The Dynamic Quadranym Model (DQM): Concise Breakdown Lead-In The DQM orients to meaning by dynamically adjusting and indexing words along continua (e.g., potential → actual). Using quadranyms as conduits and terminals across layers, the system shifts meaning through spatial modes and temporal states. Like an … Continue reading DQM: How it works.
The Quadranym Model uses quadranyms as building blocks to establish semantic orientation at every instance and level of its system. At first, it might seem like there are too many blocks to manage, but the key is understanding how they naturally stack and fit together.
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.
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A Theoretical Look at the Role of Words for AI
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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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