Проект противопоставляет эстетику классического античного искусства и генеративного метода, исследует автоматизацию художественного труда, новые формы материальной культуры и квази-археологию. Художник исследует возможность коллаборации с искусственным интеллектом, потенциал этой синтетической субъективности, мета-археологическую форму производства знания, этические проблемы внедрения методов компьютаци. Он поднимает вопросы о традиционном понимании идеи авторства, подлинности и производства искусства и истории. Проект Content Aware Studies инициирует масштабное международное исследование возможностей технологий ML (машинного обучения) и AI (искусственного интеллекта) в отношении искусства и современной культуры.

The Content Aware Studies series initiates an inquiry into the possibilities AI and machine learning technologies hold, both as tools for speculative historical investigation and means of emerging aesthetic formation. The process, developed for over a year now by an artist together with a data scientist engaged in training artificial neural networks, replenishes lost fragments of sculptures and friezes of classical antiquity and generates never before existing, yet authentic objects of that era. The research examines how our custom developed AI, utilizing the largest recent advancement in computer vision and cognition, operates when trained on datasets consisting of thousands of 3D scans of classical sculptures from renowned international museum collections (i.e., British Museum, Metropolitan, National Roman Museum etc.). The algorithm generates models, which are then 3D printed in various synthetic materials, filling the voids in the eroded and damaged marble sculptures. Some of these algorithmic outputs are turned into new entirely marble sculptures uncanny in their algorithmic integrity. They render the work of synthetic agency that lends a 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. Recent research in General Adversarial Networks (GANs, a class of machine learning systems) has shown outstanding results in hyper realistic image rendering. The technology is already in use for both investigation of historical documents (ex., Voynich Manuscript) as well as predictive instrument for modelling futures. However, we might want to critically examine a role of such form of knowledge production beforehand; how do we distinguish between accelerated forms of empirical investigation and algorithmic bias? Will this question survive, when such forms of knowledge production become ubiquitous governing agencies? The work examines questions and topics of bias, authenticity, materiality, automation, authorship, knowledge and history. It inspects what visual and aesthetic qualities for such guises are conveyed when rendered by synthetic agency and perceived through our anthropocentric lens. What of our historical knowledge and interpretation, encoded into the datasets will survive this digital digestion? It examines new forms of historical knowledge and artistic production and calls into question the ethical implications of such approaches in relation to culture and the notion of endangere