Brain-Inspired Systems and Cognitive Boldness

Attempts to introduce semantics into information theory have made some progress but fell short of having a capability to deal with information described in natural language. According to L. A. Zadeh, a theory of semantic information (TSI) is centered on a concept which plays a key role in human intelligence. A concept whose basic importance has long been and continues to be unrecognized. The concept of a restriction is pervasive in human cognition (Zadeh, 2008). Restrictions underlie the remarkable human ability to reason and make rational decisions in an environment of imprecision, uncertainty and incompleteness of information. A fundamental issue in TSI is computation with restrictions to achieve a clear meaning. TSI opens the door to modes of computation in which approximation is accepted. Acceptance of approximate computations takes the calculus of restrictions (CR) into uncharted territory (Zadeh, 1965, 1975, 1997, 2004, 2008, 2016). In fact, approximation can be of two fundamental different types. Either approximated approximation or exact approximation. They immediately give birth to two large areas of structured language systems, i.e. arbitrary entropy representation languages and minimum entropy representation languages. This is the difference that makes the difference (Bateson, 1972, pp.457-9) at semantic level! This is the main reason why traditional computational resources and systems have still to learn a lot from human brain-inspired computation and reasoning. If we, as Cognitive Informatics and Cognitive Computing Society, do really want to create the right, vital environment to develop a real, solid TSI, we, as a scientific community, must find the cognitive boldness to embrace, to face and solve this problem successfully first. Then everything else will be a gentle breeze.