This webinar will explore the use of Euclidean, hyperbolic and mixed auto-encoder and parametric embeddings for the purpose of understanding human culture, language, scientific discovery, and social networks through embedding every relational thing as data.
The speaker will begin with the case of human culture, and how dimensions induced by word differences (e.g., man – woman, rich – poor, black – white, liberal – conservative) in these vector spaces, closely correspond to dimensions of cultural meaning, and how the projection of words onto these dimensions reflects widely shared cultural connotations when compared to surveyed responses and labeled historical data.
It will show how nonparametric subsample and bootstrap approaches can reveal the stability of these associations, and will demonstrate these methods in a longitudinal analysis of the co-evolution of class and gender associations in the United States and Great Britain in the 20th century.
The speaker will also use embeddings to explore similarities and differences across the world's languages. This will reveal that while languages tend to have similar semantic clusters, with more concrete concepts that tend to be clustered consistently, those clusters are networked in different ways around the world, mapping out different organizations of meaning. The speaker will exemplify the use of hyperbolic embeddings for the purpose of recovering not social and semantic dimensions, but hierarchies in data in 21st century physics.
Finally, the webinar will explore the concepts of geometric curvature applied to social networks, and the meaning and potential for embedding networks with mixed positive, negative and neutral curvature for mapping out the social and cultural universes in ways that resonate with our modern understanding of the physical universe.
Dr. James A. Evans
Director, Knowledge Lab; Professor of Sociology, University of Chicago; Faculty Director, Masters Program in Computational Social Sciences; External Professor, Santa Fe Institute
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