Context – The smell of ecology. ― R. Scott Bakker
The Quadranym Model and Word-Sensibility Theory
A Grammar of Orientation for AI:
The intersection of semantic categories and sensory-motor areas suggests a unified mechanism for processing action, perception, and meaning. How we position ourselves in the world directly shapes our thinking and language. Designing a situated AI semantics model that can flexibly navigate complex, real-world contexts presents significant challenges. Such a model must not only process shifting lexical relations but also adapt to dynamic environments in real time, reflecting the ecological interplay of interaction, orientation, and meaning.
Approach:
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Orientation Grammar: A semantic framework that focuses on dynamic, context-driven shifts and comparisons in meaning, emphasizing adaptability rather than fixed definitions.
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Quadranyms: Hierarchical units that structure meaning across broad orientations (e.g., expansive vs. reductive) and evolvements (e.g., subjective vs. objective) for context-sensitive responses.
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Key Concepts:
- Intensions: Conceptual properties.
- Extensions: Real-world instances.
- Dimensions: Dynamic Orientations.
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Application: Used for systems like Large Language Models to adjust orientation and generate contextually relevant, responsive outputs.
This is the Dynamic Quadranym Model (DQM):
It is about building a completely different kind of artificial intelligence—one that is grounded in a continuous, dynamic process of orientation to a world teeming with meaning, rather than one that simply computes a static representation of it.
Feature Blogs
The Dynamic Quadranym Model (DQM): Integrating Semantic Structure and Responsiveness for a Situating AI
Presents the Model with Analysis of a Story
Dynamic Quadranym Model (DQM): A Researcher’s Primer
Basic Breakdown of the Orientation Grammar and Model
Orientation Semantics: Layers of Responsiveness
Orientation Layers with Analysis of a Story
Situated AI Semantics: The Simple and Powerful DQM
Perspective on Orientation Grammar Components
Dynamic Quadranym Model (DQM)
A Pre-semantic Framework for Layered Orientations
The DQM provides a framework for guiding systems like large language models (LLMs) by aligning current states with potential futures across various layers. It uses an orientation grammar to ensure that outputs remain orientationally situated and responsive to real-time situations.
(Semantic Framework: Orientation Grammar and Word-Sensibility Theory)
In these posts, we will explore word-sensibility (Q) and its potential applications in artificial intelligence and natural language processing.
We welcome an open dialogue on a range of systemic perspectives, including enactvism, cybernetics, process philosophy, phenomenology, and many other topics related to AI, nature, and human experience.
We look forward to your insights and contributions!
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Word-sensibility (Q), as presented, is a theoretical framework. While it offers insights into cognition, emotion, and dynamic systems, its application and assertions about the real world remain conceptual and speculative. The Q model is designed to simulate how meaning and semantic orientation might be structured in both human and AI systems, but it is not intended to reflect an empirical or deterministic representation of reality. Readers should approach the model as a conceptual tool for understanding complex interactions rather than a definitive explanation of real-world processes.
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