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.

Users often overestimate the accuracy of large language models (LLMs). A new approach examines user perceptions and finds that aligning LLM explanations with the models’ internal confidence improves user perception.

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.

The World Health Organization (WHO) estimates a global suicide rate of 9 per 100,000 people, amounting to 720,000 preventable deaths each year. Despite concerted multisectoral efforts, suicide prevention remains a complex public health challenge, shaped by the interplay of socioeconomic, cultural and stress-related factors. In India, the decriminalization of suicide via the 2017 Mental Healthcare Act1

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.