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.
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.
KAUST and EPFL Blue Brain Project researchers propose a new theory about a 'secret language' used by cells for internal communication regarding the external world. Using a computational model, they suggest that metabolic pathways can code details about neuromodulators that stimulate energy consumption. The model focuses on astrocytes and their cooperation with neurons in fueling the brain. Why it matters: This suggests a new avenue for understanding information processing in the brain and how cells contribute to the energy efficiency of brains compared to computers.
A Caltech researcher presented at MBZUAI on memory representation and retrieval, contrasting AI and neuroscience approaches. Current AI retrieval systems like RAG retrieve via fine-tuning and embedding similarity, while the presenter argued for exploring retrieval via combinatorial object identity or spatial proximity. The research explores circuit-level retrieval via domain fine-tuned LLMs and distributed memory for image retrieval using semantic similarity. Why it matters: The work suggests structured databases and retrieval-focused training can allow smaller models to outperform larger general-purpose models, offering efficiency gains for AI development in the region.
Prof. Mérouane Debbah of the Technology Innovation Institute (TII) warns that current AI development relies on unsustainable, energy-intensive "bruteforce computing." He argues that the field needs more energy-efficient algorithms instead of simply scaling up GPUs. Debbah suggests neuromorphic computing as a potential solution, drawing inspiration from the human brain's energy efficiency. Why it matters: This critique highlights a crucial sustainability challenge for AI in the GCC and globally, as the region invests heavily in compute-intensive AI models.
KAUST researchers led by Dr. Muhammad Hussain have developed a flexible, transparent silicon-on-polymer based FinFET inspired by the folded architecture of the human brain's cortex. The team created a 3D FinFET on a flexible platform without compromising integration density or performance. They aim to demonstrate a fully flexible silicon-based computer by the end of the year. Why it matters: This research could lead to the development of ultra-mobile, foldable computers and integrated circuits, advancing the field of flexible electronics in the region.
MBZUAI Ph.D. graduate Hilal Mohammad Hilal AlQuabeh researched methods to improve the efficiency of machine learning algorithms, specifically focusing on pairwise learning and multi-instance learning. Pairwise learning teaches AI to make decisions by comparing options in pairs, useful for ranking and anomaly detection. Multi-instance learning involves learning from sets of data points, applicable in areas like drug discovery. Why it matters: Optimizing AI for low-resource environments expands its accessibility and applicability in critical sectors like healthcare and remote area operations.