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. In today's AI research, most researchers focus on specific application problems and they develop the capabilities of their AI solutions only to the extent that these specific applications require them. While challenging AI problems such as natural language understanding require a broader view, most researchers do not begin with an all-encompassing architecture and then adapt to a specific application. It is usually more efficient to pursue a bottom-up development methodology for the experimental results, and as a result, progress in ambitious architectures for generality may have stalled.To achieve generality, a rigorous architectural approach has several benefits such as easing development, allowing future extensions while remaining backwards compatible, and exposing problems before they happen since we can conceptualize complex use-cases. In other words, it is at least better software engineering, however, there are also scientific benefits such as understanding the functions and capabilities required by a general-purpose AI system much better, and address these problems fully. Since the most general problem is attacked, the architecture can follow a rigorous design process which will eliminate redundancies, leading us to a more mathematically elegant design. And finally, since use-cases will lead the design, the result will be empirically firmer than a special-purpose application.A design from first principles is rarely undertaken, and it is arduous, but it can produce highly effective systems. We build upon the most powerful architectures for general AI, and then identify the requirements, from which we introduce refinements to the existing architectures, introducing new architectural ideas and incorporating new AI technologies in the process. The resulting deep technological integration architecture is a compact, scalable, portable, AI platform for general-purpose AI with many possible applications in wide domains. We gave the overview of an ambitious architecture based on Solomonoff's Alpha Architecture, and Schmidhuber's Gödel Machine architecture. The system is like Alpha, because it re-uses the basic design of PSMs. It is also similar to Gödel Machine architecture, because it can deploy a kind of probabilistic logical inference for reasoning and it can also observe some of its internal states and improve itself. The system also has basic provisions for intelligent agents, but it is not limited to them. We saw that the first important issue with implementing Alpha was to decide a basic set of primitives that will grant it sufficient intelligence to deal with human-scale problems. It remains to be demonstrated empirically that is the case, however, two of the eight reference machines have been implemented and seen to operate effectively.A criticism may be raised that we have not explained much about how the AI Kernel works. We only assume that it presents a generalized universal induction approximation that can optimize functions, rich enough to let us define basic machine learning tasks. It surely cannot be Levin search, but it could be any effective multi-strategy optimization method such as evolutionary architecture search BID7 . We are using an extension of the approach in Fourier Network Search BID6 which is also likely general enough. The memory update is also not detailed but it is assumed that it is possible to extend an older memory design called heuristic algorithmic memory so that it works for any reference machine. We also did not explain in detail how many components work due to lack of space, which is an issue to be tackled in a longer future version of the present paper.In the future, we would like to support the architectural design with experiments, showing if the system is imaginative enough to come up with neural architectures or hybrid solutions that did not appear to humans. The algorithms used are expensive, therefore they might not work very well with the extremely large models required by the best vision processing systems; but to accommodate such models, it might be required that the system evolves only parts of the system and not the entire architecture. The system is intended to be tested on basic psychometric tests first, and a variety of data science problems to see if we can match the competence of the solution a human data scientist would achieve.
It's a new AGI architecture for trans-sapient performance.This is a high-level overview of the Omega AGI architecture which is the basis of a data science automation system. Submitted to a workshop. 
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