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.
翻译:已知的 AGI(人工智能)与AI不同,应该用意义来操作。这就是它与AI的不同之处。任何成功的AI执行(象棋、无人驾驶驾驶、面部识别等)都与处理过的物体的含义无关,它们也没有必要。但是,对于模仿人类思维的AGI来说,这种能力至关重要。许多界定“意图”概念的尝试有一个非常重要的缺点――所有这类定义都不是严格和正规化的,因此不能编程。搜索程序应该使用正式的描述其存在及其可能的看法形式。为了实际实施AGI,有必要在其用于处理“意图”和“知识”等相关认知概念时,制定这样的“可变码”描述。在本条中试图正式确定这些概念的定义。