“My current research is devoted to the design and implementation of a new class of autopoietic and cognitive distributed computing machines (known as Super-Symbolic Computing Machines) which utilize current general-purpose computing and neural network programs to create a higher level of intelligence going beyond current generation machine intelligence that includes both process automation using symbolic computing, and pattern recognition with sub-symbolic computing (AI). Autopoiesis refers to a system with a well-defined identity and is capable of reproducing and maintaining itself. Cognition, on the other hand, is the ability to process information, apply knowledge, and change the circumstance. Autopoietic and cognitive systems improve the resilience and performance, at scale, of our current generation information systems by orders of magnitude by repurposing them just as the new (mammalian) brain in humans did with the old (reptilian) brain. This research is based on the General Theory of Information and Information Theoretical Principles of Software Development.”
Here are recent publications that describe my current research into extending the mind functions using super-symbolic computing just as sub-symbolic computing extends some functions of the human brain and symbolic computing extends the human body functions to interact with the material world and other machines and humans.
Theoretical and Foundational Problems (TFP) in Information Studies 