Table Of ContentArtificial Intelligence
in a Throughput Model
Some Major Algorithms
Waymond Rodgers
Chair Professor, University of Hull, UK
University of Texas El Paso, Texas, USA
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A SCIENCE PUBLISHERS BOOK
A SCIENCE PUBLISHERS BOOK
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Preface
“[Artificial Intelligence] is going to change the world more than anything
in the history of mankind. More than electricity.”
— AI oracle and venture capitalist Dr. Kai-Fu Lee, 2018
In 1964, Isaac Asimov envisioned the 2014 World’s Fair for The New York
Times. He was correct about the smartphone, self-driving cars and the
Keurig machine; however, he was not able to predict advanced battery
technology and space colonization. Foresight is challenging, and is only
becoming more and more so.
Artificial Intelligence (AI) is the next major disruptor in the way we
live, learn, work and adopt to different situations. Disruptors represent
a person, place or thing that prevents something, especially a system,
process or event, from continuing as usual or as expected. Our houses,
our cars, our toasters, all of seem to be teeming, even overflowing with
intelligence, like some huge fungus gone amuck. Artificial Intelligence
is here to stay, and society needs it, right now! Artificial Intelligence is
changing the world each day. At one time, a domain of science fiction,
today many of our systems are powered by Artificial Intelligence.
The field of Artificial Intelligence has awed researchers and users
alike. Artificial Intelligence is the intelligence of machines and a subset
of computer science and cognitive science. Fundamental challenges of
Artificial Intelligence embrace such features as reasoning, knowledge,
planning, learning, communication, perception and the capability to move
and manipulate objects.
This book addresses the promise of Artificial Intelligence that is
enhancing our lives. Artificial Intelligence-based systems are now
outshining medical specialists in diagnosing certain diseases. The
implementation of Artificial Intelligence in the financial system is
magnifying access to credit to borrowers whose loan applications were
once rejected. In addition, automated hiring systems have the potential
iv Artificial Intelligence in a Throughput Model: Some Major Algorithms
to evaluate candidates on the basis of their authentic qualifications as
opposed to characteristics such as age or appearance that oftentimes
mislead decision makers from making the correct decision.
One of the many advantages of utilizing Artificial Intelligence and
machine learning is that it has the ability to ingest huge amounts of data,
often in real-time. Further, it can take that data and begin to scrutinize
it based upon organizational necessities, conditions and constraints. In
addition, it can bring about those necessities, conditions and constraints
based on the data an organization owns.
Moreover, many physical and behavioral biometric technologies
such as fingerprint, facial recognition, voice identification, etc., have
enhanced the level of security substantially. Governments and corporates
have embraced these technologies to increase customer satisfaction.
Nevertheless, the current state of biometrics still faces challenges to lessen
criminal and terrorist activities as well as other digital-based financial
frauds. This is especially the case when individuals and organizations are
faced with selecting the correct algorithm to a problem. To overcome this
state of affairs, the market undertakes a host of research and development
programmes to assimilate biometrics with artificial intelligence in decision-
making modeling. The advanced software algorithm platform of Artificial
Intelligence processes information offered by biometric technology
to detect and prevent dubious activities in a bid to confront cyber and
physical crimes in the global and local communities. This development has
provided an expanded growth opportunity for the biometrics technology,
given that the technology is set to increase the security and internal control
operations many folds.
This book provides an overview of the various Artificial Intelligence
techniques, biometric technologies, decision-making algorithms and
the subsequent market expansion opportunities. Further, it proposes
a Throughput Model, which draws from computer science, economics
and psychology to model perceptual, informational sources, judgmental
processes and decision choice algorithms. This approach provides how
huge data and biometrics might be implemented to reduce risks to
individuals and organizations, especially when dealing with digital-based
mediums.
The book also examines the ethics behind Artificial Intelligence.
That is, how machine learning, neural networks, and deep learning
technology are positioned today for many individual and organizational
uses, including self-driving cars, online recommendations, search
engines, handwriting recognition, computer vision, online ad serving,
pricing, prediction of equipment failure, credit scoring, fraud detection,
Preface v
OCR (optical character recognition), spam filtering, etc. Therefore, this
book addresses ethical consideration directed at the growing ubiquity of
machine learning, neural networks, and deep learning in organizations.
This particular issue is essential in order to understand what and how to
mitigate human cognitive biases and heuristics into Artificial Intelligence
technology.
Acknowledgements
First and foremost, my thanks to the Almighty for His blessings
throughout the course of my research work without which I would
not have been able to complete this research successfully. I also
acknowledge deeply all those who have helped me to put these ideas well
above the level of simplicity into something concrete.
Further, I would like to thank my students. Learning is a collaborative
activity when it is happening at its best. We work together using each
other’s strengths to build our own challenges, developing our thinking
and problem solving skills. Therefore, the relationship we develop with
our students at every age is one that is to be respected, nurtured and
admired.
Last but not the least, I shall be forever indebted to all those who have
been with me throughout the course of this research but whose names
I am not mentioning individually.
Contents
Preface iii
Acknowledgements vi
1. Introduction to Artificial Intelligence and Biometrics Applications 1
2. Prelude to Artificial Intelligence: Decision-Making Techniques 34
3. Artificial Intelligence: Six Cognitive Driven Algorithms 58
4. Survey of Biometric Tools 77
5. Ethical Issues Addressed in Artificial Intelligence 113
6. Cyber Securities Issues: Fraud and Corruption 148
7. Artificial Intelligence and Biometrics Examples 175
8. Conclusions 199
Index 207
About the Author 211
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1
Introduction to Artificial Intelligence
and Biometrics Applications
Artificial Intelligence is the science and engineering of making intelligent
machines, especially intelligent computer programs.
—John McCarthy, Father of Artificial intelligence
“Man is only great when he acts from passion.”
—Benjamin Disraeli (former Prime Minister of Britain)
Artificial intelligence (AI) is here for stay. For individuals and organizations,
Artificial Intelligence is a disruptor in the way we live, learn, work and
adopt to different situations. Disruptors represent a person, place or thing
that prevents something, especially a system, process, or event, from
enduring as customary or as anticipated in the future. We are right in the
midst of the information revolution. Although it is an extraordinary time
and place to be in, there are caveats that come along with it. Having a
machine tell you how long your commute will be, what music you should
listen to, and what content you would likely engage with are all relatively
innocuous examples.
Artificial Intelligence is presently used as a tool to assist people. It
is adopted as point solutions across a wide array of functions such
as personal digital assistant, email filtering, search, fraud prevention,
engineering, marketing models, digital distribution, video production,
news generation, play and game-play analytics, customer service,
financial reporting, marketing optimization, energy cost management,
pricing, inventory, enterprise applications, etc. Artificial Intelligence is also
integrated into biometrics tools such as iris recognition, voice recognition,
facial recognition, content classification, gait, and natural language.
