Large language models (LLMs) developed, deployed, and researched across the GCC — including Falcon (TII), Jais (MBZUAI/Inception), AceGPT (KAUST), ALLaM (SDAIA), and Fanar (QCRI).
MBZUAI student Fatima Ahmed Khalil Mohamed Alkhoori is researching machine learning techniques to improve traffic sign recognition for autonomous vehicles. Her work focuses on using transformer model architectures to enhance the ability of autonomous vehicles to accurately recognize traffic signs in varying environmental conditions. The research aims to address challenges such as viewing angle, lighting variations, and shadows that can confuse regular models. Why it matters: This research contributes to the advancement of safe and effective autonomous vehicle navigation, aligning with the UAE's vision of having a world-class transportation system.
MBZUAI student Zain Muhammad Mujahid is researching methods to detect media bias using NLP and LLMs. His approach profiles bias across media outlets using LLMs like ChatGPT to predict bias based on 16 identifiers. The research aims to develop a tool that instantly provides a bias profile for a given media URL. Why it matters: This research has the potential to combat misinformation and enhance media literacy in the region by providing tools to identify biased reporting, and it is expanding to Arabic and other languages.
MBZUAI Professor Preslav Nakov is researching methods to identify and combat the harmful uses of large language models in generating disinformation. He notes that disinformation, unlike fake news, is weaponized with the intent to persuade, not just to lie. His research focuses on the linguistic differences between human-written and machine-generated disinformation, such as the use of rhetorical devices in human propaganda. Why it matters: As AI-generated content becomes more prevalent, understanding and mitigating its potential for spreading disinformation is critical for maintaining trust and integrity in information ecosystems, especially during major election cycles.
MBZUAI's Maxim Panov is developing uncertainty quantification methods to improve the reliability of language models. His work focuses on providing insights into the confidence level of machine learning models' predictions, especially in scenarios where accuracy is critical, such as medicine. Panov is working on post-processing techniques that can be applied to already-trained models. Why it matters: This research aims to address the issue of "hallucinations" in language models, enhancing their trustworthiness and applicability in sensitive domains within the region and globally.
MBZUAI adjunct professor Iryna Gurevych was appointed to the German National Academy of Sciences Leopoldina and received the “Social Impact Award” at the 18th Conference of the European Chapter of the Association for Computational Linguistics. The Social Impact Award recognized her study on sociodemographic prompting, a technique that steers prompt-based models towards answers reflecting specific sociodemographic profiles. Gurevych was also named one of the 15 most important women in AI in Germany by Manager Magazin for her work building an AI assistant with Amazon. Why it matters: Recognizing experts at MBZUAI raises the visibility of the university and its contributions to cutting-edge NLP research, particularly in areas like ethical and responsible AI development.
Professor Timothy Baldwin has been appointed as the Provost of Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). He previously served as Department Chair of NLP, Associate Provost for Academic Affairs, and Acting Provost. During his tenure, Baldwin co-led the technical development of Jais, the leading Arabic LLM, and spearheaded initiatives including new academic departments and the Undergraduate Research Internship Program. Why it matters: Baldwin's appointment signals continued investment in AI talent and research at MBZUAI, particularly in Arabic NLP and large language models.
MBZUAI Professor Monojit Choudhury co-authored a study on LLMs and their capacity for moral reasoning, with the study being presented at the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL) in Malta. The study included contributions from Aditi Khandelwal, Utkarsh Agarwal, and Kumar Tanmay from Microsoft. The research explores AI alignment, ensuring AI systems align with human values, moral principles, and ethical considerations. Why it matters: The study provides insight into LLMs' capabilities regarding complex ethical issues, which is important for guiding the development of AI in a way that is consistent with human values.
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.
Monojit Choudhury, formerly of Microsoft Research and Project Turing, has joined MBZUAI as a professor of natural language processing. Choudhury's work at Microsoft involved developing NLP applications and responsible AI, including manually programming LLMs to prevent toxic or biased content. He was impressed by GPT-4's capabilities and believes academia is the best place for deep research in NLP. Why it matters: Choudhury's experience at Microsoft, including his work on responsible AI and LLMs, could contribute to MBZUAI's NLP research and the development of more inclusive LLMs.
MBZUAI is highlighting five key AI innovations for UAE Innovation Month, including the Jais Arabic LLM developed with Core42 and Cerebras Systems. They also highlight the AI Operating System (AIOS) for reducing AI computing energy costs. Additionally, MBZUAI received a US patent for an AI-based handwriting generation technology. Why it matters: MBZUAI's focus on Arabic NLP and efficient AI computing positions the UAE as a leader in responsible and inclusive AI development.
