A new paper from MBZUAI introduces JEEM, a benchmark dataset for evaluating vision-language models on their understanding of images grounded in four Arabic-speaking societies (Jordan, UAE, Egypt, and Morocco) and their ability to use local dialects. The dataset comprises 2,178 images and 10,890 question-answer pairs reflecting everyday life and culturally specific scenes. Evaluation of several Arabic-capable models (Maya, PALO, Peacock, AIN, AyaV) and GPT-4o revealed that while models can generate fluent language, they struggle with genuine understanding, consistency, and relevance, especially when cultural context is important. Why it matters: This research highlights the challenges of building AI systems that can truly understand and interact with diverse cultures, emphasizing the need for culturally grounded datasets and evaluation metrics.
Michael I. Jordan, a UC Berkeley Distinguished Professor and influential figure in machine learning and AI, has been appointed laureate professor at Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI). He will also serve as honorary director of MBZUAI's new Laureate Faculty Program. The program aims to attract leading researchers to MBZUAI by providing resources and minimizing administrative burdens. Why it matters: This appointment strengthens MBZUAI's reputation and enhances the UAE's AI ecosystem by bringing in a world-renowned AI expert to foster research and innovation.
Michael I. Jordan, a renowned AI researcher from UC Berkeley, has joined Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) as a laureate professor and honorary director of the Laureate Faculty Program. MBZUAI President Eric Xing highlighted Jordan's significant influence in machine learning, noting his role as a mentor. Jordan aims to guide AI researchers and advise the university as it seeks to become a global leader in AI. Why it matters: This appointment strengthens MBZUAI's position as a prominent AI research institution in the Middle East by attracting top-tier international talent and fostering a conducive environment for cutting-edge research.
MBZUAI researchers release JEEM, a new benchmark dataset for evaluating vision-language models on Arabic dialects. The dataset covers image captioning and visual question answering tasks using images from Jordan, UAE, Egypt, and Morocco. Results show models struggle with cultural understanding and relevance despite fluent language generation.
Oman and Jordan are competing to establish themselves as key hubs for AI and data centers in the Middle East. Both countries are investing in infrastructure and regulatory frameworks to attract international tech companies. Favorable geographic locations, stable political environments, and competitive energy costs are helping to drive this growth. Why it matters: These efforts could lead to increased economic diversification and technological advancement in both countries, fostering regional innovation.
The ArabJobs dataset is a new corpus of over 8,500 Arabic job advertisements collected from Egypt, Jordan, Saudi Arabia, and the UAE. The dataset contains over 550,000 words and captures linguistic, regional, and socio-economic variation in the Arab labor market. It is available on GitHub and can be used for fairness-aware Arabic NLP and labor market research.
Kamal Mousa Al-Jahni, a Saudi Ph.D. student at the University of South Florida, is researching natural water springs in Saudi Arabia, Jordan, and Oman, leveraging facilities at the KAUST Analytical Chemistry Core Lab (ACL). Al-Jahni is expanding on his master's research, collaborating with King Saud University and utilizing KAUST's specialized testing facilities and expert staff. He aims to improve sample collection and utilize advanced lab equipment. Why it matters: This research contributes to a better understanding of water resources in the Arabian Peninsula and demonstrates KAUST's role in supporting environmental research and training Saudi researchers.
MBZUAI and UC Berkeley held a joint workshop on machine learning, featuring discussions on online learning, fair allocation in dynamic mechanism design, and causal inference. Michael I. Jordan, Laureate Professor and Honorary Program Director at MBZUAI, highlighted the institute's rapid growth during his visit. Researchers explored methods for enhancing the properties of large, complex models, such as calibration, fairness, and robustness. Why it matters: Such collaborations advance AI research and foster knowledge exchange between leading global experts and regional institutions like MBZUAI.