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.
MBZUAI hosted the Second Workshop on Collaborative Learning as part of the AI Quorum in Abu Dhabi, focusing on collaborative and federated learning for sustainable development. Researchers discussed applications in medicine, biology, ecological conservation, and humanitarian aid. Eric Xing highlighted the potential of large biology models, similar to LLMs, to revolutionize biological data analysis. Why it matters: This workshop underscores the UAE's commitment to advancing AI research in crucial sectors like healthcare and sustainability through collaborative learning approaches.
MBZUAI launched the AI Quorum, a winter series from October 2022 to March 2023, to stimulate AI research. The first session, led by Professor Michael Jordan, focused on collaborative learning with around 20 research experts. Discussions covered the use of edge devices like cell phones and hospitals providing data to build large models, as well as risks like free-riding and adversarial attacks. Why it matters: The AI Quorum initiative positions MBZUAI as a hub for global AI collaboration, addressing key challenges and opportunities in collaborative learning for real-world applications.
MBZUAI held its inaugural Human-Computer Interaction (HCI) Symposium in Abu Dhabi, focusing on the human and societal impacts of AI. The event, led by Professor Elizabeth Churchill, featured workshops and keynotes from figures like Google's Matias Duarte. Participants collaborated to address critical design aspects of human-AI interaction and co-author a book. Why it matters: The symposium highlights the increasing importance of human-centered design in AI development, ensuring AI tools are useful, desirable, and beneficial for society in the GCC region and beyond.
KAUST is hosting a workshop on distributed training in November 2025, led by Professors Peter Richtarik and Marco Canini, focusing on scaling large models like LLMs and ViTs. Richtarik's team recently solved a 75-year-old problem in asynchronous optimization, developing time-optimal stochastic gradient descent algorithms. This research improves the speed and reliability of large model training and supports applications in distributed and federated learning. Why it matters: KAUST's focus on scalable AI and federated learning contributes to Saudi Arabia's Vision 2030 goals and addresses critical challenges in AI deployment and data privacy.
MBZUAI and MIT Schwarzman College of Computing have launched a collaborative research program to advance AI across scientific discovery, human thriving, and planetary health. The program will involve faculty, students, and research staff from both institutions, with projects jointly led by principal investigators from each. The collaboration aims to strengthen AI foundations and accelerate its application to pressing societal challenges, aligning with both MIT's goals and the UAE's mission to become a global AI innovation hub. Why it matters: This partnership between a leading UAE AI university and a top US institution will foster impactful AI research and development, contributing to both regional and global advancements in the field.
MBZUAI and the University of Michigan Ann Arbor have announced a new collaboration in AI research, sponsored by the U.S. Mission to the UAE. The partnership focuses on projects addressing the cultural divide in AI, with research teams from both institutions collaborating throughout the 2023/2024 academic year. A workshop titled “Bridging the Cultural Divide in AI: Analyzing Fairness, Bias, and Transparency across Cultures” will be held on April 29-30. Why it matters: The collaboration strengthens ties between the UAE and the U.S. in AI, addressing critical issues of fairness and cultural sensitivity in AI development.