KAUST and Cerebras Systems collaborated on multi-dimensional seismic processing using the Condor Galaxy AI supercomputer, achieving record sustained memory bandwidth of 92.58 petabytes per second. They developed a Tile Low-Rank Matrix-Vector Multiplication (TLR-MVM) kernel to exploit the architecture of Cerebras CS-2 systems. This work was recognized as a finalist for the 2023 Gordon Bell Prize. Why it matters: This demonstrates the potential of AI-customized architectures for seismic processing, with broader implications for climate modeling and other scientific domains in the region and globally.
Researchers introduce AceGPT, a localized large language model (LLM) specifically for Arabic, addressing cultural sensitivity and local values not well-represented in mainstream models. AceGPT incorporates further pre-training with Arabic texts, supervised fine-tuning using native Arabic instructions and GPT-4 responses, and reinforcement learning with AI feedback using a reward model attuned to local culture. Evaluations demonstrate that AceGPT achieves state-of-the-art performance among open Arabic LLMs across several benchmarks. Why it matters: This work advances culturally-aware AI development for Arabic-speaking communities, providing a valuable resource and benchmark for future research.
Researchers at MBZUAI release SlimPajama-DC, an empirical analysis of data combinations for pretraining LLMs using the SlimPajama dataset. The study examines the impact of global vs. local deduplication and the proportions of highly-deduplicated multi-source datasets. Results show that increased data diversity after global deduplication is crucial, with the best configuration outperforming models trained on RedPajama.
This paper introduces a domain generalization (DG) method for Diabetic Retinopathy (DR) classification that maximizes mutual information using a large pretrained model. The method aims to address the challenge of domain shift in medical imaging caused by variations in data acquisition. Experiments on public datasets demonstrate that the proposed method outperforms state-of-the-art techniques, achieving a 5.25% improvement in average accuracy.