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.

VirtualMultiplexer is a generative AI tool that produces realistic multiplexed immunohistochemistry images from tissue biopsies. The generated images could be used to improve clinical predictions, enhancing histopathology workflows and accelerating cancer research.

Applying large-scale AI systems to multi-agent scenarios in real-world settings is challenging. The authors propose a decentralized model-based policy optimization framework to enable scalable decision-making.

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.

Integral equations are used in science and engineering to model complex systems with non-local dependencies; however, existing traditional and machine-learning-based methods cannot yield accurate or efficient solutions in several complex cases. Zappala and colleagues introduce a neural-network-based method that can learn an integral operator and its dynamics from data, demonstrating higher accuracy or scalability compared with several state-of-the-art methods.

Cellular phenotypic heterogeneity is a key determinant of biological functions and is challenging to identify. A deep learning method that recognizes specific nuclear signatures is discussed, which can identify cellular heterogeneity and differentiate between various cell states using a small amount of super-resolution microscopy data.

A transformer-based approach that predicts drug–drug interactions in polypharmacy has been shown, which also identifies perpetrator drugs and the chemical mechanisms causing the interactions. The method could facilitate high-throughput optimization of drug combinations and mitigate adverse drug–drug interaction risks.

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.

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