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June 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

  • Perspective |

    China’s New Generation Artificial Intelligence Development Plan was launched in 2017 and lays out an ambitious strategy, which intends to make China one of the world’s premier AI innovation centre by 2030. This Perspective presents the view from a group of Chinese AI experts from academia and industry about the origins of the plan, the motivations and main focus for attention from research and industry.

    • Fei Wu
    • , Cewu Lu
    • , Mingjie Zhu
    • , Hao Chen
    • , Jun Zhu
    • , Kai Yu
    • , Lei Li
    • , Ming Li
    • , Qianfeng Chen
    • , Xi Li
    • , Xudong Cao
    • , Zhongyuan Wang
    • , Zhengjun Zha
    • , Yueting Zhuang
    •  & Yunhe Pan
  • Article |

    Vascular abnormalities are challenging for diagnostic imaging due to the complexity of vasculature and the non-uniform scattering from biological tissues. The authors present an unsupervised learning algorithm for vascular feature recognition from small sets of biomedical images acquired from different modalities. They demonstrate the utility of their diagnostic approach on vascular images of thrombosis, internal bleeding and colitis.

    • Yong Wang
    • , Mengqi Ji
    • , Shengwei Jiang
    • , Xukang Wang
    • , Jiamin Wu
    • , Feng Duan
    • , Jingtao Fan
    • , Laiqiang Huang
    • , Shaohua Ma
    • , Lu Fang
    •  & Qionghai Dai
  • Article |

    Tumour mutational burden (TMB) shows promise as a biomarker in cancer immunotherapy, but it usually requires whole-exome sequencing, which is costly, time-consuming and unavailable at most hospitals. The authors develop a machine learning algorithm that uses standard H&E histopathological images to quickly, inexpensively and accurately predict TMB. The approach may have applications as a tool to screen and prioritize patient samples and subsequent treatments.

    • Mika S. Jain
    •  & Tarik F. Massoud
  • Article |

    Gene sets can provide valuable information for gaining insight into disease mechanisms and cellular functions. In this paper, the authors use a Gaussian approach to represent gene sets and gene networks in a low-dimensional space, allowing for accurate prediction and decreased computational complexity.

    • Sheng Wang
    • , Emily R. Flynn
    •  & Russ B. Altman
  • Article |

    Spiking neural networks and in-memory computing are both promising routes towards energy-efficient hardware for deep learning. Woźniak et al. incorporate the biologically inspired dynamics of spiking neurons into conventional recurrent neural network units and in-memory computing, and show how this allows for accurate and energy-efficient deep learning.

    • Stanisław Woźniak
    • , Angeliki Pantazi
    • , Thomas Bohnstingl
    •  & Evangelos Eleftheriou
  • Perspective |

    Medical imaging data is often subject to privacy and intellectual property restrictions. AI techniques can help out by offering tools like federated learning to bridge the gap between personal data protection and data utilisation for research and clinical routine, but these tools need to be secure.

    • Georgios A. Kaissis
    • , Marcus R. Makowski
    • , Daniel Rückert
    •  & Rickmer F. Braren

News & Comment

  • Comment |

    Artificial intelligence tools can help save lives in a pandemic. However, the need to implement technological solutions rapidly raises challenging ethical issues. We need new approaches for ethics with urgency, to ensure AI can be safely and beneficially used in the COVID-19 response and beyond.

    • Asaf Tzachor
    • , Jess Whittlestone
    • , Lalitha Sundaram
    •  & Seán Ó hÉigeartaigh
  • Editorial |

    Expectations are high for AI to help fight COVID-19. But before AI tools can make an impact, global collaboration and high-quality data and model sharing are needed.

  • Q&A |

    Contact-tracing apps could help keep countries open before a vaccine is available. But do we have a sufficient understanding of their efficacy, and can we balance protecting public health with safeguarding civil rights? We interviewed five experts, with backgrounds in digital health ethics, internet law and social sciences.

    • Yann Sweeney
  • Comment |

    The COVID-19 pandemic poses a historical challenge to society. The profusion of data requires machine learning to improve and accelerate COVID-19 diagnosis, prognosis and treatment. However, a global and open approach is necessary to avoid pitfalls in these applications.

    • Nathan Peiffer-Smadja
    • , Redwan Maatoug
    • , François-Xavier Lescure
    • , Eric D’Ortenzio
    • , Joëlle Pineau
    •  & Jean-Rémi King

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