Translated Leads Major European Initiative DVPS to Advance AI Beyond Language Models and Into the Physical World
DVPS, which stands for Diversibus Viis Plurima Solvo (Latin for “Through diverse paths, I solve many issues”), develops a new class of AI systems capable of learning through real-world interaction by combining language, vision, and sensor data.
Supported by €29 million in funding, DVPS is one of the largest European investments in AI research.
The initiative, which aims to strengthen Europe’s strategic leadership in the global AI landscape, unites twenty leading organisations across nine countries to develop advanced multimodal foundational models with wide-reaching applications.
Rome – May 28, 2025
Translated, a leader in AI-powered language solutions, announces the launch of DVPS, one of the largest Horizon Europe projects to date, backed by a €29 million seed investment and uniting twenty leading partners across nine countries to shape the next frontier in artificial intelligence.
Building on the success of large language models, DVPS explores the future of AI through multimodal foundation models. Unlike today’s systems, which learn from representations of the world via text, images, and video, these next-generation models are designed to gain a grounded understanding through direct interaction with the physical world. By integrating language, vision, and sensor data, they develop a deeper contextual awareness, extending human capabilities in domains where trust, precision, and adaptability are crucial.
"Large language models have been a breakthrough, but we’re starting to see their limits both in their architecture and how they learn from static, human-made content, only available in the digital world. To go further, AI must interact with the real world in real time. With DVPS, we’re enabling machines to grow by engaging with reality, and exchanging their learning between themselves instantly".
Marco Trombetti – founder and CEO of Translated
While today’s AI systems excel at many tasks, they still struggle with those requiring a higher level of understanding of physical context. For example, in language, these models fail at enabling live translation in challenging environments, such as crowded, noisy spaces with multiple speakers. Traditional systems struggle to identify who is speaking, while humans easily focus on the right person by using visual and acoustic cues, like gaze direction, facial orientation, voice loudness, and distance. Audio-only translation systems perform poorly in these settings because they lack contextual awareness. DVPS, by contrast, combines visual input, spatial sound, and speech direction to identify the correct speaker and deliver more accurate translations.
Potential applications span multiple areas: in language, enabling real-time translation across a wide range of languages, with an understanding of text, speech, gestures, and physical context; in health, supporting the early detection of cardiovascular risk using a 3D digital twin of the heart generated from medical imaging; and in the environment, enhancing disaster response through flood prediction powered by satellite and drone data combined with real-time observational signals.
The initiative is led by Translated, which oversees the overall vision and execution.
The DVPS founding team consists of 70 top European AI scientists from across the following partners:
- Research: University of Oxford, The Alan Turing Institute, École Polytechnique Fédérale de Lausanne, ETH Zurich, Imperial College London, Fondazione Bruno Kessler, Karlsruhe Institute of Technology, Universitat de Barcelona, and Vlaamse Instelling voor Technologisch Onderzoek
- Vertically specialized partners: Heidelberg University Hospital, Vall d’Hebron Institut de Recerca, and Amsterdam University Medical Centers, Deepset, Sistema, and MEEO, Lynkeus, Data Valley, and Pi School of AI
- High-performance computing (model training): Cyfronet, Poland’s national HPC centre
DVPS is funded by the European Union's Horizon Europe Framework under Grant Agreement No. 101213369
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