January 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.

Nature Machine Intelligence is a Transformative Journal; authors can publish using the traditional publishing route OR Open Access.
Our Open Access option complies with funder and institutional requirements.

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  • Computational augmentation of microscopic images aims at reducing the need to chemically label or stain cells to extract information. The popular U-Net model often employed for these tasks uses mostly local information. A new method for augmenting microscopic images is presented that allows for global information to be used at each step of the process.

    • Zhengyang Wang
    • Yaochen Xie
    • Shuiwang Ji
    Article
  • Autonomous flight is challenging for small flying robots, given the limited space for sensors and on-board processing capabilities, but a promising approach is to mimic optical-flow-based strategies of flying insects. A new development improves this technique, enabling smoother landings and better obstacle avoidance, by giving robots the ability to learn to estimate distances to objects by their visual appearance.

    • G. C. H. E. de Croon
    • C. De Wagter
    • T. Seidl
    Article
  • To remove artefacts from medical imaging, machine learning can be a useful tool, but supervised approaches need examples of the same image with and without artefacts. Liu et al. present a method to train an artefact removal network without needing matching images of corrupted and uncorrupted images.

    • Siyuan Liu
    • Kim-Han Thung
    • Pew-Thian Yap
    Article
  • Evolutionary computation is inspired by biological evolution and exhibits characteristics familiar from biology such as openendedness, multi-objectivity and co-evolution. This Perspective highlights where major differences still exist, and where the field of evolutionary computation could attempt to approach features from biological evolution more closely, namely neutrality and random drift, complex genotype-to-phenotype mappings with rich environmental interactions and major organizational transitions.

    • Risto Miikkulainen
    • Stephanie Forrest
    Perspective
  • The transcription process of DNA is highly complex and while short DNA sequence motifs recognized by transcription factors are well known, less is known about the context in the DNA sequence that determines whether a transcription factor will actually bind its motif. Zheng and colleagues present a method that uses convolutional neural networks to identify sequence features that help predict whether transcribing proteins can bind to their target sequences in DNA.

    • An Zheng
    • Michael Lamkin
    • Melissa Gymrek
    Article
  • Microrobotics offers great potential for precise drug delivery as medication can be released in the bloodstream only where it is needed. But the dynamic environment of the bloodstream is a challenge for navigation. An approach presented by Ahmed and colleagues combines magnetic and acoustic fields to allow swarms of particles to swim against a current.

    • Daniel Ahmed
    • Alexander Sukhov
    • Bradley J. Nelson
    Article
  • Reflecting on 2020 brings into focus clear challenges for the year ahead, including for AI research that contemplates its broader societal impact.

    Editorial
  • We invited authors of selected Comments and Perspectives published in Nature Machine Intelligence in the latter half of 2019 and first half of 2020 to describe how their topic has developed, what their thoughts are about the challenges of 2020, and what they look forward to in 2021.

    • Anna Jobin
    • Kingson Man
    • Miguel Luengo-Oroz
    Feature
  • A research paper makes the most impact when its methods, data and code are available for others to use and build on. We highlight the benefits of good sharing practices with a new type of article, reusability reports.

    Editorial
  • Artificial intelligence can be defined as intelligence demonstrated by machines. But what counts as intelligence, and how intelligence is implemented in different kinds of machines, robots and software varies across disciplines and over time.

    Editorial
  • Synthesizing robots via physical artificial intelligence is a multidisciplinary challenge for future robotics research. An education methodology is needed for researchers to develop a combination of skills in physical artificial intelligence.

    • Aslan Miriyev
    • Mirko Kovač
    Comment
  • Aims & Scope

    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.

  • Content Types

    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.

  • About the Editors

    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

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