Sketch of an AGI architecture with illustration

I will present a framework for Artificial Generla Intelligence inspired by knowledge about the only working prototype: the brain. The neurobiological findings make the directives. I define the main algorithmic modules and give solutions for each subtasks together with the available mathematical (hard) constraints. The main themes are compressed sensing, factor learning, independent process analysis and low dimensional embedding for optimal state representation to be used by a particular RL system that can be integrated with a robust controller. However, the blending of the suggested partial solutions is not a straightforward task. Nevertheless, we started to combine these modules and illustrate their working on a simulated problem. I will discuss the ongoing efforts and the steps needed to complete the integration.