MBZUAI hosted a delegation from Khalifa University to discuss collaboration on February 1, 2022. The universities explored ways to enhance recruitment and research opportunities in AI. MBZUAI President Eric Xing emphasized the interdependencies between the two institutions and the potential for future collaborative projects. Why it matters: Strengthening ties between leading UAE universities will help to build a stronger AI ecosystem and talent pool within the country.
Two student teams from MBZUAI won top prizes at the inaugural Agritech Hackathon (“Agrithon”) organized by ADAFSA. The “Masdar Boys” team developed a dashboard integrating ML models for plant disease diagnosis, optimal animal clinic placement, and disease outbreak zone classification. The “Green AI” team built a machine learning framework for plant disease classification, winning second prize. Why it matters: This highlights the growing role of AI in addressing food security challenges in the UAE and the region, with potential for real-world applications through ADAFSA's interest in further developing the students' work.
KAUST reflects on its COVID-19 response, highlighting community efforts, research contributions, and partnerships. Faculty are leveraging expertise in diagnostics, AI therapeutics, genomics, and epidemiology. KAUST is collaborating with the Saudi CDC, Ministry of Health, and other institutions. Why it matters: This demonstrates KAUST's role as a hub for research and innovation, contributing to both national and global health challenges during a crisis.
MBZUAI Executive Program participants gathered for community-building activities on Jubail Island, including a mangrove walk and dinner. MBZUAI President Eric Xing emphasized the opportunity to build partnerships and an AI community. The event aimed to foster collaboration and understanding among participants to drive positive AI progress. Why it matters: Such initiatives can help bridge divides between organizations and facilitate the responsible development of AI in the UAE.
Sai Praneeth Karimireddy from UC Berkeley presented a talk on building planetary-scale collaborative intelligence, highlighting the challenges of using distributed data in machine learning due to data silos and ethical-legal restrictions. He proposed collaborative systems like federated learning as a solution to bring together distributed data while respecting privacy. The talk addressed the need for efficiency, reliability, and management of divergent goals in these systems, suggesting the use of tools from optimization, statistics, and economics. Why it matters: Collaborative AI systems can unlock valuable distributed data in the region, especially in sensitive sectors like healthcare, while ensuring privacy and addressing ethical concerns.