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\section{Introduction}
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Regulatory texts frequently equate “AI” with three categories of \emph{learning signals}: supervised, unsupervised, and reinforcement learning \citep{EUAnnex,NISTRMF}. These categories emerged from neural/connectionist practice, not from the full breadth of artificial intelligence \citep{AIMA4}. We propose an alternative taxonomic axis: the \emph{operational premise}—the primary computational mechanism a system instantiates to improve, adapt, or decide. The resulting taxonomy, \emph{operational premise taxonomy}~(OPT) provides a transparent and consistent framework for compactly describing AI systems, including hybrids and pipelines. OPT retains biological analogs (learning vs.\ adaptation) while accommodating symbolic, probabilistic, search, control, and swarm paradigms.


