Paper short abstract:

In this paper I attempt to make sense of the manifold ways agency is modulated in novel artificial intelligence architectures and machine learning practices, paying particular attention to the computational paradigms that aim to simulate (aspects of) the world.

Paper long abstract:

This contribution elaborates on recent attempts to advance the project of artificial (general) intelligence based on my reading of emerging research in the field from the perspectives of media and cultural studies. I will focus on the process of worlding in machine learning and the blurring of boundaries between the learning subject and its environment for the purpose of constructing agency. I will discuss several technical texts that outline novel cognitive architectures and virtual environments for ML research, converge on their epistemic rendering of what the “world” is.

My hypothesis is that the project of generalist AI can be understood relationally through the careful comparison of the “world model” notion and what I refer to as “model worlds”. Model worlds are game-like simulations assembled for the purpose of ML research, expanding on Bruder’s (2021) writing on the use of microprocessors as model organisms in neuroscience. World model is a paradigm in ML that refers to AI agent’s capacity to represent the world’s dynamics (Ha and Schmidhuber 2018).

The computer science texts figure the “world” either in the sense of algorithmic knowledge domains or in relation to AI agent’s directive to grasp the world. Whether external or internal to agents, such simulations of the world allow for predictive control and, therefore, the governance of potentialities, including the drive to learn. Through the dialectics of “world” in this research corpus I aim to demonstrate how synthetic agency is engineered, negotiated, and reconfigured.