Alexander Amini, Massachusetts Institute of Technology

October 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 |

    Across disciplines, there is a rising interest in interpreting machine learning models to derive scientific knowledge from data. Genkin and Engel show that models optimized for predicting data can disagree with the ground truth and propose a new model selection principle to prioritize accurate interpretation.

    • Mikhail Genkin
    •  & Tatiana A. Engel
  • Article |

    Neural network models can predict the socioeconomic wealth of an area from aerial views, but fall short of explaining how visual features trigger a given prediction. The authors develop a pipeline for projecting class activation maps onto the underlying urban topology, to help interpret such predictions.

    • Jacob Levy Abitbol
    •  & Márton Karsai
  • Perspective |

    Robots could play an important part in transforming healthcare to cope with the COVID-19 pandemic. This Perspective highlights how robotic technology integrated in a range of tasks in the surgical environment could help to ensure a continuation of medical services while reducing the risk of infection.

    • Ajmal Zemmar
    • , Andres M. Lozano
    •  & Bradley J. Nelson
  • Review Article |

    Drug discovery has recently profited greatly from the use of deep learning models. However, these models can be notoriously hard to interpret. In this Review, Jiménez-Luna and colleagues summarize recent approaches to use explainable artificial intelligence techniques in drug discovery.

    • José Jiménez-Luna
    • , Francesca Grisoni
    •  & Gisbert Schneider
  • Article |

    To infer a previously unknown molecular formula from mass spectrometry data is a challenging, yet neglected problem. Ludwig and colleagues present a network-based approach to ranking possible formulas.

    • Marcus Ludwig
    • , Louis-Félix Nothias
    • , Kai Dührkop
    • , Irina Koester
    • , Markus Fleischauer
    • , Martin A. Hoffmann
    • , Daniel Petras
    • , Fernando Vargas
    • , Mustafa Morsy
    • , Lihini Aluwihare
    • , Pieter C. Dorrestein
    •  & Sebastian Böcker
  • Article |

    Inspired by the brain of the roundworm Caenorhabditis elegans, the authors design a highly compact neural network controller directly from raw input pixels. Compared with larger networks, this compact controller demonstrates improved generalization, robustness and interpretability on a lane-keeping task.

    • Mathias Lechner
    • , Ramin Hasani
    • , Alexander Amini
    • , Thomas A. Henzinger
    • , Daniela Rus
    •  & Radu Grosu

News & Comment

  • News & Views |

    Autonomous driving technology is improving, although doubts about their reliability remain. Controllers based on compact neural architectures could help improve their interpretability and robustness.

    • Michael Milford
  • Comment |

    Addressing the problems caused by AI applications in society with ethics frameworks is futile until we confront the political structure of such applications.

    • Jathan Sadowski
    •  & Mark Andrejevic
  • Editorial |

    Robots can relieve humans of dangerous tasks. With the pandemic making physical contact potentially dangerous due to the risk of contagion, a new focus for robotic applications in healthcare has come into view.

  • Comment |

    For machine learning developers, the use of prediction tools in real-world clinical settings can be a distant goal. Recently published guidelines for reporting clinical research that involves machine learning will help connect clinical and computer science communities, and realize the full potential of machine learning tools.

    • Bilal A. Mateen
    • , James Liley
    • , Alastair K. Denniston
    • , Chris C. Holmes
    •  & Sebastian J. Vollmer
  • News & Views |

    Finding states of matter with properties that are just right is a main challenge from metallurgy to quantum computing. A data-driven optimization approach based on gaming strategies could help.

    • Eliska Greplova

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