I propose an interactive sculpture that connects the audience in a gallery setting with a simulation of religion that is invented by neural networks.
I am fascinated by complex systems and the way individuals dynamically organize themselves around the basic human needs for hope and certainty. Therefore my main artistic focus of my thesis will be on emergent behaviors of systems, namely on how the creation of rituals influences the activity of machine learning agents in their environment. In this simulation, the agents are rewarded for inventing new rituals with the elements of their world. The role of the audience will be to temporarily add true randomness to this complex system - and therefore create non-algorithmic chaos. This uncertainty will have consequences on the agents' rituals. The audience is provoked to think about the paradox of a system that seems to be only partially rule-based.
Living in a world we try to make sense of but will never fully understand is the key to human existence. Establishing a sensitivity for our own hopes and fears when faced with the complexity of the world and ultimately create a more intuitive understanding of it is the main goal of my piece.
The theoretical part of my piece is influenced by the works of system theorists/philosophers/sociologists Theodor Adorno, Max Horkheimer, Niclas Luhmann, Juergen Habermas and Jacques Derrida, mathematical approaches in modeling complex systems and current research in the field of neural networks, especially deep reinforcement learning. My artistic approach is rooted in the Fluxus-movement, namely Joseph Beuys' performative focus on art as a collective form of healing.
While iterating with reinforcement-learning examples in Unity/Tensorflow and rendering possible versions of the final piece in c4d, I will do further theoretical research on Derrida and his view on the relationship between society and religion, more reading on the communication models of Luhmann and Habermas and gain a better understanding of chaos-theory (Lorenz attractor).
I will to talk to Gene Kogan about the main technical setup for the project, contact Ben Light to go through my fabrication plans and schedule an office hour with a faculty member of NYU Department of Computer Science with a background in deep reinforcement learning. For interaction design considerations I will consult Katherine Dillon and artistic advice Danny Rozin.
Adorno, T. W., & Horkheimer, M. (2016). Dialectic of enlightenment. London: Verso.
Baring, E., & Gordon, P. E. (2015). The trace of God: Derrida and religion. New York: Fordham University Press
Ha, D., and Schmidhuber, J. (2018). World models. arXiv preprint arXiv:1803.10122.
Hui, Y., & Stiegler, B. (2016). On the existence of digital objects. Minneapolis: University of Minnesota Press
Londei, A. (2014) Artificial neural networks and complexity: an overview. Rome: Archeologia e Calcolatori Supplemento 6
Luhmann, N. (2013). Theory of society (Vol. 2). Stanford Univ. Press
Smith, L. A. (2007). Chaos: A very short introduction. Oxford: Oxford University Press
So far in my research I gained an overview over applicable theories, thinking frameworks and tools. Most of it in the field of system theory, complex systems and devotional practices/artifacts in various cultures and times.
In the next weeks I will go deeper into the mathematical side of complex systems, the relationship between religion and sociology, the nature of rituals and reinforcement learning as a modeling tool for sociological behaviors.
My academic background is in the fields of sociology, politics and literature. I am familiar with the theoretical frameworks that are relevant for my thesis, especially the sociological and philosophical part. My private interest is in french philosophy of the 20th century with a focus on Existentialism. I have been creating artworks using machine learning / neural networks since three years after taking Gene Kogan's machine learning for artists course in Berlin (1 month intensive course). I recently took a udacity class for a deeper dive into the maths of machine learning and am now proficient enough to build my own basic ml-architectures in Pytorch from scratch. I have used the higher level interfaces of Tensorflow and am experienced with training and deploying GANs and CNNs on various remote server platforms. Last summer I did a 3 month internship at Havas New York as AI researcher in the Creative Department and built multiple prototypes for a client.
I explored in depth the element of true randomness in Project Development class with Danny Rozin and built a meditative sculpture that lets the audience engage with a rock (via measuring its truly random decay of subatomic particles with a geiger counter and mapping that to the audience actions) and true randomness.
I will use deep reinforcement learning for my setup. As this is different to the works I have done so far in the field of ML (most of it was language or image based and generative), I will focus on getting a good understanding of the algorithms underlying the Unity-ml agents and then decide which framework is the best for my piece: stick to unity, or building a pure python backend or even entirely browser based. The reward function for sociological behavior has to be developed from scratch and I have to decide if I want a physical entity (robot) in the piece. The latter depends on how much time I have left after settling on the ml-framework.
The form of interaction of the audience with the sculptural piece is technically set, I can rely on my knowledge from my last piece using geiger-counters to get true random numbers.
I envision the piece to consist of 5 random number generators that are embedded in stone-sculptures. The agent-simulation will be projected on the floor around these rocks with projection mapping techniques. The audience is encouraged to engage with the sculptures through a small hammer. Knocking the hammer against the rocks will trigger and feed random numbers into the simulation and confuse the learning process of the agents. They will then modify their observable behavior and approaches to come up with new robotic rituals - the projection on the floor will change accordingly.
So far my draft schedule will look like this:
February - 2 weeks of theoretical research and first prototypes with unity-ml while getting on top of deep q-learning in Pytorch. Rough draft of exhibition setup/fabrication ideas, early user testing
March - 2 weeks narrowing down final version of ml-side and finalizing choices on fabrication/setup, deeper user testing. 2 weeks of building network and fabrication
May - debugging, final technical touches, setup