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.
This paper introduces two methods for creating Arabic LLM prompts at scale: translating existing English prompt datasets and creating natural language prompts from Arabic NLP datasets. Using these methods, the authors generated over 67.4 million Arabic prompts covering tasks like summarization and question answering. Fine-tuning a 7B Qwen2 model on these prompts outperforms a 70B Llama3 model in handling Arabic prompts. Why it matters: The research provides a cost-effective approach to scaling Arabic LLM training data, potentially improving the performance of smaller, more accessible models for Arabic NLP.
KAUST launched the Circular Carbon Initiative (CCI) to address carbon management, capture, conversion, and storage of atmospheric CO2. The initiative involves developing materials and technologies to capture CO2 and exploring geothermal energy and geological storage. Novel fuel production will redefine CO2 as a valuable material through e-fuel developments. Why it matters: The CCI positions KAUST as a key player in developing sustainable technologies and contributing to Saudi Arabia's climate goals.
Fudan University's Zhongyu Wei presented research on social simulation driven by LLMs, covering individual and large-scale social movement simulation. Wei directs the Data Intelligence and Social Computing Lab (Fudan DISC) and has published extensively on multimodal large models and social computing. His work includes the Volcano multimodal model, DISC-MedLLM, and ElectionSim. Why it matters: Using LLMs for social simulation could provide new tools for understanding and potentially predicting social dynamics in the Arab world.