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
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
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
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
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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