Open Autonomous Intelligence Initiative

Welcome to the OAII Base Model—an evolving object-oriented framework intended to define the core classes and interfaces for personal, edge Autonomous Intelligence systems.

Our GitHub repository will host the abstract classes and design principles that underpin the Open Autonomous Intelligence Initiative (OAII). By focusing on a streamlined yet flexible model, we aim to enable everything from personal event recognition to broader AI use cases, while adhering to ethical standards and aligning with the mission of a B Corporation.

Version: 0.1 (Draft)
Status: Under active development

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Table of Contents

1. Project Vision
2. Scope & Goals
3. Key Concepts
4. Architecture Overview
5. MVP for Personal Event Recognition
6. Ethical Focus & B Corporation Alignment
7. Getting Involved
8. License

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Project Vision

OAII (Open Autonomous Intelligence Initiative) aims to define a universal yet practical model for autonomous intelligence solutions, prioritizing:

• Human-centric design: Real-world scenarios that respect user privacy and well-being.
• Modular architecture: Clear, object-oriented abstractions that simplify development and collaboration.
• Ethical standards: Aligning with B Corp principles and ensuring transparent, responsible AI practices.

We want to create a community-driven framework that fosters innovation while remaining accessible to data scientists, programmers, and organizations that share our ethical commitments.

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Scope & Goals

1. Core Classes: This repo contains abstract classes for the OAII model, such as Device, Processor, Knowledge, and Log. They serve as blueprints for future, more specialized AI components.
2. Autonomous Intelligence Standard: Define conventions and interfaces that unify how AI components are structured, named, and interact, requiring (not just optionally) standard representations for knowledge.
3. Community & Collaboration: We’re building a review panel of data scientists, software engineers, and domain experts to refine the model—your input is welcome.
4. Ethical & Sustainable: By adopting B Corporation values, we aim for a model that supports real-world impact and responsible AI deployment.

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Key Concepts

• Device: Represents sensor or hardware abstractions (e.g., GPS, microphone).
• Processor: Encapsulates data processing, inference, or AI logic. We may have device-specific Processor subclasses (e.g., GPSProcessor).
• Knowledge: Maintains a standard representation of domain facts, user profiles, labeled data, and rules—required for interoperability and advanced reasoning.
• Log: Facilitates transparency and testability, capturing key events, state changes, and inferences for auditing, debugging, and certification.

We employ a traditional application approach—rather than a complex runtime or event bus—to keep development simpler and more accessible.

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Architecture Overview

High-level:

1. Application (standard main program or orchestrator)
2. OAII Base Classes
   • Device (abstract)
   • Processor (abstract) + possible subclasses (e.g., GPSProcessor, AudioProcessor)
   • Knowledge (central repository for labeled data, rules, user profiles)
   • Log (mandatory component for capturing system activity, state transitions, recognized events)
3. Extensions or User Implementations: e.g., custom devices, specialized processors, or domain-specific knowledge modules

In this simpler architecture, the application directly instantiates and coordinates these classes. However, Log ensures that all operations (device readings, inference steps, knowledge lookups) can be recorded for accountability and debugging.

For more details, see our docs/ folder (coming soon), which will include UML diagrams and deeper technical references.

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MVP for Personal Event Recognition

While this repository defines abstract OAII classes, our MVP for Personal Event Recognition will live in a separate GitHub project—there, we’ll have concrete implementations:

• GPSProcessor, AudioProcessor, and other specialized processors
• Concrete devices (like a simulated or real GPSDevice)
• A dedicated Knowledge module for event rules (e.g., “home geofence”), user profiles, and labeled data
• Log integration that continuously tracks recognized events, sensor anomalies, and user interactions

Goal: Demonstrate how an OOP-based AI model can successfully detect real-life events like “User Returns Home,” with a strong focus on transparent logging and knowledge representation.

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Ethical Focus & B Corporation Alignment

We believe in responsible AI, meaning:

• User Privacy: Minimizing data collection, building interpretable, respectful solutions
• Transparency: Our Log class is central for auditing, compliance, and real-time debugging
• Community-Driven: Welcoming diverse opinions, especially on ethical challenges
• Long-Term Value: A sustainable, B Corp–friendly model that fosters social good over unbridled exploitation

We invite collaborators who share these values to help shape the OAII model.

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Getting Involved

1. Review Our Classes: Check the src/ or docs/ folder (once available) for definitions of Device, Processor, Knowledge, and Log.
2. Open Issues: Spot something that needs refinement? Open an Issue to start the conversation.
3. Pull Requests: Contribute improvements, add tests, or refine design patterns.
4. Join Our Team: We’re actively recruiting data scientists and programmers to further develop the model and MVP. Please reach out if you want to be part of the formal review panel.

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License

We anticipate adopting a permissive open-source license (e.g., Apache 2.0) to encourage broad usage and collaboration. A final decision will appear in our LICENSE file once finalized.

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Thank You for Your Interest!

We appreciate your curiosity about the OAII Base Model. Our mission is to create a collaborative, ethical foundation for next-gen AI solutions—starting with personal event recognition and expanding outward. If you have questions, suggestions, or want to get involved, please open an Issue or contact our team directly.

— The OAII Team