May issue out now

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 |

    Diagnostic pathology currently requires substantial human expertise, often with high inter-observer variability. A whole-slide pathology method automates the prediction process and provides computer-aided diagnosis using artificial intelligence.

    • Zizhao Zhang
    • , Pingjun Chen
    • , Mason McGough
    • , Fuyong Xing
    • , Chunbao Wang
    • , Marilyn Bui
    • , Yuanpu Xie
    • , Manish Sapkota
    • , Lei Cui
    • , Jasreman Dhillon
    • , Nazeel Ahmad
    • , Farah K. Khalil
    • , Shohreh I. Dickinson
    • , Xiaoshuang Shi
    • , Fujun Liu
    • , Hai Su
    • , Jinzheng Cai
    •  & Lin Yang
  • Article |

    Deep neural networks are a powerful tool for predicting protein function, but identifying the specific parts of a protein sequence that are relevant to its functions remains a challenge. An occlusion-based sensitivity technique helps interpret these deep neural networks, and can guide protein engineering by locating functionally relevant protein positions.

    • Julius Upmeier zu Belzen
    • , Thore Bürgel
    • , Stefan Holderbach
    • , Felix Bubeck
    • , Lukas Adam
    • , Catharina Gandor
    • , Marita Klein
    • , Jan Mathony
    • , Pauline Pfuderer
    • , Lukas Platz
    • , Moritz Przybilla
    • , Max Schwendemann
    • , Daniel Heid
    • , Mareike Daniela Hoffmann
    • , Michael Jendrusch
    • , Carolin Schmelas
    • , Max Waldhauer
    • , Irina Lehmann
    • , Dominik Niopek
    •  & Roland Eils
  • Perspective |

    There has been a recent rise of interest in developing methods for ‘explainable AI’, where models are created to explain how a first ‘black box’ machine learning model arrives at a specific decision. It can be argued that instead efforts should be directed at building inherently interpretable models in the first place, in particular where they are applied in applications that directly affect human lives, such as in healthcare and criminal justice.

    • Cynthia Rudin
  • Analysis |

    Many functions of RNA strands that do not code for proteins are still to be deciphered. Methods to classify different groups of non-coding RNA increasingly use deep learning, but the landscape is diverse and methods need to be categorized and benchmarked to move forward. The authors take a close look at six state-of-the-art deep learning non-coding RNA classifiers and compare their performance and architecture.

    • Noorul Amin
    • , Annette McGrath
    •  & Yi-Ping Phoebe Chen
  • Article |

    Accurate manoeuvring of autonomous aerial and aquatic robots requires detailed knowledge of the fluid forces, which can be challenging especially in turbulent water or air. A control method for autonomous underwater vehicles (AUVs) uses intelligent distributed sensing inspired by fish ‘lateral line’ sensing. This is used by many species of fish to feel the flow around them and respond instantly, before they are displaced by disturbances. An AUV designed with such a sensory shell similarly compensates for disturbances and has improved position tracking.

    • Michael Krieg
    • , Kevin Nelson
    •  & Kamran Mohseni
  • Perspective |

    Artificial intelligence and machine learning systems may reproduce or amplify biases. The authors discuss the literature on biases in human learning and decision-making, and propose that researchers, policymakers and the public should be aware of such biases when evaluating the output and decisions made by machines.

    • Alexander S. Rich
    •  & Todd M. Gureckis

News & Comment

  • Comment |

    To develop scientific methods for evaluation in robotics, the field requires a more stringent definition of the subject of study, says Signe Redfield, focusing on capabilities instead of physical systems.

    • Signe Redfield
  • Q&A |

    Effy Vayena runs a lab at ETH Zürich that studies ethics, legal and social implications of precision medicine and digital health. We asked her views on the code of conduct for using artificial intelligence (AI) systems in healthcare, recently published by the UK’s National Health Service (NHS).

    • Liesbeth Venema
  • Challenge Accepted |

    A new competition presents AI agents with cognition challenges to test their animal intelligence.

    • Matthew Crosby
    • , Benjamin Beyret
    •  & Marta Halina
  • Editorial |

    Deep learning has revolutionized the technology industry, but beyond eye-catching applications such as virtual assistants, recommender systems and self-driving cars, deep learning is also transforming many scientific fields.

  • Comment |

    The European Commission’s report ‘Ethics guidelines for trustworthy AI’ provides a clear benchmark to evaluate the responsible development of AI systems, and facilitates international support for AI solutions that are good for humanity and the environment, says Luciano Floridi.

    • Luciano Floridi

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