles that share the same stomach.
Albert Grandy III: Okay, wow.
Ron Eglash: And the saying is, you know, why should we fight? By feeding you, I feed myself. Right? So it's not just a circle economy of ecological value, it's also a circle economy of labor value and a circular economy of social value. So they're really hitting all three of those domains in th those that bottom up self-organization. ⁓ and then another way I found that ⁓ the fractals were enabling this was just the fact that it takes so frickin' long. ⁓ so you you know, you d you do a a fractal beadwork pattern like this one from Cameroon, ⁓ or a fractal hairstyle, what we would call cornrows in the the United States, ⁓ and that's gonna it's gonna take you all day, right? ⁓ it's very different from the way that capitalism works. So so ⁓ if you went to the parking lot after a day of work and you saw your boss stuffing 10,000 ears of corn into the trunk of his car and you only got ten ears of corn.
Albert Grandy III: Mm-hmm.
Ron Eglash: You'd say, hey, I'm getting ripped off. But you'll never see that. We make all of that invisible. ⁓ you know, my cell phone here was made by a bunch of different people, right? Somebody made the glass and somebody mined the rare earth minerals out of the earth. All of that is made invisible, as invisible as possible. So you can't see the pain and the labor that goes into it, and it becomes easier to extract value from folks. These indigenous systems work in exactly the opposite way.
Albert Grandy III: Thank
Ron Eglash: So when you're seeing this fractal detail in these systems, that's because you're trying to make the labor value as visible as possible. You want people to know that this took hours and I braided this pattern into my friend's hair and I showed my love for my friend, right? Through that that labor practice. ⁓ and so those some of those are really great examples of how fractals made it to the new world. And so the second question you asked, the first one was just what's the mechanism here? The second question you asked was, what about transmission? How does it make it through the middle passage to the the new world? If I understood you correctly.
Albert Grandy III: Yep.
Ron Eglash: Okay. ⁓ so so let me let me jump to ⁓ an example of quilting. ⁓ so so ⁓ let's see here. This is our website, Culturally Situated Design Tools. These are all free online, by the way. Anybody can use them. ⁓ And let me go to Gee's Benz. I don't know if you're familiar with ⁓ the Gee's Benz ⁓ quilts, but you can s you can you they're in Alabama, ⁓ very, very old community making these quilts, and you can see even you know, you look at these very early quilt samples, you can see the resemblance between the patterns they're making here.
Albert Grandy III: I'm not.
Ron Eglash: and the kind of patterns you would see ⁓ in in Africa, right? ⁓ but one of the the points I've made, ⁓ gosh, you know, I I I haven't tried sharing. Let me make sure that's gonna work. ⁓ tell me if you can hear this.
Albert Grandy III: Okay. I can hear it. Yeah, it came through. I liked it though. It had like very distinctive patterns and you can see how each line, each color lined up exactly like in a symmetrical pattern.
Ron Eglash: Awesome. Awesome. Exactly. And if you've ever heard folks say African music ⁓ uses call and response, right? You can see visually that the G spins quilts are using call and response too. And so so that's you know some of the transmission mechanisms. Of course, you can transmit hairstyles because you can carry your hair with you, even if ⁓ somebody has wrapped you up in in chains and threw you on a boat. You can't carry your quilts with you, but you can you can carry memories of those kinds of practices, right? And more importantly, you can remember the social features of things like call and response and these these regenerative loops that are that are at the at the basis of that. So so I wanted to give a little shout out to your question about ⁓ transmission. ⁓ and I've and I've tried to you know get the message out about fractals as black cultural capital. Right, through the the book that I wrote and the TED talk and and ⁓ doing things like this podcast. ⁓ and and ⁓ it's been really exciting seeing it get picked up. So here's a a huge modern building in Ethiopia that ⁓ the architect told me ⁓ he read the Afghan Fractals book and said, ⁓ you know, let's let's use that that pattern here. ⁓ what was so interesting, I thought, about this particular case was that ⁓ He put these in as perforations in the exterior of the building. And just like the human lungs are fractal, it gave the the building a kind of breathing system, right? And that lowered the temperature in the building enough so instead of putting in air conditioning, they just let the natural airflow cool the building. ⁓ so not only did that lower the carbon footprint of the building, it also just made it cheaper for to rent space. It's a shopping center. ⁓ so when I've ⁓ visited there in in ⁓ I think it was 2018, there were all these little mom and pop shops there. You know, and I looked at at the places around in Addis Ababa and pretty expensive stores all surrounding it. So it was one of the few places where somebody from the the low-income community could come in and and have their shop. So there's there's all kinds of really amazing benefits, you know, s ⁓ environmental benefits and social benefits that can occur ⁓ when you start thinking about ⁓ how to incorporate fractals rather than these than these grids. ⁓ and I've got a I've got a link here to ⁓ just a list of you know different researchers and artists and and so on, ⁓ folks who who have been designing ⁓ with those ⁓ fractal frameworks in mind, everything from AI algorithms to artworks to literature to transportation studies and and and so on. ⁓ but I I I wanted to ⁓ I wanted to you know, get back to your question because these are folks who are pretty well off, right? They're, you know, famous artists or university researchers or whoever. And you were asking sort of a different question, right? Which was that that question of resistance and resilience ⁓ and transformation. ⁓ so so I I think, you know, something a lot of these scholars have picked up on, really, is that ⁓ even during the slave trade, you have stuff going underground.
Albert Grandy III: Okay.
Ron Eglash: And so there are there are kinds of of hiding in the light, so to speak, right? So that cor that cornrows style ⁓ that you're you're trying to pass have, ⁓ I'm just you know, trying to make my hair look tidy and dress up for this occasion. ⁓ but in a sense you're smuggling in ⁓ a ⁓ a broader set of of concepts and practices that are much more subversive. ⁓ and so so I think, you know, that's that's a piece of it is is how does this manage to survive? ⁓ colonialism and and ⁓ the Jim Crow era and and so on. ⁓ as Malcolm X would say, by any means necessary, right? You get that that smuggling however you can and and often in ways that are just staring at you ⁓ in your face. So my my wife is is African American, Afro Caribbean, ⁓ naturalized citizen, Professor Roger Bennett, ⁓ and she actually did her her master's thesis as at Yale on ⁓ cooking.
Albert Grandy III: Okay.
Ron Eglash: And ⁓ my mother in law was just an amazing cook. She had this rich potwa and and she'd always lay out these, you know, we'd have Thanksgiving with turkey, but then there's also, you know, oxtail and plantains and ⁓ rice and feas and all this stuff. ⁓ and and and so, you know, ⁓ a a a lot of those ⁓ parts of cultural transmission get smuggled in through things like the kinds of plants that you you utilize. ⁓
Albert Grandy III: Hmm. The good stuff.
Ron Eglash: Or the the kinds of recipes and recipe sharing that you're you're putting together and so on. ⁓ so so you know when you look at something like Black Panther, it's really cool to see this stuff up on the big screen, right? ⁓ but on the other hand, they made 1.23 billion in the first month. How much of that money actually went to the people in those villages? I am not even sure you could measure the percentage. You know, it's point zero zero zero something, right?
Albert Grandy III: You
Ron Eglash: ⁓ and so so that's the the question I really wanted to turn to. ⁓ so so back ⁓ four years ago ⁓ we had gotten a National Science Foundation grant to take this idea of a ⁓ a gender justice system, a circular economy, ⁓ and apply it to a modern day economy. ⁓ and we based ourselves in Detroit ⁓ because you've got this huge African American population there. lots of low-income communities, ⁓ lots of interest in ⁓ African heritage and just kind of crafting ⁓ traditions in general. ⁓ but ⁓ at the same time, you know, it's under the pressures of of our our sort of ⁓ neoliberal ⁓ economy where everybody's competing against everybody else and you know if you want to put something up on a platform you're gonna get ripped off because there's gonna be a fee that that's
Albert Grandy III: Okay.
Ron Eglash: Associated with the platform and you want to get it up on the the website, but there's a fee associated with Squarespace or Etsy or or or whatever it is. So so ⁓ we tried to design this platform as a circular economy, keeping in mind, you know, what we had seen in the African circular economies, right? And by this time we were also working with ⁓ Native American communities and and South American indigenous communities. ⁓ I took a trip to India and Alaska, worked with some You pick folks, and so we had a good sense that ⁓ you could find these circular economies, you know, even in in in if you look at ⁓ Europe's indigenous heritage, the the the ancient Celtic traditions, right? ⁓ you saw those circular economies back then too. The only difference is that colonialism came from inside of Europe, so it sort of cannibalized itself rather than coming from the exterior. ⁓ but if you go back to those Celtic traditions, you can see those those same circular economies. So question was, how do you translate that into, you know, contemporary computing technologies, into artificial intelligence and digital fabrication and so on? ⁓ and so we started with these little mom and pop worker-owned businesses, whether it's like a local hair salon or a local clothing maker or you know, anybody who owns their own business, right? Because if I go into McDonald's and I say, hey, I'm I'm here to help you guys with robotics.
Albert Grandy III: Okay.
Ron Eglash: They're just gonna say, fine, we'll fire all the workers and replace them with robots. It's not gonna get us anywhere, right? You've gotta start with ⁓ the ownership of the means of production, as Karl Marx would say, but not doing it through, you know, state communism. You can imagine what Trump would do if he if we had socialism and and all the corporations were owned by the government. It'd be a disaster. So you you wanna keep things in the grassroots, you wanna keep that that worker ownership, right? And just empower the grassroots to to develop its own value generation. So we we went to all those little mom and pop businesses and we asked them, you know, what can AI do for you folks? What can what can digital fabrication, laser cutters and and 3D printers and so on do for you folks? What kinds of apps are you paying for every month that's draining all of your finances that we could replace with something on a worker-owned platform? So that was this project we call artisanal futures. ⁓ and here's some of the participants, ⁓ about 90% African American, ⁓ about sixty percent female, located in ⁓ low-income communities within the the greater Detroit area and all worker owned, whether it's you know urban farms or textiles or whatever it is they're doing. ⁓ and so we wanted to hit all three of those domains, right? We wanted to make sure that when we go to these urban farms, and they're already practicing organic agriculture, but the question was, how do we superpower that loop? How do we take, you know, AI-based agroecology and soil sensors and aquaponics and how how do we bring those technologies to those worker-owned farms? ⁓ and then looking at the the kinds of labor that folks were doing, how do you take, you know, 3D printing and and digital fabrication techniques?
Albert Grandy III: Okay.
