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(metamorworks/Shutterstock) Like ethically sourced diamonds or coffee beans, ethically sourced data can be hard to find. But as AI chews through all the easily sourced training data, the ways and means by which data is obtained are becoming increasingly important. One outfit that’s building a business around ethically sourced data is Prolific. Prolific was founded at Oxford University in 2014 primarily to provide data for academic research. If a behavioral scientist needed data for a study on how consumer decision-making changes with age, for instance, they could tap Prolific to help it find vetted participants and to gather the data…

arXiv:2408.01000v1 Announce Type: new Abstract: The surging demand for cloud computing resources, driven by the rapid growth of sophisticated large-scale models and data centers, underscores the critical importance of efficient and adaptive resource allocation. As major tech enterprises deploy massive infrastructures with thousands of GPUs, existing cloud platforms still struggle with low resource utilization due to key challenges: capturing hierarchical indicator structures, modeling non-Gaussian distributions, and decision-making under uncertainty. To address these challenges, we propose HRAMONY, an adaptive Hierarchical Attention-based Resource Modeling and Decision-Making System. HARMONY combines hierarchical multi-indicator distribution forecasting and uncertainty-aware Bayesian decision-making. It introduces a novel hierarchical…

arXiv:2408.01031v1 Announce Type: new Abstract: Large-scale self-supervised pre-training has paved the way for one foundation model to handle many different vision tasks. Most pre-training methodologies train a single model of a certain size at one time. Nevertheless, various computation or storage constraints in real-world scenarios require substantial efforts to develop a series of models with different sizes to deploy. Thus, in this study, we propose a novel tri-branch self-supervised training framework, termed as POA (Pre-training Once for All), to tackle this aforementioned issue. Our approach introduces an innovative elastic student branch into a modern self-distillation paradigm. At each pre-training step,…