After Constable’s Clouds: Toward A Machine Learning Paradigm for Studying Pictorial Realism

FAIN: HAA-287938-22

Pennsylvania State University (University Park, PA 16802-1503)
Elizabeth C. Mansfield (Project Director: January 2022 to present)
James Z. Wang (Co Project Director: May 2022 to present)
Jia Li (Co Project Director: August 2022 to present)

The further development of computer vision methods to compare and classify features in paintings, specifically those from the Barbizon, Realist, and Impressionist traditions.

After Constable’s Clouds will use computer vision to enhance art historical understanding of 19th-century Realism. The emergence of Realism in French landscape painting is often linked to the 1824 exhibition in Paris of John Constable’s unidealized view of the English countryside, The Hay Wain. Viewers particularly noted the veracity of Constable’s clouds. Indeed, our computational research shows that Constable’s clouds are more closely modeled on the structure of actual clouds than those of his contemporaries, with French academician Pierre-Henri de Valenciennes a near rival. Valenciennes taught a generation of landscape artists, emphasizing the importance of plein-air sky studies, yet histories of French landscape tend to cast Constable as Realism’s catalyst. After Constable’s Clouds will test this historiography by using computer vision to classify and compare the clouds in paintings by Barbizon, Realist, and Impressionist painters with those of Constable and Valenciennes.

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Hopfield and Hinton’s neural network revolution and the future of AI (Article)
Title: Hopfield and Hinton’s neural network revolution and the future of AI
Author: Brad Wyble
Author: James Wang
Abstract: In this opinion piece, the authors, from the fields of artificial intelligence (AI) and psychology, reflect on how the foundational discoveries of Nobel laureates Hopfield and Hinton have influenced their research. They also discuss emerging directions in AI and the challenges that lie ahead for neural networks and machine learning.
Year: 2024
Secondary URL: https://doi.org/10.1016/j.patter.2024.101094
Access Model: open access
Format: Journal
Periodical Title: Patterns
Publisher: Cell Press

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