Ron Eglash: ⁓ AI that's actually made for the folks working in those places ⁓ and make that available. ⁓ and then the same for the community as the whole. So now that we've got this platform, what can we do to bridge those gaps in things like education and housing and and health so that it's not some big company coming in ripping you off, but you know, actually ⁓ manifesting ⁓ those kinds of systems whether it's ⁓ banking systems or or ⁓ housing systems whatever it is within grassroots ownership. ⁓ So one of the things we found was that a lot of these worker-owned businesses were upcycling, not recycling. So in s instead of taking a bunch of plastic and melting it down in a big you know plant somewhere that's owned by a millionaire, they're just taking things out of the waste stream ⁓ and upcycling it into other products. So so this is a ⁓ one of our artisans who would take old scraps of clothing and turn it into handbags. ⁓ so so we started looking at artificial intelligence for this and we first tried some commercial artificial intelligence Dolly and we said okay Dolly I've got these textile scraps right and I want you to turn that into a jacket so Dolly produces this jacket but I don't see those scraps anywhere in this jacket do you They're really hard to find. So so I can see, you know, I've got this leopard print here. I guess that's the leopard print there, but the other parts are really, really hard to find. And we found out that's because when you look within the algorithm, what Dolly is doing is it's taking the images, turning it into a text description. And so it has a text description for leopard print, but what's the little text description for this thing or this thing? Right? There's there's no word equivalent other than you can say a bunch of stripy patterns with some orange and purple. ⁓ so we we created our own AI algorithm that would now be owned by the workers themselves, not owned by a corporation. ⁓ we call it Upsy because it was made specifically for upcycling. and and it first takes the the the words, right, uses a large language model, design a jacket using the following scraps.
Albert Grandy III: And how you gonna do that mid podcast?
Ron Eglash: But then it takes a neural network and looks at the images itself and imposes those images onto the the the the product, whatever it is. So it so it's actually using the the textures rather than converting them into a bunch of words. ⁓ and so that makes it much easier for the the artisans to now say, ⁓ yeah, I know exactly where to put this cloth and this cloth and this cloth. So it was it was a great proof of concept. You know, that you could actually, by working with local local artisans with these worker-owned ⁓ small businesses, you could improve the technical aspects of AI. You could actually advance computer science doing that. So I was super excited about that. ⁓ this is a really fun example where one of the workers w we we were ⁓ collaborating with said, design a handbag shaped like a car. Because we're in Detroit, right? ⁓ So she tried it out with these scraps here, and she said, nah, I don't like the result. It's like literally, it looks like a toy car, right? Too literal minded, too concrete. ⁓ and then she tried it with leather scraps, same words, design a handbag shaped like a car. And it was really abstract. Like I guess those are supposed to be the wheels of the car down there, but you wouldn't guess that that's a car, right? And I thought she'd be unhappy with this, but she came back to us. She said, I'm so excited. Because what this has done is it's mapped out the space of possibilities for me, right? And in between too concrete and too abstract, that's where my agency comes in. So I think, you know, developing AI in ways where people can actually use their own agency, ⁓ the way that you have, you know, artisans in Africa using their their their agency or any kind of worker-directed work using its own agency has been super important. ⁓ And in some cases, that's kind of a cyborg melding of human and machine. So ⁓ this is a different artisan who wanted something based on Kente cloth and Van Gogh's painting. She picked ⁓ Starry Knight and she calls this Kente Van Gogh. And so we just got her a a direct to to garment printer. So you can print onto textile ⁓ the way that you print onto cl onto paper with a regular printer. ⁓ and so now she's she's creating these these ⁓ Starry night textiles in Detroit. ⁓ and this is ⁓ another example where we were asking folks, you know, well what is it you hate about your job? And everybody said the same thing. They said, ⁓ if somebody comes to me with a pattern and it doesn't fit their body, now I've got to make all these adjustments to their particular body shape, right? And that's just super tedious. Could I have the AI do that for us?
Albert Grandy III: Mm-hmm.
Ron Eglash: ⁓ and so one of my grad students, Kwame Robinson, ⁓ he's now a professor in Detroit at Wayne State University. ⁓ so he's he's Dr. Kwame Robinson now. And he created this ⁓ he calls it Sancofa sizer. So so it it it automatically does that kind of adjustment of patterns. ⁓ and then the ⁓ artisan we we happen be working with said, well, you know, I don't I don't want to have to have it printed out on paper and then tape it onto the the cloth. ⁓ how about a projector? So this is a little projector that projects the the pattern onto the the cloth for her. So if you think about all of those things together, what we're looking at is a spectrum from cases where AI is mostly human and it's just offering a little inspiration to cases where AI is doing most of the work and you've offloaded the task onto the machine. And so the question you want to ask is not can AI do it. But where, at what position along the human machine agency spectrum do you want to intervene so that you're preserving the parts of the job that people love, right? Because if if we're creating jobs that people hate, that's not what you want. So that was that was at the kind of the micro scale of businesses. Then the next level up, now we want to put together an economy. So we've got to find out: can we replace competition? with business to business collaboration. And so we've came up with a great example with the local urban farmers. They were wondering, you know, what is it I should be growing, right? And we had local local fabricators saying, well, I don't want to put more plastics into the environment. What kind of natural fibers can I produce? So we can now turn that question over to AI and ask it, you know, given these farmers and these makers what should be grown in these farms
Albert Grandy III: Mm-hmm.
Ron Eglash: Such that they end up buying those fibers from the farmers and creating some products. And so it's a really neat way of now exploring, you know, you could have all sorts of value chains going on in that kind of meso-level economy, right? That are business-to-business collaborations. ⁓ This is a little project we did with a ⁓ the ⁓ African Bead Museum in Detroit. It's mostly just a bead shop. And ⁓ Dobbles, the guy who owns it, has a little outdoor sculpture garden.
Albert Grandy III: Okay.
Ron Eglash: So he ⁓ allowed us to create a greenhouse there to grow some of the plants that become ⁓ beadwork in the necklaces. So we ran a little workshop ⁓ in Ghana with students there. ⁓ and then we hired some of the local folks, and this is a mix of university students and and local folks that we hired ⁓ to actually build this thing. ⁓ and so we we then brought in some high school students who ⁓ created the ⁓ feedback regulation. So that it won't overheat in the summer. We got solar panels up on the roof so that it would be using solar electricity. And that became a model for another grant we got to now take those photoelectric systems and use them in all the urban farms that we were working with. So we managed to get enough funding for five different of these, we called it solar irrigation, right? So you're capturing rainwater and then using solar power to run the sensors and the pumps to distribute that in the in the farms. So these kinds of localized supply chains have been really crucial to thinking about this. ⁓ maybe using AI for ⁓ creating maps of where those waste streams can come from, right? And and upping the the upcycling part of this. ⁓ Kwame's been working recently on ⁓ worker-owned e-delivery. So if I use DoorDash or something like that, Uber Eats, I'm just making rich people richer. Right? The money's not going directly to the people who drive the cars and the people who farm the food. ⁓ so Kwame's been developing an al a local alternative that's actually owned by the workers for doing e-delivery. ⁓ and ⁓ one of them said, I I don't want to stand outside my farm all day handing out packages of food. ⁓ and so she ended up getting ⁓ a grant to install some of these vending machines. So Kwame's been hooking those up to this system, internet system. So when you order online, ⁓ it looks at what's available in this refrigerated vending machine, lets you know, you know, what you can pick from. And so he's brought in a ⁓ just a whole another level of of automation to this. So let me let me ⁓ stop there and ⁓ let's ⁓ just go back to our our conversation here. So ask me questions, tell me answers to questions I have, just just lay it on me, ma'am.
Albert Grandy III: Yeah, like I appreciate that walkthrough because you really gave a in-depth description on like what you do and the work that you've done and the work that continue to be built. like the first point that I thought of was this really reminded me of the circle of life in terms of like everything is growing from the ground up as you were saying, but as it grows from the ground up and people are using it, building it, it also comes back down to the ground. So it's a whole cycle. And I really think like that's important in terms of our society. So we can reuse the resources that we have instead of extracting that value from whether that's the earth, whether that's from people, whether that's from ideas, and just helping the workers at the end, because those are the people that are actually putting in the work and the time and the energy to give back to them so they can keep doing the things that they love to do. So I love that explanation that you gave. And then I also, this is actually one of the things that made me reach out to you as well, because I was looking at your page in terms of the braids and the hairstyles, because I have two strand twists in my hair right now. Like I've never really thought of it in terms of fractals and how people were grading for, you know, some people may not, you know, some people just made you do it just like you said, know, just for fun, just to have the hairstyle. But some people are grading the hair in terms of fractals to represent those patterns, represent those hard work. And I wonder if that in a sense can be like a form of energy, whether that like those fractals can represent, heritage can represent, you know, that generation as that transmission question I asked before, like how can those generations, those thoughts and ideas be passed on from one to another and Yeah, I really think that that is an example that you gave, which is beautiful.
Ron Eglash: Great, great. I'm I'm I'm so glad you liked it. Yeah, I you know, I I was I was really focused on ⁓ thinking about the the ⁓ African examples, right? ⁓ but but if you if you look at ⁓ these different indigenous cultural groups, ⁓ so here's ⁓ a Celtic example, and ⁓ here's ⁓ Anashinabe, the the indigenous group here in Michigan, ⁓ here's Maori. ⁓ So all of these indigenous cultures, as you were saying, ⁓ have that concept of the the circle of life. Absolutely.
Albert Grandy III: And then you said that you've seen these patterns across your journeys across different countries. I mean, they do look similar. Do they mean the same thing? Do they mean have different meanings?
