A vs. I in AI: Is there a Threshold to “Engineered” Intelligence?

Despite artificial intelligence reshaping the world, its development generates uncertainties regarding future capabilities. AI simultaneously exists as an artifact of engineering design and as autonomous intelligence, creating an observer-participant feedback loop. This paper proposes that embodied AI faces a bandwidth-limited intelligence threshold T_h that it arises from B = min(C_sens,C_Act). However, Shannon capacity measures bits while intelligence operates on concepts, necessitating a dual-channel model separating physical bandwidth B_io from representational capacity B_rep. Intelligence emerges as multi-dimensional rather than scalar, with components exhibiting different bandwidth dependencies. Surpassing T_h requires either new sensing methods expanding B, enhanced representational frameworks, or reconceptualization within higher cardinality ontologies.

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Publication Details

Journal
ScholarSpace (University of Hawaii at Manoa)
Published
2025-12-23
Primary Topic
Computability, Logic, AI Algorithms
Type
article
Field-Weighted Citation Impact
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article

A vs. I in AI: Is there a Threshold to “Engineered” Intelligence?

Joshit Mohanty
ScholarSpace (University of Hawaii at Manoa)
Computability, Logic, AI Algorithms
article

A vs. I in AI: Is there a Threshold to “Engineered” Intelligence?

Joshit Mohanty
article en

Abstract

Despite artificial intelligence reshaping the world, its development generates uncertainties regarding future capabilities. AI simultaneously exists as an artifact of engineering design and as autonomous intelligence, creating an observer-participant feedback loop. This paper proposes that embodied AI faces a bandwidth-limited intelligence threshold T_h that it arises from B = min(C_sens,C_Act). However, Shannon capacity measures bits while intelligence operates on concepts, necessitating a dual-channel model separating physical bandwidth B_io from representational capacity B_rep. Intelligence emerges as multi-dimensional rather than scalar, with components exhibiting different bandwidth dependencies. Surpassing T_h requires either new sensing methods expanding B, enhanced representational frameworks, or reconceptualization within higher cardinality ontologies.

ScholarSpace (University of Hawaii at Manoa)
Openalex Percentile: Top 27%
Computability, Logic, AI Algorithms
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