A Simplified a Priori Theory of Meaning; Nature Based AI 'First Principles'

Abstract

This paper names structural fundaments in ‘information’, to cover an issue seen by Claude Shannon and Warren Weaver as a missing “theory of meaning”. First, varied informatic roles are noted as likely elements for a general theory of mean- ing. Next, Shannon Signal Entropy as a likely “mother of all models” is decon- structed to note the signal literacy (logarithmic Subject-Object primitives) innate to ‘scientific’ views of information. It therein marks GENERAL intelligence ‘first principles’ and a dualist-triune (2-3) pattern. Lastly, it notes ‘intelligence building’ as named contexts wherein one details meaningful content—rendered via material trial-and-error—that we later extend abstractly. This paper thus tops today’s vague sense of Open World ‘agent intelligence’ in artificial intelligence, framed herein as a multi-level Entropic/informatic continuum of ‘functional degrees of freedom’; all as a mildly-modified view of Signal Entropy. —Related video found at: $\href{https://youtu.be/11oFq6g3Njs?si=VIRcV9H3GNJEYzXt}{The Advent of Super-Intelligence}$.