MBZUAI researchers have developed a new approach to enhance the generalizability of vision-language models when processing out-of-distribution data. The study, led by Sheng Zhang and involving multiple MBZUAI professors and researchers, addresses the challenge of AI applications needing to manage unforeseen circumstances. The new method aims to improve how these models, which combine natural language processing and computer vision, handle new information not used during training. Why it matters: Improving the adaptability of vision-language models is critical for real-world AI applications like autonomous driving and medical imaging, especially in diverse and changing environments.
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.
MBZUAI researchers presented studies at EMNLP and ArabicNLP conferences on improving NLP for diverse languages, especially Arabic. One study evaluated ChatGPT and GPT-4's performance across Arabic dialects, finding limitations compared to English. GPT-4 showed better performance than GPT-3.5 in Arabic. Why it matters: This research highlights the need for NLP models to better support the linguistic diversity of Arabic and other languages to avoid widening existing technological gaps.
MBZUAI held an appreciation ceremony recognizing over 30 industry partners for their collaboration in building the AI ecosystem in Abu Dhabi. The event included panel discussions on AI's potential in healthcare and sustainability, featuring speakers from Mubadala Healthcare, Sheikh Shakhbout Medical City, and the Department of Health – Abu Dhabi. Faculty discussed advancements in NLP and MBZUAI's role in developing Jais and Jais Climate, along with progress in computer vision and machine learning. Why it matters: This event highlights MBZUAI's commitment to fostering partnerships that advance AI research and applications, particularly in areas crucial to the UAE's strategic priorities like healthcare and climate change.
MBZUAI students won the top three awards at the Alibaba AI Hackathon in Dubai, part of GITEX Global AI InnovateFest. First place went to TalkTrain, a smart business assistant for public speaking practice, developed by Kane Lindsay and Hawau Olamide Toyin. Second place was awarded to Anaaya, a generative AI application for predicting adverse drug interactions, created by Mai A. Shaaban and Anees Ur Rehman Hashmi. Why it matters: This sweep highlights MBZUAI's strength in applied AI research and the potential for its students to create impactful solutions for real-world problems.
MBZUAI faculty won two awards and published eight papers at the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL 2023). Alham Fikri Aji and Fajri Koto won the Best Resource Award for NusaWrites, a paper on constructing high-quality corpora for low-resource Indonesian languages by engaging speaker communities. Muhammad Abdul-Mageed won an Area Chair award for ProMap, a method for constructing bilingual dictionaries via language model prompting. Why it matters: This highlights MBZUAI's contribution to NLP research, particularly in low-resource languages and bilingual lexicon induction, and strengthens its position as a hub for AI research in the region.
MBZUAI President Professor Eric Xing argues against exaggerated claims of AI existential threats, contrasting them with real dangers like climate change and nuclear warfare. He critiques the "doomer outcry" fueled by sensationalism rather than rational analysis, emphasizing the importance of evidence-based discussion. Xing suggests that overregulation risks stifling the startup and open-source community, which are vital for transparent and responsible AI development. Why it matters: The piece advocates for a balanced perspective on AI's risks and benefits, promoting informed discussion over alarmist narratives in the region's rapidly developing AI landscape.
MBZUAI has been actively involved in developing AI and generative models, contributing to models like Llama 2, Jais, Vicuna, and LaMini. Professor Preslav Nakov notes Llama 2's improvements in size and carbon footprint over Llama 1. MBZUAI aims to tackle challenges like information accuracy, economic costs, and the scarcity of Arabic online content. Why it matters: MBZUAI's work helps address the limitations of current LLMs, particularly for Arabic, and promotes sustainable AI development in the region.
MBZUAI hosted an event for UAE students to demonstrate generative AI and its potential, featuring an interactive storytelling demonstration on Jais, the Arabic LLM. A survey at the event showed that 77% of students aged 12-15 believe AI skills will be required for future employment. The event included speakers from MBZUAI and NASA, showcasing AI applications in education and creative fields. Why it matters: This event highlights the UAE's focus on AI literacy among youth and MBZUAI's role in promoting AI education and awareness, aligning with the country's national AI strategy.
