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.


  • Worldwide weather station forecasting is challenging because of high computational costs and the difficulty of modelling spatiotemporal correlations from partial observations. Wu et al. propose a transformer-based method that can reconstruct such complex correlations from scattered weather stations, leading to efficient and interpretable state-of-the-art forecasts.

    • Haixu Wu
    • Hang Zhou
    • Jianmin Wang
  • Designing methods to induce explicit and deep structural constraints in latent space at the sample level is an open problem in natural language processing-derived methods relying on transfer learning. McDermott and colleagues propose and analyse a pre-training framework imposing such structural constraints, and empirically demonstrate its advantages by showing that it outperforms existing pre-training state-of-the-art methods.

    • Matthew B. A. McDermott
    • Brendan Yap
    • Marinka Zitnik
    ArticleOpen Access
  • While single-cell multimodal datasets allow for the measurement of individual cells to understand cellular and molecular mechanisms, generating multimodal data for many cells is costly and challenging. Cohen Kalafut and colleagues develop a machine learning model capable of imputing single-cell modalities and prioritizing multimodal features, such as gene expression, chromatin accessibility and electrophysiology.

    • Noah Cohen Kalafut
    • Xiang Huang
    • Daifeng Wang
  • Immersive virtual reality requires artificial sensory perceptions to simulate what we feel and how we interact in the natural environment. Zhang and colleagues present a first-person, human-triggered, active haptic device that allows users to experience mechanical touching with various stiffness perceptions from positive to negative ranges, achieved by the unique benefits of curved origami.

    • Zhuang Zhang
    • Zhenghao Xu
    • Hanqing Jiang
  • 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
  • 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