Synopsis
Understanding Artificial Intelligence by Henry C. Mishkoff, published in 1985 by Texas Instruments and Howard W. Sams & Co., is a comprehensive introductory text designed to demystify the emerging field of artificial intelligence (AI) for a broad audience. Written in accessible language and enriched with illustrations, the book aims to answer fundamental questions about AI: what it is, what it is used for, and how it might impact society. Mishkoff, a seasoned technical writer with a background in programming and computer documentation, crafts an engaging narrative that balances technical detail with clarity, making the complex subject approachable for readers without deep technical expertise. Developed with contributions from the Texas Instruments Information Publishing Center and featuring a foreword by AI pioneer Edward A. Feigenbaum, the book reflects the state of AI research and applications in the mid-1980s, a period when the field was gaining significant traction in both academic and industrial spheres.
The book begins by exploring the history and conceptual foundations of AI, tracing its roots from early philosophical ideas about machine intelligence to the computational advancements of the 20th century. Mishkoff outlines the core objective of AI: to create systems capable of performing tasks that typically require human intelligence, such as reasoning, problem-solving, and learning. A significant portion of the text is dedicated to explaining key AI methodologies, with a particular focus on symbolic processing, which was a dominant paradigm in AI at the time. Symbolic processing involves manipulating symbols and rules to represent knowledge and solve problems, as opposed to purely numerical computation. Mishkoff illustrates how this approach enables computers to mimic human-like reasoning in specific domains, using clear examples to bridge abstract concepts with practical applications.
A major highlight of the book is its in-depth treatment of expert systems, which were among the most prominent AI applications in the 1980s. These systems emulate the decision-making abilities of human experts in specialized fields, such as medical diagnosis or equipment troubleshooting, by encoding expert knowledge into rule-based frameworks. Mishkoff explains how expert systems are built, detailing the roles of knowledge engineers who extract expertise from human specialists and translate it into machine-readable formats. He also discusses natural language processing (NLP), exploring how AI systems can understand and generate human language, a challenging area that was still in its infancy during the book’s publication. Through examples, Mishkoff shows how NLP enables computers to interpret user inputs and respond in ways that feel intuitive, laying the groundwork for future advancements in human-computer interaction.
Beyond technical explanations, the book addresses the practical applications of AI across various industries, including healthcare, finance, and manufacturing, where AI systems were beginning to enhance efficiency and decision-making. Mishkoff also examines the societal implications of AI, considering both its potential benefits—such as automating repetitive tasks—and its challenges, including ethical concerns and the fear of job displacement. While optimistic about AI’s transformative potential, he acknowledges the limitations of 1980s technology, noting that general AI, capable of human-like versatility, remained a distant goal. The book concludes with a look at emerging trends in AI research, offering readers a glimpse into the field’s future directions. Accompanied by a glossary, bibliography, and index, the 258-page text is structured to support individualized learning, making it a valuable resource for students, professionals, and curious readers alike.