Ali Marjaninejad, Darío Urbina-Meléndez, Brian A. Cohn, and Francisco J. Valero-Cuevas Cover Design: Karen Moore.

March 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 |

    To perform complex tasks, robots need to learn the relationship between their bodies and dynamic environments. A biologically plausible approach to hardware and software design shows that a robotic tendon-driven limb can make effective movements based on a short period of learning.

    • Ali Marjaninejad
    • , Darío Urbina-Meléndez
    • , Brian A. Cohn
    •  & Francisco J. Valero-Cuevas
  • Article |

    Present day quantum technologies enable computations with tens and soon hundreds of qubits. A major outstanding challenge is to measure and benchmark the complete quantum state, a task that grows exponentially with the system size. Generative models based on restricted Boltzmann machines and recurrent neural networks can be employed to solve this quantum tomography problem in a scalable manner.

    • Juan Carrasquilla
    • , Giacomo Torlai
    • , Roger G. Melko
    •  & Leandro Aolita
  • Review Article |

    Research on reinforcement learning in artificial agents focuses on a single complex problem within a static environment. In biological agents, research focuses on simple learning problems embedded in flexible, dynamic environments. The authors review the literature on these topics and suggest areas of synergy between them.

    • Emre O. Neftci
    •  & Bruno B. Averbeck
  • Article |

    Generative machine learning models are used in synthetic biology to find new structures such as DNA sequences, proteins and other macromolecules with applications in drug discovery, environmental treatment and manufacturing. Gupta and Zou propose and demonstrate in silico a feedback-loop architecture to optimize the output of a generative adversarial network that generates synthetic genes to produce ones specifically coding for antimicrobial peptides.

    • Anvita Gupta
    •  & James Zou
  • Perspective |

    A bibliometric analysis of the past and present of AI research suggests a consolidation of research influence. This may present challenges for the exchange of ideas between AI and the social sciences.

    • Morgan R. Frank
    • , Dashun Wang
    • , Manuel Cebrian
    •  & Iyad Rahwan

News & Comment

  • Q&A |

    David Oh was lead flight director for the Curiosity Mars rover and is now part of NASA’s mission to Psyche, a 200-km-wide metal asteroid. Our editor Yann Sweeney met with David at SIGGRAPH Asia to discuss whether advances in AI could improve autonomous robots for space exploration.

    • Yann Sweeney
  • Challenge Accepted |

    Juxi Leitner recounts how he and his team took part in — and won — the 2017 Amazon Robotics Challenge and reflects on the importance of solving big picture problems in robotics.

    • Jürgen Leitner
  • News Feature |

    Affordances are ways in which an animal or a robot can interact with the environment. The concept, borrowed from psychology, inspires a fresh take on the design of robots that will be able to hold their own in everyday tasks and unpredictable situations.

    • Jeremy Hsu
  • News & Views |

    Humans infer much of the intentions of others by just looking at their gaze. Similarly, we want to understand how machine learning systems solve a problem. New tools are developed to find out what strategies a learning machine is using, such as what it is paying attention to when classifying images.

    • José Hernández-Orallo
  • Editorial |

    Artificial intelligence (AI) has recently re-emerged from the intersection of many fields, directing its collective energy at the building and studying of intelligent machines.

  • Comment |

    After a difficult start, medicinal chemists are now ready to embrace AI-based methods and concepts in drug discovery, explains Gisbert Schneider.

    • Gisbert Schneider

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.

  • 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