This book describes the Throughput Model methodology that can enable individuals and organizations to better identify, understand, and use algorithms to solve daily problems. The Throughput Model is a progressive model intended to advance the artificial intelligence (AI) field since it represents symbol manipulation in six algorithmic pathways that are theorized to mimic the essential pillars of human cognition, namely, perception, information, judgment, and decision choice. The six AI algorithmic pathways are (1) Expedient Algorithmic Pathway, (2) Ruling Algorithmic Guide Pathway, (3) Analytical Algorithmic Pathway, (4) Revisionist Algorithmic Pathway, (5) Value Driven Algorithmic Pathway, and (6) Global Perspective Algorithmic Pathway.
As AI is increasingly employed for applications where decisions require explanations, the Throughput Model offers business professionals the means to look under the hood of AI and comprehend how those decisions are attained by organizations.
Key Features:
- Covers general concepts of Artificial intelligence and machine learning
- Explains the importance of dominant AI algorithms for business and AI research
- Provides information about 6 unique algorithmic pathways in the Throughput Model
- Provides information to create a roadmap towards building architectures that combine the strengths of the symbolic approaches for analyzing big data
- Explains how to understand the functions of an AI algorithm to solve problems and make good decisions
- informs managers who are interested in employing ethical and trustworthiness features in systems.
Dominant Algorithms to Evaluate Artificial Intelligence: From the view of Throughput Model is an informative reference for all professionals and scholars who are working on AI projects to solve a range of business and technical problems.
Author(s): Waymond Rodgers
Publisher: Bentham Science Publishers
Year: 2022
Language: English
Pages: 327
City: Singapore
Cover
Title
Copyright
End User License Agreement
Contents
Preface
Acknowledgements
Introduction to Artificial Intelligence and Algorithms
INTRODUCTION
AI SUB AREAS: NATURAL LANGUAGE PROCESSING, MACHINE LEARNING AND DEEP LEARNING
AI ALGORITHMS IMPACT ON SOCIETY
THE ROOTS OF MACHINE LEARNING BIAS
PROPERTIES OF ALGORITHMS
THROUGHPUT MODEL
FUTURE AI OPPORTUNITIES FOR SOCIETY
Financial Robots
Where are we headed?
CONCLUSION
REFERENCES
Understanding Throughput Decision-making Modeling
INTRODUCTION
APPLICATION OF THROUGHPUT MODELS-CREATING A TRUSTED ENVIRONMENT USING ALGORITHM PATHS
Stochastic Learning
FOUR FORMS OF AI
Reactive Machines
Limited Memory
Theory of Mind
Self-awareness
INTRODUCTION OF THE THROUGHPUT MODEL
PARALLEL PROCESSING DIMENSIONS OF THE THROUGHPUT MODEL
Types of Parallelism:
IoT and Cloud Computing
Comparison of Internet of Things and Cloud Computing
Pairing with Edge Computing
Leading to Quantum Computing
CONCLUSION
Is there a Need for Throughput Modeling to Represent Symbolic AI and Neural Networks?
REFERENCES
Six Dominant Decision-making Algorithms
INTRODUCTION
HUMAN-COMPUTER INTERACTION (HCI) AND DECISION-MAKING
Future of Human Computer Interaction (HCI)
Applications and Services Pertaining to HCI Includes:
ONWARDS TO THE USE OF ALGORITHMS
Other Algorithmic Patterns
Broader Design Algorithms and Decision-Making Processes
THROUGHPUT MODELLING SIX DOMINANT ALGORITHMS
The Process of Perception
Six Dominant Decision-Making Algorithms
ADVANTAGES AND DISADVANTAGES OF THE USE OF AI ALGORITHMS
CONCLUSION
REFERENCES
The Expedient Algorithmic Pathway
INTRODUCTION
BIOMETRICS INFUSED WITH AI TECHNOLOGY
EXAMPLE 1: EXPEDIENT ALGORITHMIC PATHWAY APPLIED TO STABLE AND UNSTABLE ENVIRONMENTS
