TII's Secure Systems Research Center (SSRC) is partnering with Imperial College London on a three-year research project focused on trustworthy cloud computing with hardware security. The project aims to design a new trustworthy cloud OS stack leveraging hardware mechanisms like ARM TrustZone. It will explore userspace isolation abstraction while maintaining compatibility with POSIX standards using ARM and RISC-V architectures. Why it matters: This collaboration addresses critical cloud security challenges by integrating hardware-based security solutions, potentially unifying cloud and edge security approaches in the region.
Researchers introduce TimeTravel, a benchmark dataset for evaluating large multimodal models (LMMs) on historical and cultural artifacts. The benchmark comprises 10,250 expert-verified samples across 266 cultures and 10 historical regions, designed to assess AI in tasks like classification and interpretation of manuscripts, artworks, inscriptions, and archaeological discoveries. The goal is to establish AI as a reliable partner in preserving cultural heritage and assisting researchers.
KAUST and the Jeddah Chamber of Commerce and Industry (JCCI) signed an MoU to foster investment in SMEs, build a digital transformation strategy, and develop AI initiatives. As part of the collaboration, KAUST will receive a seat on JCCI's Industrial council and provide access to its laboratories and technology. The partnership aims to bridge the gap between research and industry, supporting local SMEs and entrepreneurs in Jeddah. Why it matters: This partnership strengthens KAUST's role in driving economic development and AI adoption in Saudi Arabia, aligning with the Kingdom's Vision 2030 goals for SME empowerment and technological advancement.
MBZUAI researchers will present 20 papers at the 40th International Conference on Machine Learning (ICML) in Honolulu. Visiting Associate Professor Tongliang Liu leads with seven publications, followed by Kun Zhang with six. One paper investigates semi-supervised learning vs. model-based methods for noisy data annotation in deep neural networks. Why it matters: The research addresses the critical issue of data quality and accessibility in machine learning, particularly for organizations with limited resources for data annotation.