Ron Eglash: No, no. No, they have they have they have radically different ⁓ meanings and and I I don't I don't want to ⁓ start to to generalize this from a sort of you know colonial ⁓ all indigenous cultures are the the the same perspective. Let me let me zoom into one particular example that I really like. ⁓ So we were we were looking at ⁓ the the the ⁓ indigenous native American groups ⁓ and the way that ⁓ they would do these controlled burns, right? ⁓ or the way that they would do cycles of ⁓ textile fabrication. ⁓ and I had a bunch of different folks working on on this with me, ⁓ but this was really focused on the concept of entropy. And we usually think of entropy as a measure of disorder. ⁓ and rightly so. So, you know, entropy is high when you've got a gas and you cool it down to a liquid, and now ⁓ entropy is lower, and you cool that down to a crystalline solid, and now it's all ordered, and entropy is is at its lowest state, right? ⁓ and so you can you can think of entropy as how much information is it gonna take to describe this thing. So describing every single molecule in a gas is really hard. It's a little bit easier with a liquid, and it's ridiculously easy in a crystal because they're all lined up in these rows and columns. So here you have a case where diversity is is low, you have low entropy. And here you have a case where diversity is high, you have high entropy. ⁓ So in the case of these controlled burns, you go from a low entropy state where everything is ashes. To a high entropy state where you have the whole biodiversity of the system, right? And when that gets overgrown and the bushes are no longer producing all the berries you want, or the meadows no longer producing all the herbs that you want, you set it on fire and you let those nutrients go back to the soil. And when the rains come, you get this boom of biodiversity. And so it's really a back and forth modulation of entropy. And it took me a long time to kind of catch on to that. ⁓ because these systems, you know, are quite complex about where do I do the burn and how often do I do I burn it. If it's a big forest, it's maybe once every seventy-five years. So, you know, you might even ⁓ live a lifetime that has two of those burns in it, right? It's it's an intergenerational ⁓ dynamic. ⁓ so so ⁓ if you look at Navajo weaving, you actually see a very similar system. and ⁓ If you look at the sheep that's grown in the corrals, ⁓ they're pooping out seeds, and so you've got this ⁓ explosion, this biodiversity of different plants around the sheep corrals, ⁓ and those are used as the different color dyes. So you you take the plant, you turn it into a dye, and you dye your yarn from the sheep, right? ⁓ but then you reduce the entropy. You have this very low entropy weaving where everything's in these very regular grids. ⁓ And the the profits from that go back to supporting the sheep. So as you were saying, it's that that circle of life, right? ⁓ But it's a circle of life that's kind of pumped between the high entropy state of biodiversity and the low entropy state of weaving. And you can think of that as this kind of sine wave through time. ⁓ But if you think about the basis of biology, it's exactly the same. You've got DNA, which is the super compressed low entropy form, and then you know you develop as an entrop ⁓ as an embryo and you you come into being into this world as this enormously complex explosion of biomolecules, just this mind-blowing biodiversity within your body, right? ⁓ but eventually, hopefully, ⁓ you'll have children. And so once again, you've got just the the sperm and the egg, you've reduced it down to the super low. entropy state. And so just like you you saw with ⁓ with the weaving and just like you saw with the controlled burns, you've got this modulation through time between ⁓ high entropy and low entropy. ⁓ and you can think about that in all kinds of different systems, right? So if you if you think about how you teach somebody something, you have kind of a biodiverse ⁓ work diverse moment ⁓ where you're trying out all kinds of different tools, where you're experimenting. Right. And then you nail it. You go, ⁓ yeah, man, this worked out super well. And I'm going to make this thing with, I don't know, copper wire. ⁓ but then there comes a time and you say, okay, I've been there, done that. Now I want to ⁓ you know expand my repertoire again. And so you've got this modulation in in learning systems as well. ⁓ you can have the same thing with leadership systems. So one of the problems we have in our government is that things get too fossilized and ossified and fixed.
Albert Grandy III: Okay.
Ron Eglash: Right. And so you've got Supreme Court justices that are on there for a lifetime. ⁓ I was talking with one of one of the ⁓ Anishinabe professors ⁓ here at at at ⁓ the University of Michigan, ⁓ and he was saying, Yeah, you know, the the tradition was to c have constant turnover in the tribal council for exactly this reason. So you don't have an old boys network where you now you're, you know, trading favors, right? ⁓ so you have a le leader with with ex strict term limits and and ⁓ all kinds of ways of ⁓ of doing recall if somebody gets out of line and and and so on. ⁓ so so ⁓ you can think about the economy this way and and there's even you know ⁓ machine learning algorithms that use this this kind of ⁓ entropic modulation. So yeah, I I think there there's ⁓ you know quite a profound array of different techniques, whether it's fractals in Africa or control burns with the the Anishinabe or this weaving entropic modulation with the the Navajo. ⁓ all these indigenous groups have, you know, quite different ways of empowering those ⁓ circular economies.
Albert Grandy III: Okay, and to your point with AI, do you think that there could be some AI bias in terms of modulating the entropy? So in the machine learning algorithms, there could be some biases to help formulate specific entropies that are high or low, depending on what the bias wants to be represented as that could be represented in society today. Do believe that?
Ron Eglash: Well, I so so I think every I think everything's biased, right? ⁓ everything we do is produced in a social context. And so there's the s everything we do is is strongly influenced by the the the context you're you're you're in. ⁓ for example, ⁓ Andrew Pickering ⁓ has a great book called The Mangle where he talks about scientific laboratories And he shows that, you know, we we see some result in science and we think, okay, it's shown me the fundamental laws of how the universe works. But then you you backtrack in that history, and he was originally a physicist, but he became a social scientist because he was so interested in what was going on in these labs. ⁓ so he backtracks the history of the cloud chamber, for example, and he shows, yeah, you know, originally that that research could have taken off in a bunch of different directions. And this guy was at the Berkeley Livermore labs and there happened to be a bunch of liquid hydrogen sitting around. And so the particular direction that these ⁓ particle detectors took was ⁓ strongly influenced by the kinds of resources he could get his his hands on, right? And and had had he come up with a different set of resources, maybe we would have ended up, you know, going the direction of string theory instead of the the this other direction. ⁓ So so I I you know when it comes to algorithms, yeah, of course, all of our algorithms are created in a kind of social context. ⁓ and if you think about the algorithms that are used in AI today, ⁓ they're used in a way that brings you the compute as fast as possible, as massive as possible, without any regard whatsoever to the environmental consequences. Could you have used a different set of algorithms? Absolutely. Right? Absolutely. You could you could focus on, you know, low energy compute, right, as the kind of frontier that you want to explore in in computer science. ⁓ and that produces a different set of algorithms. Now I know that's not what you meant by by bias, right? You were thinking of stuff that says, you know, ⁓ look, AI is, you know, ⁓ I asked for a picture of a scientist and it shows me a picture of white person, right? And that's because it was trained on all these images, and so that bias
Albert Grandy III: Mm-hmm.
Ron Eglash: is carried over from the data into the the the outputs of of AI. ⁓ And yeah that's true, but I think the bias runs even much deeper than that, right? The the bias, ⁓ even if you eliminate bias in the data training sets, ⁓ there's still bias in the kinds of algorithms that we're producing. And I keep encouraging folks to think of this not as a negative. You know, when we say bias, it sounds like we're scolding somebody. ⁓ I'm accusing you of racism and bias, right?
Albert Grandy III: Mm-hmm.
Ron Eglash: We want to think about it in a positive sense. So how do we how do we come up with you know really profound, positive collaborations with computer science where we say, well, let's do the kinds of algorithms that are profoundly regenerative, that are are inherently good at putting value back into nature instead of extracting it, that are inherently good at putting value back into labor instead of extracting it. ⁓ So so it's it's quite a mental shift to get most computer scientists to start thinking that's that that sentence even makes sense. Right? Just just getting that concept across and getting them to wrap their heads around it ⁓ is is quite a challenge. But once you put it in those terms, often you can see folks starting to say, Well, okay, so you know, as just a you know, kind of thinking hypothesis, let me suppose I don't have a massive data center. I've got a bunch of little desktop servers. They're all networked together. And everybody, like I was showing you, everybody got has their own photovoltaic array on the roof of their worker-owned business. And their own little desktop server is now networked into a massive node that comprises a worker-owned data center. Now, what are the algorithms I need to create that data center rather than the one that we've been
Albert Grandy III: Mm-hmm.
Ron Eglash: creating that destroys the environment and makes, you know, massive amounts of money for people who are are already rich. That's a really interesting question from a computer science point of view. But it takes it takes a lot of arm twisting and and educating and nudging ⁓ to get the computer science folks to, you know, go to go to and and it is, I admit, it's a difficult leap. You've got to really think hard about, okay, so what does it mean to be working that very different context that doesn't even yet exist?
Albert Grandy III: Mm-hmm.
Ron Eglash: And and so much of our social imaginary and our technological imaginary is kind of pre-programmed to think about, you know, words like efficiency and optimization. I got my master's degree in in systems engineering, right? I I worked in in the Silicon Valley as an engineer. I I know this stuff really well. Every other word out of the mouth of the of the lecture I was getting in my engineering courses was optimization and efficiency. ⁓
Albert Grandy III: Okay.
Ron Eglash: But y you know, those words originally come from physics, and then somebody in early industry in the 1700s, you know, Adam Smith or somebody, says, ⁓ well, that's the kind of concept I need to make my factory efficient. And by efficient, I mean I pay the workers as little as possible. Right? ⁓ and so they come up with a bunch of equations that are then quote unquote the efficiency equation, and they fund a bunch of research grants for physicists. So now it starts getting reinforced, right? And it starts this positive feedback loop where what goes on in science starts to look a lot like what's going on in industry, but it's not a coincidence. It's just an echo chamber of of these folks, you know, following that same way of of thinking. So get it getting folks to shift that into other kinds of directions, I I think is absolutely imperative. But you know, in a sense they're all quote unquote biased, right? They all have a social context. It's just making that other social context more conceptually available to them.
Albert Grandy III: Right. Okay, and that makes sense. And that actually brings it back full circle because in terms of, everyone has their own subjective bias in their head and their minds and not a lot of things can change that or shift that mindset unless, you know, you go through training or you go through hard work or, you know, you're really passionate about an idea. And that goes back into the concept of the circle of life because when you have the full system, you know, you're just taking bits and pieces of everything like the nodes that you're just explaining about how everyone can have you like a local server and have bits and pieces of every computer in that community and everyone adds up in a progressive cycle and that can overall help, you know, answer that problem in terms of a large data center like that. Again, this is just generative regeneration as you were saying. So I really like that. I really have no words because like that's a perfect, in my opinion, perfect explanation of how that could happen in terms of you can still have bias, but you can still use that bias in a positive way. And I think that that's essential.
