American artist Rachel Sussman spoke at KAUST's 2019 Winter Enrichment Program about her project documenting the world's oldest living organisms. Sussman photographed 30 species alive for over 2,000 years, including trees, coral, and bacteria. She collaborated with 30 scientists to identify and document these organisms. Why it matters: The lecture highlights KAUST's interdisciplinary approach to knowledge, connecting art, science, and philosophy to explore concepts of time and longevity.
KAUST alumna Justine Braguy co-founded Thya Technology, an AI startup that automates image and video analysis. The company's platform allows users to upload and label images to generate AI detection models without coding. Thya Technology was born out of a tool developed at KAUST to count plant seeds and won the TAQADAM showcase in 2022. Why it matters: This highlights KAUST's role in fostering AI entrepreneurship and translating research into practical applications, particularly in automating scientific processes.
MBZUAI hosted a talk on causal AI, featuring Professor Jin Tian from Iowa State University. The talk covered enriching AI systems with causal reasoning capabilities, moving AI beyond prediction to understanding. Professor Tian shared research on causal inference and estimating causal effects from data, using a novel estimator with double/debiased machine learning (DML) properties. Why it matters: Causal AI can improve the explainability, robustness, and adaptability of AI systems, addressing limitations of purely statistical models.
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.
This study introduces a Probabilistic Graphical Model (PGM) framework utilizing Pearl's do-operator to causally audit LLM safety mechanisms, specifically isolating the effect of injecting cultural demographics into prompts. A large-scale empirical analysis was conducted across seven instruction-tuned models from diverse origins, including the UAE's Falcon3-7B, as well as models from the US, Europe, China, and India, using ToxiGen and BOLD datasets. The findings revealed a disparity between observational and interventional bias, demonstrating that standard fairness metrics can overestimate demographic bias. Western models exhibited higher causal refusal rates for specific demographic groups, while Eastern models showed low overall intervention rates with targeted sensitivities toward regional demographics. Why it matters: This research highlights the geopolitical nuances of LLM safety alignment and the potential for demographic-sensitive over-triggering to restrict benign discourse, which is particularly relevant for diverse regions like the Middle East in developing culturally-aware AI.