The BRAIN-CA™ Estimator and The Cincinnati Algorithm: Simplification Tools for Artificial Intelligence
Abstract: Learning is a process of creating a model of information as it is observed. The ideal storage format of such a model would represent learned information so that the model could be rapidly applied or modified, as additional observations are made. The BRAIN-CA™ Estimator is an elemental tool which enables this capability: the storage of a simple model in a manner that is accessible for rapid modification (learning) and rapid application (inference). A process called The Cincinnati Algorithm provides a simple method to update the Estimator model as additional observations are made.

Casting off the Old Guard: Achieving Superior A.I. Performance through Simplification
Abstract:
Training and deployment of huge machine learning models requires a vast amount of compute resources, and still dramatically underperforms the biological brain, which operates with six orders of magnitude less energy consumption. The trend of the conventional approach is toward more complicated engineered systems. At Brain-CA Technologies, we have designed a new architecture based on Cellular Automata. This simplified architecture casts off the old guard technologies, eliminating many of their inherent  restrictions. Our projections show that this new architecture provides significant improvement in energy consumption, while performing AI/ML tasks equivalently or better than existing systems.