A Path to Generative Artificial Selves

Abstract

Artificial intelligence output are undeniably creative, but it has been argued that creativity should be assessed in terms of, not external products, but internal self-transformation through immersion in a creative task. Self-transformation requires a self, which we define as a bounded, self-organizing, self-preserving agent that is distinct from, and interacts with, its environment. The paper explores how self-hood, as well as self-transformation as a result of creative tasks, could be achieved in a machine using autocatalytic networks. The autocatalytic framework is ideal for modeling systems that exhibit emergent network formation and growth. The approach readily scales up, and it can analyze and detect phase transitions in vastly complex networks that have proven intractable with other approaches. Autocatalytic networks have been applied to both (1) the origin of life and the onset of biological evolution, and (2) the origin of minds sufficiently complex and integrated to participate in cultural evolution. The first entails the emergence of self-hood at the level of the soma, or body, while the second entails the emergence of self-hood at the level of a mental models of the world, or worldview; we suggest that humans possess both. We discuss the feasibility of an AI with creative agency and self-hood at the second (cognitive) level, but not the first (somatic) level.