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AI Explained: A Guide for Non-Technical Readers [Hall] - mklabgr - 08-30-2026
Book name:AI Explained: A Guide for Non-Technical Readers
Authors: Wendy Hall and Pete Rai
Publication date: September 2026 — retailers list September 9, 2026
Publisher: Wiley
Edition: 1st edition
Pages: 256
ISBN: 978-1-394-43114-4
Summary
AI Explained is designed for readers who need to understand artificial intelligence without becoming programmers or machine-learning engineers. Rather than beginning with modern systems such as ChatGPT, the book builds AI from its conceptual foundations. It moves from rule- and logic-based AI through statistical approaches, neural networks and machine learning, eventually reaching generative AI and large language models. The central question is not how to program these technologies, but how AI systems arrive at the outputs and decisions they produce.
The authors connect the technical ideas to practical applications through use cases from areas such as healthcare, law and business. This makes the book particularly suitable for professionals who encounter AI in their work but do not have a computer-science background. Neural networks, machine-learning algorithms and generative models are presented conceptually rather than through mathematics or programming, allowing readers to develop a mental model of what different types of AI can and cannot do.
The book also treats AI as more than a technical subject. It discusses ethics, regulation, public policy and societal consequences, reflecting the authors' experience advising governments and international organizations. The intended readership includes educators, policymakers, lawyers, journalists, healthcare professionals and business leaders. In this respect, the book aims to provide the kind of AI literacy increasingly necessary for people who must evaluate, regulate, teach about or make decisions involving AI, rather than build the systems themselves.
Key takeaways
- AI is broader than generative AI. The book places today's LLMs within the longer development of symbolic, statistical and machine-learning approaches.
- Understanding matters more than coding. Its purpose is to explain why and how AI works, not teach Python or model development.
- Applications are used to clarify theory. Healthcare, law and business examples connect abstract AI concepts to real decisions.
- AI literacy includes ethics and policy. Regulation, societal impact and responsible use are treated as essential parts of understanding the technology.
BOOK