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.
Caltech graduate student Surya Narayanan Hari presented his research on replicating human-like memory in machines at MBZUAI. He discussed how the thalamus, which filters sensory and motor signals in the brain, inspires the development of routed monolithic models in AI. Hari explained that memory retrieval occurs on object, embedding, and circuit levels in the human brain. Why it matters: This talk highlights the potential of neuroscience-inspired AI architectures for improving memory and information processing in AI systems, which could accelerate the development of more efficient and context-aware AI models in the region.
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.
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 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.
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.
Professor Hava Siegelmann, a computer science expert, is researching lifelong learning AI, drawing inspiration from the brain's abstraction and generalization capabilities. The research aims to enable intelligent systems in satellites, robots, and medical devices to adapt and improve their expertise in real-time, even with limited communication and power. The goal is to develop AI systems applicable for far edge computing that can learn in runtime and handle unanticipated situations. Why it matters: This research could lead to more resilient and adaptable AI systems for critical applications in remote and resource-constrained environments, with potential benefits for various sectors in the Middle East.