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Книги з теми "AI DECISIONS"

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Автор: Grafiati

Опубліковано: 19 вересня 2023

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1

Phillips-Wren, Gloria, Nikhil Ichalkaranje, and Lakhmi C. Jain, eds. Intelligent Decision Making: An AI-Based Approach. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76829-6.

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2

Cox, Louis Anthony. AI-ML for Decision and Risk Analysis. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32013-2.

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3

A, Sexton George, and Langley Research Center, eds. "Diverter" AI based decision aid: Phases I & II. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1989.

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4

International Conference on Systems Research, Informatics, and Cybernetics (17th 2005 Baden-Baden, Germany). Cognitive, emotive, and ethical aspects of decision making in humans and in AI. Windsor, Ont: International Institute for Advanced Studies in Systems Research and Cybernetics, 2005.

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5

Zhaoxia, Guo, and Leung Yung-sun, eds. Optimizing decision making in the apparel supply chain using artificial intelligence (AI): From production to retail. Cambridge: Woodhead Publishing Ltd, 2013.

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6

Sifeng, Liu, and Lin Yi 1959-, eds. Hybrid rough sets and applications in uncertain decision-making. Boca Raton: Auerbach Publications, 2010.

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7

Mendel, Jerry M. Perceptual computing: Aiding people in making subjective judgments. Hoboken, N.J: John Wiley & Sons, 2010.

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8

Russell, Stuart J. Do the right thing: Studies in limited rationality. Cambridge, Mass: MIT Press, 1991.

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9

Lewis, Carroll. Ai-li-si meng you qi jing. Hong Kong: The Sunbeam Pub., 1986.

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10

Lewis, Carroll. Ai-li-si man you qi jing. Hong Kong: Da Gueng Cu Ban She, 1986.

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11

Zoppi, Corrado. Servizi pubblici e qualità della vita urbana: Discussione sul ruolo ed il significato della partecipazione delle comunità locali ai processi decisionali e attuativi della pianificazione del territorio nel quadro concettuale della valutazione ambientale strategica. Roma: Gangemi, 2003.

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12

Conference on AI, Simulation and Planning in High Autonomy Systems (2nd 1991 Cocoa Beach, Fla.). The Second Annual Conference on AI, Simulation and Planning in High Autonomy Systems, April 1-2, 1991, Cocoa Beach, Florida: Proceedings : theme, Integrating Qualitative and Quantitative System Knowledge. Los Alamitos, Calif: IEEE Computer Society Press, 1991.

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13

Lewis, Carroll. Ai li si man you yi jing ji. Shang hai: Shang hai wai yu jiao yu chu ban she, 2004.

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14

Turner, Roy M. Adaptive reasoning for real-world problems: A schema-based approach. Hillsdale, N.J: Lawrence Erlbaum Associates, 1994.

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15

Narendra, Jussien, Pinson Éric, and SpringerLink (Online service), eds. Integration of AI and OR Techniques in Contraint Programming for Combinatorial Optimzation Problems: 9th International Conference, CPAIOR 2012, Nantes, France, May 28 – June1, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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16

El Namaki, MSS, and Pooja Sharma, eds. Management of Data in AI Age. CSMFL Publications, 2020. http://dx.doi.org/10.46679/isbn9788194848349.

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Анотація:

This book is a compilation of contributed works on management of data in the age of artificial intelligence. The AI technologies have changed the way the businesses do manage themselves in modern times. It becomes much more important to manage the data a business owns when the same can be collated and used by the allied AI technologies for forming business decisions. This book highlights how AI and machine learning can help businesses categorise and manage their organizational data. The book introduces how small businesses can benefit from AI technologies for their data management with limited budgets. The book advocates for making AI processes to be core part of consumer experience and support management within the businesses. As a unique feature, this book also goes to make an awareness as to how human brain can use AI’s deep learning capabilities to make reflective decisions. The book also introduces as to how big data and big data analytics can help agriculture and farm management sector. It is hoped that the readership will find this book useful in the areas of big data management, machine learning and data decisions, AI technologies for small businesses, usage of AI in emerging sectors and those areas where data needs to managed in an environment of automation.

