ANALYSIS OF MODERN ARTIFICIAL INTELLIGENCE SYSTEMS WITH EMPHASIS ON GENERATIVE MODELS AND ETHICAL CHALLENGES

Abstract

Artificial Intelligence (AI), combined with machine learning and computer vision, is rapidly reshaping the way humans interact with technology by enabling intelligent automation and evidence-based decision systems. These technologies are enhancing efficiency and performance across major sectors such as healthcare, transportation, and education by delivering faster, more adaptive, and user-centered services. In particular, deep learning has become a core driver of recent progress, supported by both extensive theoretical advancements and large-scale real-world applications. At the same time, generative AI has introduced powerful methods for producing text, images, and source code through modern architectures such as transformers, generative adversarial networks (GANs), and diffusion-based models. These innovations are significantly influencing domains like natural language processing, software development, and immersive digital environments. Despite these advancements, critical challenges remain regarding ethical use, transparency, accountability, and data governance. This study provides a detailed review of current AI trends, assesses generative models using established evaluation metrics such as BLEU and ROUGE, and explores their broader technological and     societal            impacts. Furthermore, it highlights the importance of developing strong ethical frameworks to ensure that AI systems are deployed responsibly, securely, and transparently in practical applications.