Image: Gonzalo Rodriguez Gaspar, GRG Studios. Cover Design: Karen Moore.

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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 |

    Generative machine learning models are used in synthetic biology to find new structures such as DNA sequences, proteins and other macromolecules with applications in drug discovery, environmental treatment and manufacturing. Gupta and Zou propose and demonstrate in silico a feedback-loop architecture to optimize the output of a generative adversarial network that generates synthetic genes to produce ones specifically coding for antimicrobial peptides.

    • Anvita Gupta
    •  & James Zou
  • Perspective |

    A bibliometric analysis of the past and present of AI research suggests a consolidation of research influence. This may present challenges for the exchange of ideas between AI and the social sciences.

    • Morgan R. Frank
    • , Dashun Wang
    • , Manuel Cebrian
    •  & Iyad Rahwan
  • Perspective |

    A survey of 300 fictional and non-fictional works featuring artificial intelligence reveals that imaginings of intelligent machines may be grouped in four categories, each comprising a hope and a parallel fear. These perceptions are decoupled from what is realistically possible with current technology, yet influence scientific goals, public understanding and regulation of AI.

    • Stephen Cave
    •  & Kanta Dihal
  • Article |

    A fully convolutional neural network is used to create time-resolved three-dimensional dense segmentations of heart images. This dense motion model forms the input to a supervised system called 4Dsurvival that can efficiently predict human survival.

    • Ghalib A. Bello
    • , Timothy J. W. Dawes
    • , Jinming Duan
    • , Carlo Biffi
    • , Antonio de Marvao
    • , Luke S. G. E. Howard
    • , J. Simon R. Gibbs
    • , Martin R. Wilkins
    • , Stuart A. Cook
    • , Daniel Rueckert
    •  & Declan P. O’Regan
  • Article |

    Neuromorphic processors promise to be a low-powered platform for deep learning, but require neural networks that are adapted for binary communication. The Whetstone method achieves this by gradually sharpening activation functions during the training process.

    • William Severa
    • , Craig M. Vineyard
    • , Ryan Dellana
    • , Stephen J. Verzi
    •  & James B. Aimone

News & Comment

  • Editorial |

    Preprints provide an efficient way for scientific communities to share and discuss results. We encourage authors to post preprints on arXiv, bioRxiv or other recognized community preprint platforms.

  • Challenge Accepted |

    By organizing Kaggle competitions, astrophysicist Thomas Kitching can focus on asking the right questions.

    • Thomas Kitching
  • Comment |

    Artificial intelligence (AI) promises to be an invaluable tool for nature conservation, but its misuse could have severe real-world consequences for people and wildlife. Conservation scientists discuss how improved metrics and ethical oversight can mitigate these risks.

    • Oliver R. Wearn
    • , Robin Freeman
    •  & David M. P. Jacoby
  • Comment |

    Ken Goldberg reflects on how four exciting sub-fields of robotics — co-robotics, human–robot interaction, deep learning and cloud robotics — accelerate a renewed trend toward robots working safely and constructively with humans.

    • Ken Goldberg

About the Journal

  • Nature Machine Intelligence publishes research from a wide range of topics in machine learning, robotics and AI. The journal also explores and discusses emerging cross-disciplinary themes, such as human-robot interaction, and provides a platform to discuss the significant impact that AI has on other fields in science, society and industry.

  • Nature Machine Intelligence will publish original research as Articles. We will 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