Artificial Intelligence Textbook with Reinforcement Learning

ARTIFICIAL INTELLIGENCE TEXTBOOK WITH REINFORCEMENT LEARNING

Copyright © 2025 Harry Katzan Jr.

All rights reserved. No part of this book may be used or reproduced by any means, graphic, electronic, or mechanical, including photocopying, recording, taping or by any information storage retrieval system without the written permission of the author except in the case of brief quotations embodied in critical articles and reviews.

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ISBN: 978-1-6632-7297-3 (sc)

ISBN: 978-1-6632-7299-7 (hc)

ISBN: 978-1-6632-7298-0 (e)

Library of Congress Control Number: 2025909556

iUniverse rev. date: 05/07/2025

For Margaret Now and Forever

Contents

Introduction

Prologue

PART ONE

INTRODUCTION TO ARTIFICIAL INTELLIGENCE

Chapter 1 Review of Artificial Intelligence

Chapter 2 Thinking About Artificial Intelligence and Society

Chapter 3 Ontology of Artificial Intelligence Architecture

PART TWO

THE SCIENCE OF ARTIFICIAL INTELLIGENCE

Chapter 4 Philosophical Basis of Artificial Intelligence

Chapter 5 Natural Systems

Chapter 6 Connectionism and the Brain

Chapter 7 AI Tools and Technologies

Chapter 8 AI Applications

Chapter 9 AI Topics

PART THREE

DEEP LEARNING AND NEURAL NETWORKS

Chapter 10 Introduction

Chapter 11 Neural Networks - Basic Concepts

Chapter 12 Neural Networks – How They Work

PART FOUR

THE CLOUD

Chapter 13 The Privacy of Cloud Computing

Chapter 14 Conspectus of Cloud Computing

Chapter 15 Cloud Computing Economics

Chapter 16 Ontological View of Cloud Computing

PART FIVE

SERVICE

Chapter 17 Understanding Services

Chapter 18 Service Systems

Chapter 19 Information Services

Chapter 20 Service Management

Chapter 21 Service Business

PART SIX

ARTIFICIAL INTELLIGENCE APPLICATIONS

Chapter 22 The Artificial Intelligence Application Domain

Chapter 23 The Big Picture of Applications

Chapter 24 A Good Look at ChatGPT

Chapter 25 Reinforcement Learning

About The Author

Introduction

This a book on Artificial Intelligence. It is both a text book and a reference book. It is designed for online learning. It is one of many books on the subject of artificial intelligence. There are more than 400 of them. It is the only one on strategy that is intended to reflect on how to go about doing AI for productive purposes. It also covers about what AI is already, but it is more than that. It answers the question Can a machine think? and most people are quite tired of that question. In fact, people are now more interested in how to do what we want to do.

In fact, AI is a inportant subject in our lives and here are two outstanding books that atune to that assertion: The Singularity is Nearer (2024) by Ray Kurzweil, and Artificial Intelligence: A Modern Approach (1995) by Stuart Russell and Peter Norvig. The writers are exceedingly intelligent, and the books are useful but not that easy to read. University research is equally noteworthy. But what about the strategy of adopting AI for the modern operational environment? How do you know what to do and how to do it.

Do you have to be a scientist or a mathematician to do the job? Absolutely not. Do you need to be a manager, a major CEO, or even the President of a country. Probably yes. But you need to have the information to do the job. This book gives you what you should do to implement AI in the organization and precisely what you need to know in order to do it.

When doing the job of implementing, should you be knowledgeable about precisly what has to be done? Of course. Do you personally have to do it? Not at all. Do you need information on related subjects, of course again. Do you have to read this book serially? Of course not; it is too detailed.

Will this be happy reading? On some topics, yes. I other sections, not so much. There are a lot of pages because the environment of AI is large and complicated. Many of the subjects covered in this book will be extremely useful in other areas of business and the organizaton.

One more thing. One of the leading topics in Artificial Intelligence research is Reinforcement Learning that serves as the basis for ChatGPT, similar systems, and a wide range of AI topics. Every reader and every student should be advised of its existent and be comfortable with its subject matter.

