introduction
Introduction to this book
PART 1. Introduction to AI
Chapter 1. What is AI?
1. Definition of AI
2. The effects of AI
3.
Strong AI and Weak AI
4.
General Purpose AI (GPAI)
5.
AI Examples in Everyday Life
1) Streaming movie recommendations
2) Virtual assistant
3) Facial recognition
4) Email spam filtering
5) Machine translation
6) Generative AI
6.
summation
Chapter 2. A Brief History of AI
1.
Turing Test
2. The Birth of AI
3. The Golden Age of AI, 1956–1974
■ Logic Theorist
■ Symbolic AI and Subsymbolic AI
■ General Troubleshooter
■ ELIZA's "Chatbot"
4.
The First AI Winter, 1974-1980
5.
The Rise and Decline of Expert Systems, 1980s–Early 1990s
6.
The rise of neural networks
■ 1979: Fukushima's Neocognitron
■ 1989: LeCun's Lynette
■ 2009: Feifei Li's ImageNet
■ 2012: Krizhevsky's Alexnet
■ 2016: DeepMind's AlphaGo
■ 2022: OpenAI's ChatGPT
7.
summation
PART 2. AI TECHNOLOGY
Chapter 3.
Machine Learning Basics
1.
Basic Terms and Concepts
2.
Historical Context of Machine Learning
■ Factor #1: Increased AI Computing Power
■ Factor #2: Availability of training data
3.
Three Main Forms of Machine Learning
■ Map learning
■ Unsupervised learning
■ Reinforcement learning
4.
Other types of machine learning
5.
Overfitting and underfitting
6.
summation
Chapter 4.
Introduction to Deep Learning
1.
biological neurons
2.
artificial neurons
3.
neural network
4.
deep neural networks
5.
Deep neural network training
■ Backpropagation
■ Gradient descent
6.
Types of deep neural networks
■ Convolutional Neural Network (CNN)
■ Generative Adversarial Network (GAN)
■ Transformers and Large-Scale Language Models (LLMs)
7.
summation
PART 3. IMPORTANT CONSIDERATIONS IN AI
Chapter 5. Benefits and Concerns of AI
1.
Automation and Decision Making
■ Benefits within the workspace
■ Benefits within the home
■ The Burden of Ease
■ The so-called ontological threat
2.
Economic and financial impact
■ Economic and monetary benefits
■ Job Substitution and Economic Inequality
3.
Environmental and social impacts
■ Environmental and Social Sustainable Development Goals
■ Bias and Fairness
4.
Other benefits and concerns
■ Information and political propaganda
■ Safety, Security, and Privacy
■ Potential copyright infringement
5.
summation
Chapter 6. Myths and Truths about AI
1.
Myth #1: Savior or Destroyer?
2.
Myth #2: Superintelligence, Now or Never?
3.
Myth #3: Is it similar to the human brain or different?
4.
Myth #4: Objective or Biased?
5.
Myth #5: Economic Prosperity or Ruin?
6.
Myth #6: Trustworthy or Untrustworthy?
7.
Myth #7: Are security and privacy a threat or a defense?
8.
Myth #8: Is Perception Real? Is It Eternally Impossible?
9.
Myth #9: Process Efficiency or Defects?
10.
Myth #10: Utopia or Dystopia?
11.
summation
conclusion
Words of gratitude
supplement
■ Appendix 1: Detailed diagram of Frank Rosenblatt's perceptron
■ Appendix 2: Detailed diagram of artificial neurons
■ Appendix 3: Detailed diagram for a simple neural network example
■ Appendix 4: Diagram for a case of deep neural network
Glossary
Collection of footnotes