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Livres sur le sujet « Self-supervised learning (artificial intelligence) »

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Auteur : Grafiati

Publié le 7 juillet 2024

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1

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012.

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2

Kanerva, Pentti. The organization of an autonomous learning system. Moffett Field, CA : Research Institute for Advanced Computer Science, NASA Ames Research Center, 1988.

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3

Ekici, Berk. Towards self-sufficient high-rises : Performance optimisation using artificial intelligence. Delft : BK Books, 2022.

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4

He, Haibo. Self-adaptive systems for machine intelligence. Hoboken, N.J : Wiley-Interscience, 2011.

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5

Najim, K. Learning automata : Theory and applications. Oxford, OX, U.K : Pergamon, 1994.

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6

Wang, Huaiqing. Manufacturing intelligence for industrial engineering : Methods for system self-organization, learning, and adaptation. Hershey PA : Engineering Science Reference, 2010.

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Zhou, Zude. Manufacturing intelligence for industrial engineering : Methods for system self-organization, learning, and adaptation. Hershey PA : Engineering Science Reference, 2010.

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8

Zhou, Zude. Manufacturing intelligence for industrial engineering : Methods for system self-organization, learning, and adaptation. Hershey PA : Engineering Science Reference, 2010.

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9

Zhou, Zude. Manufacturing intelligence for industrial engineering : Methods for system self-organization, learning, and adaptation. Hershey, PA : Engineering Science Reference, 2010.

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10

Klimenko, A. V. Osnovy estestvennogo intellekta : Rekurrentnai͡a︡ teorii͡a︡ samoorganizat͡s︡ii : versii͡a︡ 3. Rostov-na-Donu : Izd-vo Rostovskogo universiteta, 1994.

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11

Friedman, Jerome. The Elements of Statistical Learning : Data Mining, Inference, and Prediction. New York, NY : Springer-Verlag New York, 2009.

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12

Song, In-sŏp. Waildŭ : Song In-sŏp Kyosu ŭi A·I sidae kamsŏng ch'angjo kyoyukpŏp = Wild. 8^e éd. Kyŏnggi-do P'aju-si : Tasan Edyu, 2020.

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13

Hastie, Trevor. The elements of statistical learning : Data mining, inference, and prediction. New York : Springer, 2001.

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14

Heuer, Herbert. Motor Behavior : Programming, Control, and Acquisition. Berlin, Heidelberg : Springer Berlin Heidelberg, 1985.

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15

Hastie, Trevor. The elements of statistical learning : Data mining, inference, and prediction : with 200 full-color illustrations. New York : Springer, 2001.

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16

Acuña, Ana Isabel González. Contributions to unsupervised and supervised learning with applications in digital image processing : Dissertation presented to the Department Of Computer Science and Artificial Intelligence in partial fulfillment of the requeriments for the degree of Doctor of Philosophy. [Lejona, Vizcaya] : Universidad del País Vasco, Servicio Editorial = Euskal Herriko Unibertsitatea, Argitalpen Zerbitzua, 2012.

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17

Partially Supervised Learning. Springer-Verlag Berlin and Heidelberg GmbH &, 2012.

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18

Goldberg, Andrew, et Xiaojin Zhu. Introduction to Semi-supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning). Morgan & Claypool Publishers, 2008.

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19

Bateman, Blaine, Benjamin Johnston, Ishita Mathur et Ashish Ranjan Jha. the Supervised Learning Workshop : A New, Interactive Approach to Understanding Supervised Learning Algorithms, 2nd Edition. Packt Publishing, Limited, 2020.

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20

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer, 2012.

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21

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin / Heidelberg, 2014.

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22

Vallor, Shannon, et George A. Bekey. Artificial Intelligence and the Ethics of Self-Learning Robots. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190652951.003.0022.

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The convergence of robotics technology with the science of artificial intelligence is rapidly enabling the development of robots that emulate a wide range of intelligent human behaviors. Recent advances in machine learning techniques have produced artificial agents that can acquire highly complex skills formerly thought to be the exclusive province of human intelligence. These developments raise a host of new ethical concerns about the responsible design, manufacture, and use of robots enabled with artificial intelligence—particularly those equipped with self-learning capacities. While the potential benefits of self-learning robots are immense, their potential dangers are equally serious. While some warn of a future where AI escapes the control of its human creators or even turns against us, this chapter focuses on other, far less cinematic risks of AI that are much nearer to hand, requiring immediate study and action by technologists, lawmakers, and other stakeholders.

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23

Sawarkar, Kunal, et Dheeraj Arremsetty. Deep Learning with PyTorch Lightning : Build and Train High-Performance Artificial Intelligence and Self-Supervised Models Using Python. Packt Publishing, Limited, 2021.

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24

Applications Of Supervised And Unsupervised Ensemble Methods. Springer, 2009.

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25

Proudfoot, Diane, et B. Jack Copeland. Artificial Intelligence. Sous la direction de Eric Margolis, Richard Samuels et Stephen P. Stich. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195309799.013.0007.

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In this article the central philosophical issues concerning human-level artificial intelligence (AI) are presented. AI largely changed direction in the 1980s and 1990s, concentrating on building domain-specific systems and on sub-goals such as self-organization, self-repair, and reliability. Computer scientists aimed to construct intelligence amplifiers for human beings, rather than imitation humans. Turing based his test on a computer-imitates-human game, describing three versions of this game in 1948, 1950, and 1952. The famous version appears in a 1950 article inMind, ‘Computing Machinery and Intelligence’ (Turing 1950). The interpretation of Turing's test is that it provides an operational definition of intelligence (or thinking) in machines, in terms of behavior. ‘Intelligent Machinery’ sets out the thesis that whether an entity is intelligent is determined in part by our responses to the entity's behavior. Wittgenstein frequently employed the idea of a human being acting like a reliable machine. A ‘living reading-machine’ is a human being or other creature that is given written signs, for example Chinese characters, arithmetical symbols, logical symbols, or musical notation, and who produces text spoken aloud, solutions to arithmetical problems, and proofs of logical theorems. Wittgenstein mentions that an entity that manipulates symbols genuinely reads only if he or she has a particular history, involving learning and training, and participates in a social environment that includes normative constraints and further uses of the symbols.

