Content Aware Studies

Marble, polyamide, machine learning algorithms, custom designed software, multichannel video installation; wall mounted PC

Technical and artistic assistance: Matthew Lenkiewicz and Artem Konevskikh 

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, utilising 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 normalisations of forms previously standardised 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 hyperrealistic 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 The technology is already in use for both investigation of historical documents (ex., Voynich Manuscript) as well as predictive instrument for modelling futures.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 endangered anthropocentric world.

The result of interpretation of antique portrait by general adversarial neural network based on the analysis of nearly 10,000 3D scans. (custom created dataset includes 3d scans of sculptures from the collections of Metropolitan Museum, Hermitage, British Museum, National Museum of Rome and other world renowned collections of antiquity);

Hellenistic Portrait GAN
Video loop 4’00”; 1:1 Aspect ratio
Machine Learning Algorithms, Custom Dataset
Parthenon Frieze Latent Space
Video, Full HD
01’12”, machine learning algorithms, custom dataset


CAS_08 Hellenistic Ruler;

Marble, Polyamide; Machine Learning Algorithms
Dimensions: 19x26x21;

CAS_09 Colos­sal head of Her­cules

Mar­ble, Polyamide; Ma­chine Learn­ing Al­go­rithms
Di­men­sions: 24x32x20

CAS_07 Tele­phos Frieze
Bot­ti­cino Mar­ble; Ma­chine Learn­ing Al­go­ry­thms
Di­men­sions: 56x67x17

CAS_10 Telephos Drapery;

Carrera marble, Machine Learning Algorithms
Dimensions: 60x40x14

Linux based server equipped with multiple GPUs per­form­ing general adversarial machine train­ing dur­ing the ex­hi­bi­tion ‘Conent Aware Studies’ at Alexander Levy Gallery, Berlin, Germany.


Marble, polyamide, machine learning algorithms, 3D scanning, 3D printing, video installation

CAS_03 Lu­cius_Verus

Car­rera Mar­ble, Polyamide, Ma­chine Learn­ing Al­go­rithms
Di­men­sions: 42x37x32

CAS_04 Parthenon_South_XI_31
Carrera Marble, Machine Learning Algorithms
Dimensions: 120x100x10cm;


‘Open Codes’, ZKM, Karlsruhe, Germany, 2018
Curator: Peter Weibel

Marble, polyamide, machine learning algorithms, 3D scanning, 3D printing, video installation

CAS_05 Julia Mamea; 2018
Crema Marfil Marble, polyamide, machine learning algorithms
Dimensions: 20x35x21

CAS_06 Female Portrait; 2018
Crema Marfil marble, polyamide, machine learning algorithms
Dimensions: 22x26x23