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 Assistant Professors Bin Gu and Huan Xiong are advancing spiking neural networks (SNNs) to improve computational power and energy efficiency. They will present their latest research on SNNs at the 38th Annual AAAI Conference on Artificial Intelligence in Vancouver. SNNs process information in discrete events, mimicking biological neurons and offering improved energy efficiency compared to traditional neural networks. Why it matters: This research could enable running advanced AI applications like GPTs on mobile devices, unlocking their full potential due to the energy efficiency of SNNs.
Tom M. Mitchell from Carnegie Mellon University discussed using machine learning to study how the brain processes natural language, using fMRI and MEG to record brain activity while reading text. The research explores neural encodings of word meaning, information flow during word comprehension, and how meanings of words combine in sentences and stories. He also touched on how understanding of the brain aligns with current AI approaches to NLP. Why it matters: This interdisciplinary research could bridge the gap between neuroscience and AI, potentially leading to more human-like NLP models.
KAUST researchers in the Sensors Lab are developing neuromorphic circuits for vision sensors, drawing inspiration from the human eye. They created flexible photoreceptors using hybrid perovskite materials, with capacitance tunable by light stimulation, mimicking the human retina. The team collaborates with experts in image characterization and brain pattern recognition to connect the 'eye' to the 'brain' for object identification. Why it matters: This biomimetic approach promises advancements in AI, machine learning, and smart city development within the region.
KAUST researchers have developed an artificial electronic retina mimicking the behavior of rod retina cells, utilizing a hybrid perovskite material (MAPbBr3) embedded in PVDF-TrFE-CEF. The photoreceptor array, made of metal-insulator-metal capacitors, detects light intensity through changes in electrical capacitance. Connected to a CMOS-sensing circuit and a spiking neural network, the 4x4 array achieved around 70 percent accuracy in recognizing handwritten numbers. Why it matters: This research paves the way for energy-efficient neuromorphic vision sensors and advanced computer vision applications, potentially revolutionizing camera technology.
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.
KAUST hosted the "Human-Machine Networks and Intelligent Infrastructures" conference, co-organized by Prof. Jeff Shamma and Asst. Prof. Meriem Laleg. The conference explored the blend of engineered devices and human elements in large-scale systems like smart grids. Keynote speaker Dr. Pramod Khargonekar discussed cyber-physical-social systems and emerging trends. Why it matters: The conference highlights the growing importance of understanding the interplay between AI, infrastructure, and human behavior in the development of smart cities and intelligent systems in the region.