February issue now live

Nature Machine Intelligence is an online-only journal for research and perspectives from the fast-moving fields of artificial intelligence, machine learning and robotics. Launched in january 2019.

Latest Research

  • Article |

    Getting safe and fast access to blood vessels is vital to many methods of treatment and diagnosis in medicine. Robot-assisted or even fully autonomous methods can potentially do the task more reliably than humans, especially when veins are hard to detect. In this work, a method is tested that uses deep learning to find blood vessels and track the movement of a patient’s arm.

    • Alvin I. Chen
    • , Max L. Balter
    • , Timothy J. Maguire
    •  & Martin L. Yarmush
  • Review Article |

    This Review surveys machine learning techniques that are currently developed for a range of research topics in biological and artificial active matter and also discusses challenges and exciting opportunities. This research direction promises to help disentangle the complexity of active matter and gain fundamental insights for instance in collective behaviour of systems at many length scales from colonies of bacteria to animal flocks.

    • Frank Cichos
    • , Kristian Gustavsson
    • , Bernhard Mehlig
    •  & Giovanni Volpe
  • Article |

    When predicting the interaction of proteins with potential drugs, the protein can be encoded as its one-dimensional sequence or a three-dimensional structure, which can capture more relevant features of the protein, but also makes the task to predict the interactions harder. A new method predicts these interactions using a two-dimensional distance matrix representation of a protein, which can be processed like a two-dimensional image, striking a balance between the data being simple to process and rich in relevant structures.

    • Shuangjia Zheng
    • , Yongjian Li
    • , Sheng Chen
    • , Jun Xu
    •  & Yuedong Yang
  • Article |

    Age-related macular degeneration is a serious eye disease which should be detected as early as possible. Using both fundus images and genetic information, a deep neural network is able to detect the severity of the disease and predict its progression seven years into the future.

    • Qi Yan
    • , Daniel E. Weeks
    • , Hongyi Xin
    • , Anand Swaroop
    • , Emily Y. Chew
    • , Heng Huang
    • , Ying Ding
    •  & Wei Chen
  • Article |

    Counting different types of circulating tumour cells can give valuable information on the severity of the disease and on whether treatments are effective for a specific patient. In this work, the authors show that their method based on autoencoders can identify and count cells more accurately and faster than human experts.

    • Leonie L. Zeune
    • , Yoeri E. Boink
    • , Guus van Dalum
    • , Afroditi Nanou
    • , Sanne de Wit
    • , Kiki C. Andree
    • , Joost F. Swennenhuis
    • , Stephan A. van Gils
    • , Leon W.M.M. Terstappen
    •  & Christoph Brune

News & Comment

  • Editorial |

    In a recent workshop at the Conference on Neural Information Processing Systems (NeurIPS), future directions at the intersection of neuroscience and AI were considered. A panel discussion at the end of the day started with a provocative question: do we need AI to understand the brain?

  • Correspondence |

    • Adam Poulsen
    • , Eduard Fosch-Villaronga
    •  & Roger Andre Søraa
  • Comment |

    Machine learning models have great potential in biomedical applications. A new platform called GradioHub offers an interactive and intuitive way for clinicians and biomedical researchers to try out models and test their reliability on real-world, out-of-training data.

    • Abubakar Abid
    • , Ali Abdalla
    • , Ali Abid
    • , Dawood Khan
    • , Abdulrahman Alfozan
    •  & James Zou

About the Journal

  • Nature Machine Intelligence aims to bring different fields together in the study, engineering and application of intelligent machines. We publish research on a large variety of topics in machine learning, robotics, cognitive science and a range of AI approaches. We also provide a platform for comments and reviews to discuss emerging inter-disciplinary themes as well as the significant impact that machine intelligence has on other fields in science and on society.

  • Nature Machine Intelligence publishes original research as Articles. We also publish a range of other content types including Reviews, Perspectives, Comments, Correspondences, News & Views and Feature articles.

  • Nature Machine Intelligence is run by a team of full-time editors. The Chief Editor is Liesbeth Venema who was previously a physics editor at Nature. Trenton Jerde started in March 2018, Yann Sweeney joined in July and Jacob Huth joined in November 2018, completing the team.

  • Contact information for editorial staff, submissions, the press office, institutional access and advertising at Nature Machine Intelligence