Intelligent AI Systems and Advanced Machine Learning: Recent Advances and Real-World Applications

Abstract

AI and Machine Learning (ML) are powerful and rapidly evolving technologies, reshaping intelligent decision-making, automation, and data-driven problem-solving in various industries. In recent years, the potential of intelligent systems to manage complex data, to learn adaptive patterns, and to assist in autonomous decision-making has been greatly improved by the emergence of new technologies, such as Deep Learning, Transformer-based Models, Generative AI, Large Language Models (LLMs), Explainable AI (XAI), Federated Learning, Edge AI, and Digital Twin. These advancements have empowered the healthcare, manufacturing, agriculture, finance, transportation, education, cybersecurity, and smart city sectors with enhanced efficiency, productivity, and service quality, driving faster AI adoption across these industries. The innovations have contributed to improved efficiency, productivity, and service quality, leading to increased adoption of AI across the healthcare, manufacturing, agriculture, finance, transportation, education, cybersecurity, and smart city sectors. The fundamentals of intelligent AI systems, key learning paradigms, notable technological advances, and applications are discussed in this chapter, providing a comprehensive review of intelligent AI systems and advanced ML. It also explores the potential of AI to solve real-world problems and discusses some of the critical challenges associated with data privacy, model interpretability, computational complexity, algorithmic bias, and ethical considerations. Finally, the chapter proposes new research directions towards the development of trustworthy, explainable, sustainable, and human-centric AI systems. This review offers a brief overview of current research progress and prospects on the development of intelligent AI systems and advanced machine learning, which will facilitate the future generation of trustworthy and ethical AI-based solutions.