KAUST researchers led by Dr. Gyorgy Szekely are developing selective porous membranes to replace energy-intensive separation techniques like distillation in the chemical manufacturing industry. These membrane processes could reduce energy consumption by up to 90% compared to traditional methods. Szekely's team uses AI to optimize separation materials by identifying patterns in previously fragmented data. Why it matters: This research has the potential to significantly reduce the environmental impact of chemical manufacturing, a sector known for its high energy consumption.
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.
KAUST researchers are exploring thin-film device technologies using materials like printable organics and metal oxides for a greener Internet of Things (IoT). They propose wirelessly powered sensor nodes using energy harvesters to reduce reliance on batteries, which are costly and environmentally harmful. Large-area electronics, printed on flexible substrates, offer a more eco-friendly alternative to silicon-based technologies due to solution-based processing and lower production temperatures. Why it matters: This research contributes to a more sustainable and environmentally friendly IoT ecosystem, aligning with global efforts to reduce electronic waste and energy consumption.
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.
KAUST's Sami Al-Ghamdi is conducting multidisciplinary research on urban sustainability to mitigate climate change and optimize resource consumption. His work supports Saudi Arabia’s Vision 2030, particularly urban gigaprojects like NEOM and Saudi Downtown. He develops computational models to assess the environmental impact of various aspects of the built environment. Why it matters: This research is crucial for advancing sustainable urban development in Saudi Arabia and achieving its ambitious environmental goals.