The Good Tech Companies - AI for Art Examining: Claude Monet, Édouard Manet, and Natalia Goncharova

Episode Date: July 10, 2025

This story was originally published on HackerNoon at: https://hackernoon.com/ai-for-art-examining-claude-monet-edouard-manet-and-natalia-goncharova. Art-tech company ArtCollecting has dedicated its resources to analyzing around 100 works from Nina Moleva’s collection. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai, #artificial-intelligence, #art, #artist, #monet, #manet, #machine-learning, #good-company, and more. This story was written by: @artcollecting. Learn more about this writer by checking @artcollecting's about page, and for more stories, please visit hackernoon.com. Art-tech company ArtCollecting has dedicated its resources to analyzing around 100 works from Nina Moleva’s collection. The study was conducted using a custom-developed algorithm. The algorithm analyzed not only visual features but also paint microstructure, signs of aging, and even hidden layers.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. AI for Art Examining, Claude Monet, Edward Manet, and Natalia Goncharova. By Art Collecting, Info, International Art Tech Company Art Collecting, specializing in art authentication technologies, has dedicated its resources to analyzing around 100 works from Nina Malev's collection. The study was conducted using a custom-developed algorithm. The machine learning process was overseen by cultural heritage expert and art collecting director Marina Nadeva, in collaboration with developers who previously worked on blockchain integration for the company. https://cdn.hackernoon.com

Images 2TT2A52YMFPGZIYQUHOJEWFT4D2320250710T 4h44m30s 959ZZWU7ALWF2x6DQU8JC9E3K. Nina Maleva's collection is of particular interest because the owner herself valued it at $2 billion and named the Russian president as the heir in her will. But more importantly, this is the first time experts have encountered a collection with such an enigmatic provenance. According to Molleiva, the wife of non-conformist artist Ili Beludin, their holdings include works by Leonardo da Vinci, Michelangelo, Titian, van Dyck, Monet, and even Natalia Goncharova. If true, this would be the largest private collection of world-class masterpieces. Authenticating even a portion of these works could have

significant repercussions for the art world. Now, researchers have a chance to unravel the mystery. Marina Nadeva, a cultural heritage expert, examined part of the collection using art collecting's innovative AI alongside traditional art analysis methods, including comparative analysis, stylistic assessment, and technical examination. The study by Marina Nadeva focused on works by Ily Beludin, 16th-19th century icons, and painting sand graphic works attributed to Claude Monet, Edward Manet, and Natalia Goncharova. The algorithm analyzed not only visual features but also paint microstructure, signs of aging, and even hidden layers, details that might escape the human eye.

This is an unprecedented case, says Marina Nadeva. Technology has never before encountered so many potential masterpieces at once. If even one work is verified, it could reshape our understanding of 20th century private collections. Research example-sortie-collectings experts have achieved significant breakthroughs in the study of provincial Russian iconography through their eye-powered examination of The Miracle of Christ in Latom, an icon from art historian Nina Malevich's private collection. Cultural heritage expert Marina Nadeeva has completed a comprehensive analysis of the icon using AI technology developed by art collecting. This project marks a new chapter in the study of provincial Russian icon painting, a field that has long been under-researched.

HowEye unlocked the icon's secrets a custom algorithm was created specifically for this study, capable of processing and cross-referencing vast datasets. The system analyzed information from over 100,000 icons, 15th-19th centuries, produced in provincial workshops, drawing from museum catalogs, antique dealer archives, historical records, and obscure private collections. The AI not only reconstructed the icon's likely original appearance but also identified works with similar stylistic and technical features, an especially valuable contribution, given that provincial iconography remains a, blank spot, in art history. Unlike major icon painting centers, provincial workshops rarely documented their output, and their surviving works

are scattered across small regional museums and private collections. Why this is a major breakthrough traditionally, such research required years of work by art historians, restorers, and chemical analysts. Now, AI accelerates the process dramatically, analyzing enormous data sets in days. Crucially, the technology doesn't replace experts, it empowers them with advanced analytical tools. We're not just scanning images, we're teaching the system to recognize stylistic traits, technical methods, even restoration marks, explains Marina Nadeva.