Ron Eglash: ⁓ Yeah, yeah. Rather rather than say no bias, ⁓ let's try to ⁓ engage in ⁓ an awareness of of ⁓ you know how the biases are running, right? And and what what are the alternative kinds of whether it's d different ⁓ data sets or or or different energy configurations and so on. Let me show you another ⁓ slide if if I'm not over overdoing it here with the with the slides. ⁓ so so ⁓ you know, if you look at the the physical components that make up a computer, right, ⁓ those are made out of out of things like tantalum ⁓ and titanium that are coming out of these ⁓ mines ⁓ in Africa and places where there's a lot of conflict, right? So just like you have conflict diamonds, you have conflict minerals as well. ⁓ and unfortunately though the the wealth of those mines is reinforcing a lot of the you know authoritarian ⁓ governments and and and violence and so on in those those regions. So so if we wanted to think about this other kind of future in which we have a regenerative basis for everything, you don't want to do it just at one scale, right? You want to do it at every scale involved. So you want to think about when I'm taking minerals out of the earth, how do I do that in a way in which it's the workers doing the work that actually own those those mines. And it that's that's possible. You know, there there's ⁓ artisanal gold mining, for example, is pretty common in in developing nations. ⁓ and unfortunately they tend to use mercury to to get the the gold out of the gravel, which causes a lot of mercury poisoning in in villages. But those there's alternatives. You can use ⁓ centrifuge and and so on. So we can come up with technologies that better empower ⁓ those locally owned mining techniques and we could use that to source the ⁓ components, the electrical components. ⁓ and then you want to think about how are those components put together, right? So when you when you create that motherboard, ⁓ you're soldering in not you, but some robot somewhere, you know, is is soldering in these these components. ⁓ so that too can be done in ways that are owned by workers rather than owned by big corporations and you can make that work a a lot more pleasant.
Albert Grandy III: .
Ron Eglash: ⁓ one of my favorite examples of that is Arduino, ⁓ which is a a little microprocessor board that was developed in Italy. It was the first open source hardware. and I absolutely love the Arduino board and I've gone through ⁓ a bunch of interviews that folks have done when they visited the Arduino factory. I was recently in Italy, didn't make it to to the Arduino folks, but ⁓ I managed to to interview a bunch of folks in worker cooperatives in Italy just to see, you know, what's what's that like.
Albert Grandy III: Okay.
Ron Eglash: ⁓ how are they thinking about these these ⁓ worker-owned labor conditions and it was just such a pleasure to see folks so thrilled to be at work and so so much enjoying themselves. ⁓ so it is possible to do fabrication work ⁓ instead of a sweatshop and an assembly line to make it much more ⁓ worker-owned, artisanal, worker directed. ⁓ and then you've got the the architectures and the computers and the networks and so on. So at every scale we can ask that question. What would turn this into a system of extraction where you're pulling value out of nature and pulling value out of workers ⁓ into a system of value regeneration?
Albert Grandy III: Okay, that makes sense. Hitting, things hitting, ideas and concepts that every stage should make a full rounded system, in your terms, more efficient. yeah, so I mean, my last question, which you pretty much already answered is, how can we create that change and take the next step to see what we need to do? But. You kind of already explained that in terms of not extracting value out of nature, out of things in terms of in a negative way, but how can we realign that thinking, that mindset to be progressive? But you, I mean, I want you to again, like reinforce that if you could summarize those points again, just as a closing and yeah.
Ron Eglash: Sure, yeah. ⁓ well, you know, you you need a big tent, right? You you you want everybody contributing in in whatever way they can. and ⁓ I spent a lot of time when I was a a s a student in in college ⁓ doing civil disobedience and and getting arrested during the ⁓ the anti anti-apartheid protests and and ⁓ against ⁓ nuclear weapons at at ⁓ Berkeley and so on. ⁓ And and ⁓ you know, I I think there's a really important role to be had in in ⁓ political organizing and political protests and so on. I I don't want to ignore that part of it. ⁓ but I I hate to see folks think of it as a binary, that either we have free market capitalism or we have the state owning everything, right? and and often when I talk with my colleagues, they say, well. I I I want the middle of the road, so we're gonna go to corporations like ⁓ Meta or or or or or whoever ⁓ and show them an algorithm that's more gentle or something. ⁓ but I I you know th those kinds of half measures where you're sort of on your hands and knees begging the corporation, please make things better for your workers, please make things better for the for the environment, or trying to come up with some laws that will constrain them. ⁓ I think ultimately that's a losing game because when there's a downturn in the economy, they can make a case for why, ⁓ it's now too expensive, or ⁓ we we no longer want DEI programs or whatever. ⁓ so I I I think ⁓ again, you know, thinking about it in terms of the social imaginary, ⁓ it would really help folks to say, well, it's not just capitalism versus socialism. ⁓ there's a third alternative in which you do have ⁓
Albert Grandy III: Mm-hmm.
Ron Eglash: ⁓ a a a a capitalist exchange system, but the corporations are owned by the people who are doing the work, right? It's it's worker owned and and worker managed and much more democratic forms of economy than what we have now. So my my fingers are crossed that whether it's it's people like me developing technologies or folks working in in economics or folks out there in the streets doing political protests, ⁓ that will become part of our vocabulary and part of our our repertoire is is that vision of a a bottom up grassroots owned economy.
Albert Grandy III: noted. But Dr. Eglash, thank you for your time. Thank you for hopping onto this podcast. You know, this is the first episode again. So, founding your founding guest. And this is a great conversation. I hope the audience out there when they listen to this can really take some ideas, take some inspiration of what we've talked about today. And again, like you said, move, move it along, move it forward. ⁓ Before we leave, like where can people find you work, people reach out to you they're interested, play it out.
Ron Eglash: yeah, so so ⁓ we've got our ⁓ nonprofit, the Center for Generative Justice. ⁓ let me ⁓ go back to that here. Wow, I just heard a big lightning strike. I don't know if my internet's gonna hold up for the ending here. so so the ⁓ if you click on projects in the Center for Generative Justice, you'll see everything that I just showed you. Afrobotics and the African Futurist Greenhouse and the Artisanal Futures.
Albert Grandy III: You
Ron Eglash: website and everything we're doing is is right on that page and and w everything we do is open source, free access, ⁓ inviting everybody to come on by.
Albert Grandy III: That sounds good. And I'll also add your links in your email and your bio. You can send me stuff too. put in the description so people can easily get to your site. But again, I want to thank you for your time.
Ron Eglash: Wonderful. My my pleasure. Great talking to you.
Albert Grandy III: As always, alright, see you later, Thread. This episode's coming out soon.
Ron Eglash: Sure. ⁓ so r ⁓ grew up in ⁓ San Luis Obispo, California, ⁓ total nerd when I was young, and I guess I I've I've kept that as my life theme. ⁓ my father, Albert A. Glash, ⁓ back in the nineteen fifties, was ⁓ here in in ⁓ Michigan. I'm just a few miles away from Detroit. and ⁓ He was working with incarcerated folks, ⁓ returned citizens from prison, ⁓ and ⁓ realized the justice system, you know, wasn't just and came up with an alternative framework he called restorative justice. ⁓ so so that was ⁓ sort of ⁓ a big influence on my my early life, right, growing up with with somebody who had kind of founded this whole framework for rethinking the the justice system around ⁓ restoration and and ⁓ Creative restitution, as dad called it. ⁓ so ⁓ when I started to put together the things I was doing, I was calling it generative justice. ⁓ and so that's the title, this little slideshow I I put together. ⁓ and I can hop right into that if you're ready.
Albert Grandy III: Yeah, we can hop right into it. Like generative justice, I feel like that combines, you know, how we see the world in terms of how we can embrace someone's hopes and dreams to continue pushing forward. So if you want to go into it, we can get into it.
Ron Eglash: Yeah, yeah, yeah. ⁓ all right. So ⁓ you had sent me a list of questions and and I threw those up on a slide here just so I could remember what it was I was supposed to be talking about. ⁓ not ⁓ no guarantees I'm gonna stick to that. ⁓ but let me let me start with ⁓ you know, if you could just rattle off for me what you think are some of the major problems facing humanity today, facing people.
Albert Grandy III: are you talking about just these things on these lists or just overall social problems?
Ron Eglash: No, just just ⁓ just overall what comes to mind.
Albert Grandy III: I would say social problems in terms of communication could be one in terms of how someone can relate to someone in terms of language, in terms of culture, in terms of like what they want to do or what they'd like to do. Another thing is resources. That could be a social problem as well. But yeah, I have those too.
Ron Eglash: Yeah. Yeah, yeah, yeah, absolutely. Yeah, that's gr that's that's a great list right there. ⁓ so so when you mention ⁓ communication problems, the first thing that comes to my mind is ⁓ the political polarization that's happening in this country right now, right? Where you've you've got somebody who's ⁓ low income, working class dude, but you know, he's watching Fox News and feels like the the Democrats are ripping him off and You know, he wants that that that MAGA ⁓ force to come in and make things right. ⁓ so so ⁓ the framework that I apply to this is the framework of value extraction that in every one of these cases ⁓ we're ripping value away from the thing that's generating it. And maybe that's resources that are generated in nature, like ⁓ forests, for example. You know, you you clear-cut the forest and now you've broken its regenerativity. Right, it can't grow back. ⁓ the same thing with labor. You pay people super low wages, right, and give them the these really bad jobs. ⁓ and of course they can barely drag themselves to to work each day. You're extracting that labor value away from them. And the same with whole communities. You know, it used to be your social network was all your friends, now your social network has been commodified. It's been sold on a on a platform through through ⁓ Meta or or Facebook, whatever they're calling it these days. So these are all ways in which value is is extracted from nature, from labor, ⁓ and from communities. ⁓ but we don't have to design systems that way, right? We can design systems instead for value circulation. So if you look at something like organic agriculture, it's not causing soil depletion, right? It's not destroying the land. When you ⁓ if you're an ⁓ organic farmer or small family farm, you'll take all that agricultural waste and you'll put it in a big compost pile and you'll let nature decompose it back into the soil, right? And that enriches the soil and it just happens in this this beautiful cycle. ⁓ the same thing for labor value. So ⁓ if you have worker-owned businesses, they're not gonna take those profits and give it away to a billionaire. They're gonna take those profits and plow it back into the workers. Right. And so you've got these beautiful worker-owned cooperatives and ⁓ businesses around the the country and around the world, like Mondragon and Spain, ⁓ that have shown there are these all very ⁓ beautiful alternatives ⁓ that that don't ⁓ have exploitative labor practices and so on. And the same thing for for whole community. So you can have ⁓ platforms that are owned by the people rather than platforms that are owned by some billionaire that's trying to push a bunch of ads onto there and you know. use algorithms to to optimize clicks or whatever it is they're they're doing to to to make things a terrible space. All right. So so that brings us to the definition of what I've been calling genitive justice. The universal right to generate unalienated value and directly participate in its benefits, the rights of value generators to create their own conditions of production, and the rights of communities of value generators to nurture self-sustaining paths for its circulation. So the question is How do we pull that off? ⁓ And it's not as much of a mystery as we might expect, because we've got some amazing models in indigenous societies. Now, a lot of the folks, when I start describing these problems, they say, ⁓ well, you're just describing the problems of capitalism. If you just get rid of capitalism and had everybody become communists, that problem wouldn't occur. But if you look at these, you know, big authoritarian bureaucracies from the The USSR and Nepal and Cuba, ⁓ they've had the same problems that capitalism has had. They've had pollution and civil rights problems and and poverty and and so on. And that's because they too are extracting value, right? So when Stalin was trying to figure out what should the industry of of the Soviet Union look like, he would say, ⁓ we have the largest iron smelting plant in the world, right? ⁓ I can't do a Russian accent, but you know what I mean. It was it was the all these prestige projects to say that we've got the biggest industry and we're the most we're gonna out out efficiency, efficient capitalism. Right? But if your if your point is to return value to workers, you can't extract it and then expect to be able to d redistribute it like Santa Claus. You've got to come up with a system that never does extraction in the first place.
