Journal & Article

Interdisciplinary Applications

I/F
# Journal I/F

Nature Machine Intelligence

What is in your LLM-based framework? A step forward in tracing and documenting dataset provenance. A large-scale audit of dataset licensing and attribution in AI. A question of trust for AI research in medicine. The need for reproducible research in soft robotics. Realistic morphology-preserving generative modelling of the brain. Automated construction of cognitive maps with visual predictive coding. Partial-convolution-implemented generative adversarial network for global oceanic data assimilation. Will generative AI transform robotics? Machine learning for micro- and nanorobots. Neuromorphic visual scene understanding with resonator networks. Visual odometry with neuromorphic resonator networks. Empathic AI can't get under the skin. Back to basics to open the black box. Quantum circuit synthesis with diffusion models. Augmenting large language models with chemistry tools. Accurate and robust protein sequence design with CarbonDesign. Predicting equilibrium distributions for molecular systems with deep learning. Efficient learning of accurate surrogates for simulations of complex systems. The rewards of reusable machine learning code. Dangers of speech technology for workplace diversity. Artificial intelligence tackles the nature–nurture debate. The benefits, risks and bounds of personalizing the alignment of large language models to individuals. Synthetic Lagrangian turbulence by generative diffusion models. The synergy complement control approach for seamless limb-driven prostheses. The new NeuroAI. Connecting molecular properties with plain language. A collective AI via lifelong learning and sharing at the edge. Challenges and opportunities in translating ethical AI principles into practice for children. Generative AI for designing and validating easily synthesizable and structurally novel antibiotics. AI protein shake-up. Neural multi-task learning in drug design. Codon language embeddings provide strong signals for use in protein engineering. Is it five already? Guidelines for study protocols describing predefined validations of prediction models in medical deep learning and beyond. The dangers of using proprietary LLMs for research. Anniversary AI reflections. Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics. Generation of 3D molecules in pockets via a language model. Assessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV. Autonomous 3D positional control of a magnetic microrobot using reinforcement learning Inversion dynamics of class manifolds in deep learning reveals tradeoffs underlying generalization Multi-animal 3D social pose estimation, identification and behaviour embedding with a few-shot learning framework Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves Variational autoencoder for design of synthetic viral vector serotypes Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers Leveraging large language models for predictive chemistry What comparing deep neural networks can teach us about human vision Mitigating allocative tradeoffs and harms in an environmental justice data tool Protein function prediction as approximate semantic entailment