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Automating crystal-structure phase mapping by combining deep learning with constraint reasoning.

  • Di Chen
  • Yiwei Bai
  • Carla P. Gomes
Article

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  • A growing number of researchers are developing approaches to improve fairness in machine learning applications in areas such as healthcare, employment and social services, to avoid propagating and amplifying racial and other inequities. An empirical study explores the trade-off between increasing fairness and model accuracy across several social policy areas and finds that this trade-off is negligible in practice.

    • Kit T. Rodolfa
    • Hemank Lamba
    • Rayid Ghani
    Article
  • Turbulent optical distortions in the atmosphere limit the ability of optical technologies such as laser communication and long-distance environmental monitoring. A new method using adversarial networks learns to counter the physical processes underlying the turbulence so that complex optical scenes can be reconstructed.

    • Darui Jin
    • Ying Chen
    • Xiangzhi Bai
    Article
  • The use of sparse signals in spiking neural networks, modelled on biological neurons, offers in principle a highly efficient approach for artificial neural networks when implemented on neuromorphic hardware, but new training approaches are needed to improve performance. Using a new type of activity-regularizing surrogate gradient for backpropagation combined with recurrent networks of tunable and adaptive spiking neurons, state-of-the-art performance for spiking neural networks is demonstrated on benchmarks in the time domain.

    • Bojian Yin
    • Federico Corradi
    • Sander M. Bohté
    Article
  • T-cell immunity is driven by the interaction between peptides presented by major histocompatibility complexes (pMHCs) and T-cell receptors (TCRs). Only a small proportion of neoantigens elicit T-cell responses, and it is not clear which neoantigens are recognized by which TCRs. The authors develop a transfer learning model to predict TCR binding specificity to class-I pMHCs.

    • Tianshi Lu
    • Ze Zhang
    • Tao Wang
    Article
  • Spiking neural networks promise fast and energy-efficient information processing. The ‘time-to-first-spike’ coding scheme, where the time elapsed before a neuron’s first spike is utilized as the main variable, is a particularly efficient approach and Göltz and Kriener et al. demonstrate that error backpropagation, an essential ingredient for learning in neural networks, can be implemented in this scheme.

    • J. Göltz
    • L. Kriener
    • M. A. Petrovici
    Article
  • 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
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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