A DeepMind researcher presented work on incorporating symmetries into machine learning models, with applications to lattice-QCD and molecular dynamics. The work includes permutation and translation-invariant normalizing flows for free-energy estimation in molecular dynamics. They also presented U(N) and SU(N) Gauge-equivariant normalizing flows for pure Gauge simulations and its extensions to incorporate fermions in lattice-QCD. Why it matters: Applying symmetry principles to generative models could improve AI's ability to model complex physical systems relevant to materials science and other fields in the region.
Ahmed Elhag, a PhD student at the University of Oxford, presented a new training procedure that approximates equivariance in unconstrained machine learning models via a multitask objective. The approach adds an equivariance loss to unconstrained models, allowing them to learn approximate symmetries without the computational cost of fully equivariant methods. Formulating equivariance as a flexible learning objective allows control over the extent of symmetry enforced, matching the performance of strictly equivariant baselines at a lower cost. Why it matters: This research from a speaker at MBZUAI balances rigorous theory and practical scalability in geometric deep learning, potentially accelerating drug discovery and design.
Bruno Ribeiro from Purdue University presented a talk on Asymmetry Learning and Out-of-Distribution (OOD) Robustness. The talk introduced Asymmetry Learning, a new paradigm that focuses on finding evidence of asymmetries in data to improve classifier performance in both in-distribution and out-of-distribution scenarios. Asymmetry Learning performs a causal structure search to find classifiers that perform well across different environments. Why it matters: This research addresses a key challenge in AI by proposing a novel approach to improve the reliability and generalization of classifiers in unseen environments, potentially leading to more robust AI systems.
KAUST research engineer Samy Ould-Chikh is collaborating with the Néel Institute-CNRS at the European Synchrotron Radiation Facility (ESRF) in France. They are using the ESRF's high-energy synchrotron light source to study the inner structure of matter at the atomic and molecular levels. Ould-Chikh's research focuses on catalysis and functional materials, with an emphasis on renewable energy and photocatalysis. Why it matters: This collaboration highlights KAUST's engagement with leading international research institutions to advance materials science and energy research.
KAUST researchers, in collaboration with Nanyang Technological University, have discovered a unique chiral structure in gold nanowires. The nanowires exhibit a Boerdijk-Coxeter-Bernal (BCB) helix structure, achieved through a seed-mediated substrate growth method, reaching a minimum diameter of 3 nanometers. High-resolution transmission electron microscopy (HRTEM) at KAUST was crucial in revealing the structure. Why it matters: This breakthrough in chiral metallic nanowire production could lead to advancements in chemical separation, sensing, and catalysis due to the unique properties of chiral crystals.
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 alumnus Dr. Muhammed Sameed works at CERN on the ALPHA project, studying antimatter. The project aims to understand why there is so little antimatter in the universe, given that physics equations predict equal amounts of matter and antimatter. Sameed's work involves creating, trapping, and studying antimatter particles in a controlled lab environment. Why it matters: This research advances our understanding of fundamental physics and the composition of the universe, with a KAUST alumnus playing a key role.
KAUST and the Saudi Food and Drug Authority (SFDA) have partnered to develop a new method using nuclear magnetic resonance (NMR) to detect adulterants in olive oil. The method aims to identify and quantify vegetable oils mixed with olive oil, addressing concerns about the mislabeling of olive oil in the Saudi market. KAUST's comprehensive suite of NMR machines was critical for the project. Why it matters: This collaboration enhances food safety and quality control in Saudi Arabia, a major olive oil importer, and helps to ensure consumers receive authentic, high-quality products.