Date of Publication :8th May 2024

Abstract:Artificial intelligence systems require computer models and software beyond those currently available. Modern forms of information presentation still do not allow semantic modelling of complex textual information. When solving clustering or classification tasks, machine learning treats a set of data as knowledge. We work through the spoken language syntax of symbols, single words, or different types of literals, which is why machine translation approaches are still far from perfect. Computers are still unable to accurately translate from one language to another. Existing models of knowledge representation are only a first-order approach to the natural. The main essence of our approach lies in the fact that information should be considered in an immaterial aspect, in a quantum form, in the form of a continuous hologram. What we call information is only its material reflection. When processing textual information, Our communication is often based on single words, symbols, or various types of literal expressions. That is why this article is dedicated to a new understanding of knowledge representation and acquisition, which can become the basis for working not only on simple textual information, but also on high-level knowledge and knowledge models, and thus the beginning of a new paradigm of artificial intelligence.

Reference :