AMRC researchers Jide Oyebanji and Tarcisio Silvia will present papers at the MATLAB User Group Meeting in Abu Dhabi. Oyebanji's paper focuses on the 'Design of an Interactive TPMS Designing Desktop App' using MATLAB's numerical capabilities. Silvia's presentation discusses the optimization of MIMO active vibration controllers for electromechanical systems using MATLAB Simulink and Particle Swarm Optimization. Why it matters: The presentations showcase the application of computational tools like MATLAB in advanced materials research and digital engineering within the UAE.
The Advanced Materials Research Center (AMRC) held its second Board of Advisors meeting on November 23, 2021. Board members from Khalifa University, University of Sheffield, City University Hong Kong, Ludwig Maximilians Universität München, and Instituto Superior Técnico provided feedback on AMRC’s strategic plans. The meeting focused on AMRC’s strategic growth and future directions. Why it matters: The meeting indicates continued investment in advanced materials research in the UAE, signaling potential advancements in related technological applications.
Nvidia is expanding its market beyond GPUs with the development of a central processing unit (CPU) based on Arm architecture. This move positions Nvidia to compete directly with established CPU manufacturers like Intel and AMD. The company aims to offer integrated hardware and software solutions optimized for AI and data science workloads. Why it matters: Nvidia's entry into the CPU market could accelerate AI development and adoption in the Gulf region by providing more specialized and efficient computing solutions.
Three researchers from the UAE's Advanced Materials Research Center (AMRC) are pursuing advanced degrees at the University of Manchester through the Advanced Technology Research Council’s NexTech program. Shamma Alhashmi is pursuing a PhD in chemical engineering, while Mohamed Alnuaimi and Omar BaNabila are working towards master's degrees in Advanced Engineering Materials. Their research focuses on nanomaterials, piezoelectrics, and material damage analysis, respectively. Why it matters: This initiative demonstrates the UAE's investment in STEM education and aims to enhance domestic research capabilities in advanced materials by providing international training opportunities for promising Emirati researchers.
The Advanced Materials Research Center (AMRC) has announced strategic partnerships with institutions including the University of Sheffield, Khalifa University, and McGill University. These partnerships include visiting scholar fellowships, graduate programs with internships, and dual-degree programs. These initiatives aim to foster collaboration between AMRC experts and students from around the world, providing access to resources, mentorship, and financial support. Why it matters: This collaboration will strengthen Abu Dhabi's innovation ecosystem and R&D sectors through global knowledge transfer and talent development.
Professor Marco Amabili, advisor at the Advanced Materials Research Center (AMRC), received the 'Cataldo Agostinelli and Angiola Gili Agostinelli' International Prize from the Lincei National Academy of Sciences of Italy. The award recognizes Prof. Amabili's research in mechanical vibrations, composite structures, and vascular biomechanics. He received the award in Rome from Nobel laureate Professor Giorgio Parisi. Why it matters: The recognition highlights the growing international visibility of UAE-based researchers and the increasing commitment of UAE institutions like TII to deep-tech research.
KAUST and Saudi healthtech company amplifAI health have signed an MoU to develop a new disease detection system. The system will combine amplifAI's AI technology with KAUST's HyplexTM hyperspectral imaging, initially for diabetic foot complications. Clinical trials are planned, with aims to reduce amputations and save Saudi Arabia over 2 billion Riyals annually. Why it matters: This partnership showcases the potential of combining Saudi AI and advanced imaging technologies to address pressing healthcare challenges in the region, particularly diabetes.
This paper introduces a novel evaluation framework for Arabic language models, addressing gaps in linguistic accuracy and cultural alignment. The authors analyze existing datasets and present the Arabic Depth Mini Dataset (ADMD), a curated collection of 490 questions across ten domains. Evaluating GPT-4, Claude 3.5 Sonnet, Gemini Flash 1.5, CommandR 100B, and Qwen-Max using ADMD reveals performance variations, with Claude 3.5 Sonnet achieving the highest accuracy at 30%. Why it matters: The work emphasizes the importance of cultural competence in Arabic language model evaluation, providing practical insights for improvement.