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.
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 held its first Enrichment in the Spring (SEP) program from March 20–28, focusing on the human brain and mind, coinciding with Brain Awareness Week. The program featured lectures from neuroscientists like Professor Alim-Louis Benabid, and presentations by KAUST's Ali Awami and Corrado Cali on visualization technology for studying the brain. KAUST researchers are collaborating with the Human Brain Project and Harvard University to develop comprehensive brain models and visualize connectome data. Why it matters: This initiative highlights KAUST's commitment to advancing neuroscience research and fostering interdisciplinary collaborations to understand the complexities of the human brain.
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.
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 researchers in the Image and Video Understanding Lab are applying machine learning to computer vision for automated navigation, including self-driving cars and UAVs. They tested their algorithms on KAUST roads, aiming to replicate the brain's efficiency in tasks like activity and object recognition. The team is also exploring the possibility of creative algorithms that can transfer skills without direct training. Why it matters: This research contributes to the advancement of autonomous systems and explores the fundamental questions of replicating human intelligence in machines within the GCC region.