June  24 Issue

June issue now live

Renner, A., Supic, L., Danielescu, A. et al. "Neuromorphic visual scene understanding with resonator networks"  and  "Visual odometry with neuromorphic resonator networks"

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.


  • Data assimilation (DA) techniques are commonly used to assess global Earth system variability but require considerable computational resources and struggle to handle sparse observational data. Ham and colleagues introduce a partial convolution and generative adversarial network-based global oceanic DA system and successfully reconstruct the observed global temperature in a real case study with smaller computational costs than traditional DA systems.

    • Yoo-Geun Ham
    • Yong-Sik Joo
    • Jeong-Gil Lee
  • Constructing spatial maps from sensory inputs is challenging in both neuroscience and artificial intelligence. Gornet and Thomson show that as an agent navigates an environment, a self-attention neural network using predictive coding can recover the environment’s map in its latent space.

    • James Gornet
    • Matt Thomson
    ArticleOpen Access
  • ROAM, based on large regions of interest and a pyramid transformer, can automatically capture key morphological features consistent with the experience of pathologists to provide accurate, reliable and adaptable clinical-grade diagnoses of gliomas while advancing the discovery of molecular and morphological markers related to glioma diagnosis.

    • Rui Jiang
    • Xiaoxu Yin
    • Hairong Lv
  • Medical imaging research is limited by data availability. To address this challenge, Tudosiu and colleagues develop a 3D generative model of the human brain that can generate high-resolution morphologically correct brains conditioned on patient characteristics.

    • Petru-Daniel Tudosiu
    • Walter H. L. Pinaya
    • M. Jorge Cardoso
    ArticleOpen Access
  • In the current wave of excitement about applying large vision–language models and generative AI to robotics, expectations are running high, but conquering real-world complexities remains challenging for robots.

  • Personalized LLMs built with the capacity for emulating empathy are right around the corner. The effects on individual users need careful consideration.

  • Most research efforts in machine learning focus on performance and are detached from an explanation of the behaviour of the model. We call for going back to basics of machine learning methods, with more focus on the development of a basic understanding grounded in statistical theory.

    • Diego Marcondes
    • Adilson Simonis
    • Junior Barrera