A Framework for Quantifying Reliable Intelligence Efficiency in Artificial Intelligence Systems

Abstract
Artificial intelligence has advanced rapidly through scaling of model parameters, datasets, “
“and computational resources. Scaling laws have demonstrated predictable improvements in model “
“capability; however, capability alone does not fully describe the value of an AI system deployed “
“in real-world environments.

Selective Realization of Conscious Experience

Consciousness science possesses quantitative markers of neural integration,
differentiation, criticality, multisensory inference, and bodily self
consciousness, yet lacks consensus on the organizational conditions supporting
unified first-person experience. Gravity is universal to ordinary physical
systems; conscious experience is selective.

The Self That Reports

Abstract
A companion paper develops the Boundary–Membrane Integrative Core (BMIC), a falsifiable model in
which a low-dimensional global state, C(t), coordinates an organism’s physical subsystems, and deliberately
stops short of any claim about subjective experience. This paper takes up that further question directly,
under its own, stricter evidential standard. Self-referential capacity — body ownership, agency, self
location, interoceptive awareness, metacognitive access, and self/world discrimination — is decomposed
into seven independently measurable components, none proposed as a dedicated receptor organ.

From Exclusion to Accountability

Abstract
Insurance imposes two related costs on the people it is meant to protect: the underwriting cost of exclusion
before a policy exists, and the claims cost of undervaluation after a loss occurs. Both stem from institutional
information and effort asymmetry, but they differ in a way that matters for how artificial intelligence should
be deployed against each. Part-One addresses life insurance underwriting, where hereditary or chronic
conditions are priced from cohort-level mortality data that can be too coarse to reflect an individual’s
evidence-backed prognosis.

A Framework for Understanding Network Intelligence

The term network intelligence is widely used in the field of communication net
works. A number of new and potentially limiting concepts and products based on
the concept of network intelligence have been introduced, including smart flows,
intelligent routing, and intelligent web switching. Many intelligent systems focus on
a specific service, function, or device, and do not provide true end-to-end network
intelligence.