Chapter 1: The Basics of AI

1-1 What is AI? Defining a Vague Concept
1-2 Rapid development and stagnation of AI
1-3 The First AI Boom: "The Beginning and Potential of AI"
1-4 The Second AI Boom: "How to Handle Data and Knowledge"
1-5 The Third AI Boom: "A Leap Forward in Machine Learning"
1-6 How did connectionism develop?
1-7 How did symbolism develop?
1-8 Relationship between AI technology and products

Chapter 2: Basic Structure of AI and Programs

2-1 Challenges and Solutions for AI
2-2 Types and Overview of Search Algorithms
2-3 Types and Overview of Sorting Algorithms
2-4 Types and Overview of Encryption Algorithms
2-5 Architecture that becomes the blueprint for AI
2-6 The existence of AI that works independently
AI's mindset and decision-making techniques according to the 2-7 Rule
2-8 AI's Thinking Methods and Decision-Making Techniques Tailored to the Goal
Learning AI's Thinking Methods and Decision-Making Techniques from Case Studies 2-9
2-10 Approaches to Thinking Flexiblely

Chapter 3: Data Handling in AI

3-1 Information required by AI
3-2 When handling data, what is easy to handle and what is difficult to handle
3-3 Approaches to conveying knowledge and concepts
3-4 Data Science and Statistics in AI
3-5 Analyze data and find value
3-6 Analysis Methods to Know ① - Finding Relationships in Data
3-7 Analysis Methods to Know ② - Divide the data and then analyze it.
3-8 Theory used to understand the ambiguous world ① - Representation of information~
3-9 Theory used to understand the ambiguous world ② - Predicting the future
3-10 Difficulties in Processing Data Correctly
3-11 Creating AI concepts from knowledge and statistics

Chapter 4: Technologies Related to Machine Learning

4-1 Create a judgment criterion from statistics
4-2 Machine Learning Using Networks
4-3 Most Common Learning Styles
4-4 Learning Styles with High Potential
4-5 Learning styles that adapt to the real world
Reinforcement learning that achieves 4-6 development
4-7 Two theorems illustrating the challenges of machine learning
4-8 Learning methods similar to reinforcement learning
4-9 Improving Machine Learning Efficiency ① - Supplementing Learning Data
4-10 Improving Machine Learning Efficiency ② - Dedicating Learning Models
4-11 Improving Machine Learning Efficiency ③ - Scientific Learning Measures

Chapter 5 Deep Learning

5-1 What is a neural network?
5-2 The Road to Deep Learning
5-3 Deep Learning's Feature Extraction Capability
5-4 Deep neural networks strong in image and speech recognition
5-5 Deep neural networks strong in language processing and time series processing
5-6 Applications of Recurrent Neural Networks
5-7 GANs that complement the shortcomings of deep learning
5-8 Information handled by neural networks
How to represent the meaning of 5-9 words in numbers
5-10 Understanding the Neural Network's Mindset
5-11 Establishing a Deep Learning Environment
5-12 Machine Learning Methods Changed by Deep Learning

Chapter 6: Various AI and Their Applications

6-1 Image Recognition: Evolving from Images to Videos
6-2 AI's Communication Methods
6-3 Transformer and writing sentences that have been changed from a huge database
6-4 Technology required for textualization of speech
6-5 Data analysis combining video, audio, and multiple pieces of information
6-6 Learn human creative techniques
6-7 Learn how to use the human body
6-8 Platformizing AI
6-9 Distributed and Spreading AI
6-10 Execute the given task ① - Perception of autonomous driving
6-11 Execute the given task ② - Judgment and operation of autonomous vehicles
6-12 Game AI that promotes the development and growth of AI
6-13 Strategies that vary depending on the information visible
6-14 Game theory explaining human judgment criteria
6-15 Collaboration between AI and humans in both private and business life

Chapter 7: AI Evolving in Harmony with Other Fields

7-1 Medical AI ① - Assisting the Medical Field
7-2 Medical AI ② - AI Utilization in Difficult Areas
7-3 Medical AI ③ - Organizing the Data Necessary for Growth
7-4 Fintech ① - Automation of Data Analysis
7-5 Fintech ② - Customer Response and Data Management
7-6 Robotics ① - Robots that expand their scope of activity
-7 Robotics ② - Robots working in human society
7-8 Autonomous Vehicle ① - Level 0-3 with Humans Involved
7-9 Autonomous Vehicle ② - Level 4-5: Leaving Everything to the Driver
7-10 Drones and Unmanned Aerial Vehicles, Applications in Military Technology
7-11 Hardware ① - A New Computer That Will Change AI
7-12 Hardware ② - Two Types of Quantum Computers
7-13 RPA ① - Work Efficiency Gaining Attention
7-14 RPA ② - Automation Areas Expanding with AI

Chapter 8: Various Discussions on AI

8-1 Classification of artificial intelligence that must be known as a prerequisite
8-2 AI's Language Understanding ① - Can Intelligence Be Measured Through Speech?
8-3 AI's Language Understanding ② - The Wall Between Understanding Meaning and Reality
8-4 Problem of falling into a state of inability to judge
8-5 AI's Physicality ① - A Body for Approaching Humans
8-6 AI's Physicality ② - Learning sensations without having a body
8-7 Biases in AI Under Human Influence
8-8 AI-powered information control for humans
8-9 The Black Box-like, Incomprehensible Way of Thinking of AI
8-10 AI Ethics ① - The Difficult Structure and Operation of AI
8-11 AI Ethics ② - Who Protects Ethics?
8-12 Monopoly and Openness of AI

Chapter 9: AI of the Future

9-1 AI continues to grow in various forms
9-2 AI Outlook ① ~Singularity and Optimism~
9-3 AI's Prospects ② ~Winter Era and Pessimism~
9-4 The Changing Way Humans Work
9-5 Methods and feasibility of reproducing humans
9-6 Humans Follow AI's Advancement
9-7 The line between humans and AI is blurring, AI, VR, and avatars
9-8 Is AI an intelligent life form?