Albert Grandy III: you Okay.
Ron Eglash: So the problem is not whether value is being extracted to ⁓ millionaires or whether the the value is being extracted to the communist state. The problem is the extraction. You've got to come up with ways of doing things from the bottom up instead of the top down. We're so accustomed to thinking of everything as top down that when I say I want things to be organized, folks immediately assume I'm I'm imposing a grid. Right, I'm I'm imposing some kind of manager who's gonna tell everybody, you know, you have to be in this spot producing this thing at this particular moment, right? Right, imposing that control ⁓ to try to iron out any variation. ⁓ but that's not what you see in these indigenous societies. You see just the opposite. They're actually organizing things from the bottom up the way nature does. And those self-organized societies prior to to colonialism, ⁓
Albert Grandy III: Okay.
Ron Eglash: had much more flexibility, much better adaptive response to climatic changes, much better ⁓ democratic potential because they could organize through, you know, small clusters of people democratically deciding things, and then they would send representatives out and they would decide in a tribal council or or however it was arranged. So that's what I found in my work on ⁓ these these architectures in Africa. That they were designed ⁓ from the bottom up rather than imposing a grid from the top down. So it's much more organic forms of growth. ⁓ and by doing that, it could be much more democratic. So ⁓ one of the my first exposures to this was reading about a study in Tanzania where ⁓ women had an unusual amount of autonomy. And this anthropologist was saying, you know, it's so amazing to walk into this village, and it's you know, religion-wise, it's a Muslim village. ⁓ but if the women wanted to divorce their their husbands, they would just build their own house out of the same, you know, ⁓ grass and sticks and mud that everybody else was using. ⁓ and that flexibility in land use was instrumental to a kind of autonomy and democracy. ⁓ even, you know, your kids are bothering you, so you build your teens a an extra house, your mother-in-law is getting on her nerves, you build her a house. That that flexibility in design ⁓ grounded in ⁓ the kind of legal systems that would allow it, right? So it's what what's called usufructory rights. If you can go to the council and say, hey, I have a use for this plot of land, you're you're able to use it. It's not imposing this grid of of ownership on on everything. So those those commons commons-based ⁓ sharing mechanisms prevent monopolies from forming, right? ⁓ and I found that it wasn't just in the architecture, it was also in the
Albert Grandy III: Okay.
Ron Eglash: textiles and in sculpture and in construction techniques for metal and all sorts of things were embodying this spirit of ⁓ organic growth and things, you know, evolving from the bottom up rather than being planned out from the from the top down. So I published that book, African Fractals, ⁓ talked about, you know, even in cases where Christianity had come in, so here in in Ethiopia, you've got these 900-year-old churches. But you can see they've they've kind of taken that fractal structure and combined it with the cross. So you have the cross within the cross within the cross, or these beautiful fractals in the ⁓ processional, big processional crosses that are used in ceremonies. ⁓ Here I'm I'm at the base of one of these big rock churches, and you can see ⁓ it's got this beautiful ⁓ curve, nonlinear curve, ⁓ where the staircase progresses in a a scaling sequence to indicate that. ⁓ the curves of life, right? These organic curves. ⁓ so so ⁓ that leaves us with this question now of translation. So now that we know you have all these advantages from these self-organized bottom-up systems, how do we now translate that into something ⁓ in modern circumstances? ⁓ and the basis for that was ⁓ understanding the kind of circular economy
Albert Grandy III: Okay.
Ron Eglash: That they create. So this is my old friend ⁓ Gabriel Boace, passed away just a few years ago, unfortunately, ⁓ in the village of Antonso in Ghana. And he's got bark from the body tree, Braidelia is the Latin name. And they boil that down in these big pots ⁓ and then they strain out the bark. And the leftover bark, once the pigment has been taken out, the leftover bark goes back into the the sacred forest or into composting for farming, and that completes that circular economy, right? But if you if you look at the symbols, ⁓ and I've got they they carve these beautiful little stamps, right? And then they they they stamp it onto the ⁓ the cloth. ⁓ so if you look at the symbols that are are being stamped, it's things like Fun Tun Funafu. ⁓ this symbol here is ⁓ two crocodiles that share the same stomach.
Albert Grandy III: Okay, wow.
Ron Eglash: And the saying is, you know, why should we fight? By feeding you, I feed myself. Right? So it's not just a circle economy of ecological value, it's also a circle economy of labor value and a circular economy of social value. So they're really hitting all three of those domains in th those that bottom up self-organization. ⁓ and then another way I found that ⁓ the fractals were enabling this was just the fact that it takes so frickin' long. ⁓ so you you know, you d you do a a fractal beadwork pattern like this one from Cameroon, ⁓ or a fractal hairstyle, what we would call cornrows in the the United States, ⁓ and that's gonna it's gonna take you all day, right? ⁓ it's very different from the way that capitalism works. So so ⁓ if you went to the parking lot after a day of work and you saw your boss stuffing 10,000 ears of corn into the trunk of his car and you only got ten ears of corn.
Albert Grandy III: Mm-hmm.
Ron Eglash: You'd say, hey, I'm getting ripped off. But you'll never see that. We make all of that invisible. ⁓ you know, my cell phone here was made by a bunch of different people, right? Somebody made the glass and somebody mined the rare earth minerals out of the earth. All of that is made invisible, as invisible as possible. So you can't see the pain and the labor that goes into it, and it becomes easier to extract value from folks. These indigenous systems work in exactly the opposite way.
Albert Grandy III: Thank
Ron Eglash: So when you're seeing this fractal detail in these systems, that's because you're trying to make the labor value as visible as possible. You want people to know that this took hours and I braided this pattern into my friend's hair and I showed my love for my friend, right? Through that that labor practice. ⁓ and so those some of those are really great examples of how fractals made it to the new world. And so the second question you asked, the first one was just what's the mechanism here? The second question you asked was, what about transmission? How does it make it through the middle passage to the the new world? If I understood you correctly.
Albert Grandy III: Yep.
Ron Eglash: Okay. ⁓ so so let me let me jump to ⁓ an example of quilting. ⁓ so so ⁓ let's see here. This is our website, Culturally Situated Design Tools. These are all free online, by the way. Anybody can use them. ⁓ And let me go to Gee's Benz. I don't know if you're familiar with ⁓ the Gee's Benz ⁓ quilts, but you can s you can you they're in Alabama, ⁓ very, very old community making these quilts, and you can see even you know, you look at these very early quilt samples, you can see the resemblance between the patterns they're making here.
Albert Grandy III: I'm not.
Ron Eglash: and the kind of patterns you would see ⁓ in in Africa, right? ⁓ but one of the the points I've made, ⁓ gosh, you know, I I I haven't tried sharing. Let me make sure that's gonna work. ⁓ tell me if you can hear this.
Albert Grandy III: Okay. I can hear it. Yeah, it came through. I liked it though. It had like very distinctive patterns and you can see how each line, each color lined up exactly like in a symmetrical pattern.
Ron Eglash: Awesome. Awesome. Exactly. And if you've ever heard folks say African music ⁓ uses call and response, right? You can see visually that the G spins quilts are using call and response too. And so so that's you know some of the transmission mechanisms. Of course, you can transmit hairstyles because you can carry your hair with you, even if ⁓ somebody has wrapped you up in in chains and threw you on a boat. You can't carry your quilts with you, but you can you can carry memories of those kinds of practices, right? And more importantly, you can remember the social features of things like call and response and these these regenerative loops that are that are at the at the basis of that. So so I wanted to give a little shout out to your question about ⁓ transmission. ⁓ and I've and I've tried to you know get the message out about fractals as black cultural capital. Right, through the the book that I wrote and the TED talk and and ⁓ doing things like this podcast. ⁓ and and ⁓ it's been really exciting seeing it get picked up. So here's a a huge modern building in Ethiopia that ⁓ the architect told me ⁓ he read the Afghan Fractals book and said, ⁓ you know, let's let's use that that pattern here. ⁓ what was so interesting, I thought, about this particular case was that ⁓ He put these in as perforations in the exterior of the building. And just like the human lungs are fractal, it gave the the building a kind of breathing system, right? And that lowered the temperature in the building enough so instead of putting in air conditioning, they just let the natural airflow cool the building. ⁓ so not only did that lower the carbon footprint of the building, it also just made it cheaper for to rent space. It's a shopping center. ⁓ so when I've ⁓ visited there in in ⁓ I think it was 2018, there were all these little mom and pop shops there. You know, and I looked at at the places around in Addis Ababa and pretty expensive stores all surrounding it. So it was one of the few places where somebody from the the low-income community could come in and and have their shop. So there's there's all kinds of really amazing benefits, you know, s ⁓ environmental benefits and social benefits that can occur ⁓ when you start thinking about ⁓ how to incorporate fractals rather than these than these grids. ⁓ and I've got a I've got a link here to ⁓ just a list of you know different researchers and artists and and so on, ⁓ folks who who have been designing ⁓ with those ⁓ fractal frameworks in mind, everything from AI algorithms to artworks to literature to transportation studies and and and so on. ⁓ but I I I wanted to ⁓ I wanted to you know, get back to your question because these are folks who are pretty well off, right? They're, you know, famous artists or university researchers or whoever. And you were asking sort of a different question, right? Which was that that question of resistance and resilience ⁓ and transformation. ⁓ so so I I think, you know, something a lot of these scholars have picked up on, really, is that ⁓ even during the slave trade, you have stuff going underground.
Albert Grandy III: Okay.