Malaysian Prime Minister Anwar Ibrahim visited MBZUAI and was received by UAE Minister Dr. Sultan Al Jaber to strengthen technology ties between the two countries. MBZUAI faculty presented research on healthcare AI, the AIOS platform for efficient AI computing, and LLMs like Vicuna and Jais. MBZUAI also announced partnerships with G42, Cerebras, and India’s Centre for Development of Advanced Computing. Why it matters: The visit underscores the UAE's commitment to AI research and international collaboration, particularly in areas like sustainable AI and Arabic language models.
MBZUAI hosted a two-day workshop on "Big Model AI in Drug Design" starting February 20, 2023. The workshop featured presentations from researchers in public and private institutions working on AI and health. MBZUAI Adjunct Professor Eran Segal opened the workshop with a talk on the Human Phenotype Project. Why it matters: The event highlights the growing interest and activity in applying AI, particularly large models, to advance drug discovery and personalized medicine within the UAE's research ecosystem.
MBZUAI's AI Quorum launched its second workshop, "Building Ecosystems for AI at Scale," focusing on AI scalability and business applications. The first CASL workshop aims to define steps for organizations to become self-sufficient with AI and explore new use cases. Speakers include MBZUAI faculty and researchers from CMU, Stanford, KAUST, UC Berkeley, and Google. Why it matters: The workshop highlights the UAE's growing role in fostering AI innovation and bridging the gap between academic research and industry applications in the region.
MBZUAI launched the AI Quorum, a winter series from October 2022 to March 2023, to stimulate AI research. The first session, led by Professor Michael Jordan, focused on collaborative learning with around 20 research experts. Discussions covered the use of edge devices like cell phones and hospitals providing data to build large models, as well as risks like free-riding and adversarial attacks. Why it matters: The AI Quorum initiative positions MBZUAI as a hub for global AI collaboration, addressing key challenges and opportunities in collaborative learning for real-world applications.
MBZUAI's Qirong Ho and colleagues are developing an Artificial Intelligence Operating System (AIOS) for decarbonization, aiming to reduce energy waste in AI development. The AIOS focuses on improving communication efficiency between machines during AI model training, as inefficient communication leads to prolonged tasks and increased energy consumption. This system addresses the high computing power demands of large language models like ChatGPT and LLaMA-2. Why it matters: By optimizing energy usage in AI development, the AIOS could significantly reduce the carbon footprint of AI technologies in the region and globally.
MBZUAI President Eric Xing discussed the transformative potential of AI with Abu Dhabi's Department of Community Development, likening its impact to that of the printing press. He emphasized AI's role in democratizing knowledge and enabling discoveries in genetics, pharmaceuticals, and healthcare. Xing also highlighted the UAE's strategic thinking and openness to innovation, positioning the nation as an international leader in AI, exemplified by MBZUAI's Jais LLM. Why it matters: This signals the UAE's continued focus on AI as a strategic priority, with emphasis on its potential to drive societal and economic development.
MBZUAI is prioritizing natural language processing (NLP) research, aiming to be a top university in the field within 12-18 months according to Professor Timothy Baldwin. MBZUAI's NLP department is focusing on deep learning, algorithmic fairness, computational social science and social media analytics. A key area is Arabic NLP, addressing the challenges of dialectal variations and code-switching in social media. Why it matters: This focus on Arabic NLP and real-world problem-solving will contribute to the UAE's ambitious agenda of growing a local AI industry and integrating AI into various sectors.
MBZUAI Professor Chih-Jen Lin gave a keynote at the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval in Taipei. Lin's address, titled ‘On the “Rough Use” of Machine Learning Techniques’, focused on instances where machine learning techniques are employed inappropriately, using examples from graph representation learning and deep neural networks. He advocated for the development of high-quality, user-friendly software to improve the practical application of machine learning and mitigate misuse. Why it matters: Showcases MBZUAI's faculty expertise and contributions to the discussion on responsible AI research and deployment on a global stage.
MBZUAI 2023 graduate Muhammad Umar is researching propaganda detection in low-resource, code-switched languages like Roman Urdu. His master's thesis focuses on detecting propaganda techniques in social media text using deep learning models. Umar aims to submit a paper on his findings to the EMNLP 2023 conference. Why it matters: This research addresses the under-explored area of propaganda detection in low-resource languages, which is crucial for combating misinformation in bilingual communities.
MBZUAI graduate Gokul Karthik Kumar credits the university's interdisciplinary approach for helping him discover his passion for working at the intersection of computer vision and NLP. He chose MBZUAI over the University of Waterloo due to the flexibility to explore different AI domains. Kumar will join G42’s Inception Institute of Artificial Intelligence (IIAI) as an applied scientist to develop large language models tailored for UAE-focused applications. Why it matters: This highlights MBZUAI's role in nurturing AI talent for the UAE and G42's focus on developing local LLMs.
