Nature Machine Intelligence Nature Machine Intelligence will publish high-quality original research and reviews in a wide range of topics in machine learning, robotics and AI. The journal will also explore and discuss the significant impact that these fields are beginning to have on other scientific disciplines as well as many aspects of society and industry. There are countless opportunities where machine intelligence can augment human capabilities and knowledge in fields such as scientific discovery, healthcare, medical diagnostics and safe and sustainable cities, transport and agriculture. At the same time, many important questions on ethical, social and legal issues arise, especially given the fast pace of developments Nature Machine Intelligence will provide a platform to discuss these wide implications — encouraging a cross-disciplinary dialogue — with Comments, News Features, News & Views articles and also Correspondence.
- Probabilistic modelling of single-cell bisulfite sequencing data with MethylVI
por Ethan Weinberger en abril 28, 2026 a las 12:00 amNature Machine Intelligence, Published online: 28 April 2026; doi:10.1038/s42256-026-01225-9MethylVI enhances analyses of single-cell bisulfite sequencing methylomic data via a deep generative model that accounts for the unique technical and biological sources of variability in this data modality.
- A domain-adapted large language model to support clinicians in psychiatric clinical practicepor Ruoxi Wang en abril 27, 2026 a las 12:00 am
Nature Machine Intelligence, Published online: 27 April 2026; doi:10.1038/s42256-026-01224-wThe authors present PsychFound, a psychiatry-specialized large language model trained on expert knowledge and clinical records. It achieves clinical-grade performance and enhances diagnostic and treatment decisions when deployed in clinical workflows.
- Programmable RNA translation through deep learning-driven IRES discovery and de novo generationpor Yanyi Chu en abril 24, 2026 a las 12:00 am
Nature Machine Intelligence, Published online: 24 April 2026; doi:10.1038/s42256-026-01213-zChu et al. present a framework for programmable RNA translation, of interest for RNA therapeutics. The method enables large-scale discovery and engineering of internal ribosome entry sites, which are validated by high-throughput functional assays.
- A multimodal large language model for materials sciencepor Yingheng Tang en abril 24, 2026 a las 12:00 am
Nature Machine Intelligence, Published online: 24 April 2026; doi:10.1038/s42256-026-01214-yTang et al. introduce MatterChat, a multimodal framework effectively integrating material structural data with large language models. It achieves high-precision property predictions and provides interpretable reasoning to accelerate materials discovery.
- Adopting a human developmental visual diet yields robust and shape-based AI visionpor Zejin Lu en abril 24, 2026 a las 12:00 am
Nature Machine Intelligence, Published online: 24 April 2026; doi:10.1038/s42256-026-01228-6Lu, Thorat and colleagues report that training AI vision systems with a human-inspired developmental visual diet results in a stronger reliance on shape over texture features, enabling substantially more robust visual inference. This finding points to a resource-efficient path to robust vision.