Sofian Audry

Artengine's at Artificial Imagination

Sofian Audry, an assistant professor of new media at the University of Maine, delivered a thought-provoking presentation on the intersection of art and technology. With a robust background in computer science, Sofian explored the philosophical and practical aspects of machine learning and artificial intelligence within the realm of contemporary art. They delved into how these technologies could be leveraged to question and expand our understanding of human nature and autonomy.

Sofian’s work primarily focuses on subverting traditional applications of AI and computer science to uncover insights about human imperfections and societal constructs. They described various projects where machine learning was not just a tool but a medium for artistic expression, revealing the intrinsic behaviors and unintended consequences embedded in these technologies. One highlight was a robot programmed with reinforcement learning, tasked with navigating the dilemma of sun exposure necessary for charging its solar panels while inherently avoiding light.

Throughout the talk, Sofian emphasized the concept of ‘agents’—entities acting in the world in response to observations—and how their behaviors can be perceived and interpreted differently, depending on the viewer’s engagement and the context provided by the artist. This approach invites viewers to reflect on the deeper implications of technology as a mirror to humanity. By interlacing AI with artistic creativity, Sofian advocates for a nuanced exploration of how these tools can both emulate and enhance human experiences, highlighting the blurred lines between creator and creation in the digital age.

This presentation was part of the symposium ARTIFICIAL IMAGINATION which unites innovative artists engaged with emerging technologies. This focused on exploring and sharing their individual practices, experiences, and insights related to algorithms, artificial intelligence, and machine learning. It served as a platform for an enriching exchange of ideas between the artists and the audience, aiming to contribute a distinctive artistic viewpoint to the ongoing discussions about our evolving relationships with machine collaborators. Each session, including this one, highlighted how these technologies are being integrated and reflected in contemporary artistic processes, encouraging a broader understanding and appreciation of the creative potential of new digital tools.

Sofian Audry is an artist, scholar, Professor of Interactive Media within the School of Media at the University of Quebec in Montreal (UQAM) and Co-Director of the Hexagram Network for Research-Creation in Art, Culture and Technology.

Their work explores the behavior of hybrid agents at the frontier of art, artificial intelligence, and artificial life, through artworks and writings. Audry’s book Art in the Age of Machine Learning examines machine learning art and its practice in art and music (MIT Press, 2021). Their artistic practice branches through multiple forms including robotics, installations, bio-art, and electronic literature.

Audrey studied computer science and mathematics (BSc, 2001), machine learning (MSc, 2003), and communication (interactive media) (MA, 2010) before completing a PhD in Humanities from Concordia University (2016). In 2017, they were a Postdoctoral Fellow at the Massachusetts Institute of Technology, and between 2017 and 2019, held Assistant Professor positions at the University of Maine and at Clarkson University. Sofian is an honorary member of artist-run center Perte de Signal (Montréal, Canada) which they led as president of the board in 2009-2017, and is actively involved in many open source softwares for new media.

Sofian Audry’s work and research have been shown at major international events and venues such as Ars Electronica, Barbican, Centre Pompidou, Club Transmediale, Dutch Design Week, Festival Elektra, International Digital Arts Biennale, International Symposium on Electronic Art, LABoral, La Gaîté Lyrique, Marrakech Biennale, Nuit Blanche Paris, Society for Arts and Technology, V2 Institute for Unstable Media, Muffathalle Munich and the Vitra Design Museum.

I’m interested in hijacking and subverting computer science and AI technologies and sort of use them to reveal some aspects of what makes us human. AI and most technologies are designed to be powerful, to control, to enslave. Like Susan mentioned earlier, to enslave nature, to enslave us all, and at the same time, to maybe solve some of our problems, to cope up with our deficiencies and our imperfections

Machine learning is a subset of artificial intelligence, trying to reproduce human performance in all kinds of spheres.

Revealing Humanity Through the Lens of AI and Cybernetics

Sofian Audry

In my work, I’m interested in hijacking and subverting computer science and A.I. technologies and sort of use them to reveal some aspects of Indian of I guess, of what makes us human. AI Most technologies are designed to be powerful, to control. Like Susan mentioned earlier, to enslave. I really like that idea, to enslave nature, to enslave us all and to to the same time, to maybe solve some of our problems, to cope up with our deficiencies and our imperfections. In my work, I’m interested in flipping that and revealing these very imperfections that I think makes makes us what we are. And I think that these these things can be found in these technologies that we we designed. So so most of my work revolves around these notions of agent and behavior agents being also coming from this computer science notion of agent based model comes from cybernetics. An agent being a an entity that acts in the world in response to observations and behavior of being like how we can how another entity would perceive this agent. So as an unchanging form of event due to the activity within an assembly. This come from Gordon Pass. Pask has a very broad notion of what an assembly is. He talks about the behavior of statue. I would personally I would argue on that, but so I would I would personally replace the word assembly with agent. But I’m interested in how we experience these behaviors now can be can be used to generate new experiences. So this is an example of a work of one of the first actually works where I used started using machine learning in 2011. It is a robot that was hanging to a cliff in the the catalog kept the Catalonian. And this robot is looking at the cliff right now because it it it doesn’t like sun. It doesn’t like to see the light. At the same time. It needs the light because it needs to recharge the batteries with the solar panels. So it needs to, you know, once in a while, once in a while look at the light. I gave it a I used a reinforcement learning system where to to to let this agent try to solve this conundrum over the summer. So I’m going to throw a few concepts here just to get the conversation ready. But I mean, like Ben already introduced machine learning, but I’m just going to give like what you know, I think what for me is a very good summary of to try to understand what machine learning is in comparison to artificial intelligence, which is so. Machine learning is a subset is a subfield of artificial intelligence. And artificial intelligence being this big project of trying to reproduce human performance in all kinds of spheres. And the problem is like is the following how can we get the probe? How can we program machines to do things we do when we ourselves do not know how we do these things? This is almost everything when we talk, when we walk, when we make decisions, most of the time we use our intuitions. We do these things without even thinking about them. So if we don’t know how we do them, how could we even program them into a machine? And the intuition here are from the basis of base of machine learning is, well, we don’t know how we do these things, but what we know is that we once did not know how to do these things and we learn how to do them by subjecting ourselves to experience and by adapting.

The Role of Machine Learning in Artistic Expression

Sofian Audry

Machine learning has again taken that very interesting idea of this, an agent adapting to the real world. And and once again, I’m going to reuse that. I really like this concept of that that that Susan was talking about earlier. Enslavement, enslavement. So so machine learning has enslaved these little agents into doing something, something where the the agency of the agent doesn’t matter anymore. What we’re after is to solve a problem. So using a model, we train an agent on some data and we try to adjust it, make it adjust itself to to solve whatever problem we have, whether it’s the it’s going to be like speech recognition or recognizing faces or or making as much money as much money as possible into the stock market. I’m so this is a project I’m working right now. We’re I’m, I’m, I’m, I’m following kind of this similar idea I’m trying to unfold this process that of, of, of learning. So, I mean, like nowadays, if you look at machine learning algorithms, we use them every day. We don’t necessarily need to know how they work. But I think that for me, what’s interesting is how they work, how they do this learning, because I think there are some very interesting I see that there’s a potential there in looking at the system, learning, there’s a potential for revealing, again, some things about ourselves. This is a new project I’m working on right now. Going more focuses with was that it’s an ongoing you and Josette is a visual artist really interested in artificial life and and embodiment. And so we’re creating this we’re working on this kind of ball robot that will have a strange non continuous body and we’ll have to try to, to learn how to roll.