The Future of Artificial Intelligence: Generative, Explainable and Agentic Paradigms
TL;DR
The opening chapters establish the historical and conceptual ground, tracing the evolution of artificial intelligence and then examining generative architectures, large language models and the practice of prompt engineering, before turning to interpretability, agency and responsibility.
Abstract
Artificial intelligence has entered a phase in which three distinct research programmes, once pursued largely in isolation, have begun to converge. Generative models have made machines fluent producers of text, image and code. Explainable AI has grown from a niche concern into a precondition for deployment in regulated settings. Agentic systems have started to close the loop between reasoning and action, allowing models to plan, use tools and pursue goals over extended horizons. This book takes that convergence as its subject. The fifteen chapters that follow move from foundations to frontiers. The opening chapters establish the historical and conceptual ground, tracing the evolution of artificial intelligence and then examining generative architectures, large language models and the practice of prompt engineering. The middle chapters turn to interpretability, agency and responsibility, covering explainable AI methods, autonomous intelligent systems, human-centric design, governance and fairness frameworks, and applications across healthcare, education and business. Later chapters address the systems context in which these technologies operate: AI-driven automation and Industry 5.0, multi-agent decision-making, integration with the Internet of Things and blockchain, cybersecurity and privacy, sustainable and green computing, and quantum and other emerging paradigms. A closing chapter draws the threads together and sets out the open research directions the authors consider most consequential. The book is written for postgraduate students, researchers and practitioners who need a structured account of a field that is moving quickly. Each chapter is self-contained enough to be read on its own, while the sequence as a whole is intended to build a coherent picture. Technical detail is provided where it aids understanding, but the emphasis throughout is on concepts, trade-offs and consequences rather than implementation specifics. The authors are grateful to their institutions and colleagues for the support that made this volume possible, and to Academic Scholar Press for bringing it to publication.
