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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
  • The COVID-19 pandemic is not over and the future is uncertain, but there has lately been a semblance of what life was like before. As thoughts turn to the possibility of a summer holiday, we offer suggestions for books and podcasts on AI to refresh the mind.

    Editorial
  • A new international competition aims to speed up the development of AI models that can assist radiologists in detecting suspicious lesions from hundreds of millions of pixels in 3D mammograms. The top three winning teams compare notes.

    • Jungkyu Park
    • Yoel Shoshan
    • Krzysztof J. Geras
    Challenge Accepted
  • Accurate and fair medical machine learning requires large amounts and diverse data to train on. Privacy-preserving methods such as federated learning can help improve machine learning models by making use of datasets in different hospitals and institutes while the data stays where it is collected.

    Editorial
  • Large language models, which are increasingly used in AI applications, display undesirable stereotypes such as persistent associations between Muslims and violence. New approaches are needed to systematically reduce the harmful bias of language models in deployment.

    • Abubakar Abid
    • Maheen Farooqi
    • James Zou
    Comment
  • A white paper from Partnership on AI provides timely advice on tackling the urgent challenge of navigating risks of AI research and responsible publication.

    Editorial
  • We spoke with Mariarosaria Taddeo, an associate professor and senior research fellow at the Oxford Internet Institute and Dstl Ethics Fellow at the Alan Turing Institute working on digital and AI ethics about two recent reports from the UK and the US on using AI in national defence and security.

    • Liesbeth Venema
    Q&A
  • Citizen scientists are empowered by mobile technology to collect data and crowdsource knowledge. Furthermore, automated machine learning tools allow non-experts in AI to analyse data. Ethical and regulatory questions arise, however, as data collection and AI technologies become enmeshed in people’s lives.

    Editorial
  • The COVID-19 pandemic has highlighted key challenges for patient care and health provider safety. Adaptable robotic systems, with enhanced sensing, manipulation and autonomy capabilities could help address these challenges in future infectious disease outbreaks.

    • Hao Su
    • Antonio Di Lallo
    • Axel Krieger
    Comment
  • It has been a little over a year since a worldwide COVID-19 pandemic was declared. Science has moved fast to fight the virus but preparations need to be underway for fighting future outbreaks.

    Editorial
  • To truly understand the societal impact of AI, we need to look beyond the exclusive focus on quantitative methods, and focus on qualitative methods like ethnography, which shed light on the actors and institutions that wield power through the use of these technologies.

    • Vidushi Marda
    • Shivangi Narayan
    Comment
  • Hearing and vision are powerful and important senses for interacting with our surroundings. So far, advances in the area of machine vision have been the most prominent, but machine hearing research that closely mimics the complex sound processing in the human ear has exciting opportunities to offer.

    Editorial