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  Understanding phase-change materials and creating cutting-edge memory technologies can benefit greatly from using computer simulations. However, direct quantum-mechanical simulations can only handle relatively simple models with hundreds or thousands of atoms at most. Recently, researchers at the University of Oxford and the Xi’an Jiaotong University in China developed a machine learning model that might…

  Researchers from the Intelligent Autonomous Systems Group, Locomotion Laboratory, German Research Center for AI, Centre for Cognitive Science, and Hessian.AI introduced a benchmark to advance research in Imitation Learning (IL) for locomotion, addressing the limitations of existing measures that often focus on simplified tasks. This new benchmark comprises diverse environments, including quadrupeds, bipeds, and…

  In an era dominated by AI advancements, distinguishing between human and machine-generated content, especially in scientific publications, has become increasingly pressing. This paper addresses this concern head-on, proposing a robust solution to identify and differentiate between human and AI-generated writing accurately for chemistry papers. Current AI text detectors, including the latest OpenAI classifier and…

  As technology continues to advance at an astonishing pace, Cerebras Systems and G42 have just taken a giant leap forward in the world of artificial intelligence. In a groundbreaking partnership, they have successfully completed a 4-Exaflop AI supercomputer, marking a significant milestone in the quest for unprecedented computational power. This achievement also signifies the…

  Modern self-driving systems frequently use Large-scale manually annotated datasets to train object detectors to recognize the traffic participants in the picture. Auto-labeling methods that automatically produce sensor data labels have recently gained more attention. Auto-labeling may provide far bigger datasets at a fraction of the expense of human annotation if its computational cost is…