Open Autonomous intelligence initiative
OAII is advancing a unified approach to building autonomous intelligent systems by integrating the Polarity Modeling Framework (PMF) as a foundational structural layer. This integration separates operational components from the underlying structure that defines state, context, and transformation, enabling systems that are more coherent, interoperable, and transparent. It establishes a practical path toward standardization and certification of autonomous intelligence. Read the OAII Concepts post
The Polarity Modeling Framework (PMF) Papers 1-9 are available for review. Download the Paper 1-9 Abstracts, Read the first post, or download the White Paper PDF
OAII Strategy: From Conceptual Foundations to Edge-Based Demonstration A four-step plan for advancing the Polarity Modeling Framework from concept to implementation, including outreach, system design, and a Minimum Viable Model.
How to review the OAII Base Model
Introducing the Personal Event Recognition model
Open Autonomous Intelligence Initiative
Open object-oriented models for accountable AuI
-
OAII Base Model — Common Types v0.1
This post defines shared abstract types used by OAII Base Model object definitions.
-
OAII Base Model — World v0.1
The World object provides the foundational contextual container within which all OAII objects operate. It establishes the structural, semantic, and interpretive boundaries that make signals, events, agents, knowledge, and policies intelligible and interoperable. In an Edge‑primary system, the World object enables local coherence without dependence on centralized or global state.
-
From Axioms to Architecture: Formalizing the OAII Foundations and Base Object Model
OAII is transitioning from an axiomatic presentation to a layered architectural model that separates foundational principles from base object classes and system-specific implementations.
-
The World Baseline Level (WBL): Making Levels Interpretable in World-Based Intelligence
Open SGI Levels are used to organize data, programs, and structures within Worlds. Levels are positive integers, built using multiplication only, with no inverses. Each prime level corresponds to an irreducible architectural foundation, and composite levels represent combinations of those foundations. This design has consequences: The number of possible composite levels is unbounded. Numeric magnitude…
-
Understanding the Difference Between Generative Base and Computer Operating Systems
Autonomous intelligence relies on two very different kinds of foundations. One is ontological—a foundation for meaning. The other is computational—a foundation for execution. These two foundations are often confused, especially when discussing the architecture of Open SGI, OAII’s world-based framework for safe and intelligible autonomous intelligence. This post clarifies a critical distinction: Generative Base (GB)…
-
Open Standards and Interoperability: The Foundation for Safe Autonomous Intelligence
Open standards are not optional—they are the conditions of safety, coordination, and global compatibility for autonomous intelligence. OAII calls on researchers, engineers, policymakers, and industry leaders to join in establishing a global standard for autonomous intelligence—one that ensures systems are not only powerful, but understandable, compatible, and safe by design.
Open Autonomous Intelligence Initiative 