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 the 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.
Table of Contents
Chapter 1 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
- 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? 64 References
- 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
- 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
- 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
- Introduction
- Example 6.1 -- Analytical Pathway (I→J→D)
- Company Profile
- Internal Controls System
- Biometrics
- Example 6.2 -- 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
- 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
- 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
- Subject Index
Author
- Waymond Rodgers
