Douglas Arellanes: Okay – we're here with Jen Southern, an artist based in the UK one of the artists exhibiting at the Mozilla Festival. And we're here to talk about her work, which is called Seeding Things. Jen, how are you today?
Jen Southern: I'm good, thanks.
Douglas: Your piece really intrigued me for a lot of different reasons, but maybe you can explain better than I could ever do – explain what is Seeding Things and what is what's going on in the work?
Jen: Seeding Things is a project that I started nearly a year ago now, it was during the first lock down of the pandemic. And I was trying to work out how to continue a practice where I often collaborate with people or other people participate in work, and I'm really interested in kind of sharing authorship. And so when I was started to be confined to just my house and garden, I started to try and think how could I kind of work with something else that would kind of have an input into the work. And I was really interested in… I mean, I'm interested in growing things anyway – that's part of what what I do. And I wanted to work out how I could develop a work that involved the kind of collaboration you do when you grow things or that you start something off and then it has its own process, and I became interested in how AI might (or machine learning might) do a similar thing, or I could use it to do a similar thing. So the work is it's a small clay mountain (I've been learning to work with clay again before the lockdown) that I embedded with grass seeds and it sits in greenhouse, and I water it every so often. Every month I take maybe 100 photographs of it, or more. And then after some months, I then took all of those photographs and used those to train a GAN machine learning model, which, which this video kind of comes out of. And the video appears to kind of… there's ice that's it starts out with, and it's sort appears to go through a kind of almost geological time of the mountain eroding and the grass growing on the ice melting. Does that make sense? I think.
Douglas: So the GAN is working with images of the plants?
Jen: Yeah.
Douglas: And so the machine learning is happening on the images after the plants have grown?
Jen: So the images have been taken all the way through the growth. So it starts with… It starts with just the clay mountain and I placed… I did a cast of ice over the mountains so that the ice would kind of melt almost like a glacier melting and that was the first way that the seeds were watered and then they grew on from that in the greenhouse. Because I take the photographs every few weeks, then the grass has been all different stages of growth and it's that that it sort of seemed to evolve over the… so the machine learning has kind of learned all the different stages of the growth and the death of the grass.
Douglas: Oh, interesting. You know, one of the things that it brought to mind was some of those… the sci fi films from the seventies specifically silent running with the idea of, you know, the greenhouse and the robots in the greenhouse and things like that. was that something that came to mind?
Jen: It didn't, although not that specific reference. But I've also been reading, science fiction like Jeff VanderMeer's Southern Reach trilogy and N. K. Jemisin's, trilogy that I can't remember the name of, but both were either nature or geology has a different kind of agency in the world. And so I think science fiction is something that's kind of there in the background of this kind of work. Although, I think obviously with science fiction, it's always the underlying things. It tells us about how we live now that are the more interesting outcomes of it. So I'm hoping that this work kind of has… has a bigger resonance to it. Very cool. You are a… a professor of art as well?
Jen: I'm a senior lecturer, yeah.
Douglas: Senior lecturer? You know, I'm new to the lecturing game myself, so I always have to have to relearn the different ranks.
Jen: Well, in the UK, you only get to be a professor. It's like being a chair in North America. So you go through all the other lecturer grades before you get there.
Douglas: Okay, But as a senior lecturer, then you've been, teaching as well. And you're a teacher of art, I assume.
Jen: Uhuh.
Douglas: And are you working with with AI in your work as an art lecturer?
Jen: No, I don't, actually currently, partly because the AI platforms that I've been using… I've used Runway – Runway ML. And it's, you know, it's a paid for service. And it's not one that our university buys a software university students could use. I teach fine art and new media, and we use a really broad range of new media, but it's not… but yeah, AI hasn't come into that so far, but I think there are students who are really interested in thinking about AI.
Douglas: It's good that you mentioned Runway because some people from Runway are going to be presenting as part of the Creative AI space as well. So you were using Runway for your work as well? Very cool. Now, when you're working with a tool like a GAN, how difficult was it to get started doing this kind of work? I mean, for a lot… And the reason I asked us on behalf of people who are just getting started, you know, with working with any type of AI tool.
Jen: I mean, I'm still getting started. I'm still… I would see myself as an absolute beginner, and it was really easy. I mean, I chose Runway ML because it was it was so easy to get to get started with it. Yeah, in fact, in some ways, it couldn't be more easy to do. It was, and I think that's part of the attraction to using it. But it also, I think – because you don't have to get very technically involved. It's an easy way in for artists. But I think there is a kind of particular look to things that come out of Runway and I think that's very quickly among artists it's going to be, you know, it's gonna be very obvious that that's how people have made things. So we'll have to see how that develops and how unique an output you can make using that particular piece of software.
Jen: Part of what I'm involved in at Lancaster University is the Center for Mobility's research. And so my own work always has ways of really tracking and tracing and thinking about mobility and what moves in the world. So this work, I'm working with an AI to make something that evolves over time – that appears to have this sort of almost like long term geology to it. It kind of moves quickly, but it feels like it's a long period of time that's compressed. And in some ways that's influenced by writers like Doreen Massey, who talked about the way that mountains have moved so that even the things that we think of as being like this really solid things beneath our feet, the rock beneath our feet over time have moved. So, for instance, in my local area just north of here in Cumbria, there are mountains, which was sedemented on the equator and of course over time as continents moved… have moved and are now here. But even the rock beneath our feet is something that's mobile, and so I'm really interested in how mobility and how things move, change how we think about the world. And by thinking of the world as something in motion it changes how we… how we perceive what's… what's happening around us. I think it changes how we think about things like climate change or even networked technology like we're using now – to think of that as a form of virtual mobility changes how we think about it. So all of those kind of… that kind of research is involved in how I approach making something like an AI.
Douglas: So Seeding Things itself is a work in… it's in video form?
Jen: Yeah, at the moment it's a short video. It's around about five minutes. And it's from the photographs that I've taken, but what I'd like to do in the future and I've been trying to work out ways to do this, to ask other people, to make their own versions and to grow them in terms of physical versions and to send me photographs and then to start trying to make a new version of the work, because it could have, multiple versions over time and particularly as the grass keeps growing and I keep taking photographs and keep feeding them back into the GAN then new things come out of it, so potentially it could be a participatory project wher people grow their own, build their own little clay mountains, grow grass on them, send me photographs, and it could become something that's much more collaborative like that.
Douglas: Wonderful. One of the things that we're planning with you at MozFest is not only to show the work, but then you also have a session where people can discuss this type of thing with you, right?
Jen: Yeah, absolutely. There's gonna be a session where I'll be talking about how I produce the work and how people can get involved.
Douglas: Wonderful. I'm really looking forward to that. Once again, we're talking with Jen Southern, and her piece, which will be shown at MozFest, is called Seeding Things. Jen thank you so much. And I'm really looking forward to seeing you at MozFest.
Jen: Great. Thank you