MBZUAI's computer science department, led by Xiaosong Ma, focuses on improving AI efficiency and sustainability by reducing wasted resources. Xiaosong's background in high-performance computing informs her approach to optimizing AI workloads. She aims to collaborate with experts across different AI domains at MBZUAI to address these challenges. Why it matters: Optimizing AI efficiency is crucial for reducing the environmental impact and computational costs associated with increasingly complex AI models in the GCC region and globally.
MBZUAI PhD graduate William de Vazelhes is researching hard-thresholding algorithms to enable AI to work from smaller datasets. His work focuses on optimization algorithms that simplify data, making it easier to analyze and work with, useful for energy-saving and deploying AI models on low-memory devices. He demonstrated that his approach can obtain results similar to those of convex algorithms in many usual settings. Why it matters: This research could broaden AI accessibility by reducing computational costs, and has potential applications in sectors like finance, particularly for portfolio management under budgetary constraints.
MBZUAI's Samuel Horváth presented a new framework called Maestro at ICML 2024 for efficiently training machine learning models in federated settings. Maestro identifies and removes redundant components of a model through trainable decomposition to increase efficiency on edge devices. The approach decomposes layers into low-dimensional approximations, discarding unused aspects to reduce model size. Why it matters: This research addresses the challenge of running complex models on resource-constrained devices, crucial for expanding AI applications while preserving data privacy.
MBZUAI researchers developed GroupMamba, a new set of state-space models (SSMs) for computer vision that addresses limitations in existing SSMs related to computational efficiency and optimization challenges. GroupMamba introduces a new layer called modulated group mamba, improving efficiency and stability. In benchmark tests, GroupMamba performed as well as similar SSM systems, but more efficiently, offering a backbone for tasks like image classification, object detection, and segmentation. Why it matters: This research aims to bridge the gap between vision transformers and CNNs by improving SSMs, potentially leading to more efficient and powerful computer vision models.
KAUST researchers developed a tandem solar cell with 32.5% conversion efficiency by optimizing the silicon-perovskite connection. Another team combined spectroscopy and reactor technologies to reveal details on catalyst function and reaction mechanisms. A KAUST team also developed a mathematical framework improving data rates by 30% and optimizing terrestrial network speeds. Why it matters: These advances highlight KAUST's contributions to sustainable energy, industrial processes, and network optimization, addressing key challenges in the region and globally.
MBZUAI researchers are developing spiking neural networks (SNNs) to emulate the energy efficiency of the human brain. Traditional deep learning models like those powering ChatGPT consume significant energy, with a single query using 3.96 watts. SNNs aim to mimic biological neurons more closely to reduce energy consumption, as the human brain uses only a fraction of the energy compared to these models. Why it matters: This research could lead to more sustainable and energy-efficient AI technologies, addressing a major challenge in deploying large-scale AI systems.
MBZUAI researchers are applying federated learning to optimize smart grids while protecting user data privacy. This approach leverages techniques from smart healthcare systems to enhance energy efficiency and local energy sharing. The research addresses the challenge of balancing grid optimization with the risk of user identity theft associated with traditional data-intensive smart grids. Why it matters: This research demonstrates a practical application of privacy-preserving AI in critical infrastructure, addressing key concerns around data security and fostering trust in smart grid technologies.