Artificial Intelligence

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