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17

Cappelen, Herman, and Josh Dever. Making AI Intelligible. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192894724.001.0001.

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Анотація:

Can humans and artificial intelligences share concepts and communicate? One aim of Making AI Intelligible is to show that philosophical work on the metaphysics of meaning can help answer these questions. Cappelen and Dever use the externalist tradition in philosophy of to create models of how AIs and humans can understand each other. In doing so, they also show ways in which that philosophical tradition can be improved: our linguistic encounters with AIs revel that our theories of meaning have been excessively anthropocentric. The questions addressed in the book are not only theoretically interesting, but the answers have pressing practical implications. Many important decisions about human life are now influenced by AI. In giving that power to AI, we presuppose that AIs can track features of the world that we care about (e.g. creditworthiness, recidivism, cancer, and combatants.) If AIs can share our concepts, that will go some way towards justifying this reliance on AI. The book can be read as a proposal for how to take some first steps towards achieving interpretable AI. Making AI Intelligible is of interest to both philosophers of language and anyone who follows current events or interacts with AI systems. It illustrates how philosophy can help us understand and improve our interactions with AI.

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18

Smith, Gary. The AI Delusion. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198824305.001.0001.

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Анотація:

We live in an incredible period in history. The Computer Revolution may be even more life-changing than the Industrial Revolution. We can do things with computers that could never be done before, and computers can do things for us that could never be done before. But our love of computers should not cloud our thinking about their limitations. We are told that computers are smarter than humans and that data mining can identify previously unknown truths, or make discoveries that will revolutionize our lives. Our lives may well be changed, but not necessarily for the better. Computers are very good at discovering patterns, but are useless in judging whether the unearthed patterns are sensible because computers do not think the way humans think. We fear that super-intelligent machines will decide to protect themselves by enslaving or eliminating humans. But the real danger is not that computers are smarter than us, but that we think computers are smarter than us and, so, trust computers to make important decisions for us. The AI Delusion explains why we should not be intimidated into thinking that computers are infallible, that data-mining is knowledge discovery, and that black boxes should be trusted.

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19

Dubber, Markus D., Frank Pasquale, and Sunit Das, eds. The Oxford Handbook of Ethics of AI. Oxford University Press, 2020. http://dx.doi.org/10.1093/oxfordhb/9780190067397.001.0001.

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Анотація:

This book explores the intertwining domains of artificial intelligence (AI) and ethics—two highly divergent fields which at first seem to have nothing to do with one another. AI is a collection of computational methods for studying human knowledge, learning, and behavior, including by building agents able to know, learn, and behave. Ethics is a body of human knowledge—far from completely understood—that helps agents (humans today, but perhaps eventually robots and other AIs) decide how they and others should behave. Despite these differences, however, the rapid development in AI technology today has led to a growing number of ethical issues in a multitude of fields, ranging from disciplines as far-reaching as international human rights law to issues as intimate as personal identity and sexuality. In fact, the number and variety of topics in this volume illustrate the width, diversity of content, and at times exasperating vagueness of the boundaries of “AI Ethics” as a domain of inquiry. Within this discourse, the book points to the capacity of sociotechnical systems that utilize data-driven algorithms to classify, to make decisions, and to control complex systems. Given the wide-reaching and often intimate impact these AI systems have on daily human lives, this volume attempts to address the increasingly complicated relations between humanity and artificial intelligence. It considers not only how humanity must conduct themselves toward AI but also how AI must behave toward humanity.

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20

Big Data Demystified: How to use big data, data science and AI to make better business decisions and gain competitive advantage. FT Press, 2018.

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21

Zweig, Katharina A. Awkward Intelligence. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/13915.001.0001.

