A new “blueprint” for building AI that highlights how the technology can learn from different kinds of data—beyond vision and language—to make it more deployable in the real world, has been developed by researchers at the University of Sheffield and the Alan Turing Institute.
The framework, which can act like a guide in how to create and deploy AI, could make the technology more practical, ethical and effective in solving real-world problems.
Published in the journal Nature Machine Intelligence, the framework is a roadmap for building multimodal AI—systems that learn from different types of data such as text, images, sound, and sensor readings.
AI typically learns from one type of information, such as text or images, but these more advanced multimodal AI systems integrate different data sources to form a more complete picture of the world. However, despite these advantages, the study has found that most multimodal AI systems and research are still mainly learning from vision and language data, which the researchers say limits its ability and potential to tackle complex challenges that require broader data.
For example, combining visual, sensor and environmental data could help self-driving cars perform more safely in complex conditions, while integrating medical, clinical and genomic data could make AI tools more accurate at diagnosing diseases and supporting drug discovery.
The new framework could be used by both developers in industry and researchers in academia, particularly in light of findings showing that 88.9% of papers featuring AI that draw on exactly two different types of data posted on arXiv—a leading open repository for computer-science preprints—in 2024 involved vision or language data.
Professor Haiping Lu, who led the study from the University of Sheffield’s School of Computer Science and Center for Machine Intelligence, said, “AI has made great progress in vision and language, but the real world is far richer and more complex. To address global challenges like pandemics, sustainable energy, and climate change, we need multimodal AI that integrates broader types of data and expertise.
“The study provides a deployment blueprint for AI that works beyond the lab—focusing on safety, reliability, and real-world usefulness.”
The research illustrates the new approach through three real-world use cases—pandemic response, self-driving car design, and climate change adaptation—bringing together 48 contributors from 22 institutions across the U.K. and worldwide.
Dr. Louisa van Zeeland, Research Lead at the Alan Turing Institute, said, “By integrating and modeling large, diverse sets of data through multimodal AI, our work together with Turing collaborators is setting a new standard for environmental forecasting. This sophisticated approach enables us to generate predictions over various spatial and temporal scales, driving real-world results in areas from Arctic conservation to agricultural resilience.”
More information:
Xianyuan Liu et al, Towards deployment-centric multimodal AI beyond vision and language, Nature Machine Intelligence (2025). DOI: 10.1038/s42256-025-01116-5. www.nature.com/articles/s42256-025-01116-5
University of Sheffield
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A new ‘blueprint’ for advancing practical, trustworthy AI (2025, October 21)
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