Understanding the impact of machine learning on labor and education : a time-dependent Turing test / Joseph Ganem.
Ganem, Joseph, author.
2023
Q325.5 .G35 2023
Available Online

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Title Understanding the impact of machine learning on labor and education : a time-dependent Turing test / Joseph Ganem.
Author Ganem, Joseph, author.
ISBN 3031310047 electronic book
9783031310041 (electronic bk.)
9783031310034
3031310039
DOI https://doi.org/10.1007/978-3-031-31004-1
Published Cham, Switzerland : Springer, [2023]
Language English
Description 1 online resource (xvii, 74 pages) : illustrations (some color).
Item Number 10.1007/978-3-031-31004-1 doi
Call Number Q325.5 .G35 2023
Dewey Decimal Classification 006.3/101
Summary This book provides a novel framework for understanding and revising labor markets and education policies in an era of machine learning. It posits that while learning and knowing both require thinking, learning is fundamentally different than knowing because it results in cognitive processes that change over time. Learning, in contrast to knowing, requires time and agency. Therefore, "learning algorithms" -- that enable machines to modify their actions based on real-world experiences -- are a fundamentally new form of artificial intelligence that have potential to be even more disruptive to labor markets than prior introductions of digital technology. To explore the difference between knowing and learning, Turing's "Imitation Game," -- that he proposed as a test for machine thinking -- is expanded to include time dependence. The arguments presented in the book introduce three novel concepts: (1) Comparative learning advantage: This is a concept analogous to comparative labor advantage but arises from the disparate times required to learn new knowledge bases/skillsets. It is argued that in the future, comparative learning advantages between humans and machines will determine their division of labor. (2) Two dimensions of job performance -- expertise and interpersonal: Job tasks can be sorted into two broad categories. Tasks that require expertise have stable endpoints, which makes these tasks inherently repetitive and subject to automation. Tasks that are interpersonal are highly context-dependent and lack stable endpoints, which makes these tasks inherently non-routine. Humans compared to machines have a comparative learning advantage along the interpersonal dimension, which is increasing in value economically. (3) The Learning Game is a time-dependent version of Turing's "Imitation Game." It is more than a thought experiment. The "Learning Game" provides a mathematical framework with quantitative criteria for training and assessing comparative learning advantages. The book is highly interdisciplinary -- presenting philosophical arguments in economics, artificial intelligence, and education. It also provides data, mathematical analysis, and testable criteria that researchers in these fields will find of practical use. The book calls for a rethinking of how labor markets operate and how the education system should prepare students for future jobs. It concludes with a list of counterintuitive recommendations for future education and labor policies that all stakeholders -- employers, employees, educators, students, and political leaders -- should heed.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Description based on online resource; title from digital title page (viewed on July 12, 2023).
Series SpringerBriefs in philosophy, 2211-4556
Available in Other Form Print version: 9783031310034
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Record Appears in Online Resources > Ebooks
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Introduction: The difference between knowing and learning
Labor Markets: Comparative learning advantages
Learning to Work: The two dimensions of job performance
The Judgment Game: The Turing Test as a general research framework
The Learning Game: A time-dependent Turing Test
Implications: Recommendations for future education and labor policies.

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