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Human Level Artificial Intelligence -- second edition

By Mitchell Kwok


Table of contents:


Introduction and Summary of Invention

Chapter 1:  Review of human level artificial intelligence

Chapter 2:  Self-generating computer programs inside the robotís conscious

Chapter 3:  Forming intelligent pathways to think and act

Chapter 4:  Managing multiple-hierarchical tasks and solving task interruptions

Chapter 5:  Searching for data in the robotís brain

Chapter 6:  Learning, configuring and storing data in memory (part 1)

Chapter 7:  Learning, configuring and storing data in memory (part 2)

Chapter 8:  Decision making and planning tasks

Chapter 9:  Accomplishing various complex tasks in the real world (part 1)

Chapter 10:  Accomplishing various complex tasks in the real world (part 2)

Chapter 11:  Playing videogames

Chapter 12:  Other topics

Chapter 13:  Self-awareness

Chapter 14: Super Intelligent Robots



If you read Mit and Stanford University's books on Artificial Intelligence, they seperate AI into topics. These topics include: NLP, logical induction and deduction, decision making, recursive planning, hierarchical planning, predicate calculus, semantic networks, neural networks, decision trees, searching algorithms, and generating common sense knowledge. However, they don't combine these topics together to form one cohesive AI program that can do everything. What is special about this book is that I combine all Artificial Intelligence topics together and present an AI program that is comparable to intelligence of a human brain, with college-level intelligence. Capabilities of said AI program includes: thinking, making decisions, generating common sense knowledge, logical inferencing, solving problems, understanding natural language, learning a new skill by itself, practicing said skill, and making decisions using said skill, and so forth.  

Keep in mind the data structure of my invention, a digital human brain, is very very long. In order to understand how my robot works, every chapter has to be read. If you skip a chapter you might miss something important. All major topics in AI is covered in this book.  

All knowledge and skills from the robot is learned through teachers in school. This includes: decision making, predicting the future, generating common sense knowledge, mobility skills, induction/deduction, solving problems, understanding natural language, and so on. For example, there are no semantic networks or decision trees in my robot's data structure to make decisions. The knowledge of making decisions is learned through teachers in school. Teachers/parents teach the robot how to make decisions.  

Humans learn knowledge from kindergarden to college. Old information is used to learn new information and knowledge from the robot's brain builds on top of each other to form complex intelligence. MIT and stanford university call this ability: learning information in terms of a bootstrapping process.  

Below is a simple example of how my robot learns complex subject matters like English grammar. In FIG. 16A, first, the robot learns to write words and understand basic grammar rules (V1-V2). Next, he takes those skills to write a sentence (V3). Then he takes the knowledge of writing a sentence to write a paragraph (V4). Finally, the robot takes previous knowledge to write a book (V5).


A robot must go to school from kindergarten to college to learn knowledge. If the robot graduates from college with a difficult degree, like an engineering degree or computer science degree, then he has acheived human level artificial intelligence. An art degree doesn't count.  

Another major topic in this book is the robot's conscious (aka the robot's mind), which is the voice in his head that controls all decision making. This book will invest a lot of time discussing what the human mind is and how it works.



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