Russian designer Denis Simachev is set to contribute to the evolution of the Sber Kandinsky neural network by supplying a dedicated dataset to train a generative machine learning model. The arrangement, formalized in a signed agreement between Simachev and Maxim Eremenko, Deputy Head of the Artificial Intelligence and Machine Learning Development Department at Sberbank, confirms a shared commitment to advancing artificial intelligence. Sber’s press service described the document as a framework for ongoing collaboration in AI development and practical experimentation.
The collaboration centers on using Simachev’s own datasets to guide the creation of a historically faithful Khokhloma design, a traditional Russian decorative painting style renowned for its vibrant patterns and cultural resonance. The initiative aims to ensure that the generated outputs reflect authentic regional artistry while exploring how these motifs can translate into modern design processes within the network.
Beyond the initial dataset use, the parties plan to explore scenario development for applying Kandinsky’s machine learning models to future pilot projects spanning the fashion sector and adjacent creative industries. This forward-looking approach seeks to test how AI-generated designs can augment human creativity in real-world settings, from concept to prototype to production.
Maxim Eremenko underscored that training generative neural networks benefits greatly from the involvement of seasoned creative professionals. He noted that collaborations with a figure like Denis Simachev bring practical artistic intuition to the training process, helping the Kandinsky model acquire a deeper, more nuanced artistic education that aligns with human design sensibilities.
According to Eremenko, the signed agreement foresees a broad range of studies across multiple creative domains. He expressed confidence that pairing a talented designer with advanced AI will yield new, compelling works that highlight and preserve traditional Russian cultural values while translating them into contemporary forms.
Simachev expressed optimism that working with the Sber Kandinsky neural network will broaden research horizons in the field of art, enabling more personalized exploration and the adoption of technologies that keep artistic practice aligned with current times. He described the collaboration as a meaningful step toward integrating symbolic cultural aesthetics with cutting-edge computational tools.
He emphasized that the partnership marks a significant milestone for both technical development and creative innovation within modern culture. The designer spoke of an exciting path ahead, where neural network-assisted design processes can reach wider audiences and stimulate deeper engagement with Russia’s artistic heritage on global platforms.
Simachev concluded by sharing his belief that this integration will empower more people to study, interpret, and appreciate the Russian cultural code and its elements—not only within the country but also across international stages. He sees the project as a catalyst for cross-cultural dialogue and a broader appreciation of traditional design through AI-enabled exploration, with an eye toward long-term impact on the way art and technology intersect on the world stage.