August 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 |

    When neural networks are retrained to solve more than one problem, they tend to forget what they have learned earlier. Here, the authors propose orthogonal weights modification, a method to avoid this so-called catastrophic forgetting problem. Capitalizing on such an ability, a new module is introduced to enable the network to continually learn context-dependent processing.

    • Guanxiong Zeng
    • , Yang Chen
    • , Bo Cui
    •  & Shan Yu
  • Article |

    Deep neural networks can contain arbitrary mathematical operators, as long as they are derivable. The authors investigate how knowledge about a problem can be incorporated into machine learning through the use of operators that are related to the problem.

    • Andreas K. Maier
    • , Christopher Syben
    • , Bernhard Stimpel
    • , Tobias Würfl
    • , Mathis Hoffmann
    • , Frank Schebesch
    • , Weilin Fu
    • , Leonid Mill
    • , Lasse Kling
    •  & Silke Christiansen
  • Article |

    An approach to protein structure prediction is to assemble candidate structures from template fragments, which are extracted from known protein structures. Wang et al. demonstrate that combining deep neural network architectures with a relatively small but high-resolution fragment dataset can improve the quality of the sample fragment libraries used for protein structure prediction.

    • Tong Wang
    • , Yanhua Qiao
    • , Wenze Ding
    • , Wenzhi Mao
    • , Yaoqi Zhou
    •  & Haipeng Gong
  • Perspective |

    Traditional robotic grasping focuses on manipulating an object, often without considering the goal or task involved in the movement. The authors propose a new metric for success in manipulation that is based on the task itself.

    • V. Ortenzi
    • , M. Controzzi
    • , F. Cini
    • , J. Leitner
    • , M. Bianchi
    • , M. A. Roa
    •  & P. Corke
  • Article |

    For some combinatorial puzzles, solutions can be verified to be optimal, for others, the state space is too large to be certain that a solution is optimal. A new deep learning based search heuristic performs well on the iconic Rubik’s cube and can also generalize to puzzles in which optimal solvers are intractable.

    • Forest Agostinelli
    • , Stephen McAleer
    • , Alexander Shmakov
    •  & Pierre Baldi

News & Comment

  • Challenge Accepted |

    As nations come together in Tokyo next summer to celebrate the spirit of human potential in the 2020 Olympic Games, they will have a chance to take part in another international competition hosted by Japan soon after, this time with challenges designed for robot contenders.

    • Liesbeth Venema
  • Comment |

    To create less harmful technologies and ignite positive social change, AI engineers need to enlist ideas and expertise from a broad range of social science disciplines, including those embracing qualitative methods, say Mona Sloane and Emanuel Moss.

    • Mona Sloane
    •  & Emanuel Moss
  • Editorial |

    As machine learning methods are adopted across the scientific community, strong code sharing and reviewing practices are required. Our policy mandates that code essential to the main results is made available to reviewers, and to readers on publication. Our partnership with Code Ocean helps authors and reviewers navigate this process.

  • News & Views |

    DeepMind’s AlphaFold recently demonstrated the potential of deep learning for protein structure prediction. DeepFragLib, a new protein-specific fragment library built using deep neural networks, may have advanced the field to the next stage.

    • Guo-Wei Wei
  • Comment |

    Deepfakes are a new dimension of the fake news problem. The criminal misuse of this technology poses far-reaching challenges and can threaten national security. Technological and governance solutions are needed to address this.

    • Irakli Beridze
    •  & James Butcher

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