Content Aware Studies (CAS) series is comprised of a vast amount of data, AI experiments, objects, moving image works, films and essays. It inquires how the use of machine learning in historical analysis and reproduction as a scientific tool brings to the forefront ethical questions of bias contamination within data and automation of its analysis. Inspired by examples of confusing para-scientific interventions such as AI-based Voynich Manuscript decryptions, the CAS series examines the various sides of this inquiry. It also speculates about material objects as synthetic documents of machine-rendered histories.
The objects from the initial iteration of the CAS series came through methodologies developed with data scientists and based on training artificial neural networks aiming to replenish lost fragments of sculptures, friezes and other objects of classical antiquity as well as to generate never-before-existing, yet algorithmically genuine, objects of that era. The research examined outputs of advanced AI models trained on datasets consisting of thousands of 3D scans of classical sculptures from renowned international museum collections. The models generated by the algorithm were then 3D-printed in various synthetic materials, filling the voids in eroded and damaged marble sculptures. Some of these algorithmic outputs were turned into entirely new marble sculptures carved by machines. Uncanny in their algorithmic integrity, they posed questions about whether they can be considered objects of classical antiquity. They render the work of a synthetic agency that lends faithful authenticity to the forms, while also producing bizarre errors and algorithmic normalizations of forms previously standardized and regulated by the canon of Hellenistic and Roman art.
The objects from the initial iteration of the CAS series came through methodologies developed with data scientists and based on training artificial neural networks aiming to replenish lost fragments of sculptures, friezes and other objects of classical antiquity as well as to generate never-before-existing, yet algorithmically genuine, objects of that era. The research examined outputs of advanced AI models trained on datasets consisting of thousands of 3D scans of classical sculptures from renowned international museum collections. The models generated by the algorithm were then 3D-printed in various synthetic materials, filling the voids in eroded and damaged marble sculptures. Some of these algorithmic outputs were turned into entirely new marble sculptures carved by machines. Uncanny in their algorithmic integrity, they posed questions about whether they can be considered objects of classical antiquity. They render the work of a synthetic agency that lends faithful authenticity to the forms, while also producing bizarre errors and algorithmic normalizations of forms previously standardized and regulated by the canon of Hellenistic and Roman art.
The objects from the initial iteration of the CAS series came through methodologies developed with data scientists and based on training artificial neural networks aiming to replenish lost fragments of sculptures, friezes and other objects of classical antiquity as well as to generate never-before-existing, yet algorithmically genuine, objects of that era. The research examined outputs of advanced AI models trained on datasets consisting of thousands of 3D scans of classical sculptures from renowned international museum collections. The models generated by the algorithm were then 3D-printed in various synthetic materials, filling the voids in eroded and damaged marble sculptures. Some of these algorithmic outputs were turned into entirely new marble sculptures carved by machines. Uncanny in their algorithmic integrity, they posed questions about whether they can be considered objects of classical antiquity. They render the work of a synthetic agency that lends faithful authenticity to the forms, while also producing bizarre errors and algorithmic normalizations of forms previously standardized and regulated by the canon of Hellenistic and Roman art.
The objects from the initial iteration of the CAS series came through methodologies developed with data scientists and based on training artificial neural networks aiming to replenish lost fragments of sculptures, friezes and other objects of classical antiquity as well as to generate never-before-existing, yet algorithmically genuine, objects of that era. The research examined outputs of advanced AI models trained on datasets consisting of thousands of 3D scans of classical sculptures from renowned international museum collections. The models generated by the algorithm were then 3D-printed in various synthetic materials, filling the voids in eroded and damaged marble sculptures. Some of these algorithmic outputs were turned into entirely new marble sculptures carved by machines. Uncanny in their algorithmic integrity, they posed questions about whether they can be considered objects of classical antiquity. They render the work of a synthetic agency that lends faithful authenticity to the forms, while also producing bizarre errors and algorithmic normalizations of forms previously standardized and regulated by the canon of Hellenistic and Roman art.
Thoughts on Materiality
Materiality has reappeared as a highly contested topic in recent art. Modernist criticism tended to privilege form over matter — considering the material as the essentialized basis of medium specificity - and technically based approaches in art history reinforced connoisseurship through the science of artistic materials. But in order to engage critically with materiality in the post-digital era, the time of big data and automation, we may need a very different set of methodological tools.
We may need to address digital infrastructures as entirely physical and to re-examine the notion of “dematerialization”, by addressing materialist critiques of artistic production, surveying relationships between matter and bodies, exploring the vitality of substances; and looking closely at the concepts of inter-materiality and trans-materiality emerging in the hybrid zones of digital experimentation. The image below is a result of the interpretation of an antique portrait by a general adversarial neural network based on the analysis of nearly 10,000 3D scans. The custom-created dataset included 3d scans of sculptures from the collections of the Metropolitan Museum, Hermitage, British Museum, National Museum of Rome and other world-renowned collections of antiquity.
CAS Essays on Synthetic Histories
A series of essays on Synthetic Histories, Hylomorphism and Materiality, and Predispositions by Design in which the work Content Aware Studies is at the centre as a case study for the proposed critique of AI-driven methodology in historiography.
Links to downloadable PDFs:
2022 ‘Museum of Synthetic Histories’ - E. Kraft, E. Kormilitsyna | Published by BCS. Learning and Development Ltd. Proceedings of POM Conference, UDK Berlin 2021
2021 ‘On Content Aware and Other Case-Studies’ - E. Kraft, E. Kormilitsyna | Published by City University of Hong Kong, HKG
‘Museum of Synthetic Histories’ Essay Book Cover.
Content Aware Studies, solo show, Alexander Levy, Berlin, DEU, 2019.
NTAA’22, New Technological Art Award, group show, Zebrastraat Ghent, BEL, 2021.
‘Museum of Synthetic Histories’ Essay Book Cover.