Example 2: Expedient Algorithmic Pathway applied Vault Doors
CONCLUSION
REFERENCES
The Ruling Guide Algorithmic Pathway
INTRODUCTION
Human Rights
Contracts and Liability
Data Privacy
Intellectual Property
EXAMPLE 1 –RULING GUIDE ALGORITHMIC PATHWAY
Machine Learning
Deep Learning
Biometric Technology
Recognition: Identification vs. Verification
THROUGHPUT MODELING ALGORITHMS AND FRAUD PREVENTION
Biometric Technologies: Physiological vs. Behavioral
Fraud and Biometrics
Decision Tree and Biometrics
Type 1 and 2 Errors
EXAMPLE 2 –RULING GUIDE ALGORITHMIC PATHWAY
Can Blockchain Augment XBRL
AI Generated Solutions for Fitness Training
CONCLUSION
REFERENCES
The Analytical Algorithmic Pathway
INTRODUCTION
EXAMPLE 6.1 -- ANALYTICAL PATHWAY (I→J→D)
Company Profile
Internal Controls System
Biometrics
EXAMPLE 6.1 -- ANALYTICAL PATHWAY (I→J→D) EMPLOYED IN ELANDA COMPANY
Background and Organization for Elanda Inc., Pharmaceutical Business
Biometric Internal Control Needs
Fraud Analysis
Inventory and Purchasing Cycle
Misrepresentation of Inventory and Falsification of Documents
Vendor Selection
Safeguard of Drugs and Chemical Components and Theft of Inventory
Benefits of Biometrics
Awareness of Bill of Rights
CONCLUSION
Classification of Recommended Biometrics
REFERENCES
The Revisionist Algorithmic Pathway
INTRODUCTION
EXAMPLE 1: REVISIONIST PATHWAY (I→P→D) FOR PAY CARD SYSTEMS
Machine Learning Implemented with the Revisionist Pathway (I→P→D)
Supervised Learning
Unsupervised Learning
Deep Learning
Application for Accountants, Auditors and Forensic Accountants
Biometrics Enhancing the Revisionist Algorithmic Pathway
Fraud and Artificial Intelligence
Decision Trees
Type 1 and Type 2 Errors
Pay Card Access System
EXAMPLE 2: REVISIONIST PATHWAY (I→P→D)
AI Technologies Employed in Airports
Deep Learning
Big Data in Relationship to the Throughput Model
Algorithms
The Relationship of Biometrics and Fraud
Reservation
Check-in
Checkpoint Screening
CONCLUSION
REFERENCES
The Value-driven Algorithmic Pathway
INTRODUCTION
DECISION TREES
Different Kinds of Decision Tree Models
Prediction of Continuous Variables
Prediction of Categorical Variables
Entropy
Information Gain
Leaf Node
Root Node
How Decision Trees in AI Are Developed
EXAMPLE 1: THE VALUE-DRIVEN ALGORITHMIC PATHWAY APPLIED TO HEALTHCARE SYSTEMS
MACRA and its Correlation with AI
EXAMPLE 2: THE VALUE-DRIVEN ALGORITHMIC PATHWAY APPLIED TO WAREHOUSE SECURITY SYSTEMS
Background
Algorithms
Biometrics
Machine Learning
Deep Learning
Decision Tree Applied to the Warehouse
Positives:
Negatives:
Positives:
Negatives:
Type I and Type II Errors
Aadhaar Use of Biometrics
CONCLUSION
REFERENCES
The Global Perspective Algorithmic Pathway
INTRODUCTION
EXAMPLE 1: GLOBAL PERSPECTIVE ALGORITHMIC PATHWAY
EXAMPLE 2: GLOBAL PERSPECTIVE ALGORITHMIC PATHWAY DECREASING FRAUD
SOURCE: PARTIALLY ADAPTED BY: RODGERS, AL FAYI, AL-REFIAY, MURRAY [8].
CONCLUSION
REFERENCES
Moving Forward with Throughput Modelling and Advancing Technologies
INTRODUCTION
3. 5G Technology
4. Internet of Things (IoT)
5. Enhancing augmented reality (AR), virtual reality (VR), and mixed reality (MR)
6. Cyber Security
7. DARQ Technology
8. As-a-Service
9. IoB
10. Human Enhancement (HE)
11. Automation and Robotics
CONCLUSION
REFERENCES
The Coming Era of Artificial Intelligence willProvide Prosperity and Peace
INTRODUCTION
THROUGHPUT MODELLING
AI BASED BIOMETRICS TECHNOLOGY AND THE FRAUD TRIANGLE
ARTIFICIAL INTELLIGENCE RE-SHAPING THE WORLD
THROUGHPUT MODELING AND ALGORITHMS
THE ROLE OF CLOUD COMPUTING ON THE INTERNET OF THINGS
CONCLUSION
REFERENCES
Subject Index
Back Cover
