The theorem yield management of knowledge was conceived in 1997 during the operation of GWIN (Global Web Interactive Network). The theorem posits that knowledge creation and transfer within distributed networks is a dynamic and complex undertaking consisting of physical and psychological properties with potential for a malleable yield curve. Two decades of R&D resulted in a distributed artificial intelligence (AI) operating system (OS) designed to provide optimal knowledge yield at the confluence of human and machine intelligence. The now patented modular AI system core is fully adaptive and tailored to the unique profiles of each entity with a simple natural language interface.
Knowledge yield is the output from knowledge optimization processes as determined by the specific needs of the subject entity. If the system is the sum of the separate parts, has the function to capture highly specific particle value (knowledge) and the ability to tailor to specific needs of each entity, we can then manage the yield curve of knowledge for specific missions in near-real time. Relevance can then be further defined by specific location and time as an extension of relativity discovered by Einstein more than a century earlier.
Among many benefits of the Kyield OS include data optimization at scale far beyond the ability of humans alone, enhanced data integrity, stronger security, crises prevention, improved productivity, and continuous learning for each individual, team, and organization. Data ownership and control remains with customers unless required by regulatory or per agreement for specific purposes.