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An expert offers a guide to where we should use artificial intelligence—and where we should not. Before we know it, artificial intelligence (AI) will work its way into every corner of our lives, making decisions about, with, and for us. Is this a good thing? There's a tendency to think that machines can be more “objective” than humans—can make better decisions about job applicants, for example, or risk assessments. In Awkward Intelligence, AI expert Katharina Zweig offers readers the inside story, explaining how many levers computer and data scientists must pull for AI's supposedly objective decision making. She presents the good and the bad: AI is good at processing vast quantities of data that humans cannot—but it's bad at making judgments about people. AI is accurate at sifting through billions of websites to offer up the best results for our search queries and it has beaten reigning champions in games of chess and Go. But, drawing on her own research, Zweig shows how inaccurate AI is, for example, at predicting whether someone with a previous conviction will become a repeat offender. It's no better than simple guesswork, and yet it's used to determine people's futures. Zweig introduces readers to the basics of AI and presents a toolkit for designing AI systems. She explains algorithms, big data, and computer intelligence, and how they relate to one another. Finally, she explores the ethics of AI and how we can shape the process. With Awkward Intelligence, Zweig equips us to confront the biggest question concerning AI: where we should use it—and where we should not.

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22

Vashishtha, Himanshu, and Melodena Stephens. AI Smart Kit: Agile Decision-Making on AI. Information Age Publishing, Incorporated, 2021.

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23

Vashishtha, Himanshu, and Melodena Stephens. AI Smart Kit: Agile Decision-Making on AI. Information Age Publishing, Incorporated, 2021.

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24

Intelligent Decision Making : An AI-Based Approach: An AI-Based Approach. Springer, 2010.

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25

Hartmann, Stephan, Huw Price, and Yang Liu. Decision Theory and the Future of AI. Springer, 2022.

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26

Lakhmi Jain,Gloria Phillips-Wren,Nikhil Ichalkaranje. Intelligent Decision Making: An AI-Based Approach. Springer, 2008.

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27

Phillips-Wren, Gloria, and Nikhil Ichalkaranje. Intelligent Decision Making: An AI-Based Approach. Springer, 2008.

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28

Natarajan, Mausam, and Andrey Poole. Planning with Markov Decision Processes: An AI Perspective. Springer International Publishing AG, 2012.

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29

Mausam and Andrey Kolobov. Planning with Markov Decision : Processes: An Ai Perspective. Morgan & Claypool Publishers, 2012.

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30

Mausam and Andrey Kolobov. Planning with Markov Decision Processes: An AI Perspective. Morgan & Claypool Publishers, 2012.

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31

Brachman, Ronald J., and Hector J. Levesque. Machines like Us. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/14299.001.0001.

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Анотація:

How we can create artificial intelligence with broad, robust common sense rather than narrow, specialized expertise. It's sometime in the not-so-distant future, and you send your fully autonomous self-driving car to the store to pick up your grocery order. The car is endowed with as much capability as an artificial intelligence agent can have, programmed to drive better than you do. But when the car encounters a traffic light stuck on red, it just sits there—indefinitely. Its obstacle-avoidance, lane-following, and route-calculation capacities are all irrelevant; it fails to act because it lacks the common sense of a human driver, who would quickly figure out what's happening and find a workaround. In Machines like Us, Ron Brachman and Hector Levesque—both leading experts in AI—consider what it would take to create machines with common sense rather than just the specialized expertise of today's AI systems. Using the stuck traffic light and other relatable examples, Brachman and Levesque offer an accessible account of how common sense might be built into a machine. They analyze common sense in humans, explain how AI over the years has focused mainly on expertise, and suggest ways to endow an AI system with both common sense and effective reasoning. Finally, they consider the critical issue of how we can trust an autonomous machine to make decisions, identifying two fundamental requirements for trustworthy autonomous AI systems: having reasons for doing what they do, and being able to accept advice. Both in the end are dependent on having common sense.

