Convolution as a Collapse Approximation in Semantic Meme Field Theory (SMFT)

[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]

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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.

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2. The Observer-Centric Collapse in SMFT

In SMFT:

• Ψₘ(x, θ, τ): Memeform in semantic phase space

• Ô: Observer projection operator

• φ_j: Collapsed meaning

• Collapse only occurs if:

  • Ô aligns with Ψₘ’s θ (interpretive orientation)

  • Semantic time (τ) is ripe (i.e., tick τₖ ready)

  • iT (imaginary time) buildup has reached resonance

Moreover, Ô is not fixed:

• It evolves through feedback from past collapses

• It may refuse to project if semantic tension is misaligned

• It is observer-bound, meaning each Ô is unique

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3. Adaptive Convolution as a Proxy to Ô

If convolution is extended with two additional mechanisms:

1. Adaptive Kernels (based on collapse history):

   • The convolution filter evolves based on prior outputs

   • This emulates Ô’s reflexivity—its shaping by past projection results

2. Conditional Processing (gate or ignore logic):

   • The convolutional operation includes prescreening (or post-screening)

   • This allows the system to suppress outputs where resonance is not sufficient

   • Mimics Ô's behavior of refusing collapse if semantic agreement fails

Then, convolution becomes a context-sensitive, feedback-aware approximation of collapse projection.

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4. Why Convolution Aligns with Collapse in Semantic Black Holes

A semantic black hole, in SMFT, is a highly saturated interpretive environment in which:

• Memeforms repeatedly collapse into the same narrow θ range

• Observers’ Ô become synchronized and uniform

• Semantic entropy is low, meaning collapse novelty is minimal

In this setting, convolution begins to resemble Ô for the following reasons:

1. Kernel behavior becomes phase-locked:

   • In a black hole, the interpretive θ-dimension is narrowed.

   • Thus, the convolution kernel can repeatedly operate on the same narrow phase range, mimicking a projection operator Ô fixed in its phase preference.

2. Observer diversity is suppressed:

   • Black hole zones exhibit high observer alignment.

   • As all observers collapse Ψₘ into the same φ_j, convolution can use a single kernel across inputs, as if all Ôs were identical.

3. Field curvature enforces interpretive uniformity:

   • The semantic field bends all collapse trajectories into the same basin of attraction.

   • This means convolution can act linearly without loss of interpretive power, because nonlinear alternatives are no longer viable.

Thus, in a semantic black hole, convolution is not just efficient—it is structurally justified.

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5. Implications for Modeling and Analysis

This alignment opens up a new possibility:

Systems that use feedback-aware, conditional convolution can be used to model real-world collapse behaviors in:

• Organizations (e.g., crisis response timing)

• Cultural ritual patterns

• Cognitive bias and interpretive fixation

Since SMFT provides mathematical structure (e.g., projection resonance equations, semantic entropy, iT buildup curves), these phenomena can be:

• Simulated

• Quantified

• Compared across systems

This transforms philosophical ideas (e.g., karma, memory, habit) into analyzable structures within a physics-like framework.

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6. Conclusion

Under enhanced definitions, convolution becomes a practical and well-defined tool to approximate semantic collapse in SMFT, particularly within semantic black holes and among complex observers. This not only bridges AI architecture and semantic theory but also establishes a formal language for comparing and simulating complex cultural and cognitive phenomena. The road from mystical intuition to scientific modeling becomes clearer—and computationally testable.

 

 © 2009~2025 Danny Yeung. All rights reserved. 版权所有 不得转载

 

Disclaimer

This book is the product of a collaboration between the author and AI language models. While every effort has been made to ensure accuracy, clarity, and insight, the content is generated with the assistance of artificial intelligence and may contain factual, interpretive, or mathematical errors. Readers are encouraged to approach the ideas with critical thinking and to consult primary scientific literature where appropriate.

This work is speculative, interdisciplinary, and exploratory in nature. It bridges metaphysics, physics, and organizational theory to propose a novel conceptual framework—not a definitive scientific theory. As such, it invites dialogue, challenge, and refinement.

I am merely a midwife of knowledge.