MBZUAI's AI Quorum launched its second workshop, "Building Ecosystems for AI at Scale," focusing on AI scalability and business applications. The first CASL workshop aims to define steps for organizations to become self-sufficient with AI and explore new use cases. Speakers include MBZUAI faculty and researchers from CMU, Stanford, KAUST, UC Berkeley, and Google. Why it matters: The workshop highlights the UAE's growing role in fostering AI innovation and bridging the gap between academic research and industry applications in the region.
MBZUAI is developing the AI Operating System (AIOS) to reduce the energy, time, and talent costs of AI computing. AIOS aims to make AI models smaller, faster, and more efficient, reducing reliance on expensive hardware and speeding up compute operations. It also enables cost-aware model tuning and standardizes AI modules for reliable operation. Why it matters: By addressing the environmental impact and resource demands of AI, AIOS could promote more sustainable and accessible AI development in the region and globally.
MBZUAI faculty Eric Xing and Qirong Ho are developing AI operating systems (AI OS) for efficient AI development, similar to mobile OS. They co-founded AI startup Petuum and lead the CASL community, which focuses on composable, automatic, and scalable learning. CASL provides a unified toolkit for distributed training and compositional model construction, with contributions from MBZUAI, CMU, Berkeley, and Stanford. Why it matters: The development of AI OS aims to optimize AI applications by efficiently connecting software and hardware, fostering innovation and broader adoption of AI solutions across industries in the region.
Qirong Ho, co-founder and CTO of Petuum Inc., will be contributing to the "ML Systems for Many" initiative. Petuum is recognized for creating standardized building blocks for AI assembly. Ho also holds a Ph.D. from Carnegie Mellon University and is part of the CASL open-source consortium. Why it matters: Showcases the ongoing efforts to democratize AI development and deployment, making it more accessible and sustainable, although the specific initiative is not further detailed.
The Technology Innovation Institute's (TII) Cryptography Research Center (CRC) has launched CLAASP, a cryptographic library for the automated analysis of symmetric primitives. CLAASP, built on SageMath and Python3, automates the design analysis of block ciphers, cryptographic permutations, hash functions, and stream ciphers. Released as an open-source tool with a GPLv3 license, CLAASP aims to ensure design sovereignty for organizations creating symmetric ciphers. Why it matters: This tool provides an important resource for the region to strengthen its cryptographic capabilities and contribute to global efforts in safeguarding digital infrastructure against evolving threats, including quantum computing.
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.
Michael Hickner, an Associate Professor from Penn State University, visited KAUST as part of the CRDF-KAUST-OSR Visiting Scholar Fellowship Program. Hickner specializes in Materials Science and Engineering, Chemistry, and Chemical Engineering. The visit was documented with photos by Meres J. Weche. Why it matters: Such programs foster international collaboration and knowledge exchange in science and engineering between KAUST and other leading institutions.
MBZUAI researchers introduced CausalVerse, a new benchmark for causal representation learning (CRL) presented at NeurIPS 2025. CausalVerse combines high-fidelity visual complexity with access to underlying causal variables and graphs, featuring 200,000 images and 300 million video frames across 24 sub-scenes in four domains. It aims to provide a realistic and precise testbed to evaluate whether CRL methods can truly learn the right causes. Why it matters: By bridging the gap between toy datasets and real-world data, CausalVerse can drive advances in AI systems capable of understanding causality in complex scenarios.