NYU Abu Dhabi and MBZUAI researchers have developed ARWI, a free web application to help Arabic language learners improve their writing skills in Modern Standard Arabic. ARWI provides essay prompts aligned with CEFR skill levels, features an Arabic text editor, and gives personalized feedback. The tool won the Diversity Award at the Workshop on Intelligent and Interactive Writing Assistants (In2Writing). Why it matters: This tool can help preserve the quality and personal voice of Arabic writing amid the rise of LLMs.
The Autonomous Robotics Research Center (ARRC) is developing underwater communication systems, including a multimode modem prototype, and has filed three patents. One key technology is the Universal Underwater Software Defined Modem (UniSDM), which supports sound, magnetic induction, light, and radio waves. ARRC also developed a network management framework for automatic network slicing (ANS) of communication resources. Why it matters: These advancements are crucial for improving underwater exploration, industrial maintenance, and marine monitoring in the region, enabling more efficient and reliable communication for underwater robots.
This paper introduces a novel fuzzy clustering method for circular time series based on a new dependence measure that considers circular arcs. The algorithm groups series generated from similar stochastic processes and demonstrates computational efficiency. The method is applied to time series of wind direction in Saudi Arabia, showcasing its practical potential.
MBZUAI researchers introduce ARB, the first comprehensive benchmark for evaluating step-by-step multimodal reasoning in Arabic across textual and visual modalities. The benchmark spans 11 diverse domains and includes 1,356 multimodal samples with 5,119 human-curated reasoning steps. Evaluations of 12 state-of-the-art LMMs revealed challenges in coherence, faithfulness, and cultural grounding, highlighting the need for culturally aware AI systems.
The Arab Reform Initiative published a paper summarizing the national AI strategies of countries in the Arab region. The paper identifies common themes such as economic diversification, government efficiency, and education reform. It also notes the varying levels of investment and implementation across different countries. Why it matters: The report provides a useful overview of AI policy in the region, highlighting both opportunities and challenges for responsible AI development.
KAUST students Daniya Boges and Dr. Corrado Calì developed an AR tool for medical applications, leading to the startup IntraVides. The project was supported by KAUST's Smart Health Initiative, which provided access to AR/VR facilities and seed funding through the KAUST Innovation Fund. The KAUST Entrepreneurship Center also helped incubate the idea from concept to business. Why it matters: This highlights KAUST's role in fostering innovation and entrepreneurship in healthcare through strategic investments in advanced technology and dedicated support programs.
The paper introduces the concept of Arabic Level of Dialectness (ALDi), a continuous variable representing the degree of dialectal Arabic in a sentence, arguing that Arabic exists on a spectrum between MSA and DA. They present the AOC-ALDi dataset, comprising 127,835 sentences manually labeled for dialectness level, derived from news articles and user comments. Experiments show a model trained on AOC-ALDi can identify dialectness levels across various corpora and genres. Why it matters: ALDi provides a more nuanced approach to analyzing Arabic text than binary dialect identification, enabling sociolinguistic analysis of stylistic choices.