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This paper explores ways of thinking about digital data that lie somewhere between blithe faith and critical dismissal. It focuses on the machine learning, an increasingly prevalent bundle of techniques and approaches that lies at the centre of contemporary data processing. Machine learning is used to program computers to find patterns, associations, and correlations, to classify events and make predictions on a large scale. As a set of techniques for classifying and predicting, machine learning lies close to centre of calculation in social network media, finance markets, robotics, and contemporary sciences such as genomics and epidemiology. This paper will discuss who is doing machine learning, who could do machine learning, and how they might do it differently.

4pm, Cowling Room, Social Sciences Building.