Existence and perception as the basis of AGI (Artificial General Intelligence)
This addresses the foundational challenge of enabling AGI to emulate human thinking by formalizing meaning, though it appears incremental as it builds on existing philosophical and AI discussions without demonstrating new implementations.
The paper tackles the problem of formalizing the concepts of 'meaning' and 'knowledge' for AGI, proposing a method to create 'ready-to-code' descriptions to enable AGI to operate with meanings, but does not report any concrete results or numbers.
As is known, AGI (Artificial General Intelligence), unlike AI, should operate with meanings. And that's what distinguishes it from AI. Any successful AI implementations (playing chess, unmanned driving, face recognition etc.) do not operate with the meanings of the processed objects in any way and do not recognize the meaning. And they don't need to. But for AGI, which emulates human thinking, this ability is crucial. Numerous attempts to define the concept of "meaning" have one very significant drawback - all such definitions are not strict and formalized, so they cannot be programmed. The meaning search procedure should use a formalized description of its existence and possible forms of its perception. For the practical implementation of AGI, it is necessary to develop such "ready-to-code" descriptions in the context of their use for processing the related cognitive concepts of "meaning" and "knowledge". An attempt to formalize the definition of such concepts is made in this article.