  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.
