Authors: Dr. Latha Gadepaka, Mr. A. Ramesh Khanna, Mr. Ravula Rajashekhar, and Mrs. V. Subhalakshmi

ISBN: 978-81-69857-96-3

DOI: https://doi.org/10.59646/852

Date of Publication: October 20, 2026

Preface

The book Next-Generation Artificial Intelligence and Machine Learning presents a comprehensive and systematic exploration of advanced computational techniques, intelligent learning paradigms, and emerging technological developments that are transforming the field of artificial intelligence. The rapid evolution of AI from rule-based symbolic systems to sophisticated data-driven models has created new opportunities for developing intelligent systems capable of learning, reasoning, generating content, adapting to changing environments, and making autonomous decisions. This book introduces the fundamental mathematical principles underlying next-generation AI, including optimization, probability, information theory, and representation learning, while examining the limitations of conventional machine learning approaches and the growing importance of large-scale data, computational resources, and general-purpose intelligence. It explores advanced machine learning architectures such as graph neural networks, capsule networks, equivariant neural networks, sparse models, mixture-of-experts, continual learning, meta-learning, Bayesian deep learning, and hybrid symbolic–neural systems, highlighting their contributions to improving model adaptability, scalability, and computational efficiency. Particular attention is devoted to foundation models and generative intelligence, including large language models, multimodal learning, self-supervised pretraining, contrastive learning, prompt engineering, in-context learning, diffusion models, and synthetic data generation.

The book also investigates the principles of autonomous learning and decision-making through reinforcement learning, hierarchical and multi-agent learning, world models, simulation-based training, planning, reasoning, and autonomous agents capable of interacting with external tools and environments. Furthermore, it addresses the engineering and deployment challenges associated with modern AI systems, covering distributed training, large-scale optimization, federated learning, privacy-preserving machine learning, MLOps, model monitoring, drift detection, edge intelligence, model compression, hardware–software co-design, and cloud-native AI architectures. Alongside these technical developments, the book emphasizes responsible AI principles, model alignment, hallucination mitigation, safety constraints, privacy protection, and energy efficiency, recognizing that the development of reliable and trustworthy intelligent systems requires both computational innovation and careful consideration of their broader implications. Through its integrated coverage of theoretical foundations, advanced architectures, generative technologies, autonomous intelligence, and scalable deployment strategies, this book aims to bridge the gap between foundational knowledge and contemporary research developments. It is intended to serve as a valuable reference for undergraduate and postgraduate students, research scholars, academicians, AI developers, and industry professionals seeking to strengthen their understanding of emerging artificial intelligence and machine learning methodologies. By presenting the concepts in a structured and technically oriented manner, the book supports further research, practical experimentation, and the development of innovative intelligent solutions across healthcare, robotics, finance, engineering, scientific computing, and smart infrastructure, preparing readers to engage with the evolving opportunities and challenges of next-generation artificial intelligence.