Artificial Intelligence

Name: Artificial Intelligence | A Systems Approach from Architecture Principles to Deployment
Brand: MIT Press
Price: 107.99 EUR
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A Systems Approach from Architecture Principles to Deployment

David R. Martinez Bruke M. Kifle(Autor*in)

MIT Press

Erschienen am 11. Juni 2024

580 Seiten

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978-0-262-37871-0 (ISBN)

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Inhalt

Intro
Contents
Preface
Acknowledgments
1. Overview
1.1. AI Notable Events in the Past Decades
1.2. AI Pipeline: A System Architecture Approach
1.3. High-Level Description of AI System Architecture Building Blocks
1.4. Effective AI Deployment
1.5. AI Horizons: Content-Based Insights, Collaboration-Based Insights, and Context-Based Insights
1.6. Chapter Road Map
1.7. Main Takeaways
1.8. Exercises
1.9. References
Part I. AI System Architecture
2. Fundamentals of Systems Engineering
2.1. Systems Engineering Common Definitions
2.2. Characteristics Espoused within the Systems Engineering Discipline
2.3. A Systems Engineering Approach Applied to Artificial Intelligence
2.4. Architecture Framework: The What and the How
2.5. Leadership: Systems Thinker
2.6. Systems Engineering Challenges
2.7. Main Takeaways
2.8. Exercises
2.9. References
3. Data Conditioning
3.1. Exponential Data Growth
3.2. Digital Transformation
3.3. Databases: Management and Evolution
3.4. Data Quality, Cleaning, and Preparation
3.5. Curated Data Set Examples and Attributes
3.6. Data Conditioning Challenges
3.7. Main Takeaways
3.8. Exercises
3.9. References
4. Machine Learning
4.1. Machine Learning Classes
4.2. Common Measures of Performance
4.3. Introduction to Deep Learning and Neural Nets
4.4. Training Neural Networks with Backpropagation
4.5. Designing a Neural Network
4.6. Introduction to Convolutional Neural Networks
4.7. Machine Learning Challenges
4.8. Main Takeaways
4.9. Exercises
4.10. References
5. Modern Computing
5.1. A Short History of Computing Technologies
5.2. Computing at the Enterprise versus Computing at the Edge
5.3. Neural Network: Key Computational Kernels
5.4. Arithmetic Precision
5.5. Confluence of ML Algorithm Improvements and Computing Technology
5.6. Domain-Specific Hardware and Software
5.7. Contemporary Computing Engines and Integrated Systems
5.8. Roofline as a Metric
5.9. Securing Modern Computing
5.10. Modern Computing Challenges
5.11. Main Takeaways
5.12. Exercises
5.13. References
6. Human-Machine Teaming
6.1. Augmenting Human Capabilities
6.2. AI as a Search-and-Discovery Tool
6.3. AI as a Teammate
6.4. Autonomy and Near-Term Barriers
6.5. Quantitative and Qualitative Performance Metrics
6.6. Human-Machine Teaming Challenges
6.7. Main Takeaways
6.8. Exercises
6.9. References
7. Robust AI Systems
7.1. Systems Perspective on AI Vulnerabilities
7.2. Classes of Adversarial Artificial Intelligence
7.3. Deepfakes and Examples
7.4. Explainable Artificial Intelligence
7.5. Mitigation Techniques
7.6. Methodology for Testing against Adversarial Attacks
7.7. Robust AI System Challenges
7.8. Main Takeaways
7.9. Exercises
7.10. References
8. Responsible Artificial Intelligence
8.1. AI and Society
8.2. Case Studies: Harms from Artificial Intelligence
8.3. Considerations for Sociotechnical Systems
8.4. Responsible AI Principles
8.5. RAI Considerations in the AI Development Life Cycle
8.6. RAI Challenges
8.7. Main Takeaways
8.8. Exercises
8.9. References
Part II. Strategic Principles
9. AI Strategy and Road Map
9.1. Introduction to Strategic Thinking
9.2. AI Strategic Development Model
9.3. Mission/Vision and Envisioned Future
9.4. Organization Core Values and Strategic Direction
9.5. AI Value Proposition
9.6. AI Strategic Road Map: A Blueprint
9.7. Strategy and Execution: A Complementary Duo
9.8. Main Takeaways
9.9. Exercises
9.10. References
10. AI Deployment Guidelines
10.1. Challenges in Deploying Artificial Intelligence
10.2. Ten Guidelines for Successfully Deploying AI Capabilities
10.3. A Process for Applying a Systems Engineering Discipline to AI Deployment
10.4. AI Adoption: Four Distinct Organizational Maturity Clusters
10.5. The AI Ecosystem
10.6. Gold Standard: Test Harness, Performance Metrics, and Benchmarks
10.7. AI Platform Characteristics and Benefits
10.8. Main Takeaways
10.9. Exercises
10.10. References
11. MLOps: Transitioning from Development to Deployment
11.1. Introduction to MLOps Fundamentals
11.2. AI System Architecture Implementation Using MLOps
11.3. MLOps Enabling Techniques and Contemporary Tools
11.4. MLOps Platforms, AutoML, and LCNC Application Development
11.5. AI Development and Deployment: Common Pitfalls
11.6. Main Takeaways
11.7. Exercises
11.8. References
12. Fostering an Innovative Team Environment
12.1. Organizational Culture
12.2. Organizational Structure and Innovation
12.3. AI Talent and the Future of Work
12.4. Preparing You for a Successful Career
12.5. AI Technical Depth and Breadth
12.6. Metrics for Measuring Progress and Results
12.7. AI Leadership and Resilience
12.8. Mentoring, Networking, and Recruiting AI Talent
12.9. Sustaining High-Performance Teams
12.10. Main Takeaways
12.11. Exercises
12.12. References
13. Communicating Effectively
13.1. VSN-C for Structuring Communications
13.2. Winston Star: Essentials for Being Remembered
13.3. Essentials of Outlining
13.4. Writing and Presentation Fundamentals
13.5. Main Takeaways
13.6. Exercises
13.7. References
Part III. Human-Machine Augmentation: Use Cases
14. Use-Case Example 1: Misty Companion Robot as Alzheimer's Application
14.1. Exercises
14.2. References
15. Use-Case Example 2: Bose AI-Powered Cycling Coach and Warning System
15.1. Exercises
15.2. Reference
16. Use-Case Example 3: Meal Evaluation and Attainment Logistics System (MEALS)
16.1. Exercises
17. Use-Case Example 4: Managing Energy for Smart Homes (MESH)
17.1. Exercises
17.2. References
18. Use-Case Example 5: AquaAI, an AI-Powered Modernized Marine Maintenance System
18.1. Exercises
18.2. Reference
Appendix
A.1. Representative AI Industries and Sample Applications
A.2. Setting Up Your Interactive Development Environment (for Either PC or Mac OS Operating Systems)
A.3. ML Performance Metrics
A.4. Multilayer Perceptron Algorithm
A.5. CNN with MNIST Fashion Data Set
A.6. Raspberry Pi: Introduction and Setup
Abbreviations
Index

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