Nature Machine Intelligence, Published online: 15 July 2026; doi:10.1038/s42256-026-01265-1
Moving beyond brute-force simulations, local neural operators—combined with equation-free methods and Krylov subspace techniques—enable system-level stability and bifurcation analysis of complex spatiotemporal systems directly from data.Nature Machine Intelligence, Published online: 14 July 2026; doi:10.1038/s42256-026-01271-3
Realigning AI technology towards the Sustainable Development GoalsNature Machine Intelligence, Published online: 14 July 2026; doi:10.1038/s42256-026-01259-z
Kindt et al. present a unifying framework for superposition in neural networks. Their three-step approach clarifies how latent features can be identified, disentangled and assessed.Nature Machine Intelligence, Published online: 13 July 2026; doi:10.1038/s42256-026-01273-1
The brain’s architecture exhibits diversity across many temporal and spatial scales, yet our computing architectures remain largely homogeneous. Low-powered neuromorphic hardware offers a path towards energy-efficient AI, but could these approaches be improved with heterogeneous computing architectures?Nature Machine Intelligence, Published online: 13 July 2026; doi:10.1038/s42256-026-01272-2
Sartore et al. present ergoCub, a humanoid robot that prioritizes human safety at hardware and motion levels. Using a shared embodied intelligence framework, design and control are jointly optimized with human-related metrics such as back stress alongside locomotion objectives, reducing spinal load and improving walking robustness.Nature Machine Intelligence, Published online: 13 July 2026; doi:10.1038/s42256-026-01260-6
Song et al. propose Sustainability Robotics as a new discipline to overcome fragmentation and enhance societal and environmental impact. They define three guiding principles, alongside two dimensions spanning sustainable design and robotics for sustainability.Nature Machine Intelligence, Published online: 10 July 2026; doi:10.1038/s42256-026-01270-4
The push to make biological data ‘AI-ready’ is accelerating worldwide. But to understand what AI-ready means for biological data, there are several questions to answer.Nature Machine Intelligence, Published online: 06 July 2026; doi:10.1038/s42256-026-01262-4
Park and Walsh introduce a reinforcement learning framework that could accelerate the discovery of new, thermodynamically stable and diverse crystalline materials with desired properties.Nature Machine Intelligence, Published online: 03 July 2026; doi:10.1038/s42256-026-01267-z
Berner et al. show how to adapt popular neural networks into discretization-agnostic neural operators that learn from continuous scientific data, enabling scientific simulations that generalize more reliably across resolutions.
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