To maintain high standards in clarity and reproducibility, authors need to clearly mention and describe the use of GPT-4 and other large language models in their work.

The Data Provenance Initiative audits over 1,800 text artificial intelligence (AI) datasets, analysing trends, permissions of use and global representation. It exposes frequent errors on several major data hosting sites and offers tools for transparent and informed use of AI training data.

Large language models can be queried to perform chain-of-thought reasoning on text descriptions of data or computational tools, which can enable flexible and autonomous workflows. Bran et al. developed ChemCrow, a GPT-4-based agent that has access to computational chemistry tools and a robotic chemistry platform, which can autonomously solve tasks for designing or synthesizing chemicals such as drugs or materials.

Large language models (LLMs) present challenges, including a tendency to produce false or misleading content and the potential to create misinformation or disinformation. Augenstein and colleagues explore issues related to factuality in LLMs and their impact on fact-checking.

With increasing reliance on public data sources, researchers are concerned whether low-quality or even adversarial data could have detrimental effects on medical models. Yang et al. developed Scorpius, a malicious text generator, to investigate whether large language models can mislead medical knowledge graphs. They show that a single generated paper abstract can mislead a medical reasoning system that has read millions of papers.

Stabilization of proteins is a key task in protein engineering; however, current methods to predict mutant stability face a number of limitations. Reeves and Kalyaanamoorthy study the performance of self-supervised protein sequence likelihood models for stability prediction and find that combining them with task-specific supervised models can lead to appreciable practical gains.

Inspired by mechanoreceptors on flying insects, a flapping-wing drone that makes use of strain sensors on the wings and reinforcement-learning-based flight control has been developed. The drone can fly in various unsteady environments, including in windy conditions.

Extracting time traces and spatial footprints of single neurons from population calcium imaging data presents challenges. Zhang et al. introduce a deep learning method that efficiently segments neuronal footprints and extracts activity traces from these data. The method surpasses existing approaches in both quality and speed, providing a robust tool for large-scale neuronal circuit analysis.

Deep learning excels in medical signal processing but lacks interpretability. An efficient, interpretable architecture that matches the performance of larger models at orders of magnitude fewer parameters in tasks common to wearable devices has been proposed.

Denoising methods introduce useful priors in pre-training methods for molecular property prediction, but chemically unaware noise can lead to inaccurate predictions in downstream tasks. A molecular pre-training framework that uses fractional denoising to improve molecular distribution modelling is proposed, resulting in better predictions in various property prediction tasks.

New multi-modal AI methods fuse different biological data types that span multiple scales, offering promising clinical utility.

To maintain high standards in clarity and reproducibility, authors need to clearly mention and describe the use of GPT-4 and other large language models in their work.

Medical research is one of the most impactful areas for machine learning applications, but access to large and diverse health datasets is needed for models to be useful. Winning trust from patients by demonstrating that data are handled securely and effectively is key.

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.