• 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.

    AI economics for the common good

    https://www.nature.com/articles/s42256-026-01212-0
    Francesco Fuso Nerini

    Learning to be uncertain before learning from data

    https://www.nature.com/articles/s42256-026-01205-z
    Takuya Isomura

    Brain-inspired warm-up training with random noise for uncertainty calibration

    https://www.nature.com/articles/s42256-026-01215-x
    Jeonghwan Cheon

    Two-dimensional geometric template diffusion for boosting single-sequence protein structure prediction

    https://www.nature.com/articles/s42256-026-01210-2
    Xudong Wang

    Predicting new research directions in materials science using large language models and concept graphs

    https://www.nature.com/articles/s42256-026-01206-y
    Thomas Marwitz

    Recognizing reproducibility and reusability in times of fast science

    https://www.nature.com/articles/s42256-026-01219-7

    Machine learning global atomic representations with Euclidean fast attention

    https://www.nature.com/articles/s42256-026-01195-y
    J. Thorben Frank

    Reverse predictivity for bidirectional comparison of neural networks and biological brains

    https://www.nature.com/articles/s42256-026-01204-0
    Sabine Muzellec