KAUST and Elm, a digital solutions company owned by the Saudi Public Investment Fund (PIF), have signed an R&D cooperation agreement. The partnership aims to develop joint educational programs and events, building on previous collaborations such as hackathons focused on energy and smart city solutions in 2018. The agreement will support entrepreneurial training and the development of specialist skills. Why it matters: The partnership aligns with Saudi Vision 2030 by fostering a knowledge-based economy and providing educational and employment opportunities for Saudi youth.
KAUST researchers have identified a protein complex of HuR and YB1 that stabilizes messenger RNA during muscle-fiber formation. The complex protects RNA as it carries muscle-forming code through the cell. Further research aims to elucidate the individual roles of each protein in the stabilization process. Why it matters: Understanding this RNA-stabilizing complex could lead to new therapies for muscle recovery and the prevention of muscle-related pathologies.
This paper introduces ProgramFC, a fact-checking model that decomposes complex claims into simpler sub-tasks using a library of functions. The model uses LLMs to generate reasoning programs and executes them by delegating sub-tasks, enhancing explainability and data efficiency. Experiments on fact-checking datasets demonstrate ProgramFC's superior performance compared to baseline methods, with publicly available code and data.
Researchers at ETH Zurich have formalized models of the EMV payment protocol using the Tamarin model checker. They discovered flaws allowing attackers to bypass PIN requirements for high-value purchases on EMV cards like Mastercard and Visa. The team also collaborated with an EMV consortium member to verify the improved EMV Kernel C-8 protocol. Why it matters: This research highlights the importance of formal methods in identifying critical vulnerabilities in widely used payment systems, potentially impacting financial security for consumers in the GCC region and worldwide.
Technology Innovation Institute's (TII) Directed Energy Research Center (DERC) is integrating machine learning (ML) techniques into signal processing to accelerate research. One project used convolutional neural networks to predict COVID-19 pneumonia from chest x-rays with 97.5% accuracy. DERC researchers also demonstrated that ML-based signal and image processing can retrieve up to 68% of text information from electromagnetic emanations. Why it matters: This adoption of ML for signal processing at TII highlights the potential for advanced AI techniques to enhance research and security applications in the UAE.
MBZUAI researchers at the Institute of Foundation Models (IFM) investigated the role of reinforcement learning (RL) in improving reasoning abilities of language models. Their study found that RL acts as an 'elicitor' for reasoning in domains frequently encountered during pre-training (e.g., math, coding), while genuinely teaching new reasoning skills in underrepresented domains (e.g., logic, simulations). To support their analysis, they created a new dataset called GURU containing 92,000 examples across six domains. Why it matters: This research clarifies the impact of reinforcement learning on language model reasoning, paving the way for developing models with more generalizable reasoning abilities across diverse domains, an important direction for more capable AI systems.
William Tang from Princeton spoke at KAUST about using deep learning to achieve nuclear fusion. Nuclear fusion, recreating stellar conditions on Earth, is considered the "holy grail" of power sources because it is clean and does not produce radioactive waste. Tokamaks, invented by Soviet physicists, are devices used to contain plasma, the superheated ionized gas required for fusion. Why it matters: KAUST is contributing to research on sustainable energy solutions, including exploring the potential of AI in nuclear fusion, a potentially transformative clean energy source.
This paper proposes a machine learning method for early detection and classification of date fruit diseases, which are economically important to countries like Saudi Arabia. The method uses a hybrid feature extraction approach combining L*a*b color features, statistical features, and Discrete Wavelet Transform (DWT) texture features. Experiments using a dataset of 871 images achieved the highest average accuracy using Random Forest (RF), Multilayer Perceptron (MLP), Naïve Bayes (NB), and Fuzzy Decision Trees (FDT) classifiers.