Algorithms & Theory

Research Published 5 December 2024 Advancing adaptive AI agents, empowering 3D scene creation, and innovating LLM training for a smarter, safer future Next week, AI researchers worldwide will gather for the 38th Annual Conference on Neural Information Processing Systems (NeurIPS), taking place December 10-15 in Vancouver, Two papers led by Google DeepMind researchers will be

Science Published 4 December 2024 Authors Ilan Price and Matthew Willson New AI model advances the prediction of weather uncertainties and risks, delivering faster, more accurate forecasts up to 15 days ahead Weather impacts all of us — shaping our decisions, our safety, and our way of life. As climate change drives more extreme weather

Acknowledgements Genie 2 was led by Jack Parker-Holder with technical leadership by Stephen Spencer, with key contributions from Philip Ball, Jake Bruce, Vibhavari Dasagi, Kristian Holsheimer, Christos Kaplanis, Alexandre Moufarek, Guy Scully, Jeremy Shar, Jimmy Shi and Jessica Yung, and contributions from Michael Dennis, Sultan Kenjeyev and Shangbang Long. Yusuf Aytar, Jeff Clune, Sander Dieleman,

Machine Intelligence

The successful application of machine learning to understand the behavior of complex real-world systems from healthcare to climate requires robust methods for processing time series data. This type of data is made up of streams of values that change over time, and can represent topics as varied as a patient’s ECG signal in the ICU

AI can have a tremendous potential impact on healthcare by helping to improve diagnostic accuracy, broadening access to care, and easing administrative burden to enable care teams to focus on their patients. However, the field of healthcare is vast and there are more potential use-cases than developers can cover. In addition, AI development for health

Vulnerability gap in DP-SGD privacy analysis Most practical implementations of DP-SGD shuffle the training examples and divide them into fixed-size mini-batches, but directly analyzing the privacy of this process is challenging. Since the mini-batches have a fixed size, if we know that a certain example is in a mini-batch, then other examples have a smaller

Augmenting large language models (LLMs) with external tools, rather than relying solely on their internal knowledge, could unlock their potential to solve more challenging problems. Common approaches for such “tool learning” fall into two categories: (1) supervised methods to generate tool calling functions, or (2) in-context learning, which uses tool documents that describe the intended