
Artificial Intelligence
An Introduction to the Big Ideas and their Development
- 338 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Artificial Intelligence
An Introduction to the Big Ideas and their Development
About this book
Artificial Intelligence: An Introduction to Big Ideas and their Development, Second Edition guides readers through the history and development of artificial intelligence (AI), from its early mathematical beginnings through to the exciting possibilities of its potential future applications. To make this journey as accessible as possible, the authors build their narrative around accounts of some of the more popular and well-known demonstrations of artificial intelligence, including Deep Blue, AlphaGo and even Texas Hold'em, followed by their historical background, so that AI can be seen as a natural development of the mathematics and computer science of AI. As the book proceeds, more technical descriptions are presented at a pace that should be suitable for all levels of readers, gradually building a broad and reasonably deep understanding and appreciation for the basic mathematics, physics, and computer science that is rapidly developing artificial intelligence as it is today. Features
- Only mathematical prerequisite is an elementary knowledge of calculus.
- Accessible to anyone with an interest in AI and its mathematics and computer science.
- Suitable as a supplementary reading for a course in AI or the History of Mathematics and Computer Science in regard to artificial intelligence.
New to the Second Edition
- Fully revised and corrected throughout to bring the material up-to-date.
- Greater technical detail and exploration of basic mathematical concepts, while retaining the simplicity of explanation of the first edition.
- Entirely new chapters on large language models (LLMs), ChatGPT, and quantum computing.
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Information
Table of contents
- Cover
- Half-Title
- Series
- Title
- Copyright
- Contents
- Preface to the Second Edition
- Preface to the First Edition
- Prologue
- About the Authors
- CHAPTER 1 • Computing Hardware
- CHAPTER 2 • The Integrated Circuit
- CHAPTER 3 • Software
- CHAPTER 4 • Open-Source Software
- CHAPTER 5 • Expert Systems
- CHAPTER 6 • Inverted Decision Trees
- CHAPTER 7 • Deep Blue
- CHAPTER 8 • Jeopardy and Miss Debater
- CHAPTER 9 • The Perceptron
- CHAPTER 10 • Parameterization
- CHAPTER 11 • Gradient Descent and Backpropagation
- CHAPTER 12 • The Cross-Entropy Cost Function
- CHAPTER 13 • Convolutional Neural Networks
- CHAPTER 14 • Imagenet and Model Fitting
- CHAPTER 15 • Markov Chain Monte Carlo Simulation
- CHAPTER 16 • Reinforcement Learning
- CHAPTER 17 • AlphaGo
- CHAPTER 18 • Game Theory
- CHAPTER 19 • Predictive Analytics
- CHAPTER 20 • Support Vector Machines
- CHAPTER 21 • Top-Down Speech Recognition
- CHAPTER 22 • Bottom-Up Speech Recognition
- CHAPTER 23 • Speech Synthesis
- CHAPTER 24 • RBMs, GANs, and LFCF
- CHAPTER 25 • LLMs and GPTs
- CHAPTER 26 • Massive Parallel Processing and Supercomputers
- CHAPTER 27 • Quantum Computing
- CHAPTER 28 • Industrial Robots: Robot Physicians
- CHAPTER 29 • Autonomous Vehicles
- CHAPTER 30 • Exoplanet/Exomoon Astronomer
- CHAPTER 31 • Protein Folding
- CHAPTER 32 • Intelligence
- CHAPTER 33 • The AI Singularity
- AFTERWORD
- APPENDIX
- BIBLIOGRAPHY
- INDEX