As countries around the world heavily invest in artificial intelligence (AI) and related infrastructure, the sustainable development of AI technology needs to be higher on the global agenda.

Akiba et al. developed an evolutionary approach to automatically merge artificial intelligence models, creating powerful hybrid models without extensive training. The method produces models with enhanced mathematical and visual capabilities that outperform larger models.

COMET, an artificial intelligence method that improves the analysis of small medical studies using large clinical databases, has been created. COMET can help develop better artificial intelligence tools and identify key biomarkers across many diseases, potentially changing medical research.

Machine learning models are promising approaches to tackle partial differential equations, which are foundational descriptions of many scientific and engineering problems. However, in speaking with several experts about progress in the area, questions are emerging over what realistic advantages machine learning models have and how their performance should be evaluated.

FLuID enables privacy-preserving knowledge sharing in drug discovery using knowledge distillation. The results show that the approach expands applicability domains and fosters collaboration across organizations without compromising data privacy or security.

UniPMT, a multitask learning model, is presented, which integrates three key biological relationships into a unified framework for accurate peptide–MHC–TCR binding prediction.

Motion planning for a robot generally requires full knowledge of its structure. Here Hu and colleagues present a method for inferring the structure of a robot from visual information.

Zheng et al. developed LLM4SD, a framework using large language models to predict molecular properties. The method leverages the ability of large language models to synthesize knowledge from literature and to reason about scientific data with domain expertise.

Bar-Lev et al. propose a high-efficiency DNA-based storage pipeline that integrates deep neural networks, error-correcting codes and safety margins, achieving a 3,200× speed improvement and a 40% accuracy gain, paving the way for commercially viable DNA data storage.

It is challenging to compare how well robots perform a task, as the evaluation depends on the process and skills required. It is proposed to group robots into a taxonomy based on their performance on a set of embodied skill benchmarks.

As countries around the world heavily invest in artificial intelligence (AI) and related infrastructure, the sustainable development of AI technology needs to be higher on the global agenda.

There is a growing awareness of the substantial environmental costs of large language models (LLMs), but discussing the sustainability of LLMs only in terms of CO₂ emissions is not enough. This Comment emphasizes the need to take into account the social and ecological costs and benefits of LLMs as well.

Machine learning models are promising approaches to tackle partial differential equations, which are foundational descriptions of many scientific and engineering problems. However, in speaking with several experts about progress in the area, questions are emerging over what realistic advantages machine learning models have and how their performance should be evaluated.

Researchers urgently need more guidance to help them identify and mitigate potential risks when designing projects that involve AI developments.

Clear descriptions of intelligence in both living organisms and machines are essential to avoid confusion, sharpen thinking and guide interdisciplinary research. A Comment in this issue encourages researchers to answer key questions to improve clarity on the terms they use.