Show HN: AI latent space with overlapping manifolds
A developer shares a Python script called "integrated_egregore_core_test_v6_4.py" on GitHub, which explores AI latent space by modeling overlapping manifolds. The project appears to be an experimental implementation for testing how different conceptual regions can intersect within a neural network's internal representation space.
Background
This is a Python proof-of-concept ("Egregore") exploring how AI models might internally group related concepts into overlapping "manifolds" — mathematical structures that capture the latent (hidden) geometry of meaning inside a neural network.
- The author, PJHkorea, is showing work-in-progress research on how conceptual boundaries in AI latent space can blur or overlap (e.g., where "dog" and "cat" manifolds might share a region for "pet").
- "Manifold" in machine learning refers to the idea that high-dimensional data (like words or images) actually lies on lower-dimensional curved surfaces within the model's internal representation.
- The code tests whether different semantic categories can be modeled as intersecting manifolds rather than fully separate clusters, which is closer to how human concepts actually relate.
- This is relevant to interpretability research (trying to understand what large language models "know" inside) and to improving AI's ability to handle fuzzy, context-dependent meanings.