LANGUAGE MODELING: FEATURES OF THE SEMANTIC AND MORPHOLOGICAL STRUCTURE OF ARTIFICIAL INTELLIGENCE AND NATURAL LANGUAGE

Abstract

The era of digitalization has brought significant changes to the world. It is known that this is significant in the development of the branch of science. In linguistics, the process of digitalization is also characterized by the emergence of new research directions and a significant impact on scientific studies. Artificial intelligence (AI), which is the result of digitalization, is recognized as one of the important achievements of modern science and technology, and the use of these technologies is aimed at improving the efficiency of human life. Identifying the mechanisms of linguistic features of texts generated by artificial intelligence, as well as systematizing the differences between human intelligence and artificial intelligence in written and spoken communication, are relevant tasks for linguistic research. Scientific and philological analysis and examination of texts produced by artificial intelligence are of great importance for the national language. There are significant differences between the structure of human language and the language generated by artificial intelligence. The rapid integration of artificial intelligence into everyday life and communication also has a noticeable impact on language. The aim of the study is to identify the characteristics of language modeling and structural differences, as well as to reveal changes in semantic meaning.

The article examines the stages of development and transformation of neural networks and language. It analyzes the differences and specific features of texts generated by ChatGPT and texts written in natural language. The ability of artificial intelligence to understand semantic meaning and recognize morphological structures is also determined.

The study is based on theoretical concepts related to language evolution and machine learning. In the methodological section of the article, the methods of scientific reasoning, generalization, comparison, modeling, and analysis are used. As a result of the study, semantic meanings are analyzed using examples, the limitations and challenges of language modeling are identified, and the causes of ethical contradictions in neural network modeling are clarified.

The scientific value of the study lies in analyzing the language of artificial intelligence and substantiating the importance of preserving the national linguistic specificity and cultural coloring of language in the context of digitalization. The practical significance of the research lies in the fact that its results can be used as teaching material for the disciplines "Lexicology of the Modern Kazakh Language" and "Morphology of the Modern Kazakh Language", studied in universities.