Using artificial intelligence to generate text and recognize it

Abstract

The article discusses modern approaches to text generation using artificial intelligence (AI) and methods of their recognition. The Introduction highlights the urgency of the problem: the growing use of AI for content creation in education, media and business requires the development of tools for identifying machine texts, which is related to issues of academic integrity, copyright and the fight against misinformation. The Methods section analyzes neural network-based text generation technologies, including model training on large data sets, as well as recognition methods such as statistical analysis (uniform word distribution, template constructions), machine learning algorithms, and specialized programs. The Results demonstrate that AI texts are often characterized by predictable syntax and lack of emotional variability, however, the complexity of models (for example, the introduction of errors to simulate human style) makes it difficult to detect them. The Discussion highlights ethical and legal challenges: the risks of fraud, the need for legislative regulation and the protection of personal data. Special attention is paid to the prospects of hybrid solutions combining automatic generation with human editing, as well as the development of multimodal systems integrating text, audio and visual data. The article emphasizes that, despite the progress of AI, the key role in quality control and creative thinking remains with humans.