Self-Concern Across Scales: A Biologically Inspired Direction for Embodied Artificial Intelligence - Sims, Matthew (Frontiers in Neurorobotics, 2022)
Intelligence in current AI research is measured according to designer assigned tasks that lack any relevance for the agent itself. As such, tasks and their evaluation reveal a lot more about our intelligence than about the possible intelligence of the agents which we design and evaluate. As a possible first step in remedying this, this paper introduces the notion of ‘self-concern’, a property of a complex system that describes its tendency to bring about states that are compatible with its continued self-maintenance. Self-concern, it is argued, is the foundation of the kind of basic intelligence found across all biological systems because it reflects such systems' existential tasks of continued viability. This paper aims to cautiously progress a few steps closer to a better understanding of some of the necessary organizational conditions that are central to self-concern in biological systems. By emulating these conditions in embodied AI, perhaps something like genuine self-concern in machines can be implemented in machines, bringing AI one step closer to its original goal of emulating human-like intelligence.