Summary of original article: The Dynamic Quadranym Model (DQM): Integrating Semantic Structure and Responsiveness for a Situating AI Introduction Language is alive, shifting with context and intention. Yet, AI often treats meaning as static—a pattern to retrieve rather than a process to adapt. Imagine a … Continue reading DQM Summary: Integrating Semantic Structure and Responsiveness for a Situated AI
Imagine sitting on your couch, remote control in hand. You press a button to change the channel, adjust the volume, or browse streaming options. The remote, a simple and familiar tool, embodies an intricate interplay of meaning and action. It is proximal—immediate and tangible—while the … Continue reading From Remote to Rhythm: The Dynamic Symphony of Meaning-Making
Meaning is the coupling between the orientation and the situation.
Introduction In cognitive science, Daniel Kahneman and Amos Tversky revolutionized our understanding of human decision-making with their dual-system framework—System 1 (fast, intuitive) and System 2 (slow, analytical). This dual-system model has become a cornerstone in both psychology and artificial intelligence (AI), offering a structured way … Continue reading From System 1 & 2 to Adaptive Intelligence: Extending Kahneman and Tversky’s Insights
Introduction: The Child and the Doll Imagination is often simple to observe in daily life. Think of a child playing with a doll. There’s no deep or mysterious process happening—the child knows the doll isn’t real, but they still interact with it as though it … Continue reading How Orientation Supports Imagination
Lead-in Today, AI excels at generating content through effective but fundamentally static methods. It relies on vast data and learned patterns to produce responses that suit many contexts. But imagine a new way of thinking about meaning—one that moves beyond rigid definitions to offer a … Continue reading The Dynamic Quadranym Model (DQM): Integrating Semantic Structure and Responsiveness for a Situating AI
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 contextually coherent and responsive to real-time situations.
The purpose of the DQM is not about clarifying or defining the meaning already present in the text—it is about orienting to that meaning in a dynamic and context-sensitive way. While traditional natural language processing (NLP) systems, like transformers, excel at extracting and representing meaning through embeddings, sentiment, or syntax, the DQM focuses on situating the agent within the context of the meaning.
The DQM offers insights into designing (or supplementing) natural language process (NLP) systems by managing overarching themes, situational responsiveness, and adaptive context-awareness:
Quadranyms as Scalable Units of Orientation:
Inputs are organized hierarchically into quadranyms, which guide natural language process systems by providing broad orientations and specific, context-driven orientational responses.
Intensions, Extensions, and Dimensions:
Traditional approaches focus on intensions (conceptual properties) and extensions (physical instances). The DQM introduces dimensions as a third aspect, framing meaning through dynamic engagement with the world, creating a Dynamical Context where orientational responses are continuously adapted to the situation.
Orientation and Responsiveness:
Meaning for the DQM is an orientational process, shaped by the agent’s goals and context. It shifts from passive representation to active, goal-oriented responsiveness, where meaning is framed as a goal, not a given; adjusting orientation based on real-time feedback.
The DQM is particularly relevant for systems requiring real-time situational alignment, such as conversational agents, decision-making systems, and context-aware robotics. It provides a flexible, scalable approach to guiding responses in human-centered environments.
The DQM shifts how AI systems process and respond to situations by moving between orientations rather than relying exclusively on fixed semantic meanings (word associations). This transition turns meaning into a dynamic, actionable process, prioritizing context, adaptability, and responsiveness. It allows AI systems to engage with the world as active, responsive agents, dynamically adapting to ever-changing contexts.
By emphasizing orientation over meaning, the DQM represents a significant departure from traditional semantic models, enabling fixed meaning systems to interact with contexts and real world environments in more human-like ways, with real-time situational awareness.
(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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