Nate Hagens from the University of Minnesota spoke at KAUST's Winter Enrichment Program (WEP) 2018 about the intersection of energy, human behavior, and economics. Hagens argued that society functions as an energy-dissipating "superorganism," with human preferences correlated with increasing energy needs. He emphasized that energy, not money, is the real capital, but global society is running out of it. Why it matters: The talk highlights the importance of viewing society through an ecological lens, particularly in the context of the GCC region's reliance on energy resources.
AI's energy consumption is a growing concern, with AI, data centers, and cryptocurrency consuming nearly 2% of the world's energy in 2022, potentially doubling by 2026. Training an LLM like GPT-3 uses the equivalent energy of 130 homes per year, and AI tasks consume 33 times more energy than task-specific software. MBZUAI's computer science department, led by Xiaosong Ma, is researching energy efficiency in AI hardware to address this problem. Why it matters: As AI adoption accelerates in the GCC, energy-efficient AI hardware and algorithms are critical for sustainable development and reducing carbon emissions in the region.
MBZUAI's Qirong Ho and colleagues are developing an Artificial Intelligence Operating System (AIOS) for decarbonization, aiming to reduce energy waste in AI development. The AIOS focuses on improving communication efficiency between machines during AI model training, as inefficient communication leads to prolonged tasks and increased energy consumption. This system addresses the high computing power demands of large language models like ChatGPT and LLaMA-2. Why it matters: By optimizing energy usage in AI development, the AIOS could significantly reduce the carbon footprint of AI technologies 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.
Siemens CTO Rainer Speh spoke at KAUST about smart cities, noting that urban populations are growing, especially in cities like Riyadh and Jeddah. Cities consume two-thirds of the world's energy and generate 70% of CO2 emissions. Siemens is working on a driverless subway system in Riyadh as part of its smart city initiatives. Why it matters: Smart city initiatives are crucial for managing resources and reducing emissions in rapidly growing urban centers in Saudi Arabia.
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.
KAUST researchers are addressing the challenge of growing electricity consumption in cooling technologies, as the global demand for air conditioning increases by 3-4% annually. In Saudi Arabia, cooling systems account for up to 70% of electricity usage during the summer. Researchers at KAUST's Water Desalination and Reuse Center are exploring ways to improve the energy efficiency of chillers to reduce costs and CO2 emissions. Why it matters: Improving cooling efficiency is critical for reducing energy consumption and carbon emissions, especially in hot climates like Saudi Arabia and other GCC countries.
Excyton, a startup based at KAUST, has developed a novel display technology called “TurboLED” that reduces power consumption by 50% and increases the color range rendered on displays to 76%. The technology utilizes a six sub-pixel format (light and deep RGB) compared to the standard three, saving energy by using lighter colors most of the time. Excyton received $2 million in funding from KAUST Innovation Ventures and collaborated with KAUST to develop the technology. Why it matters: This innovation could significantly improve the battery life of mobile devices while also enhancing display quality, providing a competitive advantage for devices manufactured in the region.