MBZUAI showcased AI applications for health, education, and climate at Abu Dhabi Sustainability Week (ADSW) 2023. Talks highlighted AI's capacity to address challenges in climate change and other sectors, aligning with the UAE’s vision for technology-enabled solutions. MBZUAI researchers will demonstrate real-world AI applications and discuss research related to climate, health, and education. Why it matters: MBZUAI's focus on AI for sustainability demonstrates the UAE's commitment to leveraging advanced technology for addressing global challenges and advancing its leadership in clean energy and emissions reduction.
MBZUAI and the Abu Dhabi School of Government (ADSG) signed an MoU at GITEX 2021 to develop AI training programs for UAE leadership. The programs will focus on integrating AI technologies into governance, industry, education, and healthcare. MBZUAI and ADSG will jointly develop training programs and offer seminars/workshops on key AI research areas. Why it matters: This partnership strengthens the UAE's knowledge economy and supports Abu Dhabi's digitalization agenda by enhancing AI expertise in the government sector.
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.
TII and ADSB have launched a Naval Technology Innovation Hub to accelerate naval technology R&D. The hub will focus on autonomy, propulsion, materials, and secure systems, with joint initiatives exploring quantum magnetic sensing, autonomous robotics, and advanced materials. The collaboration aims to transition advanced technologies from concept to deployment, reinforcing Abu Dhabi’s position in maritime innovation. Why it matters: This partnership signifies a major push towards indigenous development of advanced naval capabilities in the UAE, fostering innovation and reducing reliance on foreign technologies.
Researchers introduce ASAD, a new large-scale, high-quality Arabic Sentiment Analysis Dataset based on 95K tweets with positive, negative, and neutral labels. The dataset is launched with a competition sponsored by KAUST offering a total of 17000 USD in prizes. Baseline models are implemented and results reported to provide a reference for competition participants.
KAUST's Water Desalination and Reuse Center (WDRC) is developing solar-powered seawater desalination technologies, including the MEDAD cycle which combines adsorption desalination (AD) and multi-effect distillation (MED). The MEDAD cycle, developed by Professor Kim Choon Ng, doubles water production at the same temperature, reducing costs to $0.48/m3 compared to $1.201/m3 for multi-stage flash distillation. A 100 m3/day commercial-scale MEDAD project was commissioned in Riyadh in 2017 in collaboration with KACST, and a larger 2,000 m3/day project is planned for Yanbu. Why it matters: This highlights Saudi Arabia's move towards sustainable energy and the role of research institutions like KAUST in developing cost-effective desalination technologies suitable for the region.
This article discusses adversarial training (AT) as a method to improve the robustness of machine learning models against adversarial attacks. AT aims to correctly classify data and ensure no data fall near decision boundaries, simulating adversarial attacks during training. Dr. Jingfeng Zhang from RIKEN-AIP will present on improvements to AT and its application in evaluating and enhancing the reliability of ML methods. Why it matters: As ML models become more prevalent in real-world applications in the GCC region, ensuring their robustness against adversarial attacks is crucial for maintaining their reliability and security.
The paper introduces ADAB (Arabic Politeness Dataset), a new annotated Arabic dataset for politeness detection collected from online platforms. The dataset covers Modern Standard Arabic and multiple dialects (Gulf, Egyptian, Levantine, and Maghrebi). It contains 10,000 samples across 16 politeness categories and achieves substantial inter-annotator agreement (kappa = 0.703). Why it matters: This dataset addresses the under-explored area of Arabic-language resources for politeness detection, which is crucial for culturally-aware NLP systems.