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      The Cultural Life of Machine Learning: An Incursion into Critical AI Studies 

      Mechanized Significance and Machine Learning: Why It Became Thinkable and Preferable to Teach Machines to Judge the World

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      Springer International Publishing

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          Abstract

          The slow and uneven forging of a novel constellation of practices, concerns, and values that became machine learning occurred in 1950s and 1960s pattern recognition research through attempts to mechanize contextual significance that involved building “learning machines” that imitated human judgment by learning from examples. By the 1960s two crises emerged: the first was an inability to evaluate, compare, and judge different pattern recognition systems; the second was an inability to articulate what made pattern recognition constitute a distinct discipline. The resolution of both crises through the problem-framing strategies of supervised and unsupervised learning and the incorporation of statistical decision theory changed what it meant to provide an adequate description of the world even as it caused researchers to reimagine their own scientific self-identities.

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          Machine learning: Trends, perspectives, and prospects.

          Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
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            A logical calculus of the ideas immanent in nervous activity

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              The perceptron: a probabilistic model for information storage and organization in the brain.

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                Author and book information

                Book Chapter
                2021
                December 01 2020
                : 31-78
                10.1007/978-3-030-56286-1_2
                66a147d3-4a8b-4a89-ad6b-e306549aec44
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