AI

  Researchers from Google Research, Google DeepMind, and the University of Waterloo introduce SWIM-IR, a synthetic retrieval training dataset encompassing 33 languages, addressing the challenge of limited human-labeled training pairs in multilingual retrieval. Leveraging the SAP (summarize-then-ask prompting) method, SWIM-IR is constructed to enable synthetic fine-tuning of multilingual dense retrieval models without human supervision. SWIM-X…

  The most diversified marine environment on Earth is said to be found in coral reefs. Over 4,000 kinds of fish may be found in the coral reefs, home to an estimated 25% of all marine life. In coral, underwater parasite algae, or zooxanthellae, produces vibrant calcium carbonate structures known as reefs. When the water…

  Latent Diffusion Models are generative models used in machine learning, particularly in probabilistic modeling. These models aim to capture a dataset’s underlying structure or latent variables, often focusing on generating realistic samples or making predictions. These describe the evolution of a system over time. This can refer to transforming a set of random variables…

  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…

  In deep learning, Transformer neural networks have garnered significant attention for their effectiveness in various domains, especially in natural language processing and emerging applications like computer vision, robotics, and autonomous driving. However, while enhancing performance, the ever-increasing scale of these models brings about a substantial rise in compute cost and inference latency. The fundamental…

  Researchers from MIT, CarperAI, and Parametrix.AI introduced Neural MMO 2.0, a massively multi-agent environment for reinforcement learning research, emphasizing a versatile task system enabling users to define diverse objectives and reward signals. The key enhancement involves challenging researchers to train agents capable of generalizing to unseen tasks, maps, and opponents. Version 2.0 is a…