{"product_id":"artificial-intelligence-isbn-9780262048989","title":"Artificial Intelligence","description":"\u003cb\u003eThe first text to take a systems engineering approach to artificial intelligence (AI), from architecture principles to the development and deployment of AI capabilities.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eMost books on artificial intelligence (AI) focus on a single functional building block, such as machine learning or human-machine teaming. \u003cu\u003eArtificial Intelligence\u003c\/u\u003e 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. \u003cbr\u003e\u003cbr\u003eKey features:\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eIn-depth look at modern computing technologies \u003c\/li\u003e\n\u003cli\u003eSystems engineering description and means to successfully undertake an AI product or service development through deployment\u003c\/li\u003e\n\u003cli\u003eExisting methods for applying machine learning operations (MLOps)\u003c\/li\u003e\n\u003cli\u003eAI system architecture including a description of each of the AI pipeline building blocks\u003c\/li\u003e\n\u003cli\u003eChallenges and approaches to attend to responsible AI in practice    \u003c\/li\u003e\n\u003cli\u003eTools to develop a strategic roadmap and techniques to foster an innovative team environment \u003c\/li\u003e\n\u003cli\u003eMultiple 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 \u003c\/li\u003e\n\u003cli\u003eExercises and Jupyter notebook examples \u003c\/li\u003e\n\u003c\/ul\u003eTable of Contents\u003cbr\u003ePreface 3\u003cbr\u003eAcknowledgements 6\u003cbr\u003e1 Overview 17\u003cbr\u003ePart I AI System Architecture 49\u003cbr\u003e2 Fundamentals of Systems Engineering 50\u003cbr\u003e3 Data Conditioning 86\u003cbr\u003e4 Machine Learning 127\u003cbr\u003e5 Modern Computing 181\u003cbr\u003e6 Human-Machine Teaming 258\u003cbr\u003e7 Robust AI Systems 297\u003cbr\u003e8 Responsible AI 343\u003cbr\u003ePart II Strategic Principles 375\u003cbr\u003e9 AI Strategy and Roadmap 376\u003cbr\u003e10 AI Deployment Guidelines 427\u003cbr\u003e11 MLOps: Transitioning from Development into Deployment 473\u003cbr\u003e12 Fostering an Innovative Team Environment 518\u003cbr\u003e13 Communicating Effectively 574\u003cbr\u003e14 Use-Case Example #1: Misty Companion Robot as Alzheimer’s Application 605\u003cbr\u003e15 Use-Case Example #2: Bose AI-Powered Cycling Coach and Warning System 614\u003cbr\u003e16 Use-Case Example #3: Meal Evaluation \u0026amp; Attainment Logistics System (MEALS) 622\u003cbr\u003e17 Use-Case Example #4: Managing Energy for Smart Homes (MESH) 632\u003cbr\u003e18 Use-Case Example #5: AquaAI—An AI-Powered Modernized Marine Maintenance System 641\u003cbr\u003eAppendices 649\u003cbr\u003eGlossary 677\u003cbr\u003eIndex 680“Truly a masterpiece, providing a unique how-to guide on developing Al strategies and roadmaps using a system engineering approach, including academic and industry insights and human-machine augmentation use cases.”\u003cbr\u003e\u003cb\u003e—Liliana Horne, Executive Director, IBM; Top 100 Most Influential Global Hispanic Technology Executives\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e“A comprehensive and engaging guide to AI implementation drawing on history, best practice, and the authors' impressive experience. This is an empowering read for industry leaders looking to build AI capability into their workflows.”\u003cbr\u003e\u003cb\u003e—Mike Moulin-Ramsden, Haslam Advisory\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e“Unlike typical books in AI or machine learning, the authors have succeeded in bringing a wide range of topics in technology, processes, and people at sufficient depth to explain what it takes to ‘engineer’ complex AI systems.” \u003cbr\u003e\u003cb\u003e —Padmanaban Santhanam, Principal Research Staff Member, IBM Research; coauthor of \u003ci\u003eBeyond Algorithms: Delivering AI for Business\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003eDavid R. Martinez \u003c\/b\u003eis 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. \u003cbr\u003e\u003cb\u003eBruke Mesfin Kifle\u003c\/b\u003e is management consultant and former AI product manager at Microsoft Turing. He co-instructs MIT’s \"AI Strategies and Roadmap \" course.","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":46304020725989,"sku":"NP9780262048989","price":110.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262048989.jpg?v=1767721823","url":"https:\/\/k12savings.com\/products\/artificial-intelligence-isbn-9780262048989","provider":"K12savings","version":"1.0","type":"link"}