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

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.