• raseliarison
  • nirinA
  • adrien
  • blog
  • code
  • FAQ
  •  home  
  •  news  
    • arXiv
      • astro-ph
      • cond-mat
      • cs
      • eess
      • gr-qc
      • hep-ex
      • hep-lat
      • hep-ph
      • hep-th
      • math
      • math-ph
      • nlin
      • nucl-ex
      • nucl-th
      • physics
      • q-bio
      • quant-ph
      • stat
    • physics
      • phys.org
      • physics world
    • linux
      • kernel
      • slackware
    • nature
      • natcomputsci
      • natastron
      • natbiomedeng
      • nenergy
      • nnano
      • natmachintell
      • nbt
      • nmeth
      • natecolevol
      • nmicrobiol
      • ng
      • nchembio
      • natelectron
      • micronano
      • nphoton
    • bioRxiv
    • plos one
    • world
      • BBC
      • Al Jazeera
    • earth
      • earth observatory
      • weather
      • weather forecast
    • universe
      • apod
      • hubble
      • atel
      • nasa
  •  wiki  
  •  gemini  
  • 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.

    Conditional Monge Gap enables generalizable single-cell perturbation modelling

    https://www.nature.com/articles/s42256-026-01242-8
    Alice Driessen

    Learning the coupled dynamics of global climate modes

    https://www.nature.com/articles/s42256-026-01245-5
    Yuan Yuan

    Human–AI interactions reshape the self and our social networks

    https://www.nature.com/articles/s42256-026-01248-2
    Andreia Sofia Teixeira

    Generalizable mutation-effect prediction across adaptive immune recognition via unified multimodal framework

    https://www.nature.com/articles/s42256-026-01243-7
    Rong Han

    A large-scale unified deep learning model for peptide mass spectrum interpretation trained on multimodal data

    https://www.nature.com/articles/s42256-026-01234-8
    Jiale Zhao

    Neural operators for free-boundary problems

    https://www.nature.com/articles/s42256-026-01238-4
    Constantinos Siettos

    Deep neural operator for free boundary problems

    https://www.nature.com/articles/s42256-026-01233-9
    Zongjia Long

    Plagiarism of ideas in the age of generative artificial intelligence

    https://www.nature.com/articles/s42256-026-01247-3
    David Resnik