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26

Autonomous Learning Systems. John Wiley & Sons, 2013.

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27

Ehrenmueller-Jensen, Markus. Self-Service AI with Power BI Desktop : Machine Learning Insights for Business. Apress L. P., 2020.

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28

Najim, K., et A. S. Poznyak. Learning Automata : Theory and Applications. Elsevier Science & Technology Books, 2014.

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29

Supervised and Unsupervised Ensemble Methods and Their Applications Studies in Computational Intelligence. Springer, 2008.

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30

Villmann, Thomas, Frank-Michael Schleif, Marika Kaden et Mandy Lange. Advances in Self-Organizing Maps and Learning Vector Quantization : Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4 2014. Springer London, Limited, 2014.

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31

Eliot, Lance. Improving AI Self-Driving Cars : Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2021.

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32

Eliot, Lance. Revelatory AI Self-Driving Cars : Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2021.

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33

Eliot, Lance. AI Self-Driving Cars Imminence : Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2021.

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34

Eliot, Lance. Edifying AI Self-Driving Cars : Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2021.

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35

Villmann, Thomas, Frank-Michael Schleif, Marika Kaden et Mandy Lange. Advances in Self-Organizing Maps and Learning Vector Quantization : Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, ... in Intelligent Systems and Computing). Springer, 2014.

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36

Lanham, Micheal. Hands-On Reinforcement Learning for Games : Implementing Self-Learning Agents in Games Using Artificial Intelligence Techniques. Packt Publishing, Limited, 2020.

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37

Dutta, Sayon. Reinforcement Learning with TensorFlow : A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym. Packt Publishing, 2018.

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38

Angelov, Plamen. Autonomous Learning Systems : From Data Streams to Knowledge in Real-Time. Wiley & Sons, Limited, John, 2012.

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39

Angelov, Plamen. Autonomous Learning Systems : From Data Streams to Knowledge in Real-Time. Wiley & Sons, Incorporated, John, 2012.

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40

Angelov, Plamen. Autonomous Learning Systems : From Data Streams to Knowledge in Real-Time. Wiley & Sons, Incorporated, John, 2012.

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41

Angelov, Plamen. Autonomous Learning Systems : From Data Streams to Knowledge in Real-Time. Wiley & Sons, Incorporated, John, 2012.

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42

Geraci, Robert M. Futures of Artificial Intelligence. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9788194831679.001.0001.

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Twenty-first-century life is increasingly governed by artificial intelligence (AI) technologies such as machine learning, big data analysis, facial recognition, and robotics. For decades, an ideology of apocalyptic progress and cosmic transformation has accompanied the advancement of AI in the U.S.; that vision is intimately connected to transhumanism—the idea that humanity can transcend its limits, even mortality, using technology. Based on contributions from science and science fiction, advocates of such Apocalyptic AI suggest that the world will soon see godlike machine intelligence and that human beings will upload their minds into immortal machine bodies. The arrival of this ideology in India raises questions about how global cultures can contribute to AI technology and our beliefs about AI. These beliefs have gained a foothold in Indian visions of AI, but they have not been accepted uncritically; rather, Indian scientists and futurists revise the transhumanist vision and illustrate how traditional Hindu values can add to the global perspective. By describing the arrival and reconfiguration of transhumanist ideas in India, this book reveals how the nexus of religion and technology contributes to public life and our modern self-understanding while suggesting that the apocalyptic approach to AI should be tempered by other visions. By tracing the movement of Apocalyptic AI into India and exploring Indian efforts to redefine those transhumanist aspirations, Futures of Artificial Intelligence opens the door for rethinking our global approach to AI and advocates for technologies and visions of technology that advance human flourishing.

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43

Eliot, Dr Lance. Transformative Artificial Intelligence Driverless Self-Driving Cars : Practical Advances in AI and Machine Learning. LBE Press Publishing, 2018.

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44

Menshawy, Ahmed. Deep Learning By Example : A hands-on guide to implementing advanced machine learning algorithms and neural networks. Packt Publishing - ebooks Account, 2018.

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45

Eliot, Dr Lance. Disruptive Artificial Intelligence and Driverless Self-Driving Cars : Practical Advances in Machine Learning and AI. LBE Press Publishing, 2018.

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46

Eliot, Dr Lance. Revolutionary Innovations of AI Self-Driving Cars : Practical Advances in Artificial Intelligence and Machine Learning. LBE Press Publishing, 2018.

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47

Wodecki, Andrzej. Artificial Intelligence in Management : Self-Learning and Autonomous Systems As Key Drivers of Value Creation. Elgar Publishing Limited, Edward, 2020.

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48

Zhou, Zhi-Hua, et Friedhelm Schwenker. Partially Supervised Learning : Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected Papers. Springer, 2013.

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49

Zhou, Zhi-Hua, et Friedhelm Schwenker. Partially Supervised Learning : Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected Papers. Springer Berlin / Heidelberg, 2013.

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50

Schwenker, Friedhelm, et Edmondo Trentin. Partially Supervised Learning : First IAPR TC3 Workshop, PSL 2011, Ulm, Germany, September 15-16, 2011, Revised Selected Papers. Springer, 2012.

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