Open Autonomous intelligence initiative
Defining the objects, policies, and boundaries that make autonomy governable
How to Review the Base Model and the PER MVM
Key Concepts
OAII Base Model
Introducing the PER mvm
Open Autonomous Intelligence Initiative
Advocates for Open, ethical AI Models
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Geometric Realizations of UPA (Part 8)
Parts 1–7 established polarity, multi‑axis hyperspheres, learning on curved manifolds, safety invariants, multi‑agent geometry, and novelty/emergence. We now turn to one of the most profound structural features of UPA: identity across levels—the recursive, scale‑spanning coherence represented by hierarchical embeddings.
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Geometric Realizations of UPA (Part 7)
Parts 1–6 established polarity geometry (S²), multi-axis hyperspheres (Sⁿ), manifold learning, safety invariants, and multi-agent interaction. Now we turn to one of the deepest and most powerful consequences of UPA geometry: novelty—the lawful emergence of new distinctions, new dimensions, new identities, and new forms of intelligence.
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Geometric Realizations of UPA (Part 5)
In Parts 1–4, we introduced spheres (S²), hyperspheres (Sⁿ), multi‑axis polarity, and learning on curved manifolds. In Part 5, we turn to the most important question for SGI: how do we guarantee safety?
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Geometric Realizations of UPA (Part 4)
In Part 3, we expanded from a single polarity (S²) to many interacting polarities on hyperspheres (Sⁿ). In Part 4, we turn to the operational question: how does learning actually work on these curved manifolds?
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Geometric Realizations of UPA (Part 3)
In Part 1, we motivated geometric realization. In Part 2, we developed the geometric atom: a single polarity encoded on the sphere S². In this post, we expand from one polarity to many—moving from S² to the hypersphere Sⁿ. This is where UPA becomes a genuinely multi‑dimensional, multi‑polar, and fully integrative geometric framework.
Open Autonomous Intelligence Initiative 