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Does artificial intelligence (AI) exhibit consciousness or self? While this question is hotly debated, here we take a slightly different stance by focusing on those features that make possible both, namely a basic or fundamental subjectivity. Learning from humans and their brain, we first ask what we mean by subjectivity. Subjectivity is manifest in the perspectiveness and mineness of our experience which, ontologically, can be traced to a point of view. Adopting a non-reductive neurophilosophical strategy, we assume that the point of view exhibits two layers, a most basic neuroecological and higher order mental layer. The neuroecological layer of the point of view is mediated by the timescales of world and brain, as further evidenced by empirical data on our sense of self. Are there corresponding timescales shared with the world in AI and is there a point of view with perspectiveness and mineness? Discussing current neuroscientific evidence, we deny that current AI exhibits a point of view, let alone perspectiveness and mineness. We therefore conclude that, as per current state, AI does not exhibit a basic or fundamental subjectivity and henceforth no consciousness or self is possible in models such as ChatGPT and similar technologies.
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Much of current artificial intelligence (AI) and the drive toward artificial general intelligence (AGI) focuses on developing machines for functional tasks that humans accomplish. These may be narrowly specified tasks as in AI, or more general tasks as in AGI – but typically these tasks do not target higher-level human cognitive abilities, such as consciousness or morality; these are left to the realm of so-called “strong AI” or “artificial consciousness.” In this paper, we focus on how a machine can augment humans rather than do what they do, and we extend this beyond AGI-style tasks to augmenting peculiarly personal human capacities, such as wellbeing and morality. We base this proposal on associating such capacities with the “self,” which we define as the “environment-agent nexus”; namely, a fine-tuned interaction of brain with environment in all its relevant variables. We consider richly adaptive architectures that have the potential to implement this interaction by taking lessons from the brain. In particular, we suggest conjoining the free energy principle (FEP) with the dynamic temporo-spatial (TSD) view of neuro-mental processes. Our proposed integration of FEP and TSD – in the implementation of artificial agents – offers a novel, expressive, and explainable way for artificial agents to adapt to different environmental contexts. The targeted applications are broad: from adaptive intelligence augmenting agents (IA’s) that assist psychiatric self-regulation to environmental disaster prediction and personal assistants. This reflects the central role of the mind and moral decision-making in most of what we do as humans.