Irreducible worlds of inexhaustible meaning: early 1950s machine learning as subjective decision making, creative imagining and remedy for the unforeseen.

Little historical work examines the problems, practices and values of 'machine learning' as it was understood and justified within pattern recognition research communities prior to the 1980s. This omission has led to a failure to appreciate how the efficacy of machine learning was often justified by its perceived capacity for 'originality' rooted in machine (and human) subjectivity. This paper examines why and how early 1950s pattern recognition researchers came to see 'machine learning' as a technical and epistemological set of nominalist strategies for performing induction and abduction given incomplete, complex or contradictory information that might also spur 'creative' insight in such diverse activities as political judgement and scientific inquiry. I document local research problems, epistemological commitments, institutional contexts and the circulation of 'machine-learning' practices and values through three cases of early-career researchers imagining, building and programming digital computers to 'learn' from 1950 to 1953. This machine learning implemented in learning programs came to be seen by some researchers as more efficacious descriptions of the natural and social world because these descriptions were perspective-dependent, profoundly contingent and contextually non-exhaustive. Often indexed as colloquial appeals to greater 'generality', conceptions of machine learning's efficacy as the capacity to make meaning from contradictory information continues to inform contemporary debates regarding artificial intelligence, society and possibility. [ABSTRACT FROM AUTHOR]