Beckett Dillon's picture

Beckett Dillon PRO

Severian

AI & ML interests

I make music, teach machines, study nature, and build things.

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published a Space 1 day ago
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Just a unique tag to give the symbol (Similar to a hashtag) while defining the logic and flow with the LLM that will use it. You can name it anything. There is no metaphysical weirdness trying to be projected in this framework

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Thanks for at least engaging with my proposal! I appreciate the critical feedback, as it helps refine and invalidate ideas. However, I believe there may be some misunderstanding, as my proposal actually aligns with several of your key points.

I explicitly emphasize that this framework:

  • Leverages existing model capabilities - not inventing new ones

-Works because of prior training - not claiming any magical properties

  • Uses established patterns in language models - keeping it simple

  • The term "glyph" isn't meant to replace symbolic logic notation, but rather to describe a specific use case: using symbols as semantic anchors to organize and access existing patterns in model training, like ! does currently. The proposal focuses on practical application - how to structure these symbols to tap into and organize pre-existing capabilities efficiently.

I agree completely that "AI is much simpler than it's made out to be" - that's actually central to my argument. I'm suggesting a way to leverage that simplicity through structured organization, not complicate it.

posted an update 18 days ago
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Computational Model for Symbolic Representations: An Interaction Framework for Human-AI Collaboration

Hey everyone. I need your help to see if this concept, scientific logic, and testing with prompts can invalidate or validate it. My goal isn’t to make any bold statements or claims about AI, I just really want to know if I’ve stumbled upon something that can be useful in AI interactions. Here’s my proposal in a nutshell:

The Computational Model for Symbolic Representations Framework introduces a method for enhancing human-AI collaboration by assigning user-defined symbolic representations (glyphs) to guide interactions with computational models. This interaction and syntax is called Glyph Code-Prompting. Glyphs function as conceptual tags or anchors, representing abstract ideas, storytelling elements, or domains of focus (e.g., pacing, character development, thematic resonance). Users can steer the AI’s focus within specific conceptual domains by using these symbols, creating a shared framework for dynamic collaboration. Glyphs do not alter the underlying

The Core Point: Glyphs, acting as collaboratively defined symbols linking related concepts, add a layer of multidimensional semantic richness to user-AI interactions by serving as contextual anchors that guide the AI's focus. This enhances the AI's ability to generate more nuanced and contextually appropriate responses. For instance, a symbol like ! can carry multidimensional semantic meaning and connections, demonstrating the practical value of glyphs in conveying complex intentions efficiently.

Link to my full initial overview and sharing: https://huggingface.co/blog/Severian/computational-model-for-symbolic-representations

Try out the HF Assistant Version: https://hf.co/chat/assistant/678cfe9655026c306f0a4dab