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Artificial Intelligence

A Systems Approach from Architecture Principles to Deployment

Part of MIT Lincoln Laboratory Series

Author David R. Martinez, Bruke M. Kifle
Ebook (EPUB)
The MIT Press
On sale Jun 11, 2024 | 580 Pages | 9780262378710
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The first text to take a systems engineering approach to artificial intelligence (AI), from architecture principles to the development and deployment of AI capabilities.

Most books on artificial intelligence (AI) focus on a single functional building block, such as machine learning or human-machine teaming. Artificial Intelligence takes a more holistic approach, addressing AI from the view of systems engineering. The book centers on the people-process-technology triad that is critical to successful development of AI products and services. Development starts with an AI design, based on the AI system architecture, and culminates with successful deployment of the AI capabilities. Directed toward AI developers and operational users, this accessibly written volume of the MIT Lincoln Laboratory Series can also serve as a text for undergraduate seniors and graduate-level students and as a reference book. 

Key features:
  • 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 
David R. Martinez is a laboratory fellow at the MIT Lincoln Laboratory and the lead instructor for MIT’s “AI Strategies and Roadmap: Systems Engineering Approach to AI Development and Deployment” and “AI and ML: Leading Business Growth” courses. 
Bruke Mesfin Kifle is management consultant and former AI product manager at Microsoft Turing. He co-instructs MIT’s "AI Strategies and Roadmap " course.
David R. Martinez View titles by David R. Martinez
Bruke M. Kifle View titles by Bruke M. Kifle
Table of Contents
Preface 3
Acknowledgements 6
1 Overview 17
Part I AI System Architecture 49
2 Fundamentals of Systems Engineering 50
3 Data Conditioning 86
4 Machine Learning 127
5 Modern Computing 181
6 Human-Machine Teaming 258
7 Robust AI Systems 297
8 Responsible AI 343
Part II Strategic Principles 375
9 AI Strategy and Roadmap 376
10 AI Deployment Guidelines 427
11 MLOps: Transitioning from Development into Deployment 473
12 Fostering an Innovative Team Environment 518
13 Communicating Effectively 574
14 Use-Case Example #1: Misty Companion Robot as Alzheimer’s Application 605
15 Use-Case Example #2: Bose AI-Powered Cycling Coach and Warning System 614
16 Use-Case Example #3: Meal Evaluation & Attainment Logistics System (MEALS) 622
17 Use-Case Example #4: Managing Energy for Smart Homes (MESH) 632
18 Use-Case Example #5: AquaAI—An AI-Powered Modernized Marine Maintenance System 641
Appendices 649
Glossary 677
Index 680

About

The first text to take a systems engineering approach to artificial intelligence (AI), from architecture principles to the development and deployment of AI capabilities.

Most books on artificial intelligence (AI) focus on a single functional building block, such as machine learning or human-machine teaming. Artificial Intelligence takes a more holistic approach, addressing AI from the view of systems engineering. The book centers on the people-process-technology triad that is critical to successful development of AI products and services. Development starts with an AI design, based on the AI system architecture, and culminates with successful deployment of the AI capabilities. Directed toward AI developers and operational users, this accessibly written volume of the MIT Lincoln Laboratory Series can also serve as a text for undergraduate seniors and graduate-level students and as a reference book. 

Key features:
  • 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 

Author

David R. Martinez is a laboratory fellow at the MIT Lincoln Laboratory and the lead instructor for MIT’s “AI Strategies and Roadmap: Systems Engineering Approach to AI Development and Deployment” and “AI and ML: Leading Business Growth” courses. 
Bruke Mesfin Kifle is management consultant and former AI product manager at Microsoft Turing. He co-instructs MIT’s "AI Strategies and Roadmap " course.
David R. Martinez View titles by David R. Martinez
Bruke M. Kifle View titles by Bruke M. Kifle

Table of Contents

Table of Contents
Preface 3
Acknowledgements 6
1 Overview 17
Part I AI System Architecture 49
2 Fundamentals of Systems Engineering 50
3 Data Conditioning 86
4 Machine Learning 127
5 Modern Computing 181
6 Human-Machine Teaming 258
7 Robust AI Systems 297
8 Responsible AI 343
Part II Strategic Principles 375
9 AI Strategy and Roadmap 376
10 AI Deployment Guidelines 427
11 MLOps: Transitioning from Development into Deployment 473
12 Fostering an Innovative Team Environment 518
13 Communicating Effectively 574
14 Use-Case Example #1: Misty Companion Robot as Alzheimer’s Application 605
15 Use-Case Example #2: Bose AI-Powered Cycling Coach and Warning System 614
16 Use-Case Example #3: Meal Evaluation & Attainment Logistics System (MEALS) 622
17 Use-Case Example #4: Managing Energy for Smart Homes (MESH) 632
18 Use-Case Example #5: AquaAI—An AI-Powered Modernized Marine Maintenance System 641
Appendices 649
Glossary 677
Index 680

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