Prologue

This book covers the innformation necessary to develop a strategy for an AI based system, hhow to manage it, and how to sustain it. It is a difficult job job and will take fortitude to do it. It can be done and this book details the information necessary to do the job,

Here are the general topics included in this book in a meaningful order;

Introduction to Artificial Intelligence

Basic Elements of Artificial Intelligence

Modern Artificial Intellikgence

Neural Networks an Deep Learning

Generative Computing

Cloud Computing

Service Science

Artificial Intelligence Appplications

Renforcement Learning

Some items are strictly Artificial Intelligence. Other subjects contain information you will probably need to sustain an Artificial Intelligence implementation after it is developed and installed..

The secondary subjects are fruitful when considering what a computer can do. The real question should be is, Does a computer have to think like a human to do intelligent things, like play chess or solve a math problem or run a large organization like a corporation or a government? Well honestly looking at world events and modern business, it would appear that we human ‘thinking’ beings could use a little help. Perhaps, we aren’t smart enough to do what we are supposed to be doing. Here are a couple of examples of computer stuff and then you can go back to sleep.

Example number one. Research people have developed neural network programs that can learn to play chess all by themselves by playing against each other. Please note the operant word learn. These programs do not think like human being but can beat human beings, hands down. These programs make unbelievable extraneous moves but still win. Example two. A noble prize level biological research person came to me with the following request, I need to find the root of this equation to complete my work and the report/paper is due at the end of the week. You are a smart person, can you solve this problem? The root of an equation was required. The equation was indeed exceedingly complicated. I said, Let me look at it and I’ll try to get something for you by tomorrow morning. I know this guy had a picture of me slaving away after dinner. I just wrote a short Fortran program in twenty minutes that, using interval halving to determine where the function passed through the abscissa. Sorry, I shoould have said the x-axis. I sent it to the supercomputer and the next morning the result was on my desk. I transferred the result to his desk and went about the day. He later asked how I did it, and I explained my methodology. It did not use analytical mathematics. It used computer analysis. These examples are an example of so called ‘AI thinking.’

There are various opinions on the subject. Some say that thinking is an activity that is peculiar to human beings. Accordingly, machines cannot think. Although thought as something unique to humans may have been in the minds of early philosophers when they first considered the subject of thinking; intelligence, this does not really define the activity. So, the name Artificial Intelligence is particularly appropriate.

Others maintain that a machine is thinking when it is performing activities that normally require thought when performed by human beings. Thus, adding 2+3 must be a form of thinking. To continue, some psychologists have defined intelligence in the following simple way: intelligence is what an intelligence test measures. In light of the preceding section on information systems, all that needs to be done is to feed enough information into an information system and to develop an appropriate query language, and the result is an intelligent machine. This line of reasoning also skirts a clear definition. Perhaps, it is a waste of time to worry about precise definitions, but the fact remains that computers are doing some amazing things - such as playing chess, guiding robots, controlling space vehicles, recognizing patterns, proving theorems, and answering questions - and that these applications require much more than the conventional computer program. Richard Hamming, developer of the prestigious Hamming code for error detection and correction in computers, gives a definition of intelligent behavior that may be useful here:

The ability to act in subtle ways when presented with a class of situations that have not been exhaustively analyzed in advance, but which require rather different combinations of responses if the result in many specific cases is to be acceptable.

Artificial Intelligence is an important subject because it may indicate the direction in which society is moving. Currently, machines are used for two reasons: (1) The job cannot be done by a human being, and (2) The job can be performed more economically by machine. To this list, another reason must be added: some jobs are simply too dull to be done by humans, and it is desirable from a social point of view to have such jobs done by machine. This requires a greater number of ‘intelligent’ machines, since people seem to be finding more and more work they consider to be dull and routine.

Here are two items of before you get started with the book:

Artificial general intelligence (AGI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of some AI research and is a common topic in science fiction and future studies. (Author unknown.)

The singularity is the hypothesis that the invention of artificial super intelligence (ASI) will abruptly trigger runaway technical growth, resulting in unfathomable change to human civilization. (Author is Ray Kurzweil, mentioned above.)