For instance, our algorithm can pinpoint characteristics of a specific workshop or even an individual icon painter by comparing minute details, the brushwork of highlights, the rendering of fabric folds, or the use of particular pigments. Discoveries about, the miracle of Christ in Latom, beyond stylistic analysis, a full technical examination was conducted. Researchers determined the icon's approximate creation period, identified pigments and binding materials, and clarified its artistic tradition. The most striking achievement, however, was the eye-assisted reconstruction of damaged elements. Since iconography follows strict compositional rules, recurring motifs allowed the algorithm to compare the damaged areas with thousands of analogues,

proposing the most probable original composition. Why this matters for art history Russian iconography isn't limited to famed schools like Moscow, Novgorod, or Pollock. Hundreds of unnamed provincial masters created works of equal significance, yet without systematic study, many such icons are dismissed as ordinary, or even mislabeled as forgeries. This research helped reclaim Thier place in art history. Authenticating a work attributed to Edouard Manet experts have begun verifying the authenticity of a painting attributed to Edouard Manet using artificial intelligence. A specialized algorithm analyzes the artist's brushwork by comparing it to verified masterpieces.

The neural network has already been trained on open access databases from the Musée d'Orsay, the Musée Marmotton Monet in Paris, and the National Gallery of Art in Washington. Researchers are now refining the analysis by studying Manet's drawings and watercolors to cross-reference details. Simultaneously, the algorithm is being adjusted to identify other potential authors should the attribution to Manet prove incorrect. Microscopic technical analysis has helped narrow down the likely timeframe of the painting's creation. Additionally, archival research in Russian libraries, including French publications that entered Imperial Russia and Lat-Earth USSR, has been completed. Investigators examined records dating back to 1912 for further clues on provenance.

The study is ongoing, with new findings expected soon. AI helps date the transfiguration. ICANA custom algorithm developed by Art Collecting's team enabled a comprehensive analysis of the artwork. The system processed over 100,000 samples of 15th-19th century iconography from Russian provincial workshops, as well as Greek, Bulgarian, and Turkish collections. This large-scale study was made possible by a unique database aggregating museum catalogues, antique collections, and archival materials.

A key focus was the analysis of inscriptions on the icon. The algorithm was specifically trained to recognize stylistic evolutions in lettering across five centuries, 15th to early 20th century, incorporating all known alphabets, typefaces, and their usage in iconography. This allowed precise dating based on palaeographic features. Parallel technical examinations, including pigment and binding medium analysis, corroborated the AI's findings through microscopy, creating a multi-layered understanding of the icon's origins. About the technology in development art collecting is pioneering the world's first AI system specifically trained for artwork attribution and authentication.

Unlike traditional methods requiring months of meticulous analysis, its algorithm evaluates style, technique, and materials within hours by cross-referencing data against 10 million verified reference samples. While currently tested on only one-third of the collection, even preliminary results could revolutionize art market technologies. If the AI confirms authentic masterpieces, it would prove that lost treasures had been hidden four decades in an ordinary Moscow apartment. places, it would prove that lost treasures had been hidden for decades in an ordinary Moscow apartment. Conversely, should the collection bed-abunked as a hoax, the revelation would be equally sensational.

One thing is already clear, art collecting's technology provides the first viable tool for solving such art historical mysteries. And who knows, perhaps the next, Moliwilist, is already awaiting its investigator. Note. Mollivalist is retained as a proper noun referencing the collection's controversial nature, similar to terms like, girled trove. For broader audiences, consider adding a brief contextual phrase like, the so-called, Mollivalist, of disputed artworks. Thank you for listening to this Hacker Noon story, read by Artificial Intelligence. Visit HackerNoon.com to read, write, learn and publish.