Olivier Oullier, Visiting Professor at MBZUAI, is working on brain-computer interfaces, founding Inclusive Brains to develop a Neural Foundation Model using neurophysiological and behavioral signals. This model integrates data from brainwaves, eye-tracking, and other modalities to allow machines to build a representation of the world closer to human cognition. Why it matters: Such advancements can transform human-computer interaction, with particular implications for people of determination in the region.
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.
Fatima Ali AlNuaimi from the Autonomous Robotics Research Center (ARRC) had two research papers on brain-computer interface (BCI) technology published at the IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2022. The papers are titled “Real-time Control of UGV Robot in Gazebo Simulator using P300-based Brain-Computer Interface” and “Secure Password Using EEG-based BrainPrint System: Unlock Smartphone Password Using Brain-Computer Interface Technology”. AlNuaimi is recognized as a young Emirati scientist advancing BCI knowledge in the UAE. Why it matters: This highlights growing BCI research capabilities in the UAE and the contributions of Emirati researchers to this emerging field.
This article discusses a talk on "Assistive Augmentation," designing human-computer interfaces to augment human abilities. Examples include 'AiSee' for blind users, 'Prospero' for memory training, and 'MuSS-Bits' for deaf users to feel music. Suranga Nanayakkara from the National University of Singapore will present the talk, highlighting insights from psychology, human-centered machine learning, and design thinking. Why it matters: Such assistive technologies can significantly improve the quality of life for individuals with disabilities and extend human capabilities.
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 will present two assistive AI prototypes at GITEX 2025: smart glasses with a camera and eye tracker that identify objects and medication, and a brain-computer interface (BCI) device integrated with robotics to control a robotic dog's movements. The smart glasses use a multimodal large language model (LLM) to help visually impaired individuals, while the BCI aims to restore hands-free communication for people with mobility limitations. Hisham Cholakkal leads the research team, which received a Meta Regional Research Grant 2025 for its work on multimodal LLM for smart wearables. Why it matters: The research demonstrates the potential of AI to improve the quality of life for vulnerable populations and addresses the challenge of providing cost-effective care for aging societies.
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 Discovery highlights the contributions of Magistretti to the field of neuroenergetics. His research explores the cellular and molecular basis of brain energy metabolism and brain imaging. Magistretti's group discovered mechanisms underlying the coupling between neuronal activity and energy consumption, revealing the role of astrocytes. Why it matters: Understanding brain energy metabolism and the role of glial cells can advance brain imaging techniques and our understanding of neuronal processes.