May 2023 Issue

May issue now live

Liang, J., Xu, C. & Cai, S. Recurrent graph optimal transport for learning 3D flow motion in particle tracking. 

Nature Machine Intelligence is a Transformative Journal; authors can publish using the traditional publishing route OR via immediate gold Open Access.

Our Open Access option complies with funder and institutional requirements.


  • Memory efficient online training of recurrent spiking neural networks without compromising accuracy is an open challenge in neuromorphic computing. Yin and colleagues demonstrate that training a recurrent neural network consisting of so-called liquid time-constant spiking neurons using an algorithm called Forward-Propagation Through Time allows for online learning and state-of-the-art performance at a reduced computational cost compared with existing approaches.

    • Bojian Yin
    • Federico Corradi
    • Sander M. Bohté
  • To detect phenotype-related cell subpopulations from single-cell data, appropriate feature sets need to be chosen or learned simultaneously. Ren et al. present here a tool based on Learning with Rejection, a method that during training learns features from cells that can be predicted with high confidence, while cells that the model is not yet certain about are rejected.

    • Tao Ren
    • Canping Chen
    • Zheng Xia
  • Particle tracking velocimetry to estimate particle displacements in fluid flows in complex experimental scenarios is a challenging task and often comes with high computational cost. Liang and colleagues propose a graph neural network and optimal transport-based algorithm that can greatly improve the accuracy of existing tracking algorithms in real-world applications.

    • Jiaming Liang
    • Chao Xu
    • Shengze Cai
  • Online commerce is increasingly relying on pricing algorithms. Using a network-based approach inspired by adversarial machine learning, a firm can learn the strategy of its competitors and use it to unilaterally increase all firms’ profits. This approach, termed as ‘adversarial collusion’, calls for new regulatory measures.

    • Luc Rocher
    • Arnaud J. Tournier
    • Yves-Alexandre de Montjoye
  • Deep learning can be used to predict molecular properties, but such methods usually need a large amount of data and are hard to generalize to different chemical spaces. To provide a useful primer for deep learning models models, Fang and colleagues use contrastive learning and a knowledge graph based on the Periodic Table and Wikipedia pages on chemical functional groups.

    • Yin Fang
    • Qiang Zhang
    • Huajun Chen
    ArticleOpen Access
  • As many authors are experimenting with using large language models in writing articles, some guidelines are becoming clear, but these will need to evolve as the capabilities and integration of such tools develop further.

  • Metaverse-enabled healthcare is no longer hypothetical. Developers must now contend with ethical, legal and social hazards if they are to overcome the systematic inefficiencies and inequities that exist for patients who seek care in the real world.

    • Kristin Kostick-Quenet
    • Vasiliki Rahimzadeh
  • Generative AI programs can produce high-quality written and visual content that may be used for good or ill. We argue that a credit–blame asymmetry arises for assigning responsibility for these outputs and discuss urgent ethical and policy implications focused on large-scale language models.

    • Sebastian Porsdam Mann
    • Brian D. Earp
    • Julian Savulescu
  • Fairness approaches in machine learning should involve more than an assessment of performance metrics across groups. Shifting the focus away from model metrics, we reframe fairness through the lens of intersectionality, a Black feminist theoretical framework that contextualizes individuals in interacting systems of power and oppression.

    • Elle Lett
    • William G. La Cava