The series includes sculptures, videos and texts as inquiries into reverse-archeology via Ai models and datasets of 3d scans. Aimed at reconstructing missing fragments within the sculptures of classical antiquities and paleontological objects these computational systems are exercised at speculative object-oriented historicism of never-existed, yet algorithmically genuine, documents of synthetic histories.

Content Aware Studies series inquires into the aesthetic, philosophical, and historiographical outcomes of computational and particularly in relation to modern generative AI models, reconstruction and generative reinterpretation of the objects of art of classical antiquity and in particular to fill the voids of the missing fragments. The automated algorithmic training is performed on the manually assembled dataset over thousands of 3D scans of friezes and sculptures of the era. The series concerns the prospects of methods involving data, ML, AI, and other computational automations turning into semi- and quasi-archeological knowledge productions when performed to augment historical and cultural studies in the era of ubiquitous planetary-scale computation. Some of these algorithmic outputs are then turned into new machine-fabricated sculptures uncanny in their algorithmic integrity. They render the work of synthetic agency that lends faithful authenticity to the forms, while also producing eerie errors and algorithmically bizarre normalisations of forms previously standardised and regulated by the canon of Hellenistic and Roman art. These speculative forms of restoration, museology, and historiography provide a case study for critical examination of possibly misleading trajectories of algorithmically augmented research and knowledge production, poisoned by epistemic focal biases occurring at the level of software architecture of radical complexity. Preoccupied with biases, misleading guises, quasi-authenticities, and mixed-up entanglements of material and informational domains, it seeks to examine epistemological issues. The series questions: what epistemics do such methodologies hold by uncovering deeper and sharply unsuspected new knowledge or instead masking unacknowledged biases? In the optics of a non-human agency of the AI-investigator, what of our historical knowledge and interpretation encoded into the datasets will survive this digital digestion? How are historical narratives, documents, their meaning, and function perverted when their analysis has been outsourced to machine vision and cognition? What changes can we anticipate in historical knowledge and documentation during the era of the information overproduction epidemic and generative reality modeling?