Open Autonomous intelligence initiative
About
How to review the OAII Base Model
Key Concepts
OAII Base Model
Introducing the Personal Event Recognition model
Open Autonomous Intelligence Initiative
Advocate for Open AI Models
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Open SGI MVP Subclass — HomeWorldProfile v0.1
HomeWorldProfile constrains and configures a World to represent a single private residence in which edge‑primary event recognition operates. It establishes the minimum context required to interpret Signals as Events while maintaining privacy, dignity, and auditability
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Open SGI MVP — Aging-in-Place Event Recognition Use Case
This document defines the primary use case for the Open SGI Minimum Viable Product (MVP). This document exists to: ground Open SGI development in human reality prevent scope creep into surveillance or diagnosis guide subclassing of OAII Base Model objects support transparent review by collaborators
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OAII Base Model v0.1 — Index
This index provides a consolidated view of the OAII Base Model v0.1 object set, their roles, and their relationships. The Base Model defines an open, object‑oriented, edge‑primary architecture for autonomous intelligence systems, with aging‑in‑place event recognition used as a reference domain. The Base Model is normative at the object level and non‑normative at the implementation…
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OAII Base Model — Interface v0.1
The Interface object represents a controlled interaction surface through which Agents and systems communicate with humans or other systems within a World. Interfaces enable assistance, notifications, consent, and configuration while enforcing Policy constraints and privacy boundaries. Interfaces are where autonomy meets human oversight.
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OAII Base Model — Log v0.1
The Log object provides an explicit, structured, and privacy-aware record of system activity within a World. Logs enable auditability, accountability, debugging, and review without requiring continuous data retention or surveillance. In the OAII Base Model, Logs are first-class objects designed to support trust, not monitoring.
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World-Scoped Intelligence: Why Context Boundaries Are Essential for Ethical AI
One of the most common sources of harm in AI systems is not malicious intent, poor data, or even flawed models. It is context leakage. When AI systems fail, they often fail because observations, interpretations, or rules escape the context in which they were valid. Meaning is treated as portable when it is not. This…
Open Autonomous Intelligence Initiative 