Researchers from Alexandria University introduce AlexU-Word, a new dataset for offline Arabic handwriting recognition. The dataset contains 25,114 samples of 109 unique Arabic words, covering all letter shapes, collected from 907 writers. The dataset is designed for closed-vocabulary word recognition and to support segmented letter recognition-based systems. Why it matters: This dataset can help advance Arabic handwriting recognition systems, addressing a need for high-quality Arabic datasets in NLP research.
MBZUAI alumnus Abdulwahab Sahyoun launched SnowHeap LLC, an AI-powered data analytics company. Sahyoun, a machine learning engineer with roots in Lebanon, aims to provide strategic tech consulting and develop in-house AI products. He was inspired by MBZUAI and the UAE's startup ecosystem to pursue his entrepreneurial ambitions. Why it matters: The story highlights MBZUAI's role in fostering AI entrepreneurship and the UAE's attractiveness for AI ventures.
Educational institutions from the United Arab Emirates and Israel have formally signed a Memorandum of Understanding (MoU) focused on artificial intelligence. This agreement aims to foster collaboration in AI research, development, and education between the two countries. The initiative is set to facilitate knowledge exchange and joint projects in the rapidly evolving field of AI. Why it matters: This MoU signifies a notable expansion of academic and technological cooperation in AI between the UAE and Israel, highlighting a growing trend of regional collaboration in key technological sectors.
MBZUAI researchers have released ALM Bench, a new benchmark dataset for evaluating the performance of multimodal LLMs on cultural visual question-answer tasks across 100 languages. The dataset includes over 22,000 question-answer pairs across 19 categories, with a focus on low-resource languages and cultural nuances, including three Arabic dialects. They tested 16 open- and closed-source multimodal LLMs on it, revealing a significant need for greater cultural and linguistic inclusivity. Why it matters: The benchmark aims to improve the inclusivity of multimodal AI systems by addressing the underrepresentation of low-resource languages and cultural contexts.