The United Nations has appointed Omar Sultan Al Olama, the UAE's Minister of State for Artificial Intelligence, Digital Economy, and Remote Work Applications, to its new High-Level AI Advisory Body. This body, composed of 39 global experts, is tasked with supporting international efforts to govern artificial intelligence and bridge AI divides. Its mandate includes fostering collaboration on AI development and ensuring its responsible use worldwide. Why it matters: This appointment underscores the UAE's increasing influence in global AI governance and its commitment to shaping international AI frameworks and standards.
G42, an Abu Dhabi-based technology group, and Banco Santander, a global banking group, have signed a Memorandum of Understanding to establish a framework for strategic cooperation in artificial intelligence. The partnership aims to co-develop AI-enabled advisory and savings solutions for banking customers, as well as a banking intelligence layer spanning Santander’s global operations. G42 companies Inception (agentic AI) and Presight (applied AI) are expected to play central roles in this collaboration. Why it matters: This significant partnership between a leading UAE AI entity and a global bank highlights the growing integration of advanced AI, including sovereign AI principles, into core financial services and could set a precedent for large-scale AI adoption in the global banking sector.
This paper presents a methodology for digitizing and encoding the Al-Mawrid Arabic-English dictionary using the ISO Lexical Markup Framework (LMF) and TEI Lex-0 guidelines. The research resolves structural ambiguities and inconsistencies, achieving a structural parsing accuracy of 91% and high precision/recall for information extraction, such as 85% precision for synonyms. It also discusses limitations of TEI Lex-0 for Arabic phenomena and explores Linguistic Linked Open Data (LLOD) integration. Why it matters: This work provides a crucial, standardized computational lexicon for Arabic, addressing a significant gap in Arabic lexical infrastructure and offering a reproducible workflow for retro-digitization efforts in Arabic NLP and Digital Humanities.
This paper presents a methodology for digitizing and encoding the Al-Mawrid Arabic-English dictionary, transforming it into a standardized computational lexicon using the ISO Lexical Markup Framework (LMF) and TEI Lex-0 guidelines. The research, based on an empirical analysis of the letter Ayn (4.6% of the dictionary), achieved a structural parsing accuracy of 91%. Quantitative evaluation showed high performance for information extraction rules, including 85% precision and 98% recall for synonyms. Why it matters: This work addresses a significant gap in Arabic lexical infrastructure, providing an interoperable, machine-tractable resource and a reproducible workflow for retro-digitizing complex legacy bilingual lexicons for Arabic NLP and Digital Humanities.
This study introduces a Probabilistic Graphical Model (PGM) framework utilizing Pearl's do-operator to causally audit LLM safety mechanisms, specifically isolating the effect of injecting cultural demographics into prompts. A large-scale empirical analysis was conducted across seven instruction-tuned models from diverse origins, including the UAE's Falcon3-7B, as well as models from the US, Europe, China, and India, using ToxiGen and BOLD datasets. The findings revealed a disparity between observational and interventional bias, demonstrating that standard fairness metrics can overestimate demographic bias. Western models exhibited higher causal refusal rates for specific demographic groups, while Eastern models showed low overall intervention rates with targeted sensitivities toward regional demographics. Why it matters: This research highlights the geopolitical nuances of LLM safety alignment and the potential for demographic-sensitive over-triggering to restrict benign discourse, which is particularly relevant for diverse regions like the Middle East in developing culturally-aware AI.
TII's Secure Systems Research Center (SSRC) has developed 'Shaheen', the UAE's first RISC-V-based System-on-Chip (SoC), in collaboration with Khalifa University and other global partners. Shaheen is a test chip for drone flight control, incorporating features like a hypervisor extension, Zero Trust security, and a PULP ML accelerator. It serves as a pilot version for the Al Saqr SoC planned for 2023. Why it matters: This achievement provides the UAE with the foundational knowledge for domestic silicon manufacturing, promoting high-skilled jobs and autonomy in critical technology sectors like UAV security.
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.
This paper proposes a framework for understanding AI sovereignty as a balance between autonomy and interdependence, considering global data, supply chains, and standards. It introduces a planner's model with policy heuristics for equalizing marginal returns across sovereignty pillars and setting openness. The model is applied to India and the Middle East (Saudi Arabia and UAE), finding that managed interdependence, rather than isolation, is key for AI sovereignty.