G42 and Cerebras, in partnership with MBZUAI and C-DAC, will deploy an 8 exaflop AI supercomputer in India. The system will operate under India's governance frameworks, with all data remaining within national jurisdiction to meet sovereign security and compliance requirements. The supercomputer will be accessible to Indian researchers, startups, and government entities under the India AI Mission.
MBZUAI researchers release JEEM, a new benchmark dataset for evaluating vision-language models on Arabic dialects. The dataset covers image captioning and visual question answering tasks using images from Jordan, UAE, Egypt, and Morocco. Results show models struggle with cultural understanding and relevance despite fluent language generation.
Researchers in Abu Dhabi developed H-SURF, a swarm of bio-inspired robotic fish for underwater data collection. Funded by the Technology Innovation Institute (TII) and conducted at Khalifa University, H-SURF uses swarm intelligence and optical communication to minimize disturbance to marine life. The project was recently recognized with the Sheikh Hamdan bin Zayed Award for Environmental Research.
MBZUAI has received a Google Academic Research Award to study how AI can better understand and respond to human loneliness in digital spaces. The project will examine how loneliness is expressed online, how conversational agents can detect it, and what healthier AI companionship could look like. The research aims to define digital loneliness and address the potential negative impacts of AI chatbots on users.
MBZUAI has launched the Ruwwad AI Scholars (RAIS) program, a postdoctoral fellowship for Emirati Ph.D. graduates to undertake two-year, fully-funded research positions at leading global institutions. The program aims to cultivate local talent in AI and computational research, with the goal of strengthening participants' eligibility for faculty positions at MBZUAI. The fellowship covers a stipend, research funds, insurance, relocation support, and conference travel.
MBZUAI researchers have developed 'Byzantine antidote' (Bant), a novel defense mechanism against Byzantine attacks in federated learning. Bant uses trust scores and a trial function to dynamically filter and neutralize corrupted updates, even when a majority of nodes are compromised. The research was presented at the 40th Annual AAAI Conference on Artificial Intelligence.