Ron Eglash: And so there are there are kinds of of hiding in the light, so to speak, right? So that cor that cornrows style ⁓ that you're you're trying to pass have, ⁓ I'm just you know, trying to make my hair look tidy and dress up for this occasion. ⁓ but in a sense you're smuggling in ⁓ a ⁓ a broader set of of concepts and practices that are much more subversive. ⁓ and so so I think, you know, that's that's a piece of it is is how does this manage to survive? ⁓ colonialism and and ⁓ the Jim Crow era and and so on. ⁓ as Malcolm X would say, by any means necessary, right? You get that that smuggling however you can and and often in ways that are just staring at you ⁓ in your face. So my my wife is is African American, Afro Caribbean, ⁓ naturalized citizen, Professor Roger Bennett, ⁓ and she actually did her her master's thesis as at Yale on ⁓ cooking.
Albert Grandy III: Okay.
Ron Eglash: And ⁓ my mother in law was just an amazing cook. She had this rich potwa and and she'd always lay out these, you know, we'd have Thanksgiving with turkey, but then there's also, you know, oxtail and plantains and ⁓ rice and feas and all this stuff. ⁓ and and and so, you know, ⁓ a a a lot of those ⁓ parts of cultural transmission get smuggled in through things like the kinds of plants that you you utilize. ⁓
Albert Grandy III: Hmm. The good stuff.
Ron Eglash: Or the the kinds of recipes and recipe sharing that you're you're putting together and so on. ⁓ so so you know when you look at something like Black Panther, it's really cool to see this stuff up on the big screen, right? ⁓ but on the other hand, they made 1.23 billion in the first month. How much of that money actually went to the people in those villages? I am not even sure you could measure the percentage. You know, it's point zero zero zero something, right?
Albert Grandy III: You
Ron Eglash: ⁓ and so so that's the the question I really wanted to turn to. ⁓ so so back ⁓ four years ago ⁓ we had gotten a National Science Foundation grant to take this idea of a ⁓ a gender justice system, a circular economy, ⁓ and apply it to a modern day economy. ⁓ and we based ourselves in Detroit ⁓ because you've got this huge African American population there. lots of low-income communities, ⁓ lots of interest in ⁓ African heritage and just kind of crafting ⁓ traditions in general. ⁓ but ⁓ at the same time, you know, it's under the pressures of of our our sort of ⁓ neoliberal ⁓ economy where everybody's competing against everybody else and you know if you want to put something up on a platform you're gonna get ripped off because there's gonna be a fee that that's
Albert Grandy III: Okay.
Ron Eglash: Associated with the platform and you want to get it up on the the website, but there's a fee associated with Squarespace or Etsy or or or whatever it is. So so ⁓ we tried to design this platform as a circular economy, keeping in mind, you know, what we had seen in the African circular economies, right? And by this time we were also working with ⁓ Native American communities and and South American indigenous communities. ⁓ I took a trip to India and Alaska, worked with some You pick folks, and so we had a good sense that ⁓ you could find these circular economies, you know, even in in in if you look at ⁓ Europe's indigenous heritage, the the the ancient Celtic traditions, right? ⁓ you saw those circular economies back then too. The only difference is that colonialism came from inside of Europe, so it sort of cannibalized itself rather than coming from the exterior. ⁓ but if you go back to those Celtic traditions, you can see those those same circular economies. So question was, how do you translate that into, you know, contemporary computing technologies, into artificial intelligence and digital fabrication and so on? ⁓ and so we started with these little mom and pop worker-owned businesses, whether it's like a local hair salon or a local clothing maker or you know, anybody who owns their own business, right? Because if I go into McDonald's and I say, hey, I'm I'm here to help you guys with robotics.
Albert Grandy III: Okay.
Ron Eglash: They're just gonna say, fine, we'll fire all the workers and replace them with robots. It's not gonna get us anywhere, right? You've gotta start with ⁓ the ownership of the means of production, as Karl Marx would say, but not doing it through, you know, state communism. You can imagine what Trump would do if he if we had socialism and and all the corporations were owned by the government. It'd be a disaster. So you you wanna keep things in the grassroots, you wanna keep that that worker ownership, right? And just empower the grassroots to to develop its own value generation. So we we went to all those little mom and pop businesses and we asked them, you know, what can AI do for you folks? What can what can digital fabrication, laser cutters and and 3D printers and so on do for you folks? What kinds of apps are you paying for every month that's draining all of your finances that we could replace with something on a worker-owned platform? So that was this project we call artisanal futures. ⁓ and here's some of the participants, ⁓ about 90% African American, ⁓ about sixty percent female, located in ⁓ low-income communities within the the greater Detroit area and all worker owned, whether it's you know urban farms or textiles or whatever it is they're doing. ⁓ and so we wanted to hit all three of those domains, right? We wanted to make sure that when we go to these urban farms, and they're already practicing organic agriculture, but the question was, how do we superpower that loop? How do we take, you know, AI-based agroecology and soil sensors and aquaponics and how how do we bring those technologies to those worker-owned farms? ⁓ and then looking at the the kinds of labor that folks were doing, how do you take, you know, 3D printing and and digital fabrication techniques?
Albert Grandy III: Okay.
Ron Eglash: ⁓ AI that's actually made for the folks working in those places ⁓ and make that available. ⁓ and then the same for the community as the whole. So now that we've got this platform, what can we do to bridge those gaps in things like education and housing and and health so that it's not some big company coming in ripping you off, but you know, actually ⁓ manifesting ⁓ those kinds of systems whether it's ⁓ banking systems or or ⁓ housing systems whatever it is within grassroots ownership. ⁓ So one of the things we found was that a lot of these worker-owned businesses were upcycling, not recycling. So in s instead of taking a bunch of plastic and melting it down in a big you know plant somewhere that's owned by a millionaire, they're just taking things out of the waste stream ⁓ and upcycling it into other products. So so this is a ⁓ one of our artisans who would take old scraps of clothing and turn it into handbags. ⁓ so so we started looking at artificial intelligence for this and we first tried some commercial artificial intelligence Dolly and we said okay Dolly I've got these textile scraps right and I want you to turn that into a jacket so Dolly produces this jacket but I don't see those scraps anywhere in this jacket do you They're really hard to find. So so I can see, you know, I've got this leopard print here. I guess that's the leopard print there, but the other parts are really, really hard to find. And we found out that's because when you look within the algorithm, what Dolly is doing is it's taking the images, turning it into a text description. And so it has a text description for leopard print, but what's the little text description for this thing or this thing? Right? There's there's no word equivalent other than you can say a bunch of stripy patterns with some orange and purple. ⁓ so we we created our own AI algorithm that would now be owned by the workers themselves, not owned by a corporation. ⁓ we call it Upsy because it was made specifically for upcycling. and and it first takes the the the words, right, uses a large language model, design a jacket using the following scraps.
Albert Grandy III: And how you gonna do that mid podcast?
Ron Eglash: But then it takes a neural network and looks at the images itself and imposes those images onto the the the the product, whatever it is. So it so it's actually using the the textures rather than converting them into a bunch of words. ⁓ and so that makes it much easier for the the artisans to now say, ⁓ yeah, I know exactly where to put this cloth and this cloth and this cloth. So it was it was a great proof of concept. You know, that you could actually, by working with local local artisans with these worker-owned ⁓ small businesses, you could improve the technical aspects of AI. You could actually advance computer science doing that. So I was super excited about that. ⁓ this is a really fun example where one of the workers w we we were ⁓ collaborating with said, design a handbag shaped like a car. Because we're in Detroit, right? ⁓ So she tried it out with these scraps here, and she said, nah, I don't like the result. It's like literally, it looks like a toy car, right? Too literal minded, too concrete. ⁓ and then she tried it with leather scraps, same words, design a handbag shaped like a car. And it was really abstract. Like I guess those are supposed to be the wheels of the car down there, but you wouldn't guess that that's a car, right? And I thought she'd be unhappy with this, but she came back to us. She said, I'm so excited. Because what this has done is it's mapped out the space of possibilities for me, right? And in between too concrete and too abstract, that's where my agency comes in. So I think, you know, developing AI in ways where people can actually use their own agency, ⁓ the way that you have, you know, artisans in Africa using their their their agency or any kind of worker-directed work using its own agency has been super important. ⁓ And in some cases, that's kind of a cyborg melding of human and machine. So ⁓ this is a different artisan who wanted something based on Kente cloth and Van Gogh's painting. She picked ⁓ Starry Knight and she calls this Kente Van Gogh. And so we just got her a a direct to to garment printer. So you can print onto textile ⁓ the way that you print onto cl onto paper with a regular printer. ⁓ and so now she's she's creating these these ⁓ Starry night textiles in Detroit. ⁓ and this is ⁓ another example where we were asking folks, you know, well what is it you hate about your job? And everybody said the same thing. They said, ⁓ if somebody comes to me with a pattern and it doesn't fit their body, now I've got to make all these adjustments to their particular body shape, right? And that's just super tedious. Could I have the AI do that for us?
Albert Grandy III: Mm-hmm.
Ron Eglash: ⁓ and so one of my grad students, Kwame Robinson, ⁓ he's now a professor in Detroit at Wayne State University. ⁓ so he's he's Dr. Kwame Robinson now. And he created this ⁓ he calls it Sancofa sizer. So so it it it automatically does that kind of adjustment of patterns. ⁓ and then the ⁓ artisan we we happen be working with said, well, you know, I don't I don't want to have to have it printed out on paper and then tape it onto the the cloth. ⁓ how about a projector? So this is a little projector that projects the the pattern onto the the cloth for her. So if you think about all of those things together, what we're looking at is a spectrum from cases where AI is mostly human and it's just offering a little inspiration to cases where AI is doing most of the work and you've offloaded the task onto the machine. And so the question you want to ask is not can AI do it. But where, at what position along the human machine agency spectrum do you want to intervene so that you're preserving the parts of the job that people love, right? Because if if we're creating jobs that people hate, that's not what you want. So that was that was at the kind of the micro scale of businesses. Then the next level up, now we want to put together an economy. So we've got to find out: can we replace competition? with business to business collaboration. And so we've came up with a great example with the local urban farmers. They were wondering, you know, what is it I should be growing, right? And we had local local fabricators saying, well, I don't want to put more plastics into the environment. What kind of natural fibers can I produce? So we can now turn that question over to AI and ask it, you know, given these farmers and these makers what should be grown in these farms
Albert Grandy III: Mm-hmm.
Ron Eglash: Such that they end up buying those fibers from the farmers and creating some products. And so it's a really neat way of now exploring, you know, you could have all sorts of value chains going on in that kind of meso-level economy, right? That are business-to-business collaborations. ⁓ This is a little project we did with a ⁓ the ⁓ African Bead Museum in Detroit. It's mostly just a bead shop. And ⁓ Dobbles, the guy who owns it, has a little outdoor sculpture garden.
Albert Grandy III: Okay.