Now here is the objective of this book, and you should like it. ‘It’ being the fact that AI will eventually be an important part of our lives. We don’t know exactly how all of this will take place. We don’t know how it will affect us and what it will look like. One point is clear, when it does appear on the scene, we should be prepared to accept it, analyze it, and control the environment in which it operates. This book covers AI itself (briefly), and then the areas in which it will operate such as cybersecurity, cloud computing, and service management. The information given is definitely not the final result in any form, but it is a start to a way of looking at it. Universities are an important part of this endeavor, as are business organizations. No singlle university, business, or product will control the future of AI, and also the leaders will change as proper adoption takes place. Some businesses will have to rely on universities and some products will be dependent on helper products.

Generative Artificial Inttelligence is currently a very popular topic and applications using Deep Learning are in demand. Reinforcement Learning, a form of Deep Learning, is also introduced in the book.

PART ONE

Introduction to Artificial Intelligence

CHAPTER

1

Review of Artificial Intelligence

Fundamental Artificial Intelligence

The question, Can a machine think? is one that has been debated for some time now and the question is not likely to be answered in this book. However, the subject is fruitful when considering What a computer can do.

There are various opinions on the subject. Some say that thinking is an activity that is peculiar to human beings. Accordingly, machines cannot think. Although thought as something unique to humans may have been in the minds of early philosophers when they first considered the subject of thinking and intelligence, this does not really define the activity. Others maintain that a machine is thinking when it is performing activities that normally require thought when performed by human beings. Thus, adding 2+3 must be a form of thinking. To continue, some psychologists have defined have defined intelligence in the following simple way: intelligence is what an intelligence test measures. All that needs to be done is to feed enough information into an information system and to develop an appropriate query language, and the result is an intelligent machine. This line of reasoning also skirts a clear definition. Perhaps, it is a waste of time to worry about precise definitions, but the fact remains that computers are doing some amazing things - such as playing chess, guiding robots, controlling space vehicles, recognizing patterns, proving theorems, and answering questions - and that these applications require much more than the conventional computer program. Richard Hamming, developer of the prestigious Hamming code for error detection and correction in computers, gives a definition of intelligent behavior that may be useful here:

The ability to act in subtle ways when presented with a class of situations that have not been exhaustively analyzed in advance, but which require rather different combinations of responses if the result in many specific cases is to be acceptable.

Artificial Intelligence (AI) is an important subject because it may indicate the direction in which society is moving. Currently, machines are used for two reasons: (1) The job cannot be done by a human being, and (2) The job can be performed more economically by a machine. To this list, another reason must be added: some jobs are simply too dull to be done by humans, and it is desirable from a social point of view to have such jobs done by machine. This requires a greater number of intelligent machines, since people seem to be finding more and more work they consider to be dull and routine. Here are two items of interest before we starting somewhere with introducation:

Artificial general intelligence (AGI) is the intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of some AI research and is a common topic in science fiction and future studies. (Author unknown.)

The singularity is the hypothesis that the invention of artificial super intelligence (ASI) will abruptly trigger runaway technical growth, resulting in unfathomable change to human civilization. (Author: Ray Kurtzweil in the introduction.)

It is possible to approach Artificial Intelligence from two points of view. Both approaches make use of programs and programming techniques. The first approach is to investigate the general principles of intelligence. The second is to study human thought, in particular.

Those persons engaged in the investigation of the principles of intelligence are normally charged with the development of systems that appear to be intelligent. This activity is commonly regarded as artificial intelligence, which incorporates both engineering and computer science components.

Those same persons engaged in the study of human thought attempt to emulate human mental processes to a lesser or greater degree. This activity can be regarded as a form of computer simulation, such that the elements of a relevant psychological theory are represented in a computer program. The objective of this approach is to generate psychological theories of human thought. The discipline is generally known as Cognitive Science.

In reality, the differences between artificial intelligence and cognitive science tend to vary between not so much and quite a lot - depending upon the complexity of the underlying task. Most applications, as a matter of fact, contain elements from both approaches.

