MBZUAI Assistant Professor Alham Fikri Aji is presenting research at EACL 2024 on efficient NLP for low-resource languages. The study uses knowledge distillation, transferring knowledge from a larger model (ChatGPT) to a smaller one using synthetic instruction data. The goal is to achieve similar performance with less computational resources, focusing on underrepresented languages. Why it matters: This work addresses the need for more accessible and inclusive NLP technologies, especially for languages lacking extensive datasets and computational resources.
MBZUAI faculty Alham Fikri Aji, Timothy Baldwin, and Fajri Koto won an Outstanding Paper Award at EACL 2023 for their paper "NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages." The paper introduces the first parallel resource for 10 Indonesian low-resource languages to boost performance in sentiment analysis and machine translation. The dataset is available on HuggingFace. Why it matters: This work highlights MBZUAI's commitment to advancing NLP research in low-resource languages, which can help preserve linguistic diversity and improve access to digital resources for speakers of underrepresented languages.
Abdullah Hamdi, a Ph.D. student at KAUST, is researching AI, deep learning, and computer vision in the Image and Video Understanding Lab under Associate Professor Bernard Ghanem. His work focuses on developing reliable testing methods for deep learning tools, particularly for sensitive applications like self-driving vehicles. Hamdi aims to disseminate AI knowledge and contribute to the AI ecosystem in the region. Why it matters: This highlights KAUST's role in fostering local AI talent and contributing to critical research areas like autonomous vehicles, aligning with Saudi Arabia's broader technology goals.
MBZUAI researchers had 26 papers accepted at ACL 2023, a top NLP conference. Assistant Professor Alham Fikri Aji co-authored eight papers, including one on crosslingual generalization through multitask finetuning (MTF). Deputy Department Chair Preslav Nakov co-authored a paper on a Bulgarian language understanding benchmark dedicated to the memory of Yale Computer Scientist Dragomir R. Radev. Why it matters: MBZUAI's strong presence at ACL highlights its growing influence in the NLP field and its contributions to multilingual AI research.
MBZUAI recognized seven faculty members for outstanding contributions in research, teaching, and mentorship at its annual Faculty Recognition and Welcome Reception. Associate Professor Salman Khan received the Distinguished Research Award for his work on multimodal models for remote Earth observation, including projects like AI4Weather and the AI Global Agriculture Advisory. Assistant Professor Alham Fikri Aji received the Early Career Researcher Award for his contributions to low-resource NLP and international collaborations. Why it matters: The awards highlight MBZUAI's focus on advancing AI for global challenges and recognizing faculty contributions to research and education.
This paper presents a UI-level evaluation of ALLaM-34B, an Arabic-centric LLM developed by SDAIA and deployed in the HUMAIN Chat service. The evaluation used a prompt pack spanning various Arabic dialects, code-switching, reasoning, and safety, with outputs scored by frontier LLM judges. Results indicate strong performance in generation, code-switching, MSA handling, reasoning, and improved dialect fidelity, positioning ALLaM-34B as a robust Arabic LLM suitable for real-world use.
This paper proposes a smart dome model for mosques that uses AI to control dome movements based on weather conditions and overcrowding. The model utilizes Congested Scene Recognition Network (CSRNet) and fuzzy logic techniques in Python to determine when to open and close the domes to maintain fresh air and sunlight. The goal is to automatically manage dome operation based on real-time data, specifying the duration for which the domes should remain open each hour.
Al-Maha Systems, a startup founded by KAUST students, has developed an IoT system for livestock health tracking. The system uses sensors attached to cows to monitor vital data like heart rate and body temperature, transmitting it to a cloud server. The goal is to detect health problems early and optimize breeding times for dairy farms. Why it matters: This innovation can improve efficiency and productivity in Saudi Arabia's dairy industry by leveraging IoT for animal husbandry.