Ron Eglash: So he ⁓ allowed us to create a greenhouse there to grow some of the plants that become ⁓ beadwork in the necklaces. So we ran a little workshop ⁓ in Ghana with students there. ⁓ and then we hired some of the local folks, and this is a mix of university students and and local folks that we hired ⁓ to actually build this thing. ⁓ and so we we then brought in some high school students who ⁓ created the ⁓ feedback regulation. So that it won't overheat in the summer. We got solar panels up on the roof so that it would be using solar electricity. And that became a model for another grant we got to now take those photoelectric systems and use them in all the urban farms that we were working with. So we managed to get enough funding for five different of these, we called it solar irrigation, right? So you're capturing rainwater and then using solar power to run the sensors and the pumps to distribute that in the in the farms. So these kinds of localized supply chains have been really crucial to thinking about this. ⁓ maybe using AI for ⁓ creating maps of where those waste streams can come from, right? And and upping the the upcycling part of this. ⁓ Kwame's been working recently on ⁓ worker-owned e-delivery. So if I use DoorDash or something like that, Uber Eats, I'm just making rich people richer. Right? The money's not going directly to the people who drive the cars and the people who farm the food. ⁓ so Kwame's been developing an al a local alternative that's actually owned by the workers for doing e-delivery. ⁓ and ⁓ one of them said, I I don't want to stand outside my farm all day handing out packages of food. ⁓ and so she ended up getting ⁓ a grant to install some of these vending machines. So Kwame's been hooking those up to this system, internet system. So when you order online, ⁓ it looks at what's available in this refrigerated vending machine, lets you know, you know, what you can pick from. And so he's brought in a ⁓ just a whole another level of of automation to this. So let me let me ⁓ stop there and ⁓ let's ⁓ just go back to our our conversation here. So ask me questions, tell me answers to questions I have, just just lay it on me, ma'am.
Albert Grandy III: Yeah, like I appreciate that walkthrough because you really gave a in-depth description on like what you do and the work that you've done and the work that continue to be built. like the first point that I thought of was this really reminded me of the circle of life in terms of like everything is growing from the ground up as you were saying, but as it grows from the ground up and people are using it, building it, it also comes back down to the ground. So it's a whole cycle. And I really think like that's important in terms of our society. So we can reuse the resources that we have instead of extracting that value from whether that's the earth, whether that's from people, whether that's from ideas, and just helping the workers at the end, because those are the people that are actually putting in the work and the time and the energy to give back to them so they can keep doing the things that they love to do. So I love that explanation that you gave. And then I also, this is actually one of the things that made me reach out to you as well, because I was looking at your page in terms of the braids and the hairstyles, because I have two strand twists in my hair right now. Like I've never really thought of it in terms of fractals and how people were grading for, you know, some people may not, you know, some people just made you do it just like you said, know, just for fun, just to have the hairstyle. But some people are grading the hair in terms of fractals to represent those patterns, represent those hard work. And I wonder if that in a sense can be like a form of energy, whether that like those fractals can represent, heritage can represent, you know, that generation as that transmission question I asked before, like how can those generations, those thoughts and ideas be passed on from one to another and Yeah, I really think that that is an example that you gave, which is beautiful.
Ron Eglash: Great, great. I'm I'm I'm so glad you liked it. Yeah, I you know, I I was I was really focused on ⁓ thinking about the the ⁓ African examples, right? ⁓ but but if you if you look at ⁓ these different indigenous cultural groups, ⁓ so here's ⁓ a Celtic example, and ⁓ here's ⁓ Anashinabe, the the indigenous group here in Michigan, ⁓ here's Maori. ⁓ So all of these indigenous cultures, as you were saying, ⁓ have that concept of the the circle of life. Absolutely.
Albert Grandy III: And then you said that you've seen these patterns across your journeys across different countries. I mean, they do look similar. Do they mean the same thing? Do they mean have different meanings?
Ron Eglash: No, no. No, they have they have they have radically different ⁓ meanings and and I I don't I don't want to ⁓ start to to generalize this from a sort of you know colonial ⁓ all indigenous cultures are the the the same perspective. Let me let me zoom into one particular example that I really like. ⁓ So we were we were looking at ⁓ the the the ⁓ indigenous native American groups ⁓ and the way that ⁓ they would do these controlled burns, right? ⁓ or the way that they would do cycles of ⁓ textile fabrication. ⁓ and I had a bunch of different folks working on on this with me, ⁓ but this was really focused on the concept of entropy. And we usually think of entropy as a measure of disorder. ⁓ and rightly so. So, you know, entropy is high when you've got a gas and you cool it down to a liquid, and now ⁓ entropy is lower, and you cool that down to a crystalline solid, and now it's all ordered, and entropy is is at its lowest state, right? ⁓ and so you can you can think of entropy as how much information is it gonna take to describe this thing. So describing every single molecule in a gas is really hard. It's a little bit easier with a liquid, and it's ridiculously easy in a crystal because they're all lined up in these rows and columns. So here you have a case where diversity is is low, you have low entropy. And here you have a case where diversity is high, you have high entropy. ⁓ So in the case of these controlled burns, you go from a low entropy state where everything is ashes. To a high entropy state where you have the whole biodiversity of the system, right? And when that gets overgrown and the bushes are no longer producing all the berries you want, or the meadows no longer producing all the herbs that you want, you set it on fire and you let those nutrients go back to the soil. And when the rains come, you get this boom of biodiversity. And so it's really a back and forth modulation of entropy. And it took me a long time to kind of catch on to that. ⁓ because these systems, you know, are quite complex about where do I do the burn and how often do I do I burn it. If it's a big forest, it's maybe once every seventy-five years. So, you know, you might even ⁓ live a lifetime that has two of those burns in it, right? It's it's an intergenerational ⁓ dynamic. ⁓ so so ⁓ if you look at Navajo weaving, you actually see a very similar system. and ⁓ If you look at the sheep that's grown in the corrals, ⁓ they're pooping out seeds, and so you've got this ⁓ explosion, this biodiversity of different plants around the sheep corrals, ⁓ and those are used as the different color dyes. So you you take the plant, you turn it into a dye, and you dye your yarn from the sheep, right? ⁓ but then you reduce the entropy. You have this very low entropy weaving where everything's in these very regular grids. ⁓ And the the profits from that go back to supporting the sheep. So as you were saying, it's that that circle of life, right? ⁓ But it's a circle of life that's kind of pumped between the high entropy state of biodiversity and the low entropy state of weaving. And you can think of that as this kind of sine wave through time. ⁓ But if you think about the basis of biology, it's exactly the same. You've got DNA, which is the super compressed low entropy form, and then you know you develop as an entrop ⁓ as an embryo and you you come into being into this world as this enormously complex explosion of biomolecules, just this mind-blowing biodiversity within your body, right? ⁓ but eventually, hopefully, ⁓ you'll have children. And so once again, you've got just the the sperm and the egg, you've reduced it down to the super low. entropy state. And so just like you you saw with ⁓ with the weaving and just like you saw with the controlled burns, you've got this modulation through time between ⁓ high entropy and low entropy. ⁓ and you can think about that in all kinds of different systems, right? So if you if you think about how you teach somebody something, you have kind of a biodiverse ⁓ work diverse moment ⁓ where you're trying out all kinds of different tools, where you're experimenting. Right. And then you nail it. You go, ⁓ yeah, man, this worked out super well. And I'm going to make this thing with, I don't know, copper wire. ⁓ but then there comes a time and you say, okay, I've been there, done that. Now I want to ⁓ you know expand my repertoire again. And so you've got this modulation in in learning systems as well. ⁓ you can have the same thing with leadership systems. So one of the problems we have in our government is that things get too fossilized and ossified and fixed.
Albert Grandy III: Okay.
Ron Eglash: Right. And so you've got Supreme Court justices that are on there for a lifetime. ⁓ I was talking with one of one of the ⁓ Anishinabe professors ⁓ here at at at ⁓ the University of Michigan, ⁓ and he was saying, Yeah, you know, the the tradition was to c have constant turnover in the tribal council for exactly this reason. So you don't have an old boys network where you now you're, you know, trading favors, right? ⁓ so you have a le leader with with ex strict term limits and and ⁓ all kinds of ways of ⁓ of doing recall if somebody gets out of line and and and so on. ⁓ so so ⁓ you can think about the economy this way and and there's even you know ⁓ machine learning algorithms that use this this kind of ⁓ entropic modulation. So yeah, I I think there there's ⁓ you know quite a profound array of different techniques, whether it's fractals in Africa or control burns with the the Anishinabe or this weaving entropic modulation with the the Navajo. ⁓ all these indigenous groups have, you know, quite different ways of empowering those ⁓ circular economies.
Albert Grandy III: Okay, and to your point with AI, do you think that there could be some AI bias in terms of modulating the entropy? So in the machine learning algorithms, there could be some biases to help formulate specific entropies that are high or low, depending on what the bias wants to be represented as that could be represented in society today. Do believe that?
Ron Eglash: Well, I so so I think every I think everything's biased, right? ⁓ everything we do is produced in a social context. And so there's the s everything we do is is strongly influenced by the the the context you're you're you're in. ⁓ for example, ⁓ Andrew Pickering ⁓ has a great book called The Mangle where he talks about scientific laboratories And he shows that, you know, we we see some result in science and we think, okay, it's shown me the fundamental laws of how the universe works. But then you you backtrack in that history, and he was originally a physicist, but he became a social scientist because he was so interested in what was going on in these labs. ⁓ so he backtracks the history of the cloud chamber, for example, and he shows, yeah, you know, originally that that research could have taken off in a bunch of different directions. And this guy was at the Berkeley Livermore labs and there happened to be a bunch of liquid hydrogen sitting around. And so the particular direction that these ⁓ particle detectors took was ⁓ strongly influenced by the kinds of resources he could get his his hands on, right? And and had had he come up with a different set of resources, maybe we would have ended up, you know, going the direction of string theory instead of the the this other direction. ⁓ So so I I you know when it comes to algorithms, yeah, of course, all of our algorithms are created in a kind of social context. ⁓ and if you think about the algorithms that are used in AI today, ⁓ they're used in a way that brings you the compute as fast as possible, as massive as possible, without any regard whatsoever to the environmental consequences. Could you have used a different set of algorithms? Absolutely. Right? Absolutely. You could you could focus on, you know, low energy compute, right, as the kind of frontier that you want to explore in in computer science. ⁓ and that produces a different set of algorithms. Now I know that's not what you meant by by bias, right? You were thinking of stuff that says, you know, ⁓ look, AI is, you know, ⁓ I asked for a picture of a scientist and it shows me a picture of white person, right? And that's because it was trained on all these images, and so that bias
Albert Grandy III: Mm-hmm.