The Scope of AI

It is possible to zoom in on the scope of AI by focusing on the processes involved. At one extreme, the concentration is on the practicalities of doing AI programming, with an emphasis on symbolic programming languages and AI machines. In this context, AI can be regarded as a new way of doing programming. It necessarily follows that hardware/software systems with AI components have the potential for enhanced end-user effectiveness.

At the other extreme, AI could be regarded as the study of intelligent computation. This is a more grandiose and encompassing focus with the objective of building a systematic and encompassing focus with the objective of building a systematic theory of intellectual processes - regardless if they model human thought or not.

It would appear, therefore, that AI is more concerned with intelligence in general and less involved with human thought in particular. Thus, it may be contended that humans and computers are simply two options in the genus of information processing systems.

The Modern Era of Artificial Intelligence

The modern era of artificial intelligence effectively began with the summer conference at Dartmouth College in Hanover, New Hampshire in 1956. The key participants were Shannon from Bell Labs, Minsky from Harvard (later M.I.T.), McCarthy from Dartmouth (later M.I.T. and Stanford), and Simon from Carnegie Tech (renamed Carnegie Mellon). The key results from the conference were twofold: The question, Can a machine think? is one that has been debated for some time now and the question is no likely to be answered in this book. However, the subject is fruitful when considering What a computer can do.

•It legitimized the notion of AI and brought together a raft of piecemeal research activities.

•The name Artificial Intelligence was coined and the name more than anything had a profound influence of the future direction of artificial intelligence.

The stars of the conference were Simon, and his associate Allen Newell, who demonstrated the Logic Theorist - the first well-known reasoning program. They preferred the name, Complex Information Processing, for the new fledging science of the artificial. In the end, Shannon and McCarthy won out with the zippy and provocative name, artificial intelligence. In all probability, the resulting controversy surrounding the name artificial intelligence served to sustain a certain critical mass of academic interest in the subject - even during periods of sporadic activity and questionable results.

One of the disadvantages of the pioneering AI conference was the simple fact that an elite group of scientists was created that would effectively decide what AI is and what AI isn’t, and how to best achieve it. The end result was that AI became closely aligned with psychology and not with neurophysiology and to a lesser degree with electrical engineering. AI became a software science with the main objective of producing intelligent artifacts. In short, it became a closed group, and this effectively constrained the field for a large degree.

In recent years, the direction of AI research has been altered somewhat by an apparent relationship with brain research and cognitive technology, which is known as the design of joint human-machine cognitive systems. Two obvious fallouts of the new direction are the well-known Connection Machine, and the computer vision projects at the National Bureau of Standards in their United States. That information is somewhat out of date, but the history gives some insight into what AI is today and where it will be heading.

Early Work on the Concept of Artificial Intelligence

The history of AI essentially goes back to the philosophy of Plato, who wrote that. All knowledge must be state able in explicit definitions which anyone could apply, thereby eliminating appeals to judgment and intuition. Plato’s student Aristotle continued in this noble tradition in the development of the categorical syllogism, which plays an important part in modern logic.

The mathematician Leibnitz attempted to quantify all knowledge and reasoning through an exact algebraic system by which all objects are assigned a unique characteristic number. Using these characteristic numbers, therefore, rules for the combination of problems would be establishes and controversies could be resolved by calculation.

The underlying philosophical idea was conceptually simple: Reduce the whole of human knowledge into a single formal system. The notion of formal representation has become the basis of AI and cognitive science theories since it involves the reduction of the totality of human experience to a set of basoic elements that can be glued together in various ways.

To sum up, the philosophical phenomenologists argue that it impossible to subject pure phenomena - i.e., mental acts which give meaning to the world - to formal analysis. Of course, AI people do not agree. They contend that there is no ghost in the machine, and this is meant to imply that intelligence is a set of well-defined physical processes.

The discussion is reminiscent of the mind/brain controversy and it appears that the AI perspective is that the mind is what the brain does. Of course, the phenomenologists would reply that the definition of mind exists beyond the physical neurons; it also incorporates the intangible concepts of what the neurons do.

Accordingly, strong AI is