Given the exponential growth of Artificial Intelligence (AI) over the past few decades, AI and its related applications have become part of daily life in ways that we could never have dreamt of only a century ago. Our routines have been changed beyond measure by robotics and AI, which are now used in a vast array of services. Though AI is still in its infancy, we have already benefited immensely. This book introduces readers to basic Artificial Intelligence concepts, and helps them understand the relationship between AI and daily life.

In the interest of clarity, the content is divided into four major parts. Part I (AI Concepts) presents fundamental concepts of and information on AI; while Part II (AI Technology) introduces readers to the five core AI Technologies that provide the building blocks for various AI applications, namely: Machine Learning (ML), Data Mining (DM), Computer Vision (CV), Natural Languages Processing (NLP), and Ontology-based Search Engine (OSE). In turn, Part III (AI Applications) reviews major contemporary applications that are impacting our ways of life, working styles and environment, ranging from intelligent agents and robotics to smart campus and smart city projects. Lastly, Part IV (Beyond AI) addresses related topics that are vital to the future development of AI. It also discusses a number of critical issues, such as AI ethics and privacy, the development of a conscious mind, and autonomous robotics in our daily lives.

Dr. Raymond Lee is the founder of Quantum Finance Forecast System QFFC, and currently an Associate Professor and IT Director at United International College (UIC). With more than 20 years of experience in IT consultancy and research into AI, Chaotic Neural Networks, Intelligent Fintech Systems, Quantum Finance, and Intelligent E-Commerce Systems, he has published over 100 publications and seven textbooks and research monographs on chaotic neural networks, AI-based fintech systems, intelligent agent technologies, chaotic cryptosystems, ontological agents, neural oscillators, biometrics, and weather simulation and forecasting systems. Dr. Lee's latest book, Quantum Finance: Intelligent Forecast and Trading Systems, published by Springer in Nov 2019, serves as a textbook for a new course on Quantum Finance at the UIC. He has successfully commercialized his AI fintech inventions in the business sectors in China and Hong Kong.

From 2012 to 2017, Dr. Lee served as Group CTO/Chief Analyst at Leanda Investment Group, China, where he applied his AI fintech invention - the Quantum Finance Forecast System - to major commodities in China for 1000+ investors. In Mar 2017, he set up the Quantum Finance Forecast Center (QFFC, http://qffc.org), a non-profit, AI fintech R&D and worldwide financial forecast center where traders and individual investors around the globe can access (free of charge) 129 financial forecasts based on the latest AI, chaotic neural network and quantum field theory technologies. QFFC currently has over 10,000 registered members, which include professional traders, quants, and independent investors from major funding houses and financial institutions.

Part I AI Concepts

As the introductory section, this section will discuss the history and basic concepts of AI.

Chap 1 AI and Human Civilization From Greek Mythology to AI Robotics

Chap 2 AI Fundamentals

2.1 Definition of AI

2.2 A Brief History of AI

2.3 Turing Test and AI

2.4 Strong AI vs Weak AI

2.5 Main components of AI

2.6 Case Study: Does Turing Test Really Work to Test for AI?

Part II AI Technologies

This section discusses FIVE core AI Technologies which provide the building blocks of various different kind of AI applications, they are: Machine Learning (ML), Data Mining (DM), Computer Vision (CV), Natural Languages Processing (NLP), Ontology-based Search Engine (OSE).

Chap 3 Machine Learning (ML)

3.1 What is Machine Learning (ML)?

3.2 Supervised Learning (SL)

3.3 Unsupervised Learning (UL)

3.4 Reinforcement Learning (RL)

3.5 Case Study How can we improve our memory by using Reinforcement Learning?

Chap 4 Data Mining (DM) & Big Data (BD)

4.1 What is Knowledge?

4.2 Data Mining, Big Data and Knowledge Discovery

4.3 Traditional Data Mining Technology

4.4 AI-based Data Mining Technology

4.5 Case Study How to mine our purchase habit using Data Mining Technology?

Chap 5 Computer Vision (CV)

5.1 Computer Vision vs Human Vision

5.2 3 Levels of Computer Vision Figure-ground Segmentation, Pattern Recognition, Active Vision

5.3 Active Vision and Robotics

5.4 Computer Vision Technology

5.5 Case Study How AI-based Facial Recognition System works?

Chap 6 Natural Language Processing (NLP)

6.1 Human Language and Intelligence

6.2 Natural Language Processing in AI

6.3 Speech Recognition and Voice Synthesis

6.4 Machine Translation

6.5 Case Study English Language Tutoring Robots

Chap 7 Ontological-based Search Engine (OSE)

7.1 Human Knowledge and Ontology

7.2 How Search Engine Works?

7.3 Traditional Search Engine vs. Ontological-based Search Engine

7.4 Ontological-based Search Engine and Semantic Web

7.5 Case Study Turing Test and Ontology Search Engine

Part III AI Applications