The Artificial Intelligence Papers: Original Research Papers With Tutorial Commentaries

Modern artificial intelligence (AI) is built upon a relatively small number of foundational research papers, which have been collected and republished in this unique 350-page book. The first chapter provides a summary of the historical roots of AI, and subsequent chapters trace its development, from Rosenblatt's perceptron in 1958 to one of the early GPT models in 2019. Each paper is introduced with a commentary on its historical context and a tutorial-style technical summary. In several chapters, additional context is provided by the paper's original author(s). Written in an informal style, with a comprehensive glossary and tutorial appendices, this book is essential reading for students and researchers who wish to understand the fundamental building blocks of modern AI.

About the Author :
Visiting Professor, University of Sheffield, UK.

Review :
"James Stone has done it again: another masterful book that takes you straight to the heart of current thinking in artificial intelligence (AI) -- and its foundations. From perceptrons in 1958 to generative pre-trained transformers (GPTs), this book scaffolds the history of AI with landmark papers that chart progress over the last half-century -- as witnessed by the author. In short, this book represents an intellectual string of pearls that would complement the bookshelf of anyone invested in the forthcoming age of artificial intelligence.'' Karl J Friston, FRS. Scientific Director: Wellcome Centre for Human Neuroimaging. "I learned a lot from this collection of classic papers about the neural network approach to artificial intelligence. Spanning all the major advances from perceptrons to large language models (e.g. GPT), the collection is expertly curated and accompanied by insightful tutorials, along with intimate reminiscences from several of the pioneering researchers themselves.'' Steven Strogatz, Professor of Mathematics, Cornell University, USA. "To define the future, one must study the past. Stone's book collects together the most significant papers on neural networks from the perceptron to GPT-2. Each paper is explained in modern terms and, in many cases, comments by the original authors are included. This book describes a riveting intellectual journey that is only just beginning.'' Simon Prince, Honorary Professor of Computer Science, University of Bath, England. "Connectionist models of the brain date back to the work of Hebb in 1949, and the first faltering first steps towards practical applications followed soon after Rosenblatt's seminal 1958 paper on the perceptron. As of 2024, models firmly rooted in connectionism, from generative adversarial networks (GANs) to transformers, have heralded a renaissance in artificial intelligence that is revolutionising the nature of our digital age. This latest volume by James Stone collects the pivotal connectionist papers from 1958 right up to today's radical innovations, and provides an illuminating descriptive narrative charting the theoretical, technical, and application-based historical development in a lucid tutorial style. A welcome, much needed, and valuable addition to the current canon on artificial intelligence." Mark A Girolami, FREng FRSE. Chief Scientist: The Alan Turing Institute. Sir Kirby Laing Professor of Civil Engineering, University of Cambridge, England.