Omega: An Architecture for AI Unification

TL;DR

Omega is a modular, self-improving AI architecture inspired by Solomonoff's Alpha, designed to unify various AI components with diverse representations, neural networks, and problem-solving capabilities for data science automation.

Contribution

It introduces a comprehensive, open-ended AI architecture that integrates multiple representations, neural networks, and self-improvement mechanisms based on first principles.

Findings

Includes eight representation languages and six neural network classes.

Addresses data science automation with problem-solving methods.

Features a modular, self-improving architecture with higher-order cognition.

Abstract

We introduce the open-ended, modular, self-improving Omega AI unification architecture which is a refinement of Solomonoff's Alpha architecture, as considered from first principles. The architecture embodies several crucial principles of general intelligence including diversity of representations, diversity of data types, integrated memory, modularity, and higher-order cognition. We retain the basic design of a fundamental algorithmic substrate called an "AI kernel" for problem solving and basic cognitive functions like memory, and a larger, modular architecture that re-uses the kernel in many ways. Omega includes eight representation languages and six classes of neural networks, which are briefly introduced. The architecture is intended to initially address data science automation, hence it includes many problem solving methods for statistical tasks. We review the broad software architecture, higher-order cognition, self-improvement, modular neural architectures, intelligent agents, the process and memory hierarchy, hardware abstraction, peer-to-peer computing, and data abstraction facility.