International Journal of Advance Research Publication and Reviews

International Journal of Advance Research Publication and Reviews
Peer-Reviewed | Multi-Disciplinary Journal

ISSN: 3049-0103 (O)

Impact Factor: 4.33

An Admissibility-Theoretic Taxonomy of AI Capability Levels: From Narrow AI to Artificial Superintelligence

Author

Ian Staley

Abstract

 Current taxonomies of artificial intelligence capability, running from narrow AI through artificial general intelligence (AGI) to artificial superintelligence (ASI), are overwhelmingly behavioral: they define levels by what systems can be observed to do. This approach faces well-documented coherence problems at the AGI/ASI boundary, where behavioral signatures underdetermine the type of system under examination, definitions drift with each model release, and disagreements resist empirical resolution. This paper proposes a non-behavioral alternative: an admissibility-theoretic taxonomy in which capability levels are characterized not by observed outputs but by the structure of admissible trajectories through capability space, with structural inspiration drawn from final-state-constrained formalisms in physics and information theory. The framework defines narrow AI as the regime in which admissible histories are bounded by externally specified terminal conditions, AGI as the regime in which terminal conditions become endogenously selected, and ASI as the regime in which the system modifies its own admissibility criteria. It gives a formal handle on whether transitions between levels are continuous or phase-transitional, clarifies how behavioral benchmarks can mistake narrow capability scaling for general capability emergence, and supplies vocabulary for instrumental convergence and mesa-optimization. The borrowing is structural rather than literal: no quantum mechanism is claimed.

Keywords

artificial general intelligence; artificial superintelligence; capability taxonomy; admissible histories; final-state constraints.

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References

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