Super-Intelligence functional equivalence Litti theorem

Current classifications of artificial intelligence (AI) often rely on an implicit view of the human mind as the ultimate and unattainable reference. In this context, many widespread claims such as that "machines can never really think", or that they can never approach emotions and feelings, derive more from an anthropocentric bias than from a rigorous analysis of cognitive science.
This work proposes a functional equivalence theorem between some fundamental structures of natural cognitive systems and possible distributed artificial architectures, based on cloud and Edge AI. The central thesis argues that, accepting the dominant neuroscientific models such as Global Workspace Theory, predictive processing, somatic marker hypothesis and affective neuroscience, emotions, feelings, instincts and thought emerge as memory configurations, predictive models, control cycles and affective-instinctual constraints.
On this basis, the paper:
1. interprets instincts as a "biological firmware" in a functional sense, in the light of the fixed action patterns of classical ethology;
2. analyzes the individual consciousness/collective consciousness pair through Durkheim and Jung, paralleling it with the cloud/edge axis in contemporary AI;
3. defines a distributed artificial architecture in which edge nodes develop "local biographies" from shared global models, according to the most recent definitions of Edge AI.
A theorem of functional equivalence and cognitive superability is then enunciated, with related hypotheses and corollaries, which shows how the possibility of advanced artificial intelligences, superior to humans in various domains, is not only admissible but consistent with the current state of the sciences of the mind and with the most solid philosophical analyses (Bostrom, orthogonality and instrumental convergence).