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The cultural life of machine learnin...

Castelle, Michael.

The cultural life of machine learningan incursion into critical AI studies /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The cultural life of machine learningedited by Jonathan Roberge, Michael Castelle.
其他題名:	an incursion into critical AI studies /
其他作者:	Roberge, Jonathan.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xv, 289 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-3-030-56286-1
ISBN:	9783030562861$q(electronic bk.)

The cultural life of machine learningan incursion into critical AI studies /
The cultural life of machine learningan incursion into critical AI studies /[electronic resource] :edited by Jonathan Roberge, Michael Castelle. - Cham :Springer International Publishing :2021. - xv, 289 p. :ill., digital ;24 cm.

1. Toward an End-to-End Sociology of 21st-Century Machine Learning -- 2. Mechanized Significance and Machine Learning: Why it Became Thinkable and Preferable to Teach Machines to Judge the World -- 3. What Kind of Learning Is Machine Learning? -- 4. The Other Cambridge Analytics: Early "Artificial Intelligence" in American Political Science -- 5. Machinic Encounters: A Relational Approach to the Sociology of AI -- 6. AlphaGo's Deep Play: Technological Breakthrough as Social Drama -- 7. Adversariality in Machine Learning Systems: On Neural Networks and the Limits of Knowledge -- 8. Planetary Intelligence -- 9. Critical Perspectives on Governance Mechanisms for AI/ML Systems.

This book brings together the work of sociologists and historians along with perspectives from media studies, communication studies, cultural studies, and information studies to address the origins, practices, and possible futures of contemporary machine learning. From its foundations in 1950s and 1960s pattern recognition and neural network research to the modern-day social and technological dramas of DeepMind's AlphaGo, predictive political forecasting, and the governmentality of extractive logistics, machine learning has become controversial precisely because of its increased embeddedness and agency in our everyday lives. How can we disentangle the history of machine learning from conventional histories of artificial intelligence? How can machinic agents' capacity for novelty be theorized? Can reform initiatives for fairness and equity in AI and machine learning be realized, or are they doomed to cooptation and failure? And just what kind of "learning" does machine learning truly represent? Contributors empirically address these questions and more to provide a baseline for future research. Jonathan Roberge is an Associate Professor at the Institut National de la Recherche Scientifique in Montreal, Canada. He funded the Nenic Lab as part of the Canada Research Chair in Digital Culture he has held since 2012. His most recent edited volume is Algorithmic Cultures (2016) Michael Castelle is an Assistant Professor at the University of Warwick's Centre for Interdisciplinary Methodologies, UK and a Turing Fellow at the Alan Turing Institute, UK. He has a Ph.D. in Sociology from the University of Chicago and a Sc.B. in Computer Science from Brown University. Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

ISBN: 9783030562861$q(electronic bk.)

Standard No.: 10.1007/978-3-030-56286-1doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .C85 2021

Dewey Class. No.: 006.31

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505 0 $a 1. Toward an End-to-End Sociology of 21st-Century Machine Learning -- 2. Mechanized Significance and Machine Learning: Why it Became Thinkable and Preferable to Teach Machines to Judge the World -- 3. What Kind of Learning Is Machine Learning? -- 4. The Other Cambridge Analytics: Early "Artificial Intelligence" in American Political Science -- 5. Machinic Encounters: A Relational Approach to the Sociology of AI -- 6. AlphaGo's Deep Play: Technological Breakthrough as Social Drama -- 7. Adversariality in Machine Learning Systems: On Neural Networks and the Limits of Knowledge -- 8. Planetary Intelligence -- 9. Critical Perspectives on Governance Mechanisms for AI/ML Systems.
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