Ron Eglash: is carried over from the data into the the the outputs of of AI. ⁓ And yeah that's true, but I think the bias runs even much deeper than that, right? The the bias, ⁓ even if you eliminate bias in the data training sets, ⁓ there's still bias in the kinds of algorithms that we're producing. And I keep encouraging folks to think of this not as a negative. You know, when we say bias, it sounds like we're scolding somebody. ⁓ I'm accusing you of racism and bias, right?
Albert Grandy III: Mm-hmm.
Ron Eglash: We want to think about it in a positive sense. So how do we how do we come up with you know really profound, positive collaborations with computer science where we say, well, let's do the kinds of algorithms that are profoundly regenerative, that are are inherently good at putting value back into nature instead of extracting it, that are inherently good at putting value back into labor instead of extracting it. ⁓ So so it's it's quite a mental shift to get most computer scientists to start thinking that's that that sentence even makes sense. Right? Just just getting that concept across and getting them to wrap their heads around it ⁓ is is quite a challenge. But once you put it in those terms, often you can see folks starting to say, Well, okay, so you know, as just a you know, kind of thinking hypothesis, let me suppose I don't have a massive data center. I've got a bunch of little desktop servers. They're all networked together. And everybody, like I was showing you, everybody got has their own photovoltaic array on the roof of their worker-owned business. And their own little desktop server is now networked into a massive node that comprises a worker-owned data center. Now, what are the algorithms I need to create that data center rather than the one that we've been
Albert Grandy III: Mm-hmm.
Ron Eglash: creating that destroys the environment and makes, you know, massive amounts of money for people who are are already rich. That's a really interesting question from a computer science point of view. But it takes it takes a lot of arm twisting and and educating and nudging ⁓ to get the computer science folks to, you know, go to go to and and it is, I admit, it's a difficult leap. You've got to really think hard about, okay, so what does it mean to be working that very different context that doesn't even yet exist?
Albert Grandy III: Mm-hmm.
Ron Eglash: And and so much of our social imaginary and our technological imaginary is kind of pre-programmed to think about, you know, words like efficiency and optimization. I got my master's degree in in systems engineering, right? I I worked in in the Silicon Valley as an engineer. I I know this stuff really well. Every other word out of the mouth of the of the lecture I was getting in my engineering courses was optimization and efficiency. ⁓
Albert Grandy III: Okay.
Ron Eglash: But y you know, those words originally come from physics, and then somebody in early industry in the 1700s, you know, Adam Smith or somebody, says, ⁓ well, that's the kind of concept I need to make my factory efficient. And by efficient, I mean I pay the workers as little as possible. Right? ⁓ and so they come up with a bunch of equations that are then quote unquote the efficiency equation, and they fund a bunch of research grants for physicists. So now it starts getting reinforced, right? And it starts this positive feedback loop where what goes on in science starts to look a lot like what's going on in industry, but it's not a coincidence. It's just an echo chamber of of these folks, you know, following that same way of of thinking. So get it getting folks to shift that into other kinds of directions, I I think is absolutely imperative. But you know, in a sense they're all quote unquote biased, right? They all have a social context. It's just making that other social context more conceptually available to them.
Albert Grandy III: Right. Okay, and that makes sense. And that actually brings it back full circle because in terms of, everyone has their own subjective bias in their head and their minds and not a lot of things can change that or shift that mindset unless, you know, you go through training or you go through hard work or, you know, you're really passionate about an idea. And that goes back into the concept of the circle of life because when you have the full system, you know, you're just taking bits and pieces of everything like the nodes that you're just explaining about how everyone can have you like a local server and have bits and pieces of every computer in that community and everyone adds up in a progressive cycle and that can overall help, you know, answer that problem in terms of a large data center like that. Again, this is just generative regeneration as you were saying. So I really like that. I really have no words because like that's a perfect, in my opinion, perfect explanation of how that could happen in terms of you can still have bias, but you can still use that bias in a positive way. And I think that that's essential.
Ron Eglash: ⁓ Yeah, yeah. Rather rather than say no bias, ⁓ let's try to ⁓ engage in ⁓ an awareness of of ⁓ you know how the biases are running, right? And and what what are the alternative kinds of whether it's d different ⁓ data sets or or or different energy configurations and so on. Let me show you another ⁓ slide if if I'm not over overdoing it here with the with the slides. ⁓ so so ⁓ you know, if you look at the the physical components that make up a computer, right, ⁓ those are made out of out of things like tantalum ⁓ and titanium that are coming out of these ⁓ mines ⁓ in Africa and places where there's a lot of conflict, right? So just like you have conflict diamonds, you have conflict minerals as well. ⁓ and unfortunately though the the wealth of those mines is reinforcing a lot of the you know authoritarian ⁓ governments and and and violence and so on in those those regions. So so if we wanted to think about this other kind of future in which we have a regenerative basis for everything, you don't want to do it just at one scale, right? You want to do it at every scale involved. So you want to think about when I'm taking minerals out of the earth, how do I do that in a way in which it's the workers doing the work that actually own those those mines. And it that's that's possible. You know, there there's ⁓ artisanal gold mining, for example, is pretty common in in developing nations. ⁓ and unfortunately they tend to use mercury to to get the the gold out of the gravel, which causes a lot of mercury poisoning in in villages. But those there's alternatives. You can use ⁓ centrifuge and and so on. So we can come up with technologies that better empower ⁓ those locally owned mining techniques and we could use that to source the ⁓ components, the electrical components. ⁓ and then you want to think about how are those components put together, right? So when you when you create that motherboard, ⁓ you're soldering in not you, but some robot somewhere, you know, is is soldering in these these components. ⁓ so that too can be done in ways that are owned by workers rather than owned by big corporations and you can make that work a a lot more pleasant.
Albert Grandy III: .
Ron Eglash: ⁓ one of my favorite examples of that is Arduino, ⁓ which is a a little microprocessor board that was developed in Italy. It was the first open source hardware. and I absolutely love the Arduino board and I've gone through ⁓ a bunch of interviews that folks have done when they visited the Arduino factory. I was recently in Italy, didn't make it to to the Arduino folks, but ⁓ I managed to to interview a bunch of folks in worker cooperatives in Italy just to see, you know, what's what's that like.
Albert Grandy III: Okay.
Ron Eglash: ⁓ how are they thinking about these these ⁓ worker-owned labor conditions and it was just such a pleasure to see folks so thrilled to be at work and so so much enjoying themselves. ⁓ so it is possible to do fabrication work ⁓ instead of a sweatshop and an assembly line to make it much more ⁓ worker-owned, artisanal, worker directed. ⁓ and then you've got the the architectures and the computers and the networks and so on. So at every scale we can ask that question. What would turn this into a system of extraction where you're pulling value out of nature and pulling value out of workers ⁓ into a system of value regeneration?
Albert Grandy III: Okay, that makes sense. Hitting, things hitting, ideas and concepts that every stage should make a full rounded system, in your terms, more efficient. yeah, so I mean, my last question, which you pretty much already answered is, how can we create that change and take the next step to see what we need to do? But. You kind of already explained that in terms of not extracting value out of nature, out of things in terms of in a negative way, but how can we realign that thinking, that mindset to be progressive? But you, I mean, I want you to again, like reinforce that if you could summarize those points again, just as a closing and yeah.
Ron Eglash: Sure, yeah. ⁓ well, you know, you you need a big tent, right? You you you want everybody contributing in in whatever way they can. and ⁓ I spent a lot of time when I was a a s a student in in college ⁓ doing civil disobedience and and getting arrested during the ⁓ the anti anti-apartheid protests and and ⁓ against ⁓ nuclear weapons at at ⁓ Berkeley and so on. ⁓ And and ⁓ you know, I I think there's a really important role to be had in in ⁓ political organizing and political protests and so on. I I don't want to ignore that part of it. ⁓ but I I hate to see folks think of it as a binary, that either we have free market capitalism or we have the state owning everything, right? and and often when I talk with my colleagues, they say, well. I I I want the middle of the road, so we're gonna go to corporations like ⁓ Meta or or or or or whoever ⁓ and show them an algorithm that's more gentle or something. ⁓ but I I you know th those kinds of half measures where you're sort of on your hands and knees begging the corporation, please make things better for your workers, please make things better for the for the environment, or trying to come up with some laws that will constrain them. ⁓ I think ultimately that's a losing game because when there's a downturn in the economy, they can make a case for why, ⁓ it's now too expensive, or ⁓ we we no longer want DEI programs or whatever. ⁓ so I I I think ⁓ again, you know, thinking about it in terms of the social imaginary, ⁓ it would really help folks to say, well, it's not just capitalism versus socialism. ⁓ there's a third alternative in which you do have ⁓
Albert Grandy III: Mm-hmm.
Ron Eglash: ⁓ a a a a capitalist exchange system, but the corporations are owned by the people who are doing the work, right? It's it's worker owned and and worker managed and much more democratic forms of economy than what we have now. So my my fingers are crossed that whether it's it's people like me developing technologies or folks working in in economics or folks out there in the streets doing political protests, ⁓ that will become part of our vocabulary and part of our our repertoire is is that vision of a a bottom up grassroots owned economy.
Albert Grandy III: noted. But Dr. Eglash, thank you for your time. Thank you for hopping onto this podcast. You know, this is the first episode again. So, founding your founding guest. And this is a great conversation. I hope the audience out there when they listen to this can really take some ideas, take some inspiration of what we've talked about today. And again, like you said, move, move it along, move it forward. ⁓ Before we leave, like where can people find you work, people reach out to you they're interested, play it out.
Ron Eglash: yeah, so so ⁓ we've got our ⁓ nonprofit, the Center for Generative Justice. ⁓ let me ⁓ go back to that here. Wow, I just heard a big lightning strike. I don't know if my internet's gonna hold up for the ending here. so so the ⁓ if you click on projects in the Center for Generative Justice, you'll see everything that I just showed you. Afrobotics and the African Futurist Greenhouse and the Artisanal Futures.
Albert Grandy III: You
Ron Eglash: website and everything we're doing is is right on that page and and w everything we do is open source, free access, ⁓ inviting everybody to come on by.
Albert Grandy III: That sounds good. And I'll also add your links in your email and your bio. You can send me stuff too. put in the description so people can easily get to your site. But again, I want to thank you for your time.
Ron Eglash: Wonderful. My my pleasure. Great talking to you.
Albert Grandy III: As always, alright, see you later, Thread. This episode's coming out soon.
