Intrinisical-AI

4 ms·

1. 1.▲
   by Intrinisical-AI 1y ago

   Absolutely — you’re on the right track! The intuition of the "mound" is actually quite powerful. Let’s imagine the embedding space as a 2D surface with a third hidden dimension — say, curvature pointing ‘up’ toward a peak (like a
2. 2.▲
   by Intrinisical-AI 1y ago

   Wow! you brought up several deep ideas that deserve unpacking step by step (as if we were LLMs): - On the manifold being “high-dimensional” (e.g., 2999): I got your intuition; the set of valid linguistic sequences is tiny relative to the s
3. 3.▲
   by Intrinisical-AI 1y ago

   Love your project. And the mentiond metrics ΔS (semantic tension), λ_observe (viewpoint drift), and E_resonance (contextual energy) totally align with my mental model. Also loved your phrase: "kind of like how meaning resists compressi
4. 4.▲
   by Intrinisical-AI 1y ago

   Hey man! Thanks a lot for your support! Might sound like just common words, but honestly — knowing this helped or inspired someone really motivates me. Makes me feel a bit less like a madman hahaha. About your question — I think I get y
5. 5.▲
   by Intrinisical-AI 1y ago

   That's fascinating! — but I don't fully agree with the framing. Using G(t) in the context of embeddings seems problematic, specially given the probabilitistic nature. Example - Take a sentence with a typo but semantically clear an
6. 6.▲
   Why do we still flatten embedding spaces?

   7 points by Intrinisical-AI 1y ago|11 comments