Ontoepistemological Limitations of Computational Intelligence Arising from Electronic Hardware: Binary Substrate Problem, Possible Solutions and Theories

Contemporary advances in computational intelligence (CI) remain constrained by a fundamental ontological limitation: the binary substrate of modern electronic hardware. This paper formulates and investigates the Binary Substrate Problem, hypothesizing that intelligence— defined as semantically emergent, ethically self-organizing, and ontologically plastic cognition—cannot arise fully from binary, voltagethreshold-based architectures. Through a comparative simulation framework involving digital neural networks, spiking neuromorphic systems, and quantum neural models, we demonstrate that only non-binary substrates support the emergence of semantic coherence, moral ambiguity resolution, and category redefinition. To address this limitation, we propose a shift from binary logic gates to substrate-sensitive computational architectures capable of continuous, field-based, and non-discrete operations. We outline two foundational theories to guide this transition: the Special Theory of Non-Binary Electronics (STNBE), which models cognition as field-based integration of charge, frequency, and semantic phase; and the General Theory of Non-Binary Electronics (GTNBE), which introduces semantic permittivity into charge flow equations, enabling topological and axiological computation. A hypothetical experimental design—the Dual Pulse Differentiation Test—is introduced to quantify semantic fidelity using a new substrate-based constant γ*, analogous to the Lorentz factor in relativity. Our findings and theoretical framework suggest a future in which intelligence is co-designed with its physical substrate, moving beyond the dichotomy of zero and one toward a post-binary ontology of machine cognition.