Tag: chatgpt

The LLM on The DQM

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Imagine a race car speeding down the track, its engine roaring as it navigates sharp turns and accelerates on straightaways. The race car—sleek, agile, and immensely powerful—represents the cutting-edge Large Language Model (LLM), while the carefully engineered track, with its twists, turns, and guardrails, symbolizes the Classic AI (CAI) system that provides the structure and direction of the Dynamic Quadranym Model (DQM). Together, they form a hybrid system that blends raw computational power with refined, rule-based guidance, creating a dynamic interplay that ensures both speed and stability.

The Dynamic Quadranym Model (DQM): A Researcher’s Primer

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The DQM targets understanding as an evolving process, coupling the agent’s orientation with the situation to produce dynamic-semantic-responses. By organizing semantic elements into quadranyms—four-faceted structures—the DQM bridges contextual analysis with adaptive reasoning. It integrates foundational concepts with AI systems such as Large Language Models (LLMs) and Classic AI (CAI).

DQM Summary: Integrating Semantic Structure and Responsiveness for a Situated AI

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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

From System 1 & 2 to Adaptive Intelligence: Extending Kahneman and Tversky’s Insights

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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

Q Model: About

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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.