Made in Brooklyn provides a belated critique of the Maker Movement: from its origins in the nineteenth century to its impact on labor and its entanglement in the neoliberal economic model of the tech industry. This critique is rooted in a case study of one neighborhood in Brooklyn, where artists occupy former factory buildings as makers. Although the Maker Movement promises to revitalize the city and its dying industrial infrastructure by remaking these areas as centers of small-scale production, it often falls short of its utopian ideals. Through her analysis of the Maker Movement, the author addresses broader questions around the nature of artistic work after the internet, as well as what the term ‘hipster’ means in the context of youth culture, gentrification, labor, and the influence of the internet. Part history, part ethnography, this book is an attempt to provide a unified analysis of how the tech industry has infiltrated artistic practice and urban space.
Made in Brooklyn provides a belated critique of the Maker Movement: from its origins in the nineteenth century to its impact on labor and its entanglement in the neoliberal economic model of the tech industry. This critique is rooted in a case study of one neighborhood in Brooklyn, where artists occupy former factory buildings as makers. Although the Maker Movement promises to revitalize the city and its dying industrial infrastructure by remaking these areas as centers of small-scale production, it often falls short of its utopian ideals. Through her analysis of the Maker Movement, the author addresses broader questions around the nature of artistic work after the internet, as well as what the term ‘hipster’ means in the context of youth culture, gentrification, labor, and the influence of the internet. Part history, part ethnography, this book is an attempt to provide a unified analysis of how the tech industry has infiltrated artistic practice and urban space.
How the use of machine learning to analyze art images has revived formalism in art history, presenting a golden opportunity for art historians and computer scientists to learn from one another. Though formalism is an essential tool for art historians, much recent art history has focused on the social and political aspects of art. But now art historians are adopting machine learning methods to develop new ways to analyze the purely visual in datasets of art images. Amanda Wasielewski uses the term “computational formalism” to describe this use of machine learning and computer vision technique in art historical research. At the same time that art historians are analyzing art images in new ways, computer scientists are using art images for experiments in machine learning and computer vision. Their research, says Wasielewski, would be greatly enriched by the inclusion of humanistic issues. The main purpose in applying computational techniques such as machine learning to art datasets is to automate the process of categorization using metrics such as style, a historically fraught concept in art history. After examining a fifteen-year trajectory in image categorization and art dataset creation in the fields of machine learning and computer vision, Wasielewski considers deep learning techniques that both create and detect forgeries and fakes in art. She investigates examples of art historical analysis in the fields of computer and information sciences, placing this research in the context of art historiography. She also raises questions as which artworks are chosen for digitization, and of those artworks that are born digital, which works gain acceptance into the canon of high art.
How the use of machine learning to analyze art images has revived formalism in art history, presenting a golden opportunity for art historians and computer scientists to learn from one another. Though formalism is an essential tool for art historians, much recent art history has focused on the social and political aspects of art. But now art historians are adopting machine learning methods to develop new ways to analyze the purely visual in datasets of art images. Amanda Wasielewski uses the term “computational formalism” to describe this use of machine learning and computer vision technique in art historical research. At the same time that art historians are analyzing art images in new ways, computer scientists are using art images for experiments in machine learning and computer vision. Their research, says Wasielewski, would be greatly enriched by the inclusion of humanistic issues. The main purpose in applying computational techniques such as machine learning to art datasets is to automate the process of categorization using metrics such as style, a historically fraught concept in art history. After examining a fifteen-year trajectory in image categorization and art dataset creation in the fields of machine learning and computer vision, Wasielewski considers deep learning techniques that both create and detect forgeries and fakes in art. She investigates examples of art historical analysis in the fields of computer and information sciences, placing this research in the context of art historiography. She also raises questions as which artworks are chosen for digitization, and of those artworks that are born digital, which works gain acceptance into the canon of high art.
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