The self as agent-environment nexus: crossing disciplinary boundaries to help human selves and anticipate artificial selves

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Both human and artificial agents learn from and potentially change their environments, but also align and adapt to them. Examples abound, from humans unconsciously tapping feet to the rhythm of music to a cleaning robot keeping a house dust-free. In humans, however, brain-world alignment is also closely linked with mental features like perception or self and mental health disorders lead to profound disruptions in how one considers 'the self'.As AI agents acquire human and superhuman skill levels and Artificial General Intelligence looms closer, ethical considerations about how an agent will interact with its environment become paramount. What is it about an AI agent that will ensure it can co-exist peacefully in human society and transfer skills seamlessly from one setting to another? Let us consider a real-world example of an adolescent who is watching television (on their laptop) via a streaming service. Suppose that individual has displayed anti-social behaviours at school or in their home and suppose that he has streamed several violent movies on his laptop over the past few days. Should the recommender system of the streaming service suggest a 'movie that you might like' that, again, contains strong scenes of violence? Or should the recommender system suggest, instead, a documentary on polar bears? This example illustrates three open questions and challenges in AI research. First, how would the recommender system infer the state of the young man? Second, how would a recommender system adapt its recommendations based upon this state, particularly given that the system itself has its own internal goals - i.e. to motivate people to consume more content? And third, is it ethical to probe the human at the human-computer interface in the first place?The current proposal aims to converge both "free energy" and "dynamic temporo-spatial" approaches to mental features to develop a mathematical and computational framework for environmental alignment and adaptation of intelligent agents. The primary, direct goal of this approach is to help human agents suffering from abnormal changes in their perception and self, as in psychiatric conditions like schizophrenia. This not only carries major ethical implications for understanding of ourselves, but will also inform the second, broader, aim of this project: paving the way for an understanding and mathematical theory of artificial selves.