About the 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.

Contents:

1.    Computing Hardware

2.    The Integrated Circuit

3.    Software

4.    Open Source Software

5.    Expert Systems

6.    Inverted Decision Tree

7.    Deep Blue

8.    Jeopardy and Miss Debater

9.    The Perceptron

10. Parameterization

11. Gradient Descent and Backpropagation

12. The Cross-Entropy Cost Function

13. Convolutional Neural Networks

14. Imagenet and Model Fitting

15. Markov Chain Monte Carlo Simulation

16. Reinforcement Learning

17. AlphaGo

18. Game Theory

19. Predictive Analytics

20. Support Vector Machines

21. Top-Down Speech Recognition

22. Bottom-Up Speech Recognition

23. Speech Synthesis

24. RBMs, GANs, and LFCF

25. LLMs and GPTs

26. Massive Parallel Processing and Supercompuers

27. Quantum Computing

28. Industrial Robots: Robot Physicians

29. Autonomous Vehicles

30. Exoplanets/Exomoon Astronomer

31. Protein Folding

32. Intelligence

33. The AI Singularity

About the Authors: