Empathi AI uses a large Bayesian Network of concepts in context to interpret and natural language input and resolve ambiguity. Each context and concept effectively resides at the center of a constellation of related concepts that describe how the words fit together to represent things that are and things that happen. The symbolic modeling we use captures both obvious and nuanced phenomena with fuzzy reasoning where each discreet knowledge proposition has a confidence value that represents its likelihood in the given context. This is a whole-brained approach because it is holistic.
Neural network technologies are great at two dimensional problems like speech recognition and optical character recognition. As the size of the problem increases, the computational resources required explode. Witness Large Language Model (LLM) solutions. They are grotesquely expensive to build and run because they try to use simple patterns to achieve complex results. Artificial Neural Networks model left-brained thinking very well.
Our suite of tools will be used to add music, film and storytelling to the cognitive toolchest Empathi uses to provide insights to people with complex needs.