MBZUAI's Class of 2023 valedictorian, Klea Ziu, credits her meditation practice for balancing her studies in machine learning. Ziu, the first Albanian graduate from MBZUAI, was among 59 graduates receiving master's degrees in computer vision, machine learning, and NLP. She will represent MBZUAI at COP28 as part of the Climate Ambassador Program, focusing her AI research on reducing carbon dioxide production in the oil and gas industry. Why it matters: This highlights MBZUAI's focus on attracting international talent and applying AI research to sustainability challenges relevant to the UAE and the broader region.
MBZUAI's President addressed the graduating class of 2023, consisting of 59 graduates from 25 countries, at a ceremony in Abu Dhabi. The speech emphasized AI's transformative power and the need to shift education towards problem-solving and knowledge creation. It encouraged graduates to embrace AI while nurturing independent thinking and contributing to interdisciplinary problem-solving. Why it matters: The address highlights MBZUAI's vision of shaping future AI leaders and promoting a balanced perspective on AI's potential and risks in the region.
MBZUAI and RIKEN-AIP (Japan) co-hosted a joint workshop at MBZUAI's Masdar City campus. The workshop facilitated the sharing of research and perspectives across machine learning, computer vision, and natural language processing. Researchers from both institutions explored interdisciplinary cooperation to enhance AI's capacity to address real-world problems. Why it matters: This collaboration strengthens MBZUAI's position as a hub for cross-disciplinary AI research and fosters international partnerships in the field.
MBZUAI faculty Kun Zhang is researching methods to improve the reliability of generative AI, particularly in healthcare applications. Current generative AI models often act as "black boxes," making it difficult to understand why a specific result was produced. Zhang's research focuses on incorporating causal relationships into AI systems to ensure more accurate and meaningful information. Why it matters: Improving the trustworthiness of generative AI is crucial for sensitive sectors like healthcare and ensuring responsible AI deployment across the region.
A Mixture of Experts (MoE) layer is a sparsely activated deep learning layer. It uses a router network to direct each token to one of the experts. Yuanzhi Li, an assistant professor at CMU and affiliated faculty at MBZUAI, researches deep learning theory and NLP. Why it matters: This highlights MBZUAI's engagement with cutting-edge deep learning research, specifically in efficient model design.
MBZUAI's president Eric Xing discussed AI career prospects after the university's first graduation. He also addressed the risks of ChatGPT and other AI developments. MBZUAI's Cesare Stefanini contributed to the development of underwater robots, while researchers Thamar Solorio, Monojit Choudhury, and Aseem Srivastava will study loneliness in digital spaces. Why it matters: The interview highlights MBZUAI's role in shaping AI talent and addressing societal impacts of AI in the UAE and beyond.
MBZUAI appointed Ekaterina Kochmar as an assistant professor of NLP to advance AI-assisted learning. Kochmar co-founded Korbit AI, an AI-powered dialogue-based tutoring system for STEM subjects. Korbit AI aims to democratize education by providing personalized, high-quality education globally at minimal cost. Why it matters: This appointment highlights MBZUAI's commitment to AI in education and personalized learning, positioning the UAE as a hub for innovation in educational technology.
A presentation discusses using programmable network devices to reduce communication bottlenecks in distributed deep learning. It explores in-network aggregation and data processing to lower memory needs and increase bandwidth usage. The talk also covers gradient compression and the potential role of programmable NICs. Why it matters: Optimizing distributed deep learning infrastructure is critical for scaling AI model training in resource-constrained environments.
A recent study questions the necessity of deep ensembles, which improve accuracy and match larger models. The study demonstrates that ensemble diversity does not meaningfully improve uncertainty quantification on out-of-distribution data. It also reveals that the out-of-distribution performance of ensembles is strongly determined by their in-distribution performance. Why it matters: The findings suggest that larger, single neural networks can replicate the benefits of deep ensembles, potentially simplifying model deployment and reducing computational costs in the region.
MBZUAI's Hanan Al Darmaki is working to improve automated speech recognition (ASR) for low-resource languages, where labeled data is scarce. She notes that Arabic presents unique challenges due to dialectal variations and a lack of written resources corresponding to spoken dialects. Al Darmaki's research focuses on unsupervised speech recognition to address this gap. Why it matters: Overcoming these challenges can improve virtual assistant effectiveness across diverse languages and enable more inclusive AI applications in the Arabic-speaking world.
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.
