Machine learning underlies Big Data and drives today’s massive investment.

Evidence

Opacity is also a practical consequence of operations. Data is collected, processed, and purchased so that particular actors can do particular things. The computing complexes available to corporate and state actors today are capable not just of automatically tracing the relations between large numbers of variables and categories but also of finding patterns in massively large data sets in which no order appears to exist. Underlying the much-hyped Big Data is a new model of knowledge called Machine Learning. — Nick Couldry & Ulises A. Mejias, The Costs of Connection, 392

Categorization

• DEFINE: Establishes the contested definitional construct — machine learning as the new model of knowledge beneath Big Data.
• XREF: connects to: Machine learning demands computation so massive it drove Google’s data center investments. — Shoshana Zuboff, The Age of Surveillance Capitalism, 591; Machine-learning data operations have grown too complex for humans to comprehend or audit. — Shoshana Zuboff, The Age of Surveillance Capitalism, 1546; Machine learning divides into unsupervised, supervised, and reinforcement learning — Brian Christian, The Alignment Problem, 58 (+1 more)

Broader Connections

• Machine learning demands computation so massive it drove Google’s data center investments. #exemplifies (0.85) — Shoshana Zuboff, The Age of Surveillance Capitalism, 591
• Machine-learning data operations have grown too complex for humans to comprehend or audit. #expands (0.75) — Shoshana Zuboff, The Age of Surveillance Capitalism, 1546
• Big data correlations alone cannot deliver causal knowledge to prediction systems #qualifies (0.72) — Shoshana Zuboff, The Age of Surveillance Capitalism, 930
• Machine learning divides into unsupervised, supervised, and reinforcement learning #expands (0.70) — Brian Christian, The Alignment Problem, 58