Revolutionary Mathematics: Artificial Intelligence, Statistics, and the Logic of Capitalism
Aug 21, 2023
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Justin Joque, visualization librarian at the University of Michigan and author of Revolutionary Mathematics: Artificial Intelligence, Statistics, and the Logic of Capitalism, explores the impact of algorithms on financial systems and discusses the consequences of relying on digital systems for decision-making. He delves into the differences between frequentist and Bayesian statistical models, examines capitalist realism and its limitations, and highlights the history and use of statistical models in relation to capital.
Statistical models in financial systems prioritize maximizing rewards, contributing to the financialization of markets.
Understanding the subjectivity of statistical models, such as Bayesian statistics, is essential to grasp their potential for automation and assigning probabilities.
Deep dives
Role of algorithms and statistical models in financial systems
The podcast episode explores the role of algorithms and statistical models in financial systems. The author of Revolutionary Mathematics discusses how these models are based on maximizing rewards, contributing to the financialization of markets. The author highlights the need to understand not only algorithmic bias but also the larger questions of political economy that shape these systems.
Subjectivity in statistical models and the concept of probability
The podcast delves into the subjectivity of statistical models and the concept of probability. The author explains the differences between frequentist and Bayesian statistical models, emphasizing that probability is a metaphysical category and a supplement to deal with uncertainty. The author also links the subjective nature of Bayesian statistics to its potential for automation, making it easier to assign probabilities to individual events.
The tie between statistical models and market-driven metaphysics
The podcast discusses the connection between statistical models, such as Bayesian statistics, and market-driven metaphysics. The author explores the idea that probability and value function as metaphysical supplements, similar to how objects and value are objectified in Marxist analysis. The author argues that the ways in which these models produce knowledge are rooted in political economy, specifically the pursuit of value and optimization for certain actors.
Challenges and risks associated with AI and statistical models
The podcast addresses challenges and risks associated with AI and statistical models. It highlights the potential for data pools to be poisoned, impacting the quality of modeling outcomes. Additionally, the conversation touches on the deterioration of AI models, potential data pollution, and the generation of nonsensical or incomprehensible content. The podcast also raises concerns about the monopolization of AI and its adverse effects on search engine optimization and information quality.
Justin Joque is a visualization librarian at the University of Michigan and the author of the book Revolutionary Mathematics: Artificial Intelligence, Statistics, and the Logic of Capitalism. His book examines the statistical models on which our algorithms, machine learning, and financial systems are built, highlighting the mechanisms of abstraction which lend these models an air of misleading objectivity. Can statistical models be used towards emancipatory aims?
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