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32

National Aeronautics and Space Administration (NASA) Staff. Diverter AI Based Decision Aid, Phases 1 And 2. Independently Published, 2018.

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33

Kautish, Sandeep, and Gaurav Dhiman. AI-Enabled Multiple Criteria Decision-Making Approaches for Healthcare Management. IGI Global, 2021.

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34

Kautish, Sandeep, and Gaurav Dhiman, eds. AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4405-4.

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35

Kautish, Sandeep, and Gaurav Dhiman. AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management. IGI Global, 2022.

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36

Kautish, Sandeep, and Gaurav Dhiman. AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management. IGI Global, 2022.

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37

Kautish, Sandeep, and Gaurav Dhiman. AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management. IGI Global, 2022.

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38

Kautish, Sandeep, and Gaurav Dhiman. AI-Enabled Multiple Criteria Decision-Making Approaches for Healthcare Management. IGI Global, 2022.

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39

Yeung, Jacky T., and Michael E. Sughrue. Connectomic Medicine: Guide to Brain AI in Treatment Decision Planning. Elsevier Science & Technology Books, 2024.

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40

Decision-Theoretic Methods for Learning Probabilistic Models. Chapman & Hall/CRC, 2009.

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41

Lee, Daeyeol. Birth of Intelligence. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190908324.001.0001.

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Анотація:

What is intelligence? How did it begin and evolve to human intelligence? Does a high level of biological intelligence require a complex brain? Can man-made machines be truly intelligent? Is artificial intelligence (AI) fundamentally different from human intelligence? Rapid expansion of AI applications has made these questions pressing. To better prepare for the future society and its technology, including how the use of AI will impact our lives, it is essential to understand the biological root and limits of human intelligence. After systematically reviewing biological and computational underpinnings of decision-making and intelligent behaviors, this book proposes that true intelligence requires life.

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42

Zwingmann, Tobias. AI-Powered Business Intelligence: Improving Forecasts and Decision Making with Machine Learning. O'Reilly Media, Incorporated, 2022.

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43

Wong, W. K., Z. X. Guo, and S. Y. S. Leung. Optimizing decision making in the apparel supply chain using artificial intelligence (AI). Woodhead Publishing Limited, 2013. http://dx.doi.org/10.1533/9780857097842.

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44

AI, Simulation Conf on, and Planning in High Autonomy Systems. Ai. Simulation and Planning in High Autonomy Systems. Ieee Computer Society, 1991.

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45

Boden, Margaret A. 7. The Singularity. Oxford University Press, 2018. http://dx.doi.org/10.1093/actrade/9780199602919.003.0007.

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Анотація:

AI’s future has been hyped since its inception. Today, the prime example is the Singularity: the proposed point in time at which machines become more intelligent than humans. First, AI would reach human-level intelligence. Soon afterwards, AGI would morph into ASI—‘S’ for superhuman, with systems intelligent enough to copy themselves to outnumber us and improve themselves to out-think us. The most important problems and decisions would then be addressed by computers. ‘The Singularity’ explains that this notion is hugely contentious. It considers competing predictions, concluding that even if the probability of the Singularity is extremely small, the possible consequences are so grave that we should start taking precautions now.

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46

Konior, Bogna, Anna Greenspan, and Benjamin H. Bratton. Machine Decision Is Not Final: China and the History and Future of AI. Urbanomic, 2022.

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47

Machine Learning Algorithms for Data Scientists: An Overview. Vinaitheerthan Renganathan, 2021.

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48

Rahman, Mahmudur, ed. Artificial Intelligence (AI) and Machine Learning (ML) in Medical Imaging Informatics towards Diagnostic Decision Making. MDPI, 2023. http://dx.doi.org/10.3390/books978-3-0365-8129-3.

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49

Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner. SAS Publishing, 2006.

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50

Liu, Sifeng, Yi Lin, and Lirong Jian. Hybrid Rough Sets and Applications in Uncertain Decision-Making. Taylor & Francis Group, 2018.

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