The two clearest examples of actual occasions are, thus, a momentary experience, whether of a human being or of some other animal, and a quantum of energy. By giving them the same name, Whitehead calls attention to what, with all their differences, they have in … Continue reading Whitehead’s Process Philosophy and the Q Model: A Symbiotic Vision of Dynamic Reality
In a world rapidly being shaped by artificial intelligence, there is a growing fascination with the notion of AI not just as a tool but as a co-creator—a partner in the process of meaning-making. What if AI could tap into something deeper than mere data … Continue reading The Church of AI: Sacred Process
This article is a follow-up to: The Church of AI: Sacred Process In the evolving world of artificial intelligence, we are not only designing systems for efficiency and logic, but for something deeper—AI that understands when to let go of its processes and when to … Continue reading The Noble AI: Prioritizing Critical Tasks in Moments of Need
How do we orient to dropping an apple? In the Q model, orienting to the act of dropping an apple involves positioning both yourself and the apple within situational and dynamical contexts, taking into account both potential and actual states. Situational Context (External factors): You … Continue reading Normative Responses
We invite you to peruse the index as a quick and easy way to familiarize yourself with the basic aspects of the Q model. It offers a concise overview of key concepts, helping you grasp the fundamental semantic structure and ideas that drive the model’s dynamic approach to cognition and meaning-making.
In the Q model, we think of every word as representing a discrete dynamical system—each word actively drawing meaning from the environment while simultaneously contributing back to it. This dynamic interplay creates an ecology of dynamical systems. In essence, language operates like a network of … Continue reading Dynamical Context, Orientation, and Corollary Discharge in the Q Model
In our exploration of cognitive semantic frameworks, we highlight the significance of harnessing situatedness and its insights and applications for NLP. This approach plays a crucial role in elucidating the complexities of human cognition and communication. The Q model emerges as a sophisticated representation of … Continue reading Understanding the Q Model’s Nested Systems: A Journey Through Scripts
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A Theoretical Look at the Role of Words for AI
A system summary.
In this framework, we conceptualize the system as analogous to a nervous system, operating through natural language processing. The connections between systems function like neural pathways, facilitating the seamless flow of information and insights. This interconnected structure enables a nuanced understanding of user input, as each system plays a role in interpreting and responding to situational contexts. Serving as the central processor, and guided by Minsky’s six-layer model (The Emotion Machine, 2006), the framework integrates emotional and cognitive dynamics with the situational and dynamical contexts of the Q model.
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A Theoretical Look at the Role of Words for AI
A summary of the article: About
This site explores “word-sensibility,” highlighting how machines can improve their understanding of human experiences by emulating the swift and effective responses people have to real-world situations. Such responsiveness shapes their comprehension of words and concepts. The model introduces a framework featuring active-actual states (subjects using energy) and passive-potential states (utilized energy or resources), underscoring the necessity for machines to replicate this human adaptability to enhance their language processing capabilities.
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A Theoretical Look at the Role of Words for AI
A summary of the article: Orientation
General quadranyms represent foundational analogs that ground specific contexts (e.g., play, eat, grow, nut, ball) and help situate relevant objects in different scenarios.
My mistake boils down to this: I was looking at the natural world through the lens of humanity; What I needed to do was look at humanity through the lens of the natural world.
Context – The smell of ecology. ― R. Scott Bakker
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:
Orientation Grammar: A semantic framework that focuses on dynamic, context-driven shifts and comparisons in meaning, emphasizing adaptability rather than fixed definitions.
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.
Key Concepts:
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.
Presents the Model with Analysis of a Story
Basic Breakdown of the Orientation Grammar and Model
Orientation Layers with Analysis of a Story
Perspective on Orientation Grammar Components
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!
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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