MBZUAI has launched a Metaverse Lab led by Hao Li, focusing on integrating computer vision, graphics, and machine learning for metaverse applications. The lab aims to develop AI algorithms for photorealistic virtual humans and dynamic environment digitization. Pinscreen, Li's AI startup, previously created avatars for Expo 2020 Dubai. Why it matters: This initiative positions MBZUAI and the UAE as key players in the development of core technologies underpinning the metaverse and digital communication.
This article discusses retrieval augmentation in text generation, where information retrieved from an external source is used to condition predictions. It references recent work on retrieval-augmented image captioning, showing that model size can be greatly reduced when training data is available through retrieval. The author intends to continue this work focusing on the intersection of retrieval augmentation and in-context learning, and controllable image captioning for language learning materials. Why it matters: This research direction has the potential to improve transfer learning in vision-language models, which could be especially relevant for downstream applications in Arabic NLP and multimodal tasks.
MBZUAI hosted a panel discussion in collaboration with the Manara Center for Coexistence and Dialogue. Chaoyang He, co-founder of FedML, presented on federated learning (FL), covering privacy/security, resource constraints, label scarcity, and scalable system design. FedML is a platform for zero-code, cross-platform, secure federated learning across industries like healthcare and finance. Why it matters: Federated learning is an important subfield for the GCC region, allowing privacy-preserving model training across distributed data sources.
MBZUAI Visiting Assistant Professor Gus Xia studies music to understand how AI can act more human-like in high-context activities. Xia analyzes and creates music with computers to explore the differences between human and machine perception. He aims to leverage music's abstract nature to study creative intelligence in AI. Why it matters: This research could lead to AI systems that interact more naturally with humans, particularly in creative fields.
MBZUAI Department Chair Le Song served as a program co-chair at the 39th International Conference on Machine Learning (ICML). MBZUAI faculty, researchers, and students had 7 papers accepted at ICML 2022. Song noted the increasing focus on biomedicine and other science areas within the AI research community. Why it matters: Song's leadership role at ICML and MBZUAI's strong presence highlights the university's growing influence in the global machine learning landscape.
Dr. Najwa Aaraj from MBZUAI and TII discussed the impact of quantum computers and machine learning on cryptographic algorithms. The talk covered post-quantum cryptographic (PQC) schemes, standardization efforts, and the role of machine learning in advancing cybersecurity solutions. Dr. Aaraj also highlighted the challenges of transitioning current cryptographic systems to quantum-resistant alternatives. Why it matters: As quantum computing advances, understanding and implementing post-quantum cryptography is crucial for maintaining secure communications and data protection in the UAE and globally.
MBZUAI President Eric Xing delivered a talk at Carnegie Mellon University on May 13, 2022, titled “From Learning, to Meta-Learning, to Lego-Learning — theory, systems, and engineering.” Xing discussed the development of a standard model for learning, inspired by the standard model in physics, which aims to unify various machine learning paradigms. Before joining MBZUAI, Xing was a professor at CMU and founder of Petuum Inc., an AI development platform company. Why it matters: This talk highlights MBZUAI's leadership in advancing theoretical frameworks for machine learning and its commitment to unifying different AI approaches.
UC Berkeley professor Michael I. Jordan will lead a session on AI, Machine Learning and Economy as part of the MBZUAI Executive Program. The program is headed by MBZUAI President Eric Xing and includes 42 participants from ministerial leadership and top industry executives. The 12-week program aims to support the UAE's AI leadership mission through education, capacity building, innovation, and R&D. Why it matters: The involvement of a prominent academic figure like Jordan highlights the UAE's commitment to attracting global expertise in AI education and solidifying its position as an AI hub.
MBZUAI has appointed Professor Timothy Baldwin as Associate Provost and acting chair of its new NLP Department. Baldwin will focus on strengthening the curriculum and building a world-class faculty team. He previously spent 17 years at the University of Melbourne. Why it matters: The recruitment signals MBZUAI's commitment to becoming a leading center for NLP research and education in the region.
MBZUAI's president Eric Xing warns against the unchecked pursuit of increasingly large AI models, drawing an analogy to an "atomic bomb" due to the unpredictability of their behavior. He argues that the field lacks sufficient understanding of what these models learn and whether their outputs are reliable, advocating for more efficient models. Xing emphasizes the need for debuggability and error tracking in AI, similar to established engineering practices. Why it matters: The piece highlights growing concerns within the AI community about the scalability and potential risks associated with increasingly complex AI models, particularly regarding transparency and control.