The Artificial Intelligence Papers
Original Research Papers With Tutorial Commentaries
By: James V Stone
Paperback | 19 August 2024
At a Glance
378 Pages
27.94 x 20.96 x 1.98
Paperback
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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.
Industry Reviews
"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.
Preface
1. The Origins of Modern Artificial Intelligence
1.1 Introduction
1.2 Turing on Computing Machinery and Intelligence
1.3 The Dartmouth Summer Research Project
1.4 The Origins of Artificial Neural Networks
1.5 Modern Neural Networks
1.6 Reinforcement Learning
2. The Perceptron - 1958
Research Paper: The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain
3. Hopfield Nets - 1982
Research Paper: Neural Networks and Physical Systems with Emergent Collective Computational Abilities
4. Boltzmann Machines - 1984
Research Paper: Boltzmann Machines: Constraint Satisfaction Networks That Learn
5. Backpropagation Networks - 1985
Research Paper: Learning Internal Representations by Error Propagation
6. Reinforcement Learning - 1983
Research Paper: Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems
7. Convolutional Neural Networks - 1989
Research Paper: Backpropagation Applied to Handwritten Zip Code Recognition
8. Deep Convolutional Neural Networks - 2012
Research Paper: ImageNet Classification With Deep Convolutional Neural Networks
9. Variational Autoencoders - 2013
Research Paper: Auto-Encoding Variational Bayes
10. Generative Adversarial Networks - 2014
Research Paper: Generative Adversarial Nets
11. Diffusion Models - 2015
Research Paper: Deep Unsupervised Learning Using Nonequilibrium Thermodynamics
12. Interlude: Learning Sequences
13. Neural Probabilistic Language Model - 2000
Research Paper: A Neural Probabilistic Language Model
14. Transformer Networks - 2017
Research Paper: Attention Is All You Need
15. GPT-2 - 2019
Research Paper: Language Models Are Unsupervised Multitask Learners
16. Conclusion
16.1 Steam-Powered AI
16.2 Black Boxes
16.3 AI: Back to the Future
Appendices
A. Glossary
B. A Vector Matrix Tutorial
C. Maximum Likelihood Estimation
D. Bayes' Theorem
References
Index
ISBN: 9781068620003
ISBN-10: 1068620005
Published: 19th August 2024
Format: Paperback
Language: English
Number of Pages: 378
Audience: Professional and Scholarly
Publisher: Sebtel Press
Dimensions (cm): 27.94 x 20.96 x 1.98
Weight (kg): 0.85
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