
Artificial Intelligence
Description
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- In-depth look at modern computing technologies
- Systems engineering description and means to successfully undertake an AI product or service development through deployment
- Existing methods for applying machine learning operations (MLOps)
- AI system architecture including a description of each of the AI pipeline building blocks
- Challenges and approaches to attend to responsible AI in practice
- Tools to develop a strategic roadmap and techniques to foster an innovative team environment
- Multiple use cases that stem from the authors' MIT classes, as well as from AI practitioners, AI project managers, early-career AI team leaders, technical executives, and entrepreneurs
- Exercises and Jupyter notebook examples
More details
Other editions
Additional editions

Persons
Bruke Mesfin Kifle is management consultant and former AI product manager at Microsoft Turing. He co-instructs MIT's "AI Strategies and Roadmap " course.
Content
- 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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