SYSTEMS REASONING ™
ThinkingNode™ brings a breakthrough Distributed Artificial Intelligence technology based on Systems Reasoning through Causal Context Networks. Systems Reasoning is a new AI approach for modeling complex systems into reasoning networks using a unified cognitive framework to represent human reasoning at a highly scaleable level. Systems Reasoning Computing technology models causation and will lead to a major transition in the AI markets, particularly in Life Sciences. Imagine if a doctor could gather all the known cause-effect interactions of her patient’s health factors such as current medications, vitals, genetics, environment, history, etc. AND have the reasoning capacity to combine all of them intelligently to diagnose or intervene with the most personalized treatments. ThinkingNode™ makes this possible.
THINKINGNODE™ AUTOMATES & ACCELERATES
PROBLEM SOLVING THROUGH CAUSAL MODELING AND SYSTEMS REASONING
THE NEXT GENERATION OF AI
Today, AI companies are focused on Data-driven Machine Learning based on Correlation Patterns. With ThinkingNode™, the focus is shifted to using Knowledge-driven Reasoning Computing based on Causal Context Models to build Reasoning-Driven applications.
Our technology produces sophisticated reasoning and provides a powerful way to model complex knowledge and then computationally reason through any number of possible calculations, inferences, and simulations – with or without data.
ThinkingNode™ improves discovery and decision-making in Life Sciences by scaling the reasoning process. We create causal context models that can run simulations for complex applications with cellular dynamics, the genome, metabolic networks, the immune system, drug combinations, and drug safety... Once models have been formalized into Reasoning Networks, scientists can make sophisticated hypotheses based on an understanding of the system and test them faster using as many parameters as needed, far beyond the few concepts that humans can process simultaneously. Reasoning Computing is necessary to combine Complex Knowledge in domains such as immunology, synthetic biology, drug development, and personalized medicine. ThinkingNode™ solutions are ideal for:
Solving difficult problems using large quantities of knowledge or complex combinations.
Gaining a true understanding of what’s going on in a complex system - to be able to justify the results and improve the system in addition to simply getting an answer.
Keeping current with and making sense of the flood of information and knowledge continuously generated by both human experts and Machine Learning.
Inferring new knowledge from existing knowledge, something we humans do quite well though we are limited in our ability to scale our reasoning.
Solving problems where Complex Knowledge must be combined using both correlations and causations, and in particular when only sparse or partial data may exist.