Long horizon planning in robotics can benefit from combining classic control methods with the real-world knowledge capabilities of large language models.

To function in the real world, autonomous robots will have to respond to unanticipated situations. A vision-language-model-based approach is proposed to solve long-horizon robotic tasks, which can adapt to a dynamic environment.

A systematic review of peer-reviewed AI safety research reveals extensive work on practical and immediate concerns. The findings advocate for an inclusive approach to AI safety that embraces diverse motivations and perspectives.

InstaNovo, a transformer-based model, and InstaNovo+, a multinomial diffusion model, enhance de novo peptide sequencing, enabling discovery of novel peptides, improved therapeutics sequencing coverage and detection of unreported organisms in proteomics studies

MeshHeart, a conditional generative model for time-resolved 3D heart mesh generation, is proposed by Qiao et al. to unravel heart motion patterns. Their findings could advance diagnosis and treatment strategies for cardiovascular diseases.

Hocker et al. demonstrate a method for training recurrent neural networks, which they call ‘kindergarten curriculum learning’, involving pretraining on simple cognitive tasks to improve learning efficiency. This approach helps recurrent neural networks to mimic animal behaviour in solving complex tasks.

Xiong et al. introduce ReactSeq, a reaction description language that captures molecular editing operations in chemical reactions. It enables language models to excel in retrosynthesis prediction and reaction representation.

Chen et al. introduce an AI-driven method that generates 3D small-molecule drug candidates and outperforms existing approaches by leveraging ligand shape information.

Effective lossless compression requires that frequent patterns in the data can be identified. Li et al. explore using deep learning models to more effectively compress text, audio and video data.

Accurately predicting the optimal pH level for enzyme activity is challenging due to the complex relationship between enzyme structure and function. Gado and colleagues show that a language model can effectively learn the structural and biophysical features to predict the optimal pH for enzyme activity.

Countries in the global south stand to benefit considerably from AI developments and are taking the lead in determining the direction of inclusive AI research efforts.

Long horizon planning in robotics can benefit from combining classic control methods with the real-world knowledge capabilities of large language models.

As more powerful generative AI tools appear on the market, legal debates about the use of copyrighted content to develop such tools are intensifying. To resolve these issues, transparency regarding which copyrighted data have been used and where in the AI training pipeline needs to be a starting point.

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.