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Recent years have seen a surge in geospatial artificial intelligence models, with promising applications in ecological and environmental monitoring tasks. Further work should also focus on the sustainable development of such models.
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.
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.
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.
Distinguishing between real and fabricated facts has long been a societal challenge. As the Internet becomes increasingly littered with AI-generated content, the need for curation and safeguarding of high-quality data and information is more crucial than ever.
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.
Research papers can make a long-lasting impact when the code and software tools supporting the findings are made readily available and can be reused and built on. Our reusability reports explore and highlight examples of good code sharing practices.
After several decades of developments in AI, has the inspiration that can be drawn from neuroscience been exhausted? Recent initiatives make the case for taking a fresh look at the intersection between the two fields.
One of the most successful areas for deep learning in scientific discovery has been protein predictions and engineering. We take a closer look at four studies in this issue that advance protein science with innovative deep learning approaches.
