ADGM and MBZUAI have signed an MoU to advance AI applications in financial regulatory compliance. The partnership will focus on developing regtech and suptech solutions, including an AI model to extract meaning from financial regulations. A key project is enhancing FSRA’s ‘Risk Analyser’ platform, which uses AI to provide insights on supervised firms. Why it matters: This collaboration signals a push towards AI-driven regulatory innovation in the UAE's financial sector, potentially improving efficiency and compliance.
Researchers introduce a new task for generating question-passage pairs to aid in developing regulatory question-answering (QA) systems. The ObliQA dataset, comprising 27,869 questions from Abu Dhabi Global Markets (ADGM) financial regulations, is presented. A baseline Regulatory Information Retrieval and Answer Generation (RIRAG) system is designed and evaluated using the RePASs metric.
Conor McMenamin from Universitat Pompeu Fabra presented a seminar on State Machine Replication (SMR) without honest participants. The talk covered the limitations of current SMR protocols and introduced the ByRa model, a framework for player characterization free of honest participants. He then described FAIRSICAL, a sandbox SMR protocol, and discussed how the ideas could be extended to real-world protocols, with a focus on blockchains and cryptocurrencies. Why it matters: This research on SMR protocols and their incentive compatibility could lead to more robust and secure blockchain technologies in the region.
The Advanced Technology Research Council (ATRC) entities ASPIRE and TII have partnered with the Abu Dhabi Agriculture and Food Safety Authority (ADAFSA) to advance sustainable food and agriculture solutions. The collaboration will focus on applied research activities in areas like diagnostics and therapeutics, sustainable protein, resilient water and energy solutions, and R&D initiatives. TII will participate through its Biotechnology Research Center (BRC), the Renewable and Sustainable Energy Research Center (RSERC), and the Advanced Materials Research Center (AMRC). Why it matters: This partnership signifies a strategic effort to leverage technology and research to enhance food security and environmental resilience in the UAE.
Dr. Abdelrahman AlMahmoud from TII's Secure Systems Research Center (SSRC) will participate in a WGISTA webinar on adopting a digital mindset in auditing and fighting corruption. The webinar, organized by the International Organization of Supreme Audit Institutions (INTOSAI), will discuss the impact of emerging technologies on public sector auditing. Dr. AlMahmoud will share insights on how AI and Big Data can enable auditors to process data at a new scale. Why it matters: This highlights the UAE's growing role in applying advanced technologies like AI and big data to improve governance and accountability in the public sector.
KAUST and the Social Responsibility Association (SRA) are hosting their third annual AI hackathon at KAUST with 73 participants from across Saudi Arabia. The hackathon aims to deliver 14 social projects in technology and innovation across the tracks of social issues, housing, tourism, and education. KAUST supports the event to foster entrepreneurship and transform ideas into scalable solutions that serve society. Why it matters: The event highlights the growing focus on AI-driven solutions for social challenges within Saudi Arabia, aligning with Vision 2030's goals for digital entrepreneurship.
This paper introduces an enhanced Dense Passage Retrieval (DPR) framework tailored for Arabic text retrieval. The core innovation is an Attentive Relevance Scoring (ARS) mechanism that improves semantic relevance modeling between questions and passages, replacing standard interaction methods. The method integrates pre-trained Arabic language models and architectural refinements, achieving improved retrieval and ranking accuracy for Arabic question answering. Why it matters: This work addresses the underrepresentation of Arabic in NLP research by providing a novel approach and publicly available code to improve Arabic text retrieval, which can benefit various applications like Arabic search engines and question-answering systems.