Thoughtforms on AI

changing perspectives with Morgan Thomas

Xiaowei Wang: Could you introduce yourself, share where you’re located, and share what brought you into the Collective Action School space? 

Morgan Thomas: My name is Morgan Thomas and I’m a research, designer and technologist. I’m based in Naarm, or Melbourne, Australia. I came to the Collective Action School space because for years I had this interest in narratives in technology, in particular from more marginalized voices. I came to CAS to be in a space where I could make community with other like-minded people. 

Xiaowei: One of the things we did in Meeting 0 was to name the people and mentors we wanted to bring in with us to this space. Do you remember who you ended up bring in? 

Morgan: For me, it was bringing in all the teachers, writers, activists and academics whose work I’ve absorbed, read, and who have guided my path to where I am right now. 

It was an opportunity to bring in these ideas, participate, and form my own ideas within the space. I had spent so much time reading and thinking about these things, and now was actually in a space with others to talk about it and form new ideas. 

Xiaowei: Could you talk a little bit about your final project?

Morgan: I started thinking about my project early on in the school. I have always been really interested in narratives about technology and industry-led narratives about technology. These narratives are pervasive and gather a life of their own. Originally I was researching labor and AI, the industry narratives around AI. That’s how I started, and was reading everything: from articles, to whistleblower accounts, to technical specs, just everything. 

It’s sort of amazing to me how easily these industry led narratives were adopted into the mainstream without the critical gaze. And despite the amazing work of activists and academics, that has really existed since the beginning of this technology. I think about how our industry narratives—and how those industry narratives influence our thoughts—lead to how technology is imagined and produced. That’s sort of what got me on to where I started taking this work, towards what I call “Thoughtforms on AI.” 

Thoughtforms has a double meaning here. In psychology, it’s how a person’s thoughts are expressed through speech. And in mysticism, it refers to the extension of those thoughts into the real world. What I was thinking about here was how I could use this work to create thoughtforms in the viewer about AI, which could maybe change their perspective on this technology. The big challenge for me was how to communicate in a way that was accessible, sometimes funny, and sometimes confronting the dominant narrative. That’s how it ended up in this final video form, which plays out by using the imagination prompts from the Midjourney image generator as a way of playing back to people the connections between the most mundane everyday interactions that we have with this technology and the more exploitative and hidden human and material costs. 

Xiaowei: Digging in a little bit, I think with generative AI, people are convinced that it could still do a lot of good in the world: new drugs, new strategies of combating climate change that could come from it... You said a phrase right now, “playing things back,” and I’m curious if you could talk about the ways AI might put things on loop? 

Morgan: Yeah, that’s one of the big things for me when we talk about the way the hype overshadows the reality of the technology. There’s a lot of misunderstanding, even within industry, about what generative AI is actually capable of. And this obsession with this as part of the narrative, this obsession with newness. 

In making this video work, one of the things that stood out to me, and there’s a lot of people who have doing great research work in this space, is the huge amount of human labor that goes into creating and making this technology work. When we break it down, we realize that it’s the work of hundreds and thousands of data labelers, labeling past data sets in patterns that we call big data or generative AI. I don’t know how to explain it, but we’re not creating anything new, we’re just working with vaster and vaster computing power, and vaster and vaster statistical functions. 

Xiaowei: That’s really helpful context. I’m wondering where your project is now? 

Morgan: Right now there is the video. The project could be sort of ongoing. It’s such a vast area and there’s room for it to take other forms. This video work, I could have just gone on forever with it, but I just wanted to make a decision to finish it and have this little snapshot of time and space. 

I think the main challenge for me was seeing how vast the existing literature, writing, and art is in this space. When I started doing the project, I felt a bit overwhelmed at the sheer volume. The challenge was trying to find a form that could convey the research and ideas that I developed in a way that could be done in a reasonable time period. 

For other cohorts that are thinking about the final project, something that really helped me was just discussing the project with other people at CAS, because it was through those meetings that people would say, “oh, this reminds me of the work of this artist or this project.” Being open to that form of shifting helped me, because there was a lot of knowledge and advice within the school that guided the project in different directions. If I had started with a fixed idea of how I wanted the project to be, it probably wouldn’t have evolved so fluidly. 

Xiaowei: Is there anything else you’d like to add? 

Morgan: I’d like to extend my thanks to you, the other stewards, presenters and the cohort. It’s very exciting to make space with like-minded people in this area, and to bring everyone together from all over the world to make space and community together. 

Xiaowei: Well thank you for showing up for 13 weeks on top of work and everything else!