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.

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