Generative AI

Willow: beating the threshold Operating “below the threshold” has been a goal for error corrected quantum computing since its inception in the 1990s. However, after almost 30 years of advancement in device fabrication, calibration, and qubit design, quantum computers still hadn’t passed this landmark. That is, until our latest 105-qubit superconducting processor, Willow. Willow represents

Willow: beating the threshold Operating “below the threshold” has been a goal for error corrected quantum computing since its inception in the 1990s. However, after almost 30 years of advancement in device fabrication, calibration, and qubit design, quantum computers still hadn’t passed this landmark. That is, until our latest 105-qubit superconducting processor, Willow. Willow represents

Willow: beating the threshold Operating “below the threshold” has been a goal for error corrected quantum computing since its inception in the 1990s. However, after almost 30 years of advancement in device fabrication, calibration, and qubit design, quantum computers still hadn’t passed this landmark. That is, until our latest 105-qubit superconducting processor, Willow. Willow represents

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