[Quick overview on SMFT vs Our Universe ==>Chapter 12: The One Assumption of SMFT: Semantic Fields, AI Dreamspace, and the Inevitability of a Physical Universe]
Convolution as a Collapse Approximation in Semantic Meme Field Theory (SMFT)
Abstract
This article explores how convolutional algorithms in artificial intelligence can approximate the observer projection mechanism (Ô) in the Semantic Meme Field Theory (SMFT), especially under the conditions of a semantic black hole. We focus on complex observers such as humans, and suggest that under specific constraints, adaptive convolution with feedback and conditional processing can emulate collapse behavior. This provides a pathway to simulate and analyze real-world cultural, organizational, and cognitive phenomena using formal mathematical structures.
[SMFT basics may refer to ==> Unified Field Theory of Everything - TOC]
1. Introduction
Semantic Meme Field Theory (SMFT) models meaning formation as a field-based process in which wave-like memeforms (Ψₘ) collapse into actual interpretations (φ_j) when observed by projection operators (Ô). The Ô is observer-specific and encodes personal interpretive biases, narrative history, and attention rhythms. Collapse only occurs when the field and observer align.
In AI, convolution is a method used to extract local patterns through repeated filtering. Traditionally considered a feedforward, static operation, convolution does not inherently model observer dynamics. However, under certain enhancements, convolution may serve as a good functional approximation of Ô, especially in environments characterized by high semantic density—i.e., semantic black holes.
