“This book offers a valuable guide for navigating the emerging landscape of AI-driven imagery. The item is both compact and elegant, while also being quite comprehensive. Lee provides multiple perspectives on understanding AI images by drawing upon concepts from art history and media archaeology. I highly recommend it to students and researchers across all disciplines that involve images, as well as to practitioners in the visual arts.”
Lev Manovich
Order Algorithm, Image, Art here.
Algorithm, Image, Art examines the history, processes, and ideas behind current visual culture. Machine learning algorithms now have a pervasive influence on the aesthetics and meaning of images. But while novel in some aspects, these recent developments are connected to much earlier methods. Analog, geometrical, optical, and procedural strategies have long shaped visual culture, and are expanded on in recent applications of machine learning. This book looks at how the production of images in terms of algorithmic instructions has shaped images and art, as well as the values used to assess them. It draws connections between the algorithmic forms of visual media we are familiar with today and the precursors from which they evolved.
Algorithm, Image, Art
New York: Atropos Press, 2024
978-1-73755-914-6
Translation to Chinese: Hangzhou: Zhejiang People’s Fine Arts Publishing House, forthcoming: 2024
Summary
Algorithms play an increasingly influential role in the production, circulation, and interpretation of images. This shift has complex implications for art, the humanities, and visual culture. As machine learning becomes pervasive beyond the technical research contexts it emerged from and had previously been largely confined to, it has found a wide variety of visual applications. From the direct generation of images to influencing the display of visual content based on the large-scale collection and analysis of data, algorithms have become pervasive.
The highly automated performance of visual processing tasks by machines allows digital aesthetics to be informed by algorithms, statistical models, and the large-scale analysis of data. As a result, images are increasingly defined by their engagement with algorithms, which structure them aesthetically, processually, and semantically. This has effects that often exceed description in terms of direct human perception, agency, and understanding, while being to a great extent informed by and entangled with these. In this sense, recent technical developments such as the computational generation of images using machine learning systems tap into long-running theoretical challenges regarding the non-visual, immaterial, and non-human aspects of art, images, and visual media.
History of Algorithmic Image Production
This book examines how the algorithmic structuring of images may offer new ways of understanding recent technical developments and their surrounding discourses. Through close examination of relevant instances from the history of visual technologies, we consider how current discourses surrounding nascent forms of image-making may in some cases disrupt while in others reinforcing established conventions in thinking about visual media.
Tracing the history of algorithmic image production back to earlier instances that have meaningfully shaped artistic practices and that offer insight into current discourses around machine learning and artificial intelligence, Algorithm, Image, Art looks into the role played by algorithms in structuring visual compositions geometrically and processually, which ultimately holds implications for how they are interpreted. This work addresses a need for counternarratives to the intense hype about artificial intelligence currently, which has resulted in a majority of attention being focused on particular talking points, whether from a critical point of view or one of endorsement.
With this project, I sought to draw awareness to the historical context that recent manifestations of the algorithmic emerge from. Highlighting this through a longer timeframe than is typically considered when speaking about algorithmically-produced images and art makes the case that current aesthetics, methods, and conceptualizations of algorithmic media are far from uniquely novel and in fact share relevant aspects with even much earlier precursors to those visible today.