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The neural resource allocation problem when enhancing human bodies with extra robotic limbs.

  • Giulia Dominijanni
  • Solaiman Shokur
  • Silvestro Micera
Review Article

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  • Can the human brain cope with controlling an extra robotic arm or digit added to the body?

    Editorial
  • In the AlphaPilot Challenge, teams compete to fly autonomous drones through an obstacle course as fast as possible. The 2019 winning team MAVLab reflects on the challenge of beating human pilots.

    • C. De Wagter
    • F. Paredes-Vallés
    • G. de Croon
    Challenge Accepted
  • Very large neural network models such as GPT-3, which have many billions of parameters, are on the rise, but so far only big tech has the resources to train, deploy and study such models. This needs to change, say Stanford AI researchers, who call for an investment in academic collaborations to build and study large neural networks.

    Editorial
  • The regulatory landscape for artificial intelligence (AI) is shaping up on both sides of the Atlantic, urgently awaited by the scientific and industrial community. Commonalities and differences start to crystallize in the approaches to AI in medicine.

    • Kerstin N. Vokinger
    • Urs Gasser
    Comment
  • Health disparities need to be addressed so that the benefits of medical progress are not limited to selected groups. Big data and machine learning approaches are transformative tools for public and population health, but need ongoing support from insights in algorithmic fairness.

    Editorial
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.
Publishing online monthly from January 2019, Nature Machine Intelligence is interested in the best research from across the fields of artificial intelligence, machine learning and robotics. All editorial decisions are made by a team of full-time professional editors.
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.
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