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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

  • Perspective |

    A new vision for robot engineering, building on advances in computational materials techniques, additive and subtractive manufacturing as well as evolutionary computing, describes how to design a range of specialized robots uniquely suited to specific tasks and environmental conditions.

    • David Howard
    • , Agoston E. Eiben
    • , Danielle Frances Kennedy
    • , Jean-Baptiste Mouret
    • , Philip Valencia
    •  & Dave Winkler
  • Article |

    Deep neural networks are increasingly popular in data-intensive applications, but are power-hungry. New types of computer chips that are suited to the task of deep learning, such as memristor arrays where data handling and computing take place within the same unit, are required. A well-used deep learning model called long short-term memory, which can handle temporal sequential data analysis, is now implemented in a memristor crossbar array, promising an energy-efficient and low-footprint deep learning platform.

    • Can Li
    • , Zhongrui Wang
    • , Mingyi Rao
    • , Daniel Belkin
    • , Wenhao Song
    • , Hao Jiang
    • , Peng Yan
    • , Yunning Li
    • , Peng Lin
    • , Miao Hu
    • , Ning Ge
    • , John Paul Strachan
    • , Mark Barnell
    • , Qing Wu
    • , R. Stanley Williams
    • , J. Joshua Yang
    •  & Qiangfei Xia
  • Review Article |

    Deep neural networks have become very successful at certain machine learning tasks partly due to the widely adopted method of training called backpropagation. An alternative way to optimize neural networks is by using evolutionary algorithms, which, fuelled by the increase in computing power, offers a new range of capabilities and modes of learning.

    • Kenneth O. Stanley
    • , Jeff Clune
    • , Joel Lehman
    •  & Risto Miikkulainen
  • Perspective |

    Arguably one of the most promising as well as critical applications of deep learning is in supporting medical sciences and decision making. It is time to develop methods for systematically quantifying uncertainty underlying deep learning processes, which would lead to increased confidence in practical applicability of these approaches.

    • Edmon Begoli
    • , Tanmoy Bhattacharya
    •  & Dimitri Kusnezov
  • Article |

    Not all mathematical questions can be resolved, according to Gödel’s famous incompleteness theorems. It turns out that machine learning can be vulnerable to undecidability too, as is illustrated with an example problem where learnability cannot be proved nor refuted.

    • Shai Ben-David
    • , Pavel Hrubeš
    • , Shay Moran
    • , Amir Shpilka
    •  & Amir Yehudayoff

News & Comment

  • 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
  • News & Views |

    To be useful in a variety of daily tasks, robots must be able to interact physically with humans and infer how to be most helpful. A new theory for interactive robot control allows a robot to learn when to assist or challenge a human during reaching movements.

    • Luke Drnach
    •  & Lena H. Ting
  • Challenge Accepted |

    Yuanfang Guan explains how taking part in data challenges has helped her learn new analytical techniques and creatively apply them on a variety of datasets.

    • Yuanfang Guan
  • Editorial |

    As artificial intelligence, robotics and machine learning are high on the agenda everywhere, Nature Machine Intelligence launches to stimulate collaborations between different disciplines.

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.

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

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