Revisiting Classic Thought Experiments to Measure Consciousness for Artificial Intelligence Safety

Abstract:This research note revisits Leibniz's mill, Turing's imitation game, and Searle's Chinese Room through the Conservation-Congruent Encoding (CCE) framework. It formalises a toy symbolic setting in which successful behaviour is measured by task performance ($W_{causal,T}$), while the efficiency with which preserved internal structure supports that behaviour is measured by operational consciousness ($\kappa_T$). Within this setup, an uncompressed lookup system and a compact generative system can in principle achieve comparable behavioural success, yet diverge sharply in $\kappa_T$: the former relies on an expanding standing store of unreused mappings, whereas the latter reuses compact internal structure. The note therefore reframes classic disputes about understanding by separating outward performance from the organisation that sustains it, and motivates why this distinction may matter for later AI-safety analysis.

Artificial Intelligence (cs.AI)

I.2.0; F.1.1; H.1.1

arXiv:2608.00001 [cs.AI]

(or arXiv:2608.00001v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.00001 Focus to learn more arXiv-issued DOI via DataCite

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