International Journal of Science, Engineering and TechnologyAuthors: Vanita Ganesh Bagal
Abstract: Artificial Intelligence (AI) has emerged as a foundational pillar of modern computer science, profoundly influencing a wide range of domains including automation, data analytics, natural language understanding, intelligent systems, and human–computer interaction. Rapid advancements in computational power, availability of large-scale datasets, and algorithmic innovations have accelerated the development and adoption of AI-based techniques across academic research and real-world applications. This review paper presents a comprehensive analysis of contemporary Artificial Intelligence techniques used in computer science, with particular emphasis on machine learning, deep learning, natural language processing, computer vision, reinforcement learning, generative models, and explainable artificial intelligence. The study systematically examines the computational foundations of these techniques, their underlying algorithms, and their practical applications in areas such as intelligent decision-making, pattern recognition, autonomous systems, and data-driven modeling. In addition, the paper highlights the advantages of AI techniques in improving efficiency, accuracy, and scalability of computing systems, while also discussing key limitations including model interpretability, data dependency, computational complexity, ethical concerns, and security challenges. Recent developments such as transformer-based architectures, large language models, and explainable AI frameworks are reviewed to illustrate current research trends and emerging directions. By synthesizing findings from recent surveys, scholarly articles, and systematic reviews, this paper provides an up-to-date overview of the state-of-the-art in Artificial Intelligence research within computer science. The review aims to serve as a valuable reference for researchers, academicians, and practitioners by identifying research gaps, outlining future opportunities, and emphasizing the need for responsible, transparent, and efficient AI